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
You: "Set up AI chat for mybakery.com using this FAQ and menu content"
Claude: I'll set up RAG-powered chat for mybakery.com.
[1/4] ragchat_setup
Splitting content into sections...
Generating embeddings for 12 documents...
Domain "mybakery.com" configured with 12 documents.
[2/4] ragchat_test
Query: "What are your hours?"
Reply: "We're open Monday-Saturday 7am-6pm, and Sunday 8am-2pm."
RAG Sources: mybakery.com-3 (0.91), mybakery.com-7 (0.84)
Latency: 340ms
[3/4] ragchat_serve
Chat server running at http://localhost:3456
POST /chat ready.
[4/4] ragchat_widget
Generated embed code. Paste this into your HTML:
<script src="http://localhost:3456/widget.js"></script>
You: Done. Live chat on my site in under 60 seconds.Quick Start
1. Clone and build
git clone https://github.com/gogabrielordonez/mcp-ragchat
cd mcp-ragchat
npm install && npm run build2. Configure Claude Code (~/.claude/mcp.json)
{
"mcpServers": {
"ragchat": {
"command": "node",
"args": ["/absolute/path/to/mcp-ragchat/dist/mcp-server.js"],
"env": {
"OPENAI_API_KEY": "sk-..."
}
}
}
}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 <script> tag -- a floating chat bubble, no dependencies. |
ragchat_status | List all configured domains with document counts and config details. |
How It Works
+------------------+
| Your Markdown |
+--------+---------+
|
ragchat_setup
|
+------------v-------------+
| Local Vector Store |
| ~/.mcp-ragchat/domains/ |
| vectors.json |
| config.json |
+------------+-------------+
|
User Question |
| |
+------v------+ +------v------+
| Embedding | | Cosine |
| Provider +->+ Similarity |
+-------------+ +------+------+
|
Top 3 chunks
|
+----------v-----------+
| System Prompt |
| + RAG Context |
| + User Message |
+----------+-----------+
|
+----------v-----------+
| LLM Provider |
+----------+-----------+
|
ReplyEverything 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
~/.mcp-ragchat/domains/
mysite.com/
config.json -- system prompt, settings
vectors.json -- documents + embedding vectors- 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
<script>tag. No frameworks, no build step.
Contributing
Issues and pull requests are welcome.
- Found a bug? Open an issue
- Want to add a feature? Fork, branch, PR.
- Questions? Start a discussion
Star History
Enterprise
Need multi-tenancy, security guardrails, audit trails, and managed infrastructure? Check out Supersonic -- the enterprise AI platform built on the same RAG pipeline.
MIT License -- Gabriel Ordonez