.vitepress/contributors.ts

@@ -68,7 +68,7 @@ export const contributors = [
         nameAliases: ['li1553770945'],
         avatar: 'https://avatars.githubusercontent.com/u/55867654?v=4',
         mapByEmailAliases: ['1553770945@qq.com'],
-        links: [{ type: '', link: 'https://peacesheep.xyz/home' }]
+        links: [{ type: '', link: 'https://peacesheep.cn/home' }]
     },
     {
         name: '星弧梦影',

.vitepress/i18n/en.ts

@@ -133,13 +133,13 @@ export default {
                 text: 'Development Examples',
                 items: [
                     { text: 'MCP Server Development Examples', link: '/plugin-tutorial/examples/mcp-examples' },
-                    // { text: 'Example 1: Weather Info MCP in Python (STDIO)', link: '/plugin-tutorial/examples/python-simple-stdio' },
+                    { text: 'Example 1: Weather Info MCP in Python (STDIO)', link: '/plugin-tutorial/examples/python-simple-stdio' },
-                    // { text: 'Example 2: Read-only Neo4j MCP in Go (SSE)', link: '/plugin-tutorial/examples/go-neo4j-sse' },
+                    { text: 'Example 2: Read-only Neo4j MCP in Go (SSE)', link: '/plugin-tutorial/examples/go-neo4j-sse' },
-                    // { text: 'Example 3: Read-only Document DB MCP in Java (HTTP)', link: '/plugin-tutorial/examples/java-es-http' },
+                    { text: 'Example 3: Read-only Document DB MCP in Java (HTTP)', link: '/plugin-tutorial/examples/java-es-http' },
-                    // { text: 'Example 4: Super Web Crawler MCP in TypeScript using crawl4ai (STDIO)', link: '/plugin-tutorial/examples/typescript-crawl4ai-stdio' },
+                    { text: 'Example 4: Super Web Crawler MCP in TypeScript using crawl4ai (STDIO)', link: '/plugin-tutorial/examples/typescript-crawl4ai-stdio' },
-                    // { text: 'Example 5: Generic Form Filling MCP in Python (STDIO)', link: '/plugin-tutorial/examples/python-form-stdio' },
+                    { text: 'Example 5: Generic Form Filling MCP in Python (STDIO)', link: '/plugin-tutorial/examples/python-form-stdio' },
-                    // { text: 'Example 6: Blender-based MCP in Python (STDIO)', link: '/plugin-tutorial/examples/python-blender-stdio' },
+                    { text: 'Example 6: Blender-based MCP in Python (STDIO)', link: '/plugin-tutorial/examples/python-blender-stdio' },
-                    // { text: 'Example 7: Cadence EDA MCP in Python (STDIO)', link: '/plugin-tutorial/examples/python-cadence-stdio' }
+                    { text: 'Example 7: Cadence EDA MCP in Python (STDIO)', link: '/plugin-tutorial/examples/python-cadence-stdio' }
                 ]
             },
             {

.vitepress/i18n/ja.ts

@@ -132,13 +132,13 @@ export default {
                 text: '開発事例',
                 items: [
                     { text: 'MCP サーバー開発事例', link: '/ja/plugin-tutorial/examples/mcp-examples' },
-                    // { text: '例 1. Python による天気情報 MCP サーバー (STDIO)', link: '/ja/plugin-tutorial/examples/python-simple-stdio' },
+                    { text: '例 1. Python による天気情報 MCP サーバー (STDIO)', link: '/ja/plugin-tutorial/examples/python-simple-stdio' },
-                    // { text: '例 2. Go による neo4j 読み取り専用 MCP サーバー (SSE)', link: '/ja/plugin-tutorial/examples/go-neo4j-sse' },
+                    { text: '例 2. Go による neo4j 読み取り専用 MCP サーバー (SSE)', link: '/ja/plugin-tutorial/examples/go-neo4j-sse' },
-                    // { text: '例 3. Java によるドキュメントデータベース MCP (HTTP)', link: '/ja/plugin-tutorial/examples/java-es-http' },
+                    { text: '例 3. Java によるドキュメントデータベース MCP (HTTP)', link: '/ja/plugin-tutorial/examples/java-es-http' },
-                    // { text: '例 4. TypeScript による crawl4ai ベースのスーパーウェブクローラー MCP (STDIO)', link: '/ja/plugin-tutorial/examples/typescript-crawl4ai-stdio' },
+                    { text: '例 4. TypeScript による crawl4ai ベースのスーパーウェブクローラー MCP (STDIO)', link: '/ja/plugin-tutorial/examples/typescript-crawl4ai-stdio' },
-                    // { text: '例 5. Python による汎用フォーム入力 MCP (STDIO)', link: '/ja/plugin-tutorial/examples/python-form-stdio' },
+                    { text: '例 5. Python による汎用フォーム入力 MCP (STDIO)', link: '/ja/plugin-tutorial/examples/python-form-stdio' },
-                    // { text: '例 6. Python による Blender ベース MCP (STDIO)', link: '/ja/plugin-tutorial/examples/python-blender-stdio' },
+                    { text: '例 6. Python による Blender ベース MCP (STDIO)', link: '/ja/plugin-tutorial/examples/python-blender-stdio' },
-                    // { text: '例 7. Python による Cadence EDA MCP (STDIO)', link: '/ja/plugin-tutorial/examples/python-cadence-stdio' }
+                    { text: '例 7. Python による Cadence EDA MCP (STDIO)', link: '/ja/plugin-tutorial/examples/python-cadence-stdio' }
                 ]
             },
             {

.vitepress/i18n/zh.ts

@@ -132,13 +132,13 @@ export default {
                 text: '开发案例',
                 items: [
                     { text: 'MCP 服务器开发案例', link: '/zh/plugin-tutorial/examples/mcp-examples' },
-                    // { text: '例子 1. python 实现天气信息 mcp 服务器 (STDIO)', link: '/zh/plugin-tutorial/examples/python-simple-stdio' },
+                    { text: '例子 1. python 实现天气信息 mcp 服务器 (STDIO)', link: '/zh/plugin-tutorial/examples/python-simple-stdio' },
-                    // { text: '例子 2. go 实现 neo4j 的只读 mcp 服务器 (SSE)', link: '/zh/plugin-tutorial/examples/go-neo4j-sse' },
+                    { text: '例子 2. go 实现 neo4j 的只读 mcp 服务器 (SSE)', link: '/zh/plugin-tutorial/examples/go-neo4j-sse' },
-                    // { text: '例子 3. java 实现文档数据库的只读 mcp (HTTP)', link: '/zh/plugin-tutorial/examples/java-es-http' },
+                    { text: '例子 3. java 实现文档数据库的只读 mcp (HTTP)', link: '/zh/plugin-tutorial/examples/java-es-http' },
-                    // { text: '例子 4. typescript 实现基于 crawl4ai 的超级网页爬虫 mcp (STDIO)', link: '/zh/plugin-tutorial/examples/typescript-crawl4ai-stdio' },
+                    { text: '例子 4. typescript 实现基于 crawl4ai 的超级网页爬虫 mcp (STDIO)', link: '/zh/plugin-tutorial/examples/typescript-crawl4ai-stdio' },
-                    // { text: '例子 5. python 实现进行通用表单填充 的 mcp (STDIO)', link: '/zh/plugin-tutorial/examples/python-form-stdio' },
+                    { text: '例子 5. python 实现进行通用表单填充 的 mcp (STDIO)', link: '/zh/plugin-tutorial/examples/python-form-stdio' },
-                    // { text: '例子 6. python 实现基于 blender 的 mcp (STDIO)', link: '/zh/plugin-tutorial/examples/python-blender-stdio' },
+                    { text: '例子 6. python 实现基于 blender 的 mcp (STDIO)', link: '/zh/plugin-tutorial/examples/python-blender-stdio' },
-                    // { text: '例子 7. python 实现 cadence EDA 的 mcp (STDIO)', link: '/zh/plugin-tutorial/examples/python-cadence-stdio' },
+                    { text: '例子 7. python 实现 cadence EDA 的 mcp (STDIO)', link: '/zh/plugin-tutorial/examples/python-cadence-stdio' },
                 ]
             },
             {

ja/preview/contributors.md

@@ -74,14 +74,6 @@ const contributors = [
     links: [
       { icon: 'github', link: 'https://github.com/ZYD045692' },
     ]
-  },
-  {
-    avatar: 'https://avatars.githubusercontent.com/u/81767213?v=4',
-    name: 'Zhenyu (Dormiveglia-elf)',
-    title: 'Developer',
-    links: [
-      { icon: 'github', link: 'https://github.com/Dormiveglia-elf' },
-    ]
   }
 ]
 </script>

plugin-tutorial/examples/mcp-examples.md

@@ -16,8 +16,11 @@ next:
 - [Cadence EDA MCP (STDIO)](./python-cadence-stdio)  
   Electronic design automation interface
 - Video Editing via FFmpeg MCP  
-- [Building a RAG-Based Memory Storage MCP Server in Python](./python-rag_memo-stdio.md)
+  AI-driven video editing workflows
+- Knowledge Base Injection with RAG MCP  
+  Dynamic document retrieval system
 - Stable Diffusion MCP Server  
+  Text-to-image generation service
 
 ## Node.js
 - [Crawl4AI Web Crawler MCP (STDIO)](./typescript-crawl4ai-stdio)  

plugin-tutorial/examples/python-rag_memo-stdio.md

@@ -1,392 +0,0 @@
-# Building a RAG-Based Memory Storage MCP Server in Python
-
-[Tutorial Code Repository](https://github.com/Dormiveglia-elf/rag_memo_mcp)
-
-## Introduction
-
-In this tutorial, we'll demonstrate how to build a simple RAG (Retrieval-Augmented Generation) based long-term memory storage MCP server using Python, and debug it using the [openmcp](https://github.com/LSTM-Kirigaya/openmcp-client) plugin. Once implemented, we'll be able to store, retrieve, and manage our memories through natural language interactions with large language models, without needing to write any specific query code.
-
-## 1. Setup
-
-The project structure is as follows:
-
-```bash
-📦rag_memo_mcp
- ┣ 📂memory_db/      # LanceDB database files, created during initialization
- ┣ 📜server.py       # MCP server implementation
- ┣ 📜pyproject.toml  # Project configuration file
- ┣ 📜uv.lock         # uv lockfile
- ┗ ...
-```
-
-First, let's prepare the runtime environment. This project recommends using [uv](https://github.com/astral-sh/uv). (`uv` is a blazingly fast Python package manager that's beloved by those who use it. Of course, if you're a loyal fan of `pip` or other package managers, that works perfectly fine too.)
-
-```bash
-# First download uv (Windows)
-powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
-# Or (macOS/Linux)
-# curl -LsSf https://astral.sh/uv/install.sh | sh
-```
-
-```bash
-# Project initialization
-uv init rag_memo_mcp
-cd rag_memo_mcp
-# We recommend creating a virtual environment
-uv venv
-# Activate virtual environment (Windows)
-.venv\Scripts\activate
-# Or (macOS/Linux)
-# source .venv/bin/activate
-
-# Install dependencies
-uv add "mcp[cli]" lancedb pandas sentence-transformers
-```
-
-## 2. Understanding the Service Implementation
-
-Unlike traditional databases that require pre-installation and configuration, this project's core `MemoryStore` uses [LanceDB](https://lancedb.github.io/), a vector database that automatically creates and initializes itself in the `memory_db` directory when the server first starts, requiring no additional configuration.
-
-Let's dive into `server.py` to understand its implementation details.
-
-### 2.1 MemoryStore Core Class
-
-The `MemoryStore` class is the heart of memory storage and retrieval functionality.
-
-```python
-class MemoryStore:
-```
-
-- **`initialize()`**: This method handles initialization. It connects to the LanceDB database (creating it if it doesn't exist), defines the memory table schema, and by default loads the `all-MiniLM-L6-v2` model for generating vector embeddings from text content.
-
-```python
-def __init__(self, db_path: str = "./memory_db"):
-    self.db_path = db_path
-    self.db = None
-    self.table = None
-    self.encoder = None
-    self._initialized = False
-
-async def initialize(self):
-    if self._initialized:
-        return
-
-    self.encoder = SentenceTransformer("all-MiniLM-L6-v2")
-
-    self.db = lancedb.connect(self.db_path)
-
-    schema = pa.schema(
-        [
-            pa.field("id", pa.string()),
-            pa.field("content", pa.string()),
-            pa.field("summary", pa.string()),
-            pa.field("tags", pa.list_(pa.string())),
-            pa.field("timestamp", pa.timestamp("us")),
-            pa.field("category", pa.string()),
-            pa.field("importance", pa.int32()),
-            pa.field(
-                "vector", pa.list_(pa.float32(), 384)
-            ),
-        ]
-    )
-
-    try:
-        self.table = self.db.open_table("memories")
-    except Exception:
-        self.table = self.db.create_table("memories", schema=schema)
-
-    self._initialized = True
-```
-
-- **`store_memory()`**: When storing a new memory, this method generates a unique ID and timestamp for the memory content. If no summary is provided, it automatically generates a simple summary, then uses the pre-loaded model to convert the content into a vector, and finally stores all information (ID, content, summary, tags, timestamp, category, importance, vector) in the LanceDB table.
-
-```python
-async def store_memory(
-    self,
-    content: str,
-    summary: Optional[str] = None,
-    tags: Optional[List[str]] = None,
-    category: str = "general",
-    importance: int = 5,
-) -> str:
-    await self.initialize()
-
-    memory_id = str(uuid.uuid4())
-    timestamp = datetime.now(timezone.utc)
-
-    if not summary:
-        summary = content[:100] + "..." if len(content) > 100 else content
-
-    embedding = self._generate_embedding(content)
-
-    data = [
-        {
-            "id": memory_id,
-            "content": content,
-            "summary": summary,
-            "tags": tags or [],
-            "timestamp": timestamp,
-            "category": category,
-            "importance": importance,
-            "vector": embedding,
-        }
-    ]
-
-    self.table.add(data)
-
-    return memory_id
-```
-
-- **`search_memories()`**: This is the key to implementing RAG. When a query is made, this method converts the query text into a vector as well, then performs vector similarity search in LanceDB to find the most relevant memories. It also supports filtering by category and importance.
-
-```python
-async def search_memories(
-    self,
-    query: str,
-    limit: int = 10,
-    category: Optional[str] = None,
-    min_importance: Optional[int] = None,
-) -> List[Dict[str, Any]]:
-    await self.initialize()
-    query_embedding = self._generate_embedding(query)
-
-    search_query = self.table.search(query_embedding)
-
-    if limit:
-        search_query = search_query.limit(limit)
-
-    filters = []
-    if category:
-        filters.append(f"category = '{category}'")
-    if min_importance is not None:
-        filters.append(f"importance >= {min_importance}")
-
-    if filters:
-        filter_str = " AND ".join(filters)
-        search_query = search_query.where(filter_str)
-
-    results = search_query.to_pandas()
-
-    memories = []
-    for _, row in results.iterrows():
-        memory = {
-            "id": row["id"],
-            "content": row["content"],
-            "summary": row["summary"],
-            "tags": row["tags"].tolist(),
-            "timestamp": row["timestamp"],
-            "category": row["category"],
-            "importance": int(row["importance"]),
-            "similarity_score": row.get(
-                "_distance", 0.0
-            ),
-        }
-        memories.append(memory)
-
-    return memories
-```
-
-### 2.2 MCP Server and Tools
-
-We use `FastMCP` to quickly build an MCP server and expose `MemoryStore` functionality as tools that large language models can call through the `@mcp.tool()` decorator.
-
-- **`store_memory`**: **Take notes!** Store a memory.
-- **`search_memories`**: **Let me think...** Search for relevant memories based on query content.
-- **`get_memory`**: **Find by reference!** Retrieve a specific memory by ID.
-- **`list_categories`**: **Organize by category!** List all memory categories.
-- **`get_memory_stats`**: **Memory inventory!** Get statistics about the memory store, such as total count, counts by category, etc.
-
-```python
-# Initialize memory store
-memory_store = MemoryStore()
-
-# Create MCP server
-mcp = FastMCP("RAG-based Memory MCP Server")
-
-
-@mcp.tool()
-async def store_memory(
-    content: str,
-    summary: Optional[str] = None,
-    tags: Optional[str] = None,
-    category: str = "general",
-    importance: int = 5,
-) -> Dict[str, str]:
-    """
-    Store content in memory.
-
-    Args:
-        content: The content to store
-        summary: Optional summary (auto-generated if not provided)
-        tags: Comma-separated tags
-        category: Memory category (default: general)
-        importance: Importance level 1-10 (default: 5)
-    """
-    try:
-        # Parse tags if provided
-        tag_list = [tag.strip() for tag in tags.split(",")] if tags else []
-
-        memory_id = await memory_store.store_memory(
-            content=content,
-            summary=summary,
-            tags=tag_list,
-            category=category,
-            importance=importance,
-        )
-
-        return {
-            "status": "success",
-            "memory_id": memory_id,
-            "message": f"Memory stored successfully with ID: {memory_id}",
-        }
-    except Exception as e:
-        return {"status": "error", "message": f"Failed to store memory: {str(e)}"}
-
-
-@mcp.tool()
-async def search_memories(
-    query: str,
-    limit: int = 10,
-    category: Optional[str] = None,
-    min_importance: Optional[int] = None,
-) -> Dict[str, Any]:
-    """
-    Search stored memories using semantic similarity.
-
-    Args:
-        query: Search query
-        limit: Maximum number of results (default: 10)
-        category: Filter by category
-        min_importance: Minimum importance level
-    """
-    try:
-        memories = await memory_store.search_memories(
-            query=query, limit=limit, category=category, min_importance=min_importance
-        )
-
-        return {
-            "status": "success",
-            "query": query,
-            "total_results": len(memories),
-            "memories": memories,
-        }
-    except Exception as e:
-        return {"status": "error", "message": f"Search failed: {str(e)}"}
-
-
-@mcp.tool()
-async def get_memory(memory_id: str) -> Dict[str, Any]:
-    """
-    Retrieve a specific memory by its ID.
-
-    Args:
-        memory_id: The unique identifier of the memory
-    """
-    try:
-        memory = await memory_store.get_memory_by_id(memory_id)
-
-        if memory:
-            return {"status": "success", "memory": memory}
-        else:
-            return {
-                "status": "error",
-                "message": f"Memory with ID {memory_id} not found",
-            }
-    except Exception as e:
-        return {"status": "error", "message": f"Failed to retrieve memory: {str(e)}"}
-
-
-@mcp.tool()
-async def list_categories() -> Dict[str, Any]:
-    try:
-        categories = await memory_store.list_categories()
-        return {"status": "success", "categories": categories}
-    except Exception as e:
-        return {"status": "error", "message": f"Failed to list categories: {str(e)}"}
-
-
-@mcp.tool()
-async def get_memory_stats() -> Dict[str, Any]:
-    try:
-        stats = await memory_store.get_stats()
-        return {"status": "success", "stats": stats}
-    except Exception as e:
-        return {"status": "error", "message": f"Failed to get stats: {str(e)}"}
-```
-
-The server startup code is at the end of `server.py`, which first initializes the `MemoryStore`, then runs the MCP server.
-
-```python
-if __name__ == "__main__":
-    # Initialize memory store on startup
-    async def init_memory():
-        await memory_store.initialize()
-
-    # Run initialization
-    asyncio.run(init_memory())
-
-    # Run MCP server
-    mcp.run()
-```
-
-## 3. Debugging with [openmcp](https://github.com/LSTM-Kirigaya/openmcp-client)
-
-### 3.1 Adding Workspace Connection
-
-Next, we'll debug using the [openmcp](https://github.com/LSTM-Kirigaya/openmcp-client) plugin. First, let's test if we can connect successfully. Here we choose `stdio`, set the working path to the project directory, then click `Connect`. In the log panel on the right, we can see that we've successfully connected.
-
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_1.png" style="width: 80%;"/>
-</div>
-
-### 3.2 Testing Tools
-
-After successful connection, let's test if the tools work properly.
-
-1. **Store a little secret**: Create a new `Tool` tab and select the `store_memory` tool. For example, we input:
-   - `content`: `Xiao Ming's birthday is 2025.6.18`
-   - `category`: `birthday`
-   - `importance`: `8`
-   
-   Click `Execute`, and if successful, it will return the stored memory ID, such as `bcc30f6c-979c-46d1-b34a-cd1a09242106`
-
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_2.png" style="width: 80%;"/>
-</div>
-
-2. **Retrieve a specific memory by ID**:
-   After successful storage, we use the returned memory ID `bcc30f6c-979c-46d1-b34a-cd1a09242106`, select the `get_memory` tool, and test if we can retrieve it from `LanceDB`.
-
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_3.png" style="width: 80%;"/>
-</div>
-
-3. **List current memory categories**:
-   We call the `list_categories` tool to view all current memory categories. Since we only added one memory with the `birthday` category, the result should only contain this category.
-
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_4.png" style="width: 80%;"/>
-</div>
-
-4. **Get memory statistics**:
-   Next, we use the `get_memory_stats` tool to get statistical information about the memory store, such as the total number of memories and the count of memories in each category.
-
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_5.png" style="width: 80%;"/>
-</div>
-
-### 3.3 Large Language Model Interaction Testing
-
-We intentionally "skipped" testing one tool `search_memories` above, saving it for the large language model interaction testing. Enter the interaction testing page (remember to set up the LLM's `api_key` and `base_url` first according to the [Connect to LLM tutorial](https://kirigaya.cn/openmcp/zh/plugin-tutorial/usage/connect-llm.html)). We can first disable all other tools, keeping only the `search_memories` tool:
-
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_6.png" style="width: 80%;"/>
-</div>
-
-Then, we casually ask:
-
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_7.png" style="width: 80%;"/>
-</div>
-
-Great! The large language model successfully helped me recall my friend Xiao Ming's birthday. Cheers!

preview/contributors.md

@@ -74,14 +74,6 @@ const contributors = [
     links: [
       { icon: 'github', link: 'https://github.com/ZYD045692' },
     ]
-  },
-  {
-    avatar: 'https://avatars.githubusercontent.com/u/81767213?v=4',
-    name: 'Zhenyu (Dormiveglia-elf)',
-    title: 'Developer',
-    links: [
-      { icon: 'github', link: 'https://github.com/Dormiveglia-elf' },
-    ]
   }
 ]
 </script>

zh/plugin-tutorial/examples/mcp-examples.md

@@ -12,7 +12,7 @@ next:
 - [python 实现基于 blender 的 mcp (STDIO)](./python-blender-stdio)
 - [python 实现 cadence EDA 的 mcp (STDIO)](./python-cadence-stdio)
 - 基于 ffmpeg mcp 实现通过对话的视频剪辑
-- [python 实现基于 RAG 的记忆存储 MCP 服务器](./python-rag_memo-stdio.md)
+- 基于 rag mcp 实现知识库的注入
 - 实现 Stable Diffusion 的 MCP 服务器
 
 ## Nodejs

zh/plugin-tutorial/examples/python-rag_memo-stdio.md

@@ -1,382 +0,0 @@
-# Python 实现基于 RAG 的记忆存储 MCP 服务器
-
-[本期教程的代码](https://github.com/Dormiveglia-elf/rag_memo_mcp)
-
-## 前言
-
-本篇教程，我们将演示如何使用 Python 构建一个简易的基于 RAG (Retrieval-Augmented Generation) 的长期记忆存储 MCP 服务器, 并通过 [openmcp](https://github.com/LSTM-Kirigaya/openmcp-client) 插件进行调试。 实现完成后，我们能够通过与大模型进行自然语言交互，轻松地存储、检索和管理我们的记忆，而无需编写任何特定的查询代码。
-
-## 1. 准备
-
-项目结构如下：
-
-```bash
-📦rag_memo_mcp
- ┣ 📂memory_db/      # LanceDB 数据库文件，初始化时会创建
- ┣ 📜server.py       # MCP 服务器实现
- ┣ 📜pyproject.toml  # 项目配置文件
- ┣ 📜uv.lock         # uv lockfile
- ┗ ...
-```
-
-首先，我们来准备运行环境。本项目推荐使用 [uv](https://github.com/astral-sh/uv)。(`uv` 是一个速度快得飞起的 Python 包管理器，用过都说好。当然，如果你是 `pip` 或者其他包管理器的忠实粉丝，也完全没问题)
-```bash
-# 首先下载 uv (Windows)
-powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
-# 或者 (macOS/Linux)
-# curl -LsSf https://astral.sh/uv/install.sh | sh
-```
-
-```bash
-# 项目初始化
-uv init rag_memo_mcp
-cd rag_memo_mcp
-# 建议创建一个虚拟环境
-uv venv
-# 激活虚拟环境 (Windows)
-.venv\Scripts\activate
-# 或者 (macOS/Linux)
-# source .venv/bin/activate
-
-# 安装依赖
-uv add "mcp[cli]" lancedb pandas sentence-transformers
-```
-
-## 2. 理解服务实现
-
-与需要预先安装和配置的传统数据库不同，本项目的核心 `MemoryStore` 使用 [LanceDB](https://lancedb.github.io/)，这是一个向量数据库，它会在服务器首次启动时自动在 `memory_db` 目录下创建并初始化，无需额外配置。
-
-让我们深入 `server.py` 来理解其实现细节。
-
-### 2.1 MemoryStore 核心类
-
-`MemoryStore` 类是记忆存储和检索功能的核心。
-```python
-class MemoryStore:
-```
-
-- **`initialize()`**: 这个方法负责初始化。它会连接到 LanceDB 数据库（如果不存在则创建），定义记忆表的 schema，并默认加载 `all-MiniLM-L6-v2` 用于将文本内容生成向量嵌入。
-```python
-def __init__(self, db_path: str = "./memory_db"):
-    self.db_path = db_path
-    self.db = None
-    self.table = None
-    self.encoder = None
-    self._initialized = False
-
-async def initialize(self):
-    if self._initialized:
-        return
-
-    self.encoder = SentenceTransformer("all-MiniLM-L6-v2")
-
-    self.db = lancedb.connect(self.db_path)
-
-    schema = pa.schema(
-        [
-            pa.field("id", pa.string()),
-            pa.field("content", pa.string()),
-            pa.field("summary", pa.string()),
-            pa.field("tags", pa.list_(pa.string())),
-            pa.field("timestamp", pa.timestamp("us")),
-            pa.field("category", pa.string()),
-            pa.field("importance", pa.int32()),
-            pa.field(
-                "vector", pa.list_(pa.float32(), 384)
-            ),
-        ]
-    )
-
-    try:
-        self.table = self.db.open_table("memories")
-    except Exception:
-        self.table = self.db.create_table("memories", schema=schema)
-
-    self._initialized = True
-```
-
-- **`store_memory()`**: 当需要存储一条新记忆时，此方法会被调用。它会为记忆内容生成一个唯一的ID和时间戳，如果未提供摘要，则自动生成一个简单的摘要，然后使用预加载的模型将内容转换为向量，最后将所有信息（ID, 内容, 摘要, 标签, 时间戳, 类别, 重要性, 向量）存入 LanceDB 表中。
-```python
-async def store_memory(
-    self,
-    content: str,
-    summary: Optional[str] = None,
-    tags: Optional[List[str]] = None,
-    category: str = "general",
-    importance: int = 5,
-) -> str:
-    await self.initialize()
-
-    memory_id = str(uuid.uuid4())
-    timestamp = datetime.now(timezone.utc)
-
-    if not summary:
-        summary = content[:100] + "..." if len(content) > 100 else content
-
-    embedding = self._generate_embedding(content)
-
-    data = [
-        {
-            "id": memory_id,
-            "content": content,
-            "summary": summary,
-            "tags": tags or [],
-            "timestamp": timestamp,
-            "category": category,
-            "importance": importance,
-            "vector": embedding,
-        }
-    ]
-
-    self.table.add(data)
-
-    return memory_id
-```
-
-- **`search_memories()`**: 这是实现 RAG 的关键。当提出一个查询时，此方法会将查询文本同样转换为向量，然后在 LanceDB 中执行向量相似度搜索，以找到最相关的记忆。它还支持按类别和重要性进行过滤。
-```python
-async def search_memories(
-    self,
-    query: str,
-    limit: int = 10,
-    category: Optional[str] = None,
-    min_importance: Optional[int] = None,
-) -> List[Dict[str, Any]]:
-    await self.initialize()
-    query_embedding = self._generate_embedding(query)
-
-    search_query = self.table.search(query_embedding)
-
-    if limit:
-        search_query = search_query.limit(limit)
-
-    filters = []
-    if category:
-        filters.append(f"category = '{category}'")
-    if min_importance is not None:
-        filters.append(f"importance >= {min_importance}")
-
-    if filters:
-        filter_str = " AND ".join(filters)
-        search_query = search_query.where(filter_str)
-
-    results = search_query.to_pandas()
-
-    memories = []
-    for _, row in results.iterrows():
-        memory = {
-            "id": row["id"],
-            "content": row["content"],
-            "summary": row["summary"],
-            "tags": row["tags"].tolist(),
-            "timestamp": row["timestamp"],
-            "category": row["category"],
-            "importance": int(row["importance"]),
-            "similarity_score": row.get(
-                "_distance", 0.0
-            ),
-        }
-        memories.append(memory)
-
-    return memories
-```
-
-### 2.2 MCP 服务器与工具
-
-我们使用 `FastMCP` 来快速构建一个 MCP 服务器，并通过 `@mcp.tool()` 装饰器将 `MemoryStore` 的功能暴露为大模型可以调用的工具。
-
-- **`store_memory`**: **记笔记！** 存储一条记忆。
-- **`search_memories`**: **让我想想...** 根据查询内容搜索相关记忆。
-- **`get_memory`**: **按图索骥！** 根据 ID 精确检索某条记忆。
-- **`list_categories`**: **分门别类！** 列出所有记忆的分类。
-- **`get_memory_stats`**: **记忆盘点！** 获取关于记忆库的统计信息，如总数、各分类数量等。
-
-```python
-# 初始化记忆存储
-memory_store = MemoryStore()
-
-# 创建 MCP 服务器
-mcp = FastMCP("RAG-based Memory MCP Server")
-
-
-@mcp.tool()
-async def store_memory(
-    content: str,
-    summary: Optional[str] = None,
-    tags: Optional[str] = None,
-    category: str = "general",
-    importance: int = 5,
-) -> Dict[str, str]:
-    """
-    Store content in memory.
-
-    Args:
-        content: The content to store
-        summary: Optional summary (auto-generated if not provided)
-        tags: Comma-separated tags
-        category: Memory category (default: general)
-        importance: Importance level 1-10 (default: 5)
-    """
-    try:
-        # Parse tags if provided
-        tag_list = [tag.strip() for tag in tags.split(",")] if tags else []
-
-        memory_id = await memory_store.store_memory(
-            content=content,
-            summary=summary,
-            tags=tag_list,
-            category=category,
-            importance=importance,
-        )
-
-        return {
-            "status": "success",
-            "memory_id": memory_id,
-            "message": f"Memory stored successfully with ID: {memory_id}",
-        }
-    except Exception as e:
-        return {"status": "error", "message": f"Failed to store memory: {str(e)}"}
-
-
-@mcp.tool()
-async def search_memories(
-    query: str,
-    limit: int = 10,
-    category: Optional[str] = None,
-    min_importance: Optional[int] = None,
-) -> Dict[str, Any]:
-    """
-    Search stored memories using semantic similarity.
-
-    Args:
-        query: Search query
-        limit: Maximum number of results (default: 10)
-        category: Filter by category
-        min_importance: Minimum importance level
-    """
-    try:
-        memories = await memory_store.search_memories(
-            query=query, limit=limit, category=category, min_importance=min_importance
-        )
-
-        return {
-            "status": "success",
-            "query": query,
-            "total_results": len(memories),
-            "memories": memories,
-        }
-    except Exception as e:
-        return {"status": "error", "message": f"Search failed: {str(e)}"}
-
-
-@mcp.tool()
-async def get_memory(memory_id: str) -> Dict[str, Any]:
-    """
-    Retrieve a specific memory by its ID.
-
-    Args:
-        memory_id: The unique identifier of the memory
-    """
-    try:
-        memory = await memory_store.get_memory_by_id(memory_id)
-
-        if memory:
-            return {"status": "success", "memory": memory}
-        else:
-            return {
-                "status": "error",
-                "message": f"Memory with ID {memory_id} not found",
-            }
-    except Exception as e:
-        return {"status": "error", "message": f"Failed to retrieve memory: {str(e)}"}
-
-
-@mcp.tool()
-async def list_categories() -> Dict[str, Any]:
-    try:
-        categories = await memory_store.list_categories()
-        return {"status": "success", "categories": categories}
-    except Exception as e:
-        return {"status": "error", "message": f"Failed to list categories: {str(e)}"}
-
-
-@mcp.tool()
-async def get_memory_stats() -> Dict[str, Any]:
-    try:
-        stats = await memory_store.get_stats()
-        return {"status": "success", "stats": stats}
-    except Exception as e:
-        return {"status": "error", "message": f"Failed to get stats: {str(e)}"}
-```
-
-
-服务器的启动代码位于 `server.py` 的末尾，它首先初始化 `MemoryStore`，然后运行 MCP 服务器。
-
-```python
-if __name__ == "__main__":
-    # 在启动时初始化记忆存储
-    async def init_memory():
-        await memory_store.initialize()
-
-    # 运行初始化
-    asyncio.run(init_memory())
-
-    # 运行 MCP 服务器
-    mcp.run()
-```
-
-## 3. 通过 [openmcp](https://github.com/LSTM-Kirigaya/openmcp-client) 来进行调试
-### 3.1 添加工作区连接
-
-接下来，我们通过 [openmcp](https://github.com/LSTM-Kirigaya/openmcp-client) 插件进行调试。首先测试是否能连接成功，这里选择 `stdio`，工作路径设置为项目所在的目录，然后点击 `Connect`。右边的日志栏里可以看到我们已经连接成功。
-
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_1.png" style="width: 80%;"/>
-</div>
-
-### 3.2 测试工具
-
-连接成功后，让我们先测试一下工具是否工作正常。
-
-1.  **存个小秘密**: 新建一个 `Tool` 标签页，选择 `store_memory` 工具。例如我们输入:
-    - `content`: `小明的生日是 2025.6.18`
-    - `category`: `birthday`
-    - `importance`: `8`
-    
-    点击 `Execute`，如果成功会返回存储的记忆 ID，比如这里返回 `bcc30f6c-979c-46d1-b34a-cd1a09242106`
-
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_2.png" style="width: 80%;"/>
-</div>
-
-2. **根据 ID 精确检索某条记忆**：
-存储成功后，我们根据返回的记忆 ID `bcc30f6c-979c-46d1-b34a-cd1a09242106`，选择 `get_memory` 工具，测试是否能够从 `Lancedb` 里面检索出来。
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_3.png" style="width: 80%;"/>
-
-3.  **列出目前的记忆分类**:
-    我们调用 `list_categories` 工具来查看当前所有记忆的分类。由于我们只添加了一个 `birthday` 分类的记忆，所以返回结果中应该只包含这个分类。
-
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_4.png" style="width: 80%;"/>
-</div>
-
-4.  **获取记忆统计数据**:
-    接着，我们使用 `get_memory_stats` 工具来获取记忆库的统计信息，例如总共有多少条记忆，以及每个分类下的记忆数量。
-
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_5.png" style="width: 80%;"/>
-</div>
-
-### 3.3 大模型交互测试
-上面我们"遗漏"了一个工具 `search_memories` 没有测试，其实是特意把它留给了大模型交互测试。进入交互测试页面(记得事先参照[连接大模型教程](https://kirigaya.cn/openmcp/zh/plugin-tutorial/usage/connect-llm.html)设置好大模型的 `api_key` 和 `base_url`)，我们可以先把其他的工具都取消配备，只保留 `search_memories` 这一个工具:
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_6.png" style="width: 80%;"/>
-</div>
-
-然后，我们假装不经意地问一句：
-
-<div align=center>
-<img src="https://raw.githubusercontent.com/Dormiveglia-elf/rag_memo_mcp/main/images/rag_memo_mcp_7.png" style="width: 80%;"/>
-</div>
-
-好! 大模型成功帮助我召回了我的朋友小明的生日, Cheers!

zh/preview/contributors.md

@@ -74,14 +74,6 @@ const contributors = [
     links: [
       { icon: 'github', link: 'https://github.com/ZYD045692' },
     ]
-  },
-  {
-    avatar: 'https://avatars.githubusercontent.com/u/81767213?v=4',
-    name: 'Zhenyu (Dormiveglia-elf)',
-    title: 'Developer',
-    links: [
-      { icon: 'github', link: 'https://github.com/Dormiveglia-elf' },
-    ]
   }
 ]
 </script>