From c547c1cee55ffe17017cb39f57ce936a9944c10c Mon Sep 17 00:00:00 2001 From: fujie Date: Tue, 10 Feb 2026 21:41:15 +0800 Subject: [PATCH] docs: fix broken relative links in example cases to resolve mkdocs build warnings --- docs/plugins/pipes/star-prediction-example.md | 17 ++++++++++++----- .../plugins/pipes/star-prediction-example.zh.md | 17 ++++++++++++----- docs/plugins/pipes/video-processing-example.md | 17 ++++++++++++----- .../pipes/video-processing-example.zh.md | 17 ++++++++++++----- 4 files changed, 48 insertions(+), 20 deletions(-) diff --git a/docs/plugins/pipes/star-prediction-example.md b/docs/plugins/pipes/star-prediction-example.md index 3d00d7b..74d0490 100644 --- a/docs/plugins/pipes/star-prediction-example.md +++ b/docs/plugins/pipes/star-prediction-example.md @@ -16,16 +16,17 @@ This case study demonstrates how to use the **GitHub Copilot SDK Pipe** with the - **Plugin Type**: Pipe (GitHub Copilot SDK) - **Base Model**: Minimax 2.1 (via Pipe integration) -- **Key Capabilities**: - - **File Processing**: Automatically reads and parses multiple CSV data files. - - **Code Generation & Execution**: On-the-fly Python scripting to calculate growth rates, conversion rates, and median trends. - - **Multimodal Output**: Generates Markdown reports, interactive HTML dashboards, and Mermaid timeline charts. +- **Key Capabilities**: + - **File Processing**: Automatically reads and parses multiple CSV data files. + - **Code Generation & Execution**: On-the-fly Python scripting to calculate growth rates, conversion rates, and median trends. + - **Multimodal Output**: Generates Markdown reports, interactive HTML dashboards, and Mermaid timeline charts. --- ## 💬 Conversation Highlights ### 📥 Import Conversation + You can download the raw chat data and import it into your Open WebUI to see the full tool calls and analysis logic: [:material-download: Download Chat JSON](./star-prediction-chat.json) @@ -33,22 +34,28 @@ You can download the raw chat data and import it into your Open WebUI to see the > In Open WebUI, click your **User Avatar** (bottom of left sidebar) -> **Settings** -> **Data** -> **Import Chats**, then select the downloaded file. ### 1. Data Submission + The **User** provided traffic source distribution and uploaded: + - `Unique visitors in last 14 days.csv` - `Total views in last 14 days.csv` - `star-history.csv` ### 2. Analysis Execution + **Minimax 2.1** received the data and immediately formulated an analysis plan: + 1. Calculate star growth trajectory and rates. 2. Analyze visitor-to-star conversion rates. 3. Build linear and median projection models. 4. Generate a milestone timeline. ### 3. Report Generation + The model produced a comprehensive report. Here are the core projections: #### 🎯 Key Projections + | Metric | Value | Insight | | :--- | :--- | :--- | | **Current Stars** | 62 | 62% of the goal reached | @@ -88,4 +95,4 @@ gantt --- -> [View GitHub Copilot SDK Pipe Source Code](../../../plugins/pipes/github-copilot-sdk/README.md) +> [View GitHub Copilot SDK Pipe Documentation](./github-copilot-sdk.md) diff --git a/docs/plugins/pipes/star-prediction-example.zh.md b/docs/plugins/pipes/star-prediction-example.zh.md index 4fd67c6..38390a0 100644 --- a/docs/plugins/pipes/star-prediction-example.zh.md +++ b/docs/plugins/pipes/star-prediction-example.zh.md @@ -16,16 +16,17 @@ - **插件类型**: Pipe (GitHub Copilot SDK) - **底层模型**: Minimax 2.1 (通过 Pipe 接入) -- **核心能力**: - - **文件处理**: 自动读取并解析多份 CSV 数据文件。 - - **代码生成与执行**: 现场编写 Python 分析代码并执行，计算增长率、转化率及中位趋势。 - - **多模态输出**: 生成 Markdown 报告、HTML 交互看板以及 Mermaid 时间轴图表。 +- **核心能力**: + - **文件处理**: 自动读取并解析多份 CSV 数据文件。 + - **代码生成与执行**: 现场编写 Python 分析代码并执行，计算增长率、转化率及中位趋势。 + - **多模态输出**: 生成 Markdown 报告、HTML 交互看板以及 Mermaid 时间轴图表。 --- ## 💬 对话实录 ### 📥 导入对话记录 + 你可以下载原始对话数据并导入到你的 Open WebUI 中，查看完整的工具调用和分析逻辑： [:material-download: 下载原始对话 JSON](./star-prediction-chat.json) @@ -33,22 +34,28 @@ > 在 Open WebUI 首页点击 **左侧侧边栏底部个人头像** -> **设置** -> **数据** -> **导入记录**，选择下载的文件即可。 ### 1. 提交原始数据 + **用户**提供了项目的流量来源分布表，并上传了： + - `Unique visitors in last 14 days.csv` - `Total views in last 14 days.csv` - `star-history.csv` ### 2. 模型执行分析 + **Minimax 2.1** 接收到数据后，立即制定了分析计划： + 1. 计算 Star 增长轨迹和增长率。 2. 分析访问者到 Star 的转化率。 3. 构建线性与中位增长模型进行预测。 4. 生成里程碑时间轴。 ### 3. 生成分析报告 + 模型输出了一份详尽的报告，以下是其核心预测： #### 🎯 关键预测结果 + | 指标 | 数值 | 洞察 | | :--- | :--- | :--- | | **当前 Star 数** | 62 | 已完成目标的 62% | @@ -88,4 +95,4 @@ gantt --- -> [查看 GitHub Copilot SDK Pipe 源码](../../../plugins/pipes/github-copilot-sdk/README.md) +> [查看 GitHub Copilot SDK Pipe 开发文档](./github-copilot-sdk.zh.md) diff --git a/docs/plugins/pipes/video-processing-example.md b/docs/plugins/pipes/video-processing-example.md index b01665d..ef1f918 100644 --- a/docs/plugins/pipes/video-processing-example.md +++ b/docs/plugins/pipes/video-processing-example.md @@ -16,18 +16,19 @@ This case study demonstrates how to use the **GitHub Copilot SDK Pipe** with **M - **Plugin Type**: Pipe (GitHub Copilot SDK) - **Base Model**: Minimax 2.1 -- **Key Capabilities**: - - **System Tool Access**: Automatically detects and invokes `ffmpeg` within the container. - - **Two-Pass Optimization**: +- **Key Capabilities**: + - **System Tool Access**: Automatically detects and invokes `ffmpeg` within the container. + - **Two-Pass Optimization**: 1. **Pass 1**: Analyzes all frames to generate a custom 256-color palette (`palettegen`). 2. **Pass 2**: Applies the palette for superior quantization and dithering (`paletteuse`). - - **Precision Parameters**: Implements 1.4x PTS scaling, Lanczos scaling, and 20fps rate control. + - **Precision Parameters**: Implements 1.4x PTS scaling, Lanczos scaling, and 20fps rate control. --- ## 💬 Conversation Highlights ### 📥 Import Conversation + You can download the raw chat data and import it into your Open WebUI to see how the model debugs and optimizes the FFmpeg parameters: [:material-download: Download Chat JSON](./video-processing-chat.json) @@ -35,14 +36,18 @@ You can download the raw chat data and import it into your Open WebUI to see how > In Open WebUI, click your **User Avatar** (bottom of left sidebar) -> **Settings** -> **Data** -> **Import Chats**, then select the downloaded file. ### 1. Processing Requirements + The **User** provided an input file and specific parameters: + - Speed: 1.4x (setpts=PTS/1.4) - Resolution: 1280px width, auto height - Optimization: Must use palette generation technology - Constraint: File size < 20MB ### 2. Analysis Execution + **Minimax 2.1** generated and executed the following core logic: + ```bash # Pass 1: Generate optimal palette ffmpeg -i input.mov -vf "fps=20,scale=1280:-1:flags=lanczos,setpts=PTS/1.4,palettegen" palette.png @@ -51,6 +56,7 @@ ffmpeg -i input.mov -i palette.png -lavfi "[0:v]fps=20,scale=1280:-1:flags=lancz ``` ### 3. Result Summary + | Metric | Original Video | Processed GIF | Status | | :--- | :--- | :--- | :--- | | **File Size** | 38 MB | **14 MB** | ✅ Success | @@ -63,6 +69,7 @@ ffmpeg -i input.mov -i palette.png -lavfi "[0:v]fps=20,scale=1280:-1:flags=lancz ## 💡 Why This Case Matters Standard LLMs can only "tell you" how to use FFmpeg. However, a Pipe powered by the **GitHub Copilot SDK** can: + 1. **Interpret** complex multimedia processing parameters. 2. **Access** raw files within the filesystem. 3. **Execute** resource-intensive binary tool tasks. @@ -70,4 +77,4 @@ Standard LLMs can only "tell you" how to use FFmpeg. However, a Pipe powered by --- -> [View GitHub Copilot SDK Pipe Source Code](../../../plugins/pipes/github-copilot-sdk/README.md) +> [View GitHub Copilot SDK Pipe Documentation](./github-copilot-sdk.md) diff --git a/docs/plugins/pipes/video-processing-example.zh.md b/docs/plugins/pipes/video-processing-example.zh.md index ae690d9..6355675 100644 --- a/docs/plugins/pipes/video-processing-example.zh.md +++ b/docs/plugins/pipes/video-processing-example.zh.md @@ -16,18 +16,19 @@ - **插件类型**: Pipe (GitHub Copilot SDK) - **底层模型**: Minimax 2.1 -- **核心能力**: - - **底层系统访问**: 自动检测并调用容器内的 `ffmpeg` 工具。 - - **双阶段优化 (Two-Pass Optimization)**: +- **核心能力**: + - **底层系统访问**: 自动检测并调用容器内的 `ffmpeg` 工具。 + - **双阶段优化 (Two-Pass Optimization)**: 1. **阶段一**: 分析全视频帧生成 256 色最优调色板 (`palettegen`)。 2. **阶段二**: 应用调色板进行高质量量化和抖动处理 (`paletteuse`)。 - - **参数精准控制**: 实现 1.4 倍 PTS 缩放、Lanczos 滤镜缩放及 20fps 帧率控制。 + - **参数精准控制**: 实现 1.4 倍 PTS 缩放、Lanczos 滤镜缩放及 20fps 帧率控制。 --- ## 💬 对话实录 ### 📥 导入对话记录 + 你可以下载原始对话数据并导入到你的 Open WebUI 中，查看模型如何一步步调试 FFmpeg 参数： [:material-download: 下载原始对话 JSON](./video-processing-chat.json) @@ -35,14 +36,18 @@ > 在 Open WebUI 首页点击 **左侧侧边栏底部个人头像** -> **设置** -> **数据** -> **导入记录**，选择下载的文件即可。 ### 1. 提交处理需求 + **用户**指定了输入文件和详细参数： + - 加速：1.4x (setpts=PTS/1.4) - 分辨率：宽度 1280px，等比例缩放 - 质量优化：必须使用调色板生成技术 - 约束：文件体积 < 20MB ### 2. 模型执行处理 + **Minimax 2.1** 自动编写并执行了以下核心逻辑： + ```bash # 生成优化调色板 ffmpeg -i input.mov -vf "fps=20,scale=1280:-1:flags=lanczos,setpts=PTS/1.4,palettegen" palette.png @@ -51,6 +56,7 @@ ffmpeg -i input.mov -i palette.png -lavfi "[0:v]fps=20,scale=1280:-1:flags=lancz ``` ### 3. 处理结果摘要 + | 指标 | 原始视频 | 处理后 GIF | 状态 | | :--- | :--- | :--- | :--- | | **文件大小** | 38 MB | **14 MB** | ✅ 达标 | @@ -63,6 +69,7 @@ ffmpeg -i input.mov -i palette.png -lavfi "[0:v]fps=20,scale=1280:-1:flags=lancz ## 💡 为什么这个案例很有意义？ 传统的 LLM 只能“告诉你”怎么做，而基于 **GitHub Copilot SDK** 的 Pipe 能够： + 1. **理解** 复杂的多媒体处理参数。 2. **感知** 文件系统中的原始素材。 3. **执行** 耗时、耗能的二进制工具任务。 @@ -70,4 +77,4 @@ ffmpeg -i input.mov -i palette.png -lavfi "[0:v]fps=20,scale=1280:-1:flags=lancz --- -> [查看 GitHub Copilot SDK Pipe 源码](../../../plugins/pipes/github-copilot-sdk/README.md) +> [查看 GitHub Copilot SDK Pipe 开发文档](./github-copilot-sdk.zh.md)