Fix: Remove duplicate parameters and correct documentation

docs: add i-iooi-i as a contributor for bug, and ideas

.agent/workflows/plugin-development.md

@@ -25,6 +25,8 @@ Every plugin **MUST** have bilingual versions for both code and documentation:
 - **Valves**: Use `pydantic` for configuration.
 - **Database**: Re-use `open_webui.internal.db` shared connection.
 - **User Context**: Use `_get_user_context` helper method.
+- **Chat Context**: Use `_get_chat_context` helper method for `chat_id` and `message_id`.
+- **Debugging**: Use `_emit_debug_log` for frontend console logging (requires `SHOW_DEBUG_LOG` valve).
 - **Chat API**: For message updates, follow the "OpenWebUI Chat API 更新规范" in `.github/copilot-instructions.md`.
   - Use Event API for immediate UI updates
   - Use Chat Persistence API for database storage
@@ -39,8 +41,8 @@ Every plugin **MUST** have bilingual versions for both code and documentation:
 When adding or updating a plugin, you **MUST** update the following documentation files to maintain consistency:
 
 ### Plugin Directory
-- `README.md`: Update version, description, and usage. **Explicitly describe new features.**
-- `README_CN.md`: Update version, description, and usage. **Explicitly describe new features.**
+- `README.md`: Update version, description, and usage. **Explicitly describe new features in a prominent position at the beginning.**
+- `README_CN.md`: Update version, description, and usage. **Explicitly describe new features in a prominent position at the beginning.**
 
 ### Global Documentation (`docs/`)
 - **Index Pages**:
@@ -82,6 +84,12 @@ Reference: `.github/workflows/release.yml`
     - Generates release notes based on changes.
     - Creates a GitHub Release tag (e.g., `v2024.01.01-1`).
     - Uploads individual `.py` files of **changed plugins only** as assets.
+4.  **Market Publishing**:
+    - Workflow: `.github/workflows/publish_plugin.yml`
+    - Trigger: Release published.
+    - Action: Automatically updates the plugin code and metadata on OpenWebUI.com using `scripts/publish_plugin.py`.
+    - **Auto-Sync**: If a local plugin has no ID but matches an existing published plugin by **Title**, the script will automatically fetch the ID, update the local file, and proceed with the update.
+    - Requirement: `OPENWEBUI_API_KEY` secret must be set.
 
 ### Pull Request Check
 - Workflow: `.github/workflows/plugin-version-check.yml`

.all-contributorsrc

@@ -0,0 +1,47 @@
+{
+  "files": [
+    "README.md"
+  ],
+  "imageSize": 100,
+  "commit": false,
+  "commitType": "docs",
+  "commitConvention": "angular",
+  "contributors": [
+    {
+      "login": "rbb-dev",
+      "name": "rbb-dev",
+      "avatar_url": "https://avatars.githubusercontent.com/u/37469229?v=4",
+      "profile": "https://github.com/rbb-dev",
+      "contributions": [
+        "ideas",
+        "code"
+      ]
+    },
+    {
+      "login": "dhaern",
+      "name": "Raxxoor",
+      "avatar_url": "https://avatars.githubusercontent.com/u/7317522?v=4",
+      "profile": "https://trade.xyz/?ref=BZ1RJRXWO",
+      "contributions": [
+        "bug",
+        "ideas"
+      ]
+    },
+    {
+      "login": "i-iooi-i",
+      "name": "ZOLO",
+      "avatar_url": "https://avatars.githubusercontent.com/u/1827701?v=4",
+      "profile": "https://github.com/i-iooi-i",
+      "contributions": [
+        "bug",
+        "ideas"
+      ]
+    }
+  ],
+  "contributorsPerLine": 7,
+  "skipCi": true,
+  "repoType": "github",
+  "repoHost": "https://github.com",
+  "projectName": "awesome-openwebui",
+  "projectOwner": "Fu-Jie"
+}

.github/workflows/community-stats.yml

@@ -1,5 +1,5 @@
 # OpenWebUI 社区统计报告自动生成
-# 每小时自动获取并更新社区统计数据
+# 只在统计数据变化时 commit，避免频繁提交
 
 name: Community Stats
 
@@ -32,23 +32,19 @@ jobs:
         run: |
           pip install requests python-dotenv
       
+
+      
       - name: Generate stats report
         env:
           OPENWEBUI_API_KEY: ${{ secrets.OPENWEBUI_API_KEY }}
           OPENWEBUI_USER_ID: ${{ secrets.OPENWEBUI_USER_ID }}
         run: |
           python scripts/openwebui_stats.py
-      
-      - name: Check for changes
-        id: check_changes
-        run: |
-          git diff --quiet docs/community-stats.md docs/community-stats.en.md README.md README_CN.md || echo "changed=true" >> $GITHUB_OUTPUT
-      
+
       - name: Commit and push changes
-        if: steps.check_changes.outputs.changed == 'true'
         run: |
           git config --local user.email "github-actions[bot]@users.noreply.github.com"
           git config --local user.name "github-actions[bot]"
-          git add docs/community-stats.md docs/community-stats.en.md docs/community-stats.json README.md README_CN.md
-          git commit -m "📊 更新社区统计数据 $(date +'%Y-%m-%d')"
+          git add docs/community-stats.zh.md docs/community-stats.md docs/community-stats.json README.md README_CN.md
+          git diff --staged --quiet || git commit -m "chore: update community stats $(date +'%Y-%m-%d')"
           git push

.github/workflows/publish_new_plugin.yml

@@ -0,0 +1,68 @@
+name: Publish New Plugin
+
+on:
+  workflow_dispatch:
+    inputs:
+      plugin_dir:
+        description: 'Plugin directory (e.g., plugins/actions/deep-dive)'
+        required: true
+        type: string
+      dry_run:
+        description: 'Dry run mode (preview only)'
+        required: false
+        type: boolean
+        default: false
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.x'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install requests
+
+      - name: Validate plugin directory
+        run: |
+          if [ ! -d "${{ github.event.inputs.plugin_dir }}" ]; then
+            echo "❌ Error: Directory '${{ github.event.inputs.plugin_dir }}' does not exist"
+            exit 1
+          fi
+          echo "✅ Found plugin directory: ${{ github.event.inputs.plugin_dir }}"
+          ls -la "${{ github.event.inputs.plugin_dir }}"
+
+      - name: Publish Plugin
+        env:
+          OPENWEBUI_API_KEY: ${{ secrets.OPENWEBUI_API_KEY }}
+        run: |
+          if [ "${{ github.event.inputs.dry_run }}" = "true" ]; then
+            echo "🔍 Dry run mode - previewing..."
+            python scripts/publish_plugin.py --new "${{ github.event.inputs.plugin_dir }}" --dry-run
+          else
+            echo "🚀 Publishing plugin..."
+            python scripts/publish_plugin.py --new "${{ github.event.inputs.plugin_dir }}"
+          fi
+
+      - name: Commit changes (if ID was added)
+        if: ${{ github.event.inputs.dry_run != 'true' }}
+        run: |
+          git config user.name "github-actions[bot]"
+          git config user.email "github-actions[bot]@users.noreply.github.com"
+          
+          # Check if there are changes to commit
+          if git diff --quiet; then
+            echo "No changes to commit"
+          else
+            git add "${{ github.event.inputs.plugin_dir }}"
+            git commit -m "feat: add openwebui_id to ${{ github.event.inputs.plugin_dir }}"
+            git push
+            echo "✅ Committed and pushed openwebui_id changes"
+          fi

.github/workflows/publish_plugin.yml

@@ -0,0 +1,33 @@
+name: Publish Plugins to OpenWebUI Market
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - 'plugins/**/*.py'
+  release:
+    types: [published]
+  workflow_dispatch:
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.x'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install requests
+
+      - name: Publish Plugins
+        env:
+          OPENWEBUI_API_KEY: ${{ secrets.OPENWEBUI_API_KEY }}
+        run: python scripts/publish_plugin.py

.github/workflows/release.yml

@@ -193,10 +193,21 @@ jobs:
             TODAY_PREFIX="v${TODAY}-"
             
             # Count existing releases with today's date prefix
-            EXISTING_COUNT=$(gh release list --limit 100 | grep -c "^${TODAY_PREFIX}" || echo "0")
+            # grep -c returns 1 if count is 0, so we use || true to avoid script failure
+            EXISTING_COUNT=$(gh release list --limit 100 2>/dev/null | grep -c "^${TODAY_PREFIX}" || true)
+            
+            # Clean up output (handle potential newlines or fallback issues)
+            EXISTING_COUNT=$(echo "$EXISTING_COUNT" | tr -cd '0-9')
+            if [ -z "$EXISTING_COUNT" ]; then EXISTING_COUNT=0; fi
+            
             NEXT_NUM=$((EXISTING_COUNT + 1))
             
             VERSION="${TODAY_PREFIX}${NEXT_NUM}"
+            
+            # Final fallback to ensure VERSION is never empty
+            if [ -z "$VERSION" ]; then
+              VERSION="v$(date +'%Y.%m.%d-%H%M%S')"
+            fi
           fi
           echo "version=$VERSION" >> $GITHUB_OUTPUT
           echo "Release version: $VERSION"
@@ -334,13 +345,34 @@ jobs:
           echo "=== Release Notes ==="
           cat release_notes.md
 
+      - name: Create Git Tag
+        run: |
+          VERSION="${{ steps.version.outputs.version }}"
+          
+          if [ -z "$VERSION" ]; then
+            echo "Error: Version is empty!"
+            exit 1
+          fi
+          
+          if ! git rev-parse "$VERSION" >/dev/null 2>&1; then
+            echo "Creating tag $VERSION"
+            git tag "$VERSION"
+            git push origin "$VERSION"
+          else
+            echo "Tag $VERSION already exists"
+          fi
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
       - name: Create GitHub Release
         uses: softprops/action-gh-release@v2
         with:
           tag_name: ${{ steps.version.outputs.version }}
+          target_commitish: ${{ github.sha }}
           name: ${{ github.event.inputs.release_title || steps.version.outputs.version }}
           body_path: release_notes.md
           prerelease: ${{ github.event.inputs.prerelease || false }}
+          make_latest: true
           files: |
             plugin_versions.json
         env:

CONTRIBUTING.md

@@ -1,87 +1,16 @@
-# 贡献指南 (Contributing Guide)
+# Contributing Guide
 
-感谢你对 **OpenWebUI Extras** 感兴趣！我们非常欢迎社区贡献更多的插件、提示词和创意。
+Thank you for your interest in **OpenWebUI Extras**!
 
-## 🤝 如何贡献
+## 🚀 How to Contribute
 
-### 1. 分享提示词 (Prompts)
+1. **Fork** this repository.
+2. **Add/Modify** the plugin file in the `plugins/` directory.
+3. **Submit PR**: We will review and merge it.
 
-如果你有一个好用的提示词：
-1. 在 `prompts/` 目录下找到合适的分类（如 `coding/`, `writing/`）。如果没有合适的，可以新建一个文件夹。
-2. 创建一个新的 `.md` 或 `.json` 文件。
-3. 提交 Pull Request (PR)。
+## 💡 Important
 
-### 2. 开发插件 (Plugins)
+- Ensure your plugin includes complete metadata (title, author, version, description).
+- If updating an existing plugin, please **increment the version number** (e.g., `0.1.0` -> `0.1.1`) to trigger the auto-update.
 
-如果你开发了一个新的 OpenWebUI 插件 (Function/Tool)：
-1. 确保你的插件代码包含完整的元数据（Frontmatter）：
-   ```python
-   """
-   title: 插件名称
-   author: 你的名字
-   version: 0.1.0
-   description: 简短描述插件的功能
-   """
-   ```
-2. 将插件文件放入 `plugins/` 目录下的合适位置：
-   - `plugins/actions/`: 用于添加按钮或修改消息的 Action 插件。
-   - `plugins/filters/`: 用于拦截请求或响应的 Filter 插件。
-   - `plugins/pipes/`: 用于自定义模型或 API 的 Pipe 插件。
-   - `plugins/tools/`: 用于 LLM 调用的 Tool 插件。
-3. 建议在 `docs/` 下添加一个简单的使用说明。
-
-### 3. 改进文档
-
-如果你发现文档有错误或可以改进的地方，直接提交 PR 即可。
-
-## 🛠️ 开发规范
-
-- **代码风格**：Python 代码请遵循 PEP 8 规范。
-- **注释**：关键逻辑请添加注释，方便他人理解。
-- **测试**：提交前请在本地 OpenWebUI 环境中测试通过。
-
-## 📝 提交 PR
-
-1. Fork 本仓库。
-2. 创建一个新的分支 (`git checkout -b feature/AmazingFeature`)。
-3. 提交你的修改 (`git commit -m 'Add some AmazingFeature'`)。
-4. 推送到分支 (`git push origin feature/AmazingFeature`)。
-5. 开启一个 Pull Request。
-
-## 📦 版本更新与发布
-
-当你更新插件时，请遵循以下流程：
-
-### 1. 更新版本号
-
-在插件文件的 docstring 中更新版本号（遵循[语义化版本](https://semver.org/lang/zh-CN/)）：
-
-```python
-"""
-title: 我的插件
-version: 0.2.0  # 更新此处
-...
-"""
-```
-
-### 2. 更新更新日志
-
-在 `CHANGELOG.md` 的 `[Unreleased]` 部分添加你的更改：
-
-```markdown
-### Added / 新增
-- 新功能描述
-
-### Fixed / 修复
-- Bug 修复描述
-```
-
-### 3. 发布流程
-
-维护者会通过以下方式发布新版本：
-- 手动触发 GitHub Actions 中的 "Plugin Release" 工作流
-- 或创建版本标签 (`v*`)
-
-详细说明请参阅 [发布工作流文档](docs/release-workflow.zh.md)。
-
-再次感谢你的贡献！🚀
+Thank you! 🚀

CONTRIBUTING_CN.md

@@ -0,0 +1,16 @@
+# 贡献指南
+
+感谢你对 **OpenWebUI Extras** 感兴趣！
+
+## 🚀 贡献流程
+
+1. **Fork** 本仓库。
+2. **修改/添加** `plugins/` 目录下的插件文件。
+3. **提交 PR**: 我们会尽快审核并合并。
+
+## 💡 注意事项
+
+- 请确保插件包含完整的元数据（title, author, version, description）。
+- 如果是更新已有插件，请记得**增加版本号**（如 `0.1.0` -> `0.1.1`），这样系统会自动同步更新。
+
+再次感谢你的贡献！🚀

README.md

@@ -1,4 +1,7 @@
 # OpenWebUI Extras
+<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
+[![All Contributors](https://img.shields.io/badge/all_contributors-3-orange.svg?style=flat-square)](#contributors-)
+<!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 English | [中文](./README_CN.md)
 
@@ -7,25 +10,28 @@ A collection of enhancements, plugins, and prompts for [OpenWebUI](https://githu
 <!-- STATS_START -->
 ## 📊 Community Stats
 
-> 🕐 Auto-updated: 2026-01-06 21:19
+> 🕐 Auto-updated: 2026-01-15 00:11
 
 | 👤 Author | 👥 Followers | ⭐ Points | 🏆 Contributions |
 |:---:|:---:|:---:|:---:|
-| [Fu-Jie](https://openwebui.com/u/Fu-Jie) | **43** | **62** | **17** |
+| [Fu-Jie](https://openwebui.com/u/Fu-Jie) | **104** | **104** | **25** |
 
 | 📝 Posts | ⬇️ Downloads | 👁️ Views | 👍 Upvotes | 💾 Saves |
 |:---:|:---:|:---:|:---:|:---:|
-| **11** | **794** | **8481** | **54** | **48** |
+| **16** | **1451** | **16966** | **91** | **108** |
 
-### 🔥 Top 5 Popular Plugins
+### 🔥 Top 6 Popular Plugins
 
-| Rank | Plugin | Downloads | Views |
-|:---:|------|:---:|:---:|
-| 🥇 | [Turn Any Text into Beautiful Mind Maps](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | 240 | 2133 |
-| 🥈 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | 171 | 459 |
-| 🥉 | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | 112 | 1236 |
-| 4️⃣ | [Flash Card ](https://openwebui.com/posts/flash_card_65a2ea8f) | 76 | 1421 |
-| 5️⃣ | [Smart Infographic](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | 65 | 909 |
+> 🕐 Auto-updated: 2026-01-15 00:11
+
+| Rank | Plugin | Version | Downloads | Views | Updated |
+|:---:|------|:---:|:---:|:---:|:---:|
+| 🥇 | [Smart Mind Map](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | 0.9.1 | 451 | 4028 | 2026-01-07 |
+| 🥈 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | 0.3.7 | 194 | 671 | 2026-01-07 |
+| 🥉 | [📊 Smart Infographic (AntV)](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | 1.4.9 | 185 | 1906 | 2026-01-11 |
+| 4️⃣ | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | 1.1.3 | 156 | 1743 | 2026-01-11 |
+| 5️⃣ | [Export to Word (Enhanced)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | 0.4.3 | 122 | 1084 | 2026-01-07 |
+| 6️⃣ | [Flash Card](https://openwebui.com/posts/flash_card_65a2ea8f) | 0.2.4 | 116 | 2059 | 2026-01-07 |
 
 *See full stats in [Community Stats Report](./docs/community-stats.md)*
 <!-- STATS_END -->
@@ -39,15 +45,18 @@ Located in the `plugins/` directory, containing Python-based enhancements:
 #### Actions
 - **Smart Mind Map** (`smart-mind-map`): Generates interactive mind maps from text.
 - **Smart Infographic** (`infographic`): Transforms text into professional infographics using AntV.
-- **Knowledge Card** (`knowledge-card`): Creates beautiful flashcards for learning.
+- **Flash Card** (`flash-card`): Quickly generates beautiful flashcards for learning.
+- **Deep Dive** (`deep-dive`): A comprehensive thinking lens that dives deep into any content.
 - **Export to Excel** (`export_to_excel`): Exports chat history to Excel files.
 - **Export to Word** (`export_to_docx`): Exports chat history to Word documents.
-- **Summary** (`summary`): Text summarization tool.
 
 #### Filters
 - **Async Context Compression** (`async-context-compression`): Optimizes token usage via context compression.
 - **Context Enhancement** (`context_enhancement_filter`): Enhances chat context.
 - **Gemini Manifold Companion** (`gemini_manifold_companion`): Companion filter for Gemini Manifold.
+- **Gemini Multimodal Filter** (`web_gemini_multimodel_filter`): Provides multimodal capabilities (PDF, Office, Video) for any model via Gemini.
+- **Markdown Normalizer** (`markdown_normalizer`): Fixes common Markdown formatting issues in LLM outputs.
+- **Multi-Model Context Merger** (`multi_model_context_merger`): Automatically merges and injects context from multiple model responses.
 
 
 #### Pipes
@@ -103,3 +112,27 @@ If you have great prompts or plugins to share:
 3. Submit a Pull Request.
 
 [Contributing](./CONTRIBUTING.md)
+
+## Contributors ✨
+
+Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
+
+<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
+<!-- prettier-ignore-start -->
+<!-- markdownlint-disable -->
+<table>
+  <tbody>
+    <tr>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/rbb-dev"><img src="https://avatars.githubusercontent.com/u/37469229?v=4?s=100" width="100px;" alt="rbb-dev"/><br /><sub><b>rbb-dev</b></sub></a><br /><a href="#ideas-rbb-dev" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/Fu-Jie/awesome-openwebui/commits?author=rbb-dev" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://trade.xyz/?ref=BZ1RJRXWO"><img src="https://avatars.githubusercontent.com/u/7317522?v=4?s=100" width="100px;" alt="Raxxoor"/><br /><sub><b>Raxxoor</b></sub></a><br /><a href="https://github.com/Fu-Jie/awesome-openwebui/issues?q=author%3Adhaern" title="Bug reports">🐛</a> <a href="#ideas-dhaern" title="Ideas, Planning, & Feedback">🤔</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/i-iooi-i"><img src="https://avatars.githubusercontent.com/u/1827701?v=4?s=100" width="100px;" alt="ZOLO"/><br /><sub><b>ZOLO</b></sub></a><br /><a href="https://github.com/Fu-Jie/awesome-openwebui/issues?q=author%3Ai-iooi-i" title="Bug reports">🐛</a> <a href="#ideas-i-iooi-i" title="Ideas, Planning, & Feedback">🤔</a></td>
+    </tr>
+  </tbody>
+</table>
+
+<!-- markdownlint-restore -->
+<!-- prettier-ignore-end -->
+
+<!-- ALL-CONTRIBUTORS-LIST:END -->
+
+This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!

README_CN.md

@@ -7,27 +7,30 @@ OpenWebUI 增强功能集合。包含个人开发与收集的插件、提示词
 <!-- STATS_START -->
 ## 📊 社区统计
 
-> 🕐 自动更新于 2026-01-06 21:19
+> 🕐 自动更新于 2026-01-15 00:11
 
 | 👤 作者 | 👥 粉丝 | ⭐ 积分 | 🏆 贡献 |
 |:---:|:---:|:---:|:---:|
-| [Fu-Jie](https://openwebui.com/u/Fu-Jie) | **43** | **62** | **17** |
+| [Fu-Jie](https://openwebui.com/u/Fu-Jie) | **104** | **104** | **25** |
 
 | 📝 发布 | ⬇️ 下载 | 👁️ 浏览 | 👍 点赞 | 💾 收藏 |
 |:---:|:---:|:---:|:---:|:---:|
-| **11** | **794** | **8481** | **54** | **48** |
+| **16** | **1451** | **16966** | **91** | **108** |
 
-### 🔥 热门插件 Top 5
+### 🔥 热门插件 Top 6
 
-| 排名 | 插件 | 下载 | 浏览 |
-|:---:|------|:---:|:---:|
-| 🥇 | [Turn Any Text into Beautiful Mind Maps](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | 240 | 2133 |
-| 🥈 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | 171 | 459 |
-| 🥉 | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | 112 | 1236 |
-| 4️⃣ | [Flash Card ](https://openwebui.com/posts/flash_card_65a2ea8f) | 76 | 1421 |
-| 5️⃣ | [Smart Infographic](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | 65 | 909 |
+> 🕐 自动更新于 2026-01-15 00:11
 
-*完整统计请查看 [社区统计报告](./docs/community-stats.md)*
+| 排名 | 插件 | 版本 | 下载 | 浏览 | 更新日期 |
+|:---:|------|:---:|:---:|:---:|:---:|
+| 🥇 | [Smart Mind Map](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | 0.9.1 | 451 | 4028 | 2026-01-07 |
+| 🥈 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | 0.3.7 | 194 | 671 | 2026-01-07 |
+| 🥉 | [📊 Smart Infographic (AntV)](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | 1.4.9 | 185 | 1906 | 2026-01-11 |
+| 4️⃣ | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | 1.1.3 | 156 | 1743 | 2026-01-11 |
+| 5️⃣ | [Export to Word (Enhanced)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | 0.4.3 | 122 | 1084 | 2026-01-07 |
+| 6️⃣ | [Flash Card](https://openwebui.com/posts/flash_card_65a2ea8f) | 0.2.4 | 116 | 2059 | 2026-01-07 |
+
+*完整统计请查看 [社区统计报告](./docs/community-stats.zh.md)*
 <!-- STATS_END -->
 
 ## 📦 项目内容
@@ -39,15 +42,18 @@ OpenWebUI 增强功能集合。包含个人开发与收集的插件、提示词
 #### Actions (交互增强)
 - **Smart Mind Map** (`smart-mind-map`): 智能分析文本并生成交互式思维导图。
 - **Smart Infographic** (`infographic`): 基于 AntV 的智能信息图生成工具。
-- **Knowledge Card** (`knowledge-card`): 快速生成精美的学习记忆卡片。
+- **Flash Card** (`flash-card`): 快速生成精美的学习记忆卡片。
+- **Deep Dive** (`deep-dive`): 深度思考透镜，从背景、逻辑、洞察到行动路径的全方位分析。
 - **Export to Excel** (`export_to_excel`): 将对话内容导出为 Excel 文件。
 - **Export to Word** (`export_to_docx`): 将对话内容导出为 Word 文档。
-- **Summary** (`summary`): 文本摘要生成工具。
 
 #### Filters (消息处理)
 - **Async Context Compression** (`async-context-compression`): 异步上下文压缩，优化 Token 使用。
 - **Context Enhancement** (`context_enhancement_filter`): 上下文增强过滤器。
 - **Gemini Manifold Companion** (`gemini_manifold_companion`): Gemini Manifold 配套增强。
+- **Gemini Multimodal Filter** (`web_gemini_multimodel_filter`): 为任意模型提供多模态能力（PDF、Office、视频等），支持智能路由和字幕精修。
+- **Markdown Normalizer** (`markdown_normalizer`): 修复 LLM 输出中常见的 Markdown 格式问题。
+- **Multi-Model Context Merger** (`multi_model_context_merger`): 自动合并并注入多模型回答的上下文。
 
 #### Pipes (模型管道)
 - **Gemini Manifold** (`gemini_mainfold`): 集成 Gemini 模型的管道。
@@ -104,4 +110,4 @@ OpenWebUI 增强功能集合。包含个人开发与收集的插件、提示词
 2. 将你的文件添加到对应的 `prompts/` 或 `plugins/` 目录。
 3. 提交 Pull Request。
 
-[贡献指南](./CONTRIBUTING.md) | [更新日志](./CHANGELOG.md)
+[贡献指南](./CONTRIBUTING_CN.md) | [更新日志](./CHANGELOG.md)

docs/community-stats.en.md

@@ -1,35 +0,0 @@
-# 📊 OpenWebUI Community Stats Report
-
-> 📅 Updated: 2026-01-06 21:19
-
-## 📈 Overview
-
-| Metric | Value |
-|------|------|
-| 📝 Total Posts | 11 |
-| ⬇️ Total Downloads | 794 |
-| 👁️ Total Views | 8481 |
-| 👍 Total Upvotes | 54 |
-| 💾 Total Saves | 48 |
-| 💬 Total Comments | 13 |
-
-## 📂 By Type
-
-- **action**: 9
-- **filter**: 2
-
-## 📋 Posts List
-
-| Rank | Title | Type | Version | Downloads | Views | Upvotes | Saves | Updated |
-|:---:|------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
-| 1 | [Turn Any Text into Beautiful Mind Maps](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | action | 0.8.2 | 240 | 2133 | 10 | 15 | 2026-01-03 |
-| 2 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | action | 0.3.6 | 171 | 459 | 3 | 3 | 2026-01-03 |
-| 3 | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | filter | 1.1.0 | 112 | 1236 | 5 | 9 | 2025-12-31 |
-| 4 | [Flash Card ](https://openwebui.com/posts/flash_card_65a2ea8f) | action | 0.2.4 | 76 | 1421 | 8 | 5 | 2026-01-03 |
-| 5 | [Smart Infographic](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | action | 1.3.2 | 65 | 909 | 6 | 8 | 2026-01-03 |
-| 6 | [Export to Word (Enhanced Formatting)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | action | 0.4.0 | 51 | 504 | 5 | 4 | 2026-01-05 |
-| 7 | [智能信息图](https://openwebui.com/posts/智能信息图_e04a48ff) | action | 1.3.1 | 33 | 398 | 3 | 0 | 2025-12-29 |
-| 8 | [导出为 Word-支持公式、流程图、表格和代码块](https://openwebui.com/posts/导出为_word_支持公式流程图表格和代码块_8a6306c0) | action | 0.4.1 | 15 | 752 | 7 | 1 | 2026-01-05 |
-| 9 | [智能生成交互式思维导图,帮助用户可视化知识](https://openwebui.com/posts/智能生成交互式思维导图帮助用户可视化知识_8d4b097b) | action | 0.8.0 | 14 | 249 | 2 | 1 | 2025-12-31 |
-| 10 | [闪记卡生成插件](https://openwebui.com/posts/闪记卡生成插件_4a31eac3) | action | 0.2.2 | 12 | 309 | 3 | 1 | 2025-12-31 |
-| 11 | [异步上下文压缩](https://openwebui.com/posts/异步上下文压缩_5c0617cb) | filter | 1.1.0 | 5 | 111 | 2 | 1 | 2025-12-31 |

docs/community-stats.json

@@ -1,191 +1,271 @@
 {
-  "total_posts": 11,
-  "total_downloads": 794,
-  "total_views": 8481,
-  "total_upvotes": 54,
-  "total_downvotes": 1,
-  "total_saves": 48,
-  "total_comments": 13,
+  "total_posts": 16,
+  "total_downloads": 1451,
+  "total_views": 16966,
+  "total_upvotes": 91,
+  "total_downvotes": 2,
+  "total_saves": 108,
+  "total_comments": 23,
   "by_type": {
-    "action": 9,
-    "filter": 2
+    "unknown": 2,
+    "action": 14
   },
   "posts": [
     {
-      "title": "Turn Any Text into Beautiful Mind Maps",
+      "title": "Smart Mind Map",
       "slug": "turn_any_text_into_beautiful_mind_maps_3094c59a",
       "type": "action",
-      "version": "0.8.2",
+      "version": "0.9.1",
       "author": "Fu-Jie",
       "description": "Intelligently analyzes text content and generates interactive mind maps to help users structure and visualize knowledge.",
-      "downloads": 240,
-      "views": 2133,
-      "upvotes": 10,
-      "saves": 15,
-      "comments": 8,
+      "downloads": 451,
+      "views": 4028,
+      "upvotes": 12,
+      "saves": 26,
+      "comments": 11,
       "created_at": "2025-12-30",
-      "updated_at": "2026-01-03",
+      "updated_at": "2026-01-07",
       "url": "https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a"
     },
     {
       "title": "Export to Excel",
       "slug": "export_mulit_table_to_excel_244b8f9d",
       "type": "action",
-      "version": "0.3.6",
+      "version": "0.3.7",
       "author": "Fu-Jie",
       "description": "Extracts tables from chat messages and exports them to Excel (.xlsx) files with smart formatting.",
-      "downloads": 171,
-      "views": 459,
+      "downloads": 194,
+      "views": 671,
       "upvotes": 3,
-      "saves": 3,
+      "saves": 4,
       "comments": 0,
       "created_at": "2025-05-30",
-      "updated_at": "2026-01-03",
+      "updated_at": "2026-01-07",
       "url": "https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d"
     },
+    {
+      "title": "📊 Smart Infographic (AntV)",
+      "slug": "smart_infographic_ad6f0c7f",
+      "type": "action",
+      "version": "1.4.9",
+      "author": "Fu-Jie",
+      "description": "AI-powered infographic generator based on AntV Infographic. Supports professional templates, auto-icon matching, and SVG/PNG downloads.",
+      "downloads": 185,
+      "views": 1906,
+      "upvotes": 9,
+      "saves": 13,
+      "comments": 2,
+      "created_at": "2025-12-28",
+      "updated_at": "2026-01-11",
+      "url": "https://openwebui.com/posts/smart_infographic_ad6f0c7f"
+    },
     {
       "title": "Async Context Compression",
       "slug": "async_context_compression_b1655bc8",
-      "type": "filter",
-      "version": "1.1.0",
+      "type": "action",
+      "version": "1.1.3",
       "author": "Fu-Jie",
-      "description": "This filter automatically compresses long conversation contexts by intelligently summarizing and removing intermediate messages while preserving critical information, thereby significantly reducing token consumption.",
-      "downloads": 112,
-      "views": 1236,
-      "upvotes": 5,
-      "saves": 9,
+      "description": "Reduces token consumption in long conversations while maintaining coherence through intelligent summarization and message compression.",
+      "downloads": 156,
+      "views": 1743,
+      "upvotes": 7,
+      "saves": 15,
       "comments": 0,
       "created_at": "2025-11-08",
-      "updated_at": "2025-12-31",
+      "updated_at": "2026-01-11",
       "url": "https://openwebui.com/posts/async_context_compression_b1655bc8"
     },
     {
-      "title": "Flash Card ",
+      "title": "Export to Word (Enhanced)",
+      "slug": "export_to_word_enhanced_formatting_fca6a315",
+      "type": "action",
+      "version": "0.4.3",
+      "author": "Fu-Jie",
+      "description": "Export current conversation from Markdown to Word (.docx) with Mermaid diagrams rendered client-side (Mermaid.js, SVG+PNG), LaTeX math, real hyperlinks, improved tables, syntax highlighting, and blockquote support.",
+      "downloads": 122,
+      "views": 1084,
+      "upvotes": 6,
+      "saves": 11,
+      "comments": 0,
+      "created_at": "2026-01-03",
+      "updated_at": "2026-01-07",
+      "url": "https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315"
+    },
+    {
+      "title": "Flash Card",
       "slug": "flash_card_65a2ea8f",
       "type": "action",
       "version": "0.2.4",
       "author": "Fu-Jie",
       "description": "Quickly generates beautiful flashcards from text, extracting key points and categories.",
-      "downloads": 76,
-      "views": 1421,
+      "downloads": 116,
+      "views": 2059,
       "upvotes": 8,
-      "saves": 5,
+      "saves": 10,
       "comments": 2,
       "created_at": "2025-12-30",
-      "updated_at": "2026-01-03",
+      "updated_at": "2026-01-07",
       "url": "https://openwebui.com/posts/flash_card_65a2ea8f"
     },
     {
-      "title": "Smart Infographic",
-      "slug": "smart_infographic_ad6f0c7f",
+      "title": "Deep Dive",
+      "slug": "deep_dive_c0b846e4",
       "type": "action",
-      "version": "1.3.2",
-      "author": "jeff",
-      "description": "AI-powered infographic generator based on AntV Infographic. Supports professional templates, auto-icon matching, and SVG/PNG downloads.",
-      "downloads": 65,
-      "views": 909,
-      "upvotes": 6,
-      "saves": 8,
-      "comments": 2,
-      "created_at": "2025-12-28",
-      "updated_at": "2026-01-03",
-      "url": "https://openwebui.com/posts/smart_infographic_ad6f0c7f"
-    },
-    {
-      "title": "Export to Word (Enhanced Formatting)",
-      "slug": "export_to_word_enhanced_formatting_fca6a315",
-      "type": "action",
-      "version": "0.4.0",
+      "version": "1.0.0",
       "author": "Fu-Jie",
-      "description": "Export the current conversation to a formatted Word doc with syntax highlighting, AI-generated titles, and perfect Markdown rendering (tables, quotes, lists).",
-      "downloads": 51,
-      "views": 504,
-      "upvotes": 5,
+      "description": "A comprehensive thinking lens that dives deep into any content - from context to logic, insights, and action paths.",
+      "downloads": 54,
+      "views": 523,
+      "upvotes": 3,
       "saves": 4,
       "comments": 0,
-      "created_at": "2026-01-03",
-      "updated_at": "2026-01-05",
-      "url": "https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315"
+      "created_at": "2026-01-08",
+      "updated_at": "2026-01-08",
+      "url": "https://openwebui.com/posts/deep_dive_c0b846e4"
     },
     {
-      "title": "智能信息图",
-      "slug": "智能信息图_e04a48ff",
-      "type": "action",
-      "version": "1.3.1",
-      "author": "jeff",
-      "description": "基于 AntV Infographic 的智能信息图生成插件。支持多种专业模板，自动图标匹配，并提供 SVG/PNG 下载功能。",
-      "downloads": 33,
-      "views": 398,
-      "upvotes": 3,
-      "saves": 0,
-      "comments": 0,
-      "created_at": "2025-12-28",
-      "updated_at": "2025-12-29",
-      "url": "https://openwebui.com/posts/智能信息图_e04a48ff"
-    },
-    {
-      "title": "导出为 Word-支持公式、流程图、表格和代码块",
+      "title": "导出为 Word (增强版)",
       "slug": "导出为_word_支持公式流程图表格和代码块_8a6306c0",
       "type": "action",
-      "version": "0.4.1",
+      "version": "0.4.3",
       "author": "Fu-Jie",
-      "description": "将当前对话内容从 Markdown 转换并导出为 Word (.docx) 文件，支持中英文无乱码。",
-      "downloads": 15,
-      "views": 752,
-      "upvotes": 7,
-      "saves": 1,
+      "description": "将对话导出为 Word (.docx)，支持 Mermaid 图表 (客户端渲染 SVG+PNG)、LaTeX 数学公式、真实超链接、增强表格格式、代码高亮和引用块。",
+      "downloads": 49,
+      "views": 1155,
+      "upvotes": 9,
+      "saves": 3,
       "comments": 1,
       "created_at": "2026-01-04",
-      "updated_at": "2026-01-05",
+      "updated_at": "2026-01-07",
       "url": "https://openwebui.com/posts/导出为_word_支持公式流程图表格和代码块_8a6306c0"
     },
     {
-      "title": "智能生成交互式思维导图,帮助用户可视化知识",
+      "title": "📊 智能信息图 (AntV Infographic)",
+      "slug": "智能信息图_e04a48ff",
+      "type": "action",
+      "version": "1.4.9",
+      "author": "Fu-Jie",
+      "description": "基于 AntV Infographic 的智能信息图生成插件。支持多种专业模板，自动图标匹配，并提供 SVG/PNG 下载功能。",
+      "downloads": 41,
+      "views": 603,
+      "upvotes": 4,
+      "saves": 0,
+      "comments": 0,
+      "created_at": "2025-12-28",
+      "updated_at": "2026-01-11",
+      "url": "https://openwebui.com/posts/智能信息图_e04a48ff"
+    },
+    {
+      "title": "Markdown Normalizer",
+      "slug": "markdown_normalizer_baaa8732",
+      "type": "action",
+      "version": "1.1.2",
+      "author": "Fu-Jie",
+      "description": "A content normalizer filter that fixes common Markdown formatting issues in LLM outputs, such as broken code blocks, LaTeX formulas, and list formatting.",
+      "downloads": 30,
+      "views": 1095,
+      "upvotes": 7,
+      "saves": 11,
+      "comments": 5,
+      "created_at": "2026-01-12",
+      "updated_at": "2026-01-13",
+      "url": "https://openwebui.com/posts/markdown_normalizer_baaa8732"
+    },
+    {
+      "title": "思维导图",
       "slug": "智能生成交互式思维导图帮助用户可视化知识_8d4b097b",
       "type": "action",
-      "version": "0.8.0",
-      "author": "",
+      "version": "0.9.1",
+      "author": "Fu-Jie",
       "description": "智能分析文本内容,生成交互式思维导图,帮助用户结构化和可视化知识。",
-      "downloads": 14,
-      "views": 249,
+      "downloads": 21,
+      "views": 369,
       "upvotes": 2,
       "saves": 1,
       "comments": 0,
       "created_at": "2025-12-31",
-      "updated_at": "2025-12-31",
+      "updated_at": "2026-01-07",
       "url": "https://openwebui.com/posts/智能生成交互式思维导图帮助用户可视化知识_8d4b097b"
     },
-    {
-      "title": "闪记卡生成插件",
-      "slug": "闪记卡生成插件_4a31eac3",
-      "type": "action",
-      "version": "0.2.2",
-      "author": "Fu-Jie",
-      "description": "快速将文本提炼为精美的学习记忆卡片，支持核心要点提取与分类。",
-      "downloads": 12,
-      "views": 309,
-      "upvotes": 3,
-      "saves": 1,
-      "comments": 0,
-      "created_at": "2025-12-30",
-      "updated_at": "2025-12-31",
-      "url": "https://openwebui.com/posts/闪记卡生成插件_4a31eac3"
-    },
     {
       "title": "异步上下文压缩",
       "slug": "异步上下文压缩_5c0617cb",
-      "type": "filter",
-      "version": "1.1.0",
+      "type": "action",
+      "version": "1.1.3",
       "author": "Fu-Jie",
-      "description": "在 LLM 响应完成后进行上下文摘要和压缩",
-      "downloads": 5,
-      "views": 111,
-      "upvotes": 2,
+      "description": "通过智能摘要和消息压缩，降低长对话的 token 消耗，同时保持对话连贯性。",
+      "downloads": 14,
+      "views": 315,
+      "upvotes": 4,
       "saves": 1,
       "comments": 0,
       "created_at": "2025-11-08",
-      "updated_at": "2025-12-31",
+      "updated_at": "2026-01-11",
       "url": "https://openwebui.com/posts/异步上下文压缩_5c0617cb"
+    },
+    {
+      "title": "闪记卡 (Flash Card)",
+      "slug": "闪记卡生成插件_4a31eac3",
+      "type": "action",
+      "version": "0.2.4",
+      "author": "Fu-Jie",
+      "description": "快速将文本提炼为精美的学习记忆卡片，支持核心要点提取与分类。",
+      "downloads": 12,
+      "views": 405,
+      "upvotes": 4,
+      "saves": 1,
+      "comments": 0,
+      "created_at": "2025-12-30",
+      "updated_at": "2026-01-07",
+      "url": "https://openwebui.com/posts/闪记卡生成插件_4a31eac3"
+    },
+    {
+      "title": "精读",
+      "slug": "精读_99830b0f",
+      "type": "action",
+      "version": "1.0.0",
+      "author": "Fu-Jie",
+      "description": "全方位的思维透镜 —— 从背景全景到逻辑脉络，从深度洞察到行动路径。",
+      "downloads": 6,
+      "views": 214,
+      "upvotes": 2,
+      "saves": 1,
+      "comments": 0,
+      "created_at": "2026-01-08",
+      "updated_at": "2026-01-08",
+      "url": "https://openwebui.com/posts/精读_99830b0f"
+    },
+    {
+      "title": "Review of Claude Haiku 4.5",
+      "slug": "review_of_claude_haiku_45_41b0db39",
+      "type": "unknown",
+      "version": "",
+      "author": "",
+      "description": "",
+      "downloads": 0,
+      "views": 5,
+      "upvotes": 0,
+      "saves": 0,
+      "comments": 0,
+      "created_at": "2026-01-14",
+      "updated_at": "2026-01-14",
+      "url": "https://openwebui.com/posts/review_of_claude_haiku_45_41b0db39"
+    },
+    {
+      "title": " 🛠️ Debug Open WebUI Plugins in Your Browser",
+      "slug": "debug_open_webui_plugins_in_your_browser_81bf7960",
+      "type": "unknown",
+      "version": "",
+      "author": "",
+      "description": "",
+      "downloads": 0,
+      "views": 791,
+      "upvotes": 11,
+      "saves": 7,
+      "comments": 2,
+      "created_at": "2026-01-10",
+      "updated_at": "2026-01-10",
+      "url": "https://openwebui.com/posts/debug_open_webui_plugins_in_your_browser_81bf7960"
     }
   ],
   "user": {
@@ -193,11 +273,11 @@
     "name": "Fu-Jie",
     "profile_url": "https://openwebui.com/u/Fu-Jie",
     "profile_image": "https://community.s3.openwebui.com/uploads/users/b15d1348-4347-42b4-b815-e053342d6cb0/profile_d9510745-4bd4-4f8f-a997-4a21847d9300.webp",
-    "followers": 43,
+    "followers": 104,
     "following": 2,
-    "total_points": 62,
-    "post_points": 53,
-    "comment_points": 9,
-    "contributions": 17
+    "total_points": 104,
+    "post_points": 89,
+    "comment_points": 15,
+    "contributions": 25
   }
 }

docs/community-stats.md

@@ -1,35 +1,40 @@
-# 📊 OpenWebUI 社区统计报告
+# 📊 OpenWebUI Community Stats Report
 
-> 📅 更新时间: 2026-01-06 21:19
+> 📅 Updated: 2026-01-15 00:11
 
-## 📈 总览
+## 📈 Overview
 
-| 指标 | 数值 |
+| Metric | Value |
 |------|------|
-| 📝 发布数量 | 11 |
-| ⬇️ 总下载量 | 794 |
-| 👁️ 总浏览量 | 8481 |
-| 👍 总点赞数 | 54 |
-| 💾 总收藏数 | 48 |
-| 💬 总评论数 | 13 |
+| 📝 Total Posts | 16 |
+| ⬇️ Total Downloads | 1451 |
+| 👁️ Total Views | 16966 |
+| 👍 Total Upvotes | 91 |
+| 💾 Total Saves | 108 |
+| 💬 Total Comments | 23 |
 
-## 📂 按类型分类
+## 📂 By Type
 
-- **action**: 9
-- **filter**: 2
+- **unknown**: 2
+- **action**: 14
 
-## 📋 发布列表
+## 📋 Posts List
 
-| 排名 | 标题 | 类型 | 版本 | 下载 | 浏览 | 点赞 | 收藏 | 更新日期 |
+| Rank | Title | Type | Version | Downloads | Views | Upvotes | Saves | Updated |
 |:---:|------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
-| 1 | [Turn Any Text into Beautiful Mind Maps](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | action | 0.8.2 | 240 | 2133 | 10 | 15 | 2026-01-03 |
-| 2 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | action | 0.3.6 | 171 | 459 | 3 | 3 | 2026-01-03 |
-| 3 | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | filter | 1.1.0 | 112 | 1236 | 5 | 9 | 2025-12-31 |
-| 4 | [Flash Card ](https://openwebui.com/posts/flash_card_65a2ea8f) | action | 0.2.4 | 76 | 1421 | 8 | 5 | 2026-01-03 |
-| 5 | [Smart Infographic](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | action | 1.3.2 | 65 | 909 | 6 | 8 | 2026-01-03 |
-| 6 | [Export to Word (Enhanced Formatting)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | action | 0.4.0 | 51 | 504 | 5 | 4 | 2026-01-05 |
-| 7 | [智能信息图](https://openwebui.com/posts/智能信息图_e04a48ff) | action | 1.3.1 | 33 | 398 | 3 | 0 | 2025-12-29 |
-| 8 | [导出为 Word-支持公式、流程图、表格和代码块](https://openwebui.com/posts/导出为_word_支持公式流程图表格和代码块_8a6306c0) | action | 0.4.1 | 15 | 752 | 7 | 1 | 2026-01-05 |
-| 9 | [智能生成交互式思维导图,帮助用户可视化知识](https://openwebui.com/posts/智能生成交互式思维导图帮助用户可视化知识_8d4b097b) | action | 0.8.0 | 14 | 249 | 2 | 1 | 2025-12-31 |
-| 10 | [闪记卡生成插件](https://openwebui.com/posts/闪记卡生成插件_4a31eac3) | action | 0.2.2 | 12 | 309 | 3 | 1 | 2025-12-31 |
-| 11 | [异步上下文压缩](https://openwebui.com/posts/异步上下文压缩_5c0617cb) | filter | 1.1.0 | 5 | 111 | 2 | 1 | 2025-12-31 |
+| 1 | [Smart Mind Map](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | action | 0.9.1 | 451 | 4028 | 12 | 26 | 2026-01-07 |
+| 2 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | action | 0.3.7 | 194 | 671 | 3 | 4 | 2026-01-07 |
+| 3 | [📊 Smart Infographic (AntV)](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | action | 1.4.9 | 185 | 1906 | 9 | 13 | 2026-01-11 |
+| 4 | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | action | 1.1.3 | 156 | 1743 | 7 | 15 | 2026-01-11 |
+| 5 | [Export to Word (Enhanced)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | action | 0.4.3 | 122 | 1084 | 6 | 11 | 2026-01-07 |
+| 6 | [Flash Card](https://openwebui.com/posts/flash_card_65a2ea8f) | action | 0.2.4 | 116 | 2059 | 8 | 10 | 2026-01-07 |
+| 7 | [Deep Dive](https://openwebui.com/posts/deep_dive_c0b846e4) | action | 1.0.0 | 54 | 523 | 3 | 4 | 2026-01-08 |
+| 8 | [导出为 Word (增强版)](https://openwebui.com/posts/导出为_word_支持公式流程图表格和代码块_8a6306c0) | action | 0.4.3 | 49 | 1155 | 9 | 3 | 2026-01-07 |
+| 9 | [📊 智能信息图 (AntV Infographic)](https://openwebui.com/posts/智能信息图_e04a48ff) | action | 1.4.9 | 41 | 603 | 4 | 0 | 2026-01-11 |
+| 10 | [Markdown Normalizer](https://openwebui.com/posts/markdown_normalizer_baaa8732) | action | 1.1.2 | 30 | 1095 | 7 | 11 | 2026-01-13 |
+| 11 | [思维导图](https://openwebui.com/posts/智能生成交互式思维导图帮助用户可视化知识_8d4b097b) | action | 0.9.1 | 21 | 369 | 2 | 1 | 2026-01-07 |
+| 12 | [异步上下文压缩](https://openwebui.com/posts/异步上下文压缩_5c0617cb) | action | 1.1.3 | 14 | 315 | 4 | 1 | 2026-01-11 |
+| 13 | [闪记卡 (Flash Card)](https://openwebui.com/posts/闪记卡生成插件_4a31eac3) | action | 0.2.4 | 12 | 405 | 4 | 1 | 2026-01-07 |
+| 14 | [精读](https://openwebui.com/posts/精读_99830b0f) | action | 1.0.0 | 6 | 214 | 2 | 1 | 2026-01-08 |
+| 15 | [Review of Claude Haiku 4.5](https://openwebui.com/posts/review_of_claude_haiku_45_41b0db39) | unknown |  | 0 | 5 | 0 | 0 | 2026-01-14 |
+| 16 | [ 🛠️ Debug Open WebUI Plugins in Your Browser](https://openwebui.com/posts/debug_open_webui_plugins_in_your_browser_81bf7960) | unknown |  | 0 | 791 | 11 | 7 | 2026-01-10 |

docs/community-stats.zh.md

@@ -0,0 +1,40 @@
+# 📊 OpenWebUI 社区统计报告
+
+> 📅 更新时间: 2026-01-15 00:11
+
+## 📈 总览
+
+| 指标 | 数值 |
+|------|------|
+| 📝 发布数量 | 16 |
+| ⬇️ 总下载量 | 1451 |
+| 👁️ 总浏览量 | 16966 |
+| 👍 总点赞数 | 91 |
+| 💾 总收藏数 | 108 |
+| 💬 总评论数 | 23 |
+
+## 📂 按类型分类
+
+- **unknown**: 2
+- **action**: 14
+
+## 📋 发布列表
+
+| 排名 | 标题 | 类型 | 版本 | 下载 | 浏览 | 点赞 | 收藏 | 更新日期 |
+|:---:|------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| 1 | [Smart Mind Map](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | action | 0.9.1 | 451 | 4028 | 12 | 26 | 2026-01-07 |
+| 2 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | action | 0.3.7 | 194 | 671 | 3 | 4 | 2026-01-07 |
+| 3 | [📊 Smart Infographic (AntV)](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | action | 1.4.9 | 185 | 1906 | 9 | 13 | 2026-01-11 |
+| 4 | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | action | 1.1.3 | 156 | 1743 | 7 | 15 | 2026-01-11 |
+| 5 | [Export to Word (Enhanced)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | action | 0.4.3 | 122 | 1084 | 6 | 11 | 2026-01-07 |
+| 6 | [Flash Card](https://openwebui.com/posts/flash_card_65a2ea8f) | action | 0.2.4 | 116 | 2059 | 8 | 10 | 2026-01-07 |
+| 7 | [Deep Dive](https://openwebui.com/posts/deep_dive_c0b846e4) | action | 1.0.0 | 54 | 523 | 3 | 4 | 2026-01-08 |
+| 8 | [导出为 Word (增强版)](https://openwebui.com/posts/导出为_word_支持公式流程图表格和代码块_8a6306c0) | action | 0.4.3 | 49 | 1155 | 9 | 3 | 2026-01-07 |
+| 9 | [📊 智能信息图 (AntV Infographic)](https://openwebui.com/posts/智能信息图_e04a48ff) | action | 1.4.9 | 41 | 603 | 4 | 0 | 2026-01-11 |
+| 10 | [Markdown Normalizer](https://openwebui.com/posts/markdown_normalizer_baaa8732) | action | 1.1.2 | 30 | 1095 | 7 | 11 | 2026-01-13 |
+| 11 | [思维导图](https://openwebui.com/posts/智能生成交互式思维导图帮助用户可视化知识_8d4b097b) | action | 0.9.1 | 21 | 369 | 2 | 1 | 2026-01-07 |
+| 12 | [异步上下文压缩](https://openwebui.com/posts/异步上下文压缩_5c0617cb) | action | 1.1.3 | 14 | 315 | 4 | 1 | 2026-01-11 |
+| 13 | [闪记卡 (Flash Card)](https://openwebui.com/posts/闪记卡生成插件_4a31eac3) | action | 0.2.4 | 12 | 405 | 4 | 1 | 2026-01-07 |
+| 14 | [精读](https://openwebui.com/posts/精读_99830b0f) | action | 1.0.0 | 6 | 214 | 2 | 1 | 2026-01-08 |
+| 15 | [Review of Claude Haiku 4.5](https://openwebui.com/posts/review_of_claude_haiku_45_41b0db39) | unknown |  | 0 | 5 | 0 | 0 | 2026-01-14 |
+| 16 | [ 🛠️ Debug Open WebUI Plugins in Your Browser](https://openwebui.com/posts/debug_open_webui_plugins_in_your_browser_81bf7960) | unknown |  | 0 | 791 | 11 | 7 | 2026-01-10 |

docs/development/frontend-console-debugging.md

@@ -0,0 +1,150 @@
+# 🛠️ Debugging Python Plugins with Frontend Console
+
+When developing plugins for Open WebUI, debugging can be challenging. Standard `print()` statements or server-side logging might not always be accessible, especially in hosted environments or when you want to see the data flow in real-time alongside the UI interactions.
+
+This guide introduces a powerful technique: **Frontend Console Debugging**. By injecting JavaScript from your Python plugin, you can print structured logs directly to the browser's Developer Tools console (F12).
+
+## Why Frontend Debugging?
+
+*   **Real-time Feedback**: See logs immediately as actions happen in the browser.
+*   **Rich Objects**: Inspect complex JSON objects (like `body` or `messages`) interactively, rather than reading massive text dumps.
+*   **No Server Access Needed**: Debug issues even if you don't have SSH/Console access to the backend server.
+*   **Clean Output**: Group logs using `console.group()` to keep your console organized.
+
+## The Core Mechanism
+
+Open WebUI plugins (both Actions and Filters) support an event system. We can leverage the `__event_call__` (or sometimes `__event_emitter__`) to send a special event of type `execute`. This tells the frontend to run the provided JavaScript code.
+
+### The Helper Method
+
+To make this easy to use, we recommend adding a helper method `_emit_debug_log` to your plugin class.
+
+```python
+import json
+from typing import List
+
+async def _emit_debug_log(
+    self, 
+    __event_call__, 
+    title: str, 
+    data: dict
+):
+    """
+    Emit debug log to browser console via JS execution.
+    
+    Args:
+        __event_call__: The event callable passed to action/outlet.
+        title: A title for the log group.
+        data: A dictionary of data to log.
+    """
+    # 1. Check if debugging is enabled (recommended)
+    if not getattr(self.valves, "show_debug_log", True) or not __event_call__:
+        return
+
+    try:
+        # 2. Construct the JavaScript code
+        # We use an async IIFE (Immediately Invoked Function Expression) 
+        # to ensure a clean scope and support await if needed.
+        js_code = f"""
+            (async function() {{
+                console.group("🛠️ Plugin Debug: {title}");
+                console.log({json.dumps(data, ensure_ascii=False)});
+                console.groupEnd();
+            }})();
+        """
+
+        # 3. Send the execute event
+        await __event_call__(
+            {
+                "type": "execute",
+                "data": {"code": js_code},
+            }
+        )
+    except Exception as e:
+        print(f"Error emitting debug log: {e}")
+```
+
+## Implementation Steps
+
+### 1. Add a Valve for Control
+
+It's best practice to make debugging optional so it doesn't clutter the console for normal users.
+
+```python
+from pydantic import BaseModel, Field
+
+class Filter:
+    class Valves(BaseModel):
+        show_debug_log: bool = Field(
+            default=False, 
+            description="Print debug logs to browser console (F12)"
+        )
+    
+    def __init__(self):
+        self.valves = self.Valves()
+```
+
+### 2. Inject `__event_call__`
+
+Ensure your `action` (for Actions) or `outlet` (for Filters) method accepts `__event_call__`.
+
+**For Filters (`outlet`):**
+
+```python
+async def outlet(
+    self,
+    body: dict,
+    __user__: Optional[dict] = None,
+    __event_call__=None,  # <--- Add this
+    __metadata__: Optional[dict] = None,
+) -> dict:
+```
+
+**For Actions (`action`):**
+
+```python
+async def action(
+    self,
+    body: dict,
+    __user__=None,
+    __event_call__=None, # <--- Add this
+    __request__=None,
+):
+```
+
+### 3. Call the Helper
+
+Now you can log anything, anywhere in your logic!
+
+```python
+# Inside your logic...
+new_content = self.process_content(content)
+
+# Log the before and after
+await self._emit_debug_log(
+    __event_call__,
+    "Content Normalization",
+    {
+        "original": content,
+        "processed": new_content,
+        "changes": diff_list
+    }
+)
+```
+
+## Best Practices
+
+1.  **Use `json.dumps`**: Always serialize your Python dictionaries to JSON strings before embedding them in the f-string. This handles escaping quotes and special characters correctly.
+2.  **Async IIFE**: Wrapping your JS in `(async function() { ... })();` is safer than raw code. It prevents variable collisions with other scripts and allows using `await` inside your debug script if you ever need to check DOM elements.
+3.  **Check for None**: Always check if `__event_call__` is not None before using it, as it might not be available in all contexts (e.g., when running tests or in older Open WebUI versions).
+
+## Example Output
+
+When enabled, your browser console will show:
+
+```text
+> 🛠️ Plugin Debug: Content Normalization
+  > {original: "...", processed: "...", changes: [...]}
+```
+
+You can expand the object to inspect every detail of your data. Happy debugging!

docs/development/mermaid-syntax-standards.md

@@ -0,0 +1,64 @@
+# Mermaid Syntax Standards & Best Practices
+
+This document summarizes the official syntax standards for Mermaid flowcharts, focusing on node labels, quoting rules, and special character handling. It serves as a reference for the `markdown_normalizer` plugin logic.
+
+## 1. Node Shapes & Syntax
+
+Mermaid supports various node shapes defined by specific wrapping characters.
+
+| Shape | Syntax | Example |
+| :--- | :--- | :--- |
+| **Rectangle** (Default) | `id[Label]` | `A[Start]` |
+| **Rounded** | `id(Label)` | `B(Process)` |
+| **Stadium** (Pill) | `id([Label])` | `C([End])` |
+| **Subroutine** | `id[[Label]]` | `D[[Subroutine]]` |
+| **Cylinder** (Database) | `id[(Label)]` | `E[(Database)]` |
+| **Circle** | `id((Label))` | `F((Point))` |
+| **Double Circle** | `id(((Label)))` | `G(((Endpoint)))` |
+| **Asymmetric** | `id>Label]` | `H>Flag]` |
+| **Rhombus** (Decision) | `id{Label}` | `I{Decision}` |
+| **Hexagon** | `id{{Label}}` | `J{{Prepare}}` |
+| **Parallelogram** | `id[/Label/]` | `K[/Input/]` |
+| **Parallelogram Alt** | `id[\Label\]` | `L[\Output\]` |
+| **Trapezoid** | `id[/Label\]` | `M[/Trap/]` |
+| **Trapezoid Alt** | `id[\Label/]` | `N[\TrapAlt/]` |
+
+## 2. Quoting Rules (Critical)
+
+### Why Quote?
+Quoting node labels is **highly recommended** and sometimes **mandatory** to prevent syntax errors.
+
+### Mandatory Quoting Scenarios
+You **MUST** enclose labels in double quotes `"` if they contain:
+1.  **Special Characters**: `()`, `[]`, `{}`, `;`, `"`, etc.
+2.  **Keywords**: Words like `end`, `subgraph`, etc., if used in specific contexts.
+3.  **Unicode/Emoji**: While often supported without quotes, quoting ensures consistent rendering across different environments.
+4.  **Markdown**: If you want to use Markdown formatting (bold, italic) inside a label.
+
+### Best Practice: Always Quote
+To ensure robustness, especially when processing LLM-generated content which may contain unpredictable characters, **always enclosing labels in double quotes is the safest strategy**.
+
+**Examples:**
+*   ❌ Risky: `id(Start: 15:00)` (Colon might be interpreted as style separator)
+*   ✅ Safe: `id("Start: 15:00")`
+*   ❌ Broken: `id(Func(x))` (Nested parentheses break parsing)
+*   ✅ Safe: `id("Func(x)")`
+
+## 3. Escape Characters
+
+Inside a quoted string:
+*   Double quotes `"` must be escaped as `\"`.
+*   HTML entities (e.g., `#35;` for `#`) can be used.
+
+## 4. Plugin Logic Verification
+
+The `markdown_normalizer` plugin implements the following logic:
+
+1.  **Detection**: Identifies Mermaid node definitions using a comprehensive regex covering all shapes above.
+2.  **Normalization**:
+    *   Checks if the label is already quoted.
+    *   If **NOT quoted**, it wraps the label in double quotes `""`.
+    *   Escapes any existing double quotes inside the label (`"` -> `\"`).
+3.  **Shape Preservation**: The regex captures the specific opening and closing delimiters (e.g., `((` and `))`) to ensure the node shape is strictly preserved during normalization.
+
+**Conclusion**: The plugin's behavior of automatically adding quotes to unquoted labels is **fully aligned with Mermaid's official best practices** for robustness and error prevention.

docs/development/plugin-guide.md

@@ -7,10 +7,10 @@
 ## 📚 Table of Contents
 
 1. [Quick Start](#1-quick-start)
-2. [Core Concepts & SDK Details](#2-core-concepts--sdk-details)
+2. [Core Concepts & SDK Details](#2-core-concepts-sdk-details)
 3. [Deep Dive into Plugin Types](#3-deep-dive-into-plugin-types)
 4. [Advanced Development Patterns](#4-advanced-development-patterns)
-5. [Best Practices & Design Principles](#5-best-practices--design-principles)
+5. [Best Practices & Design Principles](#5-best-practices-design-principles)
 6. [Troubleshooting](#6-troubleshooting)
 
 ---
@@ -351,8 +351,7 @@ async def action(self, body, __event_call__, __metadata__, ...):
 
 #### Reference Implementations
 
-- `plugins/actions/js-render-poc/infographic_markdown.py` - AntV Infographic + Data URL
-- `plugins/actions/js-render-poc/js_render_poc.py` - Basic proof of concept
+- `plugins/actions/infographic/infographic.py` - Production-ready implementation using AntV + Data URL
 
 ---
 

docs/development/plugin-guide.zh.md

@@ -4,19 +4,19 @@
 
 ## 📚 目录
 
-1.  [插件开发快速入门](#1-插件开发快速入门)
-2.  [核心概念与 SDK 详解](#2-核心概念与-sdk-详解)
-3.  [插件类型深度解析](#3-插件类型深度解析)
-    *   [Action (动作)](#31-action-动作)
-    *   [Filter (过滤器)](#32-filter-过滤器)
-    *   [Pipe (管道)](#33-pipe-管道)
-4.  [高级开发模式](#4-高级开发模式)
-5.  [最佳实践与设计原则](#5-最佳实践与设计原则)
-6.  [故障排查](#6-故障排查)
+1.  [插件开发快速入门](#1-quick-start)
+2.  [核心概念与 SDK 详解](#2-core-concepts-sdk-details)
+3.  [插件类型深度解析](#3-plugin-types)
+    *   [Action (动作)](#31-action)
+    *   [Filter (过滤器)](#32-filter)
+    *   [Pipe (管道)](#33-pipe)
+4.  [高级开发模式](#4-advanced-patterns)
+5.  [最佳实践与设计原则](#5-best-practices)
+6.  [故障排查](#6-troubleshooting)
 
 ---
 
-## 1. 插件开发快速入门
+## 1. 插件开发快速入门 {: #1-quick-start }
 
 ### 1.1 什么是 OpenWebUI 插件？
 
@@ -64,7 +64,7 @@ class Action:
 
 ---
 
-## 2. 核心概念与 SDK 详解
+## 2. 核心概念与 SDK 详解 {: #2-core-concepts-sdk-details }
 
 ### 2.1 ⚠️ 重要：同步与异步
 
@@ -107,9 +107,9 @@ class Filter:
 
 ---
 
-## 3. 插件类型深度解析
+## 3. 插件类型深度解析 {: #3-plugin-types }
 
-### 3.1 Action (动作)
+### 3.1 Action (动作) {: #31-action }
 
 **定位**：在消息下方添加按钮，用户点击触发。
 
@@ -134,7 +134,7 @@ async def action(self, body, __event_call__):
     await __event_call__({"type": "execute", "data": {"code": js}})
 ```
 
-### 3.2 Filter (过滤器)
+### 3.2 Filter (过滤器) {: #32-filter }
 
 **定位**：中间件，拦截并修改请求/响应。
 
@@ -155,7 +155,7 @@ async def inlet(self, body, __metadata__):
     return body
 ```
 
-### 3.3 Pipe (管道)
+### 3.3 Pipe (管道) {: #33-pipe }
 
 **定位**：自定义模型/代理。
 
@@ -177,7 +177,7 @@ class Pipe:
 
 ---
 
-## 4. 高级开发模式
+## 4. 高级开发模式 {: #4-advanced-patterns }
 
 ### 4.1 Pipe 与 Filter 协同
 利用 `__request__.app.state` 在不同插件间共享数据。
@@ -315,10 +315,9 @@ async def action(self, body, __event_call__, __metadata__, ...):
 
 #### 参考实现
 
-- `plugins/actions/js-render-poc/infographic_markdown.py` - AntV 信息图 + Data URL
-- `plugins/actions/js-render-poc/js_render_poc.py` - 基础概念验证
+- `plugins/actions/infographic/infographic.py` - 基于 AntV + Data URL 的生产级实现
 
-## 5. 最佳实践与设计原则
+## 5. 最佳实践与设计原则 {: #5-best-practices }
 
 ### 5.1 命名与定位
 *   **简短有力**：如 "闪记卡", "精读"。避免 "文本分析助手" 这种泛词。
@@ -344,7 +343,7 @@ except Exception as e:
 
 ---
 
-## 6. 故障排查
+## 6. 故障排查 {: #6-troubleshooting }
 
 *   **HTML 不显示？** 确保包裹在 ` ```html ... ``` ` 代码块中。
 *   **数据库报错？** 检查是否在 `async` 函数中直接调用了同步的 DB 方法，请使用 `asyncio.to_thread`。

docs/index.md

@@ -73,13 +73,13 @@ hide:
 
     [:octicons-arrow-right-24: Learn More](plugins/actions/smart-mind-map.md)
 
--   :material-card-text:{ .lg .middle } **Knowledge Card**
+-   :material-card-text:{ .lg .middle } **Flash Card**
 
     ---
 
-    Quickly generates beautiful learning memory cards, perfect for studying and quick memorization.
+    Quickly generates beautiful flashcards from text, extracting key points and categories.
 
-    [:octicons-arrow-right-24: Learn More](plugins/actions/knowledge-card.md)
+    [:octicons-arrow-right-24: Learn More](plugins/actions/flash-card.md)
 
 -   :material-arrow-collapse-vertical:{ .lg .middle } **Async Context Compression**
 

docs/index.zh.md

@@ -73,13 +73,13 @@ hide:
 
     [:octicons-arrow-right-24: 了解更多](plugins/actions/smart-mind-map.md)
 
--   :material-card-text:{ .lg .middle } **知识卡片**
+-   :material-card-text:{ .lg .middle } **Flash Card（闪记卡）**
 
     ---
 
     快速生成精美的学习记忆卡片，非常适合学习和快速记忆。
 
-    [:octicons-arrow-right-24: 了解更多](plugins/actions/knowledge-card.md)
+    [:octicons-arrow-right-24: 了解更多](plugins/actions/flash-card.md)
 
 -   :material-arrow-collapse-vertical:{ .lg .middle } **异步上下文压缩**
 

docs/plugins/actions/deep-dive.md

@@ -0,0 +1,111 @@
+# Deep Dive
+
+<span class="category-badge action">Action</span>
+<span class="version-badge">v1.0.0</span>
+
+A comprehensive thinking lens that dives deep into any content - from context to logic, insights, and action paths.
+
+---
+
+## Overview
+
+The Deep Dive plugin transforms how you understand complex content by guiding you through a structured thinking process. Rather than just summarizing, it deconstructs content across four phases:
+
+- **🔍 The Context (What?)**: Panoramic view of the situation and background
+- **🧠 The Logic (Why?)**: Deconstruction of reasoning and mental models  
+- **💎 The Insight (So What?)**: Non-obvious value and hidden implications
+- **🚀 The Path (Now What?)**: Specific, prioritized strategic actions
+
+## Features
+
+- :material-brain: **Thinking Chain**: Complete structured analysis process
+- :material-eye: **Deep Understanding**: Reveals hidden assumptions and blind spots
+- :material-lightbulb-on: **Insight Extraction**: Finds the "Aha!" moments
+- :material-rocket-launch: **Action Oriented**: Translates understanding into actionable steps
+- :material-theme-light-dark: **Theme Adaptive**: Auto-adapts to OpenWebUI light/dark theme
+- :material-translate: **Multi-language**: Outputs in user's preferred language
+
+---
+
+## Installation
+
+1. Download the plugin file: [`deep_dive.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/deep-dive)
+2. Upload to OpenWebUI: **Admin Panel** → **Settings** → **Functions**
+3. Enable the plugin
+
+---
+
+## Usage
+
+1. Provide any long text, article, or meeting notes in the chat
+2. Click the **Deep Dive** button in the message action bar
+3. Follow the visual timeline from Context → Logic → Insight → Path
+
+---
+
+## Configuration
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `SHOW_STATUS` | boolean | `true` | Show status updates during processing |
+| `MODEL_ID` | string | `""` | LLM model for analysis (empty = current model) |
+| `MIN_TEXT_LENGTH` | integer | `200` | Minimum text length for analysis |
+| `CLEAR_PREVIOUS_HTML` | boolean | `true` | Clear previous plugin results |
+| `MESSAGE_COUNT` | integer | `1` | Number of recent messages to analyze |
+
+---
+
+## Theme Support
+
+Deep Dive automatically adapts to OpenWebUI's light/dark theme:
+
+- Detects theme from parent document `<meta name="theme-color">` tag
+- Falls back to `html/body` class or `data-theme` attribute
+- Uses system preference `prefers-color-scheme: dark` as last resort
+
+!!! tip "For Best Results"
+    Enable **iframe Sandbox Allow Same Origin** in OpenWebUI:
+    **Settings** → **Interface** → **Artifacts** → Check **iframe Sandbox Allow Same Origin**
+
+---
+
+## Example Output
+
+The plugin generates a beautiful structured timeline:
+
+```
+┌─────────────────────────────────────┐
+│  🌊 Deep Dive Analysis              │
+│  👤 User  📅 Date  📊 Word count    │
+├─────────────────────────────────────┤
+│  🔍 Phase 01: The Context           │
+│  [High-level panoramic view]        │
+│                                     │
+│  🧠 Phase 02: The Logic             │
+│  • Reasoning structure...           │
+│  • Hidden assumptions...            │
+│                                     │
+│  💎 Phase 03: The Insight           │
+│  • Non-obvious value...             │
+│  • Blind spots revealed...          │
+│                                     │
+│  🚀 Phase 04: The Path              │
+│  ▸ Priority Action 1...             │
+│  ▸ Priority Action 2...             │
+└─────────────────────────────────────┘
+```
+
+---
+
+## Requirements
+
+!!! note "Prerequisites"
+    - OpenWebUI v0.3.0 or later
+    - Uses the active LLM model for analysis
+    - Requires `markdown` Python package
+
+---
+
+## Source Code
+
+[:fontawesome-brands-github: View on GitHub](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/deep-dive){ .md-button }

docs/plugins/actions/deep-dive.zh.md

@@ -0,0 +1,111 @@
+# 精读 (Deep Dive)
+
+<span class="category-badge action">Action</span>
+<span class="version-badge">v1.0.0</span>
+
+全方位的思维透镜 —— 从背景全景到逻辑脉络，从深度洞察到行动路径。
+
+---
+
+## 概述
+
+精读插件改变了您理解复杂内容的方式，通过结构化的思维过程引导您进行深度分析。它不仅仅是摘要，而是从四个阶段解构内容：
+
+- **🔍 全景 (The Context)**: 情境与背景的高层级全景视图
+- **🧠 脉络 (The Logic)**: 解构底层推理逻辑与思维模型
+- **💎 洞察 (The Insight)**: 提取非显性价值与隐藏含义
+- **🚀 路径 (The Path)**: 具体的、按优先级排列的战略行动
+
+## 功能特性
+
+- :material-brain: **思维链**: 完整的结构化分析过程
+- :material-eye: **深度理解**: 揭示隐藏的假设和思维盲点
+- :material-lightbulb-on: **洞察提取**: 发现"原来如此"的时刻
+- :material-rocket-launch: **行动导向**: 将深度理解转化为可执行步骤
+- :material-theme-light-dark: **主题自适应**: 自动适配 OpenWebUI 深色/浅色主题
+- :material-translate: **多语言**: 以用户偏好语言输出
+
+---
+
+## 安装
+
+1. 下载插件文件: [`deep_dive_cn.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/deep-dive)
+2. 上传到 OpenWebUI: **管理面板** → **设置** → **Functions**
+3. 启用插件
+
+---
+
+## 使用方法
+
+1. 在聊天中提供任何长文本、文章或会议记录
+2. 点击消息操作栏中的 **精读** 按钮
+3. 沿着视觉时间轴从"全景"探索到"路径"
+
+---
+
+## 配置参数
+
+| 选项 | 类型 | 默认值 | 描述 |
+|------|------|--------|------|
+| `SHOW_STATUS` | boolean | `true` | 处理过程中是否显示状态更新 |
+| `MODEL_ID` | string | `""` | 用于分析的 LLM 模型（空 = 当前模型） |
+| `MIN_TEXT_LENGTH` | integer | `200` | 分析所需的最小文本长度 |
+| `CLEAR_PREVIOUS_HTML` | boolean | `true` | 是否清除之前的插件结果 |
+| `MESSAGE_COUNT` | integer | `1` | 要分析的最近消息数量 |
+
+---
+
+## 主题支持
+
+精读插件自动适配 OpenWebUI 的深色/浅色主题：
+
+- 从父文档 `<meta name="theme-color">` 标签检测主题
+- 回退到 `html/body` 的 class 或 `data-theme` 属性
+- 最后使用系统偏好 `prefers-color-scheme: dark`
+
+!!! tip "最佳效果"
+    请在 OpenWebUI 中启用 **iframe Sandbox Allow Same Origin**：
+    **设置** → **界面** → **Artifacts** → 勾选 **iframe Sandbox Allow Same Origin**
+
+---
+
+## 输出示例
+
+插件生成精美的结构化时间轴：
+
+```
+┌─────────────────────────────────────┐
+│  📖 精读分析报告                     │
+│  👤 用户  📅 日期  📊 字数           │
+├─────────────────────────────────────┤
+│  🔍 阶段 01: 全景 (The Context)      │
+│  [高层级全景视图内容]                 │
+│                                     │
+│  🧠 阶段 02: 脉络 (The Logic)        │
+│  • 推理结构分析...                   │
+│  • 隐藏假设识别...                   │
+│                                     │
+│  💎 阶段 03: 洞察 (The Insight)      │
+│  • 非显性价值提取...                 │
+│  • 思维盲点揭示...                   │
+│                                     │
+│  🚀 阶段 04: 路径 (The Path)         │
+│  ▸ 优先级行动 1...                   │
+│  ▸ 优先级行动 2...                   │
+└─────────────────────────────────────┘
+```
+
+---
+
+## 系统要求
+
+!!! note "前提条件"
+    - OpenWebUI v0.3.0 或更高版本
+    - 使用当前活跃的 LLM 模型进行分析
+    - 需要 `markdown` Python 包
+
+---
+
+## 源代码
+
+[:fontawesome-brands-github: 在 GitHub 上查看](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/deep-dive){ .md-button }

docs/plugins/actions/export-to-word.md

@@ -1,7 +1,7 @@
 # Export to Word
 
 <span class="category-badge action">Action</span>
-<span class="version-badge">v0.4.1</span>
+<span class="version-badge">v0.4.3</span>
 
 Export conversation to Word (.docx) with **syntax highlighting**, **native math equations**, **Mermaid diagrams**, **citations**, and **enhanced table formatting**.
 
@@ -53,6 +53,8 @@ You can configure the following settings via the **Valves** button in the plugin
 | `MATH_ENABLE` | Enable LaTeX math block conversion. | `True` |
 | `MATH_INLINE_DOLLAR_ENABLE` | Enable inline `$ ... $` math conversion. | `True` |
 
+## 🔥 What's New in v0.4.3
+
 ### User-Level Configuration (UserValves)
 
 Users can override the following settings in their personal settings:
@@ -118,3 +120,4 @@ Users can override the following settings in their personal settings:
 ## Source Code
 
 [:fontawesome-brands-github: View on GitHub](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/export_to_docx){ .md-button }
+**Author:** [Fu-Jie](https://github.com/Fu-Jie) | **Version:** 0.4.3 | **Project:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui)

docs/plugins/actions/export-to-word.zh.md

@@ -1,7 +1,7 @@
 # Export to Word（导出为 Word）
 
 <span class="category-badge action">Action</span>
-<span class="version-badge">v0.4.1</span>
+<span class="version-badge">v0.4.3</span>
 
 将当前对话导出为完美格式的 Word 文档，支持**代码语法高亮**、**原生数学公式**、**Mermaid 图表**、**引用资料**以及**增强表格**渲染。
 
@@ -117,4 +117,4 @@ Export to Word 插件会把聊天消息从 Markdown 转成精致的 Word 文档
 
 ## 源码
 
-[:fontawesome-brands-github: 在 GitHub 查看](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/export_to_docx){ .md-button }
+[:fontawes**Author:** [Fu-Jie](https://github.com/Fu-Jie) | **Version:** 0.4.3 | **Project:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui)/tree/main/plugins/actions/export_to_docx){ .md-button }

docs/plugins/actions/flash-card.md

@@ -1,9 +1,9 @@
-# Knowledge Card
+# Flash Card
 
 <span class="category-badge action">Action</span>
-<span class="version-badge">v0.2.2</span>
+<span class="version-badge">v0.2.4</span>
 
-Quickly generates beautiful learning memory cards, perfect for studying and quick memorization.
+Quickly generates beautiful flashcards from text, extracting key points and categories.
 
 ---
 
@@ -23,7 +23,7 @@ The Knowledge Card plugin (also known as Flash Card / 闪记卡) transforms cont
 
 ## Installation
 
-1. Download the plugin file: [`knowledge_card.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/knowledge-card)
+1. Download the plugin file: [`flash_card.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/flash-card)
 2. Upload to OpenWebUI: **Admin Panel** → **Settings** → **Functions**
 3. Enable the plugin
 
@@ -85,4 +85,4 @@ The Knowledge Card plugin (also known as Flash Card / 闪记卡) transforms cont
 
 ## Source Code
 
-[:fontawesome-brands-github: View on GitHub](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/knowledge-card){ .md-button }
+[:fontawesome-brands-github: View on GitHub](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/flash-card){ .md-button }

docs/plugins/actions/flash-card.zh.md

@@ -1,7 +1,7 @@
-# Knowledge Card（知识卡片）
+# Flash Card（闪记卡）
 
 <span class="category-badge action">Action</span>
-<span class="version-badge">v0.2.0</span>
+<span class="version-badge">v0.2.4</span>
 
 快速生成精美的学习记忆卡片，适合学习和速记。
 
@@ -23,7 +23,7 @@ Knowledge Card 插件（又名 Flash Card / 闪记卡）会把内容转成视觉
 
 ## 安装
 
-1. 下载插件文件：[`knowledge_card.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/knowledge-card)
+1. 下载插件文件：[`flash_card.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/flash-card)
 2. 上传到 OpenWebUI：**Admin Panel** → **Settings** → **Functions**
 3. 启用插件
 
@@ -85,4 +85,4 @@ Knowledge Card 插件（又名 Flash Card / 闪记卡）会把内容转成视觉
 
 ## 源码
 
-[:fontawesome-brands-github: 在 GitHub 查看](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/knowledge-card){ .md-button }
+[:fontawesome-brands-github: 在 GitHub 查看](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/flash-card){ .md-button }

docs/plugins/actions/index.md

@@ -23,7 +23,7 @@ Actions are interactive plugins that:
 
     Intelligently analyzes text content and generates interactive mind maps with beautiful visualizations.
 
-    **Version:** 0.8.0
+    **Version:** 0.9.1
 
     [:octicons-arrow-right-24: Documentation](smart-mind-map.md)
 
@@ -33,19 +33,19 @@ Actions are interactive plugins that:
 
     Transform text into professional infographics using AntV visualization engine with various templates.
 
-    **Version:** 1.3.0
+    **Version:** 1.4.9
 
     [:octicons-arrow-right-24: Documentation](smart-infographic.md)
 
--   :material-card-text:{ .lg .middle } **Knowledge Card**
+-   :material-card-text:{ .lg .middle } **Flash Card**
 
     ---
 
-    Quickly generates beautiful learning memory cards, perfect for studying and memorization.
+    Quickly generates beautiful flashcards from text, extracting key points and categories.
 
-    **Version:** 0.2.2
+    **Version:** 0.2.4
 
-    [:octicons-arrow-right-24: Documentation](knowledge-card.md)
+    [:octicons-arrow-right-24: Documentation](flash-card.md)
 
 -   :material-file-excel:{ .lg .middle } **Export to Excel**
 
@@ -63,29 +63,21 @@ Actions are interactive plugins that:
 
     Export the current conversation to a formatted Word doc with **syntax highlighting**, **native math equations**, **Mermaid diagrams**, **citations**, and **enhanced table formatting**.
     
-    **Version:** 0.4.1
+    **Version:** 0.4.2
 
     [:octicons-arrow-right-24: Documentation](export-to-word.md)
 
--   :material-text-box-search:{ .lg .middle } **Summary**
+-   :material-brain:{ .lg .middle } **Deep Dive**
 
     ---
 
-    Generate concise summaries of long text content with key points extraction.
-
-    **Version:** 0.1.0
-
-    [:octicons-arrow-right-24: Documentation](summary.md)
-
--   :material-image-text:{ .lg .middle } **Infographic to Markdown**
-
-    ---
-
-    AI-powered infographic generator that renders SVG and embeds it as Markdown Data URL image.
+    A comprehensive thinking lens that dives deep into any content - Context → Logic → Insight → Path. Supports theme auto-adaptation.
 
     **Version:** 1.0.0
 
-    [:octicons-arrow-right-24: Documentation](infographic-markdown.md)
+    [:octicons-arrow-right-24: Documentation](deep-dive.md)
+
+
 
 </div>
 

docs/plugins/actions/index.zh.md

@@ -33,19 +33,19 @@ Actions 是交互式插件，能够：
 
     使用 AntV 可视化引擎，将文本转成专业的信息图。
 
-    **版本：** 1.3.0
+    **版本：** 1.4.9
 
     [:octicons-arrow-right-24: 查看文档](smart-infographic.md)
 
--   :material-card-text:{ .lg .middle } **Knowledge Card**
+-   :material-card-text:{ .lg .middle } **Flash Card（闪记卡）**
 
     ---
 
-    快速生成精美的学习记忆卡片，适合学习与记忆。
+    快速生成精美的学习记忆卡片，非常适合学习和快速记忆。
 
-    **版本：** 0.2.2
+    **版本：** 0.2.4
 
-    [:octicons-arrow-right-24: 查看文档](knowledge-card.md)
+    [:octicons-arrow-right-24: 查看文档](flash-card.md)
 
 -   :material-file-excel:{ .lg .middle } **Export to Excel**
 
@@ -63,29 +63,21 @@ Actions 是交互式插件，能够：
 
     将当前对话导出为完美格式的 Word 文档，支持**代码语法高亮**、**原生数学公式**、**Mermaid 图表**、**引用资料**以及**增强表格**渲染。
 
-    **版本：** 0.4.1
+    **版本：** 0.4.2
 
     [:octicons-arrow-right-24: 查看文档](export-to-word.md)
 
--   :material-text-box-search:{ .lg .middle } **Summary**
+-   :material-brain:{ .lg .middle } **精读 (Deep Dive)**
 
     ---
 
-    对长文本进行精简总结，提取要点。
-
-    **版本：** 0.1.0
-
-    [:octicons-arrow-right-24: 查看文档](summary.md)
-
--   :material-image-text:{ .lg .middle } **信息图转 Markdown**
-
-    ---
-
-    AI 驱动的信息图生成器，渲染 SVG 并以 Markdown Data URL 图片嵌入。
+    全方位的思维透镜 —— 全景 → 脉络 → 洞察 → 路径。支持主题自适应。
 
     **版本：** 1.0.0
 
-    [:octicons-arrow-right-24: 查看文档](infographic-markdown.zh.md)
+    [:octicons-arrow-right-24: 查看文档](deep-dive.zh.md)
+
+
 
 </div>
 

docs/plugins/actions/smart-infographic.md

@@ -1,7 +1,7 @@
 # Smart Infographic
 
 <span class="category-badge action">Action</span>
-<span class="version-badge">v1.3.0</span>
+<span class="version-badge">v1.4.9</span>
 
 An AntV Infographic engine powered plugin that transforms long text into professional, beautiful infographics with a single click.
 
@@ -14,11 +14,13 @@ The Smart Infographic plugin uses AI to analyze text content and generate profes
 ## Features
 
 - :material-robot: **AI-Powered Transformation**: Automatically analyzes text logic, extracts key points, and generates structured charts
-- :material-palette: **Professional Templates**: Includes various AntV official templates: Lists, Trees, Mindmaps, Comparison Tables, Flowcharts, and Statistical Charts
-- :material-magnify: **Auto-Icon Matching**: Built-in logic to search and match the most relevant Material Design Icons based on content
+- :material-palette: **70+ Professional Templates**: Includes various AntV official templates: Lists, Trees, Roadmaps, Timelines, Comparison Tables, SWOT, Quadrants, and Statistical Charts
+- :material-magnify: **Auto-Icon Matching**: Built-in logic to search and match the most relevant icons (Iconify) and illustrations (unDraw)
 - :material-download: **Multi-Format Export**: Download your infographics as **SVG**, **PNG**, or **Standalone HTML** file
 - :material-theme-light-dark: **Theme Support**: Supports Dark/Light modes, auto-adapts theme colors
 - :material-cellphone-link: **Responsive Design**: Generated charts look great on both desktop and mobile devices
+- :material-image: **Image Embedding**: Option to embed charts as static images for better compatibility
+- :material-monitor-screenshot: **Adaptive Sizing**: Images automatically adapt to the chat container width
 
 ---
 
@@ -35,10 +37,11 @@ The Smart Infographic plugin uses AI to analyze text content and generate profes
 
 | Category | Template Name | Use Case |
 |:---------|:--------------|:---------|
-| **Lists & Hierarchy** | `list-grid`, `tree-vertical`, `mindmap` | Features, Org Charts, Brainstorming |
-| **Sequence & Relation** | `sequence-roadmap`, `relation-circle` | Roadmaps, Circular Flows, Steps |
-| **Comparison & Analysis** | `compare-binary`, `compare-swot`, `quadrant-quarter` | Pros/Cons, SWOT, Quadrants |
-| **Charts & Data** | `chart-bar`, `chart-line`, `chart-pie` | Trends, Distributions, Metrics |
+| **Sequence** | `sequence-timeline-simple`, `sequence-roadmap-vertical-simple`, `sequence-snake-steps-compact-card` | Timelines, Roadmaps, Processes |
+| **Lists** | `list-grid-candy-card-lite`, `list-row-horizontal-icon-arrow`, `list-column-simple-vertical-arrow` | Features, Bullet Points, Lists |
+| **Comparison** | `compare-binary-horizontal-underline-text-vs`, `compare-swot`, `quadrant-quarter-simple-card` | Pros/Cons, SWOT, Quadrants |
+| **Hierarchy** | `hierarchy-tree-tech-style-capsule-item`, `hierarchy-structure` | Org Charts, Structures |
+| **Charts** | `chart-column-simple`, `chart-bar-plain-text`, `chart-line-plain-text`, `chart-wordcloud` | Trends, Distributions, Metrics |
 
 ---
 
@@ -60,6 +63,7 @@ The Smart Infographic plugin uses AI to analyze text content and generate profes
 | `MIN_TEXT_LENGTH` | integer | `100` | Minimum characters required to trigger analysis |
 | `CLEAR_PREVIOUS_HTML` | boolean | `false` | Whether to clear previous charts |
 | `MESSAGE_COUNT` | integer | `1` | Number of recent messages to use for analysis |
+| `OUTPUT_MODE` | string | `image` | `image` for static image embedding (default), `html` for interactive chart |
 
 ---
 

docs/plugins/actions/smart-infographic.zh.md

@@ -1,7 +1,7 @@
 # Smart Infographic（智能信息图）
 
 <span class="category-badge action">Action</span>
-<span class="version-badge">v1.0.0</span>
+<span class="version-badge">v1.4.9</span>
 
 基于 AntV 信息图引擎，将长文本一键转成专业、美观的信息图。
 
@@ -14,11 +14,13 @@ Smart Infographic 使用 AI 分析文本，并基于 AntV 可视化引擎生成
 ## 功能特性
 
 - :material-robot: **AI 转换**：自动分析文本逻辑，提取要点并生成结构化图表
-- :material-palette: **专业模板**：内置 AntV 官方模板：列表、树、思维导图、对比表、流程图、统计图等
-- :material-magnify: **自动匹配图标**：根据内容自动选择最合适的 Material Design Icons
+- :material-palette: **70+ 专业模板**：内置多种 AntV 官方模板，包括列表、树图、路线图、时间线、对比图、SWOT、象限图及统计图表等
+- :material-magnify: **自动匹配图标**：内置图标搜索逻辑，支持 Iconify 图标和 unDraw 插图自动匹配
 - :material-download: **多格式导出**：支持下载 **SVG**、**PNG**、**独立 HTML**
 - :material-theme-light-dark: **主题支持**：适配深色/浅色模式
 - :material-cellphone-link: **响应式**：桌面与移动端都能良好展示
+- :material-image: **图片嵌入**：支持将图表作为静态图片嵌入，兼容性更好
+- :material-monitor-screenshot: **自适应尺寸**：图片模式下自动适应聊天容器宽度
 
 ---
 
@@ -35,10 +37,11 @@ Smart Infographic 使用 AI 分析文本，并基于 AntV 可视化引擎生成
 
 | 分类 | 模板名称 | 典型场景 |
 |:---------|:--------------|:---------|
-| **列表与层级** | `list-grid`, `tree-vertical`, `mindmap` | 特性列表、组织结构、头脑风暴 |
-| **序列与关系** | `sequence-roadmap`, `relation-circle` | 路线图、循环流程、步骤拆解 |
-| **对比与分析** | `compare-binary`, `compare-swot`, `quadrant-quarter` | 优劣势、SWOT、象限分析 |
-| **图表与数据** | `chart-bar`, `chart-line`, `chart-pie` | 趋势、分布、指标对比 |
+| **时序与流程** | `sequence-timeline-simple`, `sequence-roadmap-vertical-simple`, `sequence-snake-steps-compact-card` | 时间线、路线图、步骤说明 |
+| **列表与网格** | `list-grid-candy-card-lite`, `list-row-horizontal-icon-arrow`, `list-column-simple-vertical-arrow` | 功能亮点、要点列举、清单 |
+| **对比与分析** | `compare-binary-horizontal-underline-text-vs`, `compare-swot`, `quadrant-quarter-simple-card` | 优劣势对比、SWOT 分析、象限图 |
+| **层级与结构** | `hierarchy-tree-tech-style-capsule-item`, `hierarchy-structure` | 组织架构、层级关系 |
+| **图表与数据** | `chart-column-simple`, `chart-bar-plain-text`, `chart-line-plain-text`, `chart-wordcloud` | 数据趋势、比例分布、数值对比 |
 
 ---
 
@@ -60,6 +63,7 @@ Smart Infographic 使用 AI 分析文本，并基于 AntV 可视化引擎生成
 | `MIN_TEXT_LENGTH` | integer | `100` | 触发分析的最小字符数 |
 | `CLEAR_PREVIOUS_HTML` | boolean | `false` | 是否清空之前生成的图表 |
 | `MESSAGE_COUNT` | integer | `1` | 参与分析的最近消息条数 |
+| `OUTPUT_MODE` | string | `image` | `image` 为静态图片嵌入（默认），`html` 为交互式图表 |
 
 ---
 

docs/plugins/actions/smart-mind-map.md

@@ -1,7 +1,7 @@
 # Smart Mind Map
 
 <span class="category-badge action">Action</span>
-<span class="version-badge">v0.8.0</span>
+<span class="version-badge">v0.9.1</span>
 
 Intelligently analyzes text content and generates interactive mind maps for better visualization and understanding.
 
@@ -23,7 +23,7 @@ The Smart Mind Map plugin transforms text content into beautiful, interactive mi
 
 ## Installation
 
-1. Download the plugin file: [`思维导图.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/smart-mind-map)
+1. Download the plugin file: [`smart_mind_map.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/smart-mind-map)
 2. Upload to OpenWebUI: **Admin Panel** → **Settings** → **Functions** (Actions)
 3. Enable the plugin, and optionally allow iframe same-origin access so theme auto-detection works
 

docs/plugins/actions/smart-mind-map.zh.md

@@ -1,7 +1,7 @@
 # Smart Mind Map（智能思维导图）
 
 <span class="category-badge action">Action</span>
-<span class="version-badge">v0.8.0</span>
+<span class="version-badge">v0.9.1</span>
 
 智能分析文本内容，生成交互式思维导图，帮助你更直观地理解信息结构。
 
@@ -23,7 +23,7 @@ Smart Mind Map 会将文本转换成漂亮的交互式思维导图。插件会
 
 ## 安装
 
-1. 下载插件文件：[`思维导图.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/smart-mind-map)
+1. 下载插件文件：[`smart_mind_map.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/smart-mind-map)
 2. 上传到 OpenWebUI：**Admin Panel** → **Settings** → **Functions**（Actions）
 3. 启用插件，并可在设置中允许 iframe same-origin 以启用主题自动检测
 

docs/plugins/actions/summary.md

@@ -1,82 +0,0 @@
-# Summary
-
-<span class="category-badge action">Action</span>
-<span class="version-badge">v0.1.0</span>
-
-Generate concise summaries of long text content with key points extraction.
-
----
-
-## Overview
-
-The Summary plugin helps you quickly understand long pieces of text by generating concise summaries with extracted key points. It's perfect for:
-
-- Summarizing long articles or documents
-- Extracting key points from conversations
-- Creating quick overviews of complex topics
-
-## Features
-
-- :material-text-box-search: **Smart Summarization**: AI-powered content analysis
-- :material-format-list-bulleted: **Key Points**: Extracted important highlights
-- :material-content-copy: **Easy Copy**: One-click copying of summaries
-- :material-tune: **Adjustable Length**: Control summary detail level
-
----
-
-## Installation
-
-1. Download the plugin file: [`summary.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/summary)
-2. Upload to OpenWebUI: **Admin Panel** → **Settings** → **Functions**
-3. Enable the plugin
-
----
-
-## Usage
-
-1. Get a long response from the AI or paste long text
-2. Click the **Summary** button in the message action bar
-3. View the generated summary with key points
-
----
-
-## Configuration
-
-| Option | Type | Default | Description |
-|--------|------|---------|-------------|
-| `summary_length` | string | `"medium"` | Length of summary (short/medium/long) |
-| `include_key_points` | boolean | `true` | Extract and list key points |
-| `language` | string | `"auto"` | Output language |
-
----
-
-## Example Output
-
-```markdown
-## Summary
-
-This document discusses the implementation of a new feature 
-for the application, focusing on user experience improvements 
-and performance optimizations.
-
-### Key Points
-
-- ✅ New user interface design improves accessibility
-- ✅ Backend optimizations reduce load times by 40%
-- ✅ Mobile responsiveness enhanced
-- ✅ Integration with third-party services simplified
-```
-
----
-
-## Requirements
-
-!!! note "Prerequisites"
-    - OpenWebUI v0.3.0 or later
-    - Uses the active LLM model for summarization
-
----
-
-## Source Code
-
-[:fontawesome-brands-github: View on GitHub](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/summary){ .md-button }

docs/plugins/actions/summary.zh.md

@@ -1,82 +0,0 @@
-# Summary（摘要）
-
-<span class="category-badge action">Action</span>
-<span class="version-badge">v0.1.0</span>
-
-为长文本生成简洁摘要，并提取关键要点。
-
----
-
-## 概览
-
-Summary 插件可以快速理解长文本，生成精炼摘要并列出关键点，适合：
-
-- 总结长文章或文档
-- 从对话中提炼要点
-- 为复杂主题制作快速概览
-
-## 功能特性
-
-- :material-text-box-search: **智能摘要**：AI 驱动的内容分析
-- :material-format-list-bulleted: **关键点**：提取重要信息
-- :material-content-copy: **便捷复制**：一键复制摘要
-- :material-tune: **长度可调**：可选择摘要详略程度
-
----
-
-## 安装
-
-1. 下载插件文件：[`summary.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/summary)
-2. 上传到 OpenWebUI：**Admin Panel** → **Settings** → **Functions**
-3. 启用插件
-
----
-
-## 使用方法
-
-1. 获取一段较长的 AI 回复或粘贴长文本
-2. 点击消息操作栏的 **Summary** 按钮
-3. 查看生成的摘要与关键点
-
----
-
-## 配置项
-
-| 选项 | 类型 | 默认值 | 说明 |
-|--------|------|---------|-------------|
-| `summary_length` | string | `"medium"` | 摘要长度（short/medium/long） |
-| `include_key_points` | boolean | `true` | 是否提取并列出关键点 |
-| `language` | string | `"auto"` | 输出语言 |
-
----
-
-## 输出示例
-
-```markdown
-## Summary
-
-This document discusses the implementation of a new feature 
-for the application, focusing on user experience improvements 
-and performance optimizations.
-
-### Key Points
-
-- ✅ New user interface design improves accessibility
-- ✅ Backend optimizations reduce load times by 40%
-- ✅ Mobile responsiveness enhanced
-- ✅ Integration with third-party services simplified
-```
-
----
-
-## 运行要求
-
-!!! note "前置条件"
-    - OpenWebUI v0.3.0 及以上
-    - 使用当前会话的 LLM 模型进行摘要
-
----
-
-## 源码
-
-[:fontawesome-brands-github: 在 GitHub 查看](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/actions/summary){ .md-button }

docs/plugins/filters/async-context-compression.md

@@ -1,7 +1,7 @@
 # Async Context Compression
 
 <span class="category-badge filter">Filter</span>
-<span class="version-badge">v1.1.0</span>
+<span class="version-badge">v1.1.3</span>
 
 Reduces token consumption in long conversations through intelligent summarization while maintaining conversational coherence.
 
@@ -29,6 +29,11 @@ This is especially useful for:
 - :material-clock-fast: **Async Processing**: Non-blocking background compression
 - :material-memory: **Context Preservation**: Keeps important information
 - :material-currency-usd-off: **Cost Reduction**: Minimize token usage
+- :material-console: **Frontend Debugging**: Debug logs in browser console
+- :material-alert-circle-check: **Enhanced Error Reporting**: Clear error status notifications
+- :material-check-all: **Open WebUI v0.7.x Compatibility**: Dynamic DB session handling
+- :material-account-convert: **Improved Compatibility**: Summary role changed to `assistant`
+- :material-shield-check: **Enhanced Stability**: Resolved race conditions in state management
 
 ---
 

docs/plugins/filters/async-context-compression.zh.md

@@ -1,7 +1,7 @@
 # Async Context Compression（异步上下文压缩）
 
 <span class="category-badge filter">Filter</span>
-<span class="version-badge">v1.1.0</span>
+<span class="version-badge">v1.1.3</span>
 
 通过智能摘要减少长对话的 token 消耗，同时保持对话连贯。
 
@@ -29,6 +29,11 @@ Async Context Compression 过滤器通过以下方式帮助管理长对话的 to
 - :material-clock-fast: **异步处理**：后台非阻塞压缩
 - :material-memory: **保留上下文**：尽量保留重要信息
 - :material-currency-usd-off: **降低成本**：减少 token 使用
+- :material-console: **前端调试**：支持浏览器控制台日志
+- :material-alert-circle-check: **增强错误报告**：清晰的错误状态通知
+- :material-check-all: **Open WebUI v0.7.x 兼容性**：动态数据库会话处理
+- :material-account-convert: **兼容性提升**：摘要角色改为 `assistant`
+- :material-shield-check: **稳定性增强**：解决状态管理竞态条件
 
 ---
 

docs/plugins/filters/gemini-manifold-companion.md

@@ -1,54 +0,0 @@
-# Gemini Manifold Companion
-
-<span class="category-badge filter">Filter</span>
-<span class="version-badge">v0.3.2</span>
-
-Companion filter for the Gemini Manifold pipe plugin, providing enhanced functionality.
-
----
-
-## Overview
-
-The Gemini Manifold Companion works alongside the [Gemini Manifold Pipe](../pipes/gemini-manifold.md) to provide additional processing and enhancement for Gemini model integrations.
-
-## Features
-
-- :material-handshake: **Seamless Integration**: Works with Gemini Manifold pipe
-- :material-format-text: **Message Formatting**: Optimizes messages for Gemini
-- :material-shield: **Error Handling**: Graceful handling of API issues
-- :material-tune: **Fine-tuning**: Additional configuration options
-
----
-
-## Installation
-
-1. First, install the [Gemini Manifold Pipe](../pipes/gemini-manifold.md)
-2. Download the companion filter: [`gemini_manifold_companion.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/filters/gemini_manifold_companion)
-3. Upload to OpenWebUI: **Admin Panel** → **Settings** → **Functions**
-4. Enable the filter
-
----
-
-## Configuration
-
-| Option | Type | Default | Description |
-|--------|------|---------|-------------|
-| `auto_format` | boolean | `true` | Auto-format messages for Gemini |
-| `handle_errors` | boolean | `true` | Enable error handling |
-
----
-
-## Requirements
-
-!!! warning "Dependency"
-    This filter requires the **Gemini Manifold Pipe** to be installed and configured.
-
-!!! note "Prerequisites"
-    - OpenWebUI v0.3.0 or later
-    - Gemini Manifold Pipe installed
-
----
-
-## Source Code
-
-[:fontawesome-brands-github: View on GitHub](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/filters/gemini_manifold_companion){ .md-button }

docs/plugins/filters/gemini-manifold-companion.zh.md

@@ -1,54 +0,0 @@
-# Gemini Manifold Companion
-
-<span class="category-badge filter">Filter</span>
-<span class="version-badge">v0.3.2</span>
-
-Gemini Manifold Pipe 的伴随过滤器，用于增强 Gemini 集成的处理效果。
-
----
-
-## 概览
-
-Gemini Manifold Companion 与 [Gemini Manifold Pipe](../pipes/gemini-manifold.md) 搭配使用，为 Gemini 模型集成提供额外的处理与优化。
-
-## 功能特性
-
-- :material-handshake: **无缝协同**：与 Gemini Manifold Pipe 配合工作
-- :material-format-text: **消息格式化**：针对 Gemini 优化消息
-- :material-shield: **错误处理**：更友好的 API 异常处理
-- :material-tune: **精细配置**：提供额外调优选项
-
----
-
-## 安装
-
-1. 先安装 [Gemini Manifold Pipe](../pipes/gemini-manifold.md)
-2. 下载伴随过滤器：[`gemini_manifold_companion.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/filters/gemini_manifold_companion)
-3. 上传到 OpenWebUI：**Admin Panel** → **Settings** → **Functions**
-4. 启用过滤器
-
----
-
-## 配置项
-
-| 选项 | 类型 | 默认值 | 说明 |
-|--------|------|---------|-------------|
-| `auto_format` | boolean | `true` | 为 Gemini 自动格式化消息 |
-| `handle_errors` | boolean | `true` | 开启错误处理 |
-
----
-
-## 运行要求
-
-!!! warning "依赖"
-    本过滤器需要先安装并配置 **Gemini Manifold Pipe**。
-
-!!! note "前置条件"
-    - OpenWebUI v0.3.0 及以上
-    - 已安装 Gemini Manifold Pipe
-
----
-
-## 源码
-
-[:fontawesome-brands-github: 在 GitHub 查看](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/filters/gemini_manifold_companion){ .md-button }

docs/plugins/filters/index.md

@@ -22,7 +22,7 @@ Filters act as middleware in the message pipeline:
 
     Reduces token consumption in long conversations through intelligent summarization while maintaining coherence.
 
-    **Version:** 1.1.0
+    **Version:** 1.1.3
 
     [:octicons-arrow-right-24: Documentation](async-context-compression.md)
 
@@ -36,15 +36,37 @@ Filters act as middleware in the message pipeline:
 
     [:octicons-arrow-right-24: Documentation](context-enhancement.md)
 
--   :material-google:{ .lg .middle } **Gemini Manifold Companion**
+
+
+-   :material-format-paint:{ .lg .middle } **Markdown Normalizer**
 
     ---
 
-    Companion filter for the Gemini Manifold pipe plugin.
+    Fixes common Markdown formatting issues in LLM outputs, including Mermaid syntax, code blocks, and LaTeX formulas.
 
-    **Version:** 1.7.0
+    **Version:** 1.1.2
 
-    [:octicons-arrow-right-24: Documentation](gemini-manifold-companion.md)
+    [:octicons-arrow-right-24: Documentation](markdown_normalizer.md)
+
+-   :material-merge:{ .lg .middle } **Multi-Model Context Merger**
+
+    ---
+
+    Automatically merges context from multiple model responses in the previous turn, enabling collaborative answers.
+
+    **Version:** 0.1.0
+
+    [:octicons-arrow-right-24: Documentation](multi-model-context-merger.md)
+
+-   :material-file-document-multiple:{ .lg .middle } **Web Gemini Multimodal Filter**
+
+    ---
+
+    A powerful filter that provides multimodal capabilities (PDF, Office, Images, Audio, Video) to any model in OpenWebUI.
+
+    **Version:** 0.3.2
+
+    [:octicons-arrow-right-24: Documentation](web-gemini-multimodel.md)
 
 </div>
 

docs/plugins/filters/index.zh.md

@@ -22,7 +22,7 @@ Filter 充当消息管线中的中间件：
 
     通过智能总结减少长对话的 token 消耗，同时保持连贯性。
 
-    **版本：** 1.1.0
+    **版本：** 1.1.3
 
     [:octicons-arrow-right-24: 查看文档](async-context-compression.md)
 
@@ -36,15 +36,17 @@ Filter 充当消息管线中的中间件：
 
     [:octicons-arrow-right-24: 查看文档](context-enhancement.md)
 
--   :material-google:{ .lg .middle } **Gemini Manifold Companion**
+
+
+-   :material-format-paint:{ .lg .middle } **Markdown Normalizer**
 
     ---
 
-    Gemini Manifold Pipe 插件的伴随过滤器。
+    修复 LLM 输出中常见的 Markdown 格式问题，包括 Mermaid 语法、代码块和 LaTeX 公式。
 
-    **版本：** 1.7.0
+    **版本：** 1.0.1
 
-    [:octicons-arrow-right-24: 查看文档](gemini-manifold-companion.md)
+    [:octicons-arrow-right-24: 查看文档](markdown_normalizer.zh.md)
 
 </div>
 

docs/plugins/filters/markdown_normalizer.md

@@ -0,0 +1,46 @@
+# Markdown Normalizer Filter
+
+A production-grade content normalizer filter for Open WebUI that fixes common Markdown formatting issues in LLM outputs. It ensures that code blocks, LaTeX formulas, Mermaid diagrams, and other Markdown elements are rendered correctly.
+
+## Features
+
+*   **Mermaid Syntax Fix**: Automatically fixes common Mermaid syntax errors, such as unquoted node labels (including multi-line labels and citations) and unclosed subgraphs, ensuring diagrams render correctly.
+*   **Frontend Console Debugging**: Supports printing structured debug logs directly to the browser console (F12) for easier troubleshooting.
+*   **Code Block Formatting**: Fixes broken code block prefixes, suffixes, and indentation.
+*   **LaTeX Normalization**: Standardizes LaTeX formula delimiters (`\[` -> `$$`, `\(` -> `$`).
+*   **Thought Tag Normalization**: Unifies thought tags (`<think>`, `<thinking>` -> `<thought>`).
+*   **Escape Character Fix**: Cleans up excessive escape characters (`\\n`, `\\t`).
+*   **List Formatting**: Ensures proper newlines in list items.
+*   **Heading Fix**: Adds missing spaces in headings (`#Heading` -> `# Heading`).
+*   **Table Fix**: Adds missing closing pipes in tables.
+*   **XML Cleanup**: Removes leftover XML artifacts.
+
+## Usage
+
+1.  Install the plugin in Open WebUI.
+2.  Enable the filter globally or for specific models.
+3.  Configure the enabled fixes in the **Valves** settings.
+4.  (Optional) **Show Debug Log** is enabled by default in Valves. This prints structured logs to the browser console (F12).
+    > [!WARNING]
+    > As this is an initial version, some "negative fixes" might occur (e.g., breaking valid Markdown). If you encounter issues, please check the console logs, copy the "Original" vs "Normalized" content, and submit an issue.
+
+## Configuration (Valves)
+
+*   `priority`: Filter priority (default: 50).
+*   `enable_escape_fix`: Fix excessive escape characters.
+*   `enable_thought_tag_fix`: Normalize thought tags.
+*   `enable_code_block_fix`: Fix code block formatting.
+*   `enable_latex_fix`: Normalize LaTeX formulas.
+*   `enable_list_fix`: Fix list item newlines (Experimental).
+*   `enable_unclosed_block_fix`: Auto-close unclosed code blocks.
+*   `enable_fullwidth_symbol_fix`: Fix full-width symbols in code blocks.
+*   `enable_mermaid_fix`: Fix Mermaid syntax errors.
+*   `enable_heading_fix`: Fix missing space in headings.
+*   `enable_table_fix`: Fix missing closing pipe in tables.
+*   `enable_xml_tag_cleanup`: Cleanup leftover XML tags.
+*   `show_status`: Show status notification when fixes are applied.
+*   `show_debug_log`: Print debug logs to browser console.
+
+## License
+
+MIT

docs/plugins/filters/markdown_normalizer.zh.md

@@ -0,0 +1,46 @@
+# Markdown 格式化过滤器 (Markdown Normalizer)
+
+这是一个用于 Open WebUI 的生产级内容格式化过滤器，旨在修复 LLM 输出中常见的 Markdown 格式问题。它能确保代码块、LaTeX 公式、Mermaid 图表和其他 Markdown 元素被正确渲染。
+
+## 功能特性
+
+*   **Mermaid 语法修复**: 自动修复常见的 Mermaid 语法错误，如未加引号的节点标签（支持多行标签和引用标记）和未闭合的子图 (Subgraph)，确保图表能正确渲染。
+*   **前端控制台调试**: 支持将结构化的调试日志直接打印到浏览器控制台 (F12)，方便排查问题。
+*   **代码块格式化**: 修复破损的代码块前缀、后缀和缩进问题。
+*   **LaTeX 规范化**: 标准化 LaTeX 公式定界符 (`\[` -> `$$`, `\(` -> `$`)。
+*   **思维标签规范化**: 统一思维链标签 (`<think>`, `<thinking>` -> `<thought>`)。
+*   **转义字符修复**: 清理过度的转义字符 (`\\n`, `\\t`)。
+*   **列表格式化**: 确保列表项有正确的换行。
+*   **标题修复**: 修复标题中缺失的空格 (`#标题` -> `# 标题`)。
+*   **表格修复**: 修复表格中缺失的闭合管道符。
+*   **XML 清理**: 移除残留的 XML 标签。
+
+## 使用方法
+
+1.  在 Open WebUI 中安装此插件。
+2.  全局启用或为特定模型启用此过滤器。
+3.  在 **Valves** 设置中配置需要启用的修复项。
+4.  (可选) **显示调试日志 (Show Debug Log)** 在 Valves 中默认开启。这会将结构化的日志打印到浏览器控制台 (F12)。
+    > [!WARNING]
+    > 由于这是初版，可能会出现“负向修复”的情况（例如破坏了原本正确的格式）。如果您遇到问题，请务必查看控制台日志，复制“原始 (Original)”与“规范化 (Normalized)”的内容对比，并提交 Issue 反馈。
+
+## 配置项 (Valves)
+
+*   `priority`: 过滤器优先级 (默认: 50)。
+*   `enable_escape_fix`: 修复过度的转义字符。
+*   `enable_thought_tag_fix`: 规范化思维标签。
+*   `enable_code_block_fix`: 修复代码块格式。
+*   `enable_latex_fix`: 规范化 LaTeX 公式。
+*   `enable_list_fix`: 修复列表项换行 (实验性)。
+*   `enable_unclosed_block_fix`: 自动闭合未闭合的代码块。
+*   `enable_fullwidth_symbol_fix`: 修复代码块中的全角符号。
+*   `enable_mermaid_fix`: 修复 Mermaid 语法错误。
+*   `enable_heading_fix`: 修复标题中缺失的空格。
+*   `enable_table_fix`: 修复表格中缺失的闭合管道符。
+*   `enable_xml_tag_cleanup`: 清理残留的 XML 标签。
+*   `show_status`: 应用修复时显示状态通知。
+*   `show_debug_log`: 在浏览器控制台打印调试日志。
+
+## 许可证
+
+MIT

docs/plugins/filters/multi-model-context-merger.md

@@ -0,0 +1,35 @@
+# Multi-Model Context Merger
+
+<span class="category-badge filter">Filter</span>
+<span class="version-badge">v0.1.0</span>
+
+Automatically merges context from multiple model responses in the previous turn, enabling collaborative answers.
+
+---
+
+## Overview
+
+This filter detects when multiple models have responded in the previous turn (e.g., using "Arena" mode or multiple models selected). It consolidates these responses and injects them as context for the current turn, allowing the next model to see what others have said.
+
+## Features
+
+- :material-merge: **Auto-Merge**: Consolidates responses from multiple models into a single context block.
+- :material-format-list-group: **Structured Injection**: Uses XML-like tags (`<response>`) to separate different model outputs.
+- :material-robot-confused: **Collaboration**: Enables models to build upon or critique each other's answers.
+
+---
+
+## Installation
+
+1. Download the plugin file: [`multi_model_context_merger.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/filters)
+2. Upload to OpenWebUI: **Admin Panel** → **Settings** → **Functions**
+3. Enable the filter.
+
+---
+
+## Usage
+
+1. Select **multiple models** in the chat (or use Arena mode).
+2. Ask a question. All models will respond.
+3. Ask a follow-up question.
+4. The filter will inject the previous responses from ALL models into the context of the current model(s).

docs/plugins/filters/multi-model-context-merger.zh.md

@@ -0,0 +1,35 @@
+# 多模型上下文合并 (Multi-Model Context Merger)
+
+<span class="category-badge filter">Filter</span>
+<span class="version-badge">v0.1.0</span>
+
+自动合并上一轮中多个模型的回答上下文，实现协作问答。
+
+---
+
+## 概述
+
+此过滤器检测上一轮是否由多个模型回复（例如使用“竞技场”模式或选择了多个模型）。它将这些回复合并并作为上下文注入到当前轮次，使下一个模型能够看到其他模型之前所说的内容。
+
+## 功能特性
+
+- :material-merge: **自动合并**: 将多个模型的回复合并为单个上下文块。
+- :material-format-list-group: **结构化注入**: 使用类似 XML 的标签 (`<response>`) 分隔不同模型的输出。
+- :material-robot-confused: **协作**: 允许模型基于彼此的回答进行构建或评论。
+
+---
+
+## 安装
+
+1. 下载插件文件: [`multi_model_context_merger.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/filters)
+2. 上传到 OpenWebUI: **管理员面板** → **设置** → **函数**
+3. 启用过滤器。
+
+---
+
+## 使用方法
+
+1. 在聊天中选择 **多个模型** (或使用竞技场模式)。
+2. 提问。所有模型都会回答。
+3. 提出后续问题。
+4. 过滤器会将所有模型之前的回答注入到当前模型的上下文中。

docs/plugins/filters/web-gemini-multimodel.md

@@ -0,0 +1,51 @@
+# Web Gemini Multimodal Filter
+
+<span class="category-badge filter">Filter</span>
+<span class="version-badge">v0.3.2</span>
+
+A powerful filter that provides multimodal capabilities (PDF, Office, Images, Audio, Video) to any model in OpenWebUI.
+
+---
+
+## Overview
+
+This plugin enables multimodal processing for any model by leveraging Gemini as an analyzer. It supports direct file processing for Gemini models and "Analyzer Mode" for other models (like DeepSeek, Llama), where Gemini analyzes the file and injects the result as context.
+
+## Features
+
+- :material-file-document-multiple: **Multimodal Support**: Process PDF, Word, Excel, PowerPoint, EPUB, MP3, MP4, and Images.
+- :material-router-network: **Smart Routing**:
+    - **Direct Mode**: Files are passed directly to Gemini models.
+    - **Analyzer Mode**: Files are analyzed by Gemini, and results are injected into the context for other models.
+- :material-history: **Persistent Context**: Maintains session history across multiple turns using OpenWebUI Chat ID.
+- :material-database-check: **Deduplication**: Automatically tracks analyzed file hashes to prevent redundant processing.
+- :material-subtitles: **Subtitle Enhancement**: Specialized mode for generating high-quality SRT subtitles from video/audio.
+
+---
+
+## Installation
+
+1. Download the plugin file: [`web_gemini_multimodel.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/filters/web_gemini_multimodel_filter)
+2. Upload to OpenWebUI: **Admin Panel** → **Settings** → **Functions**
+3. Configure the Gemini Adapter URL and other settings.
+4. Enable the filter globally or per chat.
+
+---
+
+## Configuration
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `gemini_adapter_url` | string | `http://...` | URL of the Gemini Adapter service |
+| `target_model_keyword` | string | `"webgemini"` | Keyword to identify Gemini models |
+| `mode` | string | `"auto"` | `auto`, `direct`, or `analyzer` |
+| `analyzer_base_model_id` | string | `"gemini-3.0-pro"` | Model used for document analysis |
+| `subtitle_keywords` | string | `"字幕,srt"` | Keywords to trigger subtitle flow |
+
+---
+
+## Usage
+
+1. **Upload a file** (PDF, Image, Video, etc.) in the chat.
+2. **Ask a question** about the file.
+3. The plugin will automatically process the file and provide context to your selected model.

docs/plugins/filters/web-gemini-multimodel.zh.md

@@ -0,0 +1,51 @@
+# Web Gemini 多模态过滤器
+
+<span class="category-badge filter">Filter</span>
+<span class="version-badge">v0.3.2</span>
+
+一个强大的过滤器，为 OpenWebUI 中的任何模型提供多模态能力：PDF、Office、图片、音频、视频等。
+
+---
+
+## 概述
+
+此插件利用 Gemini 作为分析器，为任何模型提供多模态处理能力。它支持 Gemini 模型的直接文件处理，以及其他模型（如 DeepSeek, Llama）的“分析器模式”，即由 Gemini 分析文件并将结果注入上下文。
+
+## 功能特性
+
+- :material-file-document-multiple: **多模态支持**: 处理 PDF, Word, Excel, PowerPoint, EPUB, MP3, MP4 和图片。
+- :material-router-network: **智能路由**:
+    - **直连模式 (Direct Mode)**: 对于 Gemini 模型，文件直接传递（原生多模态）。
+    - **分析器模式 (Analyzer Mode)**: 对于非 Gemini 模型，文件由 Gemini 分析，结果注入为上下文。
+- :material-history: **持久上下文**: 利用 OpenWebUI 的 Chat ID 跨多轮对话维护会话历史。
+- :material-database-check: **数据库去重**: 自动记录已分析文件的哈希值，防止重复上传和分析。
+- :material-subtitles: **字幕增强**: 针对视频/音频上传的专用模式，生成高质量 SRT 字幕。
+
+---
+
+## 安装
+
+1. 下载插件文件: [`web_gemini_multimodel.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/filters/web_gemini_multimodel_filter)
+2. 上传到 OpenWebUI: **管理员面板** → **设置** → **函数**
+3. 配置 Gemini Adapter URL 和其他设置。
+4. 启用过滤器。
+
+---
+
+## 配置
+
+| 选项 | 类型 | 默认值 | 描述 |
+|------|------|--------|------|
+| `gemini_adapter_url` | string | `http://...` | Gemini Adapter 服务的 URL |
+| `target_model_keyword` | string | `"webgemini"` | 识别 Gemini 模型的关键字 |
+| `mode` | string | `"auto"` | `auto` (自动), `direct` (直连), 或 `analyzer` (分析器) |
+| `analyzer_base_model_id` | string | `"gemini-3.0-pro"` | 用于文档分析的模型 |
+| `subtitle_keywords` | string | `"字幕,srt"` | 触发字幕流程的关键字 |
+
+---
+
+## 使用方法
+
+1. 在聊天中 **上传文件** (PDF, 图片, 视频等)。
+2. 关于文件 **提问**。
+3. 插件会自动处理文件并为所选模型提供上下文。

docs/plugins/index.md

@@ -48,16 +48,15 @@ OpenWebUI supports four types of plugins, each serving a different purpose:
 
 | Plugin | Type | Description | Version |
 |--------|------|-------------|---------|
-| [Smart Mind Map](actions/smart-mind-map.md) | Action | Generate interactive mind maps from text | 0.8.0 |
-| [Smart Infographic](actions/smart-infographic.md) | Action | Transform text into professional infographics | 1.0.0 |
-| [Knowledge Card](actions/knowledge-card.md) | Action | Create beautiful learning flashcards | 0.2.0 |
-| [Export to Excel](actions/export-to-excel.md) | Action | Export chat history to Excel files | 1.0.0 |
-| [Export to Word](actions/export-to-word.md) | Action | Export chat content to Word (.docx) with formatting | 0.1.0 |
-| [Summary](actions/summary.md) | Action | Text summarization tool | 1.0.0 |
-| [Async Context Compression](filters/async-context-compression.md) | Filter | Intelligent context compression | 1.0.0 |
-| [Context Enhancement](filters/context-enhancement.md) | Filter | Enhance chat context | 1.0.0 |
-| [Gemini Manifold Companion](filters/gemini-manifold-companion.md) | Filter | Companion for Gemini Manifold | 1.0.0 |
-| [Gemini Manifold](pipes/gemini-manifold.md) | Pipe | Gemini model integration | 1.0.0 |
+| [Smart Mind Map](actions/smart-mind-map.md) | Action | Generate interactive mind maps from text | 0.9.1 |
+| [Smart Infographic](actions/smart-infographic.md) | Action | Transform text into professional infographics | 1.4.9 |
+| [Flash Card](actions/flash-card.md) | Action | Create beautiful learning flashcards | 0.2.4 |
+| [Export to Excel](actions/export-to-excel.md) | Action | Export chat history to Excel files | 0.3.7 |
+| [Export to Word](actions/export-to-word.md) | Action | Export chat content to Word (.docx) with formatting | 0.4.3 |
+| [Async Context Compression](filters/async-context-compression.md) | Filter | Intelligent context compression | 1.1.3 |
+| [Context Enhancement](filters/context-enhancement.md) | Filter | Enhance chat context | 0.3.0 |
+| [Multi-Model Context Merger](filters/multi-model-context-merger.md) | Filter | Merge context from multiple models | 0.1.0 |
+| [Web Gemini Multimodal Filter](filters/web-gemini-multimodel.md) | Filter | Multimodal capabilities for any model | 0.3.2 |
 | [MoE Prompt Refiner](pipelines/moe-prompt-refiner.md) | Pipeline | Multi-model prompt refinement | 1.0.0 |
 
 ---

docs/plugins/index.zh.md

@@ -48,16 +48,15 @@ OpenWebUI 支持四种类型的插件，每种都有不同的用途：
 
 | 插件 | 类型 | 描述 | 版本 |
 |--------|------|-------------|---------|
-| [Smart Mind Map（智能思维导图）](actions/smart-mind-map.md) | Action | 从文本生成交互式思维导图 | 0.8.0 |
-| [Smart Infographic（智能信息图）](actions/smart-infographic.md) | Action | 将文本转成专业信息图 | 1.0.0 |
-| [Knowledge Card（知识卡片）](actions/knowledge-card.md) | Action | 生成精美学习卡片 | 0.2.0 |
-| [Export to Excel（导出到 Excel）](actions/export-to-excel.md) | Action | 导出聊天记录为 Excel | 1.0.0 |
-| [Export to Word（导出为 Word）](actions/export-to-word.md) | Action | 将聊天内容导出为 Word (.docx) 并保留格式 | 0.1.0 |
-| [Summary（摘要）](actions/summary.md) | Action | 文本摘要工具 | 1.0.0 |
-| [Async Context Compression（异步上下文压缩）](filters/async-context-compression.md) | Filter | 智能上下文压缩 | 1.0.0 |
-| [Context Enhancement（上下文增强）](filters/context-enhancement.md) | Filter | 提升对话上下文 | 1.0.0 |
-| [Gemini Manifold Companion](filters/gemini-manifold-companion.md) | Filter | Gemini Manifold 伴侣 | 1.0.0 |
-| [Gemini Manifold](pipes/gemini-manifold.md) | Pipe | Gemini 模型集成 | 1.0.0 |
+| [Smart Mind Map（智能思维导图）](actions/smart-mind-map.md) | Action | 从文本生成交互式思维导图 | 0.9.1 |
+| [Smart Infographic（智能信息图）](actions/smart-infographic.md) | Action | 将文本转成专业信息图 | 1.4.9 |
+| [Flash Card（闪记卡）](actions/flash-card.md) | Action | 生成精美学习卡片 | 0.2.4 |
+| [Export to Excel（导出到 Excel）](actions/export-to-excel.md) | Action | 导出聊天记录为 Excel | 0.3.7 |
+| [Export to Word（导出为 Word）](actions/export-to-word.md) | Action | 将聊天内容导出为 Word (.docx) 并保留格式 | 0.4.3 |
+| [Async Context Compression（异步上下文压缩）](filters/async-context-compression.md) | Filter | 智能上下文压缩 | 1.1.3 |
+| [Context Enhancement（上下文增强）](filters/context-enhancement.md) | Filter | 提升对话上下文 | 0.3.0 |
+| [Multi-Model Context Merger（多模型上下文合并）](filters/multi-model-context-merger.md) | Filter | 合并多个模型的上下文 | 0.1.0 |
+| [Web Gemini Multimodal Filter（Web Gemini 多模态过滤器）](filters/web-gemini-multimodel.md) | Filter | 为任何模型提供多模态能力 | 0.3.2 |
 | [MoE Prompt Refiner](pipelines/moe-prompt-refiner.md) | Pipeline | 多模型提示词优化 | 1.0.0 |
 
 ---

docs/plugins/pipes/gemini-manifold.md

@@ -1,106 +0,0 @@
-# Gemini Manifold
-
-<span class="category-badge pipe">Pipe</span>
-<span class="version-badge">v1.0.0</span>
-
-Integration pipeline for Google's Gemini models with full streaming support.
-
----
-
-## Overview
-
-The Gemini Manifold pipe provides seamless integration with Google's Gemini AI models. It exposes Gemini models as selectable options in OpenWebUI, allowing you to use them just like any other model.
-
-## Features
-
-- :material-google: **Full Gemini Support**: Access all Gemini model variants
-- :material-stream: **Streaming**: Real-time response streaming
-- :material-image: **Multimodal**: Support for images and text
-- :material-shield: **Error Handling**: Robust error management
-- :material-tune: **Configurable**: Customize model parameters
-
----
-
-## Installation
-
-1. Download the plugin file: [`gemini_manifold.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/pipes/gemini_mainfold)
-2. Upload to OpenWebUI: **Admin Panel** → **Settings** → **Functions**
-3. Configure your Gemini API key
-4. Select Gemini models from the model dropdown
-
----
-
-## Configuration
-
-| Option | Type | Required | Description |
-|--------|------|----------|-------------|
-| `GEMINI_API_KEY` | string | Yes | Your Google AI Studio API key |
-| `DEFAULT_MODEL` | string | No | Default Gemini model to use |
-| `TEMPERATURE` | float | No | Response temperature (0-1) |
-| `MAX_TOKENS` | integer | No | Maximum response tokens |
-
----
-
-## Available Models
-
-When configured, the following models become available:
-
-- `gemini-pro` - Text-only model
-- `gemini-pro-vision` - Multimodal model
-- `gemini-1.5-pro` - Latest Pro model
-- `gemini-1.5-flash` - Fast response model
-
----
-
-## Usage
-
-1. After installation, go to any chat
-2. Open the model selector dropdown
-3. Look for models prefixed with your pipe name
-4. Select a Gemini model
-5. Start chatting!
-
----
-
-## Getting an API Key
-
-1. Visit [Google AI Studio](https://makersuite.google.com/app/apikey)
-2. Create a new API key
-3. Copy the key and paste it in the plugin configuration
-
-!!! warning "API Key Security"
-    Keep your API key secure. Never share it publicly or commit it to version control.
-
----
-
-## Companion Filter
-
-For enhanced functionality, consider installing the [Gemini Manifold Companion](../filters/gemini-manifold-companion.md) filter.
-
----
-
-## Requirements
-
-!!! note "Prerequisites"
-    - OpenWebUI v0.3.0 or later
-    - Valid Gemini API key
-    - Internet access to Google AI APIs
-
----
-
-## Troubleshooting
-
-??? question "Models not appearing?"
-    Ensure your API key is correctly configured and the plugin is enabled.
-
-??? question "API errors?"
-    Check your API key validity and quota limits in Google AI Studio.
-
-??? question "Slow responses?"
-    Consider using `gemini-1.5-flash` for faster response times.
-
----
-
-## Source Code
-
-[:fontawesome-brands-github: View on GitHub](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/pipes/gemini_mainfold){ .md-button }

docs/plugins/pipes/gemini-manifold.zh.md

@@ -1,106 +0,0 @@
-# Gemini Manifold
-
-<span class="category-badge pipe">Pipe</span>
-<span class="version-badge">v1.0.0</span>
-
-面向 Google Gemini 模型的集成流水线，支持完整流式返回。
-
----
-
-## 概览
-
-Gemini Manifold Pipe 提供与 Google Gemini AI 模型的无缝集成。它会将 Gemini 模型作为可选项暴露在 OpenWebUI 中，你可以像使用其他模型一样使用它们。
-
-## 功能特性
-
-- :material-google: **完整 Gemini 支持**：可使用所有 Gemini 模型变体
-- :material-stream: **流式输出**：实时流式响应
-- :material-image: **多模态**：支持图像与文本
-- :material-shield: **错误处理**：健壮的错误管理
-- :material-tune: **可配置**：可自定义模型参数
-
----
-
-## 安装
-
-1. 下载插件文件：[`gemini_manifold.py`](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/pipes/gemini_mainfold)
-2. 上传到 OpenWebUI：**Admin Panel** → **Settings** → **Functions**
-3. 配置你的 Gemini API Key
-4. 在模型下拉中选择 Gemini 模型
-
----
-
-## 配置
-
-| 选项 | 类型 | 是否必填 | 说明 |
-|--------|------|----------|-------------|
-| `GEMINI_API_KEY` | string | 是 | 你的 Google AI Studio API Key |
-| `DEFAULT_MODEL` | string | 否 | 默认使用的 Gemini 模型 |
-| `TEMPERATURE` | float | 否 | 输出温度（0-1） |
-| `MAX_TOKENS` | integer | 否 | 最大回复 token 数 |
-
----
-
-## 可用模型
-
-配置完成后，你可以选择以下模型：
-
-- `gemini-pro` —— 纯文本模型
-- `gemini-pro-vision` —— 多模态模型
-- `gemini-1.5-pro` —— 最新 Pro 模型
-- `gemini-1.5-flash` —— 快速响应模型
-
----
-
-## 使用方法
-
-1. 安装后进入任意对话
-2. 打开模型选择下拉
-3. 查找以 Pipe 名称前缀的模型
-4. 选择 Gemini 模型
-5. 开始聊天！
-
----
-
-## 获取 API Key
-
-1. 访问 [Google AI Studio](https://makersuite.google.com/app/apikey)
-2. 创建新的 API Key
-3. 复制并粘贴到插件配置中
-
-!!! warning "API Key 安全"
-    请妥善保管你的 API Key，不要公开或提交到版本库。
-
----
-
-## 伴随过滤器
-
-如需增强功能，可安装 [Gemini Manifold Companion](../filters/gemini-manifold-companion.md) 过滤器。
-
----
-
-## 运行要求
-
-!!! note "前置条件"
-    - OpenWebUI v0.3.0 及以上
-    - 有效的 Gemini API Key
-    - 可访问 Google AI API 的网络
-
----
-
-## 常见问题
-
-??? question "模型没有出现？"
-    请确认 API Key 配置正确且插件已启用。
-
-??? question "出现 API 错误？"
-    检查 Google AI Studio 中的 Key 有效性和额度限制。
-
-??? question "响应较慢？"
-    可尝试使用 `gemini-1.5-flash` 获得更快速度。
-
----
-
-## 源码
-
-[:fontawesome-brands-github: 在 GitHub 查看](https://github.com/Fu-Jie/awesome-openwebui/tree/main/plugins/pipes/gemini_mainfold){ .md-button }

docs/plugins/pipes/index.md

@@ -15,19 +15,7 @@ Pipes allow you to:
 
 ## Available Pipe Plugins
 
-<div class="grid cards" markdown>
 
--   :material-google:{ .lg .middle } **Gemini Manifold**
-
-    ---
-
-    Integration pipeline for Google's Gemini models with full streaming support.
-
-    **Version:** 1.0.0
-
-    [:octicons-arrow-right-24: Documentation](gemini-manifold.md)
-
-</div>
 
 ---
 

docs/plugins/pipes/index.zh.md

@@ -15,19 +15,7 @@ Pipes 可以用于：
 
 ## 可用的 Pipe 插件
 
-<div class="grid cards" markdown>
 
--   :material-google:{ .lg .middle } **Gemini Manifold**
-
-    ---
-
-    面向 Google Gemini 的集成流水线，支持完整流式返回。
-
-    **版本：** 1.0.0
-
-    [:octicons-arrow-right-24: 查看文档](gemini-manifold.md)
-
-</div>
 
 ---
 

mkdocs.yml

@@ -97,14 +97,14 @@ plugins:
             Documentation Guide: 文档编写指南
             Smart Mind Map: 智能思维导图
             Smart Infographic: 智能信息图
-            Knowledge Card: 知识卡片
+            Flash Card: 闪记卡
             Export to Excel: 导出到 Excel
             Export to Word: 导出为 Word
             Summary: 摘要
             Async Context Compression: 异步上下文压缩
             Context Enhancement: 上下文增强
-            Gemini Manifold Companion: Gemini Manifold 伴侣
-            Gemini Manifold: Gemini Manifold
+            Multi-Model Context Merger: 多模型上下文合并
+            Web Gemini Multimodal Filter: Web Gemini 多模态过滤器
             MoE Prompt Refiner: MoE 提示词优化器
   - minify:
       minify_html: true
@@ -184,18 +184,17 @@ nav:
       - plugins/actions/index.md
       - Smart Mind Map: plugins/actions/smart-mind-map.md
       - Smart Infographic: plugins/actions/smart-infographic.md
-      - Knowledge Card: plugins/actions/knowledge-card.md
+      - Flash Card: plugins/actions/flash-card.md
       - Export to Excel: plugins/actions/export-to-excel.md
       - Export to Word: plugins/actions/export-to-word.md
-      - Summary: plugins/actions/summary.md
     - Filters:
       - plugins/filters/index.md
       - Async Context Compression: plugins/filters/async-context-compression.md
       - Context Enhancement: plugins/filters/context-enhancement.md
-      - Gemini Manifold Companion: plugins/filters/gemini-manifold-companion.md
+      - Multi-Model Context Merger: plugins/filters/multi-model-context-merger.md
+      - Web Gemini Multimodal Filter: plugins/filters/web-gemini-multimodel.md
     - Pipes:
       - plugins/pipes/index.md
-      - Gemini Manifold: plugins/pipes/gemini-manifold.md
     - Pipelines:
       - plugins/pipelines/index.md
       - MoE Prompt Refiner: plugins/pipelines/moe-prompt-refiner.md

plugins/actions/deep-dive/README.md

@@ -0,0 +1,83 @@
+# 🌊 Deep Dive
+
+**Author:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **Version:** 1.0.0 | **Project:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui)
+
+A comprehensive thinking lens that dives deep into any content - from context to logic, insights, and action paths.
+
+## 🔥 What's New in v1.0.0
+
+- ✨ **Thinking Chain Structure**: Moves from surface understanding to deep strategic action.
+- 🔍 **Phase 01: The Context**: Panoramic view of the situation and background.
+- 🧠 **Phase 02: The Logic**: Deconstruction of the underlying reasoning and mental models.
+- 💎 **Phase 03: The Insight**: Extraction of non-obvious value and hidden implications.
+- 🚀 **Phase 04: The Path**: Definition of specific, prioritized strategic directions.
+- 🎨 **Premium UI**: Modern, process-oriented design with a "Thinking Line" timeline.
+- 🌗 **Theme Adaptive**: Automatically adapts to OpenWebUI's light/dark theme.
+
+## ✨ Key Features
+
+- 🌊 **Deep Thinking**: Not just a summary, but a full deconstruction of content.
+- 🧠 **Logical Analysis**: Reveals how arguments are built and identifies hidden assumptions.
+- 💎 **Value Extraction**: Finds the "Aha!" moments and blind spots.
+- 🚀 **Action Oriented**: Translates deep understanding into immediate, actionable steps.
+- 🌍 **Multi-language**: Automatically adapts to the user's preferred language.
+- 🌗 **Theme Support**: Seamlessly switches between light and dark themes based on OpenWebUI settings.
+
+## 🚀 How to Use
+
+1. **Input Content**: Provide any text, article, or meeting notes in the chat.
+2. **Trigger Deep Dive**: Click the **Deep Dive** action button.
+3. **Explore the Chain**: Follow the visual timeline from Context to Path.
+
+## ⚙️ Configuration (Valves)
+
+| Parameter | Default | Description |
+| :--- | :--- | :--- |
+| **Show Status (SHOW_STATUS)** | `True` | Whether to show status updates during the thinking process. |
+| **Model ID (MODEL_ID)** | `Empty` | LLM model for analysis. Empty = use current model. |
+| **Min Text Length (MIN_TEXT_LENGTH)** | `200` | Minimum characters required for a meaningful deep dive. |
+| **Clear Previous HTML (CLEAR_PREVIOUS_HTML)** | `True` | Whether to clear previous plugin results. |
+| **Message Count (MESSAGE_COUNT)** | `1` | Number of recent messages to analyze. |
+
+## 🌗 Theme Support
+
+The plugin automatically detects and adapts to OpenWebUI's theme settings:
+
+- **Detection Priority**:
+  1. Parent document `<meta name="theme-color">` tag
+  2. Parent document `html/body` class or `data-theme` attribute
+  3. System preference via `prefers-color-scheme: dark`
+
+- **Requirements**: For best results, enable **iframe Sandbox Allow Same Origin** in OpenWebUI: 
+  - Go to **Settings** → **Interface** → **Artifacts** → Check **iframe Sandbox Allow Same Origin**
+
+## 🎨 Visual Preview
+
+The plugin generates a structured thinking timeline:
+
+```
+┌─────────────────────────────────────┐
+│  🌊 Deep Dive Analysis              │
+│  👤 User  📅 Date  📊 Word count    │
+├─────────────────────────────────────┤
+│  🔍 Phase 01: The Context           │
+│  [High-level panoramic view]        │
+│                                     │
+│  🧠 Phase 02: The Logic             │
+│  • Reasoning structure...           │
+│  • Hidden assumptions...            │
+│                                     │
+│  💎 Phase 03: The Insight           │
+│  • Non-obvious value...             │
+│  • Blind spots revealed...          │
+│                                     │
+│  🚀 Phase 04: The Path              │
+│  ▸ Priority Action 1...             │
+│  ▸ Priority Action 2...             │
+└─────────────────────────────────────┘
+```
+
+## 📂 Files
+
+- `deep_dive.py` - English version
+- `deep_dive_cn.py` - Chinese version (精读)

plugins/actions/deep-dive/README_CN.md

@@ -0,0 +1,83 @@
+# 📖 精读
+
+**作者:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **版本:** 1.0.0 | **项目:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui)
+
+全方位的思维透镜 —— 从背景全景到逻辑脉络，从深度洞察到行动路径。
+
+## 🔥 v1.0.0 更新内容
+
+- ✨ **思维链结构**: 从表面理解一步步深入到战略行动。
+- 🔍 **阶段 01: 全景 (The Context)**: 提供情境与背景的高层级全景视图。
+- 🧠 **阶段 02: 脉络 (The Logic)**: 解构底层推理逻辑与思维模型。
+- 💎 **阶段 03: 洞察 (The Insight)**: 提取非显性价值与隐藏的深层含义。
+- 🚀 **阶段 04: 路径 (The Path)**: 定义具体的、按优先级排列的战略方向。
+- 🎨 **高端 UI**: 现代化的过程导向设计，带有"思维导火索"时间轴。
+- 🌗 **主题自适应**: 自动适配 OpenWebUI 的深色/浅色主题。
+
+## ✨ 核心特性
+
+- 📖 **深度思考**: 不仅仅是摘要，而是对内容的全面解构。
+- 🧠 **逻辑分析**: 揭示论点是如何构建的，识别隐藏的假设。
+- 💎 **价值提取**: 发现"原来如此"的时刻与思维盲点。
+- 🚀 **行动导向**: 将深度理解转化为立即、可执行的步骤。
+- 🌍 **多语言支持**: 自动适配用户的偏好语言。
+- 🌗 **主题支持**: 根据 OpenWebUI 设置自动切换深色/浅色主题。
+
+## 🚀 如何使用
+
+1. **输入内容**: 在聊天中提供任何文本、文章或会议记录。
+2. **触发精读**: 点击 **精读** 操作按钮。
+3. **探索思维链**: 沿着视觉时间轴从"全景"探索到"路径"。
+
+## ⚙️ 配置参数 (Valves)
+
+| 参数 | 默认值 | 描述 |
+| :--- | :--- | :--- |
+| **显示状态 (SHOW_STATUS)** | `True` | 是否在思维过程中显示状态更新。 |
+| **模型 ID (MODEL_ID)** | `空` | 用于分析的 LLM 模型。留空 = 使用当前模型。 |
+| **最小文本长度 (MIN_TEXT_LENGTH)** | `200` | 进行有意义的精读所需的最小字符数。 |
+| **清除旧 HTML (CLEAR_PREVIOUS_HTML)** | `True` | 是否清除之前的插件结果。 |
+| **消息数量 (MESSAGE_COUNT)** | `1` | 要分析的最近消息数量。 |
+
+## 🌗 主题支持
+
+插件会自动检测并适配 OpenWebUI 的主题设置：
+
+- **检测优先级**:
+  1. 父文档 `<meta name="theme-color">` 标签
+  2. 父文档 `html/body` 的 class 或 `data-theme` 属性
+  3. 系统偏好 `prefers-color-scheme: dark`
+
+- **环境要求**: 为获得最佳效果，请在 OpenWebUI 中启用 **iframe Sandbox Allow Same Origin**：
+  - 进入 **设置** → **界面** → **Artifacts** → 勾选 **iframe Sandbox Allow Same Origin**
+
+## 🎨 视觉预览
+
+插件生成结构化的思维时间轴：
+
+```
+┌─────────────────────────────────────┐
+│  📖 精读分析报告                     │
+│  👤 用户  📅 日期  📊 字数           │
+├─────────────────────────────────────┤
+│  🔍 阶段 01: 全景 (The Context)      │
+│  [高层级全景视图内容]                 │
+│                                     │
+│  🧠 阶段 02: 脉络 (The Logic)        │
+│  • 推理结构分析...                   │
+│  • 隐藏假设识别...                   │
+│                                     │
+│  💎 阶段 03: 洞察 (The Insight)      │
+│  • 非显性价值提取...                 │
+│  • 思维盲点揭示...                   │
+│                                     │
+│  🚀 阶段 04: 路径 (The Path)         │
+│  ▸ 优先级行动 1...                   │
+│  ▸ 优先级行动 2...                   │
+└─────────────────────────────────────┘
+```
+
+## 📂 文件说明
+
+- `deep_dive.py` - 英文版 (Deep Dive)
+- `deep_dive_cn.py` - 中文版 (精读)

plugins/actions/deep-dive/deep_dive.py

@@ -0,0 +1,944 @@
+"""
+title: Deep Dive
+author: Fu-Jie
+author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
+version: 1.0.0
+icon_url: data:image/svg+xml;base64,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
+requirements: markdown
+description: A comprehensive thinking lens that dives deep into any content - from context to logic, insights, and action paths.
+"""
+
+# Standard library imports
+import re
+import logging
+from typing import Optional, Dict, Any, Callable, Awaitable
+from datetime import datetime
+
+# Third-party imports
+from pydantic import BaseModel, Field
+from fastapi import Request
+import markdown
+
+# OpenWebUI imports
+from open_webui.utils.chat import generate_chat_completion
+from open_webui.models.users import Users
+
+# Logging setup
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+# =================================================================
+# HTML Template - Process-Oriented Design with Theme Support
+# =================================================================
+HTML_WRAPPER_TEMPLATE = """
+<!-- OPENWEBUI_PLUGIN_OUTPUT -->
+<!DOCTYPE html>
+<html lang="{user_language}">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <style>
+        :root {
+            --dd-bg-primary: #ffffff;
+            --dd-bg-secondary: #f8fafc;
+            --dd-bg-tertiary: #f1f5f9;
+            --dd-text-primary: #0f172a;
+            --dd-text-secondary: #334155;
+            --dd-text-dim: #64748b;
+            --dd-border: #e2e8f0;
+            --dd-accent: #3b82f6;
+            --dd-accent-soft: #eff6ff;
+            --dd-header-gradient: linear-gradient(135deg, #1e293b 0%, #0f172a 100%);
+            --dd-shadow: 0 10px 40px rgba(0,0,0,0.06);
+            --dd-code-bg: #f1f5f9;
+        }
+        .theme-dark {
+            --dd-bg-primary: #1e293b;
+            --dd-bg-secondary: #0f172a;
+            --dd-bg-tertiary: #334155;
+            --dd-text-primary: #f1f5f9;
+            --dd-text-secondary: #e2e8f0;
+            --dd-text-dim: #94a3b8;
+            --dd-border: #475569;
+            --dd-accent: #60a5fa;
+            --dd-accent-soft: rgba(59, 130, 246, 0.15);
+            --dd-header-gradient: linear-gradient(135deg, #0f172a 0%, #1e1e2e 100%);
+            --dd-shadow: 0 10px 40px rgba(0,0,0,0.3);
+            --dd-code-bg: #334155;
+        }
+        body { 
+            font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif; 
+            margin: 0; 
+            padding: 10px; 
+            background-color: transparent; 
+        }
+        #main-container { 
+            display: flex; 
+            flex-direction: column;
+            gap: 24px; 
+            width: 100%;
+            max-width: 900px;
+            margin: 0 auto;
+        }
+        .plugin-item { 
+            background: var(--dd-bg-primary); 
+            border-radius: 24px; 
+            box-shadow: var(--dd-shadow); 
+            overflow: hidden; 
+            border: 1px solid var(--dd-border); 
+        }
+        /* STYLES_INSERTION_POINT */
+    </style>
+</head>
+<body>
+    <div id="main-container">
+        <!-- CONTENT_INSERTION_POINT -->
+    </div>
+    <!-- SCRIPTS_INSERTION_POINT -->
+    <script>
+    (function() {
+        const parseColorLuma = (colorStr) => {
+            if (!colorStr) return null;
+            let m = colorStr.match(/^#?([0-9a-f]{6})$/i);
+            if (m) {
+                const hex = m[1];
+                const r = parseInt(hex.slice(0, 2), 16);
+                const g = parseInt(hex.slice(2, 4), 16);
+                const b = parseInt(hex.slice(4, 6), 16);
+                return (0.2126 * r + 0.7152 * g + 0.0722 * b) / 255;
+            }
+            m = colorStr.match(/rgba?\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)/i);
+            if (m) {
+                const r = parseInt(m[1], 10);
+                const g = parseInt(m[2], 10);
+                const b = parseInt(m[3], 10);
+                return (0.2126 * r + 0.7152 * g + 0.0722 * b) / 255;
+            }
+            return null;
+        };
+        const getThemeFromMeta = (doc) => {
+            const metas = Array.from((doc || document).querySelectorAll('meta[name="theme-color"]'));
+            if (!metas.length) return null;
+            const color = metas[metas.length - 1].content.trim();
+            const luma = parseColorLuma(color);
+            if (luma === null) return null;
+            return luma < 0.5 ? 'dark' : 'light';
+        };
+        const getParentDocumentSafe = () => {
+            try {
+                if (!window.parent || window.parent === window) return null;
+                const pDoc = window.parent.document;
+                void pDoc.title;
+                return pDoc;
+            } catch (err) { return null; }
+        };
+        const getThemeFromParentClass = () => {
+            try {
+                if (!window.parent || window.parent === window) return null;
+                const pDoc = window.parent.document;
+                const html = pDoc.documentElement;
+                const body = pDoc.body;
+                const htmlClass = html ? html.className : '';
+                const bodyClass = body ? body.className : '';
+                const htmlDataTheme = html ? html.getAttribute('data-theme') : '';
+                if (htmlDataTheme === 'dark' || bodyClass.includes('dark') || htmlClass.includes('dark')) return 'dark';
+                if (htmlDataTheme === 'light' || bodyClass.includes('light') || htmlClass.includes('light')) return 'light';
+                return null;
+            } catch (err) { return null; }
+        };
+        const setTheme = () => {
+            const parentDoc = getParentDocumentSafe();
+            const metaTheme = parentDoc ? getThemeFromMeta(parentDoc) : null;
+            const parentClassTheme = getThemeFromParentClass();
+            const prefersDark = window.matchMedia && window.matchMedia('(prefers-color-scheme: dark)').matches;
+            const chosen = metaTheme || parentClassTheme || (prefersDark ? 'dark' : 'light');
+            document.documentElement.classList.toggle('theme-dark', chosen === 'dark');
+        };
+        setTheme();
+        if (window.matchMedia) {
+            window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', setTheme);
+        }
+    })();
+    </script>
+</body>
+</html>
+"""
+
+# =================================================================
+# LLM Prompts - Deep Dive Thinking Chain
+# =================================================================
+
+SYSTEM_PROMPT = """
+You are a Deep Dive Analyst. Your goal is to guide the user through a comprehensive thinking process, moving from surface understanding to deep strategic action.
+
+## Thinking Structure (STRICT)
+
+You MUST analyze the input across these four specific dimensions:
+
+### 1. 🔍 The Context (What?)
+Provide a high-level panoramic view. What is this content about? What is the core situation, background, or problem being addressed? (2-3 paragraphs)
+
+### 2. 🧠 The Logic (Why?)
+Deconstruct the underlying structure. How is the argument built? What is the reasoning, the hidden assumptions, or the mental models at play? (Bullet points)
+
+### 3. 💎 The Insight (So What?)
+Extract the non-obvious value. What are the "Aha!" moments? What are the implications, the blind spots, or the unique perspectives revealed? (Bullet points)
+
+### 4. 🚀 The Path (Now What?)
+Define the strategic direction. What are the specific, prioritized next steps? How can this knowledge be applied immediately? (Actionable steps)
+
+## Rules
+- Output in the user's specified language.
+- Maintain a professional, analytical, yet inspiring tone.
+- Focus on the *process* of understanding, not just the result.
+- No greetings or meta-commentary.
+"""
+
+USER_PROMPT = """
+Initiate a Deep Dive into the following content:
+
+**User Context:**
+- User: {user_name}
+- Time: {current_date_time_str}
+- Language: {user_language}
+
+**Content to Analyze:**
+```
+{long_text_content}
+```
+
+Please execute the full thinking chain: Context → Logic → Insight → Path.
+"""
+
+# =================================================================
+# Premium CSS Design - Deep Dive Theme
+# =================================================================
+
+CSS_TEMPLATE = """
+        .deep-dive {
+            font-family: 'Inter', -apple-system, system-ui, sans-serif;
+            color: var(--dd-text-secondary);
+        }
+
+        .dd-header {
+            background: var(--dd-header-gradient);
+            padding: 40px 32px;
+            color: white;
+            position: relative;
+        }
+
+        .dd-header-badge {
+            display: inline-block;
+            padding: 4px 12px;
+            background: rgba(255,255,255,0.1);
+            border: 1px solid rgba(255,255,255,0.2);
+            border-radius: 100px;
+            font-size: 0.75rem;
+            font-weight: 600;
+            letter-spacing: 0.05em;
+            text-transform: uppercase;
+            margin-bottom: 16px;
+        }
+
+        .dd-title {
+            font-size: 2rem;
+            font-weight: 800;
+            margin: 0 0 12px 0;
+            letter-spacing: -0.02em;
+        }
+
+        .dd-meta {
+            display: flex;
+            gap: 20px;
+            font-size: 0.85rem;
+            opacity: 0.7;
+        }
+
+        .dd-body {
+            padding: 32px;
+            display: flex;
+            flex-direction: column;
+            gap: 40px;
+            position: relative;
+            background: var(--dd-bg-primary);
+        }
+
+        /* The Thinking Line */
+        .dd-body::before {
+            content: '';
+            position: absolute;
+            left: 52px;
+            top: 40px;
+            bottom: 40px;
+            width: 2px;
+            background: var(--dd-border);
+            z-index: 0;
+        }
+
+        .dd-step {
+            position: relative;
+            z-index: 1;
+            display: flex;
+            gap: 24px;
+        }
+
+        .dd-step-icon {
+            flex-shrink: 0;
+            width: 40px;
+            height: 40px;
+            background: var(--dd-bg-primary);
+            border: 2px solid var(--dd-border);
+            border-radius: 12px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-size: 1.25rem;
+            box-shadow: 0 4px 12px rgba(0,0,0,0.03);
+            transition: all 0.3s ease;
+        }
+
+        .dd-step:hover .dd-step-icon {
+            border-color: var(--dd-accent);
+            transform: scale(1.1);
+        }
+
+        .dd-step-content {
+            flex: 1;
+        }
+
+        .dd-step-label {
+            font-size: 0.75rem;
+            font-weight: 700;
+            color: var(--dd-accent);
+            text-transform: uppercase;
+            letter-spacing: 0.1em;
+            margin-bottom: 4px;
+        }
+
+        .dd-step-title {
+            font-size: 1.25rem;
+            font-weight: 700;
+            color: var(--dd-text-primary);
+            margin: 0 0 16px 0;
+        }
+
+        .dd-text {
+            line-height: 1.7;
+            font-size: 1rem;
+        }
+
+        .dd-text p { margin-bottom: 16px; }
+        .dd-text p:last-child { margin-bottom: 0; }
+
+        .dd-list {
+            list-style: none;
+            padding: 0;
+            margin: 0;
+            display: grid;
+            gap: 12px;
+        }
+
+        .dd-list-item {
+            background: var(--dd-bg-secondary);
+            padding: 16px 20px;
+            border-radius: 12px;
+            border-left: 4px solid var(--dd-border);
+            transition: all 0.2s ease;
+        }
+
+        .dd-list-item:hover {
+            background: var(--dd-bg-tertiary);
+            border-left-color: var(--dd-accent);
+            transform: translateX(4px);
+        }
+
+        .dd-list-item strong {
+            color: var(--dd-text-primary);
+            display: block;
+            margin-bottom: 4px;
+        }
+
+        .dd-path-item {
+            background: var(--dd-accent-soft);
+            border-left-color: var(--dd-accent);
+        }
+
+        .dd-footer {
+            padding: 24px 32px;
+            background: var(--dd-bg-secondary);
+            border-top: 1px solid var(--dd-border);
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            font-size: 0.8rem;
+            color: var(--dd-text-dim);
+        }
+
+        .dd-tag {
+            padding: 2px 8px;
+            background: var(--dd-bg-tertiary);
+            border-radius: 4px;
+            font-weight: 600;
+        }
+
+        .dd-text code,
+        .dd-list-item code {
+            background: var(--dd-code-bg);
+            color: var(--dd-text-primary);
+            padding: 2px 6px;
+            border-radius: 4px;
+            font-family: 'SF Mono', 'Consolas', 'Monaco', monospace;
+            font-size: 0.85em;
+        }
+
+        .dd-list-item em {
+            font-style: italic;
+            color: var(--dd-text-dim);
+        }
+"""
+
+CONTENT_TEMPLATE = """
+        <div class="deep-dive">
+            <div class="dd-header">
+                <div class="dd-header-badge">Thinking Process</div>
+                <h1 class="dd-title">Deep Dive Analysis</h1>
+                <div class="dd-meta">
+                    <span>👤 {user_name}</span>
+                    <span>📅 {current_date_time_str}</span>
+                    <span>📊 {word_count} words</span>
+                </div>
+            </div>
+            <div class="dd-body">
+                <!-- Step 1: Context -->
+                <div class="dd-step">
+                    <div class="dd-step-icon">🔍</div>
+                    <div class="dd-step-content">
+                        <div class="dd-step-label">Phase 01</div>
+                        <h2 class="dd-step-title">The Context</h2>
+                        <div class="dd-text">{context_html}</div>
+                    </div>
+                </div>
+
+                <!-- Step 2: Logic -->
+                <div class="dd-step">
+                    <div class="dd-step-icon">🧠</div>
+                    <div class="dd-step-content">
+                        <div class="dd-step-label">Phase 02</div>
+                        <h2 class="dd-step-title">The Logic</h2>
+                        <div class="dd-text">{logic_html}</div>
+                    </div>
+                </div>
+
+                <!-- Step 3: Insight -->
+                <div class="dd-step">
+                    <div class="dd-step-icon">💎</div>
+                    <div class="dd-step-content">
+                        <div class="dd-step-label">Phase 03</div>
+                        <h2 class="dd-step-title">The Insight</h2>
+                        <div class="dd-text">{insight_html}</div>
+                    </div>
+                </div>
+
+                <!-- Step 4: Path -->
+                <div class="dd-step">
+                    <div class="dd-step-icon">🚀</div>
+                    <div class="dd-step-content">
+                        <div class="dd-step-label">Phase 04</div>
+                        <h2 class="dd-step-title">The Path</h2>
+                        <div class="dd-text">{path_html}</div>
+                    </div>
+                </div>
+            </div>
+            <div class="dd-footer">
+                <span>Deep Dive Engine v1.0</span>
+                <span><span class="dd-tag">AI-Powered</span></span>
+            </div>
+        </div>
+"""
+
+
+class Action:
+    class Valves(BaseModel):
+        SHOW_STATUS: bool = Field(
+            default=True,
+            description="Whether to show operation status updates.",
+        )
+        SHOW_DEBUG_LOG: bool = Field(
+            default=False,
+            description="Whether to print debug logs in the browser console.",
+        )
+        MODEL_ID: str = Field(
+            default="",
+            description="LLM Model ID for analysis. Empty = use current model.",
+        )
+        MIN_TEXT_LENGTH: int = Field(
+            default=200,
+            description="Minimum text length for deep dive (chars).",
+        )
+        CLEAR_PREVIOUS_HTML: bool = Field(
+            default=True,
+            description="Whether to clear previous plugin results.",
+        )
+        MESSAGE_COUNT: int = Field(
+            default=1,
+            description="Number of recent messages to analyze.",
+        )
+
+    def __init__(self):
+        self.valves = self.Valves()
+
+    def _get_user_context(self, __user__: Optional[Dict[str, Any]]) -> Dict[str, str]:
+        """Safely extracts user context information."""
+        if isinstance(__user__, (list, tuple)):
+            user_data = __user__[0] if __user__ else {}
+        elif isinstance(__user__, dict):
+            user_data = __user__
+        else:
+            user_data = {}
+
+        return {
+            "user_id": user_data.get("id", "unknown_user"),
+            "user_name": user_data.get("name", "User"),
+            "user_language": user_data.get("language", "en-US"),
+        }
+
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        Unified extraction of chat context information (chat_id, message_id).
+        Prioritizes extraction from body, then metadata.
+        """
+        chat_id = ""
+        message_id = ""
+
+        # 1. Try to get from body
+        if isinstance(body, dict):
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id is usually 'id' in body
+
+            # Check body.metadata as fallback
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
+
+        # 2. Try to get from __metadata__ (as supplement)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
+
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
+
+    def _process_llm_output(self, llm_output: str) -> Dict[str, str]:
+        """Parse LLM output and convert to styled HTML."""
+        # Extract sections using flexible regex
+        context_match = re.search(
+            r"###\s*1\.\s*🔍?\s*The Context\s*\((.*?)\)\s*\n(.*?)(?=\n###|$)",
+            llm_output,
+            re.DOTALL | re.IGNORECASE,
+        )
+        logic_match = re.search(
+            r"###\s*2\.\s*🧠?\s*The Logic\s*\((.*?)\)\s*\n(.*?)(?=\n###|$)",
+            llm_output,
+            re.DOTALL | re.IGNORECASE,
+        )
+        insight_match = re.search(
+            r"###\s*3\.\s*💎?\s*The Insight\s*\((.*?)\)\s*\n(.*?)(?=\n###|$)",
+            llm_output,
+            re.DOTALL | re.IGNORECASE,
+        )
+        path_match = re.search(
+            r"###\s*4\.\s*🚀?\s*The Path\s*\((.*?)\)\s*\n(.*?)(?=\n###|$)",
+            llm_output,
+            re.DOTALL | re.IGNORECASE,
+        )
+
+        # Fallback if numbering is different
+        if not context_match:
+            context_match = re.search(
+                r"###\s*🔍?\s*The Context.*?\n(.*?)(?=\n###|$)",
+                llm_output,
+                re.DOTALL | re.IGNORECASE,
+            )
+        if not logic_match:
+            logic_match = re.search(
+                r"###\s*🧠?\s*The Logic.*?\n(.*?)(?=\n###|$)",
+                llm_output,
+                re.DOTALL | re.IGNORECASE,
+            )
+        if not insight_match:
+            insight_match = re.search(
+                r"###\s*💎?\s*The Insight.*?\n(.*?)(?=\n###|$)",
+                llm_output,
+                re.DOTALL | re.IGNORECASE,
+            )
+        if not path_match:
+            path_match = re.search(
+                r"###\s*🚀?\s*The Path.*?\n(.*?)(?=\n###|$)",
+                llm_output,
+                re.DOTALL | re.IGNORECASE,
+            )
+
+        context_md = (
+            context_match.group(1 if context_match.lastindex == 1 else 2).strip()
+            if context_match
+            else ""
+        )
+        logic_md = (
+            logic_match.group(1 if logic_match.lastindex == 1 else 2).strip()
+            if logic_match
+            else ""
+        )
+        insight_md = (
+            insight_match.group(1 if insight_match.lastindex == 1 else 2).strip()
+            if insight_match
+            else ""
+        )
+        path_md = (
+            path_match.group(1 if path_match.lastindex == 1 else 2).strip()
+            if path_match
+            else ""
+        )
+
+        if not any([context_md, logic_md, insight_md, path_md]):
+            context_md = llm_output.strip()
+            logger.warning("LLM output did not follow format. Using as context.")
+
+        md_extensions = ["nl2br"]
+
+        context_html = (
+            markdown.markdown(context_md, extensions=md_extensions)
+            if context_md
+            else '<p class="dd-no-content">No context extracted.</p>'
+        )
+        logic_html = (
+            self._process_list_items(logic_md, "logic")
+            if logic_md
+            else '<p class="dd-no-content">No logic deconstructed.</p>'
+        )
+        insight_html = (
+            self._process_list_items(insight_md, "insight")
+            if insight_md
+            else '<p class="dd-no-content">No insights found.</p>'
+        )
+        path_html = (
+            self._process_list_items(path_md, "path")
+            if path_md
+            else '<p class="dd-no-content">No path defined.</p>'
+        )
+
+        return {
+            "context_html": context_html,
+            "logic_html": logic_html,
+            "insight_html": insight_html,
+            "path_html": path_html,
+        }
+
+    def _process_list_items(self, md_content: str, section_type: str) -> str:
+        """Convert markdown list to styled HTML cards with full markdown support."""
+        lines = md_content.strip().split("\n")
+        items = []
+        current_paragraph = []
+
+        for line in lines:
+            line = line.strip()
+
+            # Check for list item (bullet or numbered)
+            bullet_match = re.match(r"^[-*]\s+(.+)$", line)
+            numbered_match = re.match(r"^\d+\.\s+(.+)$", line)
+
+            if bullet_match or numbered_match:
+                # Flush any accumulated paragraph
+                if current_paragraph:
+                    para_text = " ".join(current_paragraph)
+                    para_html = self._convert_inline_markdown(para_text)
+                    items.append(f"<p>{para_html}</p>")
+                    current_paragraph = []
+
+                # Extract the list item content
+                text = (
+                    bullet_match.group(1) if bullet_match else numbered_match.group(1)
+                )
+
+                # Handle bold title pattern: **Title:** Description or **Title**: Description
+                title_match = re.match(r"\*\*(.+?)\*\*[:\s]*(.*)$", text)
+                if title_match:
+                    title = self._convert_inline_markdown(title_match.group(1))
+                    desc = self._convert_inline_markdown(title_match.group(2).strip())
+                    path_class = "dd-path-item" if section_type == "path" else ""
+                    item_html = f'<div class="dd-list-item {path_class}"><strong>{title}</strong>{desc}</div>'
+                else:
+                    text_html = self._convert_inline_markdown(text)
+                    path_class = "dd-path-item" if section_type == "path" else ""
+                    item_html = (
+                        f'<div class="dd-list-item {path_class}">{text_html}</div>'
+                    )
+                items.append(item_html)
+            elif line and not line.startswith("#"):
+                # Accumulate paragraph text
+                current_paragraph.append(line)
+            elif not line and current_paragraph:
+                # Empty line ends paragraph
+                para_text = " ".join(current_paragraph)
+                para_html = self._convert_inline_markdown(para_text)
+                items.append(f"<p>{para_html}</p>")
+                current_paragraph = []
+
+        # Flush remaining paragraph
+        if current_paragraph:
+            para_text = " ".join(current_paragraph)
+            para_html = self._convert_inline_markdown(para_text)
+            items.append(f"<p>{para_html}</p>")
+
+        if items:
+            return f'<div class="dd-list">{" ".join(items)}</div>'
+        return f'<p class="dd-no-content">No items found.</p>'
+
+    def _convert_inline_markdown(self, text: str) -> str:
+        """Convert inline markdown (bold, italic, code) to HTML."""
+        # Convert inline code: `code` -> <code>code</code>
+        text = re.sub(r"`([^`]+)`", r"<code>\1</code>", text)
+        # Convert bold: **text** -> <strong>text</strong>
+        text = re.sub(r"\*\*(.+?)\*\*", r"<strong>\1</strong>", text)
+        # Convert italic: *text* -> <em>text</em> (but not inside **)
+        text = re.sub(r"(?<!\*)\*([^*]+)\*(?!\*)", r"<em>\1</em>", text)
+        return text
+
+    async def _emit_status(
+        self,
+        emitter: Optional[Callable[[Any], Awaitable[None]]],
+        description: str,
+        done: bool = False,
+    ):
+        """Emits a status update event."""
+        if self.valves.SHOW_STATUS and emitter:
+            await emitter(
+                {"type": "status", "data": {"description": description, "done": done}}
+            )
+
+    async def _emit_notification(
+        self,
+        emitter: Optional[Callable[[Any], Awaitable[None]]],
+        content: str,
+        ntype: str = "info",
+    ):
+        """Emits a notification event."""
+        if emitter:
+            await emitter(
+                {"type": "notification", "data": {"type": ntype, "content": content}}
+            )
+
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """Print structured debug logs in the browser console"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            import json
+
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
+
+    def _remove_existing_html(self, content: str) -> str:
+        """Removes existing plugin-generated HTML."""
+        pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
+        return re.sub(pattern, "", content).strip()
+
+    def _extract_text_content(self, content) -> str:
+        """Extract text from message content."""
+        if isinstance(content, str):
+            return content
+        elif isinstance(content, list):
+            text_parts = []
+            for item in content:
+                if isinstance(item, dict) and item.get("type") == "text":
+                    text_parts.append(item.get("text", ""))
+                elif isinstance(item, str):
+                    text_parts.append(item)
+            return "\n".join(text_parts)
+        return str(content) if content else ""
+
+    def _merge_html(
+        self,
+        existing_html: str,
+        new_content: str,
+        new_styles: str = "",
+        user_language: str = "en-US",
+    ) -> str:
+        """Merges new content into HTML container."""
+        if "<!-- OPENWEBUI_PLUGIN_OUTPUT -->" in existing_html:
+            base_html = re.sub(r"^```html\s*", "", existing_html)
+            base_html = re.sub(r"\s*```$", "", base_html)
+        else:
+            base_html = HTML_WRAPPER_TEMPLATE.replace("{user_language}", user_language)
+
+        wrapped = f'<div class="plugin-item">\n{new_content}\n</div>'
+
+        if new_styles:
+            base_html = base_html.replace(
+                "/* STYLES_INSERTION_POINT */",
+                f"{new_styles}\n/* STYLES_INSERTION_POINT */",
+            )
+
+        base_html = base_html.replace(
+            "<!-- CONTENT_INSERTION_POINT -->",
+            f"{wrapped}\n<!-- CONTENT_INSERTION_POINT -->",
+        )
+
+        return base_html.strip()
+
+    def _build_content_html(self, context: dict) -> str:
+        """Build content HTML."""
+        html = CONTENT_TEMPLATE
+        for key, value in context.items():
+            html = html.replace(f"{{{key}}}", str(value))
+        return html
+
+    async def action(
+        self,
+        body: dict,
+        __user__: Optional[Dict[str, Any]] = None,
+        __event_emitter__: Optional[Callable[[Any], Awaitable[None]]] = None,
+        __request__: Optional[Request] = None,
+    ) -> Optional[dict]:
+        logger.info("Action: Deep Dive v1.0.0 started")
+
+        user_ctx = self._get_user_context(__user__)
+        user_id = user_ctx["user_id"]
+        user_name = user_ctx["user_name"]
+        user_language = user_ctx["user_language"]
+
+        now = datetime.now()
+        current_date_time_str = now.strftime("%b %d, %Y %H:%M")
+
+        original_content = ""
+        try:
+            messages = body.get("messages", [])
+            if not messages:
+                raise ValueError("No messages found.")
+
+            message_count = min(self.valves.MESSAGE_COUNT, len(messages))
+            recent_messages = messages[-message_count:]
+
+            aggregated_parts = []
+            for msg in recent_messages:
+                text = self._extract_text_content(msg.get("content"))
+                if text:
+                    aggregated_parts.append(text)
+
+            if not aggregated_parts:
+                raise ValueError("No text content found.")
+
+            original_content = "\n\n---\n\n".join(aggregated_parts)
+            word_count = len(original_content.split())
+
+            if len(original_content) < self.valves.MIN_TEXT_LENGTH:
+                msg = f"Content too brief ({len(original_content)} chars). Deep Dive requires at least {self.valves.MIN_TEXT_LENGTH} chars for meaningful analysis."
+                await self._emit_notification(__event_emitter__, msg, "warning")
+                return {"messages": [{"role": "assistant", "content": f"⚠️ {msg}"}]}
+
+            await self._emit_notification(
+                __event_emitter__, "🌊 Initiating Deep Dive thinking process...", "info"
+            )
+            await self._emit_status(
+                __event_emitter__, "🌊 Deep Dive: Analyzing Context & Logic...", False
+            )
+
+            prompt = USER_PROMPT.format(
+                user_name=user_name,
+                current_date_time_str=current_date_time_str,
+                user_language=user_language,
+                long_text_content=original_content,
+            )
+
+            model = self.valves.MODEL_ID or body.get("model")
+            payload = {
+                "model": model,
+                "messages": [
+                    {"role": "system", "content": SYSTEM_PROMPT},
+                    {"role": "user", "content": prompt},
+                ],
+                "stream": False,
+            }
+
+            user_obj = Users.get_user_by_id(user_id)
+            if not user_obj:
+                raise ValueError(f"User not found: {user_id}")
+
+            response = await generate_chat_completion(__request__, payload, user_obj)
+            llm_output = response["choices"][0]["message"]["content"]
+
+            processed = self._process_llm_output(llm_output)
+
+            context = {
+                "user_name": user_name,
+                "current_date_time_str": current_date_time_str,
+                "word_count": word_count,
+                **processed,
+            }
+
+            content_html = self._build_content_html(context)
+
+            # Handle existing HTML
+            existing = ""
+            match = re.search(
+                r"```html\s*(<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?)```",
+                original_content,
+            )
+            if match:
+                existing = match.group(1)
+
+            if self.valves.CLEAR_PREVIOUS_HTML or not existing:
+                original_content = self._remove_existing_html(original_content)
+                final_html = self._merge_html(
+                    "", content_html, CSS_TEMPLATE, user_language
+                )
+            else:
+                original_content = self._remove_existing_html(original_content)
+                final_html = self._merge_html(
+                    existing, content_html, CSS_TEMPLATE, user_language
+                )
+
+            body["messages"][-1][
+                "content"
+            ] = f"{original_content}\n\n```html\n{final_html}\n```"
+
+            await self._emit_status(__event_emitter__, "🌊 Deep Dive complete!", True)
+            await self._emit_notification(
+                __event_emitter__,
+                f"🌊 Deep Dive complete, {user_name}! Thinking chain generated.",
+                "success",
+            )
+
+        except Exception as e:
+            logger.error(f"Deep Dive Error: {e}", exc_info=True)
+            body["messages"][-1][
+                "content"
+            ] = f"{original_content}\n\n❌ **Error:** {str(e)}"
+            await self._emit_status(__event_emitter__, "Deep Dive failed.", True)
+            await self._emit_notification(
+                __event_emitter__, f"Error: {str(e)}", "error"
+            )
+
+        return body

plugins/actions/deep-dive/deep_dive_cn.py

@@ -0,0 +1,936 @@
+"""
+title: 精读
+author: Fu-Jie
+author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
+version: 1.0.0
+icon_url: data:image/svg+xml;base64,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
+requirements: markdown
+description: 全方位的思维透镜 —— 从背景全景到逻辑脉络，从深度洞察到行动路径。
+"""
+
+# Standard library imports
+import re
+import logging
+from typing import Optional, Dict, Any, Callable, Awaitable
+from datetime import datetime
+
+# Third-party imports
+from pydantic import BaseModel, Field
+from fastapi import Request
+import markdown
+
+# OpenWebUI imports
+from open_webui.utils.chat import generate_chat_completion
+from open_webui.models.users import Users
+
+# Logging setup
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+# =================================================================
+# HTML 模板 - 过程导向设计，支持主题自适应
+# =================================================================
+HTML_WRAPPER_TEMPLATE = """
+<!-- OPENWEBUI_PLUGIN_OUTPUT -->
+<!DOCTYPE html>
+<html lang="{user_language}">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <style>
+        :root {
+            --dd-bg-primary: #ffffff;
+            --dd-bg-secondary: #f8fafc;
+            --dd-bg-tertiary: #f1f5f9;
+            --dd-text-primary: #0f172a;
+            --dd-text-secondary: #334155;
+            --dd-text-dim: #64748b;
+            --dd-border: #e2e8f0;
+            --dd-accent: #3b82f6;
+            --dd-accent-soft: #eff6ff;
+            --dd-header-gradient: linear-gradient(135deg, #1e293b 0%, #0f172a 100%);
+            --dd-shadow: 0 10px 40px rgba(0,0,0,0.06);
+            --dd-code-bg: #f1f5f9;
+        }
+        .theme-dark {
+            --dd-bg-primary: #1e293b;
+            --dd-bg-secondary: #0f172a;
+            --dd-bg-tertiary: #334155;
+            --dd-text-primary: #f1f5f9;
+            --dd-text-secondary: #e2e8f0;
+            --dd-text-dim: #94a3b8;
+            --dd-border: #475569;
+            --dd-accent: #60a5fa;
+            --dd-accent-soft: rgba(59, 130, 246, 0.15);
+            --dd-header-gradient: linear-gradient(135deg, #0f172a 0%, #1e1e2e 100%);
+            --dd-shadow: 0 10px 40px rgba(0,0,0,0.3);
+            --dd-code-bg: #334155;
+        }
+        body { 
+            font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif; 
+            margin: 0; 
+            padding: 10px; 
+            background-color: transparent; 
+        }
+        #main-container { 
+            display: flex; 
+            flex-direction: column;
+            gap: 24px; 
+            width: 100%;
+            max-width: 900px;
+            margin: 0 auto;
+        }
+        .plugin-item { 
+            background: var(--dd-bg-primary); 
+            border-radius: 24px; 
+            box-shadow: var(--dd-shadow); 
+            overflow: hidden; 
+            border: 1px solid var(--dd-border); 
+        }
+        /* STYLES_INSERTION_POINT */
+    </style>
+</head>
+<body>
+    <div id="main-container">
+        <!-- CONTENT_INSERTION_POINT -->
+    </div>
+    <!-- SCRIPTS_INSERTION_POINT -->
+    <script>
+    (function() {
+        const parseColorLuma = (colorStr) => {
+            if (!colorStr) return null;
+            let m = colorStr.match(/^#?([0-9a-f]{6})$/i);
+            if (m) {
+                const hex = m[1];
+                const r = parseInt(hex.slice(0, 2), 16);
+                const g = parseInt(hex.slice(2, 4), 16);
+                const b = parseInt(hex.slice(4, 6), 16);
+                return (0.2126 * r + 0.7152 * g + 0.0722 * b) / 255;
+            }
+            m = colorStr.match(/rgba?\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)/i);
+            if (m) {
+                const r = parseInt(m[1], 10);
+                const g = parseInt(m[2], 10);
+                const b = parseInt(m[3], 10);
+                return (0.2126 * r + 0.7152 * g + 0.0722 * b) / 255;
+            }
+            return null;
+        };
+        const getThemeFromMeta = (doc) => {
+            const metas = Array.from((doc || document).querySelectorAll('meta[name="theme-color"]'));
+            if (!metas.length) return null;
+            const color = metas[metas.length - 1].content.trim();
+            const luma = parseColorLuma(color);
+            if (luma === null) return null;
+            return luma < 0.5 ? 'dark' : 'light';
+        };
+        const getParentDocumentSafe = () => {
+            try {
+                if (!window.parent || window.parent === window) return null;
+                const pDoc = window.parent.document;
+                void pDoc.title;
+                return pDoc;
+            } catch (err) { return null; }
+        };
+        const getThemeFromParentClass = () => {
+            try {
+                if (!window.parent || window.parent === window) return null;
+                const pDoc = window.parent.document;
+                const html = pDoc.documentElement;
+                const body = pDoc.body;
+                const htmlClass = html ? html.className : '';
+                const bodyClass = body ? body.className : '';
+                const htmlDataTheme = html ? html.getAttribute('data-theme') : '';
+                if (htmlDataTheme === 'dark' || bodyClass.includes('dark') || htmlClass.includes('dark')) return 'dark';
+                if (htmlDataTheme === 'light' || bodyClass.includes('light') || htmlClass.includes('light')) return 'light';
+                return null;
+            } catch (err) { return null; }
+        };
+        const setTheme = () => {
+            const parentDoc = getParentDocumentSafe();
+            const metaTheme = parentDoc ? getThemeFromMeta(parentDoc) : null;
+            const parentClassTheme = getThemeFromParentClass();
+            const prefersDark = window.matchMedia && window.matchMedia('(prefers-color-scheme: dark)').matches;
+            const chosen = metaTheme || parentClassTheme || (prefersDark ? 'dark' : 'light');
+            document.documentElement.classList.toggle('theme-dark', chosen === 'dark');
+        };
+        setTheme();
+        if (window.matchMedia) {
+            window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', setTheme);
+        }
+    })();
+    </script>
+</body>
+</html>
+"""
+
+# =================================================================
+# LLM 提示词 - 深度下潜思维链
+# =================================================================
+
+SYSTEM_PROMPT = """
+你是一位“深度下潜 (Deep Dive)”分析专家。你的目标是引导用户完成一个全面的思维过程，从表面理解深入到战略行动。
+
+## 思维结构 (严格遵守)
+
+你必须从以下四个维度剖析输入内容：
+
+### 1. 🔍 The Context (全景)
+提供一个高层级的全景视图。内容是关于什么的？核心情境、背景或正在解决的问题是什么？（2-3 段话）
+
+### 2. 🧠 The Logic (脉络)
+解构底层结构。论点是如何构建的？其中的推理逻辑、隐藏假设或起作用的思维模型是什么？（列表形式）
+
+### 3. 💎 The Insight (洞察)
+提取非显性的价值。有哪些“原来如此”的时刻？揭示了哪些深层含义、盲点或独特视角？（列表形式）
+
+### 4. 🚀 The Path (路径)
+定义战略方向。具体的、按优先级排列的下一步行动是什么？如何立即应用这些知识？（可执行步骤）
+
+## 规则
+- 使用用户指定的语言输出。
+- 保持专业、分析性且富有启发性的语调。
+- 聚焦于“理解的过程”，而不仅仅是结果。
+- 不要包含寒暄或元对话。
+"""
+
+USER_PROMPT = """
+对以下内容发起“深度下潜”：
+
+**用户上下文：**
+- 用户：{user_name}
+- 时间：{current_date_time_str}
+- 语言：{user_language}
+
+**待分析内容：**
+```
+{long_text_content}
+```
+
+请执行完整的思维链：全景 (Context) → 脉络 (Logic) → 洞察 (Insight) → 路径 (Path)。
+"""
+
+# =================================================================
+# 现代 CSS 设计 - 深度下潜主题
+# =================================================================
+
+CSS_TEMPLATE = """
+        .deep-dive {
+            font-family: 'Inter', -apple-system, system-ui, sans-serif;
+            color: var(--dd-text-secondary);
+        }
+
+        .dd-header {
+            background: var(--dd-header-gradient);
+            padding: 40px 32px;
+            color: white;
+            position: relative;
+        }
+
+        .dd-header-badge {
+            display: inline-block;
+            padding: 4px 12px;
+            background: rgba(255,255,255,0.1);
+            border: 1px solid rgba(255,255,255,0.2);
+            border-radius: 100px;
+            font-size: 0.75rem;
+            font-weight: 600;
+            letter-spacing: 0.05em;
+            text-transform: uppercase;
+            margin-bottom: 16px;
+        }
+
+        .dd-title {
+            font-size: 2rem;
+            font-weight: 800;
+            margin: 0 0 12px 0;
+            letter-spacing: -0.02em;
+        }
+
+        .dd-meta {
+            display: flex;
+            gap: 20px;
+            font-size: 0.85rem;
+            opacity: 0.7;
+        }
+
+        .dd-body {
+            padding: 32px;
+            display: flex;
+            flex-direction: column;
+            gap: 40px;
+            position: relative;
+            background: var(--dd-bg-primary);
+        }
+
+        /* 思维导火索 */
+        .dd-body::before {
+            content: '';
+            position: absolute;
+            left: 52px;
+            top: 40px;
+            bottom: 40px;
+            width: 2px;
+            background: var(--dd-border);
+            z-index: 0;
+        }
+
+        .dd-step {
+            position: relative;
+            z-index: 1;
+            display: flex;
+            gap: 24px;
+        }
+
+        .dd-step-icon {
+            flex-shrink: 0;
+            width: 40px;
+            height: 40px;
+            background: var(--dd-bg-primary);
+            border: 2px solid var(--dd-border);
+            border-radius: 12px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-size: 1.25rem;
+            box-shadow: 0 4px 12px rgba(0,0,0,0.03);
+            transition: all 0.3s ease;
+        }
+
+        .dd-step:hover .dd-step-icon {
+            border-color: var(--dd-accent);
+            transform: scale(1.1);
+        }
+
+        .dd-step-content {
+            flex: 1;
+        }
+
+        .dd-step-label {
+            font-size: 0.75rem;
+            font-weight: 700;
+            color: var(--dd-accent);
+            text-transform: uppercase;
+            letter-spacing: 0.1em;
+            margin-bottom: 4px;
+        }
+
+        .dd-step-title {
+            font-size: 1.25rem;
+            font-weight: 700;
+            color: var(--dd-text-primary);
+            margin: 0 0 16px 0;
+        }
+
+        .dd-text {
+            line-height: 1.7;
+            font-size: 1rem;
+        }
+
+        .dd-text p { margin-bottom: 16px; }
+        .dd-text p:last-child { margin-bottom: 0; }
+
+        .dd-list {
+            list-style: none;
+            padding: 0;
+            margin: 0;
+            display: grid;
+            gap: 12px;
+        }
+
+        .dd-list-item {
+            background: var(--dd-bg-secondary);
+            padding: 16px 20px;
+            border-radius: 12px;
+            border-left: 4px solid var(--dd-border);
+            transition: all 0.2s ease;
+        }
+
+        .dd-list-item:hover {
+            background: var(--dd-bg-tertiary);
+            border-left-color: var(--dd-accent);
+            transform: translateX(4px);
+        }
+
+        .dd-list-item strong {
+            color: var(--dd-text-primary);
+            display: block;
+            margin-bottom: 4px;
+        }
+
+        .dd-path-item {
+            background: var(--dd-accent-soft);
+            border-left-color: var(--dd-accent);
+        }
+
+        .dd-footer {
+            padding: 24px 32px;
+            background: var(--dd-bg-secondary);
+            border-top: 1px solid var(--dd-border);
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            font-size: 0.8rem;
+            color: var(--dd-text-dim);
+        }
+
+        .dd-tag {
+            padding: 2px 8px;
+            background: var(--dd-bg-tertiary);
+            border-radius: 4px;
+            font-weight: 600;
+        }
+
+        .dd-text code,
+        .dd-list-item code {
+            background: var(--dd-code-bg);
+            color: var(--dd-text-primary);
+            padding: 2px 6px;
+            border-radius: 4px;
+            font-family: 'SF Mono', 'Consolas', 'Monaco', monospace;
+            font-size: 0.85em;
+        }
+
+        .dd-list-item em {
+            font-style: italic;
+            color: var(--dd-text-dim);
+        }
+"""
+
+CONTENT_TEMPLATE = """
+        <div class="deep-dive">
+            <div class="dd-header">
+                <div class="dd-header-badge">思维过程</div>
+                <h1 class="dd-title">精读分析报告</h1>
+                <div class="dd-meta">
+                    <span>👤 {user_name}</span>
+                    <span>📅 {current_date_time_str}</span>
+                    <span>📊 {word_count} 字</span>
+                </div>
+            </div>
+            <div class="dd-body">
+                <!-- 第一步：全景 -->
+                <div class="dd-step">
+                    <div class="dd-step-icon">🔍</div>
+                    <div class="dd-step-content">
+                        <div class="dd-step-label">Phase 01</div>
+                        <h2 class="dd-step-title">全景 (The Context)</h2>
+                        <div class="dd-text">{context_html}</div>
+                    </div>
+                </div>
+
+                <!-- 第二步：脉络 -->
+                <div class="dd-step">
+                    <div class="dd-step-icon">🧠</div>
+                    <div class="dd-step-content">
+                        <div class="dd-step-label">Phase 02</div>
+                        <h2 class="dd-step-title">脉络 (The Logic)</h2>
+                        <div class="dd-text">{logic_html}</div>
+                    </div>
+                </div>
+
+                <!-- 第三步：洞察 -->
+                <div class="dd-step">
+                    <div class="dd-step-icon">💎</div>
+                    <div class="dd-step-content">
+                        <div class="dd-step-label">Phase 03</div>
+                        <h2 class="dd-step-title">洞察 (The Insight)</h2>
+                        <div class="dd-text">{insight_html}</div>
+                    </div>
+                </div>
+
+                <!-- 第四步：路径 -->
+                <div class="dd-step">
+                    <div class="dd-step-icon">🚀</div>
+                    <div class="dd-step-content">
+                        <div class="dd-step-label">Phase 04</div>
+                        <h2 class="dd-step-title">路径 (The Path)</h2>
+                        <div class="dd-text">{path_html}</div>
+                    </div>
+                </div>
+            </div>
+            <div class="dd-footer">
+                <span>Deep Dive Engine v1.0</span>
+                <span><span class="dd-tag">AI 驱动分析</span></span>
+            </div>
+        </div>
+"""
+
+
+class Action:
+    class Valves(BaseModel):
+        SHOW_STATUS: bool = Field(
+            default=True,
+            description="是否显示操作状态更新。",
+        )
+        SHOW_DEBUG_LOG: bool = Field(
+            default=False,
+            description="是否在浏览器控制台打印调试日志。",
+        )
+        MODEL_ID: str = Field(
+            default="",
+            description="用于分析的 LLM 模型 ID。留空则使用当前模型。",
+        )
+        MIN_TEXT_LENGTH: int = Field(
+            default=200,
+            description="深度下潜所需的最小文本长度（字符）。",
+        )
+        CLEAR_PREVIOUS_HTML: bool = Field(
+            default=True,
+            description="是否清除之前的插件结果。",
+        )
+        MESSAGE_COUNT: int = Field(
+            default=1,
+            description="要分析的最近消息数量。",
+        )
+
+    def __init__(self):
+        self.valves = self.Valves()
+
+    def _get_user_context(self, __user__: Optional[Dict[str, Any]]) -> Dict[str, str]:
+        """安全提取用户上下文信息。"""
+        if isinstance(__user__, (list, tuple)):
+            user_data = __user__[0] if __user__ else {}
+        elif isinstance(__user__, dict):
+            user_data = __user__
+        else:
+            user_data = {}
+
+        return {
+            "user_id": user_data.get("id", "unknown_user"),
+            "user_name": user_data.get("name", "用户"),
+            "user_language": user_data.get("language", "zh-CN"),
+        }
+
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        统一提取聊天上下文信息 (chat_id, message_id)。
+        优先从 body 中提取，其次从 metadata 中提取。
+        """
+        chat_id = ""
+        message_id = ""
+
+        # 1. 尝试从 body 获取
+        if isinstance(body, dict):
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id 在 body 中通常是 id
+
+            # 再次检查 body.metadata
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
+
+        # 2. 尝试从 __metadata__ 获取 (作为补充)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
+
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
+
+    def _process_llm_output(self, llm_output: str) -> Dict[str, str]:
+        """解析 LLM 输出并转换为样式化 HTML。"""
+        # 使用灵活的正则提取各部分
+        context_match = re.search(
+            r"###\s*1\.\s*🔍?\s*(?:全景|The Context)\s*(?:\((.*?)\))?\s*\n(.*?)(?=\n###|$)",
+            llm_output,
+            re.DOTALL | re.IGNORECASE,
+        )
+        logic_match = re.search(
+            r"###\s*2\.\s*🧠?\s*(?:脉络|The Logic)\s*(?:\((.*?)\))?\s*\n(.*?)(?=\n###|$)",
+            llm_output,
+            re.DOTALL | re.IGNORECASE,
+        )
+        insight_match = re.search(
+            r"###\s*3\.\s*💎?\s*(?:洞察|The Insight)\s*(?:\((.*?)\))?\s*\n(.*?)(?=\n###|$)",
+            llm_output,
+            re.DOTALL | re.IGNORECASE,
+        )
+        path_match = re.search(
+            r"###\s*4\.\s*🚀?\s*(?:路径|The Path)\s*(?:\((.*?)\))?\s*\n(.*?)(?=\n###|$)",
+            llm_output,
+            re.DOTALL | re.IGNORECASE,
+        )
+
+        # 兜底正则
+        if not context_match:
+            context_match = re.search(
+                r"###\s*🔍?\s*(?:全景|The Context).*?\n(.*?)(?=\n###|$)",
+                llm_output,
+                re.DOTALL | re.IGNORECASE,
+            )
+        if not logic_match:
+            logic_match = re.search(
+                r"###\s*🧠?\s*(?:脉络|The Logic).*?\n(.*?)(?=\n###|$)",
+                llm_output,
+                re.DOTALL | re.IGNORECASE,
+            )
+        if not insight_match:
+            insight_match = re.search(
+                r"###\s*💎?\s*(?:洞察|The Insight).*?\n(.*?)(?=\n###|$)",
+                llm_output,
+                re.DOTALL | re.IGNORECASE,
+            )
+        if not path_match:
+            path_match = re.search(
+                r"###\s*🚀?\s*(?:路径|The Path).*?\n(.*?)(?=\n###|$)",
+                llm_output,
+                re.DOTALL | re.IGNORECASE,
+            )
+
+        context_md = (
+            context_match.group(context_match.lastindex).strip()
+            if context_match
+            else ""
+        )
+        logic_md = (
+            logic_match.group(logic_match.lastindex).strip() if logic_match else ""
+        )
+        insight_md = (
+            insight_match.group(insight_match.lastindex).strip()
+            if insight_match
+            else ""
+        )
+        path_md = path_match.group(path_match.lastindex).strip() if path_match else ""
+
+        if not any([context_md, logic_md, insight_md, path_md]):
+            context_md = llm_output.strip()
+            logger.warning("LLM 输出未遵循格式，将作为全景处理。")
+
+        md_extensions = ["nl2br"]
+
+        context_html = (
+            markdown.markdown(context_md, extensions=md_extensions)
+            if context_md
+            else '<p class="dd-no-content">未能提取全景信息。</p>'
+        )
+        logic_html = (
+            self._process_list_items(logic_md, "logic")
+            if logic_md
+            else '<p class="dd-no-content">未能解构脉络。</p>'
+        )
+        insight_html = (
+            self._process_list_items(insight_md, "insight")
+            if insight_md
+            else '<p class="dd-no-content">未能发现洞察。</p>'
+        )
+        path_html = (
+            self._process_list_items(path_md, "path")
+            if path_md
+            else '<p class="dd-no-content">未能定义路径。</p>'
+        )
+
+        return {
+            "context_html": context_html,
+            "logic_html": logic_html,
+            "insight_html": insight_html,
+            "path_html": path_html,
+        }
+
+    def _process_list_items(self, md_content: str, section_type: str) -> str:
+        """将 markdown 列表转换为样式化卡片，支持完整的 markdown 格式。"""
+        lines = md_content.strip().split("\n")
+        items = []
+        current_paragraph = []
+
+        for line in lines:
+            line = line.strip()
+
+            # 检查列表项（无序或有序）
+            bullet_match = re.match(r"^[-*]\s+(.+)$", line)
+            numbered_match = re.match(r"^\d+\.\s+(.+)$", line)
+
+            if bullet_match or numbered_match:
+                # 清空累积的段落
+                if current_paragraph:
+                    para_text = " ".join(current_paragraph)
+                    para_html = self._convert_inline_markdown(para_text)
+                    items.append(f"<p>{para_html}</p>")
+                    current_paragraph = []
+
+                # 提取列表项内容
+                text = (
+                    bullet_match.group(1) if bullet_match else numbered_match.group(1)
+                )
+
+                # 处理粗体标题模式：**标题:** 描述 或 **标题**: 描述
+                title_match = re.match(r"\*\*(.+?)\*\*[:\s：]*(.*)$", text)
+                if title_match:
+                    title = self._convert_inline_markdown(title_match.group(1))
+                    desc = self._convert_inline_markdown(title_match.group(2).strip())
+                    path_class = "dd-path-item" if section_type == "path" else ""
+                    item_html = f'<div class="dd-list-item {path_class}"><strong>{title}</strong>{desc}</div>'
+                else:
+                    text_html = self._convert_inline_markdown(text)
+                    path_class = "dd-path-item" if section_type == "path" else ""
+                    item_html = (
+                        f'<div class="dd-list-item {path_class}">{text_html}</div>'
+                    )
+                items.append(item_html)
+            elif line and not line.startswith("#"):
+                # 累积段落文本
+                current_paragraph.append(line)
+            elif not line and current_paragraph:
+                # 空行结束段落
+                para_text = " ".join(current_paragraph)
+                para_html = self._convert_inline_markdown(para_text)
+                items.append(f"<p>{para_html}</p>")
+                current_paragraph = []
+
+        # 清空剩余段落
+        if current_paragraph:
+            para_text = " ".join(current_paragraph)
+            para_html = self._convert_inline_markdown(para_text)
+            items.append(f"<p>{para_html}</p>")
+
+        if items:
+            return f'<div class="dd-list">{" ".join(items)}</div>'
+        return f'<p class="dd-no-content">未找到条目。</p>'
+
+    def _convert_inline_markdown(self, text: str) -> str:
+        """将行内 markdown（粗体、斜体、代码）转换为 HTML。"""
+        # 转换行内代码：`code` -> <code>code</code>
+        text = re.sub(r"`([^`]+)`", r"<code>\1</code>", text)
+        # 转换粗体：**text** -> <strong>text</strong>
+        text = re.sub(r"\*\*(.+?)\*\*", r"<strong>\1</strong>", text)
+        # 转换斜体：*text* -> <em>text</em>（但不在 ** 内部）
+        text = re.sub(r"(?<!\*)\*([^*]+)\*(?!\*)", r"<em>\1</em>", text)
+        return text
+
+    async def _emit_status(
+        self,
+        emitter: Optional[Callable[[Any], Awaitable[None]]],
+        description: str,
+        done: bool = False,
+    ):
+        """发送状态更新事件。"""
+        if self.valves.SHOW_STATUS and emitter:
+            await emitter(
+                {"type": "status", "data": {"description": description, "done": done}}
+            )
+
+    async def _emit_notification(
+        self,
+        emitter: Optional[Callable[[Any], Awaitable[None]]],
+        content: str,
+        ntype: str = "info",
+    ):
+        """发送通知事件。"""
+        if emitter:
+            await emitter(
+                {"type": "notification", "data": {"type": ntype, "content": content}}
+            )
+
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """在浏览器控制台打印结构化调试日志"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            import json
+
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
+
+    def _remove_existing_html(self, content: str) -> str:
+        """移除已有的插件生成的 HTML。"""
+        pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
+        return re.sub(pattern, "", content).strip()
+
+    def _extract_text_content(self, content) -> str:
+        """从消息内容中提取文本。"""
+        if isinstance(content, str):
+            return content
+        elif isinstance(content, list):
+            text_parts = []
+            for item in content:
+                if isinstance(item, dict) and item.get("type") == "text":
+                    text_parts.append(item.get("text", ""))
+                elif isinstance(item, str):
+                    text_parts.append(item)
+            return "\n".join(text_parts)
+        return str(content) if content else ""
+
+    def _merge_html(
+        self,
+        existing_html: str,
+        new_content: str,
+        new_styles: str = "",
+        user_language: str = "zh-CN",
+    ) -> str:
+        """合并新内容到 HTML 容器。"""
+        if "<!-- OPENWEBUI_PLUGIN_OUTPUT -->" in existing_html:
+            base_html = re.sub(r"^```html\s*", "", existing_html)
+            base_html = re.sub(r"\s*```$", "", base_html)
+        else:
+            base_html = HTML_WRAPPER_TEMPLATE.replace("{user_language}", user_language)
+
+        wrapped = f'<div class="plugin-item">\n{new_content}\n</div>'
+
+        if new_styles:
+            base_html = base_html.replace(
+                "/* STYLES_INSERTION_POINT */",
+                f"{new_styles}\n/* STYLES_INSERTION_POINT */",
+            )
+
+        base_html = base_html.replace(
+            "<!-- CONTENT_INSERTION_POINT -->",
+            f"{wrapped}\n<!-- CONTENT_INSERTION_POINT -->",
+        )
+
+        return base_html.strip()
+
+    def _build_content_html(self, context: dict) -> str:
+        """构建内容 HTML。"""
+        html = CONTENT_TEMPLATE
+        for key, value in context.items():
+            html = html.replace(f"{{{key}}}", str(value))
+        return html
+
+    async def action(
+        self,
+        body: dict,
+        __user__: Optional[Dict[str, Any]] = None,
+        __event_emitter__: Optional[Callable[[Any], Awaitable[None]]] = None,
+        __request__: Optional[Request] = None,
+    ) -> Optional[dict]:
+        logger.info("Action: 精读 v1.0.0 启动")
+
+        user_ctx = self._get_user_context(__user__)
+        user_id = user_ctx["user_id"]
+        user_name = user_ctx["user_name"]
+        user_language = user_ctx["user_language"]
+
+        now = datetime.now()
+        current_date_time_str = now.strftime("%Y年%m月%d日 %H:%M")
+
+        original_content = ""
+        try:
+            messages = body.get("messages", [])
+            if not messages:
+                raise ValueError("未找到消息内容。")
+
+            message_count = min(self.valves.MESSAGE_COUNT, len(messages))
+            recent_messages = messages[-message_count:]
+
+            aggregated_parts = []
+            for msg in recent_messages:
+                text = self._extract_text_content(msg.get("content"))
+                if text:
+                    aggregated_parts.append(text)
+
+            if not aggregated_parts:
+                raise ValueError("未找到文本内容。")
+
+            original_content = "\n\n---\n\n".join(aggregated_parts)
+            word_count = len(original_content)
+
+            if len(original_content) < self.valves.MIN_TEXT_LENGTH:
+                msg = f"内容过短（{len(original_content)} 字符）。精读至少需要 {self.valves.MIN_TEXT_LENGTH} 字符才能进行有意义的分析。"
+                await self._emit_notification(__event_emitter__, msg, "warning")
+                return {"messages": [{"role": "assistant", "content": f"⚠️ {msg}"}]}
+
+            await self._emit_notification(
+                __event_emitter__, "📖 正在发起精读分析...", "info"
+            )
+            await self._emit_status(
+                __event_emitter__, "📖 精读：正在分析全景与脉络...", False
+            )
+
+            prompt = USER_PROMPT.format(
+                user_name=user_name,
+                current_date_time_str=current_date_time_str,
+                user_language=user_language,
+                long_text_content=original_content,
+            )
+
+            model = self.valves.MODEL_ID or body.get("model")
+            payload = {
+                "model": model,
+                "messages": [
+                    {"role": "system", "content": SYSTEM_PROMPT},
+                    {"role": "user", "content": prompt},
+                ],
+                "stream": False,
+            }
+
+            user_obj = Users.get_user_by_id(user_id)
+            if not user_obj:
+                raise ValueError(f"未找到用户：{user_id}")
+
+            response = await generate_chat_completion(__request__, payload, user_obj)
+            llm_output = response["choices"][0]["message"]["content"]
+
+            processed = self._process_llm_output(llm_output)
+
+            context = {
+                "user_name": user_name,
+                "current_date_time_str": current_date_time_str,
+                "word_count": word_count,
+                **processed,
+            }
+
+            content_html = self._build_content_html(context)
+
+            # 处理已有 HTML
+            existing = ""
+            match = re.search(
+                r"```html\s*(<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?)```",
+                original_content,
+            )
+            if match:
+                existing = match.group(1)
+
+            if self.valves.CLEAR_PREVIOUS_HTML or not existing:
+                original_content = self._remove_existing_html(original_content)
+                final_html = self._merge_html(
+                    "", content_html, CSS_TEMPLATE, user_language
+                )
+            else:
+                original_content = self._remove_existing_html(original_content)
+                final_html = self._merge_html(
+                    existing, content_html, CSS_TEMPLATE, user_language
+                )
+
+            body["messages"][-1][
+                "content"
+            ] = f"{original_content}\n\n```html\n{final_html}\n```"
+
+            await self._emit_status(__event_emitter__, "📖 精读完成！", True)
+            await self._emit_notification(
+                __event_emitter__,
+                f"📖 精读完成，{user_name}！思维链已生成。",
+                "success",
+            )
+
+        except Exception as e:
+            logger.error(f"Deep Dive 错误：{e}", exc_info=True)
+            body["messages"][-1][
+                "content"
+            ] = f"{original_content}\n\n❌ **错误：** {str(e)}"
+            await self._emit_status(__event_emitter__, "精读失败。", True)
+            await self._emit_notification(__event_emitter__, f"错误：{str(e)}", "error")
+
+        return body

plugins/actions/export_to_docx/README.md

@@ -1,130 +1,88 @@
-# Export to Word
+# 📝 Export to Word (Enhanced)
+
+**Author:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **Version:** 0.4.3 | **Project:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui)
 
 Export conversation to Word (.docx) with **syntax highlighting**, **native math equations**, **Mermaid diagrams**, **citations**, and **enhanced table formatting**.
 
-## Features
+## 🔥 What's New in v0.4.3
 
-- **One-Click Export**: Adds an "Export to Word" action button to the chat.
-- **Markdown Conversion**: Converts Markdown syntax to Word formatting (headings, bold, italic, code, tables, lists).
-- **Syntax Highlighting**: Code blocks are highlighted with Pygments (supports 500+ languages).
-- **Native Math Equations**: LaTeX math (`$$...$$`, `\[...\]`, `$...$`, `\(...\)`) converted to editable Word equations.
-- **Mermaid Diagrams**: Mermaid flowcharts and sequence diagrams rendered as images in the document.
-- **Citations & References**: Auto-generates a References section from OpenWebUI sources with clickable citation links.
-- **Reasoning Stripping**: Automatically removes AI thinking blocks (`<think>`, `<analysis>`) from exports.
-- **Enhanced Tables**: Smart column widths, column alignment (`:---`, `---:`, `:---:`), header row repeat across pages.
-- **Blockquote Support**: Markdown blockquotes are rendered with left border and gray styling.
-- **Multi-language Support**: Properly handles both Chinese and English text.
-- **Smarter Filenames**: Configurable title source (Chat Title, AI Generated, or Markdown Title).
+- ✨ **S3 Object Storage Support**: Direct access to images stored in S3/MinIO via boto3, bypassing API layer for faster exports.
+- 🔧 **Multi-level File Fallback**: 6-level fallback mechanism for file retrieval (DB → S3 → Local → URL → API → Attributes).
+- 🛡️ **Improved Error Handling**: Better logging and error messages for file retrieval failures.
 
-## Configuration
+## ✨ Key Features
 
-You can configure the following settings via the **Valves** button in the plugin settings:
+- 🚀 **One-Click Export**: Adds an "Export to Word" action button to the chat.
+- 📄 **Markdown Conversion**: Full Markdown syntax support (headings, bold, italic, code, tables, lists).
+- 🎨 **Syntax Highlighting**: Code blocks highlighted with Pygments (500+ languages).
+- 🔢 **Native Math Equations**: LaTeX math (`$$...$$`, `\[...\]`, `$...$`) converted to editable Word equations.
+- 📊 **Mermaid Diagrams**: Flowcharts and sequence diagrams rendered as images.
+- 📚 **Citations & References**: Auto-generates References section with clickable citation links.
+- 🧹 **Reasoning Stripping**: Automatically removes AI thinking blocks (`<think>`, `<analysis>`).
+- 📋 **Enhanced Tables**: Smart column widths, alignment, header row repeat across pages.
+- 💬 **Blockquote Support**: Markdown blockquotes with left border and gray styling.
+- 🌐 **Multi-language Support**: Proper handling of Chinese and English text.
 
-- **TITLE_SOURCE**: Choose how the document title/filename is generated.
-    - `chat_title`: Use the conversation title (default).
-    - `ai_generated`: Use AI to generate a short title based on the content.
-    - `markdown_title`: Extract the first h1/h2 heading from the Markdown content.
-- **MAX_EMBED_IMAGE_MB**: Maximum image size to embed into DOCX (MB). Default: `20`.
-- **UI_LANGUAGE**: User interface language, supports `en` (English) and `zh` (Chinese). Default: `en`.
-- **FONT_LATIN**: Font name for Latin characters. Default: `Times New Roman`.
-- **FONT_ASIAN**: Font name for Asian characters. Default: `SimSun`.
-- **FONT_CODE**: Font name for code blocks. Default: `Consolas`.
-- **TABLE_HEADER_COLOR**: Table header background color (Hex without #). Default: `F2F2F2`.
-- **TABLE_ZEBRA_COLOR**: Table alternating row background color (Hex without #). Default: `FBFBFB`.
-- **MERMAID_JS_URL**: URL for the Mermaid.js library.
-- **MERMAID_JSZIP_URL**: URL for the JSZip library (required for DOCX manipulation).
-- **MERMAID_PNG_SCALE**: Scale factor for Mermaid PNG generation (Resolution). Default: `3.0`.
-- **MERMAID_DISPLAY_SCALE**: Scale factor for Mermaid visual size in Word. Default: `1.0`.
-- **MERMAID_OPTIMIZE_LAYOUT**: Automatically convert LR (Left-Right) flowcharts to TD (Top-Down). Default: `False`.
-- **MERMAID_BACKGROUND**: Background color for Mermaid diagrams (e.g., `white`, `transparent`). Default: `transparent`.
-- **MERMAID_CAPTIONS_ENABLE**: Enable/disable figure captions for Mermaid diagrams. Default: `True`.
-- **MERMAID_CAPTION_STYLE**: Paragraph style name for Mermaid captions. Default: `Caption`.
-- **MERMAID_CAPTION_PREFIX**: Caption prefix label (e.g., 'Figure'). Empty = auto-detect based on language.
-- **MATH_ENABLE**: Enable LaTeX math block conversion (`\[...\]` and `$$...$$`). Default: `True`.
-- **MATH_INLINE_DOLLAR_ENABLE**: Enable inline `$ ... $` math conversion. Default: `True`.
+## 🚀 How to Use
 
-## Supported Markdown Syntax
+1. **Install**: Search for "Export to Word" in the Open WebUI Community and install.
+2. **Trigger**: In any chat, click the "Export to Word" action button.
+3. **Download**: The .docx file will be automatically downloaded.
 
-| Syntax                              | Word Result                           |
-| :---------------------------------- | :------------------------------------ |
-| `# Heading 1` to `###### Heading 6` | Heading levels 1-6                    |
-| `**bold**` or `__bold__`            | Bold text                             |
-| `*italic*` or `_italic_`            | Italic text                           |
-| `***bold italic***`                 | Bold + Italic                         |
-| `` `inline code` ``                 | Monospace with gray background        |
-| ` ``` code block ``` `              | **Syntax highlighted** code block     |
-| `> blockquote`                      | Left-bordered gray italic text        |
-| `[link](url)`                       | Blue underlined link text             |
-| `~~strikethrough~~`                 | Strikethrough text                    |
-| `- item` or `* item`                | Bullet list                           |
-| `1. item`                           | Numbered list                         |
-| Markdown tables                     | **Enhanced table** with smart widths  |
-| `---` or `***`                      | Horizontal rule                       |
-| `$$LaTeX$$` or `\[LaTeX\]`          | **Native Word equation** (display)    |
-| `$LaTeX$` or `\(LaTeX\)`            | **Native Word equation** (inline)     |
-| ` ```mermaid ... ``` `              | **Mermaid diagram** as image          |
-| `[1]` citation markers              | **Clickable links** to References     |
+## ⚙️ Configuration (Valves)
 
-## Usage
+| Parameter | Default | Description |
+| :--- | :--- | :--- |
+| **Title Source (TITLE_SOURCE)** | `chat_title` | `chat_title`, `ai_generated`, or `markdown_title` |
+| **Max Image Size (MAX_EMBED_IMAGE_MB)** | `20` | Maximum image size to embed (MB) |
+| **UI Language (UI_LANGUAGE)** | `en` | `en` (English) or `zh` (Chinese) |
+| **Latin Font (FONT_LATIN)** | `Times New Roman` | Font for Latin characters |
+| **Asian Font (FONT_ASIAN)** | `SimSun` | Font for Asian characters |
+| **Code Font (FONT_CODE)** | `Consolas` | Font for code blocks |
+| **Table Header Color** | `F2F2F2` | Header background color (hex) |
+| **Table Zebra Color** | `FBFBFB` | Alternating row color (hex) |
+| **Mermaid PNG Scale** | `3.0` | Resolution multiplier for Mermaid images |
+| **Math Enable** | `True` | Enable LaTeX math conversion |
 
-1. Install the plugin.
-2. In any chat, click the "Export to Word" button.
-3. The .docx file will be automatically downloaded to your device.
+## 🛠️ Supported Markdown Syntax
 
-## Requirements
+| Syntax | Word Result |
+| :--- | :--- |
+| `# Heading 1` to `###### Heading 6` | Heading levels 1-6 |
+| `**bold**` or `__bold__` | Bold text |
+| `*italic*` or `_italic_` | Italic text |
+| `` `inline code` `` | Monospace with gray background |
+| ` ``` code block ``` ` | **Syntax highlighted** code block |
+| `> blockquote` | Left-bordered gray italic text |
+| `[link](url)` | Blue underlined link |
+| `~~strikethrough~~` | Strikethrough text |
+| `- item` or `* item` | Bullet list |
+| `1. item` | Numbered list |
+| Markdown tables | **Enhanced table** with smart widths |
+| `$$LaTeX$$` or `\[LaTeX\]` | **Native Word equation** (display) |
+| `$LaTeX$` or `\(LaTeX\)` | **Native Word equation** (inline) |
+| ` ```mermaid ... ``` ` | **Mermaid diagram** as image |
+| `[1]` citation markers | **Clickable links** to References |
+
+## 📦 Requirements
 
 - `python-docx==1.1.2` - Word document generation
 - `Pygments>=2.15.0` - Syntax highlighting
 - `latex2mathml` - LaTeX to MathML conversion
 - `mathml2omml` - MathML to Office Math (OMML) conversion
 
-All dependencies are declared in the plugin docstring.
+## 📝 Changelog
 
-## Font Configuration
+### v0.4.3
+- **S3 Object Storage**: Direct S3/MinIO access via boto3 for faster image retrieval.
+- **6-Level Fallback**: Robust file retrieval: DB → S3 → Local → URL → API → Attributes.
+- **Better Logging**: Improved error messages for debugging file access issues.
 
-- **English Text**: Times New Roman
-- **Chinese Text**: SimSun (宋体) for body, SimHei (黑体) for headings
-- **Code**: Consolas
-
-## Changelog
+### v0.4.1
+- **Chinese Parameter Names**: Localized configuration names for Chinese version.
 
 ### v0.4.0
-
-- **Multi-language Support**: Added UI language switching (English/Chinese) with localized messages.
-- **Font & Style Configuration**: Customizable fonts for Latin/Asian text and code, plus table colors.
-- **Mermaid Enhancements**: 
-    - Hybrid client-side rendering (SVG+PNG) for better clarity and compatibility.
-    - Configurable background color, fixing issues in dark mode.
-    - Added error boundaries to prevent export failures on render errors.
-- **Performance**: Real-time progress updates for large document exports.
-- **Bug Fixes**: 
-    - Fixed parsing errors in Markdown tables containing code blocks or links.
-    - Fixed parsing issues with underscores (`_`), asterisks (`*`), and tildes (`~`) used as long separators.
-    - Enhanced error handling for image embedding.
-
-### v0.3.0
-
-- **Mermaid Diagrams**: Native support for rendering Mermaid diagrams as images in Word.
-- **Native Math**: Converts LaTeX equations to native Office MathML for editable equations.
-- **Citations**: Automatic bibliography generation and citation linking.
-- **Reasoning Removal**: Option to strip `<think>` blocks from the output.
-- **Table Enhancements**: Improved table formatting with smart column widths.
-
-### v0.2.0
-- Added native math equation support (LaTeX → OMML)
-- Added Mermaid diagram rendering
-- Added citations and references section generation
-- Added automatic reasoning block stripping
-- Enhanced table formatting with smart column widths and alignment
-
-### v0.1.1
-- Initial release with basic Markdown to Word conversion
-
-## Author
-
-Fu-Jie  
-GitHub: [Fu-Jie/awesome-openwebui](https://github.com/Fu-Jie/awesome-openwebui)
-
-## License
-
-MIT License
+- **Multi-language Support**: UI language switching (English/Chinese).
+- **Font & Style Configuration**: Customizable fonts and table colors.
+- **Mermaid Enhancements**: Hybrid SVG+PNG rendering, background color config.
+- **Performance**: Real-time progress updates for large exports.

plugins/actions/export_to_docx/README_CN.md

@@ -1,134 +1,88 @@
-# 导出为 Word
+# 📝 导出为 Word (增强版)
+
+**Author:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **Version:** 0.4.3 | **Project:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui)
 
 将对话导出为 Word (.docx)，支持**代码语法高亮**、**原生数学公式**、**Mermaid 图表**、**引用参考**和**增强表格格式**。
 
-## 功能特点
+## 🔥 v0.4.3 更新内容
 
-- **一键导出**：在聊天界面添加"导出为 Word"动作按钮。
-- **Markdown 转换**：将 Markdown 语法转换为 Word 格式（标题、粗体、斜体、代码、表格、列表）。
-- **代码语法高亮**：使用 Pygments 库为代码块添加语法高亮（支持 500+ 种语言）。
-- **原生数学公式**：LaTeX 公式（`$$...$$`、`\[...\]`、`$...$`、`\(...\)`）转换为可编辑的 Word 公式。
-- **Mermaid 图表**：Mermaid 流程图和时序图渲染为文档中的图片。
-- **引用与参考**：自动从 OpenWebUI 来源生成参考资料章节，支持可点击的引用链接。
-- **移除思考过程**：自动移除 AI 思考块（`<think>`、`<analysis>`）。
-- **增强表格**：智能列宽、列对齐（`:---`、`---:`、`:---:`）、表头跨页重复。
-- **引用块支持**：Markdown 引用块渲染为带左侧边框的灰色斜体样式。
-- **多语言支持**：正确处理中文和英文文本，无乱码问题。
-- **智能文件名**：可配置标题来源（对话标题、AI 生成或 Markdown 标题）。
+- ✨ **S3 对象存储支持**: 通过 boto3 直连 S3/MinIO，绕过 API 层，导出速度更快。
+- 🔧 **多级文件回退**: 6 级文件获取机制（数据库 → S3 → 本地 → URL → API → 属性）。
+- 🛡️ **错误处理优化**: 更完善的日志记录和错误提示，便于调试文件访问问题。
 
-## 配置
+## ✨ 核心特性
 
-您可以通过插件设置中的 **Valves** 按钮配置以下选项：
+- 🚀 **一键导出**: 在聊天界面添加"导出为 Word"动作按钮。
+- 📄 **Markdown 转换**: 完整支持 Markdown 语法（标题、粗体、斜体、代码、表格、列表）。
+- 🎨 **代码语法高亮**: 使用 Pygments 库高亮代码块（支持 500+ 种语言）。
+- 🔢 **原生数学公式**: LaTeX 公式（`$$...$$`、`\[...\]`、`$...$`）转换为可编辑的 Word 公式。
+- 📊 **Mermaid 图表**: 流程图和时序图渲染为文档中的图片。
+- 📚 **引用与参考**: 自动生成参考资料章节，支持可点击的引用链接。
+- 🧹 **移除思考过程**: 自动移除 AI 思考块（`<think>`、`<analysis>`）。
+- 📋 **增强表格**: 智能列宽、对齐、表头跨页重复。
+- 💬 **引用块支持**: Markdown 引用块渲染为带左侧边框的灰色斜体样式。
+- 🌐 **多语言支持**: 正确处理中文和英文文本。
 
-- **文档标题来源**：选择文档标题/文件名的生成方式。
-    - `chat_title`：使用对话标题（默认）。
-    - `ai_generated`：使用 AI 根据内容生成简短标题。
-    - `markdown_title`：从 Markdown 内容中提取第一个一级或二级标题。
-- **最大嵌入图片大小MB**：嵌入图片的最大大小 (MB)。默认：`20`。
-- **界面语言**：界面语言，支持 `en` (英语) 和 `zh` (中文)。默认：`zh`。
-- **英文字体**：英文字体名称。默认：`Calibri`。
-- **中文字体**：中文字体名称。默认：`SimSun`。
-- **代码字体**：代码字体名称。默认：`Consolas`。
-- **表头背景色**：表头背景色（十六进制，不带#）。默认：`F2F2F2`。
-- **表格隔行背景色**：表格隔行背景色（十六进制，不带#）。默认：`FBFBFB`。
-- **Mermaid_JS地址**：Mermaid.js 库的 URL。
-- **JSZip库地址**：JSZip 库的 URL（用于 DOCX 操作）。
-- **Mermaid_PNG缩放比例**：Mermaid PNG 生成缩放比例（分辨率）。默认：`3.0`。
-- **Mermaid显示比例**：Mermaid 在 Word 中的显示比例（视觉大小）。默认：`1.0`。
-- **Mermaid布局优化**：自动将 LR（左右）流程图转换为 TD（上下）。默认：`False`。
-- **Mermaid背景色**：Mermaid 图表背景色（如 `white`, `transparent`）。默认：`transparent`。
-- **启用Mermaid图注**：启用/禁用 Mermaid 图表的图注。默认：`True`。
-- **Mermaid图注样式**：Mermaid 图注的段落样式名称。默认：`Caption`。
-- **Mermaid图注前缀**：图注前缀（如 '图'）。留空则根据语言自动检测。
-- **启用数学公式**：启用 LaTeX 数学公式块转换（`\[...\]` 和 `$$...$$`）。默认：`True`。
-- **启用行内公式**：启用行内 `$ ... $` 数学公式转换。默认：`True`。
+## 🚀 使用方法
 
-## 支持的 Markdown 语法
+1. **安装**: 在 Open WebUI 社区搜索 "导出为 Word" 并安装。
+2. **触发**: 在任意对话中，点击"导出为 Word"动作按钮。
+3. **下载**: .docx 文件将自动下载到你的设备。
 
-| 语法                          | Word 效果                         |
-| :---------------------------- | :-------------------------------- |
-| `# 标题1` 到 `###### 标题6`   | 标题级别 1-6                      |
-| `**粗体**` 或 `__粗体__`      | 粗体文本                          |
-| `*斜体*` 或 `_斜体_`          | 斜体文本                          |
-| `***粗斜体***`                | 粗体 + 斜体                       |
-| `` `行内代码` ``              | 等宽字体 + 灰色背景               |
-| ` ``` 代码块 ``` `            | **语法高亮**的代码块              |
-| `> 引用文本`                  | 带左侧边框的灰色斜体文本          |
-| `[链接](url)`                 | 蓝色下划线链接文本                |
-| `~~删除线~~`                  | 删除线文本                        |
-| `- 项目` 或 `* 项目`          | 无序列表                          |
-| `1. 项目`                     | 有序列表                          |
-| Markdown 表格                 | **增强表格**（智能列宽）          |
-| `---` 或 `***`                | 水平分割线                        |
-| `$$LaTeX$$` 或 `\[LaTeX\]`    | **原生 Word 公式**（块级）        |
-| `$LaTeX$` 或 `\(LaTeX\)`      | **原生 Word 公式**（行内）        |
-| ` ```mermaid ... ``` `        | **Mermaid 图表**（图片形式）      |
-| `[1]` 引用标记                | **可点击链接**到参考资料          |
+## ⚙️ 配置参数 (Valves)
 
-## 使用方法
+| 参数 | 默认值 | 说明 |
+| :--- | :--- | :--- |
+| **文档标题来源** | `chat_title` | `chat_title`（对话标题）、`ai_generated`（AI 生成）、`markdown_title`（Markdown 标题）|
+| **最大嵌入图片大小MB** | `20` | 嵌入图片的最大大小 (MB) |
+| **界面语言** | `zh` | `en`（英语）或 `zh`（中文）|
+| **英文字体** | `Calibri` | 英文字体名称 |
+| **中文字体** | `SimSun` | 中文字体名称 |
+| **代码字体** | `Consolas` | 代码块字体名称 |
+| **表头背景色** | `F2F2F2` | 表头背景色（十六进制）|
+| **表格隔行背景色** | `FBFBFB` | 表格隔行背景色（十六进制）|
+| **Mermaid_PNG缩放比例** | `3.0` | Mermaid 图片分辨率倍数 |
+| **启用数学公式** | `True` | 启用 LaTeX 公式转换 |
 
-1. 安装插件。
-2. 在任意对话中，点击"导出为 Word"按钮。
-3. .docx 文件将自动下载到你的设备。
+## 🛠️ 支持的 Markdown 语法
 
-## 依赖
+| 语法 | Word 效果 |
+| :--- | :--- |
+| `# 标题1` 到 `###### 标题6` | 标题级别 1-6 |
+| `**粗体**` 或 `__粗体__` | 粗体文本 |
+| `*斜体*` 或 `_斜体_` | 斜体文本 |
+| `` `行内代码` `` | 等宽字体 + 灰色背景 |
+| ` ``` 代码块 ``` ` | **语法高亮**的代码块 |
+| `> 引用文本` | 带左侧边框的灰色斜体文本 |
+| `[链接](url)` | 蓝色下划线链接文本 |
+| `~~删除线~~` | 删除线文本 |
+| `- 项目` 或 `* 项目` | 无序列表 |
+| `1. 项目` | 有序列表 |
+| Markdown 表格 | **增强表格**（智能列宽）|
+| `$$LaTeX$$` 或 `\[LaTeX\]` | **原生 Word 公式**（块级）|
+| `$LaTeX$` 或 `\(LaTeX\)` | **原生 Word 公式**（行内）|
+| ` ```mermaid ... ``` ` | **Mermaid 图表**（图片形式）|
+| `[1]` 引用标记 | **可点击链接**到参考资料 |
+
+## 📦 依赖
 
 - `python-docx==1.1.2` - Word 文档生成
 - `Pygments>=2.15.0` - 语法高亮
 - `latex2mathml` - LaTeX 转 MathML
 - `mathml2omml` - MathML 转 Office Math (OMML)
 
-所有依赖已在插件文档字符串中声明。
+## 📝 更新日志
 
-## 字体配置
-
-- **英文文本**：Times New Roman
-- **中文文本**：宋体（正文）、黑体（标题）
-- **代码**：Consolas
-
-## 更新日志
+### v0.4.3
+- **S3 对象存储**: 通过 boto3 直连 S3/MinIO，图片获取速度更快。
+- **6 级回退机制**: 稳健的文件获取：数据库 → S3 → 本地 → URL → API → 属性。
+- **日志优化**: 改进错误提示，便于调试文件访问问题。
 
 ### v0.4.1
-
-- **中文参数名**: 将插件配置项名称和描述全部汉化，提升中文用户体验。
+- **中文参数名**: 配置项名称和描述全部汉化。
 
 ### v0.4.0
-
-- **多语言支持**: 新增界面语言切换（中文/英文），提示信息更友好。
+- **多语言支持**: 界面语言切换（中文/英文）。
 - **字体与样式配置**: 支持自定义中英文字体、代码字体以及表格颜色。
-- **Mermaid 增强**: 
-    - 客户端混合渲染（SVG+PNG），提高清晰度与兼容性。
-    - 支持背景色配置，修复深色模式下的显示问题。
-    - 增加错误边界，渲染失败时显示提示而非中断导出。
+- **Mermaid 增强**: 混合 SVG+PNG 渲染，支持背景色配置。
 - **性能优化**: 导出大型文档时提供实时进度反馈。
-- **Bug 修复**: 
-    - 修复 Markdown 表格中包含代码块或链接时的解析错误。
-    - 修复下划线（`_`）、星号（`*`）、波浪号（`~`）作为长分隔符时的解析问题。
-    - 增强图片嵌入的错误处理。
-
-### v0.3.0
-
-- **Mermaid 图表**: 原生支持将 Mermaid 图表渲染为 Word 中的图片。
-- **原生公式**: 将 LaTeX 公式转换为原生 Office MathML，支持在 Word 中编辑。
-- **引用参考**: 自动生成参考文献列表并链接引用。
-- **移除推理**: 选项支持从输出中移除 `<think>` 推理块。
-- **表格增强**: 改进表格格式，支持智能列宽。
-
-### v0.2.0
-- 新增原生数学公式支持（LaTeX → OMML）
-- 新增 Mermaid 图表渲染
-- 新增引用与参考资料章节生成
-- 新增自动移除 AI 思考块
-- 增强表格格式（智能列宽、对齐）
-
-### v0.1.1
-- 初始版本，支持基本 Markdown 转 Word
-
-## 作者
-
-Fu-Jie  
-GitHub: [Fu-Jie/awesome-openwebui](https://github.com/Fu-Jie/awesome-openwebui)
-
-## 许可证
-
-MIT License

plugins/actions/export_to_docx/export_to_word.py

@@ -1,9 +1,10 @@
 """
 title: Export to Word (Enhanced)
 author: Fu-Jie
-author_url: https://github.com/Fu-Jie
-funding_url: https://github.com/Fu-Jie/awesome-openwebui
-version: 0.4.1
+author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
+version: 0.4.3
+openwebui_id: fca6a315-2a45-42cc-8c96-55cbc85f87f2
 icon_url: data:image/svg+xml;base64,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
 requirements: python-docx, Pygments, latex2mathml, mathml2omml
 description: Export current conversation from Markdown to Word (.docx) with Mermaid diagrams rendered client-side (Mermaid.js, SVG+PNG), LaTeX math, real hyperlinks, improved tables, syntax highlighting, and blockquote support.
@@ -65,6 +66,16 @@ try:
 except Exception:
     LATEX_MATH_AVAILABLE = False
 
+# boto3 for S3 direct access (faster than API fallback)
+try:
+    import boto3
+    from botocore.config import Config as BotoConfig
+    import os
+
+    BOTO3_AVAILABLE = True
+except ImportError:
+    BOTO3_AVAILABLE = False
+
 
 logging.basicConfig(
     level=logging.INFO,
@@ -139,6 +150,14 @@ class Action:
             default="chat_title",
             description="Title Source: 'chat_title' (Chat Title), 'ai_generated' (AI Generated), 'markdown_title' (Markdown Title)",
         )
+        SHOW_STATUS: bool = Field(
+            default=True,
+            description="Whether to show operation status updates.",
+        )
+        SHOW_DEBUG_LOG: bool = Field(
+            default=False,
+            description="Whether to print debug logs in the browser console.",
+        )
 
         MAX_EMBED_IMAGE_MB: int = Field(
             default=20,
@@ -290,6 +309,8 @@ class Action:
         self._bookmark_id_counter: int = 1
         self._active_doc: Optional[Document] = None
         self._user_lang: str = "en"  # Will be set per-request
+        self._api_token: Optional[str] = None
+        self._api_base_url: Optional[str] = None
 
     def _get_lang_key(self, user_language: str) -> str:
         """Convert user language code to i18n key (e.g., 'zh-CN' -> 'zh', 'en-US' -> 'en')."""
@@ -307,10 +328,100 @@ class Action:
                 return msg
         return msg
 
-    async def _send_notification(self, emitter: Callable, type: str, content: str):
-        await emitter(
-            {"type": "notification", "data": {"type": type, "content": content}}
-        )
+    def _get_user_context(self, __user__: Optional[Dict[str, Any]]) -> Dict[str, str]:
+        """Safely extracts user context information."""
+        if isinstance(__user__, (list, tuple)):
+            user_data = __user__[0] if __user__ else {}
+        elif isinstance(__user__, dict):
+            user_data = __user__
+        else:
+            user_data = {}
+
+        return {
+            "user_id": user_data.get("id", "unknown_user"),
+            "user_name": user_data.get("name", "User"),
+            "user_language": user_data.get("language", "en-US"),
+        }
+
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        Unified extraction of chat context information (chat_id, message_id).
+        Prioritizes extraction from body, then metadata.
+        """
+        chat_id = ""
+        message_id = ""
+
+        # 1. Try to get from body
+        if isinstance(body, dict):
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id is usually 'id' in body
+
+            # Check body.metadata as fallback
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
+
+        # 2. Try to get from __metadata__ (as supplement)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
+
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
+
+    async def _emit_status(
+        self,
+        emitter: Optional[Callable[[Any], Awaitable[None]]],
+        description: str,
+        done: bool = False,
+    ):
+        """Emits a status update event."""
+        if self.valves.SHOW_STATUS and emitter:
+            await emitter(
+                {"type": "status", "data": {"description": description, "done": done}}
+            )
+
+    async def _emit_notification(
+        self,
+        emitter: Optional[Callable[[Any], Awaitable[None]]],
+        content: str,
+        ntype: str = "info",
+    ):
+        """Emits a notification event (info, success, warning, error)."""
+        if emitter:
+            await emitter(
+                {"type": "notification", "data": {"type": ntype, "content": content}}
+            )
+
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """Print structured debug logs in the browser console"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            import json
+
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
 
     async def action(
         self,
@@ -349,6 +460,22 @@ class Action:
         # Get user language from Valves configuration
         self._user_lang = self._get_lang_key(self.valves.UI_LANGUAGE)
 
+        # Extract API connection info for file fetching (S3/Object Storage support)
+        def _get_default_base_url() -> str:
+            port = os.environ.get("PORT") or "8080"
+            return f"http://localhost:{port}"
+
+        if __request__:
+            try:
+                self._api_token = __request__.headers.get("Authorization")
+                self._api_base_url = str(__request__.base_url).rstrip("/")
+            except Exception:
+                self._api_token = None
+                self._api_base_url = _get_default_base_url()
+        else:
+            self._api_token = None
+            self._api_base_url = _get_default_base_url()
+
         if __event_emitter__:
             last_assistant_message = body["messages"][-1]
 
@@ -368,14 +495,15 @@ class Action:
                     message_content = self._strip_reasoning_blocks(message_content)
 
                 if not message_content or not message_content.strip():
-                    await self._send_notification(
-                        __event_emitter__, "error", self._get_msg("error_no_content")
+                    await self._emit_notification(
+                        __event_emitter__, self._get_msg("error_no_content"), "error"
                     )
                     return
 
                 # Generate filename
                 title = ""
-                chat_id = self.extract_chat_id(body, __metadata__)
+                chat_ctx = self._get_chat_context(body, __metadata__)
+                chat_id = chat_ctx["chat_id"]
 
                 # Fetch chat_title directly via chat_id as it's usually missing in body
                 chat_title = ""
@@ -844,10 +972,10 @@ class Action:
                     }
                 )
 
-                await self._send_notification(
+                await self._emit_notification(
                     __event_emitter__,
-                    "success",
                     self._get_msg("success", filename=filename),
+                    "success",
                 )
 
                 return {"message": "Download triggered"}
@@ -863,10 +991,10 @@ class Action:
                         },
                     }
                 )
-                await self._send_notification(
+                await self._emit_notification(
                     __event_emitter__,
-                    "error",
                     self._get_msg("error_export", error=str(e)),
+                    "error",
                 )
 
     async def generate_title_using_ai(
@@ -1075,19 +1203,85 @@ class Action:
         b64 = m.group("b64") or ""
         return self._decode_base64_limited(b64, max_bytes)
 
+    def _read_from_s3(self, s3_path: str, max_bytes: int) -> Optional[bytes]:
+        """Read file directly from S3 using environment variables for credentials."""
+        if not BOTO3_AVAILABLE:
+            return None
+
+        # Parse s3://bucket/key
+        if not s3_path.startswith("s3://"):
+            return None
+
+        path_without_prefix = s3_path[5:]  # Remove 's3://'
+        parts = path_without_prefix.split("/", 1)
+        if len(parts) < 2:
+            return None
+
+        bucket = parts[0]
+        key = parts[1]
+
+        # Read S3 config from environment variables
+        endpoint_url = os.environ.get("S3_ENDPOINT_URL")
+        access_key = os.environ.get("S3_ACCESS_KEY_ID")
+        secret_key = os.environ.get("S3_SECRET_ACCESS_KEY")
+        addressing_style = os.environ.get("S3_ADDRESSING_STYLE", "auto")
+
+        if not all([endpoint_url, access_key, secret_key]):
+            logger.debug(
+                "S3 environment variables not fully configured, skipping S3 direct download."
+            )
+            return None
+
+        try:
+            s3_config = BotoConfig(
+                s3={"addressing_style": addressing_style},
+                connect_timeout=5,
+                read_timeout=15,
+            )
+            s3_client = boto3.client(
+                "s3",
+                endpoint_url=endpoint_url,
+                aws_access_key_id=access_key,
+                aws_secret_access_key=secret_key,
+                config=s3_config,
+            )
+
+            response = s3_client.get_object(Bucket=bucket, Key=key)
+            body = response["Body"]
+            data = body.read(max_bytes + 1)
+            body.close()
+
+            if len(data) > max_bytes:
+                return None
+
+            return data
+        except Exception as e:
+            logger.warning(f"S3 direct download failed for {s3_path}: {e}")
+            return None
+
     def _image_bytes_from_owui_file_id(
         self, file_id: str, max_bytes: int
     ) -> Optional[bytes]:
-        if not file_id or Files is None:
-            return None
-        try:
-            file_obj = Files.get_file_by_id(file_id)
-        except Exception:
-            return None
-        if not file_obj:
+        if not file_id:
             return None
 
-        # Common patterns across Open WebUI versions / storage backends.
+        if Files is None:
+            logger.error(
+                "Files model is not available (import failed). Cannot retrieve file content."
+            )
+            return None
+
+        try:
+            file_obj = Files.get_file_by_id(file_id)
+        except Exception as e:
+            logger.error(f"Files.get_file_by_id({file_id}) failed: {e}")
+            return None
+
+        if not file_obj:
+            logger.warning(f"File {file_id} not found in database.")
+            return None
+
+        # 1. Try data field (DB stored)
         data_field = getattr(file_obj, "data", None)
         if isinstance(data_field, dict):
             blob_value = data_field.get("bytes")
@@ -1099,19 +1293,119 @@ class Action:
                 if isinstance(inline, str) and inline.strip():
                     return self._decode_base64_limited(inline, max_bytes)
 
+        # 2. Try S3 direct download (fastest for object storage)
+        s3_path = getattr(file_obj, "path", None)
+        if isinstance(s3_path, str) and s3_path.startswith("s3://"):
+            s3_data = self._read_from_s3(s3_path, max_bytes)
+            if s3_data is not None:
+                return s3_data
+
+        # 3. Try file paths (Disk stored)
+        # We try multiple path variations to be robust against CWD differences (e.g. Docker vs Local)
         for attr in ("path", "file_path", "absolute_path"):
             candidate = getattr(file_obj, attr, None)
             if isinstance(candidate, str) and candidate.strip():
-                raw = self._read_file_bytes_limited(Path(candidate), max_bytes)
+                # Skip obviously non-local paths (S3, GCS, HTTP)
+                if re.match(r"^(s3://|gs://|https?://)", candidate, re.IGNORECASE):
+                    logger.debug(f"Skipping local read for non-local path: {candidate}")
+                    continue
+
+                p = Path(candidate)
+
+                # Attempt 1: As-is (Absolute or relative to CWD)
+                raw = self._read_file_bytes_limited(p, max_bytes)
                 if raw is not None:
                     return raw
 
+                # Attempt 2: Relative to ./data (Common in OpenWebUI)
+                if not p.is_absolute():
+                    try:
+                        raw = self._read_file_bytes_limited(
+                            Path("./data") / p, max_bytes
+                        )
+                        if raw is not None:
+                            return raw
+                    except Exception:
+                        pass
+
+                    # Attempt 3: Relative to /app/backend/data (Docker default)
+                    try:
+                        raw = self._read_file_bytes_limited(
+                            Path("/app/backend/data") / p, max_bytes
+                        )
+                        if raw is not None:
+                            return raw
+                    except Exception:
+                        pass
+
+        # 4. Try URL (Object Storage / S3 Public URL)
+        urls_to_try = []
+        url_attr = getattr(file_obj, "url", None)
+        if isinstance(url_attr, str) and url_attr:
+            urls_to_try.append(url_attr)
+
+        if isinstance(data_field, dict):
+            url_data = data_field.get("url")
+            if isinstance(url_data, str) and url_data:
+                urls_to_try.append(url_data)
+
+        if urls_to_try:
+            import urllib.request
+
+            for url in urls_to_try:
+                if not url.startswith(("http://", "https://")):
+                    continue
+                try:
+                    logger.info(
+                        f"Attempting to download file {file_id} from URL: {url}"
+                    )
+                    # Use a timeout to avoid hanging
+                    req = urllib.request.Request(
+                        url, headers={"User-Agent": "OpenWebUI-Export-Plugin"}
+                    )
+                    with urllib.request.urlopen(req, timeout=15) as response:
+                        if 200 <= response.status < 300:
+                            data = response.read(max_bytes + 1)
+                            if len(data) <= max_bytes:
+                                return data
+                            else:
+                                logger.warning(
+                                    f"File {file_id} from URL is too large (> {max_bytes} bytes)"
+                                )
+                except Exception as e:
+                    logger.warning(f"Failed to download {file_id} from {url}: {e}")
+
+        # 5. Try fetching via Local API (Last resort for S3/Object Storage without direct URL)
+        # If we have the API token and base URL, we can try to fetch the content through the backend API.
+        if self._api_base_url:
+            api_url = f"{self._api_base_url}/api/v1/files/{file_id}/content"
+            try:
+                import urllib.request
+
+                headers = {"User-Agent": "OpenWebUI-Export-Plugin"}
+                if self._api_token:
+                    headers["Authorization"] = self._api_token
+
+                req = urllib.request.Request(api_url, headers=headers)
+                with urllib.request.urlopen(req, timeout=15) as response:
+                    if 200 <= response.status < 300:
+                        data = response.read(max_bytes + 1)
+                        if len(data) <= max_bytes:
+                            return data
+            except Exception:
+                # API fetch failed, just fall through to the next method
+                pass
+
+        # 6. Try direct content attributes (last ditch)
         for attr in ("content", "blob", "data"):
             raw = getattr(file_obj, attr, None)
             if isinstance(raw, (bytes, bytearray)):
                 b = bytes(raw)
                 return b if len(b) <= max_bytes else None
 
+        logger.warning(
+            f"File {file_id} found but no content accessible. Attributes: {dir(file_obj)}"
+        )
         return None
 
     def _add_image_placeholder(self, paragraph, alt: str, reason: str):

plugins/actions/export_to_docx/export_to_word_cn.py

@@ -1,9 +1,10 @@
 """
 title: 导出为 Word (增强版)
 author: Fu-Jie
-author_url: https://github.com/Fu-Jie
-funding_url: https://github.com/Fu-Jie/awesome-openwebui
-version: 0.4.1
+author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
+version: 0.4.3
+openwebui_id: 8a6306c0-d005-4e46-aaae-8db3532c9ed5
 icon_url: data:image/svg+xml;base64,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
 requirements: python-docx, Pygments, latex2mathml, mathml2omml
 description: 将对话导出为 Word (.docx)，支持 Mermaid 图表 (客户端渲染 SVG+PNG)、LaTeX 数学公式、真实超链接、增强表格格式、代码高亮和引用块。
@@ -65,6 +66,16 @@ try:
 except Exception:
     LATEX_MATH_AVAILABLE = False
 
+# boto3 for S3 direct access (faster than API fallback)
+try:
+    import boto3
+    from botocore.config import Config as BotoConfig
+    import os
+
+    BOTO3_AVAILABLE = True
+except ImportError:
+    BOTO3_AVAILABLE = False
+
 
 logging.basicConfig(
     level=logging.INFO,
@@ -139,6 +150,14 @@ class Action:
             default="chat_title",
             description="Title Source: 'chat_title' (Chat Title), 'ai_generated' (AI Generated), 'markdown_title' (Markdown Title)",
         )
+        SHOW_STATUS: bool = Field(
+            default=True,
+            description="是否显示操作状态更新。",
+        )
+        SHOW_DEBUG_LOG: bool = Field(
+            default=False,
+            description="是否在浏览器控制台打印调试日志。",
+        )
 
         最大嵌入图片大小MB: int = Field(
             default=20,
@@ -290,6 +309,8 @@ class Action:
         self._bookmark_id_counter: int = 1
         self._active_doc: Optional[Document] = None
         self._user_lang: str = "en"  # Will be set per-request
+        self._api_token: Optional[str] = None
+        self._api_base_url: Optional[str] = None
 
     def _get_lang_key(self, user_language: str) -> str:
         """Convert user language code to i18n key (e.g., 'zh-CN' -> 'zh', 'en-US' -> 'en')."""
@@ -307,10 +328,100 @@ class Action:
                 return msg
         return msg
 
-    async def _send_notification(self, emitter: Callable, type: str, content: str):
-        await emitter(
-            {"type": "notification", "data": {"type": type, "content": content}}
-        )
+    def _get_user_context(self, __user__: Optional[Dict[str, Any]]) -> Dict[str, str]:
+        """安全提取用户上下文信息。"""
+        if isinstance(__user__, (list, tuple)):
+            user_data = __user__[0] if __user__ else {}
+        elif isinstance(__user__, dict):
+            user_data = __user__
+        else:
+            user_data = {}
+
+        return {
+            "user_id": user_data.get("id", "unknown_user"),
+            "user_name": user_data.get("name", "用户"),
+            "user_language": user_data.get("language", "zh-CN"),
+        }
+
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        统一提取聊天上下文信息 (chat_id, message_id)。
+        优先从 body 中提取，其次从 metadata 中提取。
+        """
+        chat_id = ""
+        message_id = ""
+
+        # 1. 尝试从 body 获取
+        if isinstance(body, dict):
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id 在 body 中通常是 id
+
+            # 再次检查 body.metadata
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
+
+        # 2. 尝试从 __metadata__ 获取 (作为补充)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
+
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
+
+    async def _emit_status(
+        self,
+        emitter: Optional[Callable[[Any], Awaitable[None]]],
+        description: str,
+        done: bool = False,
+    ):
+        """Emits a status update event."""
+        if self.valves.SHOW_STATUS and emitter:
+            await emitter(
+                {"type": "status", "data": {"description": description, "done": done}}
+            )
+
+    async def _emit_notification(
+        self,
+        emitter: Optional[Callable[[Any], Awaitable[None]]],
+        content: str,
+        ntype: str = "info",
+    ):
+        """Emits a notification event (info, success, warning, error)."""
+        if emitter:
+            await emitter(
+                {"type": "notification", "data": {"type": ntype, "content": content}}
+            )
+
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """在浏览器控制台打印结构化调试日志"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            import json
+
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
 
     async def action(
         self,
@@ -347,6 +458,22 @@ class Action:
         # Get user language from Valves configuration
         self._user_lang = self._get_lang_key(self.valves.界面语言)
 
+        # Extract API connection info for file fetching (S3/Object Storage support)
+        def _get_default_base_url() -> str:
+            port = os.environ.get("PORT") or "8080"
+            return f"http://localhost:{port}"
+
+        if __request__:
+            try:
+                self._api_token = __request__.headers.get("Authorization")
+                self._api_base_url = str(__request__.base_url).rstrip("/")
+            except Exception:
+                self._api_token = None
+                self._api_base_url = _get_default_base_url()
+        else:
+            self._api_token = None
+            self._api_base_url = _get_default_base_url()
+
         if __event_emitter__:
             last_assistant_message = body["messages"][-1]
 
@@ -366,14 +493,15 @@ class Action:
                     message_content = self._strip_reasoning_blocks(message_content)
 
                 if not message_content or not message_content.strip():
-                    await self._send_notification(
-                        __event_emitter__, "error", self._get_msg("error_no_content")
+                    await self._emit_notification(
+                        __event_emitter__, self._get_msg("error_no_content"), "error"
                     )
                     return
 
                 # Generate filename
                 title = ""
-                chat_id = self.extract_chat_id(body, __metadata__)
+                chat_ctx = self._get_chat_context(body, __metadata__)
+                chat_id = chat_ctx["chat_id"]
 
                 # Fetch chat_title directly via chat_id as it's usually missing in body
                 chat_title = ""
@@ -842,10 +970,10 @@ class Action:
                     }
                 )
 
-                await self._send_notification(
+                await self._emit_notification(
                     __event_emitter__,
-                    "success",
                     self._get_msg("success", filename=filename),
+                    "success",
                 )
 
                 return {"message": "Download triggered"}
@@ -861,10 +989,10 @@ class Action:
                         },
                     }
                 )
-                await self._send_notification(
+                await self._emit_notification(
                     __event_emitter__,
-                    "error",
                     self._get_msg("error_export", error=str(e)),
+                    "error",
                 )
 
     async def generate_title_using_ai(
@@ -1073,19 +1201,85 @@ class Action:
         b64 = m.group("b64") or ""
         return self._decode_base64_limited(b64, max_bytes)
 
+    def _read_from_s3(self, s3_path: str, max_bytes: int) -> Optional[bytes]:
+        """Read file directly from S3 using environment variables for credentials."""
+        if not BOTO3_AVAILABLE:
+            return None
+
+        # Parse s3://bucket/key
+        if not s3_path.startswith("s3://"):
+            return None
+
+        path_without_prefix = s3_path[5:]  # Remove 's3://'
+        parts = path_without_prefix.split("/", 1)
+        if len(parts) < 2:
+            return None
+
+        bucket = parts[0]
+        key = parts[1]
+
+        # Read S3 config from environment variables
+        endpoint_url = os.environ.get("S3_ENDPOINT_URL")
+        access_key = os.environ.get("S3_ACCESS_KEY_ID")
+        secret_key = os.environ.get("S3_SECRET_ACCESS_KEY")
+        addressing_style = os.environ.get("S3_ADDRESSING_STYLE", "auto")
+
+        if not all([endpoint_url, access_key, secret_key]):
+            logger.debug(
+                "S3 environment variables not fully configured, skipping S3 direct download."
+            )
+            return None
+
+        try:
+            s3_config = BotoConfig(
+                s3={"addressing_style": addressing_style},
+                connect_timeout=5,
+                read_timeout=15,
+            )
+            s3_client = boto3.client(
+                "s3",
+                endpoint_url=endpoint_url,
+                aws_access_key_id=access_key,
+                aws_secret_access_key=secret_key,
+                config=s3_config,
+            )
+
+            response = s3_client.get_object(Bucket=bucket, Key=key)
+            body = response["Body"]
+            data = body.read(max_bytes + 1)
+            body.close()
+
+            if len(data) > max_bytes:
+                return None
+
+            return data
+        except Exception as e:
+            logger.warning(f"S3 direct download failed for {s3_path}: {e}")
+            return None
+
     def _image_bytes_from_owui_file_id(
         self, file_id: str, max_bytes: int
     ) -> Optional[bytes]:
-        if not file_id or Files is None:
-            return None
-        try:
-            file_obj = Files.get_file_by_id(file_id)
-        except Exception:
-            return None
-        if not file_obj:
+        if not file_id:
             return None
 
-        # Common patterns across Open WebUI versions / storage backends.
+        if Files is None:
+            logger.error(
+                "Files model is not available (import failed). Cannot retrieve file content."
+            )
+            return None
+
+        try:
+            file_obj = Files.get_file_by_id(file_id)
+        except Exception as e:
+            logger.error(f"Files.get_file_by_id({file_id}) failed: {e}")
+            return None
+
+        if not file_obj:
+            logger.warning(f"File {file_id} not found in database.")
+            return None
+
+        # 1. Try data field (DB stored)
         data_field = getattr(file_obj, "data", None)
         if isinstance(data_field, dict):
             blob_value = data_field.get("bytes")
@@ -1097,19 +1291,119 @@ class Action:
                 if isinstance(inline, str) and inline.strip():
                     return self._decode_base64_limited(inline, max_bytes)
 
+        # 2. Try S3 direct download (fastest for object storage)
+        s3_path = getattr(file_obj, "path", None)
+        if isinstance(s3_path, str) and s3_path.startswith("s3://"):
+            s3_data = self._read_from_s3(s3_path, max_bytes)
+            if s3_data is not None:
+                return s3_data
+
+        # 3. Try file paths (Disk stored)
+        # We try multiple path variations to be robust against CWD differences (e.g. Docker vs Local)
         for attr in ("path", "file_path", "absolute_path"):
             candidate = getattr(file_obj, attr, None)
             if isinstance(candidate, str) and candidate.strip():
-                raw = self._read_file_bytes_limited(Path(candidate), max_bytes)
+                # Skip obviously non-local paths (S3, GCS, HTTP)
+                if re.match(r"^(s3://|gs://|https?://)", candidate, re.IGNORECASE):
+                    logger.debug(f"Skipping local read for non-local path: {candidate}")
+                    continue
+
+                p = Path(candidate)
+
+                # Attempt 1: As-is (Absolute or relative to CWD)
+                raw = self._read_file_bytes_limited(p, max_bytes)
                 if raw is not None:
                     return raw
 
+                # Attempt 2: Relative to ./data (Common in OpenWebUI)
+                if not p.is_absolute():
+                    try:
+                        raw = self._read_file_bytes_limited(
+                            Path("./data") / p, max_bytes
+                        )
+                        if raw is not None:
+                            return raw
+                    except Exception:
+                        pass
+
+                    # Attempt 3: Relative to /app/backend/data (Docker default)
+                    try:
+                        raw = self._read_file_bytes_limited(
+                            Path("/app/backend/data") / p, max_bytes
+                        )
+                        if raw is not None:
+                            return raw
+                    except Exception:
+                        pass
+
+        # 4. Try URL (Object Storage / S3 Public URL)
+        urls_to_try = []
+        url_attr = getattr(file_obj, "url", None)
+        if isinstance(url_attr, str) and url_attr:
+            urls_to_try.append(url_attr)
+
+        if isinstance(data_field, dict):
+            url_data = data_field.get("url")
+            if isinstance(url_data, str) and url_data:
+                urls_to_try.append(url_data)
+
+        if urls_to_try:
+            import urllib.request
+
+            for url in urls_to_try:
+                if not url.startswith(("http://", "https://")):
+                    continue
+                try:
+                    logger.info(
+                        f"Attempting to download file {file_id} from URL: {url}"
+                    )
+                    # Use a timeout to avoid hanging
+                    req = urllib.request.Request(
+                        url, headers={"User-Agent": "OpenWebUI-Export-Plugin"}
+                    )
+                    with urllib.request.urlopen(req, timeout=15) as response:
+                        if 200 <= response.status < 300:
+                            data = response.read(max_bytes + 1)
+                            if len(data) <= max_bytes:
+                                return data
+                            else:
+                                logger.warning(
+                                    f"File {file_id} from URL is too large (> {max_bytes} bytes)"
+                                )
+                except Exception as e:
+                    logger.warning(f"Failed to download {file_id} from {url}: {e}")
+
+        # 5. Try fetching via Local API (Last resort for S3/Object Storage without direct URL)
+        # If we have the API token and base URL, we can try to fetch the content through the backend API.
+        if self._api_base_url:
+            api_url = f"{self._api_base_url}/api/v1/files/{file_id}/content"
+            try:
+                import urllib.request
+
+                headers = {"User-Agent": "OpenWebUI-Export-Plugin"}
+                if self._api_token:
+                    headers["Authorization"] = self._api_token
+
+                req = urllib.request.Request(api_url, headers=headers)
+                with urllib.request.urlopen(req, timeout=15) as response:
+                    if 200 <= response.status < 300:
+                        data = response.read(max_bytes + 1)
+                        if len(data) <= max_bytes:
+                            return data
+            except Exception:
+                # API fetch failed, just fall through to the next method
+                pass
+
+        # 6. Try direct content attributes (last ditch)
         for attr in ("content", "blob", "data"):
             raw = getattr(file_obj, attr, None)
             if isinstance(raw, (bytes, bytearray)):
                 b = bytes(raw)
                 return b if len(b) <= max_bytes else None
 
+        logger.warning(
+            f"File {file_id} found but no content accessible. Attributes: {dir(file_obj)}"
+        )
         return None
 
     def _add_image_placeholder(self, paragraph, alt: str, reason: str):

plugins/actions/export_to_excel/export_to_excel.py

@@ -1,9 +1,10 @@
 """
 title: Export to Excel
 author: Fu-Jie
-author_url: https://github.com/Fu-Jie
-funding_url: https://github.com/Fu-Jie/awesome-openwebui
+author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
 version: 0.3.7
+openwebui_id: 244b8f9d-7459-47d6-84d3-c7ae8e3ec710
 icon_url: data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9ImN1cnJlbnRDb2xvciIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxwYXRoIGQ9Ik0xNSAySDZhMiAyIDAgMCAwLTIgMnYxNmEyIDIgMCAwIDAgMiAyaDEyYTIgMiAwIDAgMCAyLTJWN1oiLz48cGF0aCBkPSJNMTQgMnY0YTIgMiAwIDAgMCAyIDJoNCIvPjxwYXRoIGQ9Ik04IDEzaDIiLz48cGF0aCBkPSJNMTQgMTNoMiIvPjxwYXRoIGQ9Ik04IDE3aDIiLz48cGF0aCBkPSJNMTQgMTdoMiIvPjwvc3ZnPg==
 description: Extracts tables from chat messages and exports them to Excel (.xlsx) files with smart formatting.
 """
@@ -31,6 +32,10 @@ class Action:
             default="chat_title",
             description="Title Source: 'chat_title' (Chat Title), 'ai_generated' (AI Generated), 'markdown_title' (Markdown Title)",
         )
+        SHOW_STATUS: bool = Field(
+            default=True,
+            description="Whether to show operation status updates.",
+        )
         EXPORT_SCOPE: Literal["last_message", "all_messages"] = Field(
             default="last_message",
             description="Export Scope: 'last_message' (Last Message Only), 'all_messages' (All Messages)",
@@ -39,14 +44,57 @@ class Action:
             default="",
             description="Model ID for AI title generation. Leave empty to use the current chat model.",
         )
+        SHOW_DEBUG_LOG: bool = Field(
+            default=False,
+            description="Whether to print debug logs in the browser console.",
+        )
 
     def __init__(self):
         self.valves = self.Valves()
 
-    async def _send_notification(self, emitter: Callable, type: str, content: str):
-        await emitter(
-            {"type": "notification", "data": {"type": type, "content": content}}
-        )
+    async def _emit_status(
+        self,
+        emitter: Optional[Callable[[Any], Awaitable[None]]],
+        description: str,
+        done: bool = False,
+    ):
+        """Emits a status update event."""
+        if self.valves.SHOW_STATUS and emitter:
+            await emitter(
+                {"type": "status", "data": {"description": description, "done": done}}
+            )
+
+    async def _emit_notification(
+        self,
+        emitter: Optional[Callable[[Any], Awaitable[None]]],
+        content: str,
+        ntype: str = "info",
+    ):
+        """Emits a notification event (info, success, warning, error)."""
+        if emitter:
+            await emitter(
+                {"type": "notification", "data": {"type": ntype, "content": content}}
+            )
+
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """Print structured debug logs in the browser console"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            import json
+
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
 
     async def action(
         self,
@@ -189,17 +237,18 @@ class Action:
                 # Notify user about the number of tables found
                 table_count = len(all_tables)
                 if self.valves.EXPORT_SCOPE == "all_messages":
-                    await self._send_notification(
+                    await self._emit_notification(
                         __event_emitter__,
-                        "info",
                         f"Found {table_count} table(s) in all messages.",
+                        "info",
                     )
                     # Wait a moment for user to see the notification before download dialog
                     await asyncio.sleep(1.5)
                 # Generate Workbook Title (Filename)
                 # Use the title of the chat, or the first header of the first message with tables
                 title = ""
-                chat_id = self.extract_chat_id(body, None)
+                chat_ctx = self._get_chat_context(body, None)
+                chat_id = chat_ctx["chat_id"]
                 chat_title = ""
                 if chat_id:
                     chat_title = await self.fetch_chat_title(chat_id, user_id)
@@ -329,8 +378,8 @@ class Action:
                         },
                     }
                 )
-                await self._send_notification(
-                    __event_emitter__, "error", "No tables found to export!"
+                await self._emit_notification(
+                    __event_emitter__, "No tables found to export!", "error"
                 )
                 raise e
             except Exception as e:
@@ -344,8 +393,8 @@ class Action:
                         },
                     }
                 )
-                await self._send_notification(
-                    __event_emitter__, "error", "No tables found to export!"
+                await self._emit_notification(
+                    __event_emitter__, "No tables found to export!", "error"
                 )
 
     async def generate_title_using_ai(
@@ -388,20 +437,20 @@ class Action:
             async def notification_task():
                 # Send initial notification immediately
                 if event_emitter:
-                    await self._send_notification(
+                    await self._emit_notification(
                         event_emitter,
-                        "info",
                         "AI is generating a filename for your Excel file...",
+                        "info",
                     )
 
                 # Subsequent notifications every 5 seconds
                 while True:
                     await asyncio.sleep(5)
                     if event_emitter:
-                        await self._send_notification(
+                        await self._emit_notification(
                             event_emitter,
-                            "info",
                             "Still generating filename, please be patient...",
+                            "info",
                         )
 
             # Run tasks concurrently
@@ -431,10 +480,10 @@ class Action:
         except Exception as e:
             print(f"Error generating title: {e}")
             if event_emitter:
-                await self._send_notification(
+                await self._emit_notification(
                     event_emitter,
-                    "warning",
                     f"AI title generation failed, using default title. Error: {str(e)}",
+                    "warning",
                 )
 
         return ""
@@ -449,24 +498,56 @@ class Action:
                 return match.group(1).strip()
         return ""
 
-    def extract_chat_id(self, body: dict, metadata: Optional[dict]) -> str:
-        """Extract chat_id from body or metadata"""
-        if isinstance(body, dict):
-            chat_id = body.get("chat_id") or body.get("id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
+    def _get_user_context(self, __user__: Optional[Dict[str, Any]]) -> Dict[str, str]:
+        """Safely extracts user context information."""
+        if isinstance(__user__, (list, tuple)):
+            user_data = __user__[0] if __user__ else {}
+        elif isinstance(__user__, dict):
+            user_data = __user__
+        else:
+            user_data = {}
 
-            for key in ("chat", "conversation"):
-                nested = body.get(key)
-                if isinstance(nested, dict):
-                    nested_id = nested.get("id") or nested.get("chat_id")
-                    if isinstance(nested_id, str) and nested_id.strip():
-                        return nested_id.strip()
-        if isinstance(metadata, dict):
-            chat_id = metadata.get("chat_id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
-        return ""
+        return {
+            "user_id": user_data.get("id", "unknown_user"),
+            "user_name": user_data.get("name", "User"),
+            "user_language": user_data.get("language", "en-US"),
+        }
+
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        Unified extraction of chat context information (chat_id, message_id).
+        Prioritizes extraction from body, then metadata.
+        """
+        chat_id = ""
+        message_id = ""
+
+        # 1. Try to get from body
+        if isinstance(body, dict):
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id is usually 'id' in body
+
+            # Check body.metadata as fallback
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
+
+        # 2. Try to get from __metadata__ (as supplement)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
+
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
 
     async def fetch_chat_title(self, chat_id: str, user_id: str = "") -> str:
         """Fetch chat title from database by chat_id"""

plugins/actions/export_to_excel/export_to_excel_cn.py

@@ -1,8 +1,8 @@
 """
 title: 导出为 Excel
 author: Fu-Jie
-author_url: https://github.com/Fu-Jie
-funding_url: https://github.com/Fu-Jie/awesome-openwebui
+author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
 version: 0.3.7
 icon_url: data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9ImN1cnJlbnRDb2xvciIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxwYXRoIGQ9Ik0xNSAySDZhMiAyIDAgMCAwLTIgMnYxNmEyIDIgMCAwIDAgMiAyaDEyYTIgMiAwIDAgMCAyLTJWN1oiLz48cGF0aCBkPSJNMTQgMnY0YTIgMiAwIDAgMCAyIDJoNCIvPjxwYXRoIGQ9Ik04IDEzaDIiLz48cGF0aCBkPSJNMTQgMTNoMiIvPjxwYXRoIGQ9Ik04IDE3aDIiLz48cGF0aCBkPSJNMTQgMTdoMiIvPjwvc3ZnPg==
 description: 从聊天消息中提取表格并导出为 Excel (.xlsx) 文件，支持智能格式化。
@@ -31,6 +31,10 @@ class Action:
             default="chat_title",
             description="标题来源: 'chat_title' (对话标题), 'ai_generated' (AI生成), 'markdown_title' (Markdown标题)",
         )
+        SHOW_STATUS: bool = Field(
+            default=True,
+            description="是否显示操作状态更新。",
+        )
         EXPORT_SCOPE: Literal["last_message", "all_messages"] = Field(
             default="last_message",
             description="导出范围: 'last_message' (仅最后一条消息), 'all_messages' (所有消息)",
@@ -39,14 +43,57 @@ class Action:
             default="",
             description="AI 标题生成模型 ID。留空则使用当前对话模型。",
         )
+        SHOW_DEBUG_LOG: bool = Field(
+            default=False,
+            description="是否在浏览器控制台打印调试日志。",
+        )
 
     def __init__(self):
         self.valves = self.Valves()
 
-    async def _send_notification(self, emitter: Callable, type: str, content: str):
-        await emitter(
-            {"type": "notification", "data": {"type": type, "content": content}}
-        )
+    async def _emit_status(
+        self,
+        emitter: Optional[Callable[[Any], Awaitable[None]]],
+        description: str,
+        done: bool = False,
+    ):
+        """Emits a status update event."""
+        if self.valves.SHOW_STATUS and emitter:
+            await emitter(
+                {"type": "status", "data": {"description": description, "done": done}}
+            )
+
+    async def _emit_notification(
+        self,
+        emitter: Optional[Callable[[Any], Awaitable[None]]],
+        content: str,
+        ntype: str = "info",
+    ):
+        """Emits a notification event (info, success, warning, error)."""
+        if emitter:
+            await emitter(
+                {"type": "notification", "data": {"type": ntype, "content": content}}
+            )
+
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """在浏览器控制台打印结构化调试日志"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            import json
+
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
 
     async def action(
         self,
@@ -180,17 +227,18 @@ class Action:
                 # 通知用户提取到的表格数量
                 table_count = len(all_tables)
                 if self.valves.EXPORT_SCOPE == "all_messages":
-                    await self._send_notification(
+                    await self._emit_notification(
                         __event_emitter__,
-                        "info",
                         f"从所有消息中提取到 {table_count} 个表格。",
+                        "info",
                     )
                     # 等待片刻让用户看到通知，再触发下载
                     await asyncio.sleep(1.5)
 
                 # Generate Workbook Title (Filename)
                 title = ""
-                chat_id = self.extract_chat_id(body, None)
+                chat_ctx = self._get_chat_context(body, None)
+                chat_id = chat_ctx["chat_id"]
                 chat_title = ""
                 if chat_id:
                     chat_title = await self.fetch_chat_title(chat_id, user_id)
@@ -318,8 +366,8 @@ class Action:
                         },
                     }
                 )
-                await self._send_notification(
-                    __event_emitter__, "error", "未找到可导出的表格！"
+                await self._emit_notification(
+                    __event_emitter__, "未找到可导出的表格！", "error"
                 )
                 raise e
             except Exception as e:
@@ -333,8 +381,8 @@ class Action:
                         },
                     }
                 )
-                await self._send_notification(
-                    __event_emitter__, "error", "未找到可导出的表格！"
+                await self._emit_notification(
+                    __event_emitter__, "未找到可导出的表格！", "error"
                 )
 
     async def generate_title_using_ai(
@@ -377,20 +425,20 @@ class Action:
             async def notification_task():
                 # 立即发送首次通知
                 if event_emitter:
-                    await self._send_notification(
+                    await self._emit_notification(
                         event_emitter,
-                        "info",
                         "AI 正在为您生成文件名，请稍候...",
+                        "info",
                     )
 
                 # 之后每5秒通知一次
                 while True:
                     await asyncio.sleep(5)
                     if event_emitter:
-                        await self._send_notification(
+                        await self._emit_notification(
                             event_emitter,
-                            "info",
                             "文件名生成中，请耐心等待...",
+                            "info",
                         )
 
             # 并发运行任务
@@ -420,10 +468,10 @@ class Action:
         except Exception as e:
             print(f"生成标题时出错: {e}")
             if event_emitter:
-                await self._send_notification(
+                await self._emit_notification(
                     event_emitter,
-                    "warning",
                     f"AI 文件名生成失败，将使用默认名称。错误: {str(e)}",
+                    "warning",
                 )
 
         return ""
@@ -438,24 +486,56 @@ class Action:
                 return match.group(1).strip()
         return ""
 
-    def extract_chat_id(self, body: dict, metadata: Optional[dict]) -> str:
-        """从 body 或 metadata 中提取 chat_id"""
-        if isinstance(body, dict):
-            chat_id = body.get("chat_id") or body.get("id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
+    def _get_user_context(self, __user__: Optional[Dict[str, Any]]) -> Dict[str, str]:
+        """安全提取用户上下文信息。"""
+        if isinstance(__user__, (list, tuple)):
+            user_data = __user__[0] if __user__ else {}
+        elif isinstance(__user__, dict):
+            user_data = __user__
+        else:
+            user_data = {}
 
-            for key in ("chat", "conversation"):
-                nested = body.get(key)
-                if isinstance(nested, dict):
-                    nested_id = nested.get("id") or nested.get("chat_id")
-                    if isinstance(nested_id, str) and nested_id.strip():
-                        return nested_id.strip()
-        if isinstance(metadata, dict):
-            chat_id = metadata.get("chat_id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
-        return ""
+        return {
+            "user_id": user_data.get("id", "unknown_user"),
+            "user_name": user_data.get("name", "用户"),
+            "user_language": user_data.get("language", "zh-CN"),
+        }
+
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        统一提取聊天上下文信息 (chat_id, message_id)。
+        优先从 body 中提取，其次从 metadata 中提取。
+        """
+        chat_id = ""
+        message_id = ""
+
+        # 1. 尝试从 body 获取
+        if isinstance(body, dict):
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id 在 body 中通常是 id
+
+            # 再次检查 body.metadata
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
+
+        # 2. 尝试从 __metadata__ 获取 (作为补充)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
+
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
 
     async def fetch_chat_title(self, chat_id: str, user_id: str = "") -> str:
         """通过 chat_id 从数据库获取对话标题"""

plugins/actions/flash-card/flash_card.py

@@ -1,9 +1,10 @@
 """
 title: Flash Card
 author: Fu-Jie
-author_url: https://github.com/Fu-Jie
-funding_url: https://github.com/Fu-Jie/awesome-openwebui
+author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
 version: 0.2.4
+openwebui_id: 65a2ea8f-2a13-4587-9d76-55eea0035cc8
 icon_url: data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9ImN1cnJlbnRDb2xvciIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxwb2x5Z29uIHBvaW50cz0iMTIgMiAyIDcgMTIgMTIgMjIgNyAxMiAyIi8+PHBvbHlsaW5lIHBvaW50cz0iMiAxNyAxMiAyMiAyMiAxNyIvPjxwb2x5bGluZSBwb2ludHM9IjIgMTIgMTIgMTcgMjIgMTIiLz48L3N2Zz4=
 description: Quickly generates beautiful flashcards from text, extracting key points and categories.
 """
@@ -88,6 +89,10 @@ class Action:
             default=True,
             description="Whether to show status updates in the chat interface.",
         )
+        SHOW_DEBUG_LOG: bool = Field(
+            default=False,
+            description="Whether to print debug logs in the browser console.",
+        )
         CLEAR_PREVIOUS_HTML: bool = Field(
             default=False,
             description="Whether to force clear previous plugin results (if True, overwrites instead of merging).",
@@ -115,6 +120,42 @@ class Action:
             "user_language": user_data.get("language", "en-US"),
         }
 
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        Unified extraction of chat context information (chat_id, message_id).
+        Prioritizes extraction from body, then metadata.
+        """
+        chat_id = ""
+        message_id = ""
+
+        # 1. Try to get from body
+        if isinstance(body, dict):
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id is usually 'id' in body
+
+            # Check body.metadata as fallback
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
+
+        # 2. Try to get from __metadata__ (as supplement)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
+
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
+
     async def action(
         self,
         body: dict,
@@ -330,6 +371,26 @@ Important Principles:
                 {"type": "notification", "data": {"type": ntype, "content": content}}
             )
 
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """Print structured debug logs in the browser console"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            import json
+
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
+
     def _remove_existing_html(self, content: str) -> str:
         """Removes existing plugin-generated HTML code blocks from the content."""
         pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"

plugins/actions/flash-card/flash_card_cn.py

@@ -1,9 +1,10 @@
 """
 title: 闪记卡 (Flash Card)
 author: Fu-Jie
-author_url: https://github.com/Fu-Jie
-funding_url: https://github.com/Fu-Jie/awesome-openwebui
+author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
 version: 0.2.4
+openwebui_id: 4a31eac3-a3c4-4c30-9ca5-dab36b5fac65
 icon_url: data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9ImN1cnJlbnRDb2xvciIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxwb2x5Z29uIHBvaW50cz0iMTIgMiAyIDcgMTIgMTIgMjIgNyAxMiAyIi8+PHBvbHlsaW5lIHBvaW50cz0iMiAxNyAxMiAyMiAyMiAxNyIvPjxwb2x5bGluZSBwb2ludHM9IjIgMTIgMTIgMTcgMjIgMTIiLz48L3N2Zz4=
 description: 快速将文本提炼为精美的学习记忆卡片，支持核心要点提取与分类。
 """
@@ -85,6 +86,10 @@ class Action:
         SHOW_STATUS: bool = Field(
             default=True, description="是否在聊天界面显示状态更新。"
         )
+        SHOW_DEBUG_LOG: bool = Field(
+            default=False,
+            description="是否在浏览器控制台打印调试日志。",
+        )
         CLEAR_PREVIOUS_HTML: bool = Field(
             default=False,
             description="是否强制清除旧的插件结果（如果为 True，则不合并，直接覆盖）。",
@@ -112,6 +117,42 @@ class Action:
             "user_language": user_data.get("language", "zh-CN"),
         }
 
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        统一提取聊天上下文信息 (chat_id, message_id)。
+        优先从 body 中提取，其次从 metadata 中提取。
+        """
+        chat_id = ""
+        message_id = ""
+
+        # 1. 尝试从 body 获取
+        if isinstance(body, dict):
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id 在 body 中通常是 id
+
+            # 再次检查 body.metadata
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
+
+        # 2. 尝试从 __metadata__ 获取 (作为补充)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
+
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
+
     async def action(
         self,
         body: dict,
@@ -313,6 +354,26 @@ class Action:
                 {"type": "notification", "data": {"type": ntype, "content": content}}
             )
 
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """在浏览器控制台打印结构化调试日志"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            import json
+
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
+
     def _remove_existing_html(self, content: str) -> str:
         """移除内容中已有的插件生成 HTML 代码块 (通过标记识别)。"""
         pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"

plugins/actions/infographic/README.md

@@ -1,25 +1,31 @@
 # 📊 Smart Infographic (AntV)
 
+**Author:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **Version:** 1.4.9 | **Project:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui)
+
 An Open WebUI plugin powered by the AntV Infographic engine. It transforms long text into professional, beautiful infographics with a single click.
 
+## 🔥 What's New in v1.4.9
+
+- 🎨 **70+ Official Templates**: Integrated comprehensive AntV infographic template library.
+- 🖼️ **Iconify & unDraw Support**: Richer visuals with official icons and illustrations.
+- 📏 **Visual Optimization**: Improved text wrapping, adaptive sizing, and layout refinement.
+- ✨ **PNG Upload**: Infographics now upload as PNG format for better Word export compatibility.
+- 🔧 **Canvas Conversion**: Uses browser canvas for high-quality SVG to PNG conversion (2x scale).
+
+### Previous: v1.4.0
+
+- ✨ **Default Mode Change**: Default output mode is now `image` (static image) for better compatibility.
+- 📱 **Responsive Sizing**: Images now auto-adapt to the chat container width.
+
 ## ✨ Key Features
 
 - 🚀 **AI-Powered Transformation**: Automatically analyzes text logic, extracts key points, and generates structured charts.
-- 🎨 **Professional Templates**: Includes various AntV official templates: Lists, Trees, Mindmaps, Comparison Tables, Flowcharts, and Statistical Charts.
-- 🔍 **Auto-Icon Matching**: Built-in logic to search and match the most relevant Material Design Icons based on content.
+- 🎨 **70+ Professional Templates**: Includes various AntV official templates: Lists, Trees, Roadmaps, Timelines, Comparison Tables, SWOT, Quadrants, and Statistical Charts.
+- 🔍 **Auto-Icon Matching**: Built-in logic to search and match the most relevant icons (Iconify) and illustrations (unDraw).
 - 📥 **Multi-Format Export**: Download your infographics as **SVG**, **PNG**, or a **Standalone HTML** file.
 - 🌈 **Highly Customizable**: Supports Dark/Light modes, auto-adapts theme colors, with bold titles and refined card layouts.
 - 📱 **Responsive Design**: Generated charts look great on both desktop and mobile devices.
 
-## 🛠️ Supported Template Types
-
-| Category | Template Name | Use Case |
-| :--- | :--- | :--- |
-| **Lists & Hierarchy** | `list-grid`, `tree-vertical`, `mindmap` | Features, Org Charts, Brainstorming |
-| **Sequence & Relation** | `sequence-roadmap`, `relation-circle` | Roadmaps, Circular Flows, Steps |
-| **Comparison & Analysis** | `compare-binary`, `compare-swot`, `quadrant-quarter` | Pros/Cons, SWOT, Quadrants |
-| **Charts & Data** | `chart-bar`, `chart-line`, `chart-pie` | Trends, Distributions, Metrics |
-
 ## 🚀 How to Use
 
 1. **Install**: Search for "Smart Infographic" in the Open WebUI Community and install.
@@ -38,6 +44,17 @@ You can adjust the following parameters in the plugin settings to optimize the g
 | **Min Text Length (MIN_TEXT_LENGTH)** | `100` | Minimum characters required to trigger analysis, preventing accidental triggers on short text. |
 | **Clear Previous (CLEAR_PREVIOUS_HTML)** | `False` | Whether to clear previous charts. If `False`, new charts will be appended below. |
 | **Message Count (MESSAGE_COUNT)** | `1` | Number of recent messages to use for analysis. Increase this for more context. |
+| **Output Mode (OUTPUT_MODE)** | `image` | `image` for static image embedding (default, better compatibility), `html` for interactive chart. |
+
+## 🛠️ Supported Template Types
+
+| Category | Template Name | Use Case |
+| :--- | :--- | :--- |
+| **Sequence** | `sequence-timeline-simple`, `sequence-roadmap-vertical-simple`, `sequence-snake-steps-compact-card` | Timelines, Roadmaps, Processes |
+| **Lists** | `list-grid-candy-card-lite`, `list-row-horizontal-icon-arrow`, `list-column-simple-vertical-arrow` | Features, Bullet Points, Lists |
+| **Comparison** | `compare-binary-horizontal-underline-text-vs`, `compare-swot`, `quadrant-quarter-simple-card` | Pros/Cons, SWOT, Quadrants |
+| **Hierarchy** | `hierarchy-tree-tech-style-capsule-item`, `hierarchy-structure` | Org Charts, Structures |
+| **Charts** | `chart-column-simple`, `chart-bar-plain-text`, `chart-line-plain-text`, `chart-wordcloud` | Trends, Distributions, Metrics |
 
 ## 📝 Syntax Example (For Advanced Users)
 
@@ -54,18 +71,3 @@ data
     - label Beautiful Design
       desc Uses AntV professional design standards
 ```
-
-## 👨‍💻 Author
-
-**jeff**
-- GitHub: [Fu-Jie/awesome-openwebui](https://github.com/Fu-Jie/awesome-openwebui)
-
-## 📄 License
-
-MIT License
-
-## Changelog
-
-### v1.3.2
-
-- Removed debug messages from output

plugins/actions/infographic/README_CN.md

@@ -1,25 +1,31 @@
 # 📊 智能信息图 (AntV Infographic)
 
+**Author:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **Version:** 1.4.9 | **Project:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui)
+
 基于 AntV Infographic 引擎的 Open WebUI 插件，能够将长文本内容一键转换为专业、美观的信息图表。
 
+## 🔥 v1.4.9 更新日志
+
+- 🎨 **70+ 官方模板**：全面集成 AntV 官方信息图模板库。
+- 🖼️ **图标与插图支持**：支持 Iconify 图标库与 unDraw 插图库，视觉效果更丰富。
+- 📏 **视觉优化**：改进文本换行逻辑，优化自适应尺寸，提升卡片布局精细度。
+- ✨ **PNG 上传**：信息图现在以 PNG 格式上传，与 Word 导出完美兼容。
+- 🔧 **Canvas 转换**：使用浏览器 Canvas 高质量转换 SVG 为 PNG（2倍缩放）。
+
+### 此前: v1.4.0
+
+- ✨ **默认模式变更**：默认输出模式调整为 `image`（静态图片）。
+- 📱 **响应式尺寸**：图片模式下自动适应聊天容器宽度。
+
 ## ✨ 核心特性
 
 - 🚀 **智能转换**：自动分析文本核心逻辑，提取关键点并生成结构化图表。
-- 🎨 **专业模板**：内置多种 AntV 官方模板，包括列表、树图、思维导图、对比图、流程图及统计图表等。
-- 🔍 **自动图标匹配**：内置图标搜索逻辑，根据内容自动匹配最相关的 Material Design Icons。
+- 🎨 **70+ 专业模板**：内置多种 AntV 官方模板，包括列表、树图、路线图、时间线、对比图、SWOT、象限图及统计图表等。
+- 🔍 **自动图标匹配**：内置图标搜索逻辑，支持 Iconify 图标和 unDraw 插图自动匹配。
 - 📥 **多格式导出**：支持一键下载为 **SVG**、**PNG** 或 **独立 HTML** 文件。
 - 🌈 **高度自定义**：支持深色/浅色模式，自动适配主题颜色，主标题加粗突出，卡片布局精美。
 - 📱 **响应式设计**：生成的图表在桌面端和移动端均有良好的展示效果。
 
-## 🛠️ 支持的模板类型
-
-| 分类 | 模板名称 | 适用场景 |
-| :--- | :--- | :--- |
-| **列表与层级** | `list-grid`, `tree-vertical`, `mindmap` | 功能亮点、组织架构、思维导图 |
-| **顺序与关系** | `sequence-roadmap`, `relation-circle` | 发展历程、循环关系、步骤说明 |
-| **对比与分析** | `compare-binary`, `compare-swot`, `quadrant-quarter` | 优劣势对比、SWOT 分析、象限图 |
-| **图表与数据** | `chart-bar`, `chart-line`, `chart-pie` | 数据趋势、比例分布、数值对比 |
-
 ## 🚀 使用方法
 
 1. **安装插件**：在 Open WebUI 插件市场搜索并安装。
@@ -38,6 +44,17 @@
 | **最小文本长度 (MIN_TEXT_LENGTH)** | `100` | 触发分析所需的最小字符数，防止对过短的对话误操作。 |
 | **清除旧结果 (CLEAR_PREVIOUS_HTML)** | `False` | 每次生成是否清除之前的图表。若为 `False`，新图表将追加在下方。 |
 | **上下文消息数 (MESSAGE_COUNT)** | `1` | 用于分析的最近消息条数。增加此值可让 AI 参考更多对话背景。 |
+| **输出模式 (OUTPUT_MODE)** | `image` | `image` 为静态图片嵌入（默认，兼容性好），`html` 为交互式图表。 |
+
+## 🛠️ 支持的模板类型
+
+| 分类 | 模板名称 | 适用场景 |
+| :--- | :--- | :--- |
+| **时序与流程** | `sequence-timeline-simple`, `sequence-roadmap-vertical-simple`, `sequence-snake-steps-compact-card` | 时间线、路线图、步骤说明 |
+| **列表与网格** | `list-grid-candy-card-lite`, `list-row-horizontal-icon-arrow`, `list-column-simple-vertical-arrow` | 功能亮点、要点列举、清单 |
+| **对比与分析** | `compare-binary-horizontal-underline-text-vs`, `compare-swot`, `quadrant-quarter-simple-card` | 优劣势对比、SWOT 分析、象限图 |
+| **层级与结构** | `hierarchy-tree-tech-style-capsule-item`, `hierarchy-structure` | 组织架构、层级关系 |
+| **图表与数据** | `chart-column-simple`, `chart-bar-plain-text`, `chart-line-plain-text`, `chart-wordcloud` | 数据趋势、比例分布、数值对比 |
 
 ## 📝 语法示例 (高级用户)
 
@@ -54,18 +71,3 @@ data
     - label 视觉精美
       desc 采用 AntV 专业设计规范
 ```
-
-## 👨‍💻 作者
-
-**jeff**
-- GitHub: [Fu-Jie/awesome-openwebui](https://github.com/Fu-Jie/awesome-openwebui)
-
-## 📄 许可证
-
-MIT License
-
-## 更新日志
-
-### v1.3.2
-
-- 移除输出中的调试信息

plugins/actions/infographic/infographic.py

@@ -1,14 +1,16 @@
 """
 title: 📊 Smart Infographic (AntV)
-author: jeff
+author: Fu-Jie
 author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
 icon_url: data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9ImN1cnJlbnRDb2xvciIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPgogIDxsaW5lIHgxPSIxMiIgeTE9IjIwIiB4Mj0iMTIiIHkyPSIxMCIgLz4KICA8bGluZSB4MT0iMTgiIHkxPSIyMCIgeDI9IjE4IiB5Mj0iNCIgLz4KICA8bGluZSB4MT0iNiIgeTE9IjIwIiB4Mj0iNiIgeTI9IjE2IiAvPgo8L3N2Zz4=
-version: 1.3.2
+version: 1.4.9
+openwebui_id: ad6f0c7f-c571-4dea-821d-8e71697274cf
 description: AI-powered infographic generator based on AntV Infographic. Supports professional templates, auto-icon matching, and SVG/PNG downloads.
 """
 
 from pydantic import BaseModel, Field
-from typing import Optional, Dict, Any
+from typing import Optional, Dict, Any, Callable, Awaitable
 import logging
 import time
 import re
@@ -46,24 +48,63 @@ Infographic syntax is a Mermaid-like declarative syntax for describing infograph
 
 ### Template Library & Selection Guide
 
-Choose the most appropriate template based on the content structure:
+Choose the most appropriate template based on content structure.
 
-#### 1. List & Hierarchy
-- **List**: `list-grid` (Grid Cards), `list-vertical` (Vertical List)
-- **Tree**: `tree-vertical` (Vertical Tree), `tree-horizontal` (Horizontal Tree)
-- **Mindmap**: `mindmap` (Mind Map)
+**Template Selection Guidelines (Official):**
+- Strict sequential order (processes/steps/trends) → `sequence-*` series
+  - Timeline → `sequence-timeline-simple`
+  - Roadmap → `sequence-roadmap-vertical-simple`
+  - Zigzag steps → `sequence-horizontal-zigzag-underline-text`
+  - Snake steps → `sequence-snake-steps-compact-card`
+- Listing viewpoints → `list-row-horizontal-icon-arrow` or `list-column-simple-vertical-arrow`
+- Comparative analysis (A vs B) → `compare-binary-horizontal-underline-text-vs`
+- SWOT analysis → `compare-swot`
+- Hierarchical structure (tree) → `hierarchy-tree-tech-style-capsule-item`
+- Data charts → `chart-*` series
+- Quadrant analysis → `quadrant-quarter-simple-card`
+- Grid lists (bullet points) → `list-grid-candy-card-lite`
+- Relationship display → `relation-circle-icon-badge`
 
-#### 2. Sequence & Relationship
-- **Process**: `sequence-roadmap` (Roadmap), `sequence-zigzag` (Zigzag Process), `sequence-horizontal` (Horizontal Process)
-- **Relationship**: `relation-sankey` (Sankey Diagram), `relation-circle` (Circular Relationship)
+**Available Templates:**
 
-#### 3. Comparison & Analysis
-- **Comparison**: `compare-binary` (Binary Comparison), `list-grid` (Multi-item Grid Comparison)
-- **Analysis**: `compare-swot` (SWOT Analysis), `quadrant-quarter` (Quadrant Chart)
+*Sequence (时序/流程):*
+`sequence-timeline-simple`, `sequence-roadmap-vertical-simple`, `sequence-horizontal-zigzag-underline-text`, 
+`sequence-snake-steps-compact-card`, `sequence-zigzag-steps-underline-text`, `sequence-circular-simple`,
+`sequence-pyramid-simple`, `sequence-ascending-steps`
 
-#### 4. Charts & Data
-- **Statistics**: `statistic-card` (Statistic Cards)
-- **Charts**: `chart-bar` (Bar Chart), `chart-column` (Column Chart), `chart-line` (Line Chart), `chart-pie` (Pie Chart), `chart-doughnut` (Doughnut Chart), `chart-area` (Area Chart)
+*List (列表):*
+`list-grid-candy-card-lite`, `list-grid-badge-card`, `list-row-horizontal-icon-arrow`,
+`list-column-simple-vertical-arrow`, `list-column-done-list`
+
+*Compare (对比):*
+`compare-binary-horizontal-underline-text-vs`, `compare-binary-horizontal-simple-fold`,
+`compare-hierarchy-left-right-circle-node-pill-badge`, `compare-swot`
+
+*Hierarchy (层级):*
+`hierarchy-tree-tech-style-capsule-item`, `hierarchy-tree-curved-line-rounded-rect-node`, `hierarchy-structure`
+
+*Chart (图表):*
+`chart-column-simple`, `chart-bar-plain-text`, `chart-line-plain-text`, 
+`chart-pie-plain-text`, `chart-pie-donut-plain-text`, `chart-wordcloud`
+
+*Other:*
+`quadrant-quarter-simple-card`, `relation-circle-icon-badge`
+
+**Text Capacity by Template Type:**
+- HIGH capacity (long descriptions OK): `list-column-*`, `compare-binary-*`, `sequence-timeline-*`
+- MEDIUM capacity: `list-row-*`, `sequence-roadmap-*`
+- LOW capacity (short text only): `list-grid-*`, `hierarchy-*`, `sequence-steps`
+
+### Icon and Illustration Resources
+
+**Icons (Iconify):**
+- Format: `<collection>/<icon-name>`, e.g., `mdi/rocket-launch`
+- Popular: `mdi/*` (Material Design), `fa/*` (Font Awesome), `bi/*` (Bootstrap)
+- Examples: `mdi/code-tags`, `mdi/chart-line`, `mdi/account-group`, `mdi/cloud`
+
+**Illustrations (unDraw):**
+- Format: filename without .svg, e.g., `coding`, `team-work`
+- Use `illus` field instead of `icon`
 
 ### Data Structure Examples
 
@@ -210,6 +251,12 @@ data
 - `children`: Nested items (for trees, SWOT, etc.)
 - `illus`: Illustration icon (specific to some templates like Quadrant)
 
+### Content Refinement Principles
+1. **Brevity is King**: Infographics are visual. Keep text to a minimum.
+2. **Title Limit**: Keep `label` (item titles) under 15 characters (approx. 10 Chinese characters).
+3. **Description Limit**: Keep `desc` (item descriptions) under 40 characters (approx. 20 Chinese characters / 2 lines).
+4. **Impact**: Use strong verbs and nouns. Avoid filler words.
+
 ## Output Requirements
 1. **Language**: Output content in the user's language.
 2. **Format**: Wrap output in ```infographic ... ```.
@@ -217,6 +264,8 @@ data
 4. **Indentation**: Use 2 spaces.
 """
 
+import json
+
 USER_PROMPT_GENERATE_INFOGRAPHIC = """
 Please analyze the following text content and convert its core information into AntV Infographic syntax format.
 
@@ -232,9 +281,18 @@ User Language: {user_language}
 
 Please select the most appropriate infographic template based on text characteristics and output standard infographic syntax. Pay attention to correct indentation format (two spaces).
 
-**Important Note:** 
-- If using `list-grid` format, ensure each card's `desc` description is limited to **maximum 30 Chinese characters** (or **approximately 60 English characters**) to maintain visual consistency with all descriptions fitting in 2 lines.
-- Descriptions should be concise and highlight key points.
+**Visual Optimization Guide (MUST FOLLOW):**
+- **Point-based Generation:** Infographics are not articles. Extract KEYWORDS ONLY, avoid complete sentences.
+- **Main Title (`data.title`):** **MUST** be ≤ **15 Chinese characters** (or ≤30 English characters). Trim version numbers or details if needed.
+- **Subtitle (`data.desc`):** **MUST** be ≤ **20 Chinese characters** (or ≤40 English characters).
+- **Card Title (`label`):** **MUST** be ≤ **6 Chinese characters** (or ≤12 English characters). Use 2-4 keywords only.
+- **Card Description (`desc`):** **MUST** be ≤ **12 Chinese characters** (or ≤24 English characters). Use short phrases.
+
+⚠️ **CRITICAL**: If the original text is too long, you MUST rephrase and shorten it. Do NOT simply truncate with "...".
+Examples:
+- ❌ "多步任务与工具协作能力" → ✅ "多步任务协作"
+- ❌ "Open WebUI v0.7.x 重大版本更新" → ✅ "v0.7 核心更新"
+- ❌ "自动查找历史聊天记录" → ✅ "历史检索"
 """
 
 # =================================================================
@@ -339,8 +397,9 @@ CSS_TEMPLATE_INFOGRAPHIC = """
 .infographic-container-wrapper .infographic-render-container {
     border-radius: 8px;
     padding: 16px;
+    min-height: 600px;
     background: #fff;
-    overflow: visible; /* Ensure content is visible */
+    overflow: visible;
     transition: height 0.3s ease;
 }
 .infographic-render-container svg text {
@@ -348,35 +407,59 @@ CSS_TEMPLATE_INFOGRAPHIC = """
 }
 .infographic-render-container svg foreignObject {
     font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei", sans-serif !important;
-    line-height: 1.4 !important;
+    line-height: 1.3 !important;
+    overflow: visible !important;
 }
 /* Main title styles */
 .infographic-render-container svg foreignObject[data-element-type="title"] > * {
-    font-size: 1.5em !important;
-    font-weight: bold !important;
-    line-height: 1.4 !important;
-    white-space: nowrap !important;
+    font-size: 1.3em !important;
+    font-weight: 800 !important;
+    line-height: 1.3 !important;
+    white-space: normal !important;
+    word-break: break-word !important;
+    display: -webkit-box !important;
+    -webkit-line-clamp: 2 !important;
+    -webkit-box-orient: vertical !important;
     overflow: hidden !important;
     text-overflow: ellipsis !important;
+    text-align: center !important;
 }
-/* Page subtitle and card title styles */
-.infographic-render-container svg foreignObject[data-element-type="desc"] > *,
-.infographic-render-container svg foreignObject[data-element-type="item-label"] > * {
-    font-size: 0.6em !important;
-    line-height: 1.4 !important;
-    white-space: nowrap !important;
-    overflow: hidden !important;
-    text-overflow: ellipsis !important;
-}
-/* Card title with extra bottom spacing */
-.infographic-render-container svg foreignObject[data-element-type="item-label"] > * {
-    padding-bottom: 8px !important;
+/* Page subtitle styles */
+.infographic-render-container svg foreignObject[data-element-type="desc"] > * {
+    font-size: 0.85em !important;
+    line-height: 1.3 !important;
+    white-space: normal !important;
+    word-break: break-word !important;
+    overflow: visible !important;
+    text-align: center !important;
     display: block !important;
+    color: var(--ig-muted-text-color) !important;
 }
-/* Card description text keeps normal wrapping */
+/* Card title styles */
+.infographic-render-container svg foreignObject[data-element-type="item-label"] > * {
+    font-size: 0.9em !important;
+    font-weight: 600 !important;
+    line-height: 1.3 !important;
+    white-space: normal !important;
+    word-break: break-word !important;
+    display: -webkit-box !important;
+    -webkit-line-clamp: 2 !important;
+    -webkit-box-orient: vertical !important;
+    overflow: hidden !important;
+    text-overflow: ellipsis !important;
+    padding-bottom: 2px !important;
+}
+/* Card description text */
 .infographic-render-container svg foreignObject[data-element-type="item-desc"] > * {
+    font-size: 0.8em !important;
     line-height: 1.4 !important;
     white-space: normal !important;
+    word-break: break-word !important;
+    display: -webkit-box !important;
+    -webkit-line-clamp: 2 !important;
+    -webkit-box-orient: vertical !important;
+    overflow: hidden !important;
+    text-overflow: ellipsis !important;
 }
 .infographic-container-wrapper .download-area {
     text-align: center;
@@ -532,37 +615,41 @@ SCRIPT_TEMPLATE_INFOGRAPHIC = """
             }}
         }}
 
-        // 2. Template Mapping Configuration
+        // 2. Template Mapping Configuration (Official AntV Structure IDs)
         const TEMPLATE_MAPPING = {{
-            // List & Hierarchy
+            // List & Hierarchy - map short names to full template names
             'list-grid': 'list-grid-compact-card',
+            'list-column': 'list-column-simple-vertical-arrow',
+            'list-row': 'list-row-simple-horizontal-arrow',
+            'hierarchy-tree': 'hierarchy-tree-tech-style-capsule-item',
+            
+            // Sequence & Timeline
+            'sequence-roadmap-vertical': 'sequence-roadmap-vertical-simple',
+            'sequence-timeline': 'sequence-timeline-simple',
+            'sequence-steps': 'sequence-steps-simple',
+            'sequence-horizontal-zigzag': 'sequence-horizontal-zigzag-simple',
+            
+            // Comparison
+            'compare-binary-horizontal': 'compare-binary-horizontal-simple-vs',
+            'compare-hierarchy-row': 'compare-hierarchy-row-simple',
+            
+            // Charts
+            'chart-column': 'chart-column-simple',
+            'quadrant': 'quadrant-quarter-simple-card',
+            
+            // Legacy mappings for backward compatibility
             'list-vertical': 'list-column-simple-vertical-arrow',
             'tree-vertical': 'hierarchy-tree-tech-style-capsule-item',
-            'tree-horizontal': 'hierarchy-tree-lr-tech-style-capsule-item',
-            'mindmap': 'hierarchy-mindmap-branch-gradient-capsule-item',
-            
-            // Sequence & Relationship
             'sequence-roadmap': 'sequence-roadmap-vertical-simple',
             'sequence-zigzag': 'sequence-horizontal-zigzag-simple',
-            'sequence-horizontal': 'sequence-horizontal-zigzag-simple',
-            'relation-sankey': 'relation-sankey-simple',
-            'relation-circle': 'relation-circle-icon-badge',
-            
-            // Comparison & Analysis
             'compare-binary': 'compare-binary-horizontal-simple-vs',
-            'compare-swot': 'compare-swot',
-            'quadrant-quarter': 'quadrant-quarter-simple-card',
-            
-            // Charts & Data
-            'statistic-card': 'list-grid-compact-card',
             'chart-bar': 'chart-bar-plain-text',
-            'chart-column': 'chart-column-simple',
             'chart-line': 'chart-line-plain-text',
-            'chart-area': 'chart-area-simple',
             'chart-pie': 'chart-pie-plain-text',
             'chart-doughnut': 'chart-pie-donut-plain-text'
         }};
 
+
         // 3. Apply Mapping Strategy
         for (const [key, value] of Object.entries(TEMPLATE_MAPPING)) {{
             const regex = new RegExp(`infographic\\\\s+${{key}}(?=\\\\s|$)`, 'i');
@@ -628,10 +715,48 @@ SCRIPT_TEMPLATE_INFOGRAPHIC = """
             containerEl.dataset.infographicRendered = 'true';
             console.log('[Infographic] Rendering complete');
 
-            // Auto-adjust height
+            // Auto-adjust height and tag elements
             setTimeout(() => {
                 const svg = containerEl.querySelector('svg');
                 if (svg) {
+                    // 1. Tag elements for CSS styling
+                    const fos = Array.from(svg.querySelectorAll('foreignObject'));
+                    let titleFound = false;
+                    let descFound = false;
+                    
+                    fos.forEach((fo) => {
+                        const text = fo.textContent.trim();
+                        if (!text || fo.querySelector('i') || (fo.querySelector('svg') && fo.querySelectorAll('*').length < 5)) {
+                            fo.setAttribute('data-element-type', 'icon');
+                            return;
+                        }
+                        
+                        // Dynamically increase height and width to accommodate wrapped text
+                        const currentHeight = parseInt(fo.getAttribute('height') || '0');
+                        if (currentHeight > 0 && currentHeight < 200) {
+                            fo.setAttribute('height', Math.round(currentHeight * 1.8).toString());
+                        }
+                        const currentWidth = parseInt(fo.getAttribute('width') || '0');
+                        if (currentWidth > 0 && currentWidth < 300) {
+                            fo.setAttribute('width', Math.max(Math.round(currentWidth * 1.2), 180).toString());
+                        }
+
+                        if (!titleFound) {
+                            fo.setAttribute('data-element-type', 'title');
+                            titleFound = true;
+                        } else if (!descFound) {
+                            fo.setAttribute('data-element-type', 'desc');
+                            descFound = true;
+                        } else {
+                            if (fo.querySelector('strong') || fo.style.fontWeight === 'bold' || text.length < 15) {
+                                fo.setAttribute('data-element-type', 'item-label');
+                            } else {
+                                fo.setAttribute('data-element-type', 'item-desc');
+                            }
+                        }
+                    });
+
+                    // 2. Adjust height
                     const bbox = svg.getBoundingClientRect();
                     let contentHeight = bbox.height;
                     if (svg.viewBox && svg.viewBox.baseVal && svg.viewBox.baseVal.height) {
@@ -821,10 +946,69 @@ class Action:
             default=1,
             description="Number of recent messages to use for generation. Set to 1 for just the last message, or higher for more context.",
         )
+        OUTPUT_MODE: str = Field(
+            default="image",
+            description="Output mode: 'html' for interactive HTML, or 'image' to embed as Markdown image (default).",
+        )
+        SHOW_DEBUG_LOG: bool = Field(
+            default=False,
+            description="Whether to print debug logs in the browser console.",
+        )
 
     def __init__(self):
         self.valves = self.Valves()
 
+    def _get_user_context(self, __user__: Optional[Dict[str, Any]]) -> Dict[str, str]:
+        """Safely extracts user context information."""
+        if isinstance(__user__, (list, tuple)):
+            user_data = __user__[0] if __user__ else {}
+        elif isinstance(__user__, dict):
+            user_data = __user__
+        else:
+            user_data = {}
+
+        return {
+            "user_id": user_data.get("id", "unknown_user"),
+            "user_name": user_data.get("name", "User"),
+            "user_language": user_data.get("language", "en-US"),
+        }
+
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        Unified extraction of chat context information (chat_id, message_id).
+        Prioritizes extraction from body, then metadata.
+        """
+        chat_id = ""
+        message_id = ""
+
+        # 1. Try to get from body
+        if isinstance(body, dict):
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id is usually 'id' in body
+
+            # Check body.metadata as fallback
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
+
+        # 2. Try to get from __metadata__ (as supplement)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
+
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
+
     def _extract_infographic_syntax(self, llm_output: str) -> str:
         """Extract infographic syntax from LLM output"""
         match = re.search(r"```infographic\s*(.*?)\s*```", llm_output, re.DOTALL)
@@ -852,6 +1036,24 @@ class Action:
                 {"type": "notification", "data": {"type": ntype, "content": content}}
             )
 
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """Print structured debug logs in the browser console"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
+
     def _remove_existing_html(self, content: str) -> str:
         """Remove existing plugin-generated HTML code blocks from content"""
         pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
@@ -912,14 +1114,359 @@ class Action:
 
         return base_html.strip()
 
+    def _generate_image_js_code(
+        self,
+        unique_id: str,
+        chat_id: str,
+        message_id: str,
+        infographic_syntax: str,
+    ) -> str:
+        """Generate JavaScript code for frontend SVG rendering and image embedding"""
+
+        # Escape the syntax for JS embedding
+        syntax_escaped = (
+            infographic_syntax.replace("\\", "\\\\")
+            .replace("`", "\\`")
+            .replace("${", "\\${")
+            .replace("</script>", "<\\/script>")
+        )
+
+        return f"""
+(async function() {{
+    const uniqueId = "{unique_id}";
+    const chatId = "{chat_id}";
+    const messageId = "{message_id}";
+    const defaultWidth = 1100;
+    const defaultHeight = 500;
+    
+    // Auto-detect chat container width for responsive sizing
+    let svgWidth = defaultWidth;
+    let svgHeight = defaultHeight;
+    const chatContainer = document.getElementById('chat-container');
+    if (chatContainer) {{
+        const containerWidth = chatContainer.clientWidth;
+        if (containerWidth > 100) {{
+            // Use container width with padding (80% of container, leaving more space on the right)
+            svgWidth = Math.floor(containerWidth * 0.8);
+            // Maintain aspect ratio based on default dimensions
+            svgHeight = Math.floor(svgWidth * (defaultHeight / defaultWidth));
+            console.log("[Infographic Image] Auto-detected container width:", containerWidth, "-> SVG:", svgWidth, "x", svgHeight);
+        }}
+    }}
+    
+    console.log("[Infographic Image] Starting render...");
+    console.log("[Infographic Image] chatId:", chatId, "messageId:", messageId);
+    
+    try {{
+        // Load AntV Infographic if not loaded
+        if (typeof AntVInfographic === 'undefined') {{
+            console.log("[Infographic Image] Loading AntV Infographic...");
+            await new Promise((resolve, reject) => {{
+                const script = document.createElement('script');
+                script.src = 'https://unpkg.com/@antv/infographic@latest/dist/infographic.min.js';
+                script.onload = resolve;
+                script.onerror = reject;
+                document.head.appendChild(script);
+            }});
+        }}
+        
+        const {{ Infographic }} = AntVInfographic;
+        
+        // Get syntax content
+        let syntaxContent = `{syntax_escaped}`;
+        console.log("[Infographic Image] Syntax length:", syntaxContent.length);
+        
+        // Clean up syntax: remove code block markers
+        const backtick = String.fromCharCode(96);
+        const prefix = backtick + backtick + backtick + 'infographic';
+        const simplePrefix = backtick + backtick + backtick;
+        
+        if (syntaxContent.toLowerCase().startsWith(prefix)) {{
+            syntaxContent = syntaxContent.substring(prefix.length).trim();
+        }} else if (syntaxContent.startsWith(simplePrefix)) {{
+            syntaxContent = syntaxContent.substring(simplePrefix.length).trim();
+        }}
+        
+        if (syntaxContent.endsWith(simplePrefix)) {{
+            syntaxContent = syntaxContent.substring(0, syntaxContent.length - simplePrefix.length).trim();
+        }}
+        
+        // Fix syntax: remove colons after keywords
+        syntaxContent = syntaxContent.replace(/^(data|items|children|theme|config):/gm, '$1');
+        syntaxContent = syntaxContent.replace(/(\\s)(children|items):/g, '$1$2');
+        
+        // Ensure infographic prefix
+        if (!syntaxContent.trim().toLowerCase().startsWith('infographic')) {{
+            const firstWord = syntaxContent.trim().split(/\\s+/)[0].toLowerCase();
+            if (!['data', 'theme', 'design', 'items'].includes(firstWord)) {{
+                syntaxContent = 'infographic ' + syntaxContent;
+            }}
+        }}
+        
+        // Template mapping
+        const TEMPLATE_MAPPING = {{
+            'list-grid': 'list-grid-compact-card',
+            'list-vertical': 'list-column-simple-vertical-arrow',
+            'tree-vertical': 'hierarchy-tree-tech-style-capsule-item',
+            'tree-horizontal': 'hierarchy-tree-lr-tech-style-capsule-item',
+            'mindmap': 'hierarchy-mindmap-branch-gradient-capsule-item',
+            'sequence-roadmap': 'sequence-roadmap-vertical-simple',
+            'sequence-zigzag': 'sequence-horizontal-zigzag-simple',
+            'sequence-horizontal': 'sequence-horizontal-zigzag-simple',
+            'relation-sankey': 'relation-sankey-simple',
+            'relation-circle': 'relation-circle-icon-badge',
+            'compare-binary': 'compare-binary-horizontal-simple-vs',
+            'compare-swot': 'compare-swot',
+            'quadrant-quarter': 'quadrant-quarter-simple-card',
+            'statistic-card': 'list-grid-compact-card',
+            'chart-bar': 'chart-bar-plain-text',
+            'chart-column': 'chart-column-simple',
+            'chart-line': 'chart-line-plain-text',
+            'chart-area': 'chart-area-simple',
+            'chart-pie': 'chart-pie-plain-text',
+            'chart-doughnut': 'chart-pie-donut-plain-text'
+        }};
+        
+        for (const [key, value] of Object.entries(TEMPLATE_MAPPING)) {{
+            const regex = new RegExp(`infographic\\\\s+${{key}}(?=\\\\s|$)`, 'i');
+            if (regex.test(syntaxContent)) {{
+                syntaxContent = syntaxContent.replace(regex, `infographic ${{value}}`);
+                break;
+            }}
+        }}
+        
+        // Create offscreen container
+        const container = document.createElement('div');
+        container.id = 'infographic-offscreen-' + uniqueId;
+        container.style.cssText = 'position:absolute;left:-9999px;top:-9999px;width:' + svgWidth + 'px;height:' + svgHeight + 'px;background:#ffffff;';
+        document.body.appendChild(container);
+        
+        // Create infographic instance
+        const instance = new Infographic({{
+            container: '#' + container.id,
+            width: svgWidth,
+            height: svgHeight,
+            padding: 12,
+        }});
+        
+        console.log("[Infographic Image] Rendering infographic...");
+        instance.render(syntaxContent);
+        
+        // Wait for render to complete
+        await new Promise(resolve => setTimeout(resolve, 2000));
+        
+        // Get SVG element
+        const svgEl = container.querySelector('svg');
+        if (!svgEl) {{
+            throw new Error('SVG element not found after rendering');
+        }}
+        
+        // Get actual dimensions
+        const bbox = svgEl.getBoundingClientRect();
+        const width = bbox.width || svgWidth;
+        const height = bbox.height || svgHeight;
+        
+        // Clone and prepare SVG for export
+        const clonedSvg = svgEl.cloneNode(true);
+        clonedSvg.setAttribute('xmlns', 'http://www.w3.org/2000/svg');
+        clonedSvg.setAttribute('xmlns:xlink', 'http://www.w3.org/1999/xlink');
+        clonedSvg.setAttribute('width', width);
+        clonedSvg.setAttribute('height', height);
+        
+        // Add background rect
+        const bgRect = document.createElementNS('http://www.w3.org/2000/svg', 'rect');
+        bgRect.setAttribute('width', '100%');
+        bgRect.setAttribute('height', '100%');
+        bgRect.setAttribute('fill', '#ffffff');
+        clonedSvg.insertBefore(bgRect, clonedSvg.firstChild);
+        
+        // Serialize SVG to string
+        const svgData = new XMLSerializer().serializeToString(clonedSvg);
+        
+        // Cleanup container
+        document.body.removeChild(container);
+        
+        // Convert SVG to PNG using canvas for better compatibility
+        console.log("[Infographic Image] Converting SVG to PNG...");
+        const pngBlob = await new Promise((resolve, reject) => {{
+            const canvas = document.createElement('canvas');
+            const ctx = canvas.getContext('2d');
+            const scale = 2; // Higher resolution for clarity
+            canvas.width = Math.round(width * scale);
+            canvas.height = Math.round(height * scale);
+            
+            // Fill white background
+            ctx.fillStyle = '#ffffff';
+            ctx.fillRect(0, 0, canvas.width, canvas.height);
+            ctx.scale(scale, scale);
+            
+            const img = new Image();
+            img.onload = () => {{
+                ctx.drawImage(img, 0, 0, width, height);
+                canvas.toBlob((blob) => {{
+                    if (blob) {{
+                        resolve(blob);
+                    }} else {{
+                        reject(new Error('Canvas toBlob failed'));
+                    }}
+                }}, 'image/png');
+            }};
+            img.onerror = (e) => reject(new Error('Failed to load SVG as image: ' + e));
+            img.src = 'data:image/svg+xml;base64,' + btoa(unescape(encodeURIComponent(svgData)));
+        }});
+        
+        const file = new File([pngBlob], `infographic-${{uniqueId}}.png`, {{ type: 'image/png' }});
+        
+        // Upload file to OpenWebUI API
+        console.log("[Infographic Image] Uploading PNG file...");
+        const token = localStorage.getItem("token");
+        const formData = new FormData();
+        formData.append('file', file);
+        
+        const uploadResponse = await fetch('/api/v1/files/', {{
+            method: 'POST',
+            headers: {{
+                'Authorization': `Bearer ${{token}}`
+            }},
+            body: formData
+        }});
+        
+        if (!uploadResponse.ok) {{
+            throw new Error(`Upload failed: ${{uploadResponse.statusText}}`);
+        }}
+        
+        const fileData = await uploadResponse.json();
+        const fileId = fileData.id;
+        const imageUrl = `/api/v1/files/${{fileId}}/content`;
+        
+        console.log("[Infographic Image] PNG file uploaded, ID:", fileId);
+        
+        // Generate markdown image with file URL
+        const markdownImage = `![📊 Infographic](${{imageUrl}})`;
+        
+        // Update message via API
+        if (chatId && messageId) {{
+            
+            // Helper function with retry logic
+            const fetchWithRetry = async (url, options, retries = 3) => {{
+                for (let i = 0; i < retries; i++) {{
+                    try {{
+                        const response = await fetch(url, options);
+                        if (response.ok) return response;
+                        if (i < retries - 1) {{
+                            console.log(`[Infographic Image] Retry ${{i + 1}}/${{retries}} for ${{url}}`);
+                            await new Promise(r => setTimeout(r, 1000 * (i + 1)));
+                        }}
+                    }} catch (e) {{
+                        if (i === retries - 1) throw e;
+                        await new Promise(r => setTimeout(r, 1000 * (i + 1)));
+                    }}
+                }}
+                return null;
+            }};
+            
+            // Get current chat data
+            const getResponse = await fetch(`/api/v1/chats/${{chatId}}`, {{
+                method: "GET",
+                headers: {{ "Authorization": `Bearer ${{token}}` }}
+            }});
+            
+            if (!getResponse.ok) {{
+                throw new Error("Failed to get chat data: " + getResponse.status);
+            }}
+            
+            const chatData = await getResponse.json();
+            let updatedMessages = [];
+            let newContent = "";
+            
+            if (chatData.chat && chatData.chat.messages) {{
+                updatedMessages = chatData.chat.messages.map(m => {{
+                    if (m.id === messageId) {{
+                        const originalContent = m.content || "";
+                        // Remove existing infographic images
+                        const infographicPattern = /\\n*!\\[📊[^\\]]*\\]\\((?:data:image\\/[^)]+|(?:\\/api\\/v1\\/files\\/[^)]+))\\)/g;
+                        let cleanedContent = originalContent.replace(infographicPattern, "");
+                        cleanedContent = cleanedContent.replace(/\\n{{3,}}/g, "\\n\\n").trim();
+                        // Append new image
+                        newContent = cleanedContent + "\\n\\n" + markdownImage;
+                        
+                        // Update history object as well
+                        if (chatData.chat.history && chatData.chat.history.messages) {{
+                            if (chatData.chat.history.messages[messageId]) {{
+                                chatData.chat.history.messages[messageId].content = newContent;
+                            }}
+                        }}
+                        
+                        return {{ ...m, content: newContent }};
+                    }}
+                    return m;
+                }});
+            }}
+            
+            if (!newContent) {{
+                console.warn("[Infographic Image] Could not find message to update");
+                return;
+            }}
+            
+            // Try to update frontend display via event API
+            try {{
+                await fetch(`/api/v1/chats/${{chatId}}/messages/${{messageId}}/event`, {{
+                    method: "POST",
+                    headers: {{
+                        "Content-Type": "application/json",
+                        "Authorization": `Bearer ${{token}}`
+                    }},
+                    body: JSON.stringify({{
+                        type: "chat:message",
+                        data: {{ content: newContent }}
+                    }})
+                }});
+            }} catch (eventErr) {{
+                console.log("[Infographic Image] Event API not available, continuing...");
+            }}
+            
+            // Persist to database
+            const updatePayload = {{
+                chat: {{
+                    ...chatData.chat,
+                    messages: updatedMessages
+                }}
+            }};
+            
+            const persistResponse = await fetchWithRetry(`/api/v1/chats/${{chatId}}`, {{
+                method: "POST",
+                headers: {{
+                    "Content-Type": "application/json",
+                    "Authorization": `Bearer ${{token}}`
+                }},
+                body: JSON.stringify(updatePayload)
+            }});
+            
+            if (persistResponse && persistResponse.ok) {{
+                console.log("[Infographic Image] ✅ Message persisted successfully!");
+            }} else {{
+                console.error("[Infographic Image] ❌ Failed to persist message after retries");
+            }}
+        }} else {{
+            console.warn("[Infographic Image] ⚠️ Missing chatId or messageId, cannot persist");
+        }}
+        
+    }} catch (error) {{
+        console.error("[Infographic Image] Error:", error);
+    }}
+}})();
+"""
+
     async def action(
         self,
         body: dict,
         __user__: Optional[Dict[str, Any]] = None,
         __event_emitter__: Optional[Any] = None,
+        __event_call__: Optional[Callable[[Any], Awaitable[None]]] = None,
+        __metadata__: Optional[dict] = None,
         __request__: Optional[Request] = None,
     ) -> Optional[dict]:
-        logger.info("Action: Infographic started (v1.0.0)")
+        logger.info("Action: Infographic started (v1.4.0)")
 
         # Get user information
         if isinstance(__user__, (list, tuple)):
@@ -1114,6 +1661,46 @@ class Action:
                         user_language,
                     )
 
+            # Check output mode
+            if self.valves.OUTPUT_MODE == "image":
+                # Image mode: use JavaScript to render and embed as Markdown image
+                chat_ctx = self._get_chat_context(body, __metadata__)
+                chat_id = chat_ctx["chat_id"]
+                message_id = chat_ctx["message_id"]
+
+                await self._emit_status(
+                    __event_emitter__,
+                    "📊 Infographic: Rendering image...",
+                    False,
+                )
+
+                if __event_call__:
+                    js_code = self._generate_image_js_code(
+                        unique_id=unique_id,
+                        chat_id=chat_id,
+                        message_id=message_id,
+                        infographic_syntax=infographic_syntax,
+                    )
+
+                    await __event_call__(
+                        {
+                            "type": "execute",
+                            "data": {"code": js_code},
+                        }
+                    )
+
+                await self._emit_status(
+                    __event_emitter__, "✅ Infographic: Image generated!", True
+                )
+                await self._emit_notification(
+                    __event_emitter__,
+                    f"📊 Infographic image generated, {user_name}!",
+                    "success",
+                )
+                logger.info("Infographic generation completed in image mode")
+                return body
+
+            # HTML mode (default): embed as HTML block
             html_embed_tag = f"```html\n{final_html}\n```"
             body["messages"][-1]["content"] = f"{original_content}\n\n{html_embed_tag}"
 

plugins/actions/infographic/infographic_cn.py

@@ -1,14 +1,16 @@
 """
 title: 📊 智能信息图 (AntV Infographic)
-author: jeff
+author: Fu-Jie
 author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
 icon_url: data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9ImN1cnJlbnRDb2xvciIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPgogIDxsaW5lIHgxPSIxMiIgeTE9IjIwIiB4Mj0iMTIiIHkyPSIxMCIgLz4KICA8bGluZSB4MT0iMTgiIHkxPSIyMCIgeDI9IjE4IiB5Mj0iNCIgLz4KICA8bGluZSB4MT0iNiIgeTE9IjIwIiB4Mj0iNiIgeTI9IjE2IiAvPgo8L3N2Zz4=
-version: 1.3.2
+version: 1.4.9
+openwebui_id: e04a48ff-23ee-4a41-8ea7-66c19524e7c8
 description: 基于 AntV Infographic 的智能信息图生成插件。支持多种专业模板，自动图标匹配，并提供 SVG/PNG 下载功能。
 """
 
 from pydantic import BaseModel, Field
-from typing import Optional, Dict, Any
+from typing import Optional, Dict, Any, Callable, Awaitable
 import logging
 import time
 import re
@@ -44,32 +46,61 @@ Infographic syntax is a Mermaid-like declarative syntax for describing infograph
 - ❌ Wrong: `children:` `items:` `data:` (with colons)
 - ✅ Correct: `children` `items` `data` (without colons)
 
-### Template Library & Selection Guide
+### 模板库与选择指南
 
-#### 1. List & Hierarchy (Text-heavy)
--   **Linear & Short (Steps/Phases)** -> `list-row-horizontal-icon-arrow`
--   **Linear & Long (Rankings/Details)** -> `list-vertical`
--   **Grouped / Parallel (Features/Catalog)** -> `list-grid`
--   **Hierarchical (Org Chart/Taxonomy)** -> `tree-vertical` or `tree-horizontal`
--   **Central Idea (Brainstorming)** -> `mindmap`
+根据内容结构选择最合适的模板。
 
-#### 2. Sequence & Relationship (Flow-based)
--   **Time-based (History/Plan)** -> `sequence-roadmap-vertical-simple`
--   **Process Flow (Complex)** -> `sequence-zigzag` or `sequence-horizontal`
--   **Resource Flow / Distribution** -> `relation-sankey`
--   **Circular Relationship** -> `relation-circle`
+**模板选择指南 (官方):**
+- 严格时序 (流程/步骤/趋势) → `sequence-*` 系列
+  - 时间线 → `sequence-timeline-simple`
+  - 路线图 → `sequence-roadmap-vertical-simple`
+  - 折线步骤 → `sequence-horizontal-zigzag-underline-text`
+  - 蛇形步骤 → `sequence-snake-steps-compact-card`
+- 列举要点 → `list-row-horizontal-icon-arrow` 或 `list-column-simple-vertical-arrow`
+- 对比分析 (A vs B) → `compare-binary-horizontal-underline-text-vs`
+- SWOT 分析 → `compare-swot`
+- 层级结构 (树状图) → `hierarchy-tree-tech-style-capsule-item`
+- 数据图表 → `chart-*` 系列
+- 象限分析 → `quadrant-quarter-simple-card`
+- 网格列表 → `list-grid-candy-card-lite`
+- 关系展示 → `relation-circle-icon-badge`
 
-#### 3. Comparison & Analysis
--   **Binary Comparison (A vs B)** -> `compare-binary`
--   **SWOT Analysis** -> `compare-swot`
--   **Quadrant Analysis (Importance vs Urgency)** -> `quadrant-quarter`
--   **Multi-item Grid Comparison** -> `list-grid` (use for comparing multiple items)
+**可用模板:**
 
-#### 4. Charts & Data (Metric-heavy)
--   **Key Metrics / Data Cards** -> `statistic-card`
--   **Distribution / Comparison** -> `chart-bar` or `chart-column`
--   **Trend over Time** -> `chart-line` or `chart-area`
--   **Proportion / Part-to-Whole** -> `chart-pie` or `chart-doughnut`
+*Sequence (时序/流程):*
+`sequence-timeline-simple`, `sequence-roadmap-vertical-simple`, `sequence-horizontal-zigzag-underline-text`, 
+`sequence-snake-steps-compact-card`, `sequence-zigzag-steps-underline-text`, `sequence-circular-simple`
+
+*List (列表):*
+`list-grid-candy-card-lite`, `list-grid-badge-card`, `list-row-horizontal-icon-arrow`,
+`list-column-simple-vertical-arrow`, `list-column-done-list`
+
+*Compare (对比):*
+`compare-binary-horizontal-underline-text-vs`, `compare-swot`
+
+*Hierarchy (层级):*
+`hierarchy-tree-tech-style-capsule-item`, `hierarchy-structure`
+
+*Chart (图表):*
+`chart-column-simple`, `chart-bar-plain-text`, `chart-pie-plain-text`, `chart-wordcloud`
+
+*Other:*
+`quadrant-quarter-simple-card`, `relation-circle-icon-badge`
+
+**按容量分类:**
+- 高容量 (长描述): `list-column-*`, `compare-binary-*`, `sequence-timeline-*`
+- 中容量: `list-row-*`, `sequence-roadmap-*`
+- 低容量 (短文本): `list-grid-*`, `hierarchy-*`
+
+### 图标和插图资源
+
+**图标 (Iconify):**
+- 格式: `<集合>/<图标名>`, 如 `mdi/rocket-launch`
+- 常用: `mdi/*`, `fa/*`, `bi/*`
+
+**插图 (unDraw):**
+- 格式: 文件名 (不含 .svg), 如 `coding`, `team-work`
+- 使用 `illus` 字段
 
 ### Infographic Syntax Guide
 
@@ -202,12 +233,20 @@ data
       desc Plan for next sprint
       illus mdi/star
 
+### Content Refinement Principles
+1. **Brevity is King**: Infographics are visual. Keep text to a minimum.
+2. **Title Limit**: Keep `label` (item titles) under 15 characters.
+3. **Description Limit**: Keep `desc` (item descriptions) under 25 characters (approx. 2 lines).
+4. **Impact**: Use strong verbs and nouns. Avoid filler words.
+
 ### Output Rules
 1.  **Strict Syntax**: Follow the indentation and formatting rules exactly.
 2.  **No Explanations**: Output ONLY the syntax code block.
 3.  **Language**: Use the user's requested language for content.
 """
 
+import json
+
 USER_PROMPT_GENERATE_INFOGRAPHIC = """
 请分析以下文本内容，将其核心信息转换为 AntV Infographic 语法格式。
 
@@ -223,9 +262,11 @@ USER_PROMPT_GENERATE_INFOGRAPHIC = """
 
 请根据文本特点选择最合适的信息图模板，并输出规范的 infographic 语法。注意保持正确的缩进格式（两个空格）。
 
-**重要提示：** 
-- 如果使用 `list-grid` 格式，请确保每个卡片的 `desc` 描述文字控制在 **30个汉字**（或约60个英文字符）**以内**，以保证所有卡片描述都只占用2行，维持视觉一致性。
-- 描述应简洁精炼，突出核心要点。
+**视觉优化指南：** 
+- **要点化生成：** 信息图不是文章。请将内容转化为“关键词+短语”的形式，严禁生成长难句。
+- **标题限制：** 每个卡片的 `label`（标题）请控制在 **8个汉字**以内。
+- **描述限制：** 每个卡片的 `desc`（描述）请控制在 **15个汉字**以内，确保即使在小屏幕上也能完整显示。
+- **结构化思维：** 优先使用并列、递进或对比结构，使信息一目了然。
 """
 
 # =================================================================
@@ -332,7 +373,7 @@ CSS_TEMPLATE_INFOGRAPHIC = """
     padding: 16px;
     min-height: 600px;
     background: #fff;
-    overflow: visible; /* Ensure content is visible */
+    overflow: visible;
     transition: height 0.3s ease;
 }
 .infographic-render-container svg text {
@@ -340,35 +381,58 @@ CSS_TEMPLATE_INFOGRAPHIC = """
 }
 .infographic-render-container svg foreignObject {
     font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei", sans-serif !important;
-    line-height: 1.4 !important;
+    line-height: 1.3 !important;
+    overflow: visible !important;
 }
 /* 主标题样式 */
 .infographic-render-container svg foreignObject[data-element-type="title"] > * {
-    font-size: 1.5em !important;
-    font-weight: bold !important;
-    line-height: 1.4 !important;
-    white-space: nowrap !important;
-    overflow: hidden !important;
-    text-overflow: ellipsis !important;
-}
-/* 页面副标题和卡片标题样式 */
-.infographic-render-container svg foreignObject[data-element-type="desc"] > *,
-.infographic-render-container svg foreignObject[data-element-type="item-label"] > * {
-    font-size: 0.6em !important;
-    line-height: 1.4 !important;
-    white-space: nowrap !important;
-    overflow: hidden !important;
-    text-overflow: ellipsis !important;
-}
-/* 卡片标题额外增加底部间距 */
-.infographic-render-container svg foreignObject[data-element-type="item-label"] > * {
-    padding-bottom: 8px !important;
-    display: block !important;
-}
-/* 卡片描述文字保持正常换行 */
-.infographic-render-container svg foreignObject[data-element-type="item-desc"] > * {
-    line-height: 1.4 !important;
+    font-size: 1.3em !important;
+    font-weight: 800 !important;
+    line-height: 1.3 !important;
     white-space: normal !important;
+    word-break: break-word !important;
+    display: -webkit-box !important;
+    -webkit-line-clamp: 2 !important;
+    -webkit-box-orient: vertical !important;
+    overflow: hidden !important;
+    text-overflow: ellipsis !important;
+    text-align: center !important;
+}
+/* 页面副标题样式 */
+.infographic-render-container svg foreignObject[data-element-type="desc"] > * {
+    font-size: 0.85em !important;
+    line-height: 1.3 !important;
+    white-space: nowrap !important;
+    overflow: hidden !important;
+    text-overflow: ellipsis !important;
+    text-align: center !important;
+    display: block !important;
+    color: var(--ig-muted-text-color) !important;
+}
+/* 卡片标题样式 */
+.infographic-render-container svg foreignObject[data-element-type="item-label"] > * {
+    font-size: 0.9em !important;
+    font-weight: 600 !important;
+    line-height: 1.3 !important;
+    white-space: normal !important;
+    word-break: break-word !important;
+    display: -webkit-box !important;
+    -webkit-line-clamp: 2 !important;
+    -webkit-box-orient: vertical !important;
+    overflow: hidden !important;
+    text-overflow: ellipsis !important;
+    padding-bottom: 2px !important;
+}
+/* 卡片描述文字 */
+.infographic-render-container svg foreignObject[data-element-type="item-desc"] > * {
+    font-size: 0.82em !important;
+    line-height: 1.3 !important;
+    white-space: normal !important;
+    display: -webkit-box !important;
+    -webkit-line-clamp: 2 !important;
+    -webkit-box-orient: vertical !important;
+    overflow: hidden !important;
+    text-overflow: ellipsis !important;
 }
 .infographic-container-wrapper .download-area {
     text-align: center;
@@ -536,34 +600,36 @@ SCRIPT_TEMPLATE_INFOGRAPHIC = """
             }
         }
 
-        // 2. 模板映射配置
-        // 2. 模板映射配置
+        // 2. 模板映射配置 (官方 AntV 结构 ID)
         const TEMPLATE_MAPPING = {
-            // 列表与层级
+            // 列表与层级 - 短名称映射到完整模板名
             'list-grid': 'list-grid-compact-card',
+            'list-column': 'list-column-simple-vertical-arrow',
+            'list-row': 'list-row-simple-horizontal-arrow',
+            'hierarchy-tree': 'hierarchy-tree-tech-style-capsule-item',
+            
+            // 时序与时间线
+            'sequence-roadmap-vertical': 'sequence-roadmap-vertical-simple',
+            'sequence-timeline': 'sequence-timeline-simple',
+            'sequence-steps': 'sequence-steps-simple',
+            'sequence-horizontal-zigzag': 'sequence-horizontal-zigzag-simple',
+            
+            // 对比
+            'compare-binary-horizontal': 'compare-binary-horizontal-simple-vs',
+            'compare-hierarchy-row': 'compare-hierarchy-row-simple',
+            
+            // 图表
+            'chart-column': 'chart-column-simple',
+            'quadrant': 'quadrant-quarter-simple-card',
+            
+            // 向后兼容的旧映射
             'list-vertical': 'list-column-simple-vertical-arrow',
             'tree-vertical': 'hierarchy-tree-tech-style-capsule-item',
-            'tree-horizontal': 'hierarchy-tree-lr-tech-style-capsule-item',
-            'mindmap': 'hierarchy-mindmap-branch-gradient-capsule-item',
-            
-            // 顺序与关系
             'sequence-roadmap': 'sequence-roadmap-vertical-simple',
             'sequence-zigzag': 'sequence-horizontal-zigzag-simple',
-            'sequence-horizontal': 'sequence-horizontal-zigzag-simple',
-            'relation-sankey': 'relation-sankey-simple', // 暂无直接对应，保留原值或需移除
-            'relation-circle': 'relation-circle-icon-badge',
-            
-            // 对比与分析
             'compare-binary': 'compare-binary-horizontal-simple-vs',
-            'compare-swot': 'compare-swot',
-            'quadrant-quarter': 'quadrant-quarter-simple-card',
-            
-            // 图表与数据
-            'statistic-card': 'list-grid-compact-card',
             'chart-bar': 'chart-bar-plain-text',
-            'chart-column': 'chart-column-simple',
             'chart-line': 'chart-line-plain-text',
-            'chart-area': 'chart-area-simple', // 暂无直接对应
             'chart-pie': 'chart-pie-plain-text',
             'chart-doughnut': 'chart-pie-donut-plain-text'
         };
@@ -656,10 +722,48 @@ SCRIPT_TEMPLATE_INFOGRAPHIC = """
             containerEl.dataset.infographicRendered = 'true';
             console.log('[Infographic] 渲染完成');
 
-            // 自动调整高度
+            // 自动调整高度与元素标记
             setTimeout(() => {
                 const svg = containerEl.querySelector('svg');
                 if (svg) {
+                    // 1. 标记元素以便 CSS 应用样式
+                    const fos = Array.from(svg.querySelectorAll('foreignObject'));
+                    let titleFound = false;
+                    let descFound = false;
+                    
+                    fos.forEach((fo) => {
+                        const text = fo.textContent.trim();
+                        if (!text || fo.querySelector('i') || (fo.querySelector('svg') && fo.querySelectorAll('*').length < 5)) {
+                            fo.setAttribute('data-element-type', 'icon');
+                            return;
+                        }
+                        
+                        // 动态增加高度和宽度，容纳换行后的文字
+                        const currentHeight = parseInt(fo.getAttribute('height') || '0');
+                        if (currentHeight > 0 && currentHeight < 200) {
+                            fo.setAttribute('height', Math.round(currentHeight * 1.8).toString());
+                        }
+                        const currentWidth = parseInt(fo.getAttribute('width') || '0');
+                        if (currentWidth > 0 && currentWidth < 300) {
+                            fo.setAttribute('width', Math.max(Math.round(currentWidth * 1.2), 180).toString());
+                        }
+
+                        if (!titleFound) {
+                            fo.setAttribute('data-element-type', 'title');
+                            titleFound = true;
+                        } else if (!descFound) {
+                            fo.setAttribute('data-element-type', 'desc');
+                            descFound = true;
+                        } else {
+                            if (fo.querySelector('strong') || fo.style.fontWeight === 'bold' || text.length < 15) {
+                                fo.setAttribute('data-element-type', 'item-label');
+                            } else {
+                                fo.setAttribute('data-element-type', 'item-desc');
+                            }
+                        }
+                    });
+
+                    // 2. 调整高度
                     const bbox = svg.getBoundingClientRect();
                     let contentHeight = bbox.height;
                     if (svg.viewBox && svg.viewBox.baseVal && svg.viewBox.baseVal.height) {
@@ -849,6 +953,14 @@ class Action:
             default=1,
             description="用于生成的最近消息数量。设置为1仅使用最后一条消息，更大值可包含更多上下文。",
         )
+        OUTPUT_MODE: str = Field(
+            default="image",
+            description="输出模式：'html' 为交互式HTML，'image' 将嵌入为Markdown图片（默认）。",
+        )
+        SHOW_DEBUG_LOG: bool = Field(
+            default=False,
+            description="是否在浏览器控制台打印调试日志。",
+        )
 
     def __init__(self):
         self.valves = self.Valves()
@@ -862,6 +974,57 @@ class Action:
             "Sunday": "星期日",
         }
 
+    def _get_user_context(self, __user__: Optional[Dict[str, Any]]) -> Dict[str, str]:
+        """安全提取用户上下文信息。"""
+        if isinstance(__user__, (list, tuple)):
+            user_data = __user__[0] if __user__ else {}
+        elif isinstance(__user__, dict):
+            user_data = __user__
+        else:
+            user_data = {}
+
+        return {
+            "user_id": user_data.get("id", "unknown_user"),
+            "user_name": user_data.get("name", "用户"),
+            "user_language": user_data.get("language", "zh-CN"),
+        }
+
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        统一提取聊天上下文信息 (chat_id, message_id)。
+        优先从 body 中提取，其次从 metadata 中提取。
+        """
+        chat_id = ""
+        message_id = ""
+
+        # 1. 尝试从 body 获取
+        if isinstance(body, dict):
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id 在 body 中通常是 id
+
+            # 再次检查 body.metadata
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
+
+        # 2. 尝试从 __metadata__ 获取 (作为补充)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
+
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
+
     def _extract_infographic_syntax(self, llm_output: str) -> str:
         """提取LLM输出中的infographic语法"""
         # 1. 优先匹配 ```infographic
@@ -913,6 +1076,24 @@ class Action:
                 {"type": "notification", "data": {"type": ntype, "content": content}}
             )
 
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """在浏览器控制台打印结构化调试日志"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
+
     def _remove_existing_html(self, content: str) -> str:
         """移除内容中已有的插件生成 HTML 代码块"""
         pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
@@ -973,14 +1154,359 @@ class Action:
 
         return base_html.strip()
 
+    def _generate_image_js_code(
+        self,
+        unique_id: str,
+        chat_id: str,
+        message_id: str,
+        infographic_syntax: str,
+    ) -> str:
+        """生成前端 SVG 渲染和图片嵌入的 JavaScript 代码"""
+
+        # 转义语法以便在 JS 中嵌入
+        syntax_escaped = (
+            infographic_syntax.replace("\\", "\\\\")
+            .replace("`", "\\`")
+            .replace("${", "\\${")
+            .replace("</script>", "<\\/script>")
+        )
+
+        return f"""
+(async function() {{
+    const uniqueId = "{unique_id}";
+    const chatId = "{chat_id}";
+    const messageId = "{message_id}";
+    const defaultWidth = 1100;
+    const defaultHeight = 500;
+    
+    // 自动检测聊天容器宽度以实现响应式尺寸
+    let svgWidth = defaultWidth;
+    let svgHeight = defaultHeight;
+    const chatContainer = document.getElementById('chat-container');
+    if (chatContainer) {{
+        const containerWidth = chatContainer.clientWidth;
+        if (containerWidth > 100) {{
+            // 使用容器宽度的 80%（右边留更多空间）
+            svgWidth = Math.floor(containerWidth * 0.8);
+            // 根据默认尺寸保持宽高比
+            svgHeight = Math.floor(svgWidth * (defaultHeight / defaultWidth));
+            console.log("[Infographic Image] 自动检测容器宽度:", containerWidth, "-> SVG:", svgWidth, "x", svgHeight);
+        }}
+    }}
+    
+    console.log("[Infographic Image] 开始渲染...");
+    console.log("[Infographic Image] chatId:", chatId, "messageId:", messageId);
+    
+    try {{
+        // 加载 AntV Infographic（如果未加载）
+        if (typeof AntVInfographic === 'undefined') {{
+            console.log("[Infographic Image] 加载 AntV Infographic...");
+            await new Promise((resolve, reject) => {{
+                const script = document.createElement('script');
+                script.src = 'https://registry.npmmirror.com/@antv/infographic/0.2.1/files/dist/infographic.min.js';
+                script.onload = resolve;
+                script.onerror = reject;
+                document.head.appendChild(script);
+            }});
+        }}
+        
+        const {{ Infographic }} = AntVInfographic;
+        
+        // 获取语法内容
+        let syntaxContent = `{syntax_escaped}`;
+        console.log("[Infographic Image] 语法长度:", syntaxContent.length);
+        
+        // 清理语法：移除代码块标记
+        const backtick = String.fromCharCode(96);
+        const prefix = backtick + backtick + backtick + 'infographic';
+        const simplePrefix = backtick + backtick + backtick;
+        
+        if (syntaxContent.toLowerCase().startsWith(prefix)) {{
+            syntaxContent = syntaxContent.substring(prefix.length).trim();
+        }} else if (syntaxContent.startsWith(simplePrefix)) {{
+            syntaxContent = syntaxContent.substring(simplePrefix.length).trim();
+        }}
+        
+        if (syntaxContent.endsWith(simplePrefix)) {{
+            syntaxContent = syntaxContent.substring(0, syntaxContent.length - simplePrefix.length).trim();
+        }}
+        
+        // 修复语法：移除关键字后的冒号
+        syntaxContent = syntaxContent.replace(/^(data|items|children|theme|config):/gm, '$1');
+        syntaxContent = syntaxContent.replace(/(\\s)(children|items):/g, '$1$2');
+        
+        // 确保 infographic 前缀
+        if (!syntaxContent.trim().toLowerCase().startsWith('infographic')) {{
+            const firstWord = syntaxContent.trim().split(/\\s+/)[0].toLowerCase();
+            if (!['data', 'theme', 'design', 'items'].includes(firstWord)) {{
+                syntaxContent = 'infographic ' + syntaxContent;
+            }}
+        }}
+        
+        // 模板映射
+        const TEMPLATE_MAPPING = {{
+            'list-grid': 'list-grid-compact-card',
+            'list-vertical': 'list-column-simple-vertical-arrow',
+            'tree-vertical': 'hierarchy-tree-tech-style-capsule-item',
+            'tree-horizontal': 'hierarchy-tree-lr-tech-style-capsule-item',
+            'mindmap': 'hierarchy-mindmap-branch-gradient-capsule-item',
+            'sequence-roadmap': 'sequence-roadmap-vertical-simple',
+            'sequence-zigzag': 'sequence-horizontal-zigzag-simple',
+            'sequence-horizontal': 'sequence-horizontal-zigzag-simple',
+            'relation-sankey': 'relation-sankey-simple',
+            'relation-circle': 'relation-circle-icon-badge',
+            'compare-binary': 'compare-binary-horizontal-simple-vs',
+            'compare-swot': 'compare-swot',
+            'quadrant-quarter': 'quadrant-quarter-simple-card',
+            'statistic-card': 'list-grid-compact-card',
+            'chart-bar': 'chart-bar-plain-text',
+            'chart-column': 'chart-column-simple',
+            'chart-line': 'chart-line-plain-text',
+            'chart-area': 'chart-area-simple',
+            'chart-pie': 'chart-pie-plain-text',
+            'chart-doughnut': 'chart-pie-donut-plain-text'
+        }};
+        
+        for (const [key, value] of Object.entries(TEMPLATE_MAPPING)) {{
+            const regex = new RegExp(`infographic\\\\s+${{key}}(?=\\\\s|$)`, 'i');
+            if (regex.test(syntaxContent)) {{
+                syntaxContent = syntaxContent.replace(regex, `infographic ${{value}}`);
+                break;
+            }}
+        }}
+        
+        // 创建离屏容器
+        const container = document.createElement('div');
+        container.id = 'infographic-offscreen-' + uniqueId;
+        container.style.cssText = 'position:absolute;left:-9999px;top:-9999px;width:' + svgWidth + 'px;height:' + svgHeight + 'px;background:#ffffff;';
+        document.body.appendChild(container);
+        
+        // 创建信息图实例
+        const instance = new Infographic({{
+            container: '#' + container.id,
+            width: svgWidth,
+            height: svgHeight,
+            padding: 12,
+        }});
+        
+        console.log("[Infographic Image] 渲染信息图...");
+        instance.render(syntaxContent);
+        
+        // 等待渲染完成
+        await new Promise(resolve => setTimeout(resolve, 2000));
+        
+        // 获取 SVG 元素
+        const svgEl = container.querySelector('svg');
+        if (!svgEl) {{
+            throw new Error('渲染后未找到 SVG 元素');
+        }}
+        
+        // 获取实际尺寸
+        const bbox = svgEl.getBoundingClientRect();
+        const width = bbox.width || svgWidth;
+        const height = bbox.height || svgHeight;
+        
+        // 克隆并准备导出的 SVG
+        const clonedSvg = svgEl.cloneNode(true);
+        clonedSvg.setAttribute('xmlns', 'http://www.w3.org/2000/svg');
+        clonedSvg.setAttribute('xmlns:xlink', 'http://www.w3.org/1999/xlink');
+        clonedSvg.setAttribute('width', width);
+        clonedSvg.setAttribute('height', height);
+        
+        // 添加背景矩形
+        const bgRect = document.createElementNS('http://www.w3.org/2000/svg', 'rect');
+        bgRect.setAttribute('width', '100%');
+        bgRect.setAttribute('height', '100%');
+        bgRect.setAttribute('fill', '#ffffff');
+        clonedSvg.insertBefore(bgRect, clonedSvg.firstChild);
+        
+        // 序列化 SVG 为字符串
+        const svgData = new XMLSerializer().serializeToString(clonedSvg);
+        
+        // 清理容器
+        document.body.removeChild(container);
+        
+        // 使用 canvas 将 SVG 转换为 PNG 以提高兼容性
+        console.log("[Infographic Image] 正在将 SVG 转换为 PNG...");
+        const pngBlob = await new Promise((resolve, reject) => {{
+            const canvas = document.createElement('canvas');
+            const ctx = canvas.getContext('2d');
+            const scale = 2; // 更高分辨率以提高清晰度
+            canvas.width = Math.round(width * scale);
+            canvas.height = Math.round(height * scale);
+            
+            // 填充白色背景
+            ctx.fillStyle = '#ffffff';
+            ctx.fillRect(0, 0, canvas.width, canvas.height);
+            ctx.scale(scale, scale);
+            
+            const img = new Image();
+            img.onload = () => {{
+                ctx.drawImage(img, 0, 0, width, height);
+                canvas.toBlob((blob) => {{
+                    if (blob) {{
+                        resolve(blob);
+                    }} else {{
+                        reject(new Error('Canvas toBlob 失败'));
+                    }}
+                }}, 'image/png');
+            }};
+            img.onerror = (e) => reject(new Error('加载 SVG 图片失败: ' + e));
+            img.src = 'data:image/svg+xml;base64,' + btoa(unescape(encodeURIComponent(svgData)));
+        }});
+        
+        const file = new File([pngBlob], `infographic-${{uniqueId}}.png`, {{ type: 'image/png' }});
+        
+        // 上传文件到 OpenWebUI API
+        console.log("[Infographic Image] 上传 PNG 文件...");
+        const token = localStorage.getItem("token");
+        const formData = new FormData();
+        formData.append('file', file);
+        
+        const uploadResponse = await fetch('/api/v1/files/', {{
+            method: 'POST',
+            headers: {{
+                'Authorization': `Bearer ${{token}}`
+            }},
+            body: formData
+        }});
+        
+        if (!uploadResponse.ok) {{
+            throw new Error(`上传失败: ${{uploadResponse.statusText}}`);
+        }}
+        
+        const fileData = await uploadResponse.json();
+        const fileId = fileData.id;
+        const imageUrl = `/api/v1/files/${{fileId}}/content`;
+        
+        console.log("[Infographic Image] PNG 文件已上传, ID:", fileId);
+        
+        // 生成带文件 URL 的 markdown 图片
+        const markdownImage = `![📊 信息图](${{imageUrl}})`;
+        
+        // 通过 API 更新消息
+        if (chatId && messageId) {{
+            
+            // 带重试逻辑的辅助函数
+            const fetchWithRetry = async (url, options, retries = 3) => {{
+                for (let i = 0; i < retries; i++) {{
+                    try {{
+                        const response = await fetch(url, options);
+                        if (response.ok) return response;
+                        if (i < retries - 1) {{
+                            console.log(`[Infographic Image] 重试 ${{i + 1}}/${{retries}} for ${{url}}`);
+                            await new Promise(r => setTimeout(r, 1000 * (i + 1)));
+                        }}
+                    }} catch (e) {{
+                        if (i === retries - 1) throw e;
+                        await new Promise(r => setTimeout(r, 1000 * (i + 1)));
+                    }}
+                }}
+                return null;
+            }};
+            
+            // 获取当前聊天数据
+            const getResponse = await fetch(`/api/v1/chats/${{chatId}}`, {{
+                method: "GET",
+                headers: {{ "Authorization": `Bearer ${{token}}` }}
+            }});
+            
+            if (!getResponse.ok) {{
+                throw new Error("获取聊天数据失败: " + getResponse.status);
+            }}
+            
+            const chatData = await getResponse.json();
+            let updatedMessages = [];
+            let newContent = "";
+            
+            if (chatData.chat && chatData.chat.messages) {{
+                updatedMessages = chatData.chat.messages.map(m => {{
+                    if (m.id === messageId) {{
+                        const originalContent = m.content || "";
+                        // 移除已有的信息图图片
+                        const infographicPattern = /\\n*!\\[📊[^\\]]*\\]\\((?:data:image\\/[^)]+|(?:\\/api\\/v1\\/files\\/[^)]+))\\)/g;
+                        let cleanedContent = originalContent.replace(infographicPattern, "");
+                        cleanedContent = cleanedContent.replace(/\\n{{3,}}/g, "\\n\\n").trim();
+                        // 追加新图片
+                        newContent = cleanedContent + "\\n\\n" + markdownImage;
+                        
+                        // 同时更新 history 对象
+                        if (chatData.chat.history && chatData.chat.history.messages) {{
+                            if (chatData.chat.history.messages[messageId]) {{
+                                chatData.chat.history.messages[messageId].content = newContent;
+                            }}
+                        }}
+                        
+                        return {{ ...m, content: newContent }};
+                    }}
+                    return m;
+                }});
+            }}
+            
+            if (!newContent) {{
+                console.warn("[Infographic Image] 找不到要更新的消息");
+                return;
+            }}
+            
+            // 尝试通过事件 API 更新前端显示
+            try {{
+                await fetch(`/api/v1/chats/${{chatId}}/messages/${{messageId}}/event`, {{
+                    method: "POST",
+                    headers: {{
+                        "Content-Type": "application/json",
+                        "Authorization": `Bearer ${{token}}`
+                    }},
+                    body: JSON.stringify({{
+                        type: "chat:message",
+                        data: {{ content: newContent }}
+                    }})
+                }});
+            }} catch (eventErr) {{
+                console.log("[Infographic Image] 事件 API 不可用，继续...");
+            }}
+            
+            // 持久化到数据库
+            const updatePayload = {{
+                chat: {{
+                    ...chatData.chat,
+                    messages: updatedMessages
+                }}
+            }};
+            
+            const persistResponse = await fetchWithRetry(`/api/v1/chats/${{chatId}}`, {{
+                method: "POST",
+                headers: {{
+                    "Content-Type": "application/json",
+                    "Authorization": `Bearer ${{token}}`
+                }},
+                body: JSON.stringify(updatePayload)
+            }});
+            
+            if (persistResponse && persistResponse.ok) {{
+                console.log("[Infographic Image] ✅ 消息持久化成功!");
+            }} else {{
+                console.error("[Infographic Image] ❌ 重试后消息持久化失败");
+            }}
+        }} else {{
+            console.warn("[Infographic Image] ⚠️ 缺少 chatId 或 messageId，无法持久化");
+        }}
+        
+    }} catch (error) {{
+        console.error("[Infographic Image] 错误:", error);
+    }}
+}})();
+"""
+
     async def action(
         self,
         body: dict,
         __user__: Optional[Dict[str, Any]] = None,
         __event_emitter__: Optional[Any] = None,
+        __event_call__: Optional[Callable[[Any], Awaitable[None]]] = None,
+        __metadata__: Optional[dict] = None,
         __request__: Optional[Request] = None,
     ) -> Optional[dict]:
-        logger.info("Action: 信息图启动 (v1.0.0)")
+        logger.info("Action: 信息图启动 (v1.4.0)")
 
         # 获取用户信息
         if isinstance(__user__, (list, tuple)):
@@ -1169,6 +1695,46 @@ class Action:
                         user_language,
                     )
 
+            # 检查输出模式
+            if self.valves.OUTPUT_MODE == "image":
+                # 图片模式：使用 JavaScript 渲染并嵌入为 Markdown 图片
+                chat_ctx = self._get_chat_context(body, __metadata__)
+                chat_id = chat_ctx["chat_id"]
+                message_id = chat_ctx["message_id"]
+
+                await self._emit_status(
+                    __event_emitter__,
+                    "📊 信息图: 正在渲染图片...",
+                    False,
+                )
+
+                if __event_call__:
+                    js_code = self._generate_image_js_code(
+                        unique_id=unique_id,
+                        chat_id=chat_id,
+                        message_id=message_id,
+                        infographic_syntax=infographic_syntax,
+                    )
+
+                    await __event_call__(
+                        {
+                            "type": "execute",
+                            "data": {"code": js_code},
+                        }
+                    )
+
+                await self._emit_status(
+                    __event_emitter__, "✅ 信息图: 图片生成完成!", True
+                )
+                await self._emit_notification(
+                    __event_emitter__,
+                    f"📊 信息图图片已生成，{user_name}！",
+                    "success",
+                )
+                logger.info("信息图生成完成（图片模式）")
+                return body
+
+            # HTML 模式（默认）：嵌入为 HTML 块
             html_embed_tag = f"```html\n{final_html}\n```"
             body["messages"][-1]["content"] = f"{original_content}\n\n{html_embed_tag}"
 

plugins/actions/js-render-poc/README.md

@@ -1,170 +0,0 @@
-# Infographic to Markdown
-
-> **Version:** 1.0.0
-
-AI-powered infographic generator that renders SVG on the frontend and embeds it directly into Markdown as a Data URL image.
-
-## Overview
-
-This plugin combines the power of AI text analysis with AntV Infographic visualization to create beautiful infographics that are embedded directly into chat messages as Markdown images.
-
-### How It Works
-
-```
-┌─────────────────────────────────────────────────────────────┐
-│                    Open WebUI Plugin                         │
-├─────────────────────────────────────────────────────────────┤
-│  1. Python Action                                            │
-│     ├── Receive message content                              │
-│     ├── Call LLM to generate Infographic syntax              │
-│     └── Send __event_call__ to execute frontend JS           │
-├─────────────────────────────────────────────────────────────┤
-│  2. Browser JS (via __event_call__)                          │
-│     ├── Dynamically load AntV Infographic library            │
-│     ├── Render SVG offscreen                                 │
-│     ├── Export to Data URL via toDataURL()                   │
-│     └── Update message content via REST API                  │
-├─────────────────────────────────────────────────────────────┤
-│  3. Markdown Rendering                                       │
-│     └── Display ![description](data:image/svg+xml;base64,...) │
-└─────────────────────────────────────────────────────────────┘
-```
-
-## Features
-
-- 🤖 **AI-Powered**: Automatically analyzes text and selects the best infographic template
-- 📊 **Multiple Templates**: Supports 18+ infographic templates (lists, charts, comparisons, etc.)
-- 🖼️ **Self-Contained**: SVG/PNG embedded as Data URL, no external dependencies
-- 📝 **Markdown Native**: Results are pure Markdown images, compatible everywhere
-- 🔄 **API Writeback**: Updates message content via REST API for persistence
-
-## Plugins in This Directory
-
-### 1. `infographic_markdown.py` - Main Plugin ⭐
-- **Purpose**: Production use
-- **Features**: Full AI + AntV Infographic + Data URL embedding
-
-### 2. `js_render_poc.py` - Proof of Concept
-- **Purpose**: Learning and testing
-- **Features**: Simple SVG creation demo, `__event_call__` pattern
-
-## Configuration (Valves)
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `SHOW_STATUS` | bool | `true` | Show operation status updates |
-| `MODEL_ID` | string | `""` | LLM model ID (empty = use current model) |
-| `MIN_TEXT_LENGTH` | int | `50` | Minimum text length required |
-| `MESSAGE_COUNT` | int | `1` | Number of recent messages to use |
-| `SVG_WIDTH` | int | `800` | Width of generated SVG (pixels) |
-| `EXPORT_FORMAT` | string | `"svg"` | Export format: `svg` or `png` |
-
-## Supported Templates
-
-| Category | Template | Description |
-|----------|----------|-------------|
-| List | `list-grid` | Grid cards |
-| List | `list-vertical` | Vertical list |
-| Tree | `tree-vertical` | Vertical tree |
-| Tree | `tree-horizontal` | Horizontal tree |
-| Mind Map | `mindmap` | Mind map |
-| Process | `sequence-roadmap` | Roadmap |
-| Process | `sequence-zigzag` | Zigzag process |
-| Relation | `relation-sankey` | Sankey diagram |
-| Relation | `relation-circle` | Circular relation |
-| Compare | `compare-binary` | Binary comparison |
-| Analysis | `compare-swot` | SWOT analysis |
-| Quadrant | `quadrant-quarter` | Quadrant chart |
-| Chart | `chart-bar` | Bar chart |
-| Chart | `chart-column` | Column chart |
-| Chart | `chart-line` | Line chart |
-| Chart | `chart-pie` | Pie chart |
-| Chart | `chart-doughnut` | Doughnut chart |
-| Chart | `chart-area` | Area chart |
-
-## Syntax Examples
-
-### Grid List
-```infographic
-infographic list-grid
-data
-  title Project Overview
-  items
-    - label Module A
-      desc Description of module A
-    - label Module B
-      desc Description of module B
-```
-
-### Binary Comparison
-```infographic
-infographic compare-binary
-data
-  title Pros vs Cons
-  items
-    - label Pros
-      children
-        - label Strong R&D
-          desc Technology leadership
-    - label Cons
-      children
-        - label Weak brand
-          desc Insufficient marketing
-```
-
-### Bar Chart
-```infographic
-infographic chart-bar
-data
-  title Quarterly Revenue
-  items
-    - label Q1
-      value 120
-    - label Q2
-      value 150
-```
-
-## Technical Details
-
-### Data URL Embedding
-```javascript
-// SVG to Base64 Data URL
-const svgData = new XMLSerializer().serializeToString(svg);
-const base64 = btoa(unescape(encodeURIComponent(svgData)));
-const dataUri = "data:image/svg+xml;base64," + base64;
-
-// Markdown image syntax
-const markdownImage = `![description](${dataUri})`;
-```
-
-### AntV toDataURL API
-```javascript
-// Export as SVG (recommended, supports embedded resources)
-const svgUrl = await instance.toDataURL({
-    type: 'svg',
-    embedResources: true
-});
-
-// Export as PNG (more compatible but larger)
-const pngUrl = await instance.toDataURL({
-    type: 'png',
-    dpr: 2
-});
-```
-
-## Notes
-
-1. **Browser Compatibility**: Requires modern browsers with ES6+ and Fetch API support
-2. **Network Dependency**: First use requires loading AntV library from CDN
-3. **Data URL Size**: Base64 encoding increases size by ~33%
-4. **Chinese Fonts**: SVG export embeds fonts for correct display
-
-## Related Resources
-
-- [AntV Infographic Documentation](https://infographic.antv.vision/)
-- [Infographic API Reference](https://infographic.antv.vision/reference/infographic-api)
-- [Infographic Syntax Guide](https://infographic.antv.vision/learn/infographic-syntax)
-
-## License
-
-MIT License

plugins/actions/js-render-poc/README_CN.md

@@ -1,174 +0,0 @@
-# 信息图转 Markdown
-
-> **版本:** 1.0.0
-
-AI 驱动的信息图生成器，在前端渲染 SVG 并以 Data URL 图片格式直接嵌入到 Markdown 中。
-
-## 概述
-
-这个插件结合了 AI 文本分析能力和 AntV Infographic 可视化引擎，生成精美的信息图并以 Markdown 图片格式直接嵌入到聊天消息中。
-
-### 工作原理
-
-```
-┌─────────────────────────────────────────────────────────────┐
-│                    Open WebUI 插件                           │
-├─────────────────────────────────────────────────────────────┤
-│  1. Python Action                                            │
-│     ├── 接收消息内容                                          │
-│     ├── 调用 LLM 生成 Infographic 语法                        │
-│     └── 发送 __event_call__ 执行前端 JS                      │
-├─────────────────────────────────────────────────────────────┤
-│  2. 浏览器 JS (通过 __event_call__)                          │
-│     ├── 动态加载 AntV Infographic 库                          │
-│     ├── 离屏渲染 SVG                                          │
-│     ├── 使用 toDataURL() 导出 Data URL                        │
-│     └── 通过 REST API 更新消息内容                            │
-├─────────────────────────────────────────────────────────────┤
-│  3. Markdown 渲染                                            │
-│     └── 显示 ![描述](data:image/svg+xml;base64,...)          │
-└─────────────────────────────────────────────────────────────┘
-```
-
-## 功能特点
-
-- 🤖 **AI 驱动**: 自动分析文本并选择最佳的信息图模板
-- 📊 **多种模板**: 支持 18+ 种信息图模板（列表、图表、对比等）
-- 🖼️ **自包含**: SVG/PNG 以 Data URL 嵌入，无外部依赖
-- 📝 **Markdown 原生**: 结果是纯 Markdown 图片，兼容任何平台
-- 🔄 **API 回写**: 通过 REST API 更新消息内容实现持久化
-
-## 目录中的插件
-
-### 1. `infographic_markdown.py` - 主插件 ⭐
-- **用途**: 生产使用
-- **功能**: 完整的 AI + AntV Infographic + Data URL 嵌入
-
-### 2. `infographic_markdown_cn.py` - 主插件（中文版）
-- **用途**: 生产使用
-- **功能**: 与英文版相同，界面文字为中文
-
-### 3. `js_render_poc.py` - 概念验证
-- **用途**: 学习和测试
-- **功能**: 简单的 SVG 创建演示，`__event_call__` 模式
-
-## 配置选项 (Valves)
-
-| 参数 | 类型 | 默认值 | 描述 |
-|------|------|--------|------|
-| `SHOW_STATUS` | bool | `true` | 是否显示操作状态 |
-| `MODEL_ID` | string | `""` | LLM 模型 ID（空则使用当前模型） |
-| `MIN_TEXT_LENGTH` | int | `50` | 最小文本长度要求 |
-| `MESSAGE_COUNT` | int | `1` | 用于生成的最近消息数量 |
-| `SVG_WIDTH` | int | `800` | 生成的 SVG 宽度（像素） |
-| `EXPORT_FORMAT` | string | `"svg"` | 导出格式：`svg` 或 `png` |
-
-## 支持的模板
-
-| 类别 | 模板名称 | 描述 |
-|------|----------|------|
-| 列表 | `list-grid` | 网格卡片 |
-| 列表 | `list-vertical` | 垂直列表 |
-| 树形 | `tree-vertical` | 垂直树 |
-| 树形 | `tree-horizontal` | 水平树 |
-| 思维导图 | `mindmap` | 思维导图 |
-| 流程 | `sequence-roadmap` | 路线图 |
-| 流程 | `sequence-zigzag` | 折线流程 |
-| 关系 | `relation-sankey` | 桑基图 |
-| 关系 | `relation-circle` | 圆形关系 |
-| 对比 | `compare-binary` | 二元对比 |
-| 分析 | `compare-swot` | SWOT 分析 |
-| 象限 | `quadrant-quarter` | 四象限图 |
-| 图表 | `chart-bar` | 条形图 |
-| 图表 | `chart-column` | 柱状图 |
-| 图表 | `chart-line` | 折线图 |
-| 图表 | `chart-pie` | 饼图 |
-| 图表 | `chart-doughnut` | 环形图 |
-| 图表 | `chart-area` | 面积图 |
-
-## 语法示例
-
-### 网格列表
-```infographic
-infographic list-grid
-data
-  title 项目概览
-  items
-    - label 模块一
-      desc 这是第一个模块的描述
-    - label 模块二
-      desc 这是第二个模块的描述
-```
-
-### 二元对比
-```infographic
-infographic compare-binary
-data
-  title 优劣对比
-  items
-    - label 优势
-      children
-        - label 研发能力强
-          desc 技术领先
-    - label 劣势
-      children
-        - label 品牌曝光不足
-          desc 营销力度不够
-```
-
-### 条形图
-```infographic
-infographic chart-bar
-data
-  title 季度收入
-  items
-    - label Q1
-      value 120
-    - label Q2
-      value 150
-```
-
-## 技术细节
-
-### Data URL 嵌入
-```javascript
-// SVG 转 Base64 Data URL
-const svgData = new XMLSerializer().serializeToString(svg);
-const base64 = btoa(unescape(encodeURIComponent(svgData)));
-const dataUri = "data:image/svg+xml;base64," + base64;
-
-// Markdown 图片语法
-const markdownImage = `![描述](${dataUri})`;
-```
-
-### AntV toDataURL API
-```javascript
-// 导出 SVG（推荐，支持嵌入资源）
-const svgUrl = await instance.toDataURL({
-    type: 'svg',
-    embedResources: true
-});
-
-// 导出 PNG（更兼容但体积更大）
-const pngUrl = await instance.toDataURL({
-    type: 'png',
-    dpr: 2
-});
-```
-
-## 注意事项
-
-1. **浏览器兼容性**: 需要现代浏览器支持 ES6+ 和 Fetch API
-2. **网络依赖**: 首次使用需要从 CDN 加载 AntV Infographic 库
-3. **Data URL 大小**: Base64 编码会增加约 33% 的体积
-4. **中文字体**: SVG 导出时会嵌入字体以确保正确显示
-
-## 相关资源
-
-- [AntV Infographic 官方文档](https://infographic.antv.vision/)
-- [Infographic API 参考](https://infographic.antv.vision/reference/infographic-api)
-- [Infographic 语法规范](https://infographic.antv.vision/learn/infographic-syntax)
-
-## 许可证
-
-MIT License

plugins/actions/js-render-poc/infographic_markdown.py

@@ -1,592 +0,0 @@
-"""
-title: 📊 Infographic to Markdown
-author: Fu-Jie
-version: 1.0.0
-description: AI生成信息图语法，前端渲染SVG并转换为Markdown图片格式嵌入消息。支持AntV Infographic模板。
-"""
-
-import time
-import json
-import logging
-import re
-from typing import Optional, Callable, Awaitable, Any, Dict
-from pydantic import BaseModel, Field
-from fastapi import Request
-from datetime import datetime
-
-from open_webui.utils.chat import generate_chat_completion
-from open_webui.models.users import Users
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# =================================================================
-# LLM Prompts
-# =================================================================
-
-SYSTEM_PROMPT_INFOGRAPHIC = """
-You are a professional infographic design expert who can analyze user-provided text content and convert it into AntV Infographic syntax format.
-
-## Infographic Syntax Specification
-
-Infographic syntax is a Mermaid-like declarative syntax for describing infographic templates, data, and themes.
-
-### Syntax Rules
-- Entry uses `infographic <template-name>`
-- Key-value pairs are separated by spaces, **absolutely NO colons allowed**
-- Use two spaces for indentation
-- Object arrays use `-` with line breaks
-
-⚠️ **IMPORTANT WARNING: This is NOT YAML format!**
-- ❌ Wrong: `children:` `items:` `data:` (with colons)
-- ✅ Correct: `children` `items` `data` (without colons)
-
-### Template Library & Selection Guide
-
-Choose the most appropriate template based on the content structure:
-
-#### 1. List & Hierarchy
-- **List**: `list-grid` (Grid Cards), `list-vertical` (Vertical List)
-- **Tree**: `tree-vertical` (Vertical Tree), `tree-horizontal` (Horizontal Tree)
-- **Mindmap**: `mindmap` (Mind Map)
-
-#### 2. Sequence & Relationship
-- **Process**: `sequence-roadmap` (Roadmap), `sequence-zigzag` (Zigzag Process)
-- **Relationship**: `relation-sankey` (Sankey Diagram), `relation-circle` (Circular)
-
-#### 3. Comparison & Analysis
-- **Comparison**: `compare-binary` (Binary Comparison)
-- **Analysis**: `compare-swot` (SWOT Analysis), `quadrant-quarter` (Quadrant Chart)
-
-#### 4. Charts & Data
-- **Charts**: `chart-bar`, `chart-column`, `chart-line`, `chart-pie`, `chart-doughnut`, `chart-area`
-
-### Data Structure Examples
-
-#### A. Standard List/Tree
-```infographic
-infographic list-grid
-data
-  title Project Modules
-  items
-    - label Module A
-      desc Description of A
-    - label Module B
-      desc Description of B
-```
-
-#### B. Binary Comparison
-```infographic
-infographic compare-binary
-data
-  title Advantages vs Disadvantages
-  items
-    - label Advantages
-      children
-        - label Strong R&D
-          desc Leading technology
-    - label Disadvantages
-      children
-        - label Weak brand
-          desc Insufficient marketing
-```
-
-#### C. Charts
-```infographic
-infographic chart-bar
-data
-  title Quarterly Revenue
-  items
-    - label Q1
-      value 120
-    - label Q2
-      value 150
-```
-
-### Common Data Fields
-- `label`: Main title/label (Required)
-- `desc`: Description text (max 30 Chinese chars / 60 English chars for `list-grid`)
-- `value`: Numeric value (for charts)
-- `children`: Nested items
-
-## Output Requirements
-1. **Language**: Output content in the user's language.
-2. **Format**: Wrap output in ```infographic ... ```.
-3. **No Colons**: Do NOT use colons after keys.
-4. **Indentation**: Use 2 spaces.
-"""
-
-USER_PROMPT_GENERATE = """
-Please analyze the following text content and convert its core information into AntV Infographic syntax format.
-
----
-**User Context:**
-User Name: {user_name}
-Current Date/Time: {current_date_time_str}
-User Language: {user_language}
----
-
-**Text Content:**
-{long_text_content}
-
-Please select the most appropriate infographic template based on text characteristics and output standard infographic syntax.
-
-**Important Note:** 
-- If using `list-grid` format, ensure each card's `desc` description is limited to **maximum 30 Chinese characters** (or **approximately 60 English characters**).
-- Descriptions should be concise and highlight key points.
-"""
-
-
-class Action:
-    class Valves(BaseModel):
-        SHOW_STATUS: bool = Field(
-            default=True, description="Show operation status updates in chat interface."
-        )
-        MODEL_ID: str = Field(
-            default="",
-            description="LLM model ID for text analysis. If empty, uses current conversation model.",
-        )
-        MIN_TEXT_LENGTH: int = Field(
-            default=50,
-            description="Minimum text length (characters) required for infographic analysis.",
-        )
-        MESSAGE_COUNT: int = Field(
-            default=1,
-            description="Number of recent messages to use for generation.",
-        )
-        SVG_WIDTH: int = Field(
-            default=800,
-            description="Width of generated SVG in pixels.",
-        )
-        EXPORT_FORMAT: str = Field(
-            default="svg",
-            description="Export format: 'svg' or 'png'.",
-        )
-
-    def __init__(self):
-        self.valves = self.Valves()
-
-    def _extract_chat_id(self, body: dict, metadata: Optional[dict]) -> str:
-        """Extract chat_id from body or metadata"""
-        if isinstance(body, dict):
-            chat_id = body.get("chat_id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
-
-            body_metadata = body.get("metadata", {})
-            if isinstance(body_metadata, dict):
-                chat_id = body_metadata.get("chat_id")
-                if isinstance(chat_id, str) and chat_id.strip():
-                    return chat_id.strip()
-
-        if isinstance(metadata, dict):
-            chat_id = metadata.get("chat_id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
-
-        return ""
-
-    def _extract_message_id(self, body: dict, metadata: Optional[dict]) -> str:
-        """Extract message_id from body or metadata"""
-        if isinstance(body, dict):
-            message_id = body.get("id")
-            if isinstance(message_id, str) and message_id.strip():
-                return message_id.strip()
-
-            body_metadata = body.get("metadata", {})
-            if isinstance(body_metadata, dict):
-                message_id = body_metadata.get("message_id")
-                if isinstance(message_id, str) and message_id.strip():
-                    return message_id.strip()
-
-        if isinstance(metadata, dict):
-            message_id = metadata.get("message_id")
-            if isinstance(message_id, str) and message_id.strip():
-                return message_id.strip()
-
-        return ""
-
-    def _extract_infographic_syntax(self, llm_output: str) -> str:
-        """Extract infographic syntax from LLM output"""
-        match = re.search(r"```infographic\s*(.*?)\s*```", llm_output, re.DOTALL)
-        if match:
-            return match.group(1).strip()
-        else:
-            logger.warning("LLM output did not follow expected format, treating entire output as syntax.")
-            return llm_output.strip()
-
-    def _extract_text_content(self, content) -> str:
-        """Extract text from message content, supporting multimodal formats"""
-        if isinstance(content, str):
-            return content
-        elif isinstance(content, list):
-            text_parts = []
-            for item in content:
-                if isinstance(item, dict) and item.get("type") == "text":
-                    text_parts.append(item.get("text", ""))
-                elif isinstance(item, str):
-                    text_parts.append(item)
-            return "\n".join(text_parts)
-        return str(content) if content else ""
-
-    async def _emit_status(self, emitter, description: str, done: bool = False):
-        """Send status update event"""
-        if self.valves.SHOW_STATUS and emitter:
-            await emitter(
-                {"type": "status", "data": {"description": description, "done": done}}
-            )
-
-    def _generate_js_code(
-        self,
-        unique_id: str,
-        chat_id: str,
-        message_id: str,
-        infographic_syntax: str,
-        svg_width: int,
-        export_format: str,
-    ) -> str:
-        """Generate JavaScript code for frontend SVG rendering"""
-        
-        # Escape the syntax for JS embedding
-        syntax_escaped = (
-            infographic_syntax
-            .replace("\\", "\\\\")
-            .replace("`", "\\`")
-            .replace("${", "\\${")
-            .replace("</script>", "<\\/script>")
-        )
-        
-        # Template mapping (same as infographic.py)
-        template_mapping_js = """
-        const TEMPLATE_MAPPING = {
-            'list-grid': 'list-grid-compact-card',
-            'list-vertical': 'list-column-simple-vertical-arrow',
-            'tree-vertical': 'hierarchy-tree-tech-style-capsule-item',
-            'tree-horizontal': 'hierarchy-tree-lr-tech-style-capsule-item',
-            'mindmap': 'hierarchy-mindmap-branch-gradient-capsule-item',
-            'sequence-roadmap': 'sequence-roadmap-vertical-simple',
-            'sequence-zigzag': 'sequence-horizontal-zigzag-simple',
-            'sequence-horizontal': 'sequence-horizontal-zigzag-simple',
-            'relation-sankey': 'relation-sankey-simple',
-            'relation-circle': 'relation-circle-icon-badge',
-            'compare-binary': 'compare-binary-horizontal-simple-vs',
-            'compare-swot': 'compare-swot',
-            'quadrant-quarter': 'quadrant-quarter-simple-card',
-            'statistic-card': 'list-grid-compact-card',
-            'chart-bar': 'chart-bar-plain-text',
-            'chart-column': 'chart-column-simple',
-            'chart-line': 'chart-line-plain-text',
-            'chart-area': 'chart-area-simple',
-            'chart-pie': 'chart-pie-plain-text',
-            'chart-doughnut': 'chart-pie-donut-plain-text'
-        };
-        """
-        
-        return f"""
-(async function() {{
-    const uniqueId = "{unique_id}";
-    const chatId = "{chat_id}";
-    const messageId = "{message_id}";
-    const svgWidth = {svg_width};
-    const exportFormat = "{export_format}";
-    
-    console.log("[Infographic Markdown] Starting render...");
-    console.log("[Infographic Markdown] chatId:", chatId, "messageId:", messageId);
-    
-    try {{
-        // Load AntV Infographic if not loaded
-        if (typeof AntVInfographic === 'undefined') {{
-            console.log("[Infographic Markdown] Loading AntV Infographic library...");
-            await new Promise((resolve, reject) => {{
-                const script = document.createElement('script');
-                script.src = 'https://unpkg.com/@antv/infographic@latest/dist/infographic.min.js';
-                script.onload = resolve;
-                script.onerror = reject;
-                document.head.appendChild(script);
-            }});
-            console.log("[Infographic Markdown] Library loaded.");
-        }}
-        
-        const {{ Infographic }} = AntVInfographic;
-        
-        // Get infographic syntax
-        let syntaxContent = `{syntax_escaped}`;
-        console.log("[Infographic Markdown] Original syntax:", syntaxContent.substring(0, 200) + "...");
-        
-        // Clean up syntax
-        const backtick = String.fromCharCode(96);
-        const prefix = backtick + backtick + backtick + 'infographic';
-        const simplePrefix = backtick + backtick + backtick;
-        
-        if (syntaxContent.toLowerCase().startsWith(prefix)) {{
-            syntaxContent = syntaxContent.substring(prefix.length).trim();
-        }} else if (syntaxContent.startsWith(simplePrefix)) {{
-            syntaxContent = syntaxContent.substring(simplePrefix.length).trim();
-        }}
-        
-        if (syntaxContent.endsWith(simplePrefix)) {{
-            syntaxContent = syntaxContent.substring(0, syntaxContent.length - simplePrefix.length).trim();
-        }}
-        
-        // Fix colons after keywords
-        syntaxContent = syntaxContent.replace(/^(data|items|children|theme|config):/gm, '$1');
-        syntaxContent = syntaxContent.replace(/(\\s)(children|items):/g, '$1$2');
-        
-        // Ensure infographic prefix
-        if (!syntaxContent.trim().toLowerCase().startsWith('infographic')) {{
-            syntaxContent = 'infographic list-grid\\n' + syntaxContent;
-        }}
-        
-        // Apply template mapping
-        {template_mapping_js}
-        
-        for (const [key, value] of Object.entries(TEMPLATE_MAPPING)) {{
-            const regex = new RegExp(`infographic\\\\s+${{key}}(?=\\\\s|$)`, 'i');
-            if (regex.test(syntaxContent)) {{
-                console.log(`[Infographic Markdown] Auto-mapping: ${{key}} -> ${{value}}`);
-                syntaxContent = syntaxContent.replace(regex, `infographic ${{value}}`);
-                break;
-            }}
-        }}
-        
-        console.log("[Infographic Markdown] Cleaned syntax:", syntaxContent.substring(0, 200) + "...");
-        
-        // Create offscreen container
-        const container = document.createElement('div');
-        container.id = 'infographic-offscreen-' + uniqueId;
-        container.style.cssText = 'position:absolute;left:-9999px;top:-9999px;width:' + svgWidth + 'px;';
-        document.body.appendChild(container);
-        
-        // Create and render infographic
-        const instance = new Infographic({{
-            container: '#' + container.id,
-            width: svgWidth,
-            padding: 24,
-        }});
-        
-        console.log("[Infographic Markdown] Rendering infographic...");
-        instance.render(syntaxContent);
-        
-        // Wait for render and export
-        await new Promise(resolve => setTimeout(resolve, 1000));
-        
-        let dataUrl;
-        if (exportFormat === 'png') {{
-            dataUrl = await instance.toDataURL({{ type: 'png', dpr: 2 }});
-        }} else {{
-            dataUrl = await instance.toDataURL({{ type: 'svg', embedResources: true }});
-        }}
-        
-        console.log("[Infographic Markdown] Data URL generated, length:", dataUrl.length);
-        
-        // Cleanup
-        instance.destroy();
-        document.body.removeChild(container);
-        
-        // Generate markdown image
-        const markdownImage = `![📊 AI 生成的信息图](${{dataUrl}})`;
-        
-        // Update message via API
-        if (chatId && messageId) {{
-            const token = localStorage.getItem("token");
-            
-            // Get current message content
-            const getResponse = await fetch(`/api/v1/chats/${{chatId}}`, {{
-                method: "GET",
-                headers: {{ "Authorization": `Bearer ${{token}}` }}
-            }});
-            
-            if (!getResponse.ok) {{
-                throw new Error("Failed to get chat data: " + getResponse.status);
-            }}
-            
-            const chatData = await getResponse.json();
-            let originalContent = "";
-            
-            if (chatData.chat && chatData.chat.messages) {{
-                const targetMsg = chatData.chat.messages.find(m => m.id === messageId);
-                if (targetMsg && targetMsg.content) {{
-                    originalContent = targetMsg.content;
-                }}
-            }}
-            
-            // Remove existing infographic images
-            const infographicPattern = /\\n*!\\[📊[^\\]]*\\]\\(data:image\\/[^)]+\\)/g;
-            let cleanedContent = originalContent.replace(infographicPattern, "");
-            cleanedContent = cleanedContent.replace(/\\n{{3,}}/g, "\\n\\n").trim();
-            
-            // Append new image
-            const newContent = cleanedContent + "\\n\\n" + markdownImage;
-            
-            // Update message
-            const updateResponse = await fetch(`/api/v1/chats/${{chatId}}/messages/${{messageId}}/event`, {{
-                method: "POST",
-                headers: {{
-                    "Content-Type": "application/json",
-                    "Authorization": `Bearer ${{token}}`
-                }},
-                body: JSON.stringify({{
-                    type: "chat:message",
-                    data: {{ content: newContent }}
-                }})
-            }});
-            
-            if (updateResponse.ok) {{
-                console.log("[Infographic Markdown] ✅ Message updated successfully!");
-            }} else {{
-                console.error("[Infographic Markdown] API error:", updateResponse.status);
-            }}
-        }} else {{
-            console.warn("[Infographic Markdown] ⚠️ Missing chatId or messageId");
-        }}
-        
-    }} catch (error) {{
-        console.error("[Infographic Markdown] Error:", error);
-    }}
-}})();
-"""
-
-    async def action(
-        self,
-        body: dict,
-        __user__: dict = None,
-        __event_emitter__=None,
-        __event_call__: Optional[Callable[[Any], Awaitable[None]]] = None,
-        __metadata__: Optional[dict] = None,
-        __request__: Request = None,
-    ) -> dict:
-        """
-        Generate infographic using AntV and embed as Markdown image.
-        """
-        logger.info("Action: Infographic to Markdown started")
-
-        # Get user information
-        if isinstance(__user__, (list, tuple)):
-            user_language = __user__[0].get("language", "en") if __user__ else "en"
-            user_name = __user__[0].get("name", "User") if __user__[0] else "User"
-            user_id = __user__[0].get("id", "unknown_user") if __user__ else "unknown_user"
-        elif isinstance(__user__, dict):
-            user_language = __user__.get("language", "en")
-            user_name = __user__.get("name", "User")
-            user_id = __user__.get("id", "unknown_user")
-        else:
-            user_language = "en"
-            user_name = "User"
-            user_id = "unknown_user"
-
-        # Get current time
-        now = datetime.now()
-        current_date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
-
-        try:
-            messages = body.get("messages", [])
-            if not messages:
-                raise ValueError("No messages available.")
-
-            # Get recent messages
-            message_count = min(self.valves.MESSAGE_COUNT, len(messages))
-            recent_messages = messages[-message_count:]
-
-            # Aggregate content
-            aggregated_parts = []
-            for msg in recent_messages:
-                text_content = self._extract_text_content(msg.get("content"))
-                if text_content:
-                    aggregated_parts.append(text_content)
-
-            if not aggregated_parts:
-                raise ValueError("No text content found in messages.")
-
-            long_text_content = "\n\n---\n\n".join(aggregated_parts)
-
-            # Remove existing HTML blocks
-            parts = re.split(r"```html.*?```", long_text_content, flags=re.DOTALL)
-            clean_content = ""
-            for part in reversed(parts):
-                if part.strip():
-                    clean_content = part.strip()
-                    break
-
-            if not clean_content:
-                clean_content = long_text_content.strip()
-
-            # Check minimum length
-            if len(clean_content) < self.valves.MIN_TEXT_LENGTH:
-                await self._emit_status(
-                    __event_emitter__,
-                    f"⚠️ 内容太短 ({len(clean_content)} 字符)，至少需要 {self.valves.MIN_TEXT_LENGTH} 字符",
-                    True,
-                )
-                return body
-
-            await self._emit_status(__event_emitter__, "📊 正在分析内容...", False)
-
-            # Generate infographic syntax via LLM
-            formatted_user_prompt = USER_PROMPT_GENERATE.format(
-                user_name=user_name,
-                current_date_time_str=current_date_time_str,
-                user_language=user_language,
-                long_text_content=clean_content,
-            )
-
-            target_model = self.valves.MODEL_ID or body.get("model")
-
-            llm_payload = {
-                "model": target_model,
-                "messages": [
-                    {"role": "system", "content": SYSTEM_PROMPT_INFOGRAPHIC},
-                    {"role": "user", "content": formatted_user_prompt},
-                ],
-                "stream": False,
-            }
-
-            user_obj = Users.get_user_by_id(user_id)
-            if not user_obj:
-                raise ValueError(f"Unable to get user object: {user_id}")
-
-            await self._emit_status(__event_emitter__, "📊 AI 正在生成信息图语法...", False)
-
-            llm_response = await generate_chat_completion(__request__, llm_payload, user_obj)
-
-            if not llm_response or "choices" not in llm_response or not llm_response["choices"]:
-                raise ValueError("Invalid LLM response.")
-
-            assistant_content = llm_response["choices"][0]["message"]["content"]
-            infographic_syntax = self._extract_infographic_syntax(assistant_content)
-
-            logger.info(f"Generated syntax: {infographic_syntax[:200]}...")
-
-            # Extract IDs for API callback
-            chat_id = self._extract_chat_id(body, __metadata__)
-            message_id = self._extract_message_id(body, __metadata__)
-            unique_id = f"ig_{int(time.time() * 1000)}"
-
-            await self._emit_status(__event_emitter__, "📊 正在渲染 SVG...", False)
-
-            # Execute JS to render and embed
-            if __event_call__:
-                js_code = self._generate_js_code(
-                    unique_id=unique_id,
-                    chat_id=chat_id,
-                    message_id=message_id,
-                    infographic_syntax=infographic_syntax,
-                    svg_width=self.valves.SVG_WIDTH,
-                    export_format=self.valves.EXPORT_FORMAT,
-                )
-
-                await __event_call__(
-                    {
-                        "type": "execute",
-                        "data": {"code": js_code},
-                    }
-                )
-
-            await self._emit_status(__event_emitter__, "✅ 信息图生成完成！", True)
-            logger.info("Infographic to Markdown completed")
-
-        except Exception as e:
-            error_message = f"Infographic generation failed: {str(e)}"
-            logger.error(error_message, exc_info=True)
-            await self._emit_status(__event_emitter__, f"❌ {error_message}", True)
-
-        return body

plugins/actions/js-render-poc/infographic_markdown_cn.py

@@ -1,592 +0,0 @@
-"""
-title: 📊 信息图转 Markdown
-author: Fu-Jie
-version: 1.0.0
-description: AI 生成信息图语法，前端渲染 SVG 并转换为 Markdown 图片格式嵌入消息。支持 AntV Infographic 模板。
-"""
-
-import time
-import json
-import logging
-import re
-from typing import Optional, Callable, Awaitable, Any, Dict
-from pydantic import BaseModel, Field
-from fastapi import Request
-from datetime import datetime
-
-from open_webui.utils.chat import generate_chat_completion
-from open_webui.models.users import Users
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# =================================================================
-# LLM 提示词
-# =================================================================
-
-SYSTEM_PROMPT_INFOGRAPHIC = """
-你是一位专业的信息图设计专家，能够分析用户提供的文本内容并将其转换为 AntV Infographic 语法格式。
-
-## 信息图语法规范
-
-信息图语法是一种类似 Mermaid 的声明式语法，用于描述信息图模板、数据和主题。
-
-### 语法规则
-- 入口使用 `infographic <模板名>`
-- 键值对用空格分隔，**绝对不允许使用冒号**
-- 使用两个空格缩进
-- 对象数组使用 `-` 加换行
-
-⚠️ **重要警告：这不是 YAML 格式！**
-- ❌ 错误：`children:` `items:` `data:`（带冒号）
-- ✅ 正确：`children` `items` `data`（不带冒号）
-
-### 模板库与选择指南
-
-根据内容结构选择最合适的模板：
-
-#### 1. 列表与层级
-- **列表**：`list-grid`（网格卡片）、`list-vertical`（垂直列表）
-- **树形**：`tree-vertical`（垂直树）、`tree-horizontal`（水平树）
-- **思维导图**：`mindmap`（思维导图）
-
-#### 2. 序列与关系
-- **流程**：`sequence-roadmap`（路线图）、`sequence-zigzag`（折线流程）
-- **关系**：`relation-sankey`（桑基图）、`relation-circle`（圆形关系）
-
-#### 3. 对比与分析
-- **对比**：`compare-binary`（二元对比）
-- **分析**：`compare-swot`（SWOT 分析）、`quadrant-quarter`（象限图）
-
-#### 4. 图表与数据
-- **图表**：`chart-bar`、`chart-column`、`chart-line`、`chart-pie`、`chart-doughnut`、`chart-area`
-
-### 数据结构示例
-
-#### A. 标准列表/树形
-```infographic
-infographic list-grid
-data
-  title 项目模块
-  items
-    - label 模块 A
-      desc 模块 A 的描述
-    - label 模块 B
-      desc 模块 B 的描述
-```
-
-#### B. 二元对比
-```infographic
-infographic compare-binary
-data
-  title 优势与劣势
-  items
-    - label 优势
-      children
-        - label 研发能力强
-          desc 技术领先
-    - label 劣势
-      children
-        - label 品牌曝光弱
-          desc 营销不足
-```
-
-#### C. 图表
-```infographic
-infographic chart-bar
-data
-  title 季度收入
-  items
-    - label Q1
-      value 120
-    - label Q2
-      value 150
-```
-
-### 常用数据字段
-- `label`：主标题/标签（必填）
-- `desc`：描述文字（`list-grid` 最多 30 个中文字符）
-- `value`：数值（用于图表）
-- `children`：嵌套项
-
-## 输出要求
-1. **语言**：使用用户的语言输出内容。
-2. **格式**：用 ```infographic ... ``` 包裹输出。
-3. **无冒号**：键后面不要使用冒号。
-4. **缩进**：使用 2 个空格。
-"""
-
-USER_PROMPT_GENERATE = """
-请分析以下文本内容，将其核心信息转换为 AntV Infographic 语法格式。
-
----
-**用户上下文：**
-用户名：{user_name}
-当前时间：{current_date_time_str}
-用户语言：{user_language}
----
-
-**文本内容：**
-{long_text_content}
-
-请根据文本特征选择最合适的信息图模板，输出标准的信息图语法。
-
-**重要提示：** 
-- 如果使用 `list-grid` 格式，确保每个卡片的 `desc` 描述限制在 **最多 30 个中文字符**。
-- 描述应简洁，突出重点。
-"""
-
-
-class Action:
-    class Valves(BaseModel):
-        SHOW_STATUS: bool = Field(
-            default=True, description="在聊天界面显示操作状态更新。"
-        )
-        MODEL_ID: str = Field(
-            default="",
-            description="用于文本分析的 LLM 模型 ID。留空则使用当前对话模型。",
-        )
-        MIN_TEXT_LENGTH: int = Field(
-            default=50,
-            description="信息图分析所需的最小文本长度（字符数）。",
-        )
-        MESSAGE_COUNT: int = Field(
-            default=1,
-            description="用于生成的最近消息数量。",
-        )
-        SVG_WIDTH: int = Field(
-            default=800,
-            description="生成的 SVG 宽度（像素）。",
-        )
-        EXPORT_FORMAT: str = Field(
-            default="svg",
-            description="导出格式：'svg' 或 'png'。",
-        )
-
-    def __init__(self):
-        self.valves = self.Valves()
-
-    def _extract_chat_id(self, body: dict, metadata: Optional[dict]) -> str:
-        """从 body 或 metadata 中提取 chat_id"""
-        if isinstance(body, dict):
-            chat_id = body.get("chat_id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
-
-            body_metadata = body.get("metadata", {})
-            if isinstance(body_metadata, dict):
-                chat_id = body_metadata.get("chat_id")
-                if isinstance(chat_id, str) and chat_id.strip():
-                    return chat_id.strip()
-
-        if isinstance(metadata, dict):
-            chat_id = metadata.get("chat_id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
-
-        return ""
-
-    def _extract_message_id(self, body: dict, metadata: Optional[dict]) -> str:
-        """从 body 或 metadata 中提取 message_id"""
-        if isinstance(body, dict):
-            message_id = body.get("id")
-            if isinstance(message_id, str) and message_id.strip():
-                return message_id.strip()
-
-            body_metadata = body.get("metadata", {})
-            if isinstance(body_metadata, dict):
-                message_id = body_metadata.get("message_id")
-                if isinstance(message_id, str) and message_id.strip():
-                    return message_id.strip()
-
-        if isinstance(metadata, dict):
-            message_id = metadata.get("message_id")
-            if isinstance(message_id, str) and message_id.strip():
-                return message_id.strip()
-
-        return ""
-
-    def _extract_infographic_syntax(self, llm_output: str) -> str:
-        """从 LLM 输出中提取信息图语法"""
-        match = re.search(r"```infographic\s*(.*?)\s*```", llm_output, re.DOTALL)
-        if match:
-            return match.group(1).strip()
-        else:
-            logger.warning("LLM 输出未遵循预期格式，将整个输出作为语法处理。")
-            return llm_output.strip()
-
-    def _extract_text_content(self, content) -> str:
-        """从消息内容中提取文本，支持多模态格式"""
-        if isinstance(content, str):
-            return content
-        elif isinstance(content, list):
-            text_parts = []
-            for item in content:
-                if isinstance(item, dict) and item.get("type") == "text":
-                    text_parts.append(item.get("text", ""))
-                elif isinstance(item, str):
-                    text_parts.append(item)
-            return "\n".join(text_parts)
-        return str(content) if content else ""
-
-    async def _emit_status(self, emitter, description: str, done: bool = False):
-        """发送状态更新事件"""
-        if self.valves.SHOW_STATUS and emitter:
-            await emitter(
-                {"type": "status", "data": {"description": description, "done": done}}
-            )
-
-    def _generate_js_code(
-        self,
-        unique_id: str,
-        chat_id: str,
-        message_id: str,
-        infographic_syntax: str,
-        svg_width: int,
-        export_format: str,
-    ) -> str:
-        """生成用于前端 SVG 渲染的 JavaScript 代码"""
-        
-        # 转义语法以便嵌入 JS
-        syntax_escaped = (
-            infographic_syntax
-            .replace("\\", "\\\\")
-            .replace("`", "\\`")
-            .replace("${", "\\${")
-            .replace("</script>", "<\\/script>")
-        )
-        
-        # 模板映射
-        template_mapping_js = """
-        const TEMPLATE_MAPPING = {
-            'list-grid': 'list-grid-compact-card',
-            'list-vertical': 'list-column-simple-vertical-arrow',
-            'tree-vertical': 'hierarchy-tree-tech-style-capsule-item',
-            'tree-horizontal': 'hierarchy-tree-lr-tech-style-capsule-item',
-            'mindmap': 'hierarchy-mindmap-branch-gradient-capsule-item',
-            'sequence-roadmap': 'sequence-roadmap-vertical-simple',
-            'sequence-zigzag': 'sequence-horizontal-zigzag-simple',
-            'sequence-horizontal': 'sequence-horizontal-zigzag-simple',
-            'relation-sankey': 'relation-sankey-simple',
-            'relation-circle': 'relation-circle-icon-badge',
-            'compare-binary': 'compare-binary-horizontal-simple-vs',
-            'compare-swot': 'compare-swot',
-            'quadrant-quarter': 'quadrant-quarter-simple-card',
-            'statistic-card': 'list-grid-compact-card',
-            'chart-bar': 'chart-bar-plain-text',
-            'chart-column': 'chart-column-simple',
-            'chart-line': 'chart-line-plain-text',
-            'chart-area': 'chart-area-simple',
-            'chart-pie': 'chart-pie-plain-text',
-            'chart-doughnut': 'chart-pie-donut-plain-text'
-        };
-        """
-        
-        return f"""
-(async function() {{
-    const uniqueId = "{unique_id}";
-    const chatId = "{chat_id}";
-    const messageId = "{message_id}";
-    const svgWidth = {svg_width};
-    const exportFormat = "{export_format}";
-    
-    console.log("[信息图 Markdown] 开始渲染...");
-    console.log("[信息图 Markdown] chatId:", chatId, "messageId:", messageId);
-    
-    try {{
-        // 加载 AntV Infographic（如果尚未加载）
-        if (typeof AntVInfographic === 'undefined') {{
-            console.log("[信息图 Markdown] 正在加载 AntV Infographic 库...");
-            await new Promise((resolve, reject) => {{
-                const script = document.createElement('script');
-                script.src = 'https://unpkg.com/@antv/infographic@latest/dist/infographic.min.js';
-                script.onload = resolve;
-                script.onerror = reject;
-                document.head.appendChild(script);
-            }});
-            console.log("[信息图 Markdown] 库加载完成。");
-        }}
-        
-        const {{ Infographic }} = AntVInfographic;
-        
-        // 获取信息图语法
-        let syntaxContent = `{syntax_escaped}`;
-        console.log("[信息图 Markdown] 原始语法:", syntaxContent.substring(0, 200) + "...");
-        
-        // 清理语法
-        const backtick = String.fromCharCode(96);
-        const prefix = backtick + backtick + backtick + 'infographic';
-        const simplePrefix = backtick + backtick + backtick;
-        
-        if (syntaxContent.toLowerCase().startsWith(prefix)) {{
-            syntaxContent = syntaxContent.substring(prefix.length).trim();
-        }} else if (syntaxContent.startsWith(simplePrefix)) {{
-            syntaxContent = syntaxContent.substring(simplePrefix.length).trim();
-        }}
-        
-        if (syntaxContent.endsWith(simplePrefix)) {{
-            syntaxContent = syntaxContent.substring(0, syntaxContent.length - simplePrefix.length).trim();
-        }}
-        
-        // 修复关键字后的冒号
-        syntaxContent = syntaxContent.replace(/^(data|items|children|theme|config):/gm, '$1');
-        syntaxContent = syntaxContent.replace(/(\\s)(children|items):/g, '$1$2');
-        
-        // 确保有 infographic 前缀
-        if (!syntaxContent.trim().toLowerCase().startsWith('infographic')) {{
-            syntaxContent = 'infographic list-grid\\n' + syntaxContent;
-        }}
-        
-        // 应用模板映射
-        {template_mapping_js}
-        
-        for (const [key, value] of Object.entries(TEMPLATE_MAPPING)) {{
-            const regex = new RegExp(`infographic\\\\s+${{key}}(?=\\\\s|$)`, 'i');
-            if (regex.test(syntaxContent)) {{
-                console.log(`[信息图 Markdown] 自动映射: ${{key}} -> ${{value}}`);
-                syntaxContent = syntaxContent.replace(regex, `infographic ${{value}}`);
-                break;
-            }}
-        }}
-        
-        console.log("[信息图 Markdown] 清理后语法:", syntaxContent.substring(0, 200) + "...");
-        
-        // 创建离屏容器
-        const container = document.createElement('div');
-        container.id = 'infographic-offscreen-' + uniqueId;
-        container.style.cssText = 'position:absolute;left:-9999px;top:-9999px;width:' + svgWidth + 'px;';
-        document.body.appendChild(container);
-        
-        // 创建并渲染信息图
-        const instance = new Infographic({{
-            container: '#' + container.id,
-            width: svgWidth,
-            padding: 24,
-        }});
-        
-        console.log("[信息图 Markdown] 正在渲染信息图...");
-        instance.render(syntaxContent);
-        
-        // 等待渲染完成并导出
-        await new Promise(resolve => setTimeout(resolve, 1000));
-        
-        let dataUrl;
-        if (exportFormat === 'png') {{
-            dataUrl = await instance.toDataURL({{ type: 'png', dpr: 2 }});
-        }} else {{
-            dataUrl = await instance.toDataURL({{ type: 'svg', embedResources: true }});
-        }}
-        
-        console.log("[信息图 Markdown] Data URL 已生成，长度:", dataUrl.length);
-        
-        // 清理
-        instance.destroy();
-        document.body.removeChild(container);
-        
-        // 生成 Markdown 图片
-        const markdownImage = `![📊 AI 生成的信息图](${{dataUrl}})`;
-        
-        // 通过 API 更新消息
-        if (chatId && messageId) {{
-            const token = localStorage.getItem("token");
-            
-            // 获取当前消息内容
-            const getResponse = await fetch(`/api/v1/chats/${{chatId}}`, {{
-                method: "GET",
-                headers: {{ "Authorization": `Bearer ${{token}}` }}
-            }});
-            
-            if (!getResponse.ok) {{
-                throw new Error("获取对话数据失败: " + getResponse.status);
-            }}
-            
-            const chatData = await getResponse.json();
-            let originalContent = "";
-            
-            if (chatData.chat && chatData.chat.messages) {{
-                const targetMsg = chatData.chat.messages.find(m => m.id === messageId);
-                if (targetMsg && targetMsg.content) {{
-                    originalContent = targetMsg.content;
-                }}
-            }}
-            
-            // 移除已有的信息图图片
-            const infographicPattern = /\\n*!\\[📊[^\\]]*\\]\\(data:image\\/[^)]+\\)/g;
-            let cleanedContent = originalContent.replace(infographicPattern, "");
-            cleanedContent = cleanedContent.replace(/\\n{{3,}}/g, "\\n\\n").trim();
-            
-            // 追加新图片
-            const newContent = cleanedContent + "\\n\\n" + markdownImage;
-            
-            // 更新消息
-            const updateResponse = await fetch(`/api/v1/chats/${{chatId}}/messages/${{messageId}}/event`, {{
-                method: "POST",
-                headers: {{
-                    "Content-Type": "application/json",
-                    "Authorization": `Bearer ${{token}}`
-                }},
-                body: JSON.stringify({{
-                    type: "chat:message",
-                    data: {{ content: newContent }}
-                }})
-            }});
-            
-            if (updateResponse.ok) {{
-                console.log("[信息图 Markdown] ✅ 消息更新成功！");
-            }} else {{
-                console.error("[信息图 Markdown] API 错误:", updateResponse.status);
-            }}
-        }} else {{
-            console.warn("[信息图 Markdown] ⚠️ 缺少 chatId 或 messageId");
-        }}
-        
-    }} catch (error) {{
-        console.error("[信息图 Markdown] 错误:", error);
-    }}
-}})();
-"""
-
-    async def action(
-        self,
-        body: dict,
-        __user__: dict = None,
-        __event_emitter__=None,
-        __event_call__: Optional[Callable[[Any], Awaitable[None]]] = None,
-        __metadata__: Optional[dict] = None,
-        __request__: Request = None,
-    ) -> dict:
-        """
-        使用 AntV 生成信息图并作为 Markdown 图片嵌入。
-        """
-        logger.info("动作：信息图转 Markdown 开始")
-
-        # 获取用户信息
-        if isinstance(__user__, (list, tuple)):
-            user_language = __user__[0].get("language", "zh") if __user__ else "zh"
-            user_name = __user__[0].get("name", "用户") if __user__[0] else "用户"
-            user_id = __user__[0].get("id", "unknown_user") if __user__ else "unknown_user"
-        elif isinstance(__user__, dict):
-            user_language = __user__.get("language", "zh")
-            user_name = __user__.get("name", "用户")
-            user_id = __user__.get("id", "unknown_user")
-        else:
-            user_language = "zh"
-            user_name = "用户"
-            user_id = "unknown_user"
-
-        # 获取当前时间
-        now = datetime.now()
-        current_date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
-
-        try:
-            messages = body.get("messages", [])
-            if not messages:
-                raise ValueError("没有可用的消息。")
-
-            # 获取最近的消息
-            message_count = min(self.valves.MESSAGE_COUNT, len(messages))
-            recent_messages = messages[-message_count:]
-
-            # 聚合内容
-            aggregated_parts = []
-            for msg in recent_messages:
-                text_content = self._extract_text_content(msg.get("content"))
-                if text_content:
-                    aggregated_parts.append(text_content)
-
-            if not aggregated_parts:
-                raise ValueError("消息中未找到文本内容。")
-
-            long_text_content = "\n\n---\n\n".join(aggregated_parts)
-
-            # 移除已有的 HTML 块
-            parts = re.split(r"```html.*?```", long_text_content, flags=re.DOTALL)
-            clean_content = ""
-            for part in reversed(parts):
-                if part.strip():
-                    clean_content = part.strip()
-                    break
-
-            if not clean_content:
-                clean_content = long_text_content.strip()
-
-            # 检查最小长度
-            if len(clean_content) < self.valves.MIN_TEXT_LENGTH:
-                await self._emit_status(
-                    __event_emitter__,
-                    f"⚠️ 内容太短（{len(clean_content)} 字符），至少需要 {self.valves.MIN_TEXT_LENGTH} 字符",
-                    True,
-                )
-                return body
-
-            await self._emit_status(__event_emitter__, "📊 正在分析内容...", False)
-
-            # 通过 LLM 生成信息图语法
-            formatted_user_prompt = USER_PROMPT_GENERATE.format(
-                user_name=user_name,
-                current_date_time_str=current_date_time_str,
-                user_language=user_language,
-                long_text_content=clean_content,
-            )
-
-            target_model = self.valves.MODEL_ID or body.get("model")
-
-            llm_payload = {
-                "model": target_model,
-                "messages": [
-                    {"role": "system", "content": SYSTEM_PROMPT_INFOGRAPHIC},
-                    {"role": "user", "content": formatted_user_prompt},
-                ],
-                "stream": False,
-            }
-
-            user_obj = Users.get_user_by_id(user_id)
-            if not user_obj:
-                raise ValueError(f"无法获取用户对象：{user_id}")
-
-            await self._emit_status(__event_emitter__, "📊 AI 正在生成信息图语法...", False)
-
-            llm_response = await generate_chat_completion(__request__, llm_payload, user_obj)
-
-            if not llm_response or "choices" not in llm_response or not llm_response["choices"]:
-                raise ValueError("无效的 LLM 响应。")
-
-            assistant_content = llm_response["choices"][0]["message"]["content"]
-            infographic_syntax = self._extract_infographic_syntax(assistant_content)
-
-            logger.info(f"生成的语法：{infographic_syntax[:200]}...")
-
-            # 提取 API 回调所需的 ID
-            chat_id = self._extract_chat_id(body, __metadata__)
-            message_id = self._extract_message_id(body, __metadata__)
-            unique_id = f"ig_{int(time.time() * 1000)}"
-
-            await self._emit_status(__event_emitter__, "📊 正在渲染 SVG...", False)
-
-            # 执行 JS 进行渲染和嵌入
-            if __event_call__:
-                js_code = self._generate_js_code(
-                    unique_id=unique_id,
-                    chat_id=chat_id,
-                    message_id=message_id,
-                    infographic_syntax=infographic_syntax,
-                    svg_width=self.valves.SVG_WIDTH,
-                    export_format=self.valves.EXPORT_FORMAT,
-                )
-
-                await __event_call__(
-                    {
-                        "type": "execute",
-                        "data": {"code": js_code},
-                    }
-                )
-
-            await self._emit_status(__event_emitter__, "✅ 信息图生成完成！", True)
-            logger.info("信息图转 Markdown 完成")
-
-        except Exception as e:
-            error_message = f"信息图生成失败：{str(e)}"
-            logger.error(error_message, exc_info=True)
-            await self._emit_status(__event_emitter__, f"❌ {error_message}", True)
-
-        return body

plugins/actions/js-render-poc/js_render_poc.py

@@ -1,257 +0,0 @@
-"""
-title: JS Render PoC
-author: Fu-Jie
-version: 0.6.0
-description: Proof of concept for JS rendering + API write-back pattern. JS renders SVG and updates message via API.
-"""
-
-import time
-import json
-import logging
-from typing import Optional, Callable, Awaitable, Any
-from pydantic import BaseModel, Field
-from fastapi import Request
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-class Action:
-    class Valves(BaseModel):
-        pass
-
-    def __init__(self):
-        self.valves = self.Valves()
-
-    def _extract_chat_id(self, body: dict, metadata: Optional[dict]) -> str:
-        """Extract chat_id from body or metadata"""
-        if isinstance(body, dict):
-            # body["chat_id"] 是 chat_id
-            chat_id = body.get("chat_id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
-
-            body_metadata = body.get("metadata", {})
-            if isinstance(body_metadata, dict):
-                chat_id = body_metadata.get("chat_id")
-                if isinstance(chat_id, str) and chat_id.strip():
-                    return chat_id.strip()
-
-        if isinstance(metadata, dict):
-            chat_id = metadata.get("chat_id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
-
-        return ""
-
-    def _extract_message_id(self, body: dict, metadata: Optional[dict]) -> str:
-        """Extract message_id from body or metadata"""
-        if isinstance(body, dict):
-            # body["id"] 是 message_id
-            message_id = body.get("id")
-            if isinstance(message_id, str) and message_id.strip():
-                return message_id.strip()
-
-            body_metadata = body.get("metadata", {})
-            if isinstance(body_metadata, dict):
-                message_id = body_metadata.get("message_id")
-                if isinstance(message_id, str) and message_id.strip():
-                    return message_id.strip()
-
-        if isinstance(metadata, dict):
-            message_id = metadata.get("message_id")
-            if isinstance(message_id, str) and message_id.strip():
-                return message_id.strip()
-
-        return ""
-
-    async def action(
-        self,
-        body: dict,
-        __user__: dict = None,
-        __event_emitter__=None,
-        __event_call__: Optional[Callable[[Any], Awaitable[None]]] = None,
-        __metadata__: Optional[dict] = None,
-        __request__: Request = None,
-    ) -> dict:
-        """
-        PoC: Use __event_call__ to execute JS that renders SVG and updates message via API.
-        """
-        # 准备调试数据
-        body_for_log = {}
-        for k, v in body.items():
-            if k == "messages":
-                body_for_log[k] = f"[{len(v)} messages]"
-            else:
-                body_for_log[k] = v
-
-        body_json = json.dumps(body_for_log, ensure_ascii=False, default=str)
-        metadata_json = (
-            json.dumps(__metadata__, ensure_ascii=False, default=str)
-            if __metadata__
-            else "null"
-        )
-
-        # 转义 JSON 中的特殊字符以便嵌入 JS
-        body_json_escaped = (
-            body_json.replace("\\", "\\\\").replace("`", "\\`").replace("${", "\\${")
-        )
-        metadata_json_escaped = (
-            metadata_json.replace("\\", "\\\\")
-            .replace("`", "\\`")
-            .replace("${", "\\${")
-        )
-
-        chat_id = self._extract_chat_id(body, __metadata__)
-        message_id = self._extract_message_id(body, __metadata__)
-
-        unique_id = f"poc_{int(time.time() * 1000)}"
-
-        if __event_emitter__:
-            await __event_emitter__(
-                {
-                    "type": "status",
-                    "data": {"description": "🔄 正在渲染...", "done": False},
-                }
-            )
-
-        if __event_call__:
-            await __event_call__(
-                {
-                    "type": "execute",
-                    "data": {
-                        "code": f"""
-(async function() {{
-    const uniqueId = "{unique_id}";
-    const chatId = "{chat_id}";
-    const messageId = "{message_id}";
-    
-    // ===== DEBUG: 输出 Python 端的数据 =====
-    console.log("[JS Render PoC] ===== DEBUG INFO (from Python) =====");
-    console.log("[JS Render PoC] body:", `{body_json_escaped}`);
-    console.log("[JS Render PoC] __metadata__:", `{metadata_json_escaped}`);
-    console.log("[JS Render PoC] Extracted: chatId=", chatId, "messageId=", messageId);
-    console.log("[JS Render PoC] =========================================");
-    
-    try {{
-        console.log("[JS Render PoC] Starting SVG render...");
-        
-        // Create SVG
-        const svg = document.createElementNS("http://www.w3.org/2000/svg", "svg");
-        svg.setAttribute("width", "200");
-        svg.setAttribute("height", "200");
-        svg.setAttribute("viewBox", "0 0 200 200");
-        svg.setAttribute("xmlns", "http://www.w3.org/2000/svg");
-        
-        const defs = document.createElementNS("http://www.w3.org/2000/svg", "defs");
-        const gradient = document.createElementNS("http://www.w3.org/2000/svg", "linearGradient");
-        gradient.setAttribute("id", "grad-" + uniqueId);
-        gradient.innerHTML = `
-            <stop offset="0%" style="stop-color:#1e88e5;stop-opacity:1" />
-            <stop offset="100%" style="stop-color:#43a047;stop-opacity:1" />
-        `;
-        defs.appendChild(gradient);
-        svg.appendChild(defs);
-        
-        const circle = document.createElementNS("http://www.w3.org/2000/svg", "circle");
-        circle.setAttribute("cx", "100");
-        circle.setAttribute("cy", "100");
-        circle.setAttribute("r", "80");
-        circle.setAttribute("fill", `url(#grad-${{uniqueId}})`);
-        svg.appendChild(circle);
-        
-        const text = document.createElementNS("http://www.w3.org/2000/svg", "text");
-        text.setAttribute("x", "100");
-        text.setAttribute("y", "105");
-        text.setAttribute("text-anchor", "middle");
-        text.setAttribute("fill", "white");
-        text.setAttribute("font-size", "16");
-        text.setAttribute("font-weight", "bold");
-        text.textContent = "PoC Success!";
-        svg.appendChild(text);
-        
-        // Convert to Base64 Data URI
-        const svgData = new XMLSerializer().serializeToString(svg);
-        const base64 = btoa(unescape(encodeURIComponent(svgData)));
-        const dataUri = "data:image/svg+xml;base64," + base64;
-        
-        console.log("[JS Render PoC] SVG rendered, data URI length:", dataUri.length);
-        
-        // Call API - 完全替换方案（更稳定）
-        if (chatId && messageId) {{
-            const token = localStorage.getItem("token");
-            
-            // 1. 获取当前消息内容
-            const getResponse = await fetch(`/api/v1/chats/${{chatId}}`, {{
-                method: "GET",
-                headers: {{ "Authorization": `Bearer ${{token}}` }}
-            }});
-            
-            if (!getResponse.ok) {{
-                throw new Error("Failed to get chat data: " + getResponse.status);
-            }}
-            
-            const chatData = await getResponse.json();
-            console.log("[JS Render PoC] Got chat data");
-            
-            let originalContent = "";
-            if (chatData.chat && chatData.chat.messages) {{
-                const targetMsg = chatData.chat.messages.find(m => m.id === messageId);
-                if (targetMsg && targetMsg.content) {{
-                    originalContent = targetMsg.content;
-                    console.log("[JS Render PoC] Found original content, length:", originalContent.length);
-                }}
-            }}
-            
-            // 2. 移除已存在的 PoC 图片（如果有的话）
-            // 匹配 ![JS Render PoC 生成的 SVG](data:...) 格式
-            const pocImagePattern = /\\n*!\\[JS Render PoC[^\\]]*\\]\\(data:image\\/svg\\+xml;base64,[^)]+\\)/g;
-            let cleanedContent = originalContent.replace(pocImagePattern, "");
-            // 移除可能残留的多余空行
-            cleanedContent = cleanedContent.replace(/\\n{{3,}}/g, "\\n\\n").trim();
-            
-            if (cleanedContent !== originalContent) {{
-                console.log("[JS Render PoC] Removed existing PoC image(s)");
-            }}
-            
-            // 3. 添加新的 Markdown 图片
-            const markdownImage = `![JS Render PoC 生成的 SVG](${{dataUri}})`;
-            const newContent = cleanedContent + "\\n\\n" + markdownImage;
-            
-            // 3. 使用 chat:message 完全替换
-            const updateResponse = await fetch(`/api/v1/chats/${{chatId}}/messages/${{messageId}}/event`, {{
-                method: "POST",
-                headers: {{
-                    "Content-Type": "application/json",
-                    "Authorization": `Bearer ${{token}}`
-                }},
-                body: JSON.stringify({{
-                    type: "chat:message",
-                    data: {{ content: newContent }}
-                }})
-            }});
-            
-            if (updateResponse.ok) {{
-                console.log("[JS Render PoC] ✅ Message updated successfully!");
-            }} else {{
-                console.error("[JS Render PoC] API error:", updateResponse.status, await updateResponse.text());
-            }}
-        }} else {{
-            console.warn("[JS Render PoC] ⚠️ Missing chatId or messageId, cannot persist.");
-        }}
-        
-    }} catch (error) {{
-        console.error("[JS Render PoC] Error:", error);
-    }}
-}})();
-                        """
-                    },
-                }
-            )
-
-        if __event_emitter__:
-            await __event_emitter__(
-                {"type": "status", "data": {"description": "✅ 渲染完成", "done": True}}
-            )
-
-        return body

plugins/actions/smart-mind-map/README.md

@@ -1,6 +1,6 @@
 # Smart Mind Map - Mind Mapping Generation Plugin
 
-**Author:** [Fu-Jie](https://github.com/Fu-Jie) | **Version:** 0.9.1 | **License:** MIT
+**Author:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **Version:** 0.9.1 | **License:** MIT
 
 > **Important**: To ensure the maintainability and usability of all plugins, each plugin should be accompanied by clear and comprehensive documentation to ensure its functionality, configuration, and usage are well explained.
 
@@ -8,6 +8,25 @@ Smart Mind Map is a powerful OpenWebUI action plugin that intelligently analyzes
 
 ---
 
+## 🔥 What's New in v0.9.1
+
+**New Feature: Image Output Mode**
+
+- **Static Image Support**: Added `OUTPUT_MODE` configuration parameter.
+  - `html` (default): Interactive HTML mind map.
+  - `image`: Static SVG image embedded directly in Markdown (**No HTML code output**, cleaner chat history).
+- **Efficient Storage**: Image mode uploads SVG to `/api/v1/files`, avoiding huge base64 strings in chat history.
+- **Smart Features**: Auto-responsive width and automatic theme detection (light/dark) for generated images.
+
+| Feature | HTML Mode (Default) | Image Mode |
+| :--- | :--- | :--- |
+| **Output Format** | Interactive HTML Block | Static Markdown Image |
+| **Interactivity** | Zoom, Pan, Expand/Collapse | None (Static Image) |
+| **Chat History** | Contains HTML Code | Clean (Image URL only) |
+| **Storage** | Browser Rendering | `/api/v1/files` Upload |
+
+---
+
 ## Core Features
 
 - ✅ **Intelligent Text Analysis**: Automatically identifies core themes, key concepts, and hierarchical structures
@@ -20,7 +39,7 @@ Smart Mind Map is a powerful OpenWebUI action plugin that intelligently analyzes
 - ✅ **Real-time Rendering**: Renders mind maps directly in the chat interface without navigation
 - ✅ **Export Capabilities**: Supports PNG, SVG code, and Markdown source export
 - ✅ **Customizable Configuration**: Configurable LLM model, minimum text length, and other parameters
-- ✅ **Image Output Mode**: Generate static SVG images embedded directly in Markdown (no interactive HTML)
+- ✅ **Image Output Mode**: Generate static SVG images embedded directly in Markdown (**No HTML code output**, cleaner chat history)
 
 ---
 

plugins/actions/smart-mind-map/README_CN.md

@@ -1,6 +1,6 @@
 # 思维导图 - 思维导图生成插件
 
-**作者:** [Fu-Jie](https://github.com/Fu-Jie) | **版本:** 0.9.1 | **许可证:** MIT
+**作者:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **版本:** 0.9.1 | **许可证:** MIT
 
 > **重要提示**：为了确保所有插件的可维护性和易用性，每个插件都应附带清晰、完整的文档，以确保其功能、配置和使用方法得到充分说明。
 
@@ -8,6 +8,25 @@
 
 ---
 
+## 🔥 v0.9.1 更新亮点
+
+**新功能：图片输出模式**
+
+- **静态图片支持**：新增 `OUTPUT_MODE` 配置参数。
+  - `html`（默认）：交互式 HTML 思维导图。
+  - `image`：静态 SVG 图片直接嵌入 Markdown（**不输出 HTML 代码**，聊天记录更简洁）。
+- **高效存储**：图片模式将 SVG 上传至 `/api/v1/files`，避免聊天记录中出现超长 Base64 字符串。
+- **智能特性**：生成的图片支持自动响应式宽度和自动主题检测（亮色/暗色）。
+
+| 特性 | HTML 模式 (默认) | 图片模式 |
+| :--- | :--- | :--- |
+| **输出格式** | 交互式 HTML 代码块 | 静态 Markdown 图片 |
+| **交互性** | 缩放、拖拽、展开/折叠 | 无 (静态图片) |
+| **聊天记录** | 包含 HTML 代码 | 简洁 (仅图片链接) |
+| **存储方式** | 浏览器实时渲染 | `/api/v1/files` 上传 |
+
+---
+
 ## 核心特性
 
 - ✅ **智能文本分析**：自动识别文本的核心主题、关键概念和层次结构
@@ -20,7 +39,7 @@
 - ✅ **实时渲染**：在聊天界面中直接渲染思维导图，无需跳转
 - ✅ **导出功能**：支持 PNG、SVG 代码和 Markdown 源码导出
 - ✅ **自定义配置**：可配置 LLM 模型、最小文本长度等参数
-- ✅ **图片输出模式**：生成静态 SVG 图片直接嵌入 Markdown（无交互式 HTML）
+- ✅ **图片输出模式**：生成静态 SVG 图片直接嵌入 Markdown（**不输出 HTML 代码**，聊天记录更简洁）
 
 ---
 

plugins/actions/smart-mind-map/smart_mind_map.py

@@ -1,9 +1,11 @@
 """
 title: Smart Mind Map
 author: Fu-Jie
-author_url: https://github.com/Fu-Jie
+author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
 funding_url: https://github.com/Fu-Jie/awesome-openwebui
 version: 0.9.1
+openwebui_id: 3094c59a-b4dd-4e0c-9449-15e2dd547dc4
 icon_url: data:image/svg+xml;base64,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
 description: Intelligently analyzes text content and generates interactive mind maps to help users structure and visualize knowledge.
 """
@@ -48,6 +50,8 @@ Please strictly follow these guidelines:
     ```
 """
 
+import json
+
 USER_PROMPT_GENERATE_MINDMAP = """
 Please analyze the following long-form text and structure its core themes, key concepts, branches, and sub-branches into standard Markdown list syntax for Markmap.js rendering.
 
@@ -790,6 +794,10 @@ class Action:
             default="html",
             description="Output mode: 'html' for interactive HTML (default), or 'image' to embed as Markdown image.",
         )
+        SHOW_DEBUG_LOG: bool = Field(
+            default=False,
+            description="Whether to print debug logs in the browser console.",
+        )
 
     def __init__(self):
         self.valves = self.Valves()
@@ -818,45 +826,41 @@ class Action:
             "user_language": user_data.get("language", "en-US"),
         }
 
-    def _extract_chat_id(self, body: dict, metadata: Optional[dict]) -> str:
-        """Extract chat_id from body or metadata"""
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        Unified extraction of chat context information (chat_id, message_id).
+        Prioritizes extraction from body, then metadata.
+        """
+        chat_id = ""
+        message_id = ""
+
+        # 1. Try to get from body
         if isinstance(body, dict):
-            chat_id = body.get("chat_id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id is usually 'id' in body
 
-            body_metadata = body.get("metadata", {})
-            if isinstance(body_metadata, dict):
-                chat_id = body_metadata.get("chat_id")
-                if isinstance(chat_id, str) and chat_id.strip():
-                    return chat_id.strip()
+            # Check body.metadata as fallback
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
 
-        if isinstance(metadata, dict):
-            chat_id = metadata.get("chat_id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
+        # 2. Try to get from __metadata__ (as supplement)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
 
-        return ""
-
-    def _extract_message_id(self, body: dict, metadata: Optional[dict]) -> str:
-        """Extract message_id from body or metadata"""
-        if isinstance(body, dict):
-            message_id = body.get("id")
-            if isinstance(message_id, str) and message_id.strip():
-                return message_id.strip()
-
-            body_metadata = body.get("metadata", {})
-            if isinstance(body_metadata, dict):
-                message_id = body_metadata.get("message_id")
-                if isinstance(message_id, str) and message_id.strip():
-                    return message_id.strip()
-
-        if isinstance(metadata, dict):
-            message_id = metadata.get("message_id")
-            if isinstance(message_id, str) and message_id.strip():
-                return message_id.strip()
-
-        return ""
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
 
     def _extract_markdown_syntax(self, llm_output: str) -> str:
         match = re.search(r"```markdown\s*(.*?)\s*```", llm_output, re.DOTALL)
@@ -883,6 +887,42 @@ class Action:
                 {"type": "notification", "data": {"type": ntype, "content": content}}
             )
 
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """Print structured debug logs in the browser console"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
+
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """Print structured debug logs in the browser console"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
+
     def _remove_existing_html(self, content: str) -> str:
         """Removes existing plugin-generated HTML code blocks from the content."""
         pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
@@ -1514,8 +1554,9 @@ class Action:
             # Check output mode
             if self.valves.OUTPUT_MODE == "image":
                 # Image mode: use JavaScript to render and embed as Markdown image
-                chat_id = self._extract_chat_id(body, __metadata__)
-                message_id = self._extract_message_id(body, __metadata__)
+                chat_ctx = self._get_chat_context(body, __metadata__)
+                chat_id = chat_ctx["chat_id"]
+                message_id = chat_ctx["message_id"]
 
                 await self._emit_status(
                     __event_emitter__,

plugins/actions/smart-mind-map/smart_mind_map_cn.py

@@ -1,9 +1,10 @@
 """
 title: 思维导图
 author: Fu-Jie
-author_url: https://github.com/Fu-Jie
-funding_url: https://github.com/Fu-Jie/awesome-openwebui
+author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
 version: 0.9.1
+openwebui_id: 8d4b097b-219b-4dd2-b509-05fbe6388335
 icon_url: data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9ImN1cnJlbnRDb2xvciIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxyZWN0IHg9IjE2IiB5PSIxNiIgd2lkdGg9IjYiIGhlaWdodD0iNiIgcng9IjEiLz48cmVjdCB4PSIyIiB5PSIxNiIgd2lkdGg9IjYiIGhlaWdodD0iNiIgcng9IjEiLz48cmVjdCB4PSI5IiB5PSIyIiB3aWR0aD0iNiIgaGVpZ2h0PSI2IiByeD0iMSIvPjxwYXRoIGQ9Ik01IDE2di0zYTEgMSAwIDAgMSAxLTFoMTJhMSAxIDAgMCAxIDEgMXYzIi8+PHBhdGggZD0iTTEyIDEyVjgiLz48L3N2Zz4=
 description: 智能分析文本内容,生成交互式思维导图,帮助用户结构化和可视化知识。
 """
@@ -48,6 +49,8 @@ SYSTEM_PROMPT_MINDMAP_ASSISTANT = """
     ```
 """
 
+import json
+
 USER_PROMPT_GENERATE_MINDMAP = """
 请分析以下长篇文本,并将其核心主题、关键概念、分支和子分支结构化为标准的Markdown列表语法,以供Markmap.js渲染。
 
@@ -789,6 +792,10 @@ class Action:
             default="html",
             description="输出模式: 'html' 为交互式HTML(默认),'image' 为嵌入Markdown图片。",
         )
+        SHOW_DEBUG_LOG: bool = Field(
+            default=False,
+            description="是否在浏览器控制台打印调试日志。",
+        )
 
     def __init__(self):
         self.valves = self.Valves()
@@ -817,45 +824,41 @@ class Action:
             "user_language": user_data.get("language", "zh-CN"),
         }
 
-    def _extract_chat_id(self, body: dict, metadata: Optional[dict]) -> str:
-        """从 body 或 metadata 中提取 chat_id"""
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        统一提取聊天上下文信息 (chat_id, message_id)。
+        优先从 body 中提取，其次从 metadata 中提取。
+        """
+        chat_id = ""
+        message_id = ""
+
+        # 1. 尝试从 body 获取
         if isinstance(body, dict):
-            chat_id = body.get("chat_id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id 在 body 中通常是 id
 
-            body_metadata = body.get("metadata", {})
-            if isinstance(body_metadata, dict):
-                chat_id = body_metadata.get("chat_id")
-                if isinstance(chat_id, str) and chat_id.strip():
-                    return chat_id.strip()
+            # 再次检查 body.metadata
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
 
-        if isinstance(metadata, dict):
-            chat_id = metadata.get("chat_id")
-            if isinstance(chat_id, str) and chat_id.strip():
-                return chat_id.strip()
+        # 2. 尝试从 __metadata__ 获取 (作为补充)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
 
-        return ""
-
-    def _extract_message_id(self, body: dict, metadata: Optional[dict]) -> str:
-        """从 body 或 metadata 中提取 message_id"""
-        if isinstance(body, dict):
-            message_id = body.get("id")
-            if isinstance(message_id, str) and message_id.strip():
-                return message_id.strip()
-
-            body_metadata = body.get("metadata", {})
-            if isinstance(body_metadata, dict):
-                message_id = body_metadata.get("message_id")
-                if isinstance(message_id, str) and message_id.strip():
-                    return message_id.strip()
-
-        if isinstance(metadata, dict):
-            message_id = metadata.get("message_id")
-            if isinstance(message_id, str) and message_id.strip():
-                return message_id.strip()
-
-        return ""
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
 
     def _extract_markdown_syntax(self, llm_output: str) -> str:
         match = re.search(r"```markdown\s*(.*?)\s*```", llm_output, re.DOTALL)
@@ -880,6 +883,24 @@ class Action:
                 {"type": "notification", "data": {"type": ntype, "content": content}}
             )
 
+    async def _emit_debug_log(self, emitter, title: str, data: dict):
+        """在浏览器控制台打印结构化调试日志"""
+        if not self.valves.SHOW_DEBUG_LOG or not emitter:
+            return
+
+        try:
+            js_code = f"""
+                (async function() {{
+                    console.group("🛠️ {title}");
+                    console.log({json.dumps(data, ensure_ascii=False)});
+                    console.groupEnd();
+                }})();
+            """
+
+            await emitter({"type": "execute", "data": {"code": js_code}})
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
+
     def _remove_existing_html(self, content: str) -> str:
         """移除内容中已有的插件生成 HTML 代码块 (通过标记识别)。"""
         pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
@@ -1507,8 +1528,9 @@ class Action:
             # 检查输出模式
             if self.valves.OUTPUT_MODE == "image":
                 # 图片模式: 使用 JavaScript 渲染并嵌入为 Markdown 图片
-                chat_id = self._extract_chat_id(body, __metadata__)
-                message_id = self._extract_message_id(body, __metadata__)
+                chat_ctx = self._get_chat_context(body, __metadata__)
+                chat_id = chat_ctx["chat_id"]
+                message_id = chat_ctx["message_id"]
 
                 await self._emit_status(
                     __event_emitter__,

plugins/actions/summary/README.md

@@ -1,30 +0,0 @@
-# Deep Reading & Summary
-
-A powerful tool for analyzing long texts, generating detailed summaries, key points, and actionable insights.
-
-## Features
-
-- **Deep Analysis**: Goes beyond simple summarization to understand the core message.
-- **Key Point Extraction**: Identifies and lists the most important information.
-- **Actionable Advice**: Provides practical suggestions based on the text content.
-
-## Usage
-
-1. Install the plugin.
-2. Send a long text or article to the chat.
-3. Click the "Deep Reading" button (or trigger via command).
-
-## Author
-
-Fu-Jie
-GitHub: [Fu-Jie/awesome-openwebui](https://github.com/Fu-Jie/awesome-openwebui)
-
-## License
-
-MIT License
-
-## Changelog
-
-### v0.1.2
-
-- Removed debug messages from output

plugins/actions/summary/README_CN.md

@@ -1,30 +0,0 @@
-# 深度阅读与摘要 (Deep Reading & Summary)
-
-一个强大的长文本分析工具，用于生成详细摘要、关键信息点和可执行的行动建议。
-
-## 功能特点
-
-- **深度分析**：超越简单的总结，深入理解核心信息。
-- **关键点提取**：识别并列出最重要的信息点。
-- **行动建议**：基于文本内容提供切实可行的建议。
-
-## 使用方法
-
-1. 安装插件。
-2. 发送长文本或文章到聊天框。
-3. 点击“精读”按钮（或通过命令触发）。
-
-## 作者
-
-Fu-Jie
-GitHub: [Fu-Jie/awesome-openwebui](https://github.com/Fu-Jie/awesome-openwebui)
-
-## 许可证
-
-MIT License
-
-## 更新日志
-
-### v0.1.2
-
-- 移除输出中的调试信息

plugins/actions/summary/summary.py

@@ -1,674 +0,0 @@
-"""
-title: Deep Reading & Summary
-author: Fu-Jie
-author_url: https://github.com/Fu-Jie
-funding_url: https://github.com/Fu-Jie/awesome-openwebui
-version: 0.1.2
-icon_url: data:image/svg+xml;base64,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
-description: Provides deep reading analysis and summarization for long texts.
-requirements: jinja2, markdown
-"""
-
-from pydantic import BaseModel, Field
-from typing import Optional, Dict, Any
-import logging
-import re
-from fastapi import Request
-from datetime import datetime
-import pytz
-import markdown
-from jinja2 import Template
-
-from open_webui.utils.chat import generate_chat_completion
-from open_webui.models.users import Users
-
-logging.basicConfig(
-    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-)
-logger = logging.getLogger(__name__)
-
-# =================================================================
-# HTML Wrapper Template (supports multiple plugins and grid layout)
-# =================================================================
-HTML_WRAPPER_TEMPLATE = """
-<!-- OPENWEBUI_PLUGIN_OUTPUT -->
-<!DOCTYPE html>
-<html lang="{user_language}">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <style>
-        body { 
-            font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif; 
-            margin: 0; 
-            padding: 10px; 
-            background-color: transparent; 
-        }
-        #main-container { 
-            display: flex; 
-            flex-wrap: wrap; 
-            gap: 20px; 
-            align-items: flex-start; 
-            width: 100%;
-        }
-        .plugin-item { 
-            flex: 1 1 400px; /* Default width, allows shrinking/growing */
-            min-width: 300px; 
-            background: white; 
-            border-radius: 12px; 
-            box-shadow: 0 4px 6px rgba(0,0,0,0.05); 
-            overflow: hidden; 
-            border: 1px solid #e5e7eb; 
-            transition: all 0.3s ease;
-        }
-        .plugin-item:hover {
-            box-shadow: 0 10px 15px rgba(0,0,0,0.1);
-        }
-        @media (max-width: 768px) { 
-            .plugin-item { flex: 1 1 100%; } 
-        }
-        /* STYLES_INSERTION_POINT */
-    </style>
-</head>
-<body>
-    <div id="main-container">
-        <!-- CONTENT_INSERTION_POINT -->
-    </div>
-    <!-- SCRIPTS_INSERTION_POINT -->
-</body>
-</html>
-"""
-
-# =================================================================
-# Internal LLM Prompts
-# =================================================================
-
-SYSTEM_PROMPT_READING_ASSISTANT = """
-You are a professional Deep Text Analysis Expert, specializing in reading long texts and extracting the essence. Your task is to conduct a comprehensive and in-depth analysis.
-
-Please provide the following:
-1.  **Detailed Summary**: Summarize the core content of the text in 2-3 paragraphs, ensuring accuracy and completeness. Do not be too brief; ensure the reader fully understands the main idea.
-2.  **Key Information Points**: List 5-8 most important facts, viewpoints, or arguments. Each point should:
-    - Be specific and insightful
-    - Include necessary details and context
-    - Use Markdown list format
-3.  **Actionable Advice**: Identify and refine specific, actionable items from the text. Each suggestion should:
-    - Be clear and actionable
-    - Include execution priority or timing suggestions
-    - If there are no clear action items, provide learning suggestions or thinking directions
-
-Please strictly follow these guidelines:
--   **Language**: All output must be in the user's specified language.
--   **Format**: Please strictly follow the Markdown format below, ensuring each section has a clear header:
-    ## Summary
-    [Detailed summary content here, 2-3 paragraphs, use Markdown **bold** or *italic* to emphasize key points]
-
-    ## Key Information Points
-    - [Key Point 1: Include specific details and context]
-    - [Key Point 2: Include specific details and context]
-    - [Key Point 3: Include specific details and context]
-    - [At least 5, at most 8 key points]
-
-    ## Actionable Advice
-    - [Action Item 1: Specific, actionable, include priority]
-    - [Action Item 2: Specific, actionable, include priority]
-    - [If no clear action items, provide learning suggestions or thinking directions]
--   **Depth First**: Analysis should be deep and comprehensive, not superficial.
--   **Action Oriented**: Focus on actionable suggestions and next steps.
--   **Analysis Results Only**: Do not include any extra pleasantries, explanations, or leading text.
-"""
-
-USER_PROMPT_GENERATE_SUMMARY = """
-Please conduct a deep analysis of the following long text, providing:
-1.  Detailed Summary (2-3 paragraphs, comprehensive overview)
-2.  Key Information Points List (5-8 items, including specific details)
-3.  Actionable Advice (Specific, clear, including priority)
-
----
-**User Context:**
-User Name: {user_name}
-Current Date/Time: {current_date_time_str}
-Weekday: {current_weekday}
-Timezone: {current_timezone_str}
-User Language: {user_language}
----
-
-**Long Text Content:**
-```
-{long_text_content}
-```
-
-Please conduct a deep and comprehensive analysis, focusing on actionable advice.
-"""
-
-# =================================================================
-# Frontend HTML Template (Jinja2 Syntax)
-# =================================================================
-
-CSS_TEMPLATE_SUMMARY = """
-        :root {
-            --primary-color: #4285f4;
-            --secondary-color: #1e88e5;
-            --action-color: #34a853;
-            --background-color: #f8f9fa;
-            --card-bg-color: #ffffff;
-            --text-color: #202124;
-            --muted-text-color: #5f6368;
-            --border-color: #dadce0;
-            --header-gradient: linear-gradient(135deg, #4285f4, #1e88e5);
-            --shadow: 0 1px 3px rgba(60,64,67,.3);
-            --border-radius: 8px;
-            --font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
-        }
-        .summary-container-wrapper {
-            font-family: var(--font-family);
-            line-height: 1.8;
-            color: var(--text-color);
-            height: 100%;
-            display: flex;
-            flex-direction: column;
-        }
-        .summary-container-wrapper .header {
-            background: var(--header-gradient);
-            color: white;
-            padding: 20px 24px;
-            text-align: center;
-        }
-        .summary-container-wrapper .header h1 {
-            margin: 0;
-            font-size: 1.5em;
-            font-weight: 500;
-            letter-spacing: -0.5px;
-        }
-        .summary-container-wrapper .user-context {
-            font-size: 0.8em;
-            color: var(--muted-text-color);
-            background-color: #f1f3f4;
-            padding: 8px 16px;
-            display: flex;
-            justify-content: space-around;
-            flex-wrap: wrap;
-            border-bottom: 1px solid var(--border-color);
-        }
-        .summary-container-wrapper .user-context span { margin: 2px 8px; }
-        .summary-container-wrapper .content { padding: 20px; flex-grow: 1; }
-        .summary-container-wrapper .section {
-            margin-bottom: 16px;
-            padding-bottom: 16px;
-            border-bottom: 1px solid #e8eaed;
-        }
-        .summary-container-wrapper .section:last-child {
-            border-bottom: none;
-            margin-bottom: 0;
-            padding-bottom: 0;
-        }
-        .summary-container-wrapper .section h2 {
-            margin-top: 0;
-            margin-bottom: 12px;
-            font-size: 1.2em;
-            font-weight: 500;
-            color: var(--text-color);
-            display: flex;
-            align-items: center;
-            padding-bottom: 8px;
-            border-bottom: 2px solid var(--primary-color);
-        }
-        .summary-container-wrapper .section h2 .icon {
-            margin-right: 8px;
-            font-size: 1.1em;
-            line-height: 1;
-        }
-        .summary-container-wrapper .summary-section h2 { border-bottom-color: var(--primary-color); }
-        .summary-container-wrapper .keypoints-section h2 { border-bottom-color: var(--secondary-color); }
-        .summary-container-wrapper .actions-section h2 { border-bottom-color: var(--action-color); }
-        .summary-container-wrapper .html-content {
-            font-size: 0.95em;
-            line-height: 1.7;
-        }
-        .summary-container-wrapper .html-content p:first-child { margin-top: 0; }
-        .summary-container-wrapper .html-content p:last-child { margin-bottom: 0; }
-        .summary-container-wrapper .html-content ul {
-            list-style: none;
-            padding-left: 0;
-            margin: 12px 0;
-        }
-        .summary-container-wrapper .html-content li {
-            padding: 8px 0 8px 24px;
-            position: relative;
-            margin-bottom: 6px;
-            line-height: 1.6;
-        }
-        .summary-container-wrapper .html-content li::before {
-            position: absolute;
-            left: 0;
-            top: 8px;
-            font-family: 'Arial';
-            font-weight: bold;
-            font-size: 1em;
-        }
-        .summary-container-wrapper .keypoints-section .html-content li::before { 
-            content: '•'; 
-            color: var(--secondary-color);
-            font-size: 1.3em;
-            top: 5px;
-        }
-        .summary-container-wrapper .actions-section .html-content li::before { 
-            content: '▸'; 
-            color: var(--action-color); 
-        }
-        .summary-container-wrapper .no-content { 
-            color: var(--muted-text-color); 
-            font-style: italic;
-            padding: 12px;
-            background: #f8f9fa;
-            border-radius: 4px;
-        }
-        .summary-container-wrapper .footer {
-            text-align: center;
-            padding: 16px;
-            font-size: 0.8em;
-            color: #5f6368;
-            background-color: #f8f9fa;
-            border-top: 1px solid var(--border-color);
-        }
-"""
-
-CONTENT_TEMPLATE_SUMMARY = """
-        <div class="summary-container-wrapper">
-            <div class="header">
-                <h1>📖 Deep Reading: Analysis Report</h1>
-            </div>
-            <div class="user-context">
-                <span><strong>User:</strong> {user_name}</span>
-                <span><strong>Time:</strong> {current_date_time_str}</span>
-            </div>
-            <div class="content">
-                <div class="section summary-section">
-                    <h2><span class="icon">📝</span>Detailed Summary</h2>
-                    <div class="html-content">{summary_html}</div>
-                </div>
-                <div class="section keypoints-section">
-                    <h2><span class="icon">💡</span>Key Information Points</h2>
-                    <div class="html-content">{keypoints_html}</div>
-                </div>
-                <div class="section actions-section">
-                    <h2><span class="icon">🎯</span>Actionable Advice</h2>
-                    <div class="html-content">{actions_html}</div>
-                </div>
-            </div>
-            <div class="footer">
-                <p>&copy; {current_year} Deep Reading - Text Analysis Service</p>
-            </div>
-        </div>
-"""
-
-
-class Action:
-    class Valves(BaseModel):
-        SHOW_STATUS: bool = Field(
-            default=True,
-            description="Whether to show operation status updates in the chat interface.",
-        )
-        MODEL_ID: str = Field(
-            default="",
-            description="Built-in LLM Model ID used for text analysis. If empty, uses the current conversation's model.",
-        )
-        MIN_TEXT_LENGTH: int = Field(
-            default=200,
-            description="Minimum text length required for deep analysis (characters). Recommended 200+.",
-        )
-        RECOMMENDED_MIN_LENGTH: int = Field(
-            default=500,
-            description="Recommended minimum text length for best analysis results.",
-        )
-        CLEAR_PREVIOUS_HTML: bool = Field(
-            default=False,
-            description="Whether to force clear previous plugin results (if True, overwrites instead of merging).",
-        )
-        MESSAGE_COUNT: int = Field(
-            default=1,
-            description="Number of recent messages to use for generation. Set to 1 for just the last message, or higher for more context.",
-        )
-
-    def __init__(self):
-        self.valves = self.Valves()
-
-    def _process_llm_output(self, llm_output: str) -> Dict[str, str]:
-        """
-        Parse LLM Markdown output and convert to HTML fragments.
-        """
-        summary_match = re.search(
-            r"##\s*Summary\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL | re.IGNORECASE
-        )
-        keypoints_match = re.search(
-            r"##\s*Key Information Points\s*\n(.*?)(?=\n##|$)",
-            llm_output,
-            re.DOTALL | re.IGNORECASE,
-        )
-        actions_match = re.search(
-            r"##\s*Actionable Advice\s*\n(.*?)(?=\n##|$)",
-            llm_output,
-            re.DOTALL | re.IGNORECASE,
-        )
-
-        summary_md = summary_match.group(1).strip() if summary_match else ""
-        keypoints_md = keypoints_match.group(1).strip() if keypoints_match else ""
-        actions_md = actions_match.group(1).strip() if actions_match else ""
-
-        if not any([summary_md, keypoints_md, actions_md]):
-            summary_md = llm_output.strip()
-            logger.warning(
-                "LLM output did not follow expected Markdown format. Treating entire output as summary."
-            )
-
-        # Use 'nl2br' extension to convert newlines \n to <br>
-        md_extensions = ["nl2br"]
-        summary_html = (
-            markdown.markdown(summary_md, extensions=md_extensions)
-            if summary_md
-            else '<p class="no-content">Failed to extract summary.</p>'
-        )
-        keypoints_html = (
-            markdown.markdown(keypoints_md, extensions=md_extensions)
-            if keypoints_md
-            else '<p class="no-content">Failed to extract key information points.</p>'
-        )
-        actions_html = (
-            markdown.markdown(actions_md, extensions=md_extensions)
-            if actions_md
-            else '<p class="no-content">No explicit actionable advice.</p>'
-        )
-
-        return {
-            "summary_html": summary_html,
-            "keypoints_html": keypoints_html,
-            "actions_html": actions_html,
-        }
-
-    async def _emit_status(self, emitter, description: str, done: bool = False):
-        """Emits a status update event."""
-        if self.valves.SHOW_STATUS and emitter:
-            await emitter(
-                {"type": "status", "data": {"description": description, "done": done}}
-            )
-
-    async def _emit_notification(self, emitter, content: str, ntype: str = "info"):
-        """Emits a notification event (info/success/warning/error)."""
-        if emitter:
-            await emitter(
-                {"type": "notification", "data": {"type": ntype, "content": content}}
-            )
-
-    def _remove_existing_html(self, content: str) -> str:
-        """Removes existing plugin-generated HTML code blocks from the content."""
-        pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
-        return re.sub(pattern, "", content).strip()
-
-    def _extract_text_content(self, content) -> str:
-        """Extract text from message content, supporting multimodal message formats"""
-        if isinstance(content, str):
-            return content
-        elif isinstance(content, list):
-            # Multimodal message: [{"type": "text", "text": "..."}, {"type": "image_url", ...}]
-            text_parts = []
-            for item in content:
-                if isinstance(item, dict) and item.get("type") == "text":
-                    text_parts.append(item.get("text", ""))
-                elif isinstance(item, str):
-                    text_parts.append(item)
-            return "\n".join(text_parts)
-        return str(content) if content else ""
-
-    def _merge_html(
-        self,
-        existing_html_code: str,
-        new_content: str,
-        new_styles: str = "",
-        new_scripts: str = "",
-        user_language: str = "en-US",
-    ) -> str:
-        """
-        Merges new content into an existing HTML container, or creates a new one.
-        """
-        if (
-            "<!-- OPENWEBUI_PLUGIN_OUTPUT -->" in existing_html_code
-            and "<!-- CONTENT_INSERTION_POINT -->" in existing_html_code
-        ):
-            base_html = existing_html_code
-            base_html = re.sub(r"^```html\s*", "", base_html)
-            base_html = re.sub(r"\s*```$", "", base_html)
-        else:
-            base_html = HTML_WRAPPER_TEMPLATE.replace("{user_language}", user_language)
-
-        wrapped_content = f'<div class="plugin-item">\n{new_content}\n</div>'
-
-        if new_styles:
-            base_html = base_html.replace(
-                "/* STYLES_INSERTION_POINT */",
-                f"{new_styles}\n/* STYLES_INSERTION_POINT */",
-            )
-
-        base_html = base_html.replace(
-            "<!-- CONTENT_INSERTION_POINT -->",
-            f"{wrapped_content}\n<!-- CONTENT_INSERTION_POINT -->",
-        )
-
-        if new_scripts:
-            base_html = base_html.replace(
-                "<!-- SCRIPTS_INSERTION_POINT -->",
-                f"{new_scripts}\n<!-- SCRIPTS_INSERTION_POINT -->",
-            )
-
-        return base_html.strip()
-
-    def _build_content_html(self, context: dict) -> str:
-        """
-        Build content HTML using context data.
-        """
-        return (
-            CONTENT_TEMPLATE_SUMMARY.replace(
-                "{user_name}", context.get("user_name", "User")
-            )
-            .replace(
-                "{current_date_time_str}", context.get("current_date_time_str", "")
-            )
-            .replace("{current_year}", context.get("current_year", ""))
-            .replace("{summary_html}", context.get("summary_html", ""))
-            .replace("{keypoints_html}", context.get("keypoints_html", ""))
-            .replace("{actions_html}", context.get("actions_html", ""))
-        )
-
-    async def action(
-        self,
-        body: dict,
-        __user__: Optional[Dict[str, Any]] = None,
-        __event_emitter__: Optional[Any] = None,
-        __request__: Optional[Request] = None,
-    ) -> Optional[dict]:
-        logger.info("Action: Deep Reading Started (v2.0.0)")
-
-        if isinstance(__user__, (list, tuple)):
-            user_language = (
-                __user__[0].get("language", "en-US") if __user__ else "en-US"
-            )
-            user_name = __user__[0].get("name", "User") if __user__[0] else "User"
-            user_id = (
-                __user__[0]["id"]
-                if __user__ and "id" in __user__[0]
-                else "unknown_user"
-            )
-        elif isinstance(__user__, dict):
-            user_language = __user__.get("language", "en-US")
-            user_name = __user__.get("name", "User")
-            user_id = __user__.get("id", "unknown_user")
-
-        now = datetime.now()
-        current_date_time_str = now.strftime("%B %d, %Y %H:%M:%S")
-        current_weekday = now.strftime("%A")
-        current_year = now.strftime("%Y")
-        current_timezone_str = "Unknown Timezone"
-
-        original_content = ""
-        try:
-            messages = body.get("messages", [])
-            if not messages:
-                raise ValueError("Unable to get valid user message content.")
-
-            # Get last N messages based on MESSAGE_COUNT
-            message_count = min(self.valves.MESSAGE_COUNT, len(messages))
-            recent_messages = messages[-message_count:]
-
-            # Aggregate content from selected messages with labels
-            aggregated_parts = []
-            for i, msg in enumerate(recent_messages, 1):
-                text_content = self._extract_text_content(msg.get("content"))
-                if text_content:
-                    role = msg.get("role", "unknown")
-                    role_label = (
-                        "User"
-                        if role == "user"
-                        else "Assistant" if role == "assistant" else role
-                    )
-                    aggregated_parts.append(f"{text_content}")
-
-            if not aggregated_parts:
-                raise ValueError("Unable to get valid user message content.")
-
-            original_content = "\n\n---\n\n".join(aggregated_parts)
-
-            if len(original_content) < self.valves.MIN_TEXT_LENGTH:
-                short_text_message = f"Text content too short ({len(original_content)} chars), recommended at least {self.valves.MIN_TEXT_LENGTH} chars for effective deep analysis.\n\n💡 Tip: For short texts, consider using '⚡ Flash Card' for quick refinement."
-                await self._emit_notification(
-                    __event_emitter__, short_text_message, "warning"
-                )
-                return {
-                    "messages": [
-                        {"role": "assistant", "content": f"⚠️ {short_text_message}"}
-                    ]
-                }
-
-            # Recommend for longer texts
-            if len(original_content) < self.valves.RECOMMENDED_MIN_LENGTH:
-                await self._emit_notification(
-                    __event_emitter__,
-                    f"Text length is {len(original_content)} chars. Recommended {self.valves.RECOMMENDED_MIN_LENGTH}+ chars for best analysis results.",
-                    "info",
-                )
-
-            await self._emit_notification(
-                __event_emitter__,
-                "📖 Deep Reading started, analyzing deeply...",
-                "info",
-            )
-            await self._emit_status(
-                __event_emitter__,
-                "📖 Deep Reading: Analyzing text, extracting essence...",
-                False,
-            )
-
-            formatted_user_prompt = USER_PROMPT_GENERATE_SUMMARY.format(
-                user_name=user_name,
-                current_date_time_str=current_date_time_str,
-                current_weekday=current_weekday,
-                current_timezone_str=current_timezone_str,
-                user_language=user_language,
-                long_text_content=original_content,
-            )
-
-            # Determine model to use
-            target_model = self.valves.MODEL_ID
-            if not target_model:
-                target_model = body.get("model")
-
-            llm_payload = {
-                "model": target_model,
-                "messages": [
-                    {"role": "system", "content": SYSTEM_PROMPT_READING_ASSISTANT},
-                    {"role": "user", "content": formatted_user_prompt},
-                ],
-                "stream": False,
-            }
-
-            user_obj = Users.get_user_by_id(user_id)
-            if not user_obj:
-                raise ValueError(f"Unable to get user object, User ID: {user_id}")
-
-            llm_response = await generate_chat_completion(
-                __request__, llm_payload, user_obj
-            )
-            assistant_response_content = llm_response["choices"][0]["message"][
-                "content"
-            ]
-
-            processed_content = self._process_llm_output(assistant_response_content)
-
-            context = {
-                "user_language": user_language,
-                "user_name": user_name,
-                "current_date_time_str": current_date_time_str,
-                "current_weekday": current_weekday,
-                "current_year": current_year,
-                **processed_content,
-            }
-
-            content_html = self._build_content_html(context)
-
-            # Extract existing HTML if any
-            existing_html_block = ""
-            match = re.search(
-                r"```html\s*(<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?)```",
-                original_content,
-            )
-            if match:
-                existing_html_block = match.group(1)
-
-            if self.valves.CLEAR_PREVIOUS_HTML:
-                original_content = self._remove_existing_html(original_content)
-                final_html = self._merge_html(
-                    "", content_html, CSS_TEMPLATE_SUMMARY, "", user_language
-                )
-            else:
-                if existing_html_block:
-                    original_content = self._remove_existing_html(original_content)
-                    final_html = self._merge_html(
-                        existing_html_block,
-                        content_html,
-                        CSS_TEMPLATE_SUMMARY,
-                        "",
-                        user_language,
-                    )
-                else:
-                    final_html = self._merge_html(
-                        "", content_html, CSS_TEMPLATE_SUMMARY, "", user_language
-                    )
-
-            html_embed_tag = f"```html\n{final_html}\n```"
-            body["messages"][-1]["content"] = f"{original_content}\n\n{html_embed_tag}"
-
-            await self._emit_status(
-                __event_emitter__, "📖 Deep Reading: Analysis complete!", True
-            )
-            await self._emit_notification(
-                __event_emitter__,
-                f"📖 Deep Reading complete, {user_name}! Deep analysis report generated.",
-                "success",
-            )
-
-        except Exception as e:
-            error_message = f"Deep Reading processing failed: {str(e)}"
-            logger.error(f"Deep Reading Error: {error_message}", exc_info=True)
-            user_facing_error = f"Sorry, Deep Reading encountered an error while processing: {str(e)}.\nPlease check Open WebUI backend logs for more details."
-            body["messages"][-1][
-                "content"
-            ] = f"{original_content}\n\n❌ **Error:** {user_facing_error}"
-
-            await self._emit_status(
-                __event_emitter__, "Deep Reading: Processing failed.", True
-            )
-            await self._emit_notification(
-                __event_emitter__,
-                f"Deep Reading processing failed, {user_name}!",
-                "error",
-            )
-
-        return body

plugins/actions/summary/summary_cn.py

@@ -1,663 +0,0 @@
-"""
-title: 精读 (Deep Reading)
-icon_url: data:image/svg+xml;base64,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
-version: 0.1.2
-description: 深度分析长篇文本，提炼详细摘要、关键信息点和可执行的行动建议，适合工作和学习场景。
-requirements: jinja2, markdown
-"""
-
-from pydantic import BaseModel, Field
-from typing import Optional, Dict, Any
-import logging
-import re
-from fastapi import Request
-from datetime import datetime
-import pytz
-import markdown
-from jinja2 import Template
-
-from open_webui.utils.chat import generate_chat_completion
-from open_webui.models.users import Users
-
-logging.basicConfig(
-    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-)
-logger = logging.getLogger(__name__)
-
-# =================================================================
-# HTML 容器模板 (支持多插件共存与网格布局)
-# =================================================================
-HTML_WRAPPER_TEMPLATE = """
-<!-- OPENWEBUI_PLUGIN_OUTPUT -->
-<!DOCTYPE html>
-<html lang="{user_language}">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <style>
-        body { 
-            font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif; 
-            margin: 0; 
-            padding: 10px; 
-            background-color: transparent; 
-        }
-        #main-container { 
-            display: flex; 
-            flex-wrap: wrap; 
-            gap: 20px; 
-            align-items: flex-start; 
-            width: 100%;
-        }
-        .plugin-item { 
-            flex: 1 1 400px; /* 默认宽度，允许伸缩 */
-            min-width: 300px; 
-            background: white; 
-            border-radius: 12px; 
-            box-shadow: 0 4px 6px rgba(0,0,0,0.05); 
-            overflow: hidden; 
-            border: 1px solid #e5e7eb; 
-            transition: all 0.3s ease;
-        }
-        .plugin-item:hover {
-            box-shadow: 0 10px 15px rgba(0,0,0,0.1);
-        }
-        @media (max-width: 768px) { 
-            .plugin-item { flex: 1 1 100%; } 
-        }
-        /* STYLES_INSERTION_POINT */
-    </style>
-</head>
-<body>
-    <div id="main-container">
-        <!-- CONTENT_INSERTION_POINT -->
-    </div>
-    <!-- SCRIPTS_INSERTION_POINT -->
-</body>
-</html>
-"""
-
-# =================================================================
-# 内部 LLM 提示词设计
-# =================================================================
-
-SYSTEM_PROMPT_READING_ASSISTANT = """
-你是一个专业的深度文本分析专家，擅长精读长篇文本并提炼精华。你的任务是进行全面、深入的分析。
-
-请提供以下内容：
-1.  **详细摘要**：用 2-3 段话全面总结文本的核心内容，确保准确性和完整性。不要过于简略，要让读者充分理解文本主旨。
-2.  **关键信息点**：列出 5-8 个最重要的事实、观点或论据。每个信息点应该：
-    - 具体且有深度
-    - 包含必要的细节和背景
-    - 使用 Markdown 列表格式
-3.  **行动建议**：从文本中识别并提炼出具体的、可执行的行动项。每个建议应该：
-    - 明确且可操作
-    - 包含执行的优先级或时间建议
-    - 如果没有明确的行动项，可以提供学习建议或思考方向
-
-请严格遵循以下指导原则：
--   **语言**：所有输出必须使用用户指定的语言。
--   **格式**：请严格按照以下 Markdown 格式输出，确保每个部分都有明确的标题：
-    ## 摘要
-    [这里是详细的摘要内容，2-3段话，可以使用 Markdown 进行**加粗**或*斜体*强调重点]
-
-    ## 关键信息点
-    - [关键点1：包含具体细节和背景]
-    - [关键点2：包含具体细节和背景]
-    - [关键点3：包含具体细节和背景]
-    - [至少5个，最多8个关键点]
-
-    ## 行动建议
-    - [行动项1：具体、可执行，包含优先级]
-    - [行动项2：具体、可执行，包含优先级]
-    - [如果没有明确行动项，提供学习建议或思考方向]
--   **深度优先**：分析要深入、全面，不要浮于表面。
--   **行动导向**：重点关注可执行的建议和下一步行动。
--   **只输出分析结果**：不要包含任何额外的寒暄、解释或引导性文字。
-"""
-
-USER_PROMPT_GENERATE_SUMMARY = """
-请对以下长篇文本进行深度分析，提供：
-1.  详细的摘要（2-3段话，全面概括文本内容）
-2.  关键信息点列表（5-8个，包含具体细节）
-3.  可执行的行动建议（具体、明确，包含优先级）
-
----
-**用户上下文信息:**
-用户姓名: {user_name}
-当前日期时间: {current_date_time_str}
-当前星期: {current_weekday}
-当前时区: {current_timezone_str}
-用户语言: {user_language}
----
-
-**长篇文本内容:**
-```
-{long_text_content}
-```
-
-请进行深入、全面的分析，重点关注可执行的行动建议。
-"""
-
-# =================================================================
-# 前端 HTML 模板 (Jinja2 语法)
-# =================================================================
-
-CSS_TEMPLATE_SUMMARY = """
-        :root {
-            --primary-color: #4285f4;
-            --secondary-color: #1e88e5;
-            --action-color: #34a853;
-            --background-color: #f8f9fa;
-            --card-bg-color: #ffffff;
-            --text-color: #202124;
-            --muted-text-color: #5f6368;
-            --border-color: #dadce0;
-            --header-gradient: linear-gradient(135deg, #4285f4, #1e88e5);
-            --shadow: 0 1px 3px rgba(60,64,67,.3);
-            --border-radius: 8px;
-            --font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
-        }
-        .summary-container-wrapper {
-            font-family: var(--font-family);
-            line-height: 1.8;
-            color: var(--text-color);
-            height: 100%;
-            display: flex;
-            flex-direction: column;
-        }
-        .summary-container-wrapper .header {
-            background: var(--header-gradient);
-            color: white;
-            padding: 20px 24px;
-            text-align: center;
-        }
-        .summary-container-wrapper .header h1 {
-            margin: 0;
-            font-size: 1.5em;
-            font-weight: 500;
-            letter-spacing: -0.5px;
-        }
-        .summary-container-wrapper .user-context {
-            font-size: 0.8em;
-            color: var(--muted-text-color);
-            background-color: #f1f3f4;
-            padding: 8px 16px;
-            display: flex;
-            justify-content: space-around;
-            flex-wrap: wrap;
-            border-bottom: 1px solid var(--border-color);
-        }
-        .summary-container-wrapper .user-context span { margin: 2px 8px; }
-        .summary-container-wrapper .content { padding: 20px; flex-grow: 1; }
-        .summary-container-wrapper .section {
-            margin-bottom: 16px;
-            padding-bottom: 16px;
-            border-bottom: 1px solid #e8eaed;
-        }
-        .summary-container-wrapper .section:last-child {
-            border-bottom: none;
-            margin-bottom: 0;
-            padding-bottom: 0;
-        }
-        .summary-container-wrapper .section h2 {
-            margin-top: 0;
-            margin-bottom: 12px;
-            font-size: 1.2em;
-            font-weight: 500;
-            color: var(--text-color);
-            display: flex;
-            align-items: center;
-            padding-bottom: 8px;
-            border-bottom: 2px solid var(--primary-color);
-        }
-        .summary-container-wrapper .section h2 .icon {
-            margin-right: 8px;
-            font-size: 1.1em;
-            line-height: 1;
-        }
-        .summary-container-wrapper .summary-section h2 { border-bottom-color: var(--primary-color); }
-        .summary-container-wrapper .keypoints-section h2 { border-bottom-color: var(--secondary-color); }
-        .summary-container-wrapper .actions-section h2 { border-bottom-color: var(--action-color); }
-        .summary-container-wrapper .html-content {
-            font-size: 0.95em;
-            line-height: 1.7;
-        }
-        .summary-container-wrapper .html-content p:first-child { margin-top: 0; }
-        .summary-container-wrapper .html-content p:last-child { margin-bottom: 0; }
-        .summary-container-wrapper .html-content ul {
-            list-style: none;
-            padding-left: 0;
-            margin: 12px 0;
-        }
-        .summary-container-wrapper .html-content li {
-            padding: 8px 0 8px 24px;
-            position: relative;
-            margin-bottom: 6px;
-            line-height: 1.6;
-        }
-        .summary-container-wrapper .html-content li::before {
-            position: absolute;
-            left: 0;
-            top: 8px;
-            font-family: 'Arial';
-            font-weight: bold;
-            font-size: 1em;
-        }
-        .summary-container-wrapper .keypoints-section .html-content li::before { 
-            content: '•'; 
-            color: var(--secondary-color);
-            font-size: 1.3em;
-            top: 5px;
-        }
-        .summary-container-wrapper .actions-section .html-content li::before { 
-            content: '▸'; 
-            color: var(--action-color); 
-        }
-        .summary-container-wrapper .no-content { 
-            color: var(--muted-text-color); 
-            font-style: italic;
-            padding: 12px;
-            background: #f8f9fa;
-            border-radius: 4px;
-        }
-        .summary-container-wrapper .footer {
-            text-align: center;
-            padding: 16px;
-            font-size: 0.8em;
-            color: #5f6368;
-            background-color: #f8f9fa;
-            border-top: 1px solid var(--border-color);
-        }
-"""
-
-CONTENT_TEMPLATE_SUMMARY = """
-        <div class="summary-container-wrapper">
-            <div class="header">
-                <h1>📖 精读：深度分析报告</h1>
-            </div>
-            <div class="user-context">
-                <span><strong>用户:</strong> {user_name}</span>
-                <span><strong>时间:</strong> {current_date_time_str}</span>
-            </div>
-            <div class="content">
-                <div class="section summary-section">
-                    <h2><span class="icon">📝</span>详细摘要</h2>
-                    <div class="html-content">{summary_html}</div>
-                </div>
-                <div class="section keypoints-section">
-                    <h2><span class="icon">💡</span>关键信息点</h2>
-                    <div class="html-content">{keypoints_html}</div>
-                </div>
-                <div class="section actions-section">
-                    <h2><span class="icon">🎯</span>行动建议</h2>
-                    <div class="html-content">{actions_html}</div>
-                </div>
-            </div>
-            <div class="footer">
-                <p>&copy; {current_year} 精读 - 深度文本分析服务</p>
-            </div>
-        </div>
-"""
-
-
-class Action:
-    class Valves(BaseModel):
-        SHOW_STATUS: bool = Field(
-            default=True, description="是否在聊天界面显示操作状态更新。"
-        )
-        MODEL_ID: str = Field(
-            default="",
-            description="用于文本分析的内置LLM模型ID。如果为空，则使用当前对话的模型。",
-        )
-        MIN_TEXT_LENGTH: int = Field(
-            default=200,
-            description="进行深度分析所需的最小文本长度(字符数)。建议200字符以上。",
-        )
-        RECOMMENDED_MIN_LENGTH: int = Field(
-            default=500, description="建议的最小文本长度，以获得最佳分析效果。"
-        )
-        CLEAR_PREVIOUS_HTML: bool = Field(
-            default=False,
-            description="是否强制清除旧的插件结果（如果为 True，则不合并，直接覆盖）。",
-        )
-        MESSAGE_COUNT: int = Field(
-            default=1,
-            description="用于生成的最近消息数量。设置为1仅使用最后一条消息，更大值可包含更多上下文。",
-        )
-
-    def __init__(self):
-        self.valves = self.Valves()
-        self.weekday_map = {
-            "Monday": "星期一",
-            "Tuesday": "星期二",
-            "Wednesday": "星期三",
-            "Thursday": "星期四",
-            "Friday": "星期五",
-            "Saturday": "星期六",
-            "Sunday": "星期日",
-        }
-
-    def _process_llm_output(self, llm_output: str) -> Dict[str, str]:
-        """
-        解析LLM的Markdown输出,将其转换为HTML片段。
-        """
-        summary_match = re.search(
-            r"##\s*摘要\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL
-        )
-        keypoints_match = re.search(
-            r"##\s*关键信息点\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL
-        )
-        actions_match = re.search(
-            r"##\s*行动建议\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL
-        )
-
-        summary_md = summary_match.group(1).strip() if summary_match else ""
-        keypoints_md = keypoints_match.group(1).strip() if keypoints_match else ""
-        actions_md = actions_match.group(1).strip() if actions_match else ""
-
-        if not any([summary_md, keypoints_md, actions_md]):
-            summary_md = llm_output.strip()
-            logger.warning("LLM输出未遵循预期的Markdown格式。将整个输出视为摘要。")
-
-        # 使用 'nl2br' 扩展将换行符 \n 转换为 <br>
-        md_extensions = ["nl2br"]
-        summary_html = (
-            markdown.markdown(summary_md, extensions=md_extensions)
-            if summary_md
-            else '<p class="no-content">未能提取摘要信息。</p>'
-        )
-        keypoints_html = (
-            markdown.markdown(keypoints_md, extensions=md_extensions)
-            if keypoints_md
-            else '<p class="no-content">未能提取关键信息点。</p>'
-        )
-        actions_html = (
-            markdown.markdown(actions_md, extensions=md_extensions)
-            if actions_md
-            else '<p class="no-content">暂无明确的行动建议。</p>'
-        )
-
-        return {
-            "summary_html": summary_html,
-            "keypoints_html": keypoints_html,
-            "actions_html": actions_html,
-        }
-
-    async def _emit_status(self, emitter, description: str, done: bool = False):
-        """发送状态更新事件。"""
-        if self.valves.SHOW_STATUS and emitter:
-            await emitter(
-                {"type": "status", "data": {"description": description, "done": done}}
-            )
-
-    async def _emit_notification(self, emitter, content: str, ntype: str = "info"):
-        """发送通知事件 (info/success/warning/error)。"""
-        if emitter:
-            await emitter(
-                {"type": "notification", "data": {"type": ntype, "content": content}}
-            )
-
-    def _remove_existing_html(self, content: str) -> str:
-        """移除内容中已有的插件生成 HTML 代码块 (通过标记识别)。"""
-        pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
-        return re.sub(pattern, "", content).strip()
-
-    def _extract_text_content(self, content) -> str:
-        """从消息内容中提取文本，支持多模态消息格式"""
-        if isinstance(content, str):
-            return content
-        elif isinstance(content, list):
-            # 多模态消息: [{"type": "text", "text": "..."}, {"type": "image_url", ...}]
-            text_parts = []
-            for item in content:
-                if isinstance(item, dict) and item.get("type") == "text":
-                    text_parts.append(item.get("text", ""))
-                elif isinstance(item, str):
-                    text_parts.append(item)
-            return "\n".join(text_parts)
-        return str(content) if content else ""
-
-    def _merge_html(
-        self,
-        existing_html_code: str,
-        new_content: str,
-        new_styles: str = "",
-        new_scripts: str = "",
-        user_language: str = "zh-CN",
-    ) -> str:
-        """
-        将新内容合并到现有的 HTML 容器中，或者创建一个新的容器。
-        """
-        if (
-            "<!-- OPENWEBUI_PLUGIN_OUTPUT -->" in existing_html_code
-            and "<!-- CONTENT_INSERTION_POINT -->" in existing_html_code
-        ):
-            base_html = existing_html_code
-            base_html = re.sub(r"^```html\s*", "", base_html)
-            base_html = re.sub(r"\s*```$", "", base_html)
-        else:
-            base_html = HTML_WRAPPER_TEMPLATE.replace("{user_language}", user_language)
-
-        wrapped_content = f'<div class="plugin-item">\n{new_content}\n</div>'
-
-        if new_styles:
-            base_html = base_html.replace(
-                "/* STYLES_INSERTION_POINT */",
-                f"{new_styles}\n/* STYLES_INSERTION_POINT */",
-            )
-
-        base_html = base_html.replace(
-            "<!-- CONTENT_INSERTION_POINT -->",
-            f"{wrapped_content}\n<!-- CONTENT_INSERTION_POINT -->",
-        )
-
-        if new_scripts:
-            base_html = base_html.replace(
-                "<!-- SCRIPTS_INSERTION_POINT -->",
-                f"{new_scripts}\n<!-- SCRIPTS_INSERTION_POINT -->",
-            )
-
-        return base_html.strip()
-
-    def _build_content_html(self, context: dict) -> str:
-        """
-        使用上下文数据构建内容 HTML。
-        """
-        return (
-            CONTENT_TEMPLATE_SUMMARY.replace(
-                "{user_name}", context.get("user_name", "用户")
-            )
-            .replace(
-                "{current_date_time_str}", context.get("current_date_time_str", "")
-            )
-            .replace("{current_year}", context.get("current_year", ""))
-            .replace("{summary_html}", context.get("summary_html", ""))
-            .replace("{keypoints_html}", context.get("keypoints_html", ""))
-            .replace("{actions_html}", context.get("actions_html", ""))
-        )
-
-    async def action(
-        self,
-        body: dict,
-        __user__: Optional[Dict[str, Any]] = None,
-        __event_emitter__: Optional[Any] = None,
-        __request__: Optional[Request] = None,
-    ) -> Optional[dict]:
-        logger.info("Action: 精读启动 (v2.0.0 - Deep Reading)")
-
-        if isinstance(__user__, (list, tuple)):
-            user_language = (
-                __user__[0].get("language", "zh-CN") if __user__ else "zh-CN"
-            )
-            user_name = __user__[0].get("name", "用户") if __user__[0] else "用户"
-            user_id = (
-                __user__[0]["id"]
-                if __user__ and "id" in __user__[0]
-                else "unknown_user"
-            )
-        elif isinstance(__user__, dict):
-            user_language = __user__.get("language", "zh-CN")
-            user_name = __user__.get("name", "用户")
-            user_id = __user__.get("id", "unknown_user")
-
-        now = datetime.now()
-        current_date_time_str = now.strftime("%Y年%m月%d日 %H:%M:%S")
-        current_weekday_en = now.strftime("%A")
-        current_weekday = self.weekday_map.get(current_weekday_en, current_weekday_en)
-        current_year = now.strftime("%Y")
-        current_timezone_str = "未知时区"
-
-        original_content = ""
-        try:
-            messages = body.get("messages", [])
-            if not messages:
-                raise ValueError("无法获取有效的用户消息内容。")
-
-            # Get last N messages based on MESSAGE_COUNT
-            message_count = min(self.valves.MESSAGE_COUNT, len(messages))
-            recent_messages = messages[-message_count:]
-
-            # Aggregate content from selected messages with labels
-            aggregated_parts = []
-            for i, msg in enumerate(recent_messages, 1):
-                text_content = self._extract_text_content(msg.get("content"))
-                if text_content:
-                    role = msg.get("role", "unknown")
-                    role_label = (
-                        "用户"
-                        if role == "user"
-                        else "助手" if role == "assistant" else role
-                    )
-                    aggregated_parts.append(f"{text_content}")
-
-            if not aggregated_parts:
-                raise ValueError("无法获取有效的用户消息内容。")
-
-            original_content = "\n\n---\n\n".join(aggregated_parts)
-
-            if len(original_content) < self.valves.MIN_TEXT_LENGTH:
-                short_text_message = f"文本内容过短({len(original_content)}字符)，建议至少{self.valves.MIN_TEXT_LENGTH}字符以获得有效的深度分析。\n\n💡 提示：对于短文本，建议使用'⚡ 闪记卡'进行快速提炼。"
-                await self._emit_notification(
-                    __event_emitter__, short_text_message, "warning"
-                )
-                return {
-                    "messages": [
-                        {"role": "assistant", "content": f"⚠️ {short_text_message}"}
-                    ]
-                }
-
-            # Recommend for longer texts
-            if len(original_content) < self.valves.RECOMMENDED_MIN_LENGTH:
-                await self._emit_notification(
-                    __event_emitter__,
-                    f"文本长度为{len(original_content)}字符。建议{self.valves.RECOMMENDED_MIN_LENGTH}字符以上可获得更好的分析效果。",
-                    "info",
-                )
-
-            await self._emit_notification(
-                __event_emitter__, "📖 精读已启动，正在进行深度分析...", "info"
-            )
-            await self._emit_status(
-                __event_emitter__, "📖 精读: 深入分析文本，提炼精华...", False
-            )
-
-            formatted_user_prompt = USER_PROMPT_GENERATE_SUMMARY.format(
-                user_name=user_name,
-                current_date_time_str=current_date_time_str,
-                current_weekday=current_weekday,
-                current_timezone_str=current_timezone_str,
-                user_language=user_language,
-                long_text_content=original_content,
-            )
-
-            # 确定使用的模型
-            target_model = self.valves.MODEL_ID
-            if not target_model:
-                target_model = body.get("model")
-
-            llm_payload = {
-                "model": target_model,
-                "messages": [
-                    {"role": "system", "content": SYSTEM_PROMPT_READING_ASSISTANT},
-                    {"role": "user", "content": formatted_user_prompt},
-                ],
-                "stream": False,
-            }
-
-            user_obj = Users.get_user_by_id(user_id)
-            if not user_obj:
-                raise ValueError(f"无法获取用户对象, 用户ID: {user_id}")
-
-            llm_response = await generate_chat_completion(
-                __request__, llm_payload, user_obj
-            )
-            assistant_response_content = llm_response["choices"][0]["message"][
-                "content"
-            ]
-
-            processed_content = self._process_llm_output(assistant_response_content)
-
-            context = {
-                "user_language": user_language,
-                "user_name": user_name,
-                "current_date_time_str": current_date_time_str,
-                "current_weekday": current_weekday,
-                "current_year": current_year,
-                **processed_content,
-            }
-
-            content_html = self._build_content_html(context)
-
-            # Extract existing HTML if any
-            existing_html_block = ""
-            match = re.search(
-                r"```html\s*(<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?)```",
-                original_content,
-            )
-            if match:
-                existing_html_block = match.group(1)
-
-            if self.valves.CLEAR_PREVIOUS_HTML:
-                original_content = self._remove_existing_html(original_content)
-                final_html = self._merge_html(
-                    "", content_html, CSS_TEMPLATE_SUMMARY, "", user_language
-                )
-            else:
-                if existing_html_block:
-                    original_content = self._remove_existing_html(original_content)
-                    final_html = self._merge_html(
-                        existing_html_block,
-                        content_html,
-                        CSS_TEMPLATE_SUMMARY,
-                        "",
-                        user_language,
-                    )
-                else:
-                    final_html = self._merge_html(
-                        "", content_html, CSS_TEMPLATE_SUMMARY, "", user_language
-                    )
-
-            html_embed_tag = f"```html\n{final_html}\n```"
-            body["messages"][-1]["content"] = f"{original_content}\n\n{html_embed_tag}"
-
-            await self._emit_status(__event_emitter__, "📖 精读: 分析完成!", True)
-            await self._emit_notification(
-                __event_emitter__,
-                f"📖 精读完成，{user_name}！深度分析报告已生成。",
-                "success",
-            )
-
-        except Exception as e:
-            error_message = f"精读处理失败: {str(e)}"
-            logger.error(f"精读错误: {error_message}", exc_info=True)
-            user_facing_error = f"抱歉, 精读在处理时遇到错误: {str(e)}。\n请检查Open WebUI后端日志获取更多详情。"
-            body["messages"][-1][
-                "content"
-            ] = f"{original_content}\n\n❌ **错误:** {user_facing_error}"
-
-            await self._emit_status(__event_emitter__, "精读: 处理失败。", True)
-            await self._emit_notification(
-                __event_emitter__, f"精读处理失败, {user_name}!", "error"
-            )
-
-        return body

plugins/filters/async-context-compression/README.md

@@ -1,15 +1,26 @@
 # Async Context Compression Filter
 
-**Author:** [Fu-Jie](https://github.com/Fu-Jie) | **Version:** 1.1.0 | **License:** MIT
+**Author:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **Version:** 1.1.3 | **License:** MIT
 
 This filter reduces token consumption in long conversations through intelligent summarization and message compression while keeping conversations coherent.
 
-## What's new in 1.1.0 
+## What's new in 1.1.3
+- **Improved Compatibility**: Changed summary injection role from `user` to `assistant` for better compatibility across different LLMs.
+- **Enhanced Stability**: Fixed a race condition in state management that could cause "inlet state not found" warnings in high-concurrency scenarios.
+- **Bug Fixes**: Corrected default model handling to prevent misleading logs when no model is specified.
+
+## What's new in 1.1.2
+
+- **Open WebUI v0.7.x Compatibility**: Resolved a critical database session binding error affecting Open WebUI v0.7.x users. The plugin now dynamically discovers the database engine and session context, ensuring compatibility across versions.
+- **Enhanced Error Reporting**: Errors during background summary generation are now reported via both the status bar and browser console.
+- **Robust Model Handling**: Improved handling of missing or invalid model IDs to prevent crashes.
+
+## What's new in 1.1.1
+
+- **Frontend Debugging**: Added `show_debug_log` option to print debug info to the browser console (F12).
+- **Optimized Compression**: Improved token calculation logic to prevent aggressive truncation of history, ensuring more context is retained.
+
 
-- Reuses Open WebUI's shared database connection by default (no custom engine or env vars required).
-- Token-based thresholds (`compression_threshold_tokens`, `max_context_tokens`) for safer long-context handling.
-- Per-model overrides via `model_thresholds` for mixed-model workflows.
-- Documentation now mirrors the latest async workflow and retention-first injection.
 
 ---
 
@@ -54,6 +65,7 @@ It is recommended to keep this filter early in the chain so it runs before filte
 | `summary_temperature`          | `0.3`    | Randomness for summary generation. Lower is more deterministic.                                                                                                       |
 | `model_thresholds`             | `{}`     | Per-model overrides for `compression_threshold_tokens` and `max_context_tokens` (useful for mixed models).                                                            |
 | `debug_mode`                   | `true`   | Log verbose debug info. Set to `false` in production.                                                                                                                 |
+| `show_debug_log`               | `false`  | Print debug logs to browser console (F12). Useful for frontend debugging.                                                                                             |
 
 ---
 

plugins/filters/async-context-compression/README_CN.md

@@ -1,17 +1,28 @@
 # 异步上下文压缩过滤器
 
-**作者:** [Fu-Jie](https://github.com/Fu-Jie) | **版本:** 1.2.0 | **许可证:** MIT
+**作者:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **版本:** 1.1.3 | **许可证:** MIT
 
 > **重要提示**：为了确保所有过滤器的可维护性和易用性，每个过滤器都应附带清晰、完整的文档，以确保其功能、配置和使用方法得到充分说明。
 
 本过滤器通过智能摘要和消息压缩技术，在保持对话连贯性的同时，显著降低长对话的 Token 消耗。
 
-## 1.1.0 版本更新
+## 1.1.3 版本更新
+- **兼容性提升**: 将摘要注入角色从 `user` 改为 `assistant`，以提高在不同 LLM 之间的兼容性。
+- **稳定性增强**: 修复了状态管理中的竞态条件，解决了高并发场景下可能出现的“无法获取 inlet 状态”警告。
+- **Bug 修复**: 修正了默认模型处理逻辑，防止在未指定模型时产生误导性日志。
+
+## 1.1.2 版本更新
+
+- **Open WebUI v0.7.x 兼容性**: 修复了影响 Open WebUI v0.7.x 用户的严重数据库会话绑定错误。插件现在动态发现数据库引擎和会话上下文，确保跨版本兼容性。
+- **增强错误报告**: 后台摘要生成过程中的错误现在会通过状态栏和浏览器控制台同时报告。
+- **健壮的模型处理**: 改进了对缺失或无效模型 ID 的处理，防止程序崩溃。
+
+## 1.1.1 版本更新
+
+- **前端调试**: 新增 `show_debug_log` 选项，支持在浏览器控制台 (F12) 打印调试信息。
+- **压缩优化**: 优化 Token 计算逻辑，防止历史记录被过度截断，保留更多上下文。
+
 
-- 默认复用 OpenWebUI 内置数据库连接，无需自建引擎、无需配置 `DATABASE_URL`。
-- 基于 Token 的阈值控制（`compression_threshold_tokens`、`max_context_tokens`），长上下文更安全。
-- 支持 `model_thresholds` 为不同模型设置专属阈值，适合混用多模型场景。
-- 文档同步最新异步工作流与“先保留再注入”策略。
 
 ---
 
@@ -94,6 +105,11 @@
 - **默认值**: `true`
 - **描述**: 是否在 Open WebUI 的控制台日志中打印详细的调试信息（如 Token 计数、压缩进度、数据库操作等）。生产环境建议设为 `false`。
 
+#### `show_debug_log`
+
+- **默认值**: `false`
+- **描述**: 是否在浏览器控制台 (F12) 打印调试日志。便于前端调试。
+
 ---
 
 ## 故障排除

plugins/filters/async-context-compression/async_context_compression.py

@@ -2,10 +2,11 @@
 title: Async Context Compression
 id: async_context_compression
 author: Fu-Jie
-author_url: https://github.com/Fu-Jie
-funding_url: https://github.com/Fu-Jie/awesome-openwebui
+author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
 description: Reduces token consumption in long conversations while maintaining coherence through intelligent summarization and message compression.
-version: 1.1.0
+version: 1.1.3
+openwebui_id: b1655bc8-6de9-4cad-8cb5-a6f7829a02ce
 license: MIT
 
 ═══════════════════════════════════════════════════════════════════════════════
@@ -138,6 +139,10 @@ debug_mode
   Default: true
   Description: Prints detailed debug information to the log. Recommended to set to `false` in production.
 
+show_debug_log
+  Default: false
+  Description: Print debug logs to browser console (F12). Useful for frontend debugging.
+
 🔧 Deployment
 ═══════════════════════════════════════════════════════
 
@@ -244,6 +249,7 @@ import asyncio
 import json
 import hashlib
 import time
+import contextlib
 
 # Open WebUI built-in imports
 from open_webui.utils.chat import generate_chat_completion
@@ -252,9 +258,10 @@ from fastapi.requests import Request
 from open_webui.main import app as webui_app
 
 # Open WebUI internal database (re-use shared connection)
-from open_webui.internal.db import engine as owui_engine
-from open_webui.internal.db import Session as owui_Session
-from open_webui.internal.db import Base as owui_Base
+try:
+    from open_webui.internal import db as owui_db
+except ModuleNotFoundError:  # pragma: no cover - filter runs inside Open WebUI
+    owui_db = None
 
 # Try to import tiktoken
 try:
@@ -264,14 +271,91 @@ except ImportError:
 
 # Database imports
 from sqlalchemy import Column, String, Text, DateTime, Integer, inspect
+from sqlalchemy.orm import declarative_base, sessionmaker
+from sqlalchemy.engine import Engine
 from datetime import datetime
 
 
+def _discover_owui_engine(db_module: Any) -> Optional[Engine]:
+    """Discover the Open WebUI SQLAlchemy engine via provided db module helpers."""
+    if db_module is None:
+        return None
+
+    db_context = getattr(db_module, "get_db_context", None) or getattr(
+        db_module, "get_db", None
+    )
+    if callable(db_context):
+        try:
+            with db_context() as session:
+                try:
+                    return session.get_bind()
+                except AttributeError:
+                    return getattr(session, "bind", None) or getattr(
+                        session, "engine", None
+                    )
+        except Exception as exc:
+            print(f"[DB Discover] get_db_context failed: {exc}")
+
+    for attr in ("engine", "ENGINE", "bind", "BIND"):
+        candidate = getattr(db_module, attr, None)
+        if candidate is not None:
+            return candidate
+
+    return None
+
+
+def _discover_owui_schema(db_module: Any) -> Optional[str]:
+    """Discover the Open WebUI database schema name if configured."""
+    if db_module is None:
+        return None
+
+    try:
+        base = getattr(db_module, "Base", None)
+        metadata = getattr(base, "metadata", None) if base is not None else None
+        candidate = getattr(metadata, "schema", None) if metadata is not None else None
+        if isinstance(candidate, str) and candidate.strip():
+            return candidate.strip()
+    except Exception as exc:
+        print(f"[DB Discover] Base metadata schema lookup failed: {exc}")
+
+    try:
+        metadata_obj = getattr(db_module, "metadata_obj", None)
+        candidate = (
+            getattr(metadata_obj, "schema", None) if metadata_obj is not None else None
+        )
+        if isinstance(candidate, str) and candidate.strip():
+            return candidate.strip()
+    except Exception as exc:
+        print(f"[DB Discover] metadata_obj schema lookup failed: {exc}")
+
+    try:
+        from open_webui import env as owui_env
+
+        candidate = getattr(owui_env, "DATABASE_SCHEMA", None)
+        if isinstance(candidate, str) and candidate.strip():
+            return candidate.strip()
+    except Exception as exc:
+        print(f"[DB Discover] env schema lookup failed: {exc}")
+
+    return None
+
+
+owui_engine = _discover_owui_engine(owui_db)
+owui_schema = _discover_owui_schema(owui_db)
+owui_Base = getattr(owui_db, "Base", None) if owui_db is not None else None
+if owui_Base is None:
+    owui_Base = declarative_base()
+
+
 class ChatSummary(owui_Base):
     """Chat Summary Storage Table"""
 
     __tablename__ = "chat_summary"
-    __table_args__ = {"extend_existing": True}
+    __table_args__ = (
+        {"extend_existing": True, "schema": owui_schema}
+        if owui_schema
+        else {"extend_existing": True}
+    )
 
     id = Column(Integer, primary_key=True, autoincrement=True)
     chat_id = Column(String(255), unique=True, nullable=False, index=True)
@@ -284,14 +368,69 @@ class ChatSummary(owui_Base):
 class Filter:
     def __init__(self):
         self.valves = self.Valves()
+        self._owui_db = owui_db
         self._db_engine = owui_engine
-        self._SessionLocal = owui_Session
-        self.temp_state = {}  # Used to pass temporary data between inlet and outlet
+        self._db_engine = owui_engine
+        self._fallback_session_factory = (
+            sessionmaker(bind=self._db_engine) if self._db_engine else None
+        )
+        self._fallback_session_factory = (
+            sessionmaker(bind=self._db_engine) if self._db_engine else None
+        )
         self._init_database()
 
+    @contextlib.contextmanager
+    def _db_session(self):
+        """Yield a database session using Open WebUI helpers with graceful fallbacks."""
+        db_module = self._owui_db
+        db_context = None
+        if db_module is not None:
+            db_context = getattr(db_module, "get_db_context", None) or getattr(
+                db_module, "get_db", None
+            )
+
+        if callable(db_context):
+            with db_context() as session:
+                yield session
+                return
+
+        factory = None
+        if db_module is not None:
+            factory = getattr(db_module, "SessionLocal", None) or getattr(
+                db_module, "ScopedSession", None
+            )
+        if callable(factory):
+            session = factory()
+            try:
+                yield session
+            finally:
+                close = getattr(session, "close", None)
+                if callable(close):
+                    close()
+            return
+
+        if self._fallback_session_factory is None:
+            raise RuntimeError(
+                "Open WebUI database session is unavailable. Ensure Open WebUI's database layer is initialized."
+            )
+
+        session = self._fallback_session_factory()
+        try:
+            yield session
+        finally:
+            try:
+                session.close()
+            except Exception as exc:  # pragma: no cover - best-effort cleanup
+                print(f"[Database] ⚠️ Failed to close fallback session: {exc}")
+
     def _init_database(self):
         """Initializes the database table using Open WebUI's shared connection."""
         try:
+            if self._db_engine is None:
+                raise RuntimeError(
+                    "Open WebUI database engine is unavailable. Ensure Open WebUI is configured with a valid DATABASE_URL."
+                )
+
             # Check if table exists using SQLAlchemy inspect
             inspector = inspect(self._db_engine)
             if not inspector.has_table("chat_summary"):
@@ -354,11 +493,14 @@ class Filter:
         debug_mode: bool = Field(
             default=True, description="Enable detailed logging for debugging."
         )
+        show_debug_log: bool = Field(
+            default=False, description="Print debug logs to browser console (F12)"
+        )
 
     def _save_summary(self, chat_id: str, summary: str, compressed_count: int):
         """Saves the summary to the database."""
         try:
-            with self._SessionLocal() as session:
+            with self._db_session() as session:
                 # Find existing record
                 existing = session.query(ChatSummary).filter_by(chat_id=chat_id).first()
 
@@ -398,7 +540,7 @@ class Filter:
     def _load_summary_record(self, chat_id: str) -> Optional[ChatSummary]:
         """Loads the summary record object from the database."""
         try:
-            with self._SessionLocal() as session:
+            with self._db_session() as session:
                 record = session.query(ChatSummary).filter_by(chat_id=chat_id).first()
                 if record:
                     # Detach the object from the session so it can be used after session close
@@ -479,41 +621,137 @@ class Filter:
             "max_context_tokens": self.valves.max_context_tokens,
         }
 
-    def _inject_summary_to_first_message(self, message: dict, summary: str) -> dict:
-        """Injects the summary into the first message (prepended to content)."""
-        content = message.get("content", "")
-        summary_block = f"【Historical Conversation Summary】\n{summary}\n\n---\nBelow is the recent conversation:\n\n"
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        Unified extraction of chat context information (chat_id, message_id).
+        Prioritizes extraction from body, then metadata.
+        """
+        chat_id = ""
+        message_id = ""
 
-        # Handle different content types
-        if isinstance(content, list):  # Multimodal content
-            # Find the first text part and insert the summary before it
-            new_content = []
-            summary_inserted = False
+        # 1. Try to get from body
+        if isinstance(body, dict):
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id is usually 'id' in body
 
-            for part in content:
-                if (
-                    isinstance(part, dict)
-                    and part.get("type") == "text"
-                    and not summary_inserted
-                ):
-                    # Prepend summary to the first text part
-                    new_content.append(
-                        {"type": "text", "text": summary_block + part.get("text", "")}
-                    )
-                    summary_inserted = True
-                else:
-                    new_content.append(part)
+            # Check body.metadata as fallback
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
 
-            # If no text part, insert at the beginning
-            if not summary_inserted:
-                new_content.insert(0, {"type": "text", "text": summary_block})
+        # 2. Try to get from __metadata__ (as supplement)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
 
-            message["content"] = new_content
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
 
-        elif isinstance(content, str):  # Plain text
-            message["content"] = summary_block + content
+    async def _emit_debug_log(
+        self,
+        __event_call__,
+        chat_id: str,
+        original_count: int,
+        compressed_count: int,
+        summary_length: int,
+        kept_first: int,
+        kept_last: int,
+    ):
+        """Emit debug log to browser console via JS execution"""
+        if not self.valves.show_debug_log or not __event_call__:
+            return
 
-        return message
+        try:
+            # Prepare data for JS
+            log_data = {
+                "chatId": chat_id,
+                "originalCount": original_count,
+                "compressedCount": compressed_count,
+                "summaryLength": summary_length,
+                "keptFirst": kept_first,
+                "keptLast": kept_last,
+                "ratio": (
+                    f"{(1 - compressed_count/original_count)*100:.1f}%"
+                    if original_count > 0
+                    else "0%"
+                ),
+            }
+
+            # Construct JS code
+            js_code = f"""
+                (async function() {{
+                    console.group("🗜️ Async Context Compression Debug");
+                    console.log("Chat ID:", {json.dumps(chat_id)});
+                    console.log("Messages:", {original_count} + " -> " + {compressed_count});
+                    console.log("Compression Ratio:", {json.dumps(log_data['ratio'])});
+                    console.log("Summary Length:", {summary_length} + " chars");
+                    console.log("Configuration:", {{
+                        "Keep First": {kept_first},
+                        "Keep Last": {kept_last}
+                    }});
+                    console.groupEnd();
+                }})();
+            """
+
+            await __event_call__(
+                {
+                    "type": "execute",
+                    "data": {"code": js_code},
+                }
+            )
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
+
+    async def _log(self, message: str, type: str = "info", event_call=None):
+        """Unified logging to both backend (print) and frontend (console.log)"""
+        # Backend logging
+        if self.valves.debug_mode:
+            print(message)
+
+        # Frontend logging
+        if self.valves.show_debug_log and event_call:
+            try:
+                css = "color: #3b82f6;"  # Blue default
+                if type == "error":
+                    css = "color: #ef4444; font-weight: bold;"  # Red
+                elif type == "warning":
+                    css = "color: #f59e0b;"  # Orange
+                elif type == "success":
+                    css = "color: #10b981; font-weight: bold;"  # Green
+
+                # Clean message for frontend: remove separators and extra newlines
+                lines = message.split("\n")
+                # Keep lines that don't start with lots of equals or hyphens
+                filtered_lines = [
+                    line
+                    for line in lines
+                    if not line.strip().startswith("====")
+                    and not line.strip().startswith("----")
+                ]
+                clean_message = "\n".join(filtered_lines).strip()
+
+                if not clean_message:
+                    return
+
+                # Escape quotes in message for JS string
+                safe_message = clean_message.replace('"', '\\"').replace("\n", "\\n")
+
+                js_code = f"""
+                    console.log("%c[Compression] {safe_message}", "{css}");
+                """
+                await event_call({"type": "execute", "data": {"code": js_code}})
+            except Exception as e:
+                print(f"Failed to emit log to frontend: {e}")
 
     async def inlet(
         self,
@@ -521,36 +759,42 @@ class Filter:
         __user__: Optional[dict] = None,
         __metadata__: dict = None,
         __event_emitter__: Callable[[Any], Awaitable[None]] = None,
+        __event_call__: Callable[[Any], Awaitable[None]] = None,
     ) -> dict:
         """
         Executed before sending to the LLM.
         Compression Strategy: Only responsible for injecting existing summaries, no Token calculation.
         """
         messages = body.get("messages", [])
-        chat_id = __metadata__["chat_id"]
+        chat_ctx = self._get_chat_context(body, __metadata__)
+        chat_id = chat_ctx["chat_id"]
 
-        if self.valves.debug_mode:
-            print(f"\n{'='*60}")
-            print(f"[Inlet] Chat ID: {chat_id}")
-            print(f"[Inlet] Received {len(messages)} messages")
+        if not chat_id:
+            await self._log(
+                "[Inlet] ❌ Missing chat_id in metadata, skipping compression",
+                type="error",
+                event_call=__event_call__,
+            )
+            return body
+
+        if self.valves.debug_mode or self.valves.show_debug_log:
+            await self._log(
+                f"\n{'='*60}\n[Inlet] Chat ID: {chat_id}\n[Inlet] Received {len(messages)} messages",
+                event_call=__event_call__,
+            )
 
         # Record the target compression progress for the original messages, for use in outlet
         # Target is to compress up to the (total - keep_last) message
         target_compressed_count = max(0, len(messages) - self.valves.keep_last)
 
-        # [Optimization] Simple state cleanup check
-        if chat_id in self.temp_state:
-            if self.valves.debug_mode:
-                print(
-                    f"[Inlet] ⚠️ Overwriting unconsumed old state (Chat ID: {chat_id})"
-                )
+        # Record the target compression progress for the original messages, for use in outlet
+        # Target is to compress up to the (total - keep_last) message
+        target_compressed_count = max(0, len(messages) - self.valves.keep_last)
 
-        self.temp_state[chat_id] = target_compressed_count
-
-        if self.valves.debug_mode:
-            print(
-                f"[Inlet] Recorded target compression progress: {target_compressed_count}"
-            )
+        await self._log(
+            f"[Inlet] Recorded target compression progress: {target_compressed_count}",
+            event_call=__event_call__,
+        )
 
         # Load summary record
         summary_record = await asyncio.to_thread(self._load_summary_record, chat_id)
@@ -578,7 +822,7 @@ class Filter:
                 f"---\n"
                 f"Below is the recent conversation:"
             )
-            summary_msg = {"role": "user", "content": summary_content}
+            summary_msg = {"role": "assistant", "content": summary_content}
 
             # 3. Tail messages (Tail) - All messages starting from the last compression point
             # Note: Must ensure head messages are not duplicated
@@ -599,19 +843,32 @@ class Filter:
                     }
                 )
 
-            if self.valves.debug_mode:
-                print(
-                    f"[Inlet] Applied summary: Head({len(head_messages)}) + Summary + Tail({len(tail_messages)})"
-                )
+            await self._log(
+                f"[Inlet] Applied summary: Head({len(head_messages)}) + Summary + Tail({len(tail_messages)})",
+                type="success",
+                event_call=__event_call__,
+            )
+
+            # Emit debug log to frontend (Keep the structured log as well)
+            await self._emit_debug_log(
+                __event_call__,
+                chat_id,
+                len(messages),
+                len(final_messages),
+                len(summary_record.summary),
+                self.valves.keep_first,
+                self.valves.keep_last,
+            )
         else:
             # No summary, use original messages
             final_messages = messages
 
         body["messages"] = final_messages
 
-        if self.valves.debug_mode:
-            print(f"[Inlet] Final send: {len(body['messages'])} messages")
-            print(f"{'='*60}\n")
+        await self._log(
+            f"[Inlet] Final send: {len(body['messages'])} messages\n{'='*60}\n",
+            event_call=__event_call__,
+        )
 
         return body
 
@@ -621,29 +878,51 @@ class Filter:
         __user__: Optional[dict] = None,
         __metadata__: dict = None,
         __event_emitter__: Callable[[Any], Awaitable[None]] = None,
+        __event_call__: Callable[[Any], Awaitable[None]] = None,
     ) -> dict:
         """
         Executed after the LLM response is complete.
         Calculates Token count in the background and triggers summary generation (does not block current response, does not affect content output).
         """
-        chat_id = __metadata__["chat_id"]
-        model = body.get("model", "gpt-3.5-turbo")
+        chat_ctx = self._get_chat_context(body, __metadata__)
+        chat_id = chat_ctx["chat_id"]
+        if not chat_id:
+            await self._log(
+                "[Outlet] ❌ Missing chat_id in metadata, skipping compression",
+                type="error",
+                event_call=__event_call__,
+            )
+            return body
+        model = body.get("model") or ""
 
-        if self.valves.debug_mode:
-            print(f"\n{'='*60}")
-            print(f"[Outlet] Chat ID: {chat_id}")
-            print(f"[Outlet] Response complete")
+        # Calculate target compression progress directly
+        # Assuming body['messages'] in outlet contains the full history (including new response)
+        messages = body.get("messages", [])
+        target_compressed_count = max(0, len(messages) - self.valves.keep_last)
+
+        if self.valves.debug_mode or self.valves.show_debug_log:
+            await self._log(
+                f"\n{'='*60}\n[Outlet] Chat ID: {chat_id}\n[Outlet] Response complete\n[Outlet] Calculated target compression progress: {target_compressed_count} (Messages: {len(messages)})",
+                event_call=__event_call__,
+            )
 
         # Process Token calculation and summary generation asynchronously in the background (do not wait for completion, do not affect output)
         asyncio.create_task(
             self._check_and_generate_summary_async(
-                chat_id, model, body, __user__, __event_emitter__
+                chat_id,
+                model,
+                body,
+                __user__,
+                target_compressed_count,
+                __event_emitter__,
+                __event_call__,
             )
         )
 
-        if self.valves.debug_mode:
-            print(f"[Outlet] Background processing started")
-            print(f"{'='*60}\n")
+        await self._log(
+            f"[Outlet] Background processing started\n{'='*60}\n",
+            event_call=__event_call__,
+        )
 
         return body
 
@@ -653,7 +932,9 @@ class Filter:
         model: str,
         body: dict,
         user_data: Optional[dict],
+        target_compressed_count: Optional[int],
         __event_emitter__: Callable[[Any], Awaitable[None]] = None,
+        __event_call__: Callable[[Any], Awaitable[None]] = None,
     ):
         """
         Background processing: Calculates Token count and generates summary (does not block response).
@@ -667,36 +948,58 @@ class Filter:
                 "compression_threshold_tokens", self.valves.compression_threshold_tokens
             )
 
-            if self.valves.debug_mode:
-                print(f"\n[🔍 Background Calculation] Starting Token count...")
+            await self._log(
+                f"\n[🔍 Background Calculation] Starting Token count...",
+                event_call=__event_call__,
+            )
 
             # Calculate Token count in a background thread
             current_tokens = await asyncio.to_thread(
                 self._calculate_messages_tokens, messages
             )
 
-            if self.valves.debug_mode:
-                print(f"[🔍 Background Calculation] Token count: {current_tokens}")
+            await self._log(
+                f"[🔍 Background Calculation] Token count: {current_tokens}",
+                event_call=__event_call__,
+            )
 
             # Check if compression is needed
             if current_tokens >= compression_threshold_tokens:
-                if self.valves.debug_mode:
-                    print(
-                        f"[🔍 Background Calculation] ⚡ Compression threshold triggered (Token: {current_tokens} >= {compression_threshold_tokens})"
-                    )
+                await self._log(
+                    f"[🔍 Background Calculation] ⚡ Compression threshold triggered (Token: {current_tokens} >= {compression_threshold_tokens})",
+                    type="warning",
+                    event_call=__event_call__,
+                )
 
                 # Proceed to generate summary
                 await self._generate_summary_async(
-                    messages, chat_id, body, user_data, __event_emitter__
+                    messages,
+                    chat_id,
+                    body,
+                    user_data,
+                    target_compressed_count,
+                    __event_emitter__,
+                    __event_call__,
                 )
             else:
-                if self.valves.debug_mode:
-                    print(
-                        f"[🔍 Background Calculation] Compression threshold not reached (Token: {current_tokens} < {compression_threshold_tokens})"
-                    )
+                await self._log(
+                    f"[🔍 Background Calculation] Compression threshold not reached (Token: {current_tokens} < {compression_threshold_tokens})",
+                    event_call=__event_call__,
+                )
 
         except Exception as e:
-            print(f"[🔍 Background Calculation] ❌ Error: {str(e)}")
+            await self._log(
+                f"[🔍 Background Calculation] ❌ Error: {str(e)}",
+                type="error",
+                event_call=__event_call__,
+            )
+
+    def _clean_model_id(self, model_id: Optional[str]) -> Optional[str]:
+        """Cleans the model ID by removing whitespace and quotes."""
+        if not model_id:
+            return None
+        cleaned = model_id.strip().strip('"').strip("'")
+        return cleaned if cleaned else None
 
     async def _generate_summary_async(
         self,
@@ -704,7 +1007,9 @@ class Filter:
         chat_id: str,
         body: dict,
         user_data: Optional[dict],
+        target_compressed_count: Optional[int],
         __event_emitter__: Callable[[Any], Awaitable[None]] = None,
+        __event_call__: Callable[[Any], Awaitable[None]] = None,
     ):
         """
         Generates summary asynchronously (runs in background, does not block response).
@@ -714,18 +1019,19 @@ class Filter:
         3. Generate summary for the remaining middle messages.
         """
         try:
-            if self.valves.debug_mode:
-                print(f"\n[🤖 Async Summary Task] Starting...")
+            await self._log(
+                f"\n[🤖 Async Summary Task] Starting...", event_call=__event_call__
+            )
 
             # 1. Get target compression progress
-            # Prioritize getting from temp_state (calculated by inlet). If unavailable (e.g., after restart), assume current is full history.
-            target_compressed_count = self.temp_state.pop(chat_id, None)
+            # If target_compressed_count is not passed (should not happen with new logic), estimate it
             if target_compressed_count is None:
                 target_compressed_count = max(0, len(messages) - self.valves.keep_last)
-                if self.valves.debug_mode:
-                    print(
-                        f"[🤖 Async Summary Task] ⚠️ Could not get inlet state, estimating progress using current message count: {target_compressed_count}"
-                    )
+                await self._log(
+                    f"[🤖 Async Summary Task] ⚠️ target_compressed_count is None, estimating: {target_compressed_count}",
+                    type="warning",
+                    event_call=__event_call__,
+                )
 
             # 2. Determine the range of messages to compress (Middle)
             start_index = self.valves.keep_first
@@ -735,25 +1041,33 @@ class Filter:
 
             # Ensure indices are valid
             if start_index >= end_index:
-                if self.valves.debug_mode:
-                    print(
-                        f"[🤖 Async Summary Task] Middle messages empty (Start: {start_index}, End: {end_index}), skipping"
-                    )
+                await self._log(
+                    f"[🤖 Async Summary Task] Middle messages empty (Start: {start_index}, End: {end_index}), skipping",
+                    event_call=__event_call__,
+                )
                 return
 
             middle_messages = messages[start_index:end_index]
 
-            if self.valves.debug_mode:
-                print(
-                    f"[🤖 Async Summary Task] Middle messages to process: {len(middle_messages)}"
-                )
+            await self._log(
+                f"[🤖 Async Summary Task] Middle messages to process: {len(middle_messages)}",
+                event_call=__event_call__,
+            )
 
             # 3. Check Token limit and truncate (Max Context Truncation)
             # [Optimization] Use the summary model's (if any) threshold to decide how many middle messages can be processed
             # This allows using a long-window model (like gemini-flash) to compress history exceeding the current model's window
-            summary_model_id = self.valves.summary_model or body.get(
-                "model", "gpt-3.5-turbo"
-            )
+            summary_model_id = self._clean_model_id(
+                self.valves.summary_model
+            ) or self._clean_model_id(body.get("model"))
+
+            if not summary_model_id:
+                await self._log(
+                    "[🤖 Async Summary Task] ⚠️ Summary model does not exist, skipping compression",
+                    type="warning",
+                    event_call=__event_call__,
+                )
+                return
 
             thresholds = self._get_model_thresholds(summary_model_id)
             # Note: Using the summary model's max context limit here
@@ -761,22 +1075,26 @@ class Filter:
                 "max_context_tokens", self.valves.max_context_tokens
             )
 
-            if self.valves.debug_mode:
-                print(
-                    f"[🤖 Async Summary Task] Using max limit for model {summary_model_id}: {max_context_tokens} Tokens"
-                )
-
-            # Calculate current total Tokens (using summary model for counting)
-            total_tokens = await asyncio.to_thread(
-                self._calculate_messages_tokens, messages
+            await self._log(
+                f"[🤖 Async Summary Task] Using max limit for model {summary_model_id}: {max_context_tokens} Tokens",
+                event_call=__event_call__,
             )
 
-            if total_tokens > max_context_tokens:
-                excess_tokens = total_tokens - max_context_tokens
-                if self.valves.debug_mode:
-                    print(
-                        f"[🤖 Async Summary Task] ⚠️ Total Tokens ({total_tokens}) exceed summary model limit ({max_context_tokens}), need to remove approx {excess_tokens} Tokens"
-                    )
+            # Calculate tokens for middle messages only (plus buffer for prompt)
+            # We only send middle_messages to the summary model, so we shouldn't count the full history against its limit.
+            middle_tokens = await asyncio.to_thread(
+                self._calculate_messages_tokens, middle_messages
+            )
+            # Add buffer for prompt and output (approx 2000 tokens)
+            estimated_input_tokens = middle_tokens + 2000
+
+            if estimated_input_tokens > max_context_tokens:
+                excess_tokens = estimated_input_tokens - max_context_tokens
+                await self._log(
+                    f"[🤖 Async Summary Task] ⚠️ Middle messages ({middle_tokens} Tokens) + Buffer exceed summary model limit ({max_context_tokens}), need to remove approx {excess_tokens} Tokens",
+                    type="warning",
+                    event_call=__event_call__,
+                )
 
                 # Remove from the head of middle_messages
                 removed_tokens = 0
@@ -784,20 +1102,22 @@ class Filter:
 
                 while removed_tokens < excess_tokens and middle_messages:
                     msg_to_remove = middle_messages.pop(0)
-                    msg_tokens = self._count_tokens(str(msg_to_remove.get("content", "")))
+                    msg_tokens = self._count_tokens(
+                        str(msg_to_remove.get("content", ""))
+                    )
                     removed_tokens += msg_tokens
                     removed_count += 1
 
-                if self.valves.debug_mode:
-                    print(
-                        f"[🤖 Async Summary Task] Removed {removed_count} messages, totaling {removed_tokens} Tokens"
-                    )
+                await self._log(
+                    f"[🤖 Async Summary Task] Removed {removed_count} messages, totaling {removed_tokens} Tokens",
+                    event_call=__event_call__,
+                )
 
             if not middle_messages:
-                if self.valves.debug_mode:
-                    print(
-                        f"[🤖 Async Summary Task] Middle messages empty after truncation, skipping summary generation"
-                    )
+                await self._log(
+                    f"[🤖 Async Summary Task] Middle messages empty after truncation, skipping summary generation",
+                    event_call=__event_call__,
+                )
                 return
 
             # 4. Build conversation text
@@ -819,14 +1139,26 @@ class Filter:
                 )
 
             new_summary = await self._call_summary_llm(
-                None, conversation_text, body, user_data
+                None,
+                conversation_text,
+                {**body, "model": summary_model_id},
+                user_data,
+                __event_call__,
             )
 
-            # 6. Save new summary
-            if self.valves.debug_mode:
-                print(
-                    "[Optimization] Saving summary in a background thread to avoid blocking the event loop."
+            if not new_summary:
+                await self._log(
+                    "[🤖 Async Summary Task] ⚠️ Summary generation returned empty result, skipping save",
+                    type="warning",
+                    event_call=__event_call__,
                 )
+                return
+
+            # 6. Save new summary
+            await self._log(
+                "[Optimization] Saving summary in a background thread to avoid blocking the event loop.",
+                event_call=__event_call__,
+            )
 
             await asyncio.to_thread(
                 self._save_summary, chat_id, new_summary, target_compressed_count
@@ -844,16 +1176,34 @@ class Filter:
                     }
                 )
 
-            if self.valves.debug_mode:
-                print(
-                    f"[🤖 Async Summary Task] ✅ Complete! New summary length: {len(new_summary)} characters"
-                )
-                print(
-                    f"[🤖 Async Summary Task] Progress update: Compressed up to original message {target_compressed_count}"
-                )
+            await self._log(
+                f"[🤖 Async Summary Task] ✅ Complete! New summary length: {len(new_summary)} characters",
+                type="success",
+                event_call=__event_call__,
+            )
+            await self._log(
+                f"[🤖 Async Summary Task] Progress update: Compressed up to original message {target_compressed_count}",
+                event_call=__event_call__,
+            )
 
         except Exception as e:
-            print(f"[🤖 Async Summary Task] ❌ Error: {str(e)}")
+            await self._log(
+                f"[🤖 Async Summary Task] ❌ Error: {str(e)}",
+                type="error",
+                event_call=__event_call__,
+            )
+
+            if __event_emitter__:
+                await __event_emitter__(
+                    {
+                        "type": "status",
+                        "data": {
+                            "description": f"Summary Error: {str(e)[:100]}...",
+                            "done": True,
+                        },
+                    }
+                )
+
             import traceback
 
             traceback.print_exc()
@@ -890,12 +1240,15 @@ class Filter:
         new_conversation_text: str,
         body: dict,
         user_data: dict,
+        __event_call__: Callable[[Any], Awaitable[None]] = None,
     ) -> str:
         """
         Calls the LLM to generate a summary using Open WebUI's built-in method.
         """
-        if self.valves.debug_mode:
-            print(f"[🤖 LLM Call] Using Open WebUI's built-in method")
+        await self._log(
+            f"[🤖 LLM Call] Using Open WebUI's built-in method",
+            event_call=__event_call__,
+        )
 
         # Build summary prompt (Optimized)
         summary_prompt = f"""
@@ -932,10 +1285,19 @@ This conversation may contain previous summaries (as system messages or text) an
 Based on the content above, generate the summary:
 """
         # Determine the model to use
-        model = self.valves.summary_model or body.get("model", "")
+        model = self._clean_model_id(self.valves.summary_model) or self._clean_model_id(
+            body.get("model")
+        )
 
-        if self.valves.debug_mode:
-            print(f"[🤖 LLM Call] Model: {model}")
+        if not model:
+            await self._log(
+                "[🤖 LLM Call] ⚠️ Summary model does not exist, skipping summary generation",
+                type="warning",
+                event_call=__event_call__,
+            )
+            return ""
+
+        await self._log(f"[🤖 LLM Call] Model: {model}", event_call=__event_call__)
 
         # Build payload
         payload = {
@@ -953,18 +1315,19 @@ Based on the content above, generate the summary:
                 raise ValueError("Could not get user ID")
 
             # [Optimization] Get user object in a background thread to avoid blocking the event loop.
-            if self.valves.debug_mode:
-                print(
-                    "[Optimization] Getting user object in a background thread to avoid blocking the event loop."
-                )
+            await self._log(
+                "[Optimization] Getting user object in a background thread to avoid blocking the event loop.",
+                event_call=__event_call__,
+            )
             user = await asyncio.to_thread(Users.get_user_by_id, user_id)
 
             if not user:
                 raise ValueError(f"Could not find user: {user_id}")
 
-            if self.valves.debug_mode:
-                print(f"[🤖 LLM Call] User: {user.email}")
-                print(f"[🤖 LLM Call] Sending request...")
+            await self._log(
+                f"[🤖 LLM Call] User: {user.email}\n[🤖 LLM Call] Sending request...",
+                event_call=__event_call__,
+            )
 
             # Create Request object
             request = Request(scope={"type": "http", "app": webui_app})
@@ -977,20 +1340,31 @@ Based on the content above, generate the summary:
 
             summary = response["choices"][0]["message"]["content"].strip()
 
-            if self.valves.debug_mode:
-                print(f"[🤖 LLM Call] ✅ Successfully received summary")
+            await self._log(
+                f"[🤖 LLM Call] ✅ Successfully received summary",
+                type="success",
+                event_call=__event_call__,
+            )
 
             return summary
 
         except Exception as e:
-            error_message = f"Error occurred while calling LLM ({model}) to generate summary: {str(e)}"
+            error_msg = str(e)
+            # Handle specific error messages
+            if "Model not found" in error_msg:
+                error_message = f"Summary model '{model}' not found."
+            else:
+                error_message = f"Summary LLM Error ({model}): {error_msg}"
             if not self.valves.summary_model:
                 error_message += (
                     "\n[Hint] You did not specify a summary_model, so the filter attempted to use the current conversation's model. "
                     "If this is a pipeline (Pipe) model or an incompatible model, please specify a compatible summary model (e.g., 'gemini-2.5-flash') in the configuration."
                 )
 
-            if self.valves.debug_mode:
-                print(f"[🤖 LLM Call] ❌ {error_message}")
+            await self._log(
+                f"[🤖 LLM Call] ❌ {error_message}",
+                type="error",
+                event_call=__event_call__,
+            )
 
             raise Exception(error_message)

plugins/filters/async-context-compression/async_context_compression_cn.py

@@ -2,10 +2,11 @@
 title: 异步上下文压缩
 id: async_context_compression
 author: Fu-Jie
-author_url: https://github.com/Fu-Jie
-funding_url: https://github.com/Fu-Jie/awesome-openwebui
+author_url: https://github.com/Fu-Jie/awesome-openwebui
+funding_url: https://github.com/open-webui
 description: 通过智能摘要和消息压缩，降低长对话的 token 消耗，同时保持对话连贯性。
-version: 1.1.0
+version: 1.1.3
+openwebui_id: 5c0617cb-a9e4-4bd6-a440-d276534ebd18
 license: MIT
 
 ═══════════════════════════════════════════════════════════════════════════════
@@ -137,6 +138,10 @@ debug_mode (调试模式)
   默认: true
   说明: 在日志中打印详细的调试信息。生产环境建议设为 `false`。
 
+show_debug_log (前端调试日志)
+  默认: false
+  说明: 在浏览器控制台打印调试日志 (F12)。便于前端调试。
+
 🔧 部署配置
 ═══════════════════════════════════════════════════════
 
@@ -285,7 +290,8 @@ class Filter:
         self.valves = self.Valves()
         self._db_engine = owui_engine
         self._SessionLocal = owui_Session
-        self.temp_state = {}  # 用于在 inlet 和 outlet 之间传递临时数据
+        self._SessionLocal = owui_Session
+        self._init_database()
         self._init_database()
 
     def _init_database(self):
@@ -344,6 +350,9 @@ class Filter:
             default=0.1, ge=0.0, le=2.0, description="摘要生成的温度参数"
         )
         debug_mode: bool = Field(default=True, description="调试模式，打印详细日志")
+        show_debug_log: bool = Field(
+            default=False, description="在浏览器控制台打印调试日志 (F12)"
+        )
 
     def _save_summary(self, chat_id: str, summary: str, compressed_count: int):
         """保存摘要到数据库"""
@@ -425,9 +434,7 @@ class Filter:
         # 回退策略：粗略估算 (1 token ≈ 4 chars)
         return len(text) // 4
 
-    def _calculate_messages_tokens(
-        self, messages: List[Dict]
-    ) -> int:
+    def _calculate_messages_tokens(self, messages: List[Dict]) -> int:
         """计算消息列表的总 Token 数"""
         total_tokens = 0
         for msg in messages:
@@ -465,41 +472,137 @@ class Filter:
             "max_context_tokens": self.valves.max_context_tokens,
         }
 
-    def _inject_summary_to_first_message(self, message: dict, summary: str) -> dict:
-        """将摘要注入到第一条消息中（追加到内容前面）"""
-        content = message.get("content", "")
-        summary_block = f"【历史对话摘要】\n{summary}\n\n---\n以下是最近的对话：\n\n"
+    def _get_chat_context(
+        self, body: dict, __metadata__: Optional[dict] = None
+    ) -> Dict[str, str]:
+        """
+        统一提取聊天上下文信息 (chat_id, message_id)。
+        优先从 body 中提取，其次从 metadata 中提取。
+        """
+        chat_id = ""
+        message_id = ""
 
-        # 处理不同内容类型
-        if isinstance(content, list):  # 多模态内容
-            # 查找第一个文本部分并在其前面插入摘要
-            new_content = []
-            summary_inserted = False
+        # 1. 尝试从 body 获取
+        if isinstance(body, dict):
+            chat_id = body.get("chat_id", "")
+            message_id = body.get("id", "")  # message_id 在 body 中通常是 id
 
-            for part in content:
-                if (
-                    isinstance(part, dict)
-                    and part.get("type") == "text"
-                    and not summary_inserted
-                ):
-                    # 在第一个文本部分前插入摘要
-                    new_content.append(
-                        {"type": "text", "text": summary_block + part.get("text", "")}
-                    )
-                    summary_inserted = True
-                else:
-                    new_content.append(part)
+            # 再次检查 body.metadata
+            if not chat_id or not message_id:
+                body_metadata = body.get("metadata", {})
+                if isinstance(body_metadata, dict):
+                    if not chat_id:
+                        chat_id = body_metadata.get("chat_id", "")
+                    if not message_id:
+                        message_id = body_metadata.get("message_id", "")
 
-            # 如果没有文本部分，在开头插入
-            if not summary_inserted:
-                new_content.insert(0, {"type": "text", "text": summary_block})
+        # 2. 尝试从 __metadata__ 获取 (作为补充)
+        if __metadata__ and isinstance(__metadata__, dict):
+            if not chat_id:
+                chat_id = __metadata__.get("chat_id", "")
+            if not message_id:
+                message_id = __metadata__.get("message_id", "")
 
-            message["content"] = new_content
+        return {
+            "chat_id": str(chat_id).strip(),
+            "message_id": str(message_id).strip(),
+        }
 
-        elif isinstance(content, str):  # 纯文本
-            message["content"] = summary_block + content
+    async def _emit_debug_log(
+        self,
+        __event_call__,
+        chat_id: str,
+        original_count: int,
+        compressed_count: int,
+        summary_length: int,
+        kept_first: int,
+        kept_last: int,
+    ):
+        """Emit debug log to browser console via JS execution"""
+        if not self.valves.show_debug_log or not __event_call__:
+            return
 
-        return message
+        try:
+            # Prepare data for JS
+            log_data = {
+                "chatId": chat_id,
+                "originalCount": original_count,
+                "compressedCount": compressed_count,
+                "summaryLength": summary_length,
+                "keptFirst": kept_first,
+                "keptLast": kept_last,
+                "ratio": (
+                    f"{(1 - compressed_count/original_count)*100:.1f}%"
+                    if original_count > 0
+                    else "0%"
+                ),
+            }
+
+            # Construct JS code
+            js_code = f"""
+                (async function() {{
+                    console.group("🗜️ Async Context Compression Debug");
+                    console.log("Chat ID:", {json.dumps(chat_id)});
+                    console.log("Messages:", {original_count} + " -> " + {compressed_count});
+                    console.log("Compression Ratio:", {json.dumps(log_data['ratio'])});
+                    console.log("Summary Length:", {summary_length} + " chars");
+                    console.log("Configuration:", {{
+                        "Keep First": {kept_first},
+                        "Keep Last": {kept_last}
+                    }});
+                    console.groupEnd();
+                }})();
+            """
+
+            await __event_call__(
+                {
+                    "type": "execute",
+                    "data": {"code": js_code},
+                }
+            )
+        except Exception as e:
+            print(f"Error emitting debug log: {e}")
+
+    async def _log(self, message: str, type: str = "info", event_call=None):
+        """统一日志输出到后端 (print) 和前端 (console.log)"""
+        # 后端日志
+        if self.valves.debug_mode:
+            print(message)
+
+        # 前端日志
+        if self.valves.show_debug_log and event_call:
+            try:
+                css = "color: #3b82f6;"  # 默认蓝色
+                if type == "error":
+                    css = "color: #ef4444; font-weight: bold;"  # 红色
+                elif type == "warning":
+                    css = "color: #f59e0b;"  # 橙色
+                elif type == "success":
+                    css = "color: #10b981; font-weight: bold;"  # 绿色
+
+                # 清理前端消息：移除分隔符和多余换行
+                lines = message.split("\n")
+                # 保留不以大量等号或连字符开头的行
+                filtered_lines = [
+                    line
+                    for line in lines
+                    if not line.strip().startswith("====")
+                    and not line.strip().startswith("----")
+                ]
+                clean_message = "\n".join(filtered_lines).strip()
+
+                if not clean_message:
+                    return
+
+                # 转义消息中的引号和换行符
+                safe_message = clean_message.replace('"', '\\"').replace("\n", "\\n")
+
+                js_code = f"""
+                    console.log("%c[压缩] {safe_message}", "{css}");
+                """
+                await event_call({"type": "execute", "data": {"code": js_code}})
+            except Exception as e:
+                print(f"发送前端日志失败: {e}")
 
     async def inlet(
         self,
@@ -507,32 +610,34 @@ class Filter:
         __user__: Optional[dict] = None,
         __metadata__: dict = None,
         __event_emitter__: Callable[[Any], Awaitable[None]] = None,
+        __event_call__: Callable[[Any], Awaitable[None]] = None,
     ) -> dict:
         """
         在发送到 LLM 之前执行
         压缩策略：只负责注入已有的摘要，不进行 Token 计算
         """
         messages = body.get("messages", [])
-        chat_id = __metadata__["chat_id"]
+        chat_ctx = self._get_chat_context(body, __metadata__)
+        chat_id = chat_ctx["chat_id"]
 
-        if self.valves.debug_mode:
-            print(f"\n{'='*60}")
-            print(f"[Inlet] Chat ID: {chat_id}")
-            print(f"[Inlet] 收到 {len(messages)} 条消息")
+        if self.valves.debug_mode or self.valves.show_debug_log:
+            await self._log(
+                f"\n{'='*60}\n[Inlet] Chat ID: {chat_id}\n[Inlet] 收到 {len(messages)} 条消息",
+                event_call=__event_call__,
+            )
 
         # 记录原始消息的目标压缩进度，供 outlet 使用
         # 目标是压缩到倒数第 keep_last 条之前
         target_compressed_count = max(0, len(messages) - self.valves.keep_last)
 
-        # [优化] 简单的状态清理检查
-        if chat_id in self.temp_state:
-            if self.valves.debug_mode:
-                print(f"[Inlet] ⚠️ 覆盖未消费的旧状态 (Chat ID: {chat_id})")
+        # 记录原始消息的目标压缩进度，供 outlet 使用
+        # 目标是压缩到倒数第 keep_last 条之前
+        target_compressed_count = max(0, len(messages) - self.valves.keep_last)
 
-        self.temp_state[chat_id] = target_compressed_count
-
-        if self.valves.debug_mode:
-            print(f"[Inlet] 记录目标压缩进度: {target_compressed_count}")
+        await self._log(
+            f"[Inlet] 记录目标压缩进度: {target_compressed_count}",
+            event_call=__event_call__,
+        )
 
         # 加载摘要记录
         summary_record = await asyncio.to_thread(self._load_summary_record, chat_id)
@@ -560,7 +665,7 @@ class Filter:
                 f"---\n"
                 f"以下是最近的对话："
             )
-            summary_msg = {"role": "user", "content": summary_content}
+            summary_msg = {"role": "assistant", "content": summary_content}
 
             # 3. 尾部消息 (Tail) - 从上次压缩点开始的所有消息
             # 注意：这里必须确保不重复包含头部消息
@@ -581,19 +686,32 @@ class Filter:
                     }
                 )
 
-            if self.valves.debug_mode:
-                print(
-                    f"[Inlet] 应用摘要: Head({len(head_messages)}) + Summary + Tail({len(tail_messages)})"
-                )
+            await self._log(
+                f"[Inlet] 应用摘要: Head({len(head_messages)}) + Summary + Tail({len(tail_messages)})",
+                type="success",
+                event_call=__event_call__,
+            )
+
+            # Emit debug log to frontend (Keep the structured log as well)
+            await self._emit_debug_log(
+                __event_call__,
+                chat_id,
+                len(messages),
+                len(final_messages),
+                len(summary_record.summary),
+                self.valves.keep_first,
+                self.valves.keep_last,
+            )
         else:
             # 没有摘要，使用原始消息
             final_messages = messages
 
         body["messages"] = final_messages
 
-        if self.valves.debug_mode:
-            print(f"[Inlet] 最终发送: {len(body['messages'])} 条消息")
-            print(f"{'='*60}\n")
+        await self._log(
+            f"[Inlet] 最终发送: {len(body['messages'])} 条消息\n{'='*60}\n",
+            event_call=__event_call__,
+        )
 
         return body
 
@@ -603,29 +721,44 @@ class Filter:
         __user__: Optional[dict] = None,
         __metadata__: dict = None,
         __event_emitter__: Callable[[Any], Awaitable[None]] = None,
+        __event_call__: Callable[[Any], Awaitable[None]] = None,
     ) -> dict:
         """
         在 LLM 响应完成后执行
         在后台计算 Token 数并触发摘要生成（不阻塞当前响应，不影响内容输出）
         """
-        chat_id = __metadata__["chat_id"]
-        model = body.get("model", "gpt-3.5-turbo")
+        chat_ctx = self._get_chat_context(body, __metadata__)
+        chat_id = chat_ctx["chat_id"]
+        model = body.get("model") or ""
 
-        if self.valves.debug_mode:
-            print(f"\n{'='*60}")
-            print(f"[Outlet] Chat ID: {chat_id}")
-            print(f"[Outlet] 响应完成")
+        # 直接计算目标压缩进度
+        # 假设 outlet 中的 body['messages'] 包含完整历史（包括新响应）
+        messages = body.get("messages", [])
+        target_compressed_count = max(0, len(messages) - self.valves.keep_last)
+
+        if self.valves.debug_mode or self.valves.show_debug_log:
+            await self._log(
+                f"\n{'='*60}\n[Outlet] Chat ID: {chat_id}\n[Outlet] 响应完成\n[Outlet] 计算目标压缩进度: {target_compressed_count} (消息数: {len(messages)})",
+                event_call=__event_call__,
+            )
 
         # 在后台异步处理 Token 计算和摘要生成（不等待完成，不影响输出）
         asyncio.create_task(
             self._check_and_generate_summary_async(
-                chat_id, model, body, __user__, __event_emitter__
+                chat_id,
+                model,
+                body,
+                __user__,
+                target_compressed_count,
+                __event_emitter__,
+                __event_call__,
             )
         )
 
-        if self.valves.debug_mode:
-            print(f"[Outlet] 后台处理已启动")
-            print(f"{'='*60}\n")
+        await self._log(
+            f"[Outlet] 后台处理已启动\n{'='*60}\n",
+            event_call=__event_call__,
+        )
 
         return body
 
@@ -635,7 +768,9 @@ class Filter:
         model: str,
         body: dict,
         user_data: Optional[dict],
+        target_compressed_count: Optional[int],
         __event_emitter__: Callable[[Any], Awaitable[None]] = None,
+        __event_call__: Callable[[Any], Awaitable[None]] = None,
     ):
         """
         后台处理：计算 Token 数并生成摘要（不阻塞响应）
@@ -649,36 +784,58 @@ class Filter:
                 "compression_threshold_tokens", self.valves.compression_threshold_tokens
             )
 
-            if self.valves.debug_mode:
-                print(f"\n[🔍 后台计算] 开始 Token 计数...")
+            await self._log(
+                f"\n[🔍 后台计算] 开始 Token 计数...",
+                event_call=__event_call__,
+            )
 
             # 在后台线程中计算 Token 数
             current_tokens = await asyncio.to_thread(
                 self._calculate_messages_tokens, messages
             )
 
-            if self.valves.debug_mode:
-                print(f"[🔍 后台计算] Token 数: {current_tokens}")
+            await self._log(
+                f"[🔍 后台计算] Token 数: {current_tokens}",
+                event_call=__event_call__,
+            )
 
             # 检查是否需要压缩
             if current_tokens >= compression_threshold_tokens:
-                if self.valves.debug_mode:
-                    print(
-                        f"[🔍 后台计算] ⚡ 触发压缩阈值 (Token: {current_tokens} >= {compression_threshold_tokens})"
-                    )
+                await self._log(
+                    f"[🔍 后台计算] ⚡ 触发压缩阈值 (Token: {current_tokens} >= {compression_threshold_tokens})",
+                    type="warning",
+                    event_call=__event_call__,
+                )
 
                 # 继续生成摘要
                 await self._generate_summary_async(
-                    messages, chat_id, body, user_data, __event_emitter__
+                    messages,
+                    chat_id,
+                    body,
+                    user_data,
+                    target_compressed_count,
+                    __event_emitter__,
+                    __event_call__,
                 )
             else:
-                if self.valves.debug_mode:
-                    print(
-                        f"[🔍 后台计算] 未触发压缩阈值 (Token: {current_tokens} < {compression_threshold_tokens})"
-                    )
+                await self._log(
+                    f"[🔍 后台计算] 未触发压缩阈值 (Token: {current_tokens} < {compression_threshold_tokens})",
+                    event_call=__event_call__,
+                )
 
         except Exception as e:
-            print(f"[🔍 后台计算] ❌ 错误: {str(e)}")
+            await self._log(
+                f"[🔍 后台计算] ❌ 错误: {str(e)}",
+                type="error",
+                event_call=__event_call__,
+            )
+
+    def _clean_model_id(self, model_id: Optional[str]) -> Optional[str]:
+        """Cleans the model ID by removing whitespace and quotes."""
+        if not model_id:
+            return None
+        cleaned = model_id.strip().strip('"').strip("'")
+        return cleaned if cleaned else None
 
     async def _generate_summary_async(
         self,
@@ -686,7 +843,9 @@ class Filter:
         chat_id: str,
         body: dict,
         user_data: Optional[dict],
+        target_compressed_count: Optional[int],
         __event_emitter__: Callable[[Any], Awaitable[None]] = None,
+        __event_call__: Callable[[Any], Awaitable[None]] = None,
     ):
         """
         异步生成摘要（后台执行，不阻塞响应）
@@ -696,18 +855,17 @@ class Filter:
         3. 对剩余的中间消息生成摘要。
         """
         try:
-            if self.valves.debug_mode:
-                print(f"\n[🤖 异步摘要任务] 开始...")
+            await self._log(f"\n[🤖 异步摘要任务] 开始...", event_call=__event_call__)
 
             # 1. 获取目标压缩进度
-            # 优先从 temp_state 获取（由 inlet 计算），如果获取不到（例如重启后），则假设当前是完整历史
-            target_compressed_count = self.temp_state.pop(chat_id, None)
+            # 如果未传递 target_compressed_count（新逻辑下不应发生），则进行估算
             if target_compressed_count is None:
                 target_compressed_count = max(0, len(messages) - self.valves.keep_last)
-                if self.valves.debug_mode:
-                    print(
-                        f"[🤖 异步摘要任务] ⚠️ 无法获取 inlet 状态，使用当前消息数估算进度: {target_compressed_count}"
-                    )
+                await self._log(
+                    f"[🤖 异步摘要任务] ⚠️ target_compressed_count 为 None，进行估算: {target_compressed_count}",
+                    type="warning",
+                    event_call=__event_call__,
+                )
 
             # 2. 确定待压缩的消息范围 (Middle)
             start_index = self.valves.keep_first
@@ -717,21 +875,33 @@ class Filter:
 
             # 确保索引有效
             if start_index >= end_index:
-                if self.valves.debug_mode:
-                    print(
-                        f"[🤖 异步摘要任务] 中间消息为空 (Start: {start_index}, End: {end_index})，跳过"
-                    )
+                await self._log(
+                    f"[🤖 异步摘要任务] 中间消息为空 (Start: {start_index}, End: {end_index})，跳过",
+                    event_call=__event_call__,
+                )
                 return
 
             middle_messages = messages[start_index:end_index]
 
-            if self.valves.debug_mode:
-                print(f"[🤖 异步摘要任务] 待处理中间消息: {len(middle_messages)} 条")
+            await self._log(
+                f"[🤖 异步摘要任务] 待处理中间消息: {len(middle_messages)} 条",
+                event_call=__event_call__,
+            )
 
             # 3. 检查 Token 上限并截断 (Max Context Truncation)
             # [优化] 使用摘要模型(如果有)的阈值来决定能处理多少中间消息
             # 这样可以用长窗口模型(如 gemini-flash)来压缩超过当前模型窗口的历史记录
-            summary_model_id = self.valves.summary_model or body.get("model")
+            summary_model_id = self._clean_model_id(
+                self.valves.summary_model
+            ) or self._clean_model_id(body.get("model"))
+
+            if not summary_model_id:
+                await self._log(
+                    "[🤖 异步摘要任务] ⚠️ 摘要模型不存在，跳过压缩",
+                    type="warning",
+                    event_call=__event_call__,
+                )
+                return
 
             thresholds = self._get_model_thresholds(summary_model_id)
             # 注意：这里使用的是摘要模型的最大上下文限制
@@ -739,22 +909,26 @@ class Filter:
                 "max_context_tokens", self.valves.max_context_tokens
             )
 
-            if self.valves.debug_mode:
-                print(
-                    f"[🤖 异步摘要任务] 使用模型 {summary_model_id} 的上限: {max_context_tokens} Tokens"
-                )
-
-            # 计算当前总 Token (使用摘要模型进行计数)
-            total_tokens = await asyncio.to_thread(
-                self._calculate_messages_tokens, messages
+            await self._log(
+                f"[🤖 异步摘要任务] 使用模型 {summary_model_id} 的上限: {max_context_tokens} Tokens",
+                event_call=__event_call__,
             )
 
-            if total_tokens > max_context_tokens:
-                excess_tokens = total_tokens - max_context_tokens
-                if self.valves.debug_mode:
-                    print(
-                        f"[🤖 异步摘要任务] ⚠️ 总 Token ({total_tokens}) 超过摘要模型上限 ({max_context_tokens})，需要移除约 {excess_tokens} Token"
-                    )
+            # 计算中间消息的 Token (加上提示词的缓冲)
+            # 我们只把 middle_messages 发送给摘要模型，所以不应该把完整历史计入限制
+            middle_tokens = await asyncio.to_thread(
+                self._calculate_messages_tokens, middle_messages
+            )
+            # 增加提示词和输出的缓冲 (约 2000 Tokens)
+            estimated_input_tokens = middle_tokens + 2000
+
+            if estimated_input_tokens > max_context_tokens:
+                excess_tokens = estimated_input_tokens - max_context_tokens
+                await self._log(
+                    f"[🤖 异步摘要任务] ⚠️ 中间消息 ({middle_tokens} Tokens) + 缓冲超过摘要模型上限 ({max_context_tokens})，需要移除约 {excess_tokens} Token",
+                    type="warning",
+                    event_call=__event_call__,
+                )
 
                 # 从 middle_messages 头部开始移除
                 removed_tokens = 0
@@ -768,14 +942,16 @@ class Filter:
                     removed_tokens += msg_tokens
                     removed_count += 1
 
-                if self.valves.debug_mode:
-                    print(
-                        f"[🤖 异步摘要任务] 已移除 {removed_count} 条消息，共 {removed_tokens} Token"
-                    )
+                await self._log(
+                    f"[🤖 异步摘要任务] 已移除 {removed_count} 条消息，共 {removed_tokens} Token",
+                    event_call=__event_call__,
+                )
 
             if not middle_messages:
-                if self.valves.debug_mode:
-                    print(f"[🤖 异步摘要任务] 截断后中间消息为空，跳过摘要生成")
+                await self._log(
+                    f"[🤖 异步摘要任务] 截断后中间消息为空，跳过摘要生成",
+                    event_call=__event_call__,
+                )
                 return
 
             # 4. 构建对话文本
@@ -797,12 +973,26 @@ class Filter:
                 )
 
             new_summary = await self._call_summary_llm(
-                None, conversation_text, body, user_data
+                None,
+                conversation_text,
+                {**body, "model": summary_model_id},
+                user_data,
+                __event_call__,
             )
 
+            if not new_summary:
+                await self._log(
+                    "[🤖 异步摘要任务] ⚠️ 摘要生成返回空结果，跳过保存",
+                    type="warning",
+                    event_call=__event_call__,
+                )
+                return
+
             # 6. 保存新摘要
-            if self.valves.debug_mode:
-                print("[优化] 正在后台线程中保存摘要，以避免阻塞事件循环。")
+            await self._log(
+                "[优化] 在后台线程中保存摘要以避免阻塞事件循环。",
+                event_call=__event_call__,
+            )
 
             await asyncio.to_thread(
                 self._save_summary, chat_id, new_summary, target_compressed_count
@@ -814,32 +1004,52 @@ class Filter:
                     {
                         "type": "status",
                         "data": {
-                            "description": f"上下文摘要已更新 (已压缩 {len(middle_messages)} 条消息)",
+                            "description": f"上下文摘要已更新 (压缩了 {len(middle_messages)} 条消息)",
                             "done": True,
                         },
                     }
                 )
 
-            if self.valves.debug_mode:
-                print(f"[🤖 异步摘要任务] ✅ 完成！新摘要长度: {len(new_summary)} 字符")
-                print(
-                    f"[🤖 异步摘要任务] 进度更新: 已压缩至原始第 {target_compressed_count} 条消息"
-                )
+            await self._log(
+                f"[🤖 异步摘要任务] ✅ 完成！新摘要长度: {len(new_summary)} 字符",
+                type="success",
+                event_call=__event_call__,
+            )
+            await self._log(
+                f"[🤖 异步摘要任务] 进度更新: 已压缩至原始消息 {target_compressed_count}",
+                event_call=__event_call__,
+            )
 
         except Exception as e:
-            print(f"[🤖 异步摘要任务] ❌ 错误: {str(e)}")
+            await self._log(
+                f"[🤖 异步摘要任务] ❌ 错误: {str(e)}",
+                type="error",
+                event_call=__event_call__,
+            )
+
+            if __event_emitter__:
+                await __event_emitter__(
+                    {
+                        "type": "status",
+                        "data": {
+                            "description": f"摘要生成错误: {str(e)[:100]}...",
+                            "done": True,
+                        },
+                    }
+                )
+
             import traceback
 
             traceback.print_exc()
 
     def _format_messages_for_summary(self, messages: list) -> str:
-        """格式化消息用于摘要"""
+        """Formats messages for summarization."""
         formatted = []
         for i, msg in enumerate(messages, 1):
             role = msg.get("role", "unknown")
             content = msg.get("content", "")
 
-            # 处理多模态内容
+            # Handle multimodal content
             if isinstance(content, list):
                 text_parts = []
                 for part in content:
@@ -847,10 +1057,10 @@ class Filter:
                         text_parts.append(part.get("text", ""))
                 content = " ".join(text_parts)
 
-            # 处理角色名称
-            role_name = {"user": "用户", "assistant": "助手"}.get(role, role)
+            # Handle role name
+            role_name = {"user": "User", "assistant": "Assistant"}.get(role, role)
 
-            # 限制每条消息的长度，避免过长
+            # Limit length of each message to avoid excessive length
             if len(content) > 500:
                 content = content[:500] + "..."
 
@@ -864,12 +1074,15 @@ class Filter:
         new_conversation_text: str,
         body: dict,
         user_data: dict,
+        __event_call__: Callable[[Any], Awaitable[None]] = None,
     ) -> str:
         """
-        使用 Open WebUI 内置方法调用 LLM 生成摘要
+        调用 LLM 生成摘要，使用 Open Web UI 的内置方法。
         """
-        if self.valves.debug_mode:
-            print(f"[🤖 LLM 调用] 使用 Open WebUI 内置方法")
+        await self._log(
+            f"[🤖 LLM 调用] 使用 Open Web UI 内置方法",
+            event_call=__event_call__,
+        )
 
         # 构建摘要提示词 (优化版)
         summary_prompt = f"""
@@ -906,10 +1119,19 @@ class Filter:
 请根据上述内容，生成摘要：
 """
         # 确定使用的模型
-        model = self.valves.summary_model or body.get("model", "")
+        model = self._clean_model_id(self.valves.summary_model) or self._clean_model_id(
+            body.get("model")
+        )
 
-        if self.valves.debug_mode:
-            print(f"[🤖 LLM 调用] 模型: {model}")
+        if not model:
+            await self._log(
+                "[🤖 LLM 调用] ⚠️ 摘要模型不存在，跳过摘要生成",
+                type="warning",
+                event_call=__event_call__,
+            )
+            return ""
+
+        await self._log(f"[🤖 LLM 调用] 模型: {model}", event_call=__event_call__)
 
         # 构建 payload
         payload = {
@@ -926,17 +1148,20 @@ class Filter:
             if not user_id:
                 raise ValueError("无法获取用户 ID")
 
-            # [优化] 在后台线程中获取用户对象，以避免阻塞事件循环
-            if self.valves.debug_mode:
-                print("[优化] 正在后台线程中获取用户对象，以避免阻塞事件循环。")
+            # [优化] 在后台线程中获取用户对象以避免阻塞事件循环
+            await self._log(
+                "[优化] 在后台线程中获取用户对象以避免阻塞事件循环。",
+                event_call=__event_call__,
+            )
             user = await asyncio.to_thread(Users.get_user_by_id, user_id)
 
             if not user:
                 raise ValueError(f"无法找到用户: {user_id}")
 
-            if self.valves.debug_mode:
-                print(f"[🤖 LLM 调用] 用户: {user.email}")
-                print(f"[🤖 LLM 调用] 发送请求...")
+            await self._log(
+                f"[🤖 LLM 调用] 用户: {user.email}\n[🤖 LLM 调用] 发送请求...",
+                event_call=__event_call__,
+            )
 
             # 创建 Request 对象
             request = Request(scope={"type": "http", "app": webui_app})
@@ -949,20 +1174,31 @@ class Filter:
 
             summary = response["choices"][0]["message"]["content"].strip()
 
-            if self.valves.debug_mode:
-                print(f"[🤖 LLM 调用] ✅ 成功获取摘要")
+            await self._log(
+                f"[🤖 LLM 调用] ✅ 成功接收摘要",
+                type="success",
+                event_call=__event_call__,
+            )
 
             return summary
 
         except Exception as e:
-            error_message = f"调用 LLM ({model}) 生成摘要时发生错误: {str(e)}"
+            error_msg = str(e)
+            # Handle specific error messages
+            if "Model not found" in error_msg:
+                error_message = f"摘要模型 '{model}' 不存在。"
+            else:
+                error_message = f"摘要 LLM 错误 ({model}): {error_msg}"
             if not self.valves.summary_model:
                 error_message += (
-                    "\n[提示] 您没有指定摘要模型 (summary_model)，因此尝试使用当前对话的模型。"
-                    "如果这是一个流水线（Pipe）模型或不兼容的模型，请在配置中指定一个兼容的摘要模型（如 'gemini-2.5-flash'）。"
+                    "\n[提示] 您未指定 summary_model，因此过滤器尝试使用当前对话的模型。"
+                    "如果这是流水线 (Pipe) 模型或不兼容的模型，请在配置中指定兼容的摘要模型 (例如 'gemini-2.5-flash')。"
                 )
 
-            if self.valves.debug_mode:
-                print(f"[🤖 LLM 调用] ❌ {error_message}")
+            await self._log(
+                f"[🤖 LLM 调用] ❌ {error_message}",
+                type="error",
+                event_call=__event_call__,
+            )
 
             raise Exception(error_message)

plugins/filters/context_enhancement_filter/context_enhancement_filter.py

@@ -1,12 +1,9 @@
 """
 title: Context & Model Enhancement Filter
-author: Fu-Jie
-author_url: https://github.com/Fu-Jie
-funding_url: https://github.com/Fu-Jie/awesome-openwebui
-version: 0.2
+version: 0.3
 
 description:
-    一个功能全面的 Filter 插件，用于增强请求上下文和优化模型功能。提供四大核心功能：
+    一个专注于增强请求上下文和优化模型功能的 Filter 插件。提供三大核心功能：
 
     1. 环境变量注入：在每条用户消息前自动注入用户环境变量（用户名、时间、时区、语言等）
        - 支持纯文本、图片、多模态消息
@@ -24,222 +21,24 @@ description:
        - 动态模型重定向
        - 智能化的模型识别和适配
 
-    4. 智能内容规范化：生产级的内容清洗与修复系统
-       - 智能修复损坏的代码块（前缀、后缀、缩进）
-       - 规范化 LaTeX 公式格式（行内/块级）
-       - 优化思维链标签（</thought>）格式
-       - 自动闭合未结束的代码块
-       - 智能列表格式修复
-       - 清理冗余的 XML 标签
-       - 可配置的规则系统
-
 features:
     - 自动化环境变量管理
     - 智能模型功能适配
     - 异步状态反馈
     - 幂等性保证
     - 多模型支持
-    - 智能内容清洗与规范化
 """
 
 from pydantic import BaseModel, Field
-from typing import Optional, List, Callable
+from typing import Optional
 import re
 import logging
-from dataclasses import dataclass, field
+import asyncio
 
 
 # 配置日志
 logger = logging.getLogger(__name__)
 
-@dataclass
-class NormalizerConfig:
-    """规范化配置类,用于动态启用/禁用特定规则"""
-    enable_escape_fix: bool = True          # 修复转义字符
-    enable_thought_tag_fix: bool = True     # 修复思考链标签
-    enable_code_block_fix: bool = True      # 修复代码块格式
-    enable_latex_fix: bool = True           # 修复 LaTeX 公式格式
-    enable_list_fix: bool = False            # 修复列表换行
-    enable_unclosed_block_fix: bool = True  # 修复未闭合代码块
-    enable_fullwidth_symbol_fix: bool = False # 修复代码内的全角符号
-    enable_xml_tag_cleanup: bool = True     # 清理 XML 残留标签
-    
-    # 自定义清理函数列表（高级扩展用）
-    custom_cleaners: List[Callable[[str], str]] = field(default_factory=list)
-
-class ContentNormalizer:
-    """LLM 输出内容规范化器 - 生产级实现"""
-    
-    # --- 1. 预编译正则表达式（性能优化） ---
-    _PATTERNS = {
-        # 代码块前缀：如果 ``` 前面不是行首也不是换行符
-        'code_block_prefix': re.compile(r'(?<!^)(?<!\n)(```)', re.MULTILINE),
-        
-        # 代码块后缀：匹配 ```语言名 后面紧跟非空白字符(没有换行)
-        # 匹配 ```python code 这种情况，但不匹配 ```python 或 ```python\n
-        'code_block_suffix': re.compile(r'(```[\w\+\-\.]*)[ \t]+([^\n\r])'),
-        
-        # 代码块缩进：行首的空白字符 + ```
-        'code_block_indent': re.compile(r'^[ \t]+(```)', re.MULTILINE),
-        
-        # 思考链标签：</thought> 后可能跟空格或换行
-        'thought_tag': re.compile(r'</thought>[ \t]*\n*'),
-        
-        # LaTeX 块级公式：\[ ... \]
-        'latex_bracket_block': re.compile(r'\\\[(.+?)\\\]', re.DOTALL),
-        # LaTeX 行内公式：\( ... \)
-        'latex_paren_inline': re.compile(r'\\\((.+?)\\\)'),
-        
-        # 列表项：非换行符 + 数字 + 点 + 空格 (e.g. "Text1. Item")
-        'list_item': re.compile(r'([^\n])(\d+\. )'),
-        
-        # XML 残留标签 (如 Claude 的 artifacts)
-        'xml_artifacts': re.compile(r'</?(?:antArtifact|antThinking|artifact)[^>]*>', re.IGNORECASE),
-    }
-    
-    def __init__(self, config: Optional[NormalizerConfig] = None):
-        self.config = config or NormalizerConfig()
-        self.applied_fixes = []
-    
-    def normalize(self, content: str) -> str:
-        """主入口：按顺序应用所有规范化规则"""
-        self.applied_fixes = []
-        if not content:
-            return content
-        
-        try:
-            # 1. 转义字符修复（必须最先执行，否则影响后续正则）
-            if self.config.enable_escape_fix:
-                original = content
-                content = self._fix_escape_characters(content)
-                if content != original:
-                    self.applied_fixes.append("修复转义字符")
-            
-            # 2. 思考链标签规范化
-            if self.config.enable_thought_tag_fix:
-                original = content
-                content = self._fix_thought_tags(content)
-                if content != original:
-                    self.applied_fixes.append("规范化思考链")
-            
-            # 3. 代码块格式修复
-            if self.config.enable_code_block_fix:
-                original = content
-                content = self._fix_code_blocks(content)
-                if content != original:
-                    self.applied_fixes.append("修复代码块格式")
-            
-            # 4. LaTeX 公式规范化
-            if self.config.enable_latex_fix:
-                original = content
-                content = self._fix_latex_formulas(content)
-                if content != original:
-                    self.applied_fixes.append("规范化 LaTeX 公式")
-            
-            # 5. 列表格式修复
-            if self.config.enable_list_fix:
-                original = content
-                content = self._fix_list_formatting(content)
-                if content != original:
-                    self.applied_fixes.append("修复列表格式")
-            
-            # 6. 未闭合代码块检测与修复
-            if self.config.enable_unclosed_block_fix:
-                original = content
-                content = self._fix_unclosed_code_blocks(content)
-                if content != original:
-                    self.applied_fixes.append("闭合未结束代码块")
-            
-            # 7. 全角符号转半角（仅代码块内）
-            if self.config.enable_fullwidth_symbol_fix:
-                original = content
-                content = self._fix_fullwidth_symbols_in_code(content)
-                if content != original:
-                    self.applied_fixes.append("全角符号转半角")
-            
-            # 8. XML 标签残留清理
-            if self.config.enable_xml_tag_cleanup:
-                original = content
-                content = self._cleanup_xml_tags(content)
-                if content != original:
-                    self.applied_fixes.append("清理 XML 标签")
-            
-            # 9. 执行自定义清理函数
-            for cleaner in self.config.custom_cleaners:
-                original = content
-                content = cleaner(content)
-                if content != original:
-                    self.applied_fixes.append("执行自定义清理")
-            
-            return content
-            
-        except Exception as e:
-            # 生产环境保底机制：如果清洗过程报错，返回原始内容，避免阻断服务
-            logger.error(f"内容规范化失败: {e}", exc_info=True)
-            return content
-    
-    def _fix_escape_characters(self, content: str) -> str:
-        """修复过度转义的字符"""
-        # 注意：先处理具体的转义序列，再处理通用的双反斜杠
-        content = content.replace("\\r\\n", "\n")
-        content = content.replace("\\n", "\n")
-        content = content.replace("\\t", "\t")
-        # 修复过度转义的反斜杠 (例如路径 C:\\Users)
-        content = content.replace("\\\\", "\\")
-        return content
-    
-    def _fix_thought_tags(self, content: str) -> str:
-        """规范化 </thought> 标签，统一为空两行"""
-        return self._PATTERNS['thought_tag'].sub("</thought>\n\n", content)
-    
-    def _fix_code_blocks(self, content: str) -> str:
-        """修复代码块格式（独占行、换行、去缩进）"""
-        # C: 移除代码块前的缩进（必须先执行，否则影响下面的判断）
-        content = self._PATTERNS['code_block_indent'].sub(r"\1", content)
-        # A: 确保 ``` 前有换行
-        content = self._PATTERNS['code_block_prefix'].sub(r"\n\1", content)
-        # B: 确保 ```语言标识 后有换行
-        content = self._PATTERNS['code_block_suffix'].sub(r"\1\n\2", content)
-        return content
-    
-    def _fix_latex_formulas(self, content: str) -> str:
-        """规范化 LaTeX 公式：\[ -> $$ (块级), \( -> $ (行内)"""
-        content = self._PATTERNS['latex_bracket_block'].sub(r"$$\1$$", content)
-        content = self._PATTERNS['latex_paren_inline'].sub(r"$\1$", content)
-        return content
-    
-    def _fix_list_formatting(self, content: str) -> str:
-        """修复列表项缺少换行的问题 (如 'text1. item' -> 'text\\n1. item')"""
-        return self._PATTERNS['list_item'].sub(r"\1\n\2", content)
-    
-    def _fix_unclosed_code_blocks(self, content: str) -> str:
-        """检测并修复未闭合的代码块"""
-        if content.count("```") % 2 != 0:
-            logger.warning("检测到未闭合的代码块，自动补全")
-            content += "\n```"
-        return content
-    
-    def _fix_fullwidth_symbols_in_code(self, content: str) -> str:
-        """在代码块内将全角符号转为半角（精细化操作）"""
-        # 常见误用的全角符号映射
-        FULLWIDTH_MAP = {
-            '，': ',', '。': '.', '（': '(', '）': ')',
-            '【': '[', '】': ']', '；': ';', '：': ':',
-            '？': '?', '！': '!', '"': '"', '"': '"',
-            ''': "'", ''': "'",
-        }
-        
-        parts = content.split("```")
-        # 代码块内容位于索引 1, 3, 5... (奇数位)
-        for i in range(1, len(parts), 2):
-            for full, half in FULLWIDTH_MAP.items():
-                parts[i] = parts[i].replace(full, half)
-        
-        return "```".join(parts)
-    
-    def _cleanup_xml_tags(self, content: str) -> str:
-        """移除无关的 XML 标签"""
-        return self._PATTERNS['xml_artifacts'].sub("", content)
 
 class Filter:
     class Valves(BaseModel):
@@ -349,13 +148,9 @@ class Filter:
                 body["model"] = body["model"] + "-search"
                 features["web_search"] = False
                 search_enabled_for_model = True
-            if user_email == "yi204o@qq.com":
-                features["web_search"] = False
 
         # 如果启用了模型本身的搜索能力，发送状态提示
         if search_enabled_for_model and __event_emitter__:
-            import asyncio
-
             try:
                 asyncio.create_task(
                     self._emit_search_status(__event_emitter__, model_name)
@@ -464,8 +259,6 @@ class Filter:
 
             # 环境变量注入成功后，发送状态提示给用户
             if env_injected and __event_emitter__:
-                import asyncio
-
                 try:
                     # 如果在异步环境中，使用 await
                     asyncio.create_task(self._emit_env_status(__event_emitter__))
@@ -506,67 +299,3 @@ class Filter:
             )
         except Exception as e:
             print(f"发送搜索状态提示时出错: {e}")
-
-    async def _emit_normalization_status(self, __event_emitter__, applied_fixes: List[str] = None):
-        """
-        发送内容规范化完成的状态提示
-        """
-        description = "✓ 内容已自动规范化"
-        if applied_fixes:
-            description += f"：{', '.join(applied_fixes)}"
-
-        try:
-            await __event_emitter__(
-                {
-                    "type": "status",
-                    "data": {
-                        "description": description,
-                        "done": True,
-                    },
-                }
-            )
-        except Exception as e:
-            print(f"发送规范化状态提示时出错: {e}")
-
-    def _contains_html(self, content: str) -> bool:
-        """
-        检测内容是否包含 HTML 标签
-        """
-        # 匹配常见的 HTML 标签
-        pattern = r"<\s*/?\s*(?:html|head|body|div|span|p|br|hr|ul|ol|li|table|thead|tbody|tfoot|tr|td|th|img|a|b|i|strong|em|code|pre|blockquote|h[1-6]|script|style|form|input|button|label|select|option|iframe|link|meta|title)\b"
-        return bool(re.search(pattern, content, re.IGNORECASE))
-
-    def outlet(self, body: dict, __user__: Optional[dict] = None, __event_emitter__=None) -> dict:
-        """
-        处理传出响应体，通过修改最后一条助手消息的内容。
-        使用 ContentNormalizer 进行全面的内容规范化。
-        """
-        if "messages" in body and body["messages"]:
-            last = body["messages"][-1]
-            content = last.get("content", "") or ""
-            
-            if last.get("role") == "assistant" and isinstance(content, str):
-                # 如果包含 HTML，跳过规范化，为了防止错误格式化
-                if self._contains_html(content):
-                    return body
-
-                # 初始化规范化器
-                normalizer = ContentNormalizer()
-                
-                # 执行规范化
-                new_content = normalizer.normalize(content)
-                
-                # 更新内容
-                if new_content != content:
-                    last["content"] = new_content
-                    # 如果内容发生了改变，发送状态提示
-                    if __event_emitter__:
-                        import asyncio
-                        try:
-                            # 传入 applied_fixes
-                            asyncio.create_task(self._emit_normalization_status(__event_emitter__, normalizer.applied_fixes))
-                        except RuntimeError:
-                            # 假如不在循环中，则忽略
-                            pass
-        
-        return body

plugins/filters/markdown_normalizer/FEATURES_CN.md

@@ -0,0 +1,188 @@
+# Markdown Normalizer 功能详解
+
+本插件旨在修复 LLM 输出中常见的 Markdown 格式问题，确保在 Open WebUI 中完美渲染。以下是支持的修复功能列表及示例。
+
+## 1. 代码块修复 (Code Block Fixes)
+
+### 1.1 去除代码块缩进
+LLM 有时会在代码块前添加空格缩进，导致渲染失效。本插件会自动移除这些缩进。
+
+**Before:**
+   ```python
+   print("hello")
+   ```
+
+**After:**
+```python
+print("hello")
+```
+
+### 1.2 补全代码块前后换行
+代码块标记 ` ``` ` 必须独占一行。如果 LLM 将其与文本混在一行，插件会自动修复。
+
+**Before:**
+Here is code:```python
+print("hello")```
+
+**After:**
+Here is code:
+```python
+print("hello")
+```
+
+### 1.3 修复语言标识符后的换行
+有时 LLM 会忘记在语言标识符（如 `python`）后换行。
+
+**Before:**
+```python print("hello")
+```
+
+**After:**
+```python
+print("hello")
+```
+
+### 1.4 自动闭合代码块
+如果输出被截断或 LLM 忘记闭合代码块，插件会自动添加结尾的 ` ``` `。
+
+**Before:**
+```python
+print("unfinished code...")
+
+**After:**
+```python
+print("unfinished code...")
+```
+
+## 2. LaTeX 公式规范化 (LaTeX Normalization)
+
+Open WebUI 使用 MathJax/KaTeX 渲染公式，通常需要 `$$` 或 `$` 包裹。本插件会将常见的 LaTeX 括号语法转换为标准格式。
+
+**Before:**
+块级公式：\[ E = mc^2 \]
+行内公式：\( a^2 + b^2 = c^2 \)
+
+**After:**
+块级公式：$$ E = mc^2 $$
+行内公式：$ a^2 + b^2 = c^2 $
+
+## 3. 转义字符清理 (Escape Character Fix)
+
+修复过度转义的字符，这常见于某些 API 返回的原始字符串中。
+
+**Before:**
+Line 1\\nLine 2\\tTabbed
+
+**After:**
+Line 1
+Line 2	Tabbed
+
+## 4. 思维链标签规范化 (Thought Tag Fix)
+**功能**: 
+1.  确保 `</thought>` 标签后有足够的空行，防止思维链内容与正文粘连。
+2.  **标准化标签**: 将 `<think>` (DeepSeek 等模型常用) 或 `<thinking>` 统一转换为 Open WebUI 标准的 `<thought>` 标签，以便正确触发 UI 的折叠功能。
+
+**默认**: 开启 (`enable_thought_tag_fix = True`)
+
+**示例**:
+*   **Before**: `<think>Thinking...</think>Response starts here.`
+*   **After**: 
+    ```xml
+    <thought>Thinking...</thought>
+
+    Response starts here.
+    ```
+
+## 5. 列表格式修复 (List Formatting Fix)
+
+*默认关闭，需在设置中开启*
+
+修复列表项缺少换行的问题。
+
+**Before:**
+Header1. Item 1
+
+**After:**
+Header
+1. Item 1
+
+## 6. 全角符号转半角 (Full-width Symbol Fix)
+
+*默认关闭，需在设置中开启*
+
+仅在**代码块内部**将全角符号转换为半角符号，防止代码因符号问题无法运行。
+
+**Before:**
+```python
+if x == 1：
+    print（"hello"）
+```
+
+**After:**
+```python
+if x == 1:
+    print("hello")
+```
+
+## 7. Mermaid 语法修复 (Mermaid Syntax Fix)
+**功能**: 修复 Mermaid 图表中常见的语法错误，特别是未加引号的标签包含特殊字符、嵌套括号或 HTML 标签的情况。
+**默认**: 开启 (`enable_mermaid_fix = True`)
+
+### 7.1 基础特殊字符
+**Before**:
+```mermaid
+graph TD
+A[Label with (parens)] --> B(Label with [brackets])
+```
+**After**:
+```mermaid
+graph TD
+A["Label with (parens)"] --> B("Label with [brackets]")
+```
+
+### 7.2 嵌套括号修复 (v1.1.0+)
+**Before**:
+```mermaid
+graph TD
+A((开始: 发现可疑快照)) --> B[物理损坏(Allocation Errors)]
+```
+**After**:
+```mermaid
+graph TD
+A(("开始: 发现可疑快照")) --> B["物理损坏(Allocation Errors)"]
+```
+
+### 7.3 包含 HTML 标签 (v1.1.0+)
+**Before**:
+```mermaid
+graph TD
+A[第一步<br/>环境隔离] --> B{状态?}
+```
+**After**:
+```mermaid
+graph TD
+A["第一步<br/>环境隔离"] --> B{"状态?"}
+```
+*注：插件已优化 HTML 保护机制，允许包含 `<br/>` 等标签的 Mermaid 图表正常触发修复。*
+
+## 8. XML 标签清理 (XML Cleanup)
+
+移除 LLM 输出中残留的无用 XML 标签（如 Claude 的 artifact 标签）。
+
+**Before:**
+Here is the result <antArtifact>hidden metadata</antArtifact>.
+
+**After:**
+## 9. 标题格式修复 (Heading Format Fix)
+**功能**: 修复标题标记 `#` 后缺少空格的问题。
+**默认**: 开启 (`enable_heading_fix = True`)
+**示例**:
+*   **Before**: `#Heading 1`
+*   **After**: `# Heading 1`
+
+## 10. 表格格式修复 (Table Format Fix)
+**功能**: 修复表格行末尾缺少管道符 `|` 的问题。
+**默认**: 开启 (`enable_table_fix = True`)
+**示例**:
+*   **Before**: `| Col 1 | Col 2`
+*   **After**: `| Col 1 | Col 2 |`

plugins/filters/markdown_normalizer/README.md

@@ -0,0 +1,61 @@
+# Markdown Normalizer Filter
+
+**Author:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui)
+**Version:** 1.1.2
+
+A content normalizer filter for Open WebUI that fixes common Markdown formatting issues in LLM outputs. It ensures that code blocks, LaTeX formulas, Mermaid diagrams, and other Markdown elements are rendered correctly.
+
+## Features
+
+*   **Mermaid Syntax Fix**: Automatically fixes common Mermaid syntax errors, such as unquoted node labels (including multi-line labels and citations) and unclosed subgraphs. **New in v1.1.2**: Comprehensive protection for edge labels (text on connecting lines) across all link types (solid, dotted, thick).
+*   **Frontend Console Debugging**: Supports printing structured debug logs directly to the browser console (F12) for easier troubleshooting.
+*   **Code Block Formatting**: Fixes broken code block prefixes, suffixes, and indentation.
+*   **LaTeX Normalization**: Standardizes LaTeX formula delimiters (`\[` -> `$$`, `\(` -> `$`).
+*   **Thought Tag Normalization**: Unifies thought tags (`<think>`, `<thinking>` -> `<thought>`).
+*   **Escape Character Fix**: Cleans up excessive escape characters (`\\n`, `\\t`).
+*   **List Formatting**: Ensures proper newlines in list items.
+*   **Heading Fix**: Adds missing spaces in headings (`#Heading` -> `# Heading`).
+*   **Table Fix**: Adds missing closing pipes in tables.
+*   **XML Cleanup**: Removes leftover XML artifacts.
+
+## Usage
+
+1.  Install the plugin in Open WebUI.
+2.  Enable the filter globally or for specific models.
+3.  Configure the enabled fixes in the **Valves** settings.
+4.  (Optional) **Show Debug Log** is enabled by default in Valves. This prints structured logs to the browser console (F12).
+    > [!WARNING]
+    > As this is an initial version, some "negative fixes" might occur (e.g., breaking valid Markdown). If you encounter issues, please check the console logs, copy the "Original" vs "Normalized" content, and submit an issue.
+
+## Configuration (Valves)
+
+*   `priority`: Filter priority (default: 50).
+*   `enable_escape_fix`: Fix excessive escape characters.
+*   `enable_thought_tag_fix`: Normalize thought tags.
+*   `enable_code_block_fix`: Fix code block formatting.
+*   `enable_latex_fix`: Normalize LaTeX formulas.
+*   `enable_list_fix`: Fix list item newlines (Experimental).
+*   `enable_unclosed_block_fix`: Auto-close unclosed code blocks.
+*   `enable_fullwidth_symbol_fix`: Fix full-width symbols in code blocks.
+*   `enable_mermaid_fix`: Fix Mermaid syntax errors.
+*   `enable_heading_fix`: Fix missing space in headings.
+*   `enable_table_fix`: Fix missing closing pipe in tables.
+*   `enable_xml_tag_cleanup`: Cleanup leftover XML tags.
+*   `show_status`: Show status notification when fixes are applied.
+*   `show_debug_log`: Print debug logs to browser console.
+
+## Changelog
+
+### v1.1.2
+*   **Mermaid Edge Label Protection**: Implemented comprehensive protection for edge labels (text on connecting lines) to prevent them from being incorrectly modified. Now supports all Mermaid link types including solid (`--`), dotted (`-.`), and thick (`==`) lines with or without arrows.
+*   **Bug Fixes**: Fixed an issue where lines without arrows (e.g., `A -- text --- B`) were not correctly protected.
+
+### v1.1.0
+*   **Mermaid Fix Refinement**: Improved regex to handle nested parentheses in node labels (e.g., `ID("Label (text)")`) and avoided matching connection labels.
+*   **HTML Safeguard Optimization**: Refined `_contains_html` to allow common tags like `<br/>`, `<b>`, `<i>`, etc., ensuring Mermaid diagrams with these tags are still normalized.
+*   **Full-width Symbol Cleanup**: Fixed duplicate keys and incorrect quote mapping in `FULLWIDTH_MAP`.
+*   **Bug Fixes**: Fixed missing `Dict` import in Python files.
+
+## License
+
+MIT

plugins/filters/markdown_normalizer/README_CN.md

@@ -0,0 +1,61 @@
+# Markdown 格式化过滤器 (Markdown Normalizer)
+
+**作者:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui)
+**版本:** 1.1.2
+
+这是一个用于 Open WebUI 的内容格式化过滤器，旨在修复 LLM 输出中常见的 Markdown 格式问题。它能确保代码块、LaTeX 公式、Mermaid 图表和其他 Markdown 元素被正确渲染。
+
+## 功能特性
+
+*   **Mermaid 语法修复**: 自动修复常见的 Mermaid 语法错误，如未加引号的节点标签（支持多行标签和引用标记）和未闭合的子图 (Subgraph)。**v1.1.2 新增**: 全面保护各种类型的连线标签（实线、虚线、粗线），防止被误修改。
+*   **前端控制台调试**: 支持将结构化的调试日志直接打印到浏览器控制台 (F12)，方便排查问题。
+*   **代码块格式化**: 修复破损的代码块前缀、后缀和缩进问题。
+*   **LaTeX 规范化**: 标准化 LaTeX 公式定界符 (`\[` -> `$$`, `\(` -> `$`)。
+*   **思维标签规范化**: 统一思维链标签 (`<think>`, `<thinking>` -> `<thought>`)。
+*   **转义字符修复**: 清理过度的转义字符 (`\\n`, `\\t`)。
+*   **列表格式化**: 确保列表项有正确的换行。
+*   **标题修复**: 修复标题中缺失的空格 (`#标题` -> `# 标题`)。
+*   **表格修复**: 修复表格中缺失的闭合管道符。
+*   **XML 清理**: 移除残留的 XML 标签。
+
+## 使用方法
+
+1.  在 Open WebUI 中安装此插件。
+2.  全局启用或为特定模型启用此过滤器。
+3.  在 **Valves** 设置中配置需要启用的修复项。
+4.  (可选) **显示调试日志 (Show Debug Log)** 在 Valves 中默认开启。这会将结构化的日志打印到浏览器控制台 (F12)。
+    > [!WARNING]
+    > 由于这是初版，可能会出现“负向修复”的情况（例如破坏了原本正确的格式）。如果您遇到问题，请务必查看控制台日志，复制“原始 (Original)”与“规范化 (Normalized)”的内容对比，并提交 Issue 反馈。
+
+## 配置项 (Valves)
+
+*   `priority`: 过滤器优先级 (默认: 50)。
+*   `enable_escape_fix`: 修复过度的转义字符。
+*   `enable_thought_tag_fix`: 规范化思维标签。
+*   `enable_code_block_fix`: 修复代码块格式。
+*   `enable_latex_fix`: 规范化 LaTeX 公式。
+*   `enable_list_fix`: 修复列表项换行 (实验性)。
+*   `enable_unclosed_block_fix`: 自动闭合未闭合的代码块。
+*   `enable_fullwidth_symbol_fix`: 修复代码块中的全角符号。
+*   `enable_mermaid_fix`: 修复 Mermaid 语法错误。
+*   `enable_heading_fix`: 修复标题中缺失的空格。
+*   `enable_table_fix`: 修复表格中缺失的闭合管道符。
+*   `enable_xml_tag_cleanup`: 清理残留的 XML 标签。
+*   `show_status`: 应用修复时显示状态通知。
+*   `show_debug_log`: 在浏览器控制台打印调试日志。
+
+## 更新日志
+
+### v1.1.2
+*   **Mermaid 连线标签保护**: 实现了全面的连线标签保护机制，防止连接线上的文字被误修改。现在支持所有 Mermaid 连线类型，包括实线 (`--`)、虚线 (`-.`) 和粗线 (`==`)，无论是否带有箭头。
+*   **Bug 修复**: 修复了无箭头连线（如 `A -- text --- B`）未被正确保护的问题。
+
+### v1.1.0
+*   **Mermaid 修复优化**: 改进了正则表达式以处理节点标签中的嵌套括号（如 `ID("标签 (文本)")`），并避免误匹配连接线上的文字。
+*   **HTML 保护机制优化**: 优化了 `_contains_html` 检测，允许 `<br/>`, `<b>`, `<i>` 等常见标签，确保包含这些标签的 Mermaid 图表能被正常规范化。
+*   **全角符号清理**: 修复了 `FULLWIDTH_MAP` 中的重复键名和错误的引号映射。
+*   **Bug 修复**: 修复了 Python 文件中缺失的 `Dict` 类型导入。
+
+## 许可证
+
+MIT