官方说明7 个来源MCP 服务8 天前

gogabrielordonez/mcp-ragchat

📇 🏠 - Add RAG-based AI chat to any website: local vector store, OpenAI, Anthropic or Gemini models, a…

官方说明📇 🏠 - Add RAG-based AI chat to any website: local vector store, OpenAI, Anthropic or Gemini models, a self-contained chat server and an embeddable widget.

配置模板（请按你的客户端调整）

claude mcp add mcp-ragchat -- npx -y mcp-ragchat

这是根据仓库名生成的可粘贴骨架，命令行参数与包名请以该项目 README 为准。

★ 数取值: 2 ★ · gogabrielordonez/mcp-ragchat · 取自 GitHub 搜索 API · （2026-10-16 20:00:20 核对）
来源明细: 收录自 punkpeye/awesome-mcp-servers 榜单（我们从中收集的条目之一） · 条目类型：MCP 服务
收录与活跃: 收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
评分构成: 综合分 703 · 2 ★ · 7 个来源 · mcp

常见问题（答案都来自我们的记录）

怎么安装？

我们【没有】从该仓库取到安装命令（我们的 install 覆盖率只有约 4%）。请看它的 README 或 releases 页。

它适合谁 / 用来做什么？

我们把它归在这些标签下: MCP 服务。标签来自条目自身，不是我们强加的分类。

谁维护它、还在更新吗？

仓库 gogabrielordonez/mcp-ragchat；我们收录于 2026-10-09；最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。

第三方质量标记

mcp-ragchat

以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →

An MCP server that adds RAG-powered AI chat to any website. One command from Claude Code.
---
Tell Claude Code _"add AI chat to mysite.com"_ and it will crawl your content, build a local vector store, spin up a chat server, and hand you an embed snippet. No cloud infra. No database. Just one API key.
Demo
Quick Start
**1. Clone and build**
**2. Configure Claude Code** (~/.claude/mcp.json)
**3. Use it**
Open Claude Code and say:
> "Add AI chat to mysite.com. Here's the content: [paste your markdown]"
Claude handles the rest.
Tools
| Tool | What it does |
|------|-------------|
| ragchat_setup | Seed a knowledge base from markdown content. Each ## section becomes a searchable document with vector embeddings. |
| ragchat_test | Send a test message to verify RAG retrieval and LLM response quality. |
| ragchat_serve | Start a local HTTP chat server with CORS and input sanitization. |
| ragchat_widget | Generate a self-contained tag -- a floating chat bubble, no dependencies. |
| ragchat_status | List all configured domains with document counts and config details. |
How It Works
Everything runs locally. No cloud infrastructure. Bring your own API key.
Supported Providers
LLM (chat completions)
| Provider | Env Var | Default Model |
|----------|---------|---------------|
| OpenAI | OPENAI_API_KEY | gpt-4o-mini |
| Anthropic | ANTHROPIC_API_KEY | claude-sonnet-4-5-20250929 |
| Google Gemini | GEMINI_API_KEY | gemini-2.0-flash |
Embeddings (vector search)
| Provider | Env Var | Default Model |
|----------|---------|---------------|
| OpenAI | OPENAI_API_KEY | text-embedding-3-small |
| Google Gemini | GEMINI_API_KEY | text-embedding-004 |
| AWS Bedrock | AWS_REGION + IAM | amazon.titan-embed-text-v2:0 |
Override defaults with LLM_MODEL and EMBEDDING_MODEL environment variables.
Architecture
- **Vector store** -- Local JSON files with cosine similarity search. Zero external dependencies.
- **Chat server** -- Node.js HTTP server with CORS and input sanitization.
- **Widget** -- Self-contained tag. No frameworks, no build step.
Contributing
Issues and pull requests are welcome.