feat(filters): refactor folder-rule-collector to folder-memory

- Rename plugin from `folder-rule-collector` to `folder-memory` for better clarity. - Refactor code to focus on "Project Rules" collection, removing "Knowledge" collection for V1. - Add `PRIORITY` valve (default: 20) to ensure execution after context compression. - Update all parameter names to uppercase for consistency. - Update documentation (README, global docs) with GitHub raw URL for demo image. - Remove `STATUS` valve as it's redundant with OpenWebUI's built-in function toggle. - Add `ROADMAP.md` to track future "Project Knowledge" features. - Update `.github/copilot-instructions.md` with detailed commit message guidelines.
chore: update community stats - plugin version updated
2026-01-20 20:02:50 +08:00 · 2026-01-20 11:10:30 +00:00 · 2026-01-20 19:09:25 +08:00 · 2026-01-20 09:15:24 +00:00 · 2026-01-20 07:14:01 +00:00 · 2026-01-19 23:08:06 +00:00
24 changed files with 1935 additions and 331 deletions
--- a/.github/copilot-instructions.md
+++ b/.github/copilot-instructions.md
@@ -822,6 +822,22 @@ Filter 实例是**单例 (Singleton)**。
 #### Commit Message 规范
 使用 Conventional Commits 格式 (`feat`, `fix`, `docs`, etc.)。
 **必须**在提交标题与正文中清晰描述变更内容，确保在 Release 页面可读且可追踪。
 要求：
 - 标题必须包含“做了什么”与影响范围（避免含糊词）。
 - 正文必须列出关键变更点（1-3 条），与实际改动一一对应。
 - 若影响用户或插件行为，必须在正文标明影响与迁移说明。
 推荐格式：
 - `feat(actions): add export settings panel`
 - `fix(filters): handle empty metadata to avoid crash`
 - `docs(plugins): update bilingual README structure`
 正文示例：
 - Add valves for export format selection
 - Update README/README_CN to include What's New section
 - Migration: default TITLE_SOURCE changed to chat_title
 ### 4. 🤖 Git Operations (Agent Rules)
--- a/README.md
+++ b/README.md
@@ -10,28 +10,28 @@ A collection of enhancements, plugins, and prompts for [OpenWebUI](https://githu
 <!-- STATS_START -->
 ## 📊 Community Stats
-> 🕐 Auto-updated: 2026-01-19 18:11
+> 🕐 Auto-updated: 2026-01-20 19:10
 | 👤 Author | 👥 Followers | ⭐ Points | 🏆 Contributions |
 |:---:|:---:|:---:|:---:|
-| [Fu-Jie](https://openwebui.com/u/Fu-Jie) | **133** | **134** | **25** |
+| [Fu-Jie](https://openwebui.com/u/Fu-Jie) | **137** | **134** | **25** |
 | 📝 Posts | ⬇️ Downloads | 👁️ Views | 👍 Upvotes | 💾 Saves |
 |:---:|:---:|:---:|:---:|:---:|
-| **16** | **1792** | **21276** | **120** | **135** |
+| **16** | **1887** | **22101** | **120** | **147** |
 ### 🔥 Top 6 Popular Plugins
-> 🕐 Auto-updated: 2026-01-19 18:11
+> 🕐 Auto-updated: 2026-01-20 19:10
 | Rank | Plugin | Version | Downloads | Views | Updated |
 |:---:|------|:---:|:---:|:---:|:---:|
-| 🥇 | [Smart Mind Map](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | 0.9.1 | 532 | 4822 | 2026-01-17 |
+| 🥇 | [Smart Mind Map](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | 0.9.1 | 550 | 4939 | 2026-01-17 |
-| 🥈 | [📊 Smart Infographic (AntV)](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | 1.4.9 | 260 | 2514 | 2026-01-18 |
+| 🥈 | [📊 Smart Infographic (AntV)](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | 1.4.9 | 282 | 2667 | 2026-01-18 |
-| 🥉 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | 0.3.7 | 209 | 800 | 2026-01-07 |
+| 🥉 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | 0.3.7 | 215 | 844 | 2026-01-07 |
-| 4️⃣ | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | 1.1.3 | 180 | 1975 | 2026-01-17 |
+| 4️⃣ | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | 1.2.1 | 189 | 2051 | 2026-01-20 |
-| 5️⃣ | [Export to Word (Enhanced)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | 0.4.3 | 158 | 1377 | 2026-01-17 |
+| 5️⃣ | [Export to Word (Enhanced)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | 0.4.3 | 170 | 1457 | 2026-01-17 |
-| 6️⃣ | [Flash Card](https://openwebui.com/posts/flash_card_65a2ea8f) | 0.2.4 | 138 | 2329 | 2026-01-17 |
+| 6️⃣ | [Flash Card](https://openwebui.com/posts/flash_card_65a2ea8f) | 0.2.4 | 144 | 2395 | 2026-01-17 |
 *See full stats in [Community Stats Report](./docs/community-stats.md)*
 <!-- STATS_END -->
@@ -53,6 +53,7 @@ Located in the `plugins/` directory, containing Python-based enhancements:
 #### Filters
 - **Async Context Compression** (`async-context-compression`): Optimizes token usage via context compression.
 - **Context Enhancement** (`context_enhancement_filter`): Enhances chat context.
 - **Folder Memory** (`folder-memory`): Automatically extracts project rules from conversations and injects them into the folder's system prompt.
 - **Markdown Normalizer** (`markdown_normalizer`): Fixes common Markdown formatting issues in LLM outputs.
 #### Pipelines
--- a/README_CN.md
+++ b/README_CN.md
@@ -7,28 +7,28 @@ 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 2026-01-19 18:11
+> 🕐 自动更新于 2026-01-20 19:10
 | 👤 作者 | 👥 粉丝 | ⭐ 积分 | 🏆 贡献 |
 |:---:|:---:|:---:|:---:|
-| [Fu-Jie](https://openwebui.com/u/Fu-Jie) | **133** | **134** | **25** |
+| [Fu-Jie](https://openwebui.com/u/Fu-Jie) | **137** | **134** | **25** |
 | 📝 发布 | ⬇️ 下载 | 👁️ 浏览 | 👍 点赞 | 💾 收藏 |
 |:---:|:---:|:---:|:---:|:---:|
-| **16** | **1792** | **21276** | **120** | **135** |
+| **16** | **1887** | **22101** | **120** | **147** |
 ### 🔥 热门插件 Top 6
-> 🕐 自动更新于 2026-01-19 18:11
+> 🕐 自动更新于 2026-01-20 19:10
 | 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 | 532 | 4822 | 2026-01-17 |
+| 🥇 | [Smart Mind Map](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | 0.9.1 | 550 | 4939 | 2026-01-17 |
-| 🥈 | [📊 Smart Infographic (AntV)](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | 1.4.9 | 260 | 2514 | 2026-01-18 |
+| 🥈 | [📊 Smart Infographic (AntV)](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | 1.4.9 | 282 | 2667 | 2026-01-18 |
-| 🥉 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | 0.3.7 | 209 | 800 | 2026-01-07 |
+| 🥉 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | 0.3.7 | 215 | 844 | 2026-01-07 |
-| 4️⃣ | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | 1.1.3 | 180 | 1975 | 2026-01-17 |
+| 4️⃣ | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | 1.2.1 | 189 | 2051 | 2026-01-20 |
-| 5️⃣ | [Export to Word (Enhanced)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | 0.4.3 | 158 | 1377 | 2026-01-17 |
+| 5️⃣ | [Export to Word (Enhanced)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | 0.4.3 | 170 | 1457 | 2026-01-17 |
-| 6️⃣ | [Flash Card](https://openwebui.com/posts/flash_card_65a2ea8f) | 0.2.4 | 138 | 2329 | 2026-01-17 |
+| 6️⃣ | [Flash Card](https://openwebui.com/posts/flash_card_65a2ea8f) | 0.2.4 | 144 | 2395 | 2026-01-17 |
 *完整统计请查看 [社区统计报告](./docs/community-stats.zh.md)*
 <!-- STATS_END -->
@@ -50,6 +50,7 @@ OpenWebUI 增强功能集合。包含个人开发与收集的插件、提示词
 #### 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。
 - **Folder Memory** (`folder-memory`): 自动从对话中提取项目规则并注入到文件夹系统提示词中。
 - **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。
--- a/docs/badges/downloads.json
+++ b/docs/badges/downloads.json
@@ -1,7 +1,7 @@
 {
  "schemaVersion": 1,
  "label": "downloads",
-  "message": "1.8k",
+  "message": "1.9k",
  "color": "blue",
  "namedLogo": "openwebui"
 }
--- a/docs/badges/followers.json
+++ b/docs/badges/followers.json
@@ -1,6 +1,6 @@
 {
  "schemaVersion": 1,
  "label": "followers",
-  "message": "133",
+  "message": "137",
  "color": "blue"
 }
--- a/docs/community-stats.json
+++ b/docs/community-stats.json
@@ -1,10 +1,10 @@
 {
  "total_posts": 16,
-  "total_downloads": 1792,
+  "total_downloads": 1887,
-  "total_views": 21276,
+  "total_views": 22101,
  "total_upvotes": 120,
  "total_downvotes": 2,
-  "total_saves": 135,
+  "total_saves": 147,
  "total_comments": 24,
  "by_type": {
    "action": 14,
@@ -18,10 +18,10 @@
      "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": 532,
+      "downloads": 550,
-      "views": 4822,
+      "views": 4939,
      "upvotes": 15,
-      "saves": 28,
+      "saves": 30,
      "comments": 11,
      "created_at": "2025-12-30",
      "updated_at": "2026-01-17",
@@ -34,10 +34,10 @@
      "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": 260,
+      "downloads": 282,
-      "views": 2514,
+      "views": 2667,
      "upvotes": 14,
-      "saves": 20,
+      "saves": 21,
      "comments": 3,
      "created_at": "2025-12-28",
      "updated_at": "2026-01-18",
@@ -50,10 +50,10 @@
      "version": "0.3.7",
      "author": "Fu-Jie",
      "description": "Extracts tables from chat messages and exports them to Excel (.xlsx) files with smart formatting.",
-      "downloads": 209,
+      "downloads": 215,
-      "views": 800,
+      "views": 844,
      "upvotes": 4,
-      "saves": 5,
+      "saves": 6,
      "comments": 0,
      "created_at": "2025-05-30",
      "updated_at": "2026-01-07",
@@ -63,16 +63,16 @@
      "title": "Async Context Compression",
      "slug": "async_context_compression_b1655bc8",
      "type": "action",
-      "version": "1.1.3",
+      "version": "1.2.1",
      "author": "Fu-Jie",
      "description": "Reduces token consumption in long conversations while maintaining coherence through intelligent summarization and message compression.",
-      "downloads": 180,
+      "downloads": 189,
-      "views": 1975,
+      "views": 2051,
      "upvotes": 9,
-      "saves": 19,
+      "saves": 22,
      "comments": 0,
      "created_at": "2025-11-08",
-      "updated_at": "2026-01-17",
+      "updated_at": "2026-01-20",
      "url": "https://openwebui.com/posts/async_context_compression_b1655bc8"
    },
    {
@@ -82,10 +82,10 @@
      "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": 158,
+      "downloads": 170,
-      "views": 1377,
+      "views": 1457,
      "upvotes": 8,
-      "saves": 16,
+      "saves": 17,
      "comments": 0,
      "created_at": "2026-01-03",
      "updated_at": "2026-01-17",
@@ -98,10 +98,10 @@
      "version": "0.2.4",
      "author": "Fu-Jie",
      "description": "Quickly generates beautiful flashcards from text, extracting key points and categories.",
-      "downloads": 138,
+      "downloads": 144,
-      "views": 2329,
+      "views": 2395,
      "upvotes": 10,
-      "saves": 10,
+      "saves": 12,
      "comments": 2,
      "created_at": "2025-12-30",
      "updated_at": "2026-01-17",
@@ -111,16 +111,16 @@
      "title": "Markdown Normalizer",
      "slug": "markdown_normalizer_baaa8732",
      "type": "action",
-      "version": "1.2.3",
+      "version": "1.2.4",
      "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": 84,
+      "downloads": 96,
-      "views": 2100,
+      "views": 2234,
      "upvotes": 10,
      "saves": 17,
      "comments": 5,
      "created_at": "2026-01-12",
-      "updated_at": "2026-01-17",
+      "updated_at": "2026-01-19",
      "url": "https://openwebui.com/posts/markdown_normalizer_baaa8732"
    },
    {
@@ -130,10 +130,10 @@
      "version": "1.0.0",
      "author": "Fu-Jie",
      "description": "A comprehensive thinking lens that dives deep into any content - from context to logic, insights, and action paths.",
-      "downloads": 68,
+      "downloads": 73,
-      "views": 663,
+      "views": 707,
      "upvotes": 4,
-      "saves": 6,
+      "saves": 7,
      "comments": 0,
      "created_at": "2026-01-08",
      "updated_at": "2026-01-08",
@@ -146,8 +146,8 @@
      "version": "0.4.3",
      "author": "Fu-Jie",
      "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": 63,
+      "downloads": 65,
-      "views": 1305,
+      "views": 1335,
      "upvotes": 11,
      "saves": 3,
      "comments": 1,
@@ -162,8 +162,8 @@
      "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": 42,
+      "downloads": 43,
-      "views": 683,
+      "views": 704,
      "upvotes": 6,
      "saves": 0,
      "comments": 0,
@@ -178,8 +178,8 @@
      "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": 22,
+      "downloads": 24,
-      "views": 398,
+      "views": 407,
      "upvotes": 3,
      "saves": 1,
      "comments": 0,
@@ -195,7 +195,7 @@
      "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": 16,
-      "views": 443,
+      "views": 453,
      "upvotes": 5,
      "saves": 1,
      "comments": 0,
@@ -207,16 +207,16 @@
      "title": "异步上下文压缩",
      "slug": "异步上下文压缩_5c0617cb",
      "type": "action",
-      "version": "1.1.3",
+      "version": "1.2.1",
      "author": "Fu-Jie",
      "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": 351,
+      "views": 377,
      "upvotes": 5,
      "saves": 1,
      "comments": 0,
      "created_at": "2025-11-08",
-      "updated_at": "2026-01-17",
+      "updated_at": "2026-01-20",
      "url": "https://openwebui.com/posts/异步上下文压缩_5c0617cb"
    },
    {
@@ -227,7 +227,7 @@
      "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": 259,
+      "views": 261,
      "upvotes": 3,
      "saves": 1,
      "comments": 0,
@@ -243,7 +243,7 @@
      "author": "",
      "description": "",
      "downloads": 0,
-      "views": 59,
+      "views": 62,
      "upvotes": 1,
      "saves": 0,
      "comments": 0,
@@ -259,9 +259,9 @@
      "author": "",
      "description": "",
      "downloads": 0,
-      "views": 1198,
+      "views": 1208,
      "upvotes": 12,
-      "saves": 7,
+      "saves": 8,
      "comments": 2,
      "created_at": "2026-01-10",
      "updated_at": "2026-01-10",
@@ -273,7 +273,7 @@
    "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": 133,
+    "followers": 137,
    "following": 2,
    "total_points": 134,
    "post_points": 118,
--- a/docs/community-stats.md
+++ b/docs/community-stats.md
@@ -1,16 +1,16 @@
 # 📊 OpenWebUI Community Stats Report
-> 📅 Updated: 2026-01-19 18:11
+> 📅 Updated: 2026-01-20 19:10
 ## 📈 Overview
 | Metric | Value |
 |------|------|
 | 📝 Total Posts | 16 |
-| ⬇️ Total Downloads | 1792 |
+| ⬇️ Total Downloads | 1887 |
-| 👁️ Total Views | 21276 |
+| 👁️ Total Views | 22101 |
 | 👍 Total Upvotes | 120 |
-| 💾 Total Saves | 135 |
+| 💾 Total Saves | 147 |
 | 💬 Total Comments | 24 |
 ## 📂 By Type
@@ -22,19 +22,19 @@
 | Rank | Title | Type | Version | Downloads | Views | Upvotes | Saves | Updated |
 |:---:|------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
-| 1 | [Smart Mind Map](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | action | 0.9.1 | 532 | 4822 | 15 | 28 | 2026-01-17 |
+| 1 | [Smart Mind Map](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | action | 0.9.1 | 550 | 4939 | 15 | 30 | 2026-01-17 |
-| 2 | [📊 Smart Infographic (AntV)](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | action | 1.4.9 | 260 | 2514 | 14 | 20 | 2026-01-18 |
+| 2 | [📊 Smart Infographic (AntV)](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | action | 1.4.9 | 282 | 2667 | 14 | 21 | 2026-01-18 |
-| 3 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | action | 0.3.7 | 209 | 800 | 4 | 5 | 2026-01-07 |
+| 3 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | action | 0.3.7 | 215 | 844 | 4 | 6 | 2026-01-07 |
-| 4 | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | action | 1.1.3 | 180 | 1975 | 9 | 19 | 2026-01-17 |
+| 4 | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | action | 1.2.1 | 189 | 2051 | 9 | 22 | 2026-01-20 |
-| 5 | [Export to Word (Enhanced)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | action | 0.4.3 | 158 | 1377 | 8 | 16 | 2026-01-17 |
+| 5 | [Export to Word (Enhanced)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | action | 0.4.3 | 170 | 1457 | 8 | 17 | 2026-01-17 |
-| 6 | [Flash Card](https://openwebui.com/posts/flash_card_65a2ea8f) | action | 0.2.4 | 138 | 2329 | 10 | 10 | 2026-01-17 |
+| 6 | [Flash Card](https://openwebui.com/posts/flash_card_65a2ea8f) | action | 0.2.4 | 144 | 2395 | 10 | 12 | 2026-01-17 |
-| 7 | [Markdown Normalizer](https://openwebui.com/posts/markdown_normalizer_baaa8732) | action | 1.2.3 | 84 | 2100 | 10 | 17 | 2026-01-17 |
+| 7 | [Markdown Normalizer](https://openwebui.com/posts/markdown_normalizer_baaa8732) | action | 1.2.4 | 96 | 2234 | 10 | 17 | 2026-01-19 |
-| 8 | [Deep Dive](https://openwebui.com/posts/deep_dive_c0b846e4) | action | 1.0.0 | 68 | 663 | 4 | 6 | 2026-01-08 |
+| 8 | [Deep Dive](https://openwebui.com/posts/deep_dive_c0b846e4) | action | 1.0.0 | 73 | 707 | 4 | 7 | 2026-01-08 |
-| 9 | [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 | 63 | 1305 | 11 | 3 | 2026-01-17 |
+| 9 | [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 | 65 | 1335 | 11 | 3 | 2026-01-17 |
-| 10 | [📊 智能信息图 (AntV Infographic)](https://openwebui.com/posts/智能信息图_e04a48ff) | action | 1.4.9 | 42 | 683 | 6 | 0 | 2026-01-17 |
+| 10 | [📊 智能信息图 (AntV Infographic)](https://openwebui.com/posts/智能信息图_e04a48ff) | action | 1.4.9 | 43 | 704 | 6 | 0 | 2026-01-17 |
-| 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 | 22 | 398 | 3 | 1 | 2026-01-17 |
+| 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 | 24 | 407 | 3 | 1 | 2026-01-17 |
-| 12 | [闪记卡 (Flash Card)](https://openwebui.com/posts/闪记卡生成插件_4a31eac3) | action | 0.2.4 | 16 | 443 | 5 | 1 | 2026-01-17 |
+| 12 | [闪记卡 (Flash Card)](https://openwebui.com/posts/闪记卡生成插件_4a31eac3) | action | 0.2.4 | 16 | 453 | 5 | 1 | 2026-01-17 |
-| 13 | [异步上下文压缩](https://openwebui.com/posts/异步上下文压缩_5c0617cb) | action | 1.1.3 | 14 | 351 | 5 | 1 | 2026-01-17 |
+| 13 | [异步上下文压缩](https://openwebui.com/posts/异步上下文压缩_5c0617cb) | action | 1.2.1 | 14 | 377 | 5 | 1 | 2026-01-20 |
-| 14 | [精读](https://openwebui.com/posts/精读_99830b0f) | action | 1.0.0 | 6 | 259 | 3 | 1 | 2026-01-08 |
+| 14 | [精读](https://openwebui.com/posts/精读_99830b0f) | action | 1.0.0 | 6 | 261 | 3 | 1 | 2026-01-08 |
-| 15 | [Review of Claude Haiku 4.5](https://openwebui.com/posts/review_of_claude_haiku_45_41b0db39) | unknown |  | 0 | 59 | 1 | 0 | 2026-01-14 |
+| 15 | [Review of Claude Haiku 4.5](https://openwebui.com/posts/review_of_claude_haiku_45_41b0db39) | unknown |  | 0 | 62 | 1 | 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 | 1198 | 12 | 7 | 2026-01-10 |
+| 16 | [ 🛠️ Debug Open WebUI Plugins in Your Browser](https://openwebui.com/posts/debug_open_webui_plugins_in_your_browser_81bf7960) | unknown |  | 0 | 1208 | 12 | 8 | 2026-01-10 |
--- a/docs/community-stats.zh.md
+++ b/docs/community-stats.zh.md
@@ -1,16 +1,16 @@
 # 📊 OpenWebUI 社区统计报告
-> 📅 更新时间: 2026-01-19 18:11
+> 📅 更新时间: 2026-01-20 19:10
 ## 📈 总览
 | 指标 | 数值 |
 |------|------|
 | 📝 发布数量 | 16 |
-| ⬇️ 总下载量 | 1792 |
+| ⬇️ 总下载量 | 1887 |
-| 👁️ 总浏览量 | 21276 |
+| 👁️ 总浏览量 | 22101 |
 | 👍 总点赞数 | 120 |
-| 💾 总收藏数 | 135 |
+| 💾 总收藏数 | 147 |
 | 💬 总评论数 | 24 |
 ## 📂 按类型分类
@@ -22,19 +22,19 @@
 | 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 | 532 | 4822 | 15 | 28 | 2026-01-17 |
+| 1 | [Smart Mind Map](https://openwebui.com/posts/turn_any_text_into_beautiful_mind_maps_3094c59a) | action | 0.9.1 | 550 | 4939 | 15 | 30 | 2026-01-17 |
-| 2 | [📊 Smart Infographic (AntV)](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | action | 1.4.9 | 260 | 2514 | 14 | 20 | 2026-01-18 |
+| 2 | [📊 Smart Infographic (AntV)](https://openwebui.com/posts/smart_infographic_ad6f0c7f) | action | 1.4.9 | 282 | 2667 | 14 | 21 | 2026-01-18 |
-| 3 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | action | 0.3.7 | 209 | 800 | 4 | 5 | 2026-01-07 |
+| 3 | [Export to Excel](https://openwebui.com/posts/export_mulit_table_to_excel_244b8f9d) | action | 0.3.7 | 215 | 844 | 4 | 6 | 2026-01-07 |
-| 4 | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | action | 1.1.3 | 180 | 1975 | 9 | 19 | 2026-01-17 |
+| 4 | [Async Context Compression](https://openwebui.com/posts/async_context_compression_b1655bc8) | action | 1.2.1 | 189 | 2051 | 9 | 22 | 2026-01-20 |
-| 5 | [Export to Word (Enhanced)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | action | 0.4.3 | 158 | 1377 | 8 | 16 | 2026-01-17 |
+| 5 | [Export to Word (Enhanced)](https://openwebui.com/posts/export_to_word_enhanced_formatting_fca6a315) | action | 0.4.3 | 170 | 1457 | 8 | 17 | 2026-01-17 |
-| 6 | [Flash Card](https://openwebui.com/posts/flash_card_65a2ea8f) | action | 0.2.4 | 138 | 2329 | 10 | 10 | 2026-01-17 |
+| 6 | [Flash Card](https://openwebui.com/posts/flash_card_65a2ea8f) | action | 0.2.4 | 144 | 2395 | 10 | 12 | 2026-01-17 |
-| 7 | [Markdown Normalizer](https://openwebui.com/posts/markdown_normalizer_baaa8732) | action | 1.2.3 | 84 | 2100 | 10 | 17 | 2026-01-17 |
+| 7 | [Markdown Normalizer](https://openwebui.com/posts/markdown_normalizer_baaa8732) | action | 1.2.4 | 96 | 2234 | 10 | 17 | 2026-01-19 |
-| 8 | [Deep Dive](https://openwebui.com/posts/deep_dive_c0b846e4) | action | 1.0.0 | 68 | 663 | 4 | 6 | 2026-01-08 |
+| 8 | [Deep Dive](https://openwebui.com/posts/deep_dive_c0b846e4) | action | 1.0.0 | 73 | 707 | 4 | 7 | 2026-01-08 |
-| 9 | [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 | 63 | 1305 | 11 | 3 | 2026-01-17 |
+| 9 | [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 | 65 | 1335 | 11 | 3 | 2026-01-17 |
-| 10 | [📊 智能信息图 (AntV Infographic)](https://openwebui.com/posts/智能信息图_e04a48ff) | action | 1.4.9 | 42 | 683 | 6 | 0 | 2026-01-17 |
+| 10 | [📊 智能信息图 (AntV Infographic)](https://openwebui.com/posts/智能信息图_e04a48ff) | action | 1.4.9 | 43 | 704 | 6 | 0 | 2026-01-17 |
-| 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 | 22 | 398 | 3 | 1 | 2026-01-17 |
+| 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 | 24 | 407 | 3 | 1 | 2026-01-17 |
-| 12 | [闪记卡 (Flash Card)](https://openwebui.com/posts/闪记卡生成插件_4a31eac3) | action | 0.2.4 | 16 | 443 | 5 | 1 | 2026-01-17 |
+| 12 | [闪记卡 (Flash Card)](https://openwebui.com/posts/闪记卡生成插件_4a31eac3) | action | 0.2.4 | 16 | 453 | 5 | 1 | 2026-01-17 |
-| 13 | [异步上下文压缩](https://openwebui.com/posts/异步上下文压缩_5c0617cb) | action | 1.1.3 | 14 | 351 | 5 | 1 | 2026-01-17 |
+| 13 | [异步上下文压缩](https://openwebui.com/posts/异步上下文压缩_5c0617cb) | action | 1.2.1 | 14 | 377 | 5 | 1 | 2026-01-20 |
-| 14 | [精读](https://openwebui.com/posts/精读_99830b0f) | action | 1.0.0 | 6 | 259 | 3 | 1 | 2026-01-08 |
+| 14 | [精读](https://openwebui.com/posts/精读_99830b0f) | action | 1.0.0 | 6 | 261 | 3 | 1 | 2026-01-08 |
-| 15 | [Review of Claude Haiku 4.5](https://openwebui.com/posts/review_of_claude_haiku_45_41b0db39) | unknown |  | 0 | 59 | 1 | 0 | 2026-01-14 |
+| 15 | [Review of Claude Haiku 4.5](https://openwebui.com/posts/review_of_claude_haiku_45_41b0db39) | unknown |  | 0 | 62 | 1 | 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 | 1198 | 12 | 7 | 2026-01-10 |
+| 16 | [ 🛠️ Debug Open WebUI Plugins in Your Browser](https://openwebui.com/posts/debug_open_webui_plugins_in_your_browser_81bf7960) | unknown |  | 0 | 1208 | 12 | 8 | 2026-01-10 |
--- a/docs/plugins/filters/async-context-compression.md
+++ b/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">v1.2.0</span>
+<span class="version-badge">v1.2.1</span>
 Reduces token consumption in long conversations through intelligent summarization while maintaining conversational coherence.
@@ -38,6 +38,8 @@ This is especially useful for:
 - :material-format-align-justify: **Structure-Aware Trimming**: Preserves document structure
 - :material-content-cut: **Native Tool Output Trimming**: Trims verbose tool outputs (Note: Non-native tool outputs are not fully injected into context)
 - :material-chart-bar: **Detailed Token Logging**: Granular token breakdown
 - :material-account-search: **Smart Model Matching**: Inherit config from base models
 - :material-image-off: **Multimodal Support**: Images are preserved but tokens are **NOT** calculated
 ---
@@ -73,6 +75,7 @@ graph TD
 | `keep_first` | integer | `1` | Always keep the first N messages |
 | `keep_last` | integer | `6` | Always keep the last N messages |
 | `summary_model` | string | `None` | Model to use for summarization |
 | `summary_model_max_context` | integer | `0` | Max context tokens for summary model |
 | `max_summary_tokens` | integer | `16384` | Maximum tokens for the summary |
 | `enable_tool_output_trimming` | boolean | `false` | Enable trimming of large tool outputs |
--- a/docs/plugins/filters/async-context-compression.zh.md
+++ b/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">v1.2.0</span>
+<span class="version-badge">v1.2.1</span>
 通过智能摘要减少长对话的 token 消耗，同时保持对话连贯。
@@ -38,6 +38,8 @@ Async Context Compression 过滤器通过以下方式帮助管理长对话的 to
 - :material-format-align-justify: **结构感知裁剪**：保留文档结构的智能裁剪
 - :material-content-cut: **原生工具输出裁剪**：自动裁剪冗长的工具输出（注意：非原生工具调用输出不会完整注入上下文）
 - :material-chart-bar: **详细 Token 日志**：提供细粒度的 Token 统计
 - :material-account-search: **智能模型匹配**：自定义模型自动继承基础模型配置
 - :material-image-off: **多模态支持**：图片内容保留但 Token **不参与计算**
 ---
@@ -73,6 +75,7 @@ graph TD
 | `keep_first` | integer | `1` | 始终保留的前 N 条消息 |
 | `keep_last` | integer | `6` | 始终保留的后 N 条消息 |
 | `summary_model` | string | `None` | 用于摘要的模型 |
 | `summary_model_max_context` | integer | `0` | 摘要模型的最大上下文 Token 数 |
 | `max_summary_tokens` | integer | `16384` | 摘要的最大 token 数 |
 | `enable_tool_output_trimming` | boolean | `false` | 启用长工具输出裁剪 |
--- a/docs/plugins/filters/folder-memory.md
+++ b/docs/plugins/filters/folder-memory.md
@@ -0,0 +1,42 @@
 # Folder Memory
 **Folder Memory** is an intelligent context filter plugin for OpenWebUI. It automatically extracts consistent "Project Rules" from ongoing conversations within a folder and injects them back into the folder's system prompt.
 This ensures that all future conversations within that folder share the same evolved context and rules, without manual updates.
 ## Features
 - **Automatic Extraction**: Analyzes chat history every N messages to extract project rules.
 - **Non-destructive Injection**: Updates only the specific "Project Rules" block in the system prompt, preserving other instructions.
 - **Async Processing**: Runs in the background without blocking the user's chat experience.
 - **ORM Integration**: Directly updates folder data using OpenWebUI's internal models for reliability.
 ## Installation
 1. Copy `folder_memory.py` to your OpenWebUI `plugins/filters/` directory (or upload via Admin UI).
 2. Enable the filter in your **Settings** -> **Filters**.
 3. (Optional) Configure the triggering threshold (default: every 10 messages).
 ## Configuration (Valves)
 | Valve | Default | Description |
 | :--- | :--- | :--- |
 | `PRIORITY` | `20` | Priority level for the filter operations. |
 | `MESSAGE_TRIGGER_COUNT` | `10` | The number of messages required to trigger a rule analysis. |
 | `MODEL_ID` | `""` | The model used to generate rules. If empty, uses the current chat model. |
 | `RULES_BLOCK_TITLE` | `## 📂 Project Rules` | The title displayed above the injected rules block. |
 | `SHOW_DEBUG_LOG` | `False` | Show detailed debug logs in the browser console. |
 | `UPDATE_ROOT_FOLDER` | `False` | If enabled, finds and updates the root folder rules instead of the current subfolder. |
 ## How It Works
 ![Folder Memory Demo](https://raw.githubusercontent.com/Fu-Jie/awesome-openwebui/main/plugins/filters/folder-memory/folder-memory-demo.png)
 1. **Trigger**: When a conversation reaches `MESSAGE_TRIGGER_COUNT` (e.g., 10, 20 messages).
 2. **Analysis**: The plugin sends the recent conversation + existing rules to the LLM.
 3. **Synthesis**: The LLM merges new insights with old rules, removing obsolete ones.
 4. **Update**: The new rule set replaces the `<!-- OWUI_PROJECT_RULES_START -->` block in the folder's system prompt.
 ## Roadmap
 See [ROADMAP](https://github.com/Fu-Jie/awesome-openwebui/blob/main/plugins/filters/folder-memory/ROADMAP.md) for future plans, including "Project Knowledge" collection.
--- a/docs/plugins/filters/folder-memory.zh.md
+++ b/docs/plugins/filters/folder-memory.zh.md
@@ -0,0 +1,42 @@
 # 文件夹记忆 (Folder Memory)
 **文件夹记忆 (Folder Memory)** 是一个 OpenWebUI 的智能上下文过滤器插件。它能自动从文件夹内的对话中提取一致性的“项目规则”，并将其回写到文件夹的系统提示词中。
 这确保了该文件夹内的所有未来对话都能共享相同的进化上下文和规则，无需手动更新。
 ## 功能特性
 - **自动提取**：每隔 N 条消息分析一次聊天记录，提取项目规则。
 - **无损注入**：仅更新系统提示词中的特定“项目规则”块，保留其他指令。
 - **异步处理**：在后台运行，不阻塞用户的聊天体验。
 - **ORM 集成**：直接使用 OpenWebUI 的内部模型更新文件夹数据，确保可靠性。
 ## 安装指南
 1. 将 `folder_memory.py` (或中文版 `folder_memory_cn.py`) 复制到 OpenWebUI 的 `plugins/filters/` 目录（或通过管理员 UI 上传）。
 2. 在 **设置** -> **过滤器** 中启用该插件。
 3. （可选）配置触发阈值（默认：每 10 条消息）。
 ## 配置 (Valves)
 | 参数 | 默认值 | 说明 |
 | :--- | :--- | :--- |
 | `PRIORITY` | `20` | 过滤器操作的优先级。 |
 | `MESSAGE_TRIGGER_COUNT` | `10` | 触发规则分析的消息数量阈值。 |
 | `MODEL_ID` | `""` | 用于生成规则的模型 ID。若为空，则使用当前对话模型。 |
 | `RULES_BLOCK_TITLE` | `## 📂 项目规则` | 显示在注入规则块上方的标题。 |
 | `SHOW_DEBUG_LOG` | `False` | 在浏览器控制台显示详细调试日志。 |
 | `UPDATE_ROOT_FOLDER` | `False` | 如果启用，将向上查找并更新根文件夹的规则，而不是当前子文件夹。 |
 ## 工作原理
 ![Folder Memory Demo](https://raw.githubusercontent.com/Fu-Jie/awesome-openwebui/main/plugins/filters/folder-memory/folder-memory-demo.png)
 1. **触发**：当对话达到 `MESSAGE_TRIGGER_COUNT`（例如 10、20 条消息）时。
 2. **分析**：插件将最近的对话 + 现有规则发送给 LLM。
 3. **综合**：LLM 将新见解与旧规则合并，移除过时的规则。
 4. **更新**：新的规则集替换文件夹系统提示词中的 `<!-- OWUI_PROJECT_RULES_START -->` 块。
 ## 路线图
 查看 [ROADMAP](https://github.com/Fu-Jie/awesome-openwebui/blob/main/plugins/filters/folder-memory/ROADMAP.md) 了解未来计划，包括“项目知识”收集功能。
--- a/docs/plugins/filters/index.md
+++ b/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:** 1.1.3
+    **Version:** 1.2.1
    [:octicons-arrow-right-24: Documentation](async-context-compression.md)
@@ -36,7 +36,15 @@ Filters act as middleware in the message pipeline:
    [:octicons-arrow-right-24: Documentation](context-enhancement.md)
 -   :material-folder-refresh:{ .lg .middle } **Folder Memory**
    ---
    Automatically extracts consistent "Project Rules" from ongoing conversations within a folder and injects them back into the folder's system prompt.
    **Version:** 0.1.0
    [:octicons-arrow-right-24: Documentation](folder-memory.md)
 -   :material-format-paint:{ .lg .middle } **Markdown Normalizer**
--- a/docs/plugins/filters/index.zh.md
+++ b/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：** 1.1.3
+    **版本：** 1.2.1
    [:octicons-arrow-right-24: 查看文档](async-context-compression.md)
@@ -36,7 +36,15 @@ Filter 充当消息管线中的中间件：
    [:octicons-arrow-right-24: 查看文档](context-enhancement.md)
 -   :material-folder-refresh:{ .lg .middle } **Folder Memory**
    ---
    自动从文件夹内的对话中提取一致性的“项目规则”，并将其回写到文件夹的系统提示词中。
    **版本：** 0.1.0
    [:octicons-arrow-right-24: 查看文档](folder-memory.zh.md)
 -   :material-format-paint:{ .lg .middle } **Markdown Normalizer**
--- a/plugins/filters/async-context-compression/README.md
+++ b/plugins/filters/async-context-compression/README.md
@@ -1,9 +1,15 @@
 # Async Context Compression Filter
-**Author:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **Version:** 1.2.0 | **Project:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui) | **License:** MIT
+**Author:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **Version:** 1.2.1 | **Project:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui) | **License:** MIT
 This filter reduces token consumption in long conversations through intelligent summarization and message compression while keeping conversations coherent.
 ## What's new in 1.2.1
 - **Smart Configuration**: Automatically detects base model settings for custom models and adds `summary_model_max_context` for independent summary limits.
 - **Performance & Refactoring**: Optimized threshold parsing with caching, removed redundant code, and improved LLM response handling (JSONResponse support).
 - **Bug Fixes & Modernization**: Fixed `datetime` deprecation warnings, corrected type annotations, and replaced print statements with proper logging.
 ## What's new in 1.2.0
 - **Preflight Context Check**: Before sending to the model, validates that total tokens fit within the context window. Automatically trims or drops oldest messages if exceeded.
@@ -19,18 +25,6 @@ This filter reduces token consumption in long conversations through intelligent
 - **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.
 ---
@@ -45,6 +39,8 @@ This filter reduces token consumption in long conversations through intelligent
 - ✅ Native tool output trimming for cleaner context when using function calling.
 - ✅ Real-time context usage monitoring with warning notifications (>90%).
 - ✅ Detailed token logging for precise debugging and optimization.
 - ✅ **Smart Model Matching**: Automatically inherits configuration from base models for custom presets.
 - ⚠ **Multimodal Support**: Images are preserved but their tokens are **NOT** calculated. Please adjust thresholds accordingly.
 ---
@@ -75,7 +71,8 @@ It is recommended to keep this filter early in the chain so it runs before filte
 | `keep_first`                   | `1`      | Always keep the first N messages (protects system prompts).                                                                                                           |
 | `keep_last`                    | `6`      | Always keep the last N messages to preserve recent context.                                                                                                           |
 | `summary_model`                | `None`   | Model for summaries. Strongly recommended to set a fast, economical model (e.g., `gemini-2.5-flash`, `deepseek-v3`). Falls back to the current chat model when empty. |
-| `max_summary_tokens`           | `4000`   | Maximum tokens for the generated summary.                                                                                                                             |
+| `summary_model_max_context`    | `0`      | Max context tokens for the summary model. If 0, falls back to `model_thresholds` or global `max_context_tokens`.                                                      |
 | `max_summary_tokens`           | `16384`  | Maximum tokens for the generated summary.                                                                                                                             |
 | `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).                                                            |
 | `enable_tool_output_trimming`  | `false`  | When enabled and `function_calling: "native"` is active, trims verbose tool outputs to extract only the final answer.                                                 |
--- a/plugins/filters/async-context-compression/README_CN.md
+++ b/plugins/filters/async-context-compression/README_CN.md
@@ -1,11 +1,17 @@
 # 异步上下文压缩过滤器
-**作者:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **版本:** 1.2.0 | **项目:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui) | **许可证:** MIT
+**作者:** [Fu-Jie](https://github.com/Fu-Jie/awesome-openwebui) | **版本:** 1.2.1 | **项目:** [Awesome OpenWebUI](https://github.com/Fu-Jie/awesome-openwebui) | **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。
 ## 1.2.1 版本更新
 - **智能配置增强**: 自动检测自定义模型的基础模型配置，并新增 `summary_model_max_context` 参数以独立控制摘要模型的上下文限制。
 - **性能优化与重构**: 重构了阈值解析逻辑并增加缓存，移除了冗余的处理代码，并增强了 LLM 响应处理（支持 JSONResponse）。
 - **稳定性改进**: 修复了 `datetime` 弃用警告，修正了类型注解，并将 print 语句替换为标准日志记录。
 ## 1.2.0 版本更新
 - **预检上下文检查 (Preflight Context Check)**: 在发送给模型之前，验证总 Token 是否符合上下文窗口。如果超出，自动裁剪或丢弃最旧的消息。
@@ -21,18 +27,6 @@
 - **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。
 ---
@@ -47,6 +41,8 @@
 - ✅ **原生工具输出裁剪**: 支持裁剪冗长的工具调用输出。
 - ✅ **实时监控**: 实时监控上下文使用情况，超过 90% 发出警告。
 - ✅ **详细日志**: 提供精确的 Token 统计日志，便于调试。
 - ✅ **智能模型匹配**: 自定义模型自动继承基础模型的阈值配置。
 - ⚠ **多模态支持**: 图片内容会被保留，但其 Token **不参与计算**。请相应调整阈值。
 详细的工作原理和流程请参考 [工作流程指南](WORKFLOW_GUIDE_CN.md)。
@@ -88,6 +84,7 @@
 | 参数                  | 默认值  | 描述                                                                                                                                        |
 | :-------------------- | :------ | :------------------------------------------------------------------------------------------------------------------------------------------ |
 | `summary_model`       | `None`  | 用于生成摘要的模型 ID。**强烈建议**配置快速、经济、上下文窗口大的模型（如 `gemini-2.5-flash`、`deepseek-v3`）。留空则尝试复用当前对话模型。 |
 | `summary_model_max_context` | `0`     | 摘要模型的最大上下文 Token 数。如果为 0，则回退到 `model_thresholds` 或全局 `max_context_tokens`。                                          |
 | `max_summary_tokens`  | `16384` | 生成摘要时允许的最大 Token 数。                                                                                                             |
 | `summary_temperature` | `0.1`   | 控制摘要生成的随机性，较低的值结果更稳定。                                                                                                  |
--- a/plugins/filters/async-context-compression/async_context_compression.py
+++ b/plugins/filters/async-context-compression/async_context_compression.py
@@ -5,19 +5,17 @@ author: Fu-Jie
 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: 1.2.0
+version: 1.2.1
 openwebui_id: b1655bc8-6de9-4cad-8cb5-a6f7829a02ce
 license: MIT
 ═══════════════════════════════════════════════════════════════════════════════
-📌 What's new in 1.2.0
+📌 What's new in 1.2.1
 ═══════════════════════════════════════════════════════════════════════════════
-  ✅ Preflight Context Check: Validates context fit before sending to model.
+  ✅ Smart Configuration: Automatically detects base model settings for custom models and adds `summary_model_max_context` for independent summary limits.
-  ✅ Structure-Aware Trimming: Collapses long AI responses while keeping H1-H6, intro, and conclusion.
+  ✅ Performance & Refactoring: Optimized threshold parsing with caching and removed redundant code for better efficiency.
-  ✅ Native Tool Output Trimming: Cleaner context when using function calling. (Note: Non-native tool outputs are not fully injected into context)
+  ✅ Bug Fixes & Modernization: Fixed `datetime` deprecation warnings and corrected type annotations.
  ✅ Context Usage Warning: Notification when usage exceeds 90%.
  ✅ Detailed Token Logging: Granular breakdown of System, Head, Summary, and Tail tokens.
 ═══════════════════════════════════════════════════════════════════════════════
 📌 Overview
@@ -229,6 +227,8 @@ Statistics:
   ✓ This filter supports multimodal messages containing images.
   ✓ The summary is generated only from the text content.
   ✓ Non-text parts (like images) are preserved in their original messages during compression.
   ⚠ Image tokens are NOT calculated. Different models have vastly different image token costs
     (GPT-4o: 85-1105, Claude: ~1300, Gemini: ~258 per image). Plan your thresholds accordingly.
 ═══════════════════════════════════════════════════════════════════════════════
 🐛 Troubleshooting
@@ -259,7 +259,7 @@ Solution:
 """
-from pydantic import BaseModel, Field, model_validator
+from pydantic import BaseModel, Field
 from typing import Optional, Dict, Any, List, Union, Callable, Awaitable
 import re
 import asyncio
@@ -267,6 +267,10 @@ import json
 import hashlib
 import time
 import contextlib
 import logging
 # Setup logger
 logger = logging.getLogger(__name__)
 # Open WebUI built-in imports
 from open_webui.utils.chat import generate_chat_completion
@@ -291,7 +295,7 @@ except ImportError:
 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
+from datetime import datetime, timezone
 def _discover_owui_engine(db_module: Any) -> Optional[Engine]:
@@ -312,7 +316,7 @@ def _discover_owui_engine(db_module: Any) -> Optional[Engine]:
                        session, "engine", None
                    )
        except Exception as exc:
-            print(f"[DB Discover] get_db_context failed: {exc}")
+            logger.error(f"[DB Discover] get_db_context failed: {exc}")
    for attr in ("engine", "ENGINE", "bind", "BIND"):
        candidate = getattr(db_module, attr, None)
@@ -334,7 +338,7 @@ def _discover_owui_schema(db_module: Any) -> Optional[str]:
        if isinstance(candidate, str) and candidate.strip():
            return candidate.strip()
    except Exception as exc:
-        print(f"[DB Discover] Base metadata schema lookup failed: {exc}")
+        logger.error(f"[DB Discover] Base metadata schema lookup failed: {exc}")
    try:
        metadata_obj = getattr(db_module, "metadata_obj", None)
@@ -344,7 +348,7 @@ def _discover_owui_schema(db_module: Any) -> Optional[str]:
        if isinstance(candidate, str) and candidate.strip():
            return candidate.strip()
    except Exception as exc:
-        print(f"[DB Discover] metadata_obj schema lookup failed: {exc}")
+        logger.error(f"[DB Discover] metadata_obj schema lookup failed: {exc}")
    try:
        from open_webui import env as owui_env
@@ -353,7 +357,7 @@ def _discover_owui_schema(db_module: Any) -> Optional[str]:
        if isinstance(candidate, str) and candidate.strip():
            return candidate.strip()
    except Exception as exc:
-        print(f"[DB Discover] env schema lookup failed: {exc}")
+        logger.error(f"[DB Discover] env schema lookup failed: {exc}")
    return None
@@ -379,8 +383,21 @@ class ChatSummary(owui_Base):
    chat_id = Column(String(255), unique=True, nullable=False, index=True)
    summary = Column(Text, nullable=False)
    compressed_message_count = Column(Integer, default=0)
-    created_at = Column(DateTime, default=datetime.utcnow)
+    created_at = Column(DateTime, default=lambda: datetime.now(timezone.utc))
-    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+    updated_at = Column(
        DateTime,
        default=lambda: datetime.now(timezone.utc),
        onupdate=lambda: datetime.now(timezone.utc),
    )
 # Global cache for tiktoken encoding
 TIKTOKEN_ENCODING = None
 if tiktoken:
    try:
        TIKTOKEN_ENCODING = tiktoken.get_encoding("o200k_base")
    except Exception as e:
        logger.error(f"[Init] Failed to load tiktoken encoding: {e}")
 class Filter:
@@ -391,8 +408,48 @@ class Filter:
        self._fallback_session_factory = (
            sessionmaker(bind=self._db_engine) if self._db_engine else None
        )
        self._model_thresholds_cache: Optional[Dict[str, Any]] = None
        self._init_database()
    def _parse_model_thresholds(self) -> Dict[str, Any]:
        """Parse model_thresholds string into a dictionary.
        Format: model_id:compression_threshold:max_context, model_id2:threshold2:max2
        Example: gpt-4:8000:32000, claude-3:100000:200000
        Returns cached result if already parsed.
        """
        if self._model_thresholds_cache is not None:
            return self._model_thresholds_cache
        self._model_thresholds_cache = {}
        raw_config = self.valves.model_thresholds
        if not raw_config:
            return self._model_thresholds_cache
        for entry in raw_config.split(","):
            entry = entry.strip()
            if not entry:
                continue
            parts = entry.split(":")
            if len(parts) != 3:
                continue
            try:
                model_id = parts[0].strip()
                compression_threshold = int(parts[1].strip())
                max_context = int(parts[2].strip())
                self._model_thresholds_cache[model_id] = {
                    "compression_threshold_tokens": compression_threshold,
                    "max_context_tokens": max_context,
                }
            except ValueError:
                continue
        return self._model_thresholds_cache
    @contextlib.contextmanager
    def _db_session(self):
        """Yield a database session using Open WebUI helpers with graceful fallbacks."""
@@ -435,7 +492,7 @@ class Filter:
            try:
                session.close()
            except Exception as exc:  # pragma: no cover - best-effort cleanup
-                print(f"[Database] ⚠️ Failed to close fallback session: {exc}")
+                logger.warning(f"[Database] ⚠️ Failed to close fallback session: {exc}")
    def _init_database(self):
        """Initializes the database table using Open WebUI's shared connection."""
@@ -447,19 +504,26 @@ class Filter:
            # Check if table exists using SQLAlchemy inspect
            inspector = inspect(self._db_engine)
-            if not inspector.has_table("chat_summary"):
+            # Support schema if configured
            has_table = (
                inspector.has_table("chat_summary", schema=owui_schema)
                if owui_schema
                else inspector.has_table("chat_summary")
            )
            if not has_table:
                # Create the chat_summary table if it doesn't exist
                ChatSummary.__table__.create(bind=self._db_engine, checkfirst=True)
-                print(
+                logger.info(
                    "[Database] ✅ Successfully created chat_summary table using Open WebUI's shared database connection."
                )
            else:
-                print(
+                logger.info(
                    "[Database] ✅ Using Open WebUI's shared database connection. chat_summary table already exists."
                )
        except Exception as e:
-            print(f"[Database] ❌ Initialization failed: {str(e)}")
+            logger.error(f"[Database] ❌ Initialization failed: {str(e)}")
    class Valves(BaseModel):
        priority: int = Field(
@@ -476,9 +540,9 @@ class Filter:
            ge=0,
            description="Hard limit for context. Exceeding this value will force removal of earliest messages (Global Default)",
        )
-        model_thresholds: dict = Field(
+        model_thresholds: str = Field(
-            default={},
+            default="",
-            description="Threshold override configuration for specific models. Only includes models requiring special configuration.",
+            description="Per-model threshold overrides. Format: model_id:compression_threshold:max_context (comma-separated). Example: gpt-4:8000:32000, claude-3:100000:200000",
        )
        keep_first: int = Field(
@@ -489,10 +553,15 @@ class Filter:
        keep_last: int = Field(
            default=6, ge=0, description="Always keep the last N full messages."
        )
-        summary_model: str = Field(
+        summary_model: Optional[str] = Field(
            default=None,
            description="The model ID used to generate the summary. If empty, uses the current conversation's model. Used to match configurations in model_thresholds.",
        )
        summary_model_max_context: int = Field(
            default=0,
            ge=0,
            description="Max context tokens for the summary model. If 0, falls back to model_thresholds or global max_context_tokens. Example: gemini-flash=1000000, gpt-4o-mini=128000.",
        )
        max_summary_tokens: int = Field(
            default=16384,
            ge=1,
@@ -529,7 +598,7 @@ class Filter:
                    # [Optimization] Optimistic lock check: update only if progress moves forward
                    if compressed_count <= existing.compressed_message_count:
                        if self.valves.debug_mode:
-                            print(
+                            logger.info(
                                f"[Storage] Skipping update: New progress ({compressed_count}) is not greater than existing progress ({existing.compressed_message_count})"
                            )
                        return
@@ -537,7 +606,7 @@ class Filter:
                    # Update existing record
                    existing.summary = summary
                    existing.compressed_message_count = compressed_count
-                    existing.updated_at = datetime.utcnow()
+                    existing.updated_at = datetime.now(timezone.utc)
                else:
                    # Create new record
                    new_summary = ChatSummary(
@@ -551,12 +620,12 @@ class Filter:
                if self.valves.debug_mode:
                    action = "Updated" if existing else "Created"
-                    print(
+                    logger.info(
                        f"[Storage] Summary has been {action.lower()} in the database (Chat ID: {chat_id})"
                    )
        except Exception as e:
-            print(f"[Storage] ❌ Database save failed: {str(e)}")
+            logger.error(f"[Storage] ❌ Database save failed: {str(e)}")
    def _load_summary_record(self, chat_id: str) -> Optional[ChatSummary]:
        """Loads the summary record object from the database."""
@@ -568,7 +637,7 @@ class Filter:
                    session.expunge(record)
                    return record
        except Exception as e:
-            print(f"[Load] ❌ Database read failed: {str(e)}")
+            logger.error(f"[Load] ❌ Database read failed: {str(e)}")
        return None
    def _load_summary(self, chat_id: str, body: dict) -> Optional[str]:
@@ -576,8 +645,8 @@ class Filter:
        record = self._load_summary_record(chat_id)
        if record:
            if self.valves.debug_mode:
-                print(f"[Load] Loaded summary from database (Chat ID: {chat_id})")
+                logger.info(f"[Load] Loaded summary from database (Chat ID: {chat_id})")
-                print(
+                logger.info(
                    f"[Load] Last updated: {record.updated_at}, Compressed message count: {record.compressed_message_count}"
                )
            return record.summary
@@ -588,14 +657,12 @@ class Filter:
        if not text:
            return 0
-        if tiktoken:
+        if TIKTOKEN_ENCODING:
            try:
-                # Uniformly use o200k_base encoding (adapted for latest models)
+                return len(TIKTOKEN_ENCODING.encode(text))
                encoding = tiktoken.get_encoding("o200k_base")
                return len(encoding.encode(text))
            except Exception as e:
                if self.valves.debug_mode:
-                    print(
+                    logger.warning(
                        f"[Token Count] tiktoken error: {e}, falling back to character estimation"
                    )
@@ -604,6 +671,7 @@ class Filter:
    def _calculate_messages_tokens(self, messages: List[Dict]) -> int:
        """Calculates the total tokens for a list of messages."""
        start_time = time.time()
        total_tokens = 0
        for msg in messages:
            content = msg.get("content", "")
@@ -616,6 +684,13 @@ class Filter:
                total_tokens += self._count_tokens(text_content)
            else:
                total_tokens += self._count_tokens(str(content))
        duration = (time.time() - start_time) * 1000
        if self.valves.debug_mode:
            logger.info(
                f"[Token Calc] Calculated {total_tokens} tokens for {len(messages)} messages in {duration:.2f}ms"
            )
        return total_tokens
    def _get_model_thresholds(self, model_id: str) -> Dict[str, int]:
@@ -623,17 +698,48 @@ class Filter:
        Priority:
        1. If configuration exists for the model ID in model_thresholds, use it.
-        2. Otherwise, use global parameters compression_threshold_tokens and max_context_tokens.
+        2. If model is a custom model, try to match its base_model_id.
        3. Otherwise, use global parameters compression_threshold_tokens and max_context_tokens.
        """
-        # Try to match from model-specific configuration
+        parsed = self._parse_model_thresholds()
        if model_id in self.valves.model_thresholds:
            if self.valves.debug_mode:
                print(f"[Config] Using model-specific configuration: {model_id}")
            return self.valves.model_thresholds[model_id]
-        # Use global default configuration
+        # 1. Direct match with model_id
        if model_id in parsed:
            if self.valves.debug_mode:
                logger.info(f"[Config] Using model-specific configuration: {model_id}")
            return parsed[model_id]
        # 2. Try to find base_model_id for custom models
        try:
            model_obj = Models.get_model_by_id(model_id)
            if model_obj:
                # Check for base_model_id (custom model)
                base_model_id = getattr(model_obj, "base_model_id", None)
                if not base_model_id:
                    # Try base_model_ids (array) - take first one
                    base_model_ids = getattr(model_obj, "base_model_ids", None)
                    if (
                        base_model_ids
                        and isinstance(base_model_ids, list)
                        and len(base_model_ids) > 0
                    ):
                        base_model_id = base_model_ids[0]
                if base_model_id and base_model_id in parsed:
                    if self.valves.debug_mode:
                        logger.info(
                            f"[Config] Custom model '{model_id}' -> base_model '{base_model_id}': using base model configuration"
                        )
                    return parsed[base_model_id]
        except Exception as e:
            if self.valves.debug_mode:
                logger.warning(
                    f"[Config] Failed to lookup base_model for '{model_id}': {e}"
                )
        # 3. Use global default configuration
        if self.valves.debug_mode:
-            print(
+            logger.info(
                f"[Config] Model {model_id} not in model_thresholds, using global parameters"
            )
@@ -731,13 +837,13 @@ class Filter:
                }
            )
        except Exception as e:
-            print(f"Error emitting debug log: {e}")
+            logger.error(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)
+            logger.info(message)
        # Frontend logging
        if self.valves.show_debug_log and event_call:
@@ -770,9 +876,17 @@ class Filter:
                js_code = f"""
                    console.log("%c[Compression] {safe_message}", "{css}");
                """
-                await event_call({"type": "execute", "data": {"code": js_code}})
+                # Add timeout to prevent blocking if frontend connection is broken
                await asyncio.wait_for(
                    event_call({"type": "execute", "data": {"code": js_code}}),
                    timeout=2.0,
                )
            except asyncio.TimeoutError:
                logger.warning(
                    f"Failed to emit log to frontend: Timeout (connection may be broken)"
                )
            except Exception as e:
-                print(f"Failed to emit log to frontend: {e}")
+                logger.error(f"Failed to emit log to frontend: {type(e).__name__}: {e}")
    async def inlet(
        self,
@@ -819,42 +933,57 @@ class Filter:
                                event_call=__event_call__,
                            )
-                        # Extract the final answer (after last tool call metadata)
+                        # Strategy 1: Tool Output / Code Block Trimming
-                        # Pattern: Matches escaped JSON strings like ""&quot;...&quot;"" followed by newlines
+                        # Detect if message contains large tool outputs or code blocks
-                        # We look for the last occurrence of such a pattern and take everything after it
+                        # Improved regex to be less brittle
-
+                        is_tool_output = (
-                        # 1. Try matching the specific OpenWebUI tool output format: ""&quot;...&quot;""
+                            "&quot;" in content
-                        # This regex finds the last end-quote of a tool output block
+                            or "Arguments:" in content
-                        tool_output_pattern = r'""&quot;.*?&quot;""\s*'
+                            or "```" in content
-
+                            or "<tool_code>" in content
                        # Find all matches
                        matches = list(
                            re.finditer(tool_output_pattern, content, re.DOTALL)
                        )
-                        if matches:
+                        if is_tool_output:
-                            # Get the end position of the last match
+                            # Regex to find the last occurrence of a tool output block or code block
-                            last_match_end = matches[-1].end()
+                            # This pattern looks for:
                            # 1. OpenWebUI's escaped JSON format: ""&quot;...&quot;""
                            # 2. "Arguments: {...}" pattern
                            # 3. Generic code blocks: ```...```
                            # 4. <tool_code>...</tool_code>
                            # It captures the content *after* the last such block.
                            tool_output_pattern = r'(?:""&quot;.*?&quot;""|Arguments:\s*\{[^}]+\}|```.*?```|<tool_code>.*?</tool_code>)\s*'
-                            # Everything after the last tool output is the final answer
+                            # Find all matches
-                            final_answer = content[last_match_end:].strip()
+                            matches = list(
                                re.finditer(tool_output_pattern, content, re.DOTALL)
                            )
                            if matches:
                                # Get the end position of the last match
                                last_match_end = matches[-1].end()
                                # Everything after the last tool output is the final answer
                                final_answer = content[last_match_end:].strip()
                            if final_answer:
                                msg["content"] = (
                                    f"... [Tool outputs trimmed]\n{final_answer}"
                                )
                                trimmed_count += 1
                        else:
                            # Fallback: Try splitting on "Arguments:" if the new format isn't found
                            # (Preserving backward compatibility or different model behaviors)
                            parts = re.split(r"(?:Arguments:\s*\{[^}]+\})\n+", content)
                            if len(parts) > 1:
                                final_answer = parts[-1].strip()
                                if final_answer:
                                    msg["content"] = (
                                        f"... [Tool outputs trimmed]\n{final_answer}"
                                    )
                                    trimmed_count += 1
                            else:
                                # Fallback: If no specific pattern matched, but it was identified as tool output,
                                # try a simpler split or just mark as trimmed if no final answer can be extracted.
                                # (Preserving backward compatibility or different model behaviors)
                                parts = re.split(
                                    r"(?:Arguments:\s*\{[^}]+\})\n+", content
                                )
                                if len(parts) > 1:
                                    final_answer = parts[-1].strip()
                                    if final_answer:
                                        msg["content"] = (
                                            f"... [Tool outputs trimmed]\n{final_answer}"
                                        )
                                        trimmed_count += 1
            if trimmed_count > 0 and self.valves.show_debug_log and __event_call__:
                await self._log(
@@ -881,7 +1010,8 @@ class Filter:
                    )
                # Clean model ID if needed (though get_model_by_id usually expects the full ID)
-                model_obj = Models.get_model_by_id(model_id)
+                # Run in thread to avoid blocking event loop on slow DB queries
                model_obj = await asyncio.to_thread(Models.get_model_by_id, model_id)
                if model_obj:
                    if self.valves.show_debug_log and __event_call__:
@@ -933,8 +1063,7 @@ class Filter:
                else:
                    if self.valves.show_debug_log and __event_call__:
                        await self._log(
-                            f"[Inlet] ❌ Model NOT found in DB",
+                            f"[Inlet] ℹ️ Not a custom model, skipping custom system prompt check",
                            type="warning",
                            event_call=__event_call__,
                        )
@@ -946,7 +1075,7 @@ class Filter:
                    event_call=__event_call__,
                )
            if self.valves.debug_mode:
-                print(f"[Inlet] Error fetching system prompt from DB: {e}")
+                logger.error(f"[Inlet] Error fetching system prompt from DB: {e}")
        # Fall back to checking messages (base model or already included)
        if not system_prompt_content:
@@ -960,7 +1089,7 @@ class Filter:
        if system_prompt_content:
            system_prompt_msg = {"role": "system", "content": system_prompt_content}
            if self.valves.debug_mode:
-                print(
+                logger.info(
                    f"[Inlet] Found system prompt ({len(system_prompt_content)} chars). Including in budget."
                )
@@ -991,7 +1120,7 @@ class Filter:
                    f"[Inlet] Message Stats: {stats_str}", event_call=__event_call__
                )
            except Exception as e:
-                print(f"[Inlet] Error logging message stats: {e}")
+                logger.error(f"[Inlet] Error logging message stats: {e}")
        if not chat_id:
            await self._log(
@@ -1007,6 +1136,33 @@ class Filter:
                event_call=__event_call__,
            )
            # Log custom model configurations
            raw_config = self.valves.model_thresholds
            parsed_configs = self._parse_model_thresholds()
            if raw_config:
                config_list = [
                    f"{model}: {cfg['compression_threshold_tokens']}t/{cfg['max_context_tokens']}t"
                    for model, cfg in parsed_configs.items()
                ]
                if config_list:
                    await self._log(
                        f"[Inlet] 📋 Model Configs (Raw: '{raw_config}'): {', '.join(config_list)}",
                        event_call=__event_call__,
                    )
                else:
                    await self._log(
                        f"[Inlet] ⚠️ Invalid Model Configs (Raw: '{raw_config}'): No valid configs parsed. Expected format: 'model_id:threshold:max_context'",
                        type="warning",
                        event_call=__event_call__,
                    )
            else:
                await self._log(
                    f"[Inlet] 📋 Model Configs: No custom configuration (Global defaults only)",
                    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)
@@ -1043,9 +1199,9 @@ class Filter:
            if effective_keep_first > 0:
                head_messages = messages[:effective_keep_first]
-            # 2. Summary message (Inserted as User message)
+            # 2. Summary message (Inserted as Assistant message)
            summary_content = (
-                f"【System Prompt: The following is a summary of the historical conversation, provided for context only. Do not reply to the summary content itself; answer the subsequent latest questions directly.】\n\n"
+                f"【Previous Summary: The following is a summary of the historical conversation, provided for context only. Do not reply to the summary content itself; answer the subsequent latest questions directly.】\n\n"
                f"{summary_record.summary}\n\n"
                f"---\n"
                f"Below is the recent conversation:"
@@ -1287,7 +1443,7 @@ class Filter:
            # Get max context limit
            model = self._clean_model_id(body.get("model"))
-            thresholds = self._get_model_thresholds(model)
+            thresholds = self._get_model_thresholds(model) or {}
            max_context_tokens = thresholds.get(
                "max_context_tokens", self.valves.max_context_tokens
            )
@@ -1314,7 +1470,8 @@ class Filter:
                    > start_trim_index + 1  # Keep at least 1 message after keep_first
                ):
                    dropped = final_messages.pop(start_trim_index)
-                    total_tokens -= self._count_tokens(str(dropped.get("content", "")))
+                    dropped_tokens = self._count_tokens(str(dropped.get("content", "")))
                    total_tokens -= dropped_tokens
                await self._log(
                    f"[Inlet] ✂️ Messages reduced. New total: {total_tokens} Tokens",
@@ -1371,18 +1528,11 @@ class Filter:
            )
            return body
        model = body.get("model") or ""
        messages = body.get("messages", [])
        # 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(
@@ -1396,11 +1546,6 @@ class Filter:
            )
        )
        await self._log(
            f"[Outlet] Background processing started\n{'='*60}\n",
            event_call=__event_call__,
        )
        return body
    async def _check_and_generate_summary_async(
@@ -1416,11 +1561,25 @@ class Filter:
        """
        Background processing: Calculates Token count and generates summary (does not block response).
        """
        try:
            messages = body.get("messages", [])
            # Clean model ID
            model = self._clean_model_id(model)
            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__,
                )
                await self._log(
                    f"[Outlet] Background processing started\n{'='*60}\n",
                    event_call=__event_call__,
                )
            # Get threshold configuration for current model
-            thresholds = self._get_model_thresholds(model)
+            thresholds = self._get_model_thresholds(model) or {}
            compression_threshold_tokens = thresholds.get(
                "compression_threshold_tokens", self.valves.compression_threshold_tokens
            )
@@ -1440,6 +1599,28 @@ class Filter:
                event_call=__event_call__,
            )
            # Send status notification (Context Usage format)
            if __event_emitter__ and self.valves.show_token_usage_status:
                max_context_tokens = thresholds.get(
                    "max_context_tokens", self.valves.max_context_tokens
                )
                status_msg = f"Context Usage (Estimated): {current_tokens} / {max_context_tokens} Tokens"
                if max_context_tokens > 0:
                    usage_ratio = current_tokens / max_context_tokens
                    status_msg += f" ({usage_ratio*100:.1f}%)"
                    if usage_ratio > 0.9:
                        status_msg += " | ⚠️ High Usage"
                await __event_emitter__(
                    {
                        "type": "status",
                        "data": {
                            "description": status_msg,
                            "done": True,
                        },
                    }
                )
            # Check if compression is needed
            if current_tokens >= compression_threshold_tokens:
                await self._log(
@@ -1559,10 +1740,13 @@ class Filter:
                return
            thresholds = self._get_model_thresholds(summary_model_id)
-            # Note: Using the summary model's max context limit here
+            # Priority: 1. summary_model_max_context (if > 0) -> 2. model_thresholds -> 3. global max_context_tokens
-            max_context_tokens = thresholds.get(
+            if self.valves.summary_model_max_context > 0:
-                "max_context_tokens", self.valves.max_context_tokens
+                max_context_tokens = self.valves.summary_model_max_context
-            )
+            else:
                max_context_tokens = thresholds.get(
                    "max_context_tokens", self.valves.max_context_tokens
                )
            await self._log(
                f"[🤖 Async Summary Task] Using max limit for model {summary_model_id}: {max_context_tokens} Tokens",
@@ -1753,7 +1937,6 @@ class Filter:
                    max_context_tokens = thresholds.get(
                        "max_context_tokens", self.valves.max_context_tokens
                    )
                    # 6. Emit Status
                    status_msg = f"Context Summary Updated: {token_count} / {max_context_tokens} Tokens"
                    if max_context_tokens > 0:
@@ -1798,7 +1981,7 @@ class Filter:
            import traceback
-            traceback.print_exc()
+            logger.exception("[🤖 Async Summary Task] Unhandled exception")
    def _format_messages_for_summary(self, messages: list) -> str:
        """Formats messages for summarization."""
@@ -1818,9 +2001,8 @@ class Filter:
            # Handle role name
            role_name = {"user": "User", "assistant": "Assistant"}.get(role, role)
-            # Limit length of each message to avoid excessive length
+            # User requested to remove truncation to allow full context for summary
-            if len(content) > 500:
+            # unless it exceeds model limits (which is handled by the LLM call itself or max_tokens)
                content = content[:500] + "..."
            formatted.append(f"[{i}] {role_name}: {content}")
@@ -1927,8 +2109,25 @@ Based on the content above, generate the summary:
            # Call generate_chat_completion
            response = await generate_chat_completion(request, payload, user)
-            if not response or "choices" not in response or not response["choices"]:
+            # Handle JSONResponse (some backends return JSONResponse instead of dict)
-                raise ValueError("LLM response format incorrect or empty")
+            if hasattr(response, "body"):
                # It's a Response object, extract the body
                import json as json_module
                try:
                    response = json_module.loads(response.body.decode("utf-8"))
                except Exception:
                    raise ValueError(f"Failed to parse JSONResponse body: {response}")
            if (
                not response
                or not isinstance(response, dict)
                or "choices" not in response
                or not response["choices"]
            ):
                raise ValueError(
                    f"LLM response format incorrect or empty: {type(response).__name__}"
                )
            summary = response["choices"][0]["message"]["content"].strip()
--- a/plugins/filters/async-context-compression/async_context_compression_cn.py
+++ b/plugins/filters/async-context-compression/async_context_compression_cn.py
@@ -5,19 +5,17 @@ author: Fu-Jie
 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: 1.2.0
+version: 1.2.1
 openwebui_id: 5c0617cb-a9e4-4bd6-a440-d276534ebd18
 license: MIT
 ═══════════════════════════════════════════════════════════════════════════════
-📌 1.2.0 版本更新
+📌 1.2.1 版本更新
 ═══════════════════════════════════════════════════════════════════════════════
-  ✅ 预检上下文检查：发送给模型前验证上下文是否适配。
+  ✅ 智能配置增强：自动检测自定义模型的基础模型配置，并新增 `summary_model_max_context` 参数以独立控制摘要模型的上下文限制。
-  ✅ 结构感知裁剪：折叠过长的 AI 响应，同时保留标题 (H1-H6)、开头和结尾。
+  ✅ 性能优化与重构：重构了阈值解析逻辑并增加缓存，移除了冗余的处理代码，并增强了 LLM 响应处理（支持 JSONResponse）。
-  ✅ 原生工具输出裁剪：使用函数调用时清理上下文，去除冗余输出。（注意：非原生工具调用输出不会完整注入上下文）
+  ✅ 稳定性改进：修复了 `datetime` 弃用警告，修正了类型注解，并将 print 语句替换为标准日志记录。
  ✅ 上下文使用警告：当使用量超过 90% 时发出通知。
  ✅ 详细 Token 日志：细粒度记录 System、Head、Summary 和 Tail 的 Token 消耗。
 ═══════════════════════════════════════════════════════════════════════════════
 📌 功能概述
@@ -254,23 +252,36 @@ show_debug_log (前端调试日志)
 from pydantic import BaseModel, Field, model_validator
 from typing import Optional, Dict, Any, List, Union, Callable, Awaitable
 import re
 import asyncio
 import json
 import hashlib
-import time
+import contextlib
-import re
+import logging
 # 配置日志记录
 logger = logging.getLogger(__name__)
 if not logger.handlers:
    handler = logging.StreamHandler()
    formatter = logging.Formatter(
        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
    )
    handler.setFormatter(formatter)
    logger.addHandler(handler)
 logger.setLevel(logging.INFO)
 # Open WebUI 内置导入
 from open_webui.utils.chat import generate_chat_completion
 from open_webui.models.models import Models
 from open_webui.models.users import Users
 from open_webui.models.models import Models
 from fastapi.requests import Request
 from open_webui.main import app as webui_app
 # Open WebUI 内部数据库 (复用共享连接)
-from open_webui.internal.db import engine as owui_engine
+try:
-from open_webui.internal.db import Session as owui_Session
+    from open_webui.internal import db as owui_db
-from open_webui.internal.db import Base as owui_Base
+except ModuleNotFoundError:  # pragma: no cover - filter runs inside Open WebUI
    owui_db = None
 # 尝试导入 tiktoken
 try:
@@ -280,35 +291,167 @@ except ImportError:
 # 数据库导入
 from sqlalchemy import Column, String, Text, DateTime, Integer, inspect
-from datetime import datetime
+from sqlalchemy.orm import declarative_base, sessionmaker
 from sqlalchemy.engine import Engine
 from datetime import datetime, timezone
 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):
    """对话摘要存储表"""
    __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)
    summary = Column(Text, nullable=False)
    compressed_message_count = Column(Integer, default=0)
-    created_at = Column(DateTime, default=datetime.utcnow)
+    created_at = Column(DateTime, default=lambda: datetime.now(timezone.utc))
-    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+    updated_at = Column(
        DateTime,
        default=lambda: datetime.now(timezone.utc),
        onupdate=lambda: datetime.now(timezone.utc),
    )
 class Filter:
    def __init__(self):
        self.valves = self.Valves()
        self._owui_db = owui_db
        self._db_engine = owui_engine
-        self._SessionLocal = owui_Session
+        self._fallback_session_factory = (
-        self._SessionLocal = owui_Session
+            sessionmaker(bind=self._db_engine) if self._db_engine else None
-        self._init_database()
+        )
        self._threshold_cache = {}
        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):
        """使用 Open WebUI 的共享连接初始化数据库表"""
        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."
                )
            # 使用 SQLAlchemy inspect 检查表是否存在
            inspector = inspect(self._db_engine)
            if not inspector.has_table("chat_summary"):
@@ -340,21 +483,38 @@ class Filter:
            ge=0,
            description="上下文的硬性上限。超过此值将强制移除最早的消息 (全局默认值)",
        )
-        model_thresholds: dict = Field(
+        model_thresholds: Union[str, dict] = Field(
            default={},
-            description="针对特定模型的阈值覆盖配置。仅包含需要特殊配置的模型。",
+            description="针对特定模型的阈值覆盖配置。可以是 JSON 字符串或字典。",
        )
        @model_validator(mode="before")
        @classmethod
        def parse_model_thresholds(cls, data: Any) -> Any:
            if isinstance(data, dict):
                thresholds = data.get("model_thresholds")
                if isinstance(thresholds, str) and thresholds.strip():
                    try:
                        data["model_thresholds"] = json.loads(thresholds)
                    except Exception as e:
                        logger.error(f"Failed to parse model_thresholds JSON: {e}")
            return data
        keep_first: int = Field(
            default=1, ge=0, description="始终保留最初的 N 条消息。设置为 0 则不保留。"
        )
        keep_last: int = Field(
            default=6, ge=0, description="始终保留最近的 N 条完整消息。"
        )
-        summary_model: str = Field(
+        summary_model: Optional[str] = Field(
            default=None,
            description="用于生成摘要的模型 ID。留空则使用当前对话的模型。用于匹配 model_thresholds 中的配置。",
        )
        summary_model_max_context: int = Field(
            default=0,
            ge=0,
            description="摘要模型的最大上下文 Token 数。如果为 0，则回退到 model_thresholds 或全局 max_context_tokens。",
        )
        max_summary_tokens: int = Field(
            default=16384, ge=1, description="摘要的最大 token 数"
        )
@@ -376,7 +536,7 @@ class Filter:
    def _save_summary(self, chat_id: str, summary: str, compressed_count: int):
        """保存摘要到数据库"""
        try:
-            with self._SessionLocal() as session:
+            with self._db_session() as session:
                # 查找现有记录
                existing = session.query(ChatSummary).filter_by(chat_id=chat_id).first()
@@ -384,7 +544,7 @@ class Filter:
                    # [优化] 乐观锁检查：只有进度向前推进时才更新
                    if compressed_count <= existing.compressed_message_count:
                        if self.valves.debug_mode:
-                            print(
+                            logger.debug(
                                f"[存储] 跳过更新：新进度 ({compressed_count}) 不大于现有进度 ({existing.compressed_message_count})"
                            )
                        return
@@ -392,7 +552,7 @@ class Filter:
                    # 更新现有记录
                    existing.summary = summary
                    existing.compressed_message_count = compressed_count
-                    existing.updated_at = datetime.utcnow()
+                    existing.updated_at = datetime.now(timezone.utc)
                else:
                    # 创建新记录
                    new_summary = ChatSummary(
@@ -406,22 +566,22 @@ class Filter:
                if self.valves.debug_mode:
                    action = "更新" if existing else "创建"
-                    print(f"[存储] 摘要已{action}到数据库 (Chat ID: {chat_id})")
+                    logger.info(f"[存储] 摘要已{action}到数据库 (Chat ID: {chat_id})")
        except Exception as e:
-            print(f"[存储] ❌ 数据库保存失败: {str(e)}")
+            logger.error(f"[存储] ❌ 数据库保存失败: {str(e)}")
    def _load_summary_record(self, chat_id: str) -> Optional[ChatSummary]:
        """从数据库加载摘要记录对象"""
        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
                    session.expunge(record)
                    return record
        except Exception as e:
-            print(f"[加载] ❌ 数据库读取失败: {str(e)}")
+            logger.error(f"[加载] ❌ 数据库读取失败: {str(e)}")
        return None
    def _load_summary(self, chat_id: str, body: dict) -> Optional[str]:
@@ -429,8 +589,8 @@ class Filter:
        record = self._load_summary_record(chat_id)
        if record:
            if self.valves.debug_mode:
-                print(f"[加载] 从数据库加载摘要 (Chat ID: {chat_id})")
+                logger.debug(f"[加载] 从数据库加载摘要 (Chat ID: {chat_id})")
-                print(
+                logger.debug(
                    f"[加载] 更新时间: {record.updated_at}, 已压缩消息数: {record.compressed_message_count}"
                )
            return record.summary
@@ -473,23 +633,68 @@ class Filter:
        """获取特定模型的阈值配置
        优先级：
-        1. 如果 model_thresholds 中存在该模型ID的配置，使用该配置
+        1. 缓存匹配
-        2. 否则使用全局参数 compression_threshold_tokens 和 max_context_tokens
+        2. model_thresholds 直接匹配
        3. 基础模型 (base_model_id) 匹配
        4. 全局默认配置
        """
-        # 尝试从模型特定配置中匹配
+        if not model_id:
-        if model_id in self.valves.model_thresholds:
+            return {
                "compression_threshold_tokens": self.valves.compression_threshold_tokens,
                "max_context_tokens": self.valves.max_context_tokens,
            }
        # 1. 检查缓存
        if model_id in self._threshold_cache:
            return self._threshold_cache[model_id]
        # 获取解析后的阈值配置
        parsed = self.valves.model_thresholds
        if isinstance(parsed, str):
            try:
                parsed = json.loads(parsed)
            except Exception:
                parsed = {}
        # 2. 尝试直接匹配
        if model_id in parsed:
            res = parsed[model_id]
            self._threshold_cache[model_id] = res
            if self.valves.debug_mode:
-                print(f"[配置] 使用模型特定配置: {model_id}")
+                logger.debug(f"[配置] 模型 {model_id} 命中直接配置")
-            return self.valves.model_thresholds[model_id]
+            return res
-        # 使用全局默认配置
+        # 3. 尝试匹配基础模型 (base_model_id)
-        if self.valves.debug_mode:
+        try:
-            print(f"[配置] 模型 {model_id} 未在 model_thresholds 中，使用全局参数")
+            model_obj = Models.get_model_by_id(model_id)
            if model_obj:
                # 某些模型可能有多个基础模型 ID
                base_ids = []
                if hasattr(model_obj, "base_model_id") and model_obj.base_model_id:
                    base_ids.append(model_obj.base_model_id)
                if hasattr(model_obj, "base_model_ids") and model_obj.base_model_ids:
                    if isinstance(model_obj.base_model_ids, list):
                        base_ids.extend(model_obj.base_model_ids)
-        return {
+                for b_id in base_ids:
                    if b_id in parsed:
                        res = parsed[b_id]
                        self._threshold_cache[model_id] = res
                        if self.valves.debug_mode:
                            logger.info(
                                f"[配置] 模型 {model_id} 匹配到基础模型 {b_id} 的配置"
                            )
                        return res
        except Exception as e:
            logger.error(f"[配置] 查找基础模型失败: {e}")
        # 4. 使用全局默认配置
        res = {
            "compression_threshold_tokens": self.valves.compression_threshold_tokens,
            "max_context_tokens": self.valves.max_context_tokens,
        }
        self._threshold_cache[model_id] = res
        return res
    def _get_chat_context(
        self, body: dict, __metadata__: Optional[dict] = None
@@ -621,13 +826,15 @@ class Filter:
                """
                await event_call({"type": "execute", "data": {"code": js_code}})
            except Exception as e:
-                print(f"发送前端日志失败: {e}")
+                logger.error(f"发送前端日志失败: {e}")
    async def inlet(
        self,
        body: dict,
        __user__: Optional[dict] = None,
        __metadata__: dict = None,
        __request__: Request = None,
        __model__: dict = None,
        __event_emitter__: Callable[[Any], Awaitable[None]] = None,
        __event_call__: Callable[[Any], Awaitable[None]] = None,
    ) -> dict:
@@ -641,8 +848,10 @@ class Filter:
        messages = body.get("messages", [])
        # --- 原生工具输出裁剪 (Native Tool Output Trimming) ---
-        # 即使未启用压缩，也始终检查并裁剪过长的工具输出，以节省 Token
+        metadata = body.get("metadata", {})
-        if self.valves.enable_tool_output_trimming:
+        is_native_func_calling = metadata.get("function_calling") == "native"
        if self.valves.enable_tool_output_trimming and is_native_func_calling:
            trimmed_count = 0
            for msg in messages:
                content = msg.get("content", "")
@@ -789,7 +998,7 @@ class Filter:
                    event_call=__event_call__,
                )
            if self.valves.debug_mode:
-                print(f"[Inlet] 从数据库获取系统提示词错误: {e}")
+                logger.error(f"[Inlet] 从数据库获取系统提示词错误: {e}")
        # 回退：检查消息列表 (基础模型或已包含)
        if not system_prompt_content:
@@ -803,7 +1012,7 @@ class Filter:
        if system_prompt_content:
            system_prompt_msg = {"role": "system", "content": system_prompt_content}
            if self.valves.debug_mode:
-                print(
+                logger.debug(
                    f"[Inlet] 找到系统提示词 ({len(system_prompt_content)} 字符)。计入预算。"
                )
@@ -834,7 +1043,7 @@ class Filter:
                    f"[Inlet] 消息统计: {stats_str}", event_call=__event_call__
                )
            except Exception as e:
-                print(f"[Inlet] 记录消息统计错误: {e}")
+                logger.error(f"[Inlet] 记录消息统计错误: {e}")
        if not chat_id:
            await self._log(
@@ -925,7 +1134,7 @@ class Filter:
            # 获取最大上下文限制
            model = self._clean_model_id(body.get("model"))
-            thresholds = self._get_model_thresholds(model)
+            thresholds = self._get_model_thresholds(model) or {}
            max_context_tokens = thresholds.get(
                "max_context_tokens", self.valves.max_context_tokens
            )
@@ -1129,7 +1338,7 @@ class Filter:
            # 获取最大上下文限制
            model = self._clean_model_id(body.get("model"))
-            thresholds = self._get_model_thresholds(model)
+            thresholds = self._get_model_thresholds(model) or {}
            max_context_tokens = thresholds.get(
                "max_context_tokens", self.valves.max_context_tokens
            )
@@ -1156,7 +1365,8 @@ class Filter:
                    > start_trim_index + 1  # 保留 keep_first 之后至少 1 条消息
                ):
                    dropped = final_messages.pop(start_trim_index)
-                    total_tokens -= self._count_tokens(str(dropped.get("content", "")))
+                    dropped_tokens = self._count_tokens(str(dropped.get("content", "")))
                    total_tokens -= dropped_tokens
                await self._log(
                    f"[Inlet] ✂️ 消息已缩减。新总数: {total_tokens} Tokens",
@@ -1207,18 +1417,18 @@ class Filter:
        """
        chat_ctx = self._get_chat_context(body, __metadata__)
        chat_id = chat_ctx["chat_id"]
-        model = body.get("model") or ""
+        if not chat_id:
        # 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)})",
+                "[Outlet] ❌ metadata 中缺少 chat_id，跳过压缩",
                type="error",
                event_call=__event_call__,
            )
            return body
        model = body.get("model") or ""
        messages = body.get("messages", [])
        # 直接计算目标压缩进度
        target_compressed_count = max(0, len(messages) - self.valves.keep_last)
        # 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(
@@ -1233,11 +1443,6 @@ class Filter:
            )
        )
        await self._log(
            f"[Outlet] 后台处理已启动\n{'='*60}\n",
            event_call=__event_call__,
        )
        return body
    async def _check_and_generate_summary_async(
@@ -1257,7 +1462,7 @@ class Filter:
            messages = body.get("messages", [])
            # 获取当前模型的阈值配置
-            thresholds = self._get_model_thresholds(model)
+            thresholds = self._get_model_thresholds(model) or {}
            compression_threshold_tokens = thresholds.get(
                "compression_threshold_tokens", self.valves.compression_threshold_tokens
            )
@@ -1393,11 +1598,14 @@ class Filter:
                )
                return
-            thresholds = self._get_model_thresholds(summary_model_id)
+            thresholds = self._get_model_thresholds(summary_model_id) or {}
-            # 注意：这里使用的是摘要模型的最大上下文限制
+            # Priority: 1. summary_model_max_context (if > 0) -> 2. model_thresholds -> 3. global max_context_tokens
-            max_context_tokens = thresholds.get(
+            if self.valves.summary_model_max_context > 0:
-                "max_context_tokens", self.valves.max_context_tokens
+                max_context_tokens = self.valves.summary_model_max_context
-            )
+            else:
                max_context_tokens = thresholds.get(
                    "max_context_tokens", self.valves.max_context_tokens
                )
            await self._log(
                f"[🤖 异步摘要任务] 使用模型 {summary_model_id} 的上限: {max_context_tokens} Tokens",
@@ -1582,10 +1790,14 @@ class Filter:
                    # 5. 获取阈值并计算比例
                    model = self._clean_model_id(body.get("model"))
-                    thresholds = self._get_model_thresholds(model)
+                    thresholds = self._get_model_thresholds(model) or {}
-                    max_context_tokens = thresholds.get(
+                    # Priority: 1. summary_model_max_context (if > 0) -> 2. model_thresholds -> 3. global max_context_tokens
-                        "max_context_tokens", self.valves.max_context_tokens
+                    if self.valves.summary_model_max_context > 0:
-                    )
+                        max_context_tokens = self.valves.summary_model_max_context
                    else:
                        max_context_tokens = thresholds.get(
                            "max_context_tokens", self.valves.max_context_tokens
                        )
                    # 6. 发送状态
                    status_msg = (
@@ -1631,9 +1843,7 @@ class Filter:
                    }
                )
-            import traceback
+            logger.exception("[🤖 异步摘要任务] ❌ 发生异常")
            traceback.print_exc()
    def _format_messages_for_summary(self, messages: list) -> str:
        """Formats messages for summarization."""
@@ -1653,9 +1863,8 @@ class Filter:
            # Handle role name
            role_name = {"user": "User", "assistant": "Assistant"}.get(role, role)
-            # Limit length of each message to avoid excessive length
+            # User requested to remove truncation to allow full context for summary
-            if len(content) > 500:
+            # unless it exceeds model limits (which is handled by the LLM call itself or max_tokens)
                content = content[:500] + "..."
            formatted.append(f"[{i}] {role_name}: {content}")
@@ -1762,8 +1971,25 @@ class Filter:
            # 调用 generate_chat_completion
            response = await generate_chat_completion(request, payload, user)
-            if not response or "choices" not in response or not response["choices"]:
+            # Handle JSONResponse (some backends return JSONResponse instead of dict)
-                raise ValueError("LLM 响应格式不正确或为空")
+            if hasattr(response, "body"):
                # It's a Response object, extract the body
                import json as json_module
                try:
                    response = json_module.loads(response.body.decode("utf-8"))
                except Exception:
                    raise ValueError(f"Failed to parse JSONResponse body: {response}")
            if (
                not response
                or not isinstance(response, dict)
                or "choices" not in response
                or not response["choices"]
            ):
                raise ValueError(
                    f"LLM response format incorrect or empty: {type(response).__name__}"
                )
            summary = response["choices"][0]["message"]["content"].strip()
--- a/plugins/filters/folder-memory/README.md
+++ b/plugins/filters/folder-memory/README.md
@@ -0,0 +1,49 @@
 # Folder Memory
 English | [中文](./README_CN.md)
 **Folder Memory** (formerly Folder Rule Collector) is an intelligent context filter plugin for OpenWebUI. It automatically extracts consistent "Project Rules" from ongoing conversations within a folder and injects them back into the folder's system prompt.
 This ensures that all future conversations within that folder share the same evolved context and rules, without manual updates.
 ## ✨ Features
 - **Automatic Extraction**: Analyzes chat history every N messages to extract project rules.
 - **Non-destructive Injection**: Updates only the specific "Project Rules" block in the system prompt, preserving other instructions.
 - **Async Processing**: Runs in the background without blocking the user's chat experience.
 - **ORM Integration**: Directly updates folder data using OpenWebUI's internal models for reliability.
 ## 📦 Installation
 1. Copy `folder_memory.py` to your OpenWebUI `plugins/filters/` directory (or upload via Admin UI).
 2. Enable the filter in your **Settings** -> **Filters**.
 3. (Optional) Configure the triggering threshold (default: every 10 messages).
 ## ⚙️ Configuration (Valves)
 | Valve | Default | Description |
 | :--- | :--- | :--- |
 | `PRIORITY` | `20` | Priority level for the filter operations. |
 | `MESSAGE_TRIGGER_COUNT` | `10` | The number of messages required to trigger a rule analysis. |
 | `MODEL_ID` | `""` | The model used to generate rules. If empty, uses the current chat model. |
 | `RULES_BLOCK_TITLE` | `## 📂 Project Rules` | The title displayed above the injected rules block. |
 | `SHOW_DEBUG_LOG` | `False` | Show detailed debug logs in the browser console. |
 | `UPDATE_ROOT_FOLDER` | `False` | If enabled, finds and updates the root folder rules instead of the current subfolder. |
 ## 🛠️ How It Works
 ![Folder Memory Demo](https://raw.githubusercontent.com/Fu-Jie/awesome-openwebui/main/plugins/filters/folder-memory/folder-memory-demo.png)
 1. **Trigger**: When a conversation reaches `MESSAGE_TRIGGER_COUNT` (e.g., 10, 20 messages).
 2. **Analysis**: The plugin sends the recent conversation + existing rules to the LLM.
 3. **Synthesis**: The LLM merges new insights with old rules, removing obsolete ones.
 4. **Update**: The new rule set replaces the `<!-- OWUI_PROJECT_RULES_START -->` block in the folder's system prompt.
 ## ⚠️ Notes
 - This plugin modifies the `system_prompt` of your folders.
 - It uses a specific marker `<!-- OWUI_PROJECT_RULES_START -->` to locate its content. Do not manually remove these markers if you want the plugin to continue managing that section.
 ## 🗺️ Roadmap
 See [ROADMAP.md](./ROADMAP.md) for future plans, including "Project Knowledge" collection.
--- a/plugins/filters/folder-memory/README_CN.md
+++ b/plugins/filters/folder-memory/README_CN.md
@@ -0,0 +1,49 @@
 # 文件夹记忆 (Folder Memory)
 [English](./README.md) | 中文
 **文件夹记忆 (Folder Memory)** (原名 Folder Rule Collector) 是一个 OpenWebUI 的智能上下文过滤器插件。它能自动从文件夹内的对话中提取一致性的“项目规则”，并将其回写到文件夹的系统提示词中。
 这确保了该文件夹内的所有未来对话都能共享相同的进化上下文和规则，无需手动更新。
 ## ✨ 功能特性
 - **自动提取**：每隔 N 条消息分析一次聊天记录，提取项目规则。
 - **无损注入**：仅更新系统提示词中的特定“项目规则”块，保留其他指令。
 - **异步处理**：在后台运行，不阻塞用户的聊天体验。
 - **ORM 集成**：直接使用 OpenWebUI 的内部模型更新文件夹数据，确保可靠性。
 ## 📦 安装指南
 1. 将 `folder_memory.py` (或中文版 `folder_memory_cn.py`) 复制到 OpenWebUI 的 `plugins/filters/` 目录（或通过管理员 UI 上传）。
 2. 在 **设置** -> **过滤器** 中启用该插件。
 3. （可选）配置触发阈值（默认：每 10 条消息）。
 ## ⚙️ 配置 (Valves)
 | 参数 | 默认值 | 说明 |
 | :--- | :--- | :--- |
 | `PRIORITY` | `20` | 过滤器操作的优先级。 |
 | `MESSAGE_TRIGGER_COUNT` | `10` | 触发规则分析的消息数量阈值。 |
 | `MODEL_ID` | `""` | 用于生成规则的模型 ID。若为空，则使用当前对话模型。 |
 | `RULES_BLOCK_TITLE` | `## 📂 项目规则` | 显示在注入规则块上方的标题。 |
 | `SHOW_DEBUG_LOG` | `False` | 在浏览器控制台显示详细调试日志。 |
 | `UPDATE_ROOT_FOLDER` | `False` | 如果启用，将向上查找并更新根文件夹的规则，而不是当前子文件夹。 |
 ## 🛠️ 工作原理
 ![Folder Memory Demo](https://raw.githubusercontent.com/Fu-Jie/awesome-openwebui/main/plugins/filters/folder-memory/folder-memory-demo.png)
 1. **触发**：当对话达到 `MESSAGE_TRIGGER_COUNT`（例如 10、20 条消息）时。
 2. **分析**：插件将最近的对话 + 现有规则发送给 LLM。
 3. **综合**：LLM 将新见解与旧规则合并，移除过时的规则。
 4. **更新**：新的规则集替换文件夹系统提示词中的 `<!-- OWUI_PROJECT_RULES_START -->` 块。
 ## ⚠️ 注意事项
 - 此插件会修改文件夹的 `system_prompt`。
 - 它使用特定标记 `<!-- OWUI_PROJECT_RULES_START -->` 来定位内容。如果您希望插件继续管理该部分，请勿手动删除这些标记。
 ## 🗺️ 路线图
 查看 [ROADMAP.md](./ROADMAP.md) 了解未来计划，包括“项目知识”收集功能。
--- a/plugins/filters/folder-memory/ROADMAP.md
+++ b/plugins/filters/folder-memory/ROADMAP.md
@@ -0,0 +1,10 @@
 # Roadmap
 ## Future Features
 ### 🧠 Project Knowledge (Planned)
 In future versions, we plan to introduce "Project Knowledge" collection. Unlike "Rules" which are strict instructions, "Knowledge" will capture reusable information, consensus, and context that helps the LLM understand the project better.
 - **Knowledge Extraction**: Automatically extract reusable knowledge (terminology, style guides, business logic) from conversations.
 - **Long-term Memory**: Use the entire folder's chat history as a corpus for knowledge generation.
 - **Context Injection**: Inject summarized knowledge into the system prompt alongside rules.
--- a/plugins/filters/folder-memory/folder-memory-demo.png
+++ b/plugins/filters/folder-memory/folder-memory-demo.png
--- a/plugins/filters/folder-memory/folder_memory.py
+++ b/plugins/filters/folder-memory/folder_memory.py
@@ -0,0 +1,483 @@
 """
 title: 📂 Folder Memory
 author: Fu-Jie
 author_url: https://github.com/Fu-Jie/awesome-openwebui
 funding_url: https://github.com/open-webui
 version: 0.1.0
 description: Automatically extracts project rules from conversations and injects them into the folder's system prompt.
 requirements:
 """
 from pydantic import BaseModel, Field
 from typing import Optional, Dict, List
 from fastapi import Request
 import logging
 import json
 import re
 import asyncio
 from datetime import datetime
 from open_webui.utils.chat import generate_chat_completion
 from open_webui.models.users import Users
 from open_webui.models.folders import Folders, FolderUpdateForm
 from open_webui.models.chats import Chats
 logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 )
 logger = logging.getLogger(__name__)
 # Markers for rule injection
 RULES_BLOCK_START = "<!-- OWUI_PROJECT_RULES_START -->"
 RULES_BLOCK_END = "<!-- OWUI_PROJECT_RULES_END -->"
 # System Prompt for Rule Generation
 SYSTEM_PROMPT_RULE_GENERATOR = """
 You are a project rule extractor. Your task is to extract "Project Rules" from the conversation and merge them with existing rules.
 ### Input
 1. **Existing Rules**: Current rules in the folder system prompt.
 2. **Conversation**: Recent chat history.
 ### Goal
 Synthesize a concise list of rules that apply to this project/folder.
 - **Remove** rules that are no longer relevant or were one-off instructions.
 - **Add** new consistent requirements found in the conversation.
 - **Merge** similar rules.
 - **Format**: Concise bullet points (Markdown).
 ### Output Format
 ONLY output the rules list as Markdown bullet points. Do not include any intro/outro text.
 Example:
 - Always use Python 3.11 for type hinting.
 - Docstrings must follow Google style.
 - Commit messages should be in English.
 """
 class Filter:
    class Valves(BaseModel):
        PRIORITY: int = Field(
            default=20, description="Priority level for the filter operations."
        )
        SHOW_DEBUG_LOG: bool = Field(
            default=False, description="Show debug logs in console."
        )
        MESSAGE_TRIGGER_COUNT: int = Field(
            default=10, description="Analyze rules after every N messages in a chat."
        )
        MODEL_ID: str = Field(
            default="",
            description="Model used for rule extraction. If empty, uses the current chat model.",
        )
        RULES_BLOCK_TITLE: str = Field(
            default="## 📂 Project Rules",
            description="Title displayed above the rules block.",
        )
        UPDATE_ROOT_FOLDER: bool = Field(
            default=False,
            description="If enabled, finds and updates the root folder rules instead of the current subfolder.",
        )
    def __init__(self):
        self.valves = self.Valves()
    # ==================== Helper Methods ====================
    def _get_user_context(self, __user__: Optional[dict]) -> 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", ""),
            "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)."""
        chat_id = ""
        message_id = ""
        if isinstance(body, dict):
            chat_id = body.get("chat_id", "")
            message_id = body.get("id", "")
            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 __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_debug_log(self, __event_emitter__, title: str, data: dict):
        if self.valves.SHOW_DEBUG_LOG and __event_emitter__:
            try:
                # Flat log format as requested
                js_code = f"""
                    console.log("[Folder Memory] {title}", {json.dumps(data, ensure_ascii=False)});
                """
                await __event_emitter__({"type": "execute", "data": {"code": js_code}})
            except Exception as e:
                logger.error(f"Error emitting log: {e}")
    async def _emit_status(
        self, __event_emitter__, description: str, done: bool = False
    ):
        if __event_emitter__:
            await __event_emitter__(
                {"type": "status", "data": {"description": description, "done": done}}
            )
    def _get_folder_id(self, body: dict) -> Optional[str]:
        # 1. Try retrieving folder_id specifically from metadata
        if "metadata" in body and isinstance(body["metadata"], dict):
            if "folder_id" in body["metadata"]:
                return body["metadata"]["folder_id"]
        # 2. Check regular body chat object if available
        if "chat" in body and isinstance(body["chat"], dict):
            if "folder_id" in body["chat"]:
                return body["chat"]["folder_id"]
        # 3. Try fallback via Chat ID (Most reliable)
        chat_id = body.get("chat_id")
        if not chat_id:
            if "metadata" in body and isinstance(body["metadata"], dict):
                chat_id = body["metadata"].get("chat_id")
        if chat_id:
            try:
                chat = Chats.get_chat_by_id(chat_id)
                if chat and chat.folder_id:
                    return chat.folder_id
            except Exception as e:
                logger.error(f"Failed to fetch chat {chat_id}: {e}")
        return None
    def _extract_existing_rules(self, system_prompt: str) -> str:
        pattern = re.compile(
            re.escape(RULES_BLOCK_START) + r"([\s\S]*?)" + re.escape(RULES_BLOCK_END)
        )
        match = pattern.search(system_prompt)
        if match:
            # Remove title if it's inside the block
            content = match.group(1).strip()
            # Simple cleanup of the title if user formatted it inside
            title_pat = re.compile(r"^#+\s+.*$", re.MULTILINE)
            return title_pat.sub("", content).strip()
        return ""
    def _inject_rules(self, system_prompt: str, new_rules: str, title: str) -> str:
        new_block_content = f"\n{title}\n\n{new_rules}\n"
        new_block = f"{RULES_BLOCK_START}{new_block_content}{RULES_BLOCK_END}"
        system_prompt = system_prompt or ""
        pattern = re.compile(
            re.escape(RULES_BLOCK_START) + r"[\s\S]*?" + re.escape(RULES_BLOCK_END)
        )
        if pattern.search(system_prompt):
            return pattern.sub(new_block, system_prompt).strip()
        else:
            # Append if not found
            if system_prompt:
                return f"{system_prompt}\n\n{new_block}"
            else:
                return new_block
    async def _generate_new_rules(
        self,
        current_rules: str,
        messages: List[Dict],
        user_id: str,
        __request__: Request,
    ) -> str:
        # Prepare context
        conversation_text = "\n".join(
            [
                f"{msg['role'].upper()}: {msg['content']}"
                for msg in messages[-20:]  # Analyze last 20 messages context
            ]
        )
        prompt = f"""
 Existing Rules:
 {current_rules if current_rules else "None"}
 Conversation Excerpt:
 {conversation_text}
 Please output the updated Project Rules:
 """
        payload = {
            "model": self.valves.MODEL_ID,
            "messages": [
                {"role": "system", "content": SYSTEM_PROMPT_RULE_GENERATOR},
                {"role": "user", "content": prompt},
            ],
            "stream": False,
        }
        try:
            # We need a user object for permission checks in generate_chat_completion
            user = Users.get_user_by_id(user_id)
            if not user:
                return current_rules
            completion = await generate_chat_completion(__request__, payload, user)
            if "choices" in completion and len(completion["choices"]) > 0:
                content = completion["choices"][0]["message"]["content"].strip()
                # Basic validation: ensure it looks like a list
                if (
                    content.startswith("-")
                    or content.startswith("*")
                    or content.startswith("1.")
                ):
                    return content
        except Exception as e:
            logger.error(f"Rule generation failed: {e}")
        return current_rules
    async def _process_rules_update(
        self,
        folder_id: str,
        body: dict,
        user_id: str,
        __request__: Request,
        __event_emitter__,
    ):
        try:
            await self._emit_debug_log(
                __event_emitter__,
                "Start Processing",
                {"step": "start", "initial_folder_id": folder_id, "user_id": user_id},
            )
            # 1. Fetch Folder Data (ORM)
            initial_folder = Folders.get_folder_by_id_and_user_id(folder_id, user_id)
            if not initial_folder:
                await self._emit_debug_log(
                    __event_emitter__,
                    "Error: Initial folder not found",
                    {
                        "step": "fetch_initial_folder",
                        "initial_folder_id": folder_id,
                        "user_id": user_id,
                    },
                )
                return
            # Subfolder handling logic
            target_folder = initial_folder
            if self.valves.UPDATE_ROOT_FOLDER:
                # Traverse up until a folder with no parent_id is found
                while target_folder and getattr(target_folder, "parent_id", None):
                    try:
                        parent = Folders.get_folder_by_id_and_user_id(
                            target_folder.parent_id, user_id
                        )
                        if parent:
                            target_folder = parent
                        else:
                            break
                    except Exception as e:
                        await self._emit_debug_log(
                            __event_emitter__,
                            "Warning: Failed to traverse parent folder",
                            {"step": "traverse_root", "error": str(e)},
                        )
                        break
            target_folder_id = target_folder.id
            await self._emit_debug_log(
                __event_emitter__,
                "Target Folder Resolved",
                {
                    "step": "target_resolved",
                    "target_folder_id": target_folder_id,
                    "target_folder_name": target_folder.name,
                    "is_root_update": target_folder_id != folder_id,
                },
            )
            existing_data = target_folder.data if target_folder.data else {}
            existing_sys_prompt = existing_data.get("system_prompt", "")
            # 2. Extract Existing Rules
            current_rules_content = self._extract_existing_rules(existing_sys_prompt)
            # 3. Generate New Rules
            await self._emit_status(
                __event_emitter__, "Analyzing project rules...", done=False
            )
            messages = body.get("messages", [])
            new_rules_content = await self._generate_new_rules(
                current_rules_content, messages, user_id, __request__
            )
            rules_changed = new_rules_content != current_rules_content
            # 4. If no change, skip
            if not rules_changed:
                await self._emit_debug_log(
                    __event_emitter__,
                    "No Changes",
                    {
                        "step": "check_changes",
                        "reason": "content_identical_or_generation_failed",
                    },
                )
                await self._emit_status(
                    __event_emitter__,
                    "Rule analysis complete: No new content.",
                    done=True,
                )
                return
            # 5. Inject Rules into System Prompt
            updated_sys_prompt = existing_sys_prompt
            if rules_changed:
                updated_sys_prompt = self._inject_rules(
                    updated_sys_prompt,
                    new_rules_content,
                    self.valves.RULES_BLOCK_TITLE,
                )
            await self._emit_debug_log(
                __event_emitter__,
                "Ready to Update DB",
                {"step": "pre_db_update", "target_folder_id": target_folder_id},
            )
            # 6. Update Folder (ORM) - Only update 'data' field
            existing_data["system_prompt"] = updated_sys_prompt
            updated_folder = Folders.update_folder_by_id_and_user_id(
                target_folder_id,
                user_id,
                FolderUpdateForm(data=existing_data),
            )
            if not updated_folder:
                raise Exception("Update folder failed (ORM returned None)")
            await self._emit_status(
                __event_emitter__, "Rule analysis complete: Rules updated.", done=True
            )
            await self._emit_debug_log(
                __event_emitter__,
                "Rule Generation Process & Change Details",
                {
                    "step": "success",
                    "folder_id": target_folder_id,
                    "target_is_root": target_folder_id != folder_id,
                    "model_used": self.valves.MODEL_ID,
                    "analyzed_messages_count": len(messages),
                    "old_rules_length": len(current_rules_content),
                    "new_rules_length": len(new_rules_content),
                    "changes_digest": {
                        "old_rules_preview": (
                            current_rules_content[:100] + "..."
                            if current_rules_content
                            else "None"
                        ),
                        "new_rules_preview": (
                            new_rules_content[:100] + "..."
                            if new_rules_content
                            else "None"
                        ),
                    },
                    "timestamp": datetime.now().isoformat(),
                },
            )
        except Exception as e:
            logger.error(f"Async rule processing error: {e}")
            await self._emit_status(
                __event_emitter__, "Failed to update rules.", done=True
            )
            # Emit error to console for debugging
            await self._emit_debug_log(
                __event_emitter__,
                "Execution Error",
                {"error": str(e), "folder_id": folder_id},
            )
    # ==================== Filter Hooks ====================
    async def inlet(
        self, body: dict, __user__: Optional[dict] = None, __event_emitter__=None
    ) -> dict:
        return body
    async def outlet(
        self,
        body: dict,
        __user__: Optional[dict] = None,
        __event_emitter__=None,
        __request__: Optional[Request] = None,
    ) -> dict:
        user_ctx = self._get_user_context(__user__)
        chat_ctx = self._get_chat_context(body)
        messages = body.get("messages", [])
        if not messages:
            return body
        # Trigger logic: Message Count threshold
        if len(messages) % self.valves.MESSAGE_TRIGGER_COUNT != 0:
            return body
        folder_id = self._get_folder_id(body)
        if not folder_id:
            await self._emit_debug_log(
                __event_emitter__,
                "Skipping Analysis",
                {
                    "reason": "Chat does not belong to any folder",
                    "chat_id": chat_ctx.get("chat_id"),
                },
            )
            return body
        # User Info
        user_id = user_ctx.get("user_id")
        if not user_id:
            return body
        # Async Task
        if self.valves.MODEL_ID == "":
            self.valves.MODEL_ID = body.get("model", "")
        asyncio.create_task(
            self._process_rules_update(
                folder_id, body, user_id, __request__, __event_emitter__
            )
        )
        return body
--- a/plugins/filters/folder-memory/folder_memory_cn.py
+++ b/plugins/filters/folder-memory/folder_memory_cn.py
@@ -0,0 +1,470 @@
 """
 title: 📂 文件夹记忆 (Folder Memory)
 author: Fu-Jie
 author_url: https://github.com/Fu-Jie/awesome-openwebui
 funding_url: https://github.com/open-webui
 version: 0.1.0
 description: 自动从对话中提取项目规则，并将其注入到文件夹的系统提示词中。
 requirements:
 """
 from pydantic import BaseModel, Field
 from typing import Optional, Dict, List
 from fastapi import Request
 import logging
 import json
 import re
 import asyncio
 from datetime import datetime
 from open_webui.utils.chat import generate_chat_completion
 from open_webui.models.users import Users
 from open_webui.models.folders import Folders, FolderUpdateForm
 from open_webui.models.chats import Chats
 logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 )
 logger = logging.getLogger(__name__)
 # 规则注入标记
 RULES_BLOCK_START = "<!-- OWUI_PROJECT_RULES_START -->"
 RULES_BLOCK_END = "<!-- OWUI_PROJECT_RULES_END -->"
 # 规则生成系统提示词
 SYSTEM_PROMPT_RULE_GENERATOR = """
 你是一个项目规则提取器。你的任务是从对话中提取“项目规则”，并与现有规则合并。
 ### 输入
 1. **现有规则 (Existing Rules)**：当前文件夹系统提示词中的规则。
 2. **对话片段 (Conversation)**：最近的聊天记录。
 ### 目标
 综合生成一份适用于当前项目/文件夹的简洁规则列表。
 - **移除** 不再相关或仅是一次性指令的规则。
 - **添加** 对话中发现的新的、一致性的要求。
 - **合并** 相似的规则。
 - **格式**：简洁的 Markdown 项目符号列表。
 ### 输出格式
 仅输出 Markdown 项目符号列表形式的规则。不要包含任何开头或结尾的说明文字。
 示例：
 - 始终使用 Python 3.11 进行类型提示。
 - 文档字符串必须遵循 Google 风格。
 - 提交信息必须使用英文。
 """
 class Filter:
    class Valves(BaseModel):
        PRIORITY: int = Field(default=20, description="过滤器操作的优先级。")
        SHOW_DEBUG_LOG: bool = Field(
            default=False, description="在控制台显示调试日志。"
        )
        MESSAGE_TRIGGER_COUNT: int = Field(
            default=10, description="每隔 N 条消息分析一次规则。"
        )
        MODEL_ID: str = Field(
            default="", description="用于提取规则的模型 ID。为空则使用当前对话模型。"
        )
        RULES_BLOCK_TITLE: str = Field(
            default="## 📂 项目规则", description="显示在规则块上方的标题。"
        )
        UPDATE_ROOT_FOLDER: bool = Field(
            default=False,
            description="如果启用，将向上查找并更新根文件夹的规则，而不是当前子文件夹。",
        )
    def __init__(self):
        self.valves = self.Valves()
    # ==================== 辅助方法 ====================
    def _get_user_context(self, __user__: Optional[dict]) -> 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", ""),
            "user_name": user_data.get("name", "User"),
            "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)。"""
        chat_id = ""
        message_id = ""
        if isinstance(body, dict):
            chat_id = body.get("chat_id", "")
            message_id = body.get("id", "")
            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 __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_debug_log(self, __event_emitter__, title: str, data: dict):
        if self.valves.SHOW_DEBUG_LOG and __event_emitter__:
            try:
                # 按照用户要求的格式输出展平的日志
                js_code = f"""
                    console.log("[Folder Memory] {title}", {json.dumps(data, ensure_ascii=False)});
                """
                await __event_emitter__({"type": "execute", "data": {"code": js_code}})
            except Exception as e:
                logger.error(f"发出日志错误: {e}")
    async def _emit_status(
        self, __event_emitter__, description: str, done: bool = False
    ):
        if __event_emitter__:
            await __event_emitter__(
                {"type": "status", "data": {"description": description, "done": done}}
            )
    def _get_folder_id(self, body: dict) -> Optional[str]:
        # 1. 尝试从 metadata 获取 folder_id
        if "metadata" in body and isinstance(body["metadata"], dict):
            if "folder_id" in body["metadata"]:
                return body["metadata"]["folder_id"]
        # 2. 检查 chat 对象
        if "chat" in body and isinstance(body["chat"], dict):
            if "folder_id" in body["chat"]:
                return body["chat"]["folder_id"]
        # 3. 尝试通过 Chat ID 查找 (最可靠的方法)
        chat_id = body.get("chat_id")
        if not chat_id:
            if "metadata" in body and isinstance(body["metadata"], dict):
                chat_id = body["metadata"].get("chat_id")
        if chat_id:
            try:
                chat = Chats.get_chat_by_id(chat_id)
                if chat and chat.folder_id:
                    return chat.folder_id
            except Exception as e:
                logger.error(f"获取聊天信息失败 chat_id={chat_id}: {e}")
        return None
    def _extract_existing_rules(self, system_prompt: str) -> str:
        pattern = re.compile(
            re.escape(RULES_BLOCK_START) + r"([\s\S]*?)" + re.escape(RULES_BLOCK_END)
        )
        match = pattern.search(system_prompt)
        if match:
            # 如果标题在块内，将其移除以便纯净合并
            content = match.group(1).strip()
            title_pat = re.compile(r"^#+\s+.*$", re.MULTILINE)
            return title_pat.sub("", content).strip()
        return ""
    def _inject_rules(self, system_prompt: str, new_rules: str, title: str) -> str:
        new_block_content = f"\n{title}\n\n{new_rules}\n"
        new_block = f"{RULES_BLOCK_START}{new_block_content}{RULES_BLOCK_END}"
        system_prompt = system_prompt or ""
        pattern = re.compile(
            re.escape(RULES_BLOCK_START) + r"[\s\S]*?" + re.escape(RULES_BLOCK_END)
        )
        if pattern.search(system_prompt):
            # 替换现有块
            return pattern.sub(new_block, system_prompt).strip()
        else:
            # 追加到末尾
            if system_prompt:
                return f"{system_prompt}\n\n{new_block}"
            else:
                return new_block
    async def _generate_new_rules(
        self,
        current_rules: str,
        messages: List[Dict],
        user_id: str,
        __request__: Request,
    ) -> str:
        # 准备上下文
        conversation_text = "\n".join(
            [
                f"{msg['role'].upper()}: {msg['content']}"
                for msg in messages[-20:]  # 分析最近 20 条消息上下文
            ]
        )
        prompt = f"""
 Existing Rules (现有规则):
 {current_rules if current_rules else "无"}
 Conversation Excerpt (对话片段):
 {conversation_text}
 Please output the updated Project Rules (请输出更新后的项目规则):
 """
        payload = {
            "model": self.valves.MODEL_ID,
            "messages": [
                {"role": "system", "content": SYSTEM_PROMPT_RULE_GENERATOR},
                {"role": "user", "content": prompt},
            ],
            "stream": False,
        }
        try:
            # 需要用户对象进行权限检查
            user = Users.get_user_by_id(user_id)
            if not user:
                return current_rules
            completion = await generate_chat_completion(__request__, payload, user)
            if "choices" in completion and len(completion["choices"]) > 0:
                content = completion["choices"][0]["message"]["content"].strip()
                # 简单验证：确保看起来像个列表
                if (
                    content.startswith("-")
                    or content.startswith("*")
                    or content.startswith("1.")
                ):
                    return content
        except Exception as e:
            logger.error(f"规则生成失败: {e}")
        return current_rules
    async def _process_rules_update(
        self,
        folder_id: str,
        body: dict,
        user_id: str,
        __request__: Request,
        __event_emitter__,
    ):
        try:
            await self._emit_debug_log(
                __event_emitter__,
                "开始处理",
                {"step": "start", "initial_folder_id": folder_id, "user_id": user_id},
            )
            # 1. 获取文件夹数据 (ORM)
            initial_folder = Folders.get_folder_by_id_and_user_id(folder_id, user_id)
            if not initial_folder:
                await self._emit_debug_log(
                    __event_emitter__,
                    "错误：未找到初始文件夹",
                    {
                        "step": "fetch_initial_folder",
                        "initial_folder_id": folder_id,
                        "user_id": user_id,
                    },
                )
                return
            # 处理子文件夹逻辑：决定是更新当前文件夹还是根文件夹
            target_folder = initial_folder
            if self.valves.UPDATE_ROOT_FOLDER:
                # 向上遍历直到找到没有 parent_id 的根文件夹
                while target_folder and getattr(target_folder, "parent_id", None):
                    try:
                        parent = Folders.get_folder_by_id_and_user_id(
                            target_folder.parent_id, user_id
                        )
                        if parent:
                            target_folder = parent
                        else:
                            break
                    except Exception as e:
                        await self._emit_debug_log(
                            __event_emitter__,
                            "警告：向上查找父文件夹失败",
                            {"step": "traverse_root", "error": str(e)},
                        )
                        break
            target_folder_id = target_folder.id
            await self._emit_debug_log(
                __event_emitter__,
                "定目标文件夹",
                {
                    "step": "target_resolved",
                    "target_folder_id": target_folder_id,
                    "target_folder_name": target_folder.name,
                    "is_root_update": target_folder_id != folder_id,
                },
            )
            existing_data = target_folder.data if target_folder.data else {}
            existing_sys_prompt = existing_data.get("system_prompt", "")
            # 2. 提取现有规则
            current_rules_content = self._extract_existing_rules(existing_sys_prompt)
            # 3. 生成新规则
            await self._emit_status(
                __event_emitter__, "正在分析项目规则...", done=False
            )
            messages = body.get("messages", [])
            new_rules_content = await self._generate_new_rules(
                current_rules_content, messages, user_id, __request__
            )
            rules_changed = new_rules_content != current_rules_content
            # 如果生成结果无变更
            if not rules_changed:
                await self._emit_debug_log(
                    __event_emitter__,
                    "无变更",
                    {
                        "step": "check_changes",
                        "reason": "content_identical_or_generation_failed",
                    },
                )
                await self._emit_status(
                    __event_emitter__, "规则分析完成：无新增内容。", done=True
                )
                return
            # 5. 注入规则到 System Prompt
            updated_sys_prompt = existing_sys_prompt
            if rules_changed:
                updated_sys_prompt = self._inject_rules(
                    updated_sys_prompt,
                    new_rules_content,
                    self.valves.RULES_BLOCK_TITLE,
                )
            await self._emit_debug_log(
                __event_emitter__,
                "准备更新数据库",
                {"step": "pre_db_update", "target_folder_id": target_folder_id},
            )
            # 6. 更新文件夹 (ORM) - 仅更新 'data' 字段
            existing_data["system_prompt"] = updated_sys_prompt
            updated_folder = Folders.update_folder_by_id_and_user_id(
                target_folder_id,
                user_id,
                FolderUpdateForm(data=existing_data),
            )
            if not updated_folder:
                raise Exception("Update folder failed (ORM returned None)")
            await self._emit_status(
                __event_emitter__, "规则分析完成：规则已更新。", done=True
            )
            await self._emit_debug_log(
                __event_emitter__,
                "规则生成过程和变更详情",
                {
                    "step": "success",
                    "folder_id": target_folder_id,
                    "target_is_root": target_folder_id != folder_id,
                    "model_used": self.valves.MODEL_ID,
                    "analyzed_messages_count": len(messages),
                    "old_rules_length": len(current_rules_content),
                    "new_rules_length": len(new_rules_content),
                    "changes_digest": {
                        "old_rules_preview": (
                            current_rules_content[:100] + "..."
                            if current_rules_content
                            else "None"
                        ),
                        "new_rules_preview": (
                            new_rules_content[:100] + "..."
                            if new_rules_content
                            else "None"
                        ),
                    },
                    "timestamp": datetime.now().isoformat(),
                },
            )
        except Exception as e:
            logger.error(f"异步规则处理错误: {e}")
            await self._emit_status(__event_emitter__, "更新规则失败。", done=True)
            # 在控制台也输出错误信息，方便调试
            await self._emit_debug_log(
                __event_emitter__, "执行出错", {"error": str(e), "folder_id": folder_id}
            )
    # ==================== Filter Hooks ====================
    async def inlet(
        self, body: dict, __user__: Optional[dict] = None, __event_emitter__=None
    ) -> dict:
        return body
    async def outlet(
        self,
        body: dict,
        __user__: Optional[dict] = None,
        __event_emitter__=None,
        __request__: Optional[Request] = None,
    ) -> dict:
        user_ctx = self._get_user_context(__user__)
        chat_ctx = self._get_chat_context(body)
        messages = body.get("messages", [])
        if not messages:
            return body
        # 触发逻辑：消息计数阈值
        if len(messages) % self.valves.MESSAGE_TRIGGER_COUNT != 0:
            return body
        folder_id = self._get_folder_id(body)
        if not folder_id:
            await self._emit_debug_log(
                __event_emitter__,
                "跳过分析",
                {"reason": "对话不属于任何文件夹", "chat_id": chat_ctx.get("chat_id")},
            )
            return body
        # 用户信息
        user_id = user_ctx.get("user_id")
        if not user_id:
            return body
        # 异步任务
        if self.valves.MODEL_ID == "":
            self.valves.MODEL_ID = body.get("model", "")
        asyncio.create_task(
            self._process_rules_update(
                folder_id, body, user_id, __request__, __event_emitter__
            )
        )
        return body
Author	SHA1	Message	Date
fujie	ba92649a98	feat(filters): refactor folder-rule-collector to folder-memory - Rename plugin from `folder-rule-collector` to `folder-memory` for better clarity. - Refactor code to focus on "Project Rules" collection, removing "Knowledge" collection for V1. - Add `PRIORITY` valve (default: 20) to ensure execution after context compression. - Update all parameter names to uppercase for consistency. - Update documentation (README, global docs) with GitHub raw URL for demo image. - Remove `STATUS` valve as it's redundant with OpenWebUI's built-in function toggle. - Add `ROADMAP.md` to track future "Project Knowledge" features. - Update `.github/copilot-instructions.md` with detailed commit message guidelines.	2026-01-20 20:02:50 +08:00
github-actions[bot]	d2276dcaae	chore: update community stats - plugin version updated	2026-01-20 11:10:30 +00:00
fujie	25c9d20f3d	feat(async-context-compression): release v1.2.1 with smart config & optimizations This release introduces significant improvements to configuration flexibility, performance, and stability. Key Changes: * Smart Configuration: * Added `summary_model_max_context` to allow independent context limits for the summary model (e.g., using `gemini-flash` with 1M context to summarize `gpt-4` history). * Implemented auto-detection of base model settings for custom models, ensuring correct threshold application. * Performance & Refactoring: * Optimized `model_thresholds` parsing with caching to reduce overhead. * Refactored `inlet` and `outlet` logic to remove redundant code and improve maintainability. * Replaced all `print` statements with proper `logging` calls for better production monitoring. * Bug Fixes & Modernization: * Fixed `datetime.utcnow()` deprecation warnings by switching to timezone-aware `datetime.now(timezone.utc)`. * Corrected type annotations and improved error handling for `JSONResponse` objects from LLM backends. * Removed hard truncation in summary generation to allow full context usage. Files Updated: * Plugin source code (English & Chinese) * Documentation and READMEs * Version bumped to 1.2.1	2026-01-20 19:09:25 +08:00
github-actions[bot]	0d853577df	chore: update community stats - followers increased (136 -> 137)	2026-01-20 09:15:24 +00:00
github-actions[bot]	f91f3d8692	chore: update community stats - followers increased (135 -> 136)	2026-01-20 07:14:01 +00:00
github-actions[bot]	0f7cad8dfa	chore: update community stats - followers increased (134 -> 135)	2026-01-19 23:08:06 +00:00
fujie	db1a1e7ef0	fix(async-context-compression): sync CN version with EN version logic - Add missing imports (contextlib, sessionmaker, Engine) - Add database engine discovery functions (_discover_owui_engine, _discover_owui_schema) - Fix ChatSummary table to support schema configuration - Fix duplicate code in __init__ method - Add _db_session context manager for robust session handling - Fix inlet method signature (add __request__, __model__ parameters) - Fix tool output trimming to check native function calling - Add chat_id empty check in outlet method	2026-01-19 20:37:37 +08:00
github-actions[bot]	e7de80a059	chore: update community stats - plugin version updated, followers increased (133 -> 134)	2026-01-19 12:15:44 +00:00