Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP RAG What are the supported document formats?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
基于模型上下文协议（MCP）的智能知识库系统，提供文档处理、知识问答和向量库管理功能。
支持使用豆包与OpenAI
🧠 智能知识库：基于向量检索的 RAG 系统，支持语义搜索和智能问答
📄 多格式文档处理：支持超过 25 种文档格式，包括 PDF、DOCX、PPTX、XLSX、图片、邮件等
🌐 直观 Web 界面：Bento 风格布局，分类展示所有工具功能
🤖 多模型支持：兼容 OpenAI、豆包、Ollama 等主流 AI 模型
🔍 高级过滤搜索：支持按文件类型、内容结构等条件进行精确检索
📊 统计分析：提供知识库统计、嵌入缓存分析等数据洞察
⚡ 本地化处理：支持本地模型推理，保护数据隐私
🔧 向量库管理：提供缓存清理、数据库优化等维护功能
Related MCP server: Chalee MCP RAG
# 安装工具
uv tool install mcp_rag
# 升级工具
uv tool install mcp_rag --upgrade
# 卸载工具
uv tool uninstall mcp_ragmcp_rag servermcp_rag webWeb 界面提供直观的 Bento 布局，支持以下工具分类：
📥 添加内容：添加文本和文档到知识库
❓ 智能问答：基于知识库进行问答和检索
📊 数据统计：查看知识库和系统统计信息
⚙️ 向量库管理：优化和维护向量数据库
在项目根目录创建 .env 文件进行配置：
# OpenAI 配置
OPENAI_API_KEY=
OPENAI_API_BASE=https://api.openai.com/v1
OPENAI_MODEL=gpt-4o-mini
OPENAI_TEMPERATURE=0
OPENAI_EMBEDDING_MODEL=text-embedding-3-large
# 豆包 配置
# OPENAI_API_KEY=
# OPENAI_API_BASE=https://ark.cn-beijing.volces.com/api/v3
# OPENAI_MODEL=doubao-1-5-pro-32k-250115
# OPENAI_TEMPERATURE=0
# OPENAI_EMBEDDING_MODEL=doubao-embedding-text-240715{
"mcpServers": {
"rag": {
"command": "uv",
"args": [
"run",
"mcp-rag",
"serve"
],
"env": {
"PYTHONUNBUFFERED": "1",
"OPENAI_API_KEY": "key",
"OPENAI_API_BASE": "https://ark.cn-beijing.volces.com/api/v3",
"OPENAI_MODEL": "doubao-1-5-pro-32k-250115",
"OPENAI_TEMPERATURE": "0",
"OPENAI_EMBEDDING_MODEL": "doubao-embedding-text-240715",
}
}
}
}learn_text(text, source_name) - 添加文本到知识库
learn_document(file_path) - 处理并添加文档到知识库
ask_rag(query) - 基于知识库回答问题
ask_rag_filtered(query, file_type, min_tables, min_titles, processing_method) - 带过滤条件的智能检索
支持超过 25 种文档格式，包括 PDF、DOCX、PPTX、XLSX、图片、邮件等。
用户想查询已有资料或者需要知识库时调用
Inputs: query (required). Returns: result
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 1 tool
Only one tool exists, so there is no risk of ambiguity between tools. The tool's purpose is clear enough for an agent to identify when to use it.
The single tool name 'ask_rag' follows a clear verb_noun convention, which is consistent even with only one tool.
A RAG server typically requires tools for managing the knowledge base (add, delete, list) in addition to querying. Having only one tool is too few for the apparent scope of a RAG server.
The server only supports querying an existing knowledge base, with no way to ingest, update, or manage documents. This leaves significant gaps in the RAG workflow.
Ingest, manage, and retrieve documents for RAG-powered AI applications
Cloud or self-hosted knowledge for AI agents: hybrid search, reranking, GraphRAG, scoped MCP tools.
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
