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4a543d15d1
- Added rate limiting functionality to the chat API, allowing a maximum number of requests per IP within a specified time window. - Implemented error handling for rate limit exceeded responses, including appropriate headers and retry instructions. - Enhanced error handling for other API errors, providing user-friendly messages for various failure scenarios. - Updated README to include new environment variables for rate limiting configuration.
101 lines
2.6 KiB
Markdown
101 lines
2.6 KiB
Markdown
# Docs Chat
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Docs chatbot that uses RAG (Retrieval-Augmented Generation) to answer questions
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from the OpenClaw documentation via semantic search.
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## RAG Pipeline (Recommended)
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The vector-based RAG pipeline uses OpenAI embeddings and LanceDB for semantic
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search. This provides much better results than keyword matching.
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### Build the vector index
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```bash
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OPENAI_API_KEY=sk-... pnpm docs:chat:index:vector
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```
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This generates embeddings for all doc chunks and stores them in
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`scripts/docs-chat/.lance-db/` (gitignored).
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### Run the RAG API
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```bash
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OPENAI_API_KEY=sk-... pnpm docs:chat:serve:vector
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```
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Defaults to `http://localhost:3001`. Optional environment variables:
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `PORT` | `3001` | Server port |
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| `RATE_LIMIT` | `20` | Max requests per window per IP |
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| `RATE_WINDOW_MS` | `60000` | Rate limit window in milliseconds |
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Health check:
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```bash
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curl http://localhost:3001/health
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# Returns: {"ok":true,"chunks":N,"mode":"vector"}
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```
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## Legacy Keyword Pipeline
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The original keyword-based implementation is still available for backward
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compatibility.
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### Build the keyword index
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```bash
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pnpm docs:chat:index
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```
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This generates `scripts/docs-chat/search-index.json` from `docs/**/*.md`.
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### Run the keyword API
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```bash
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OPENAI_API_KEY=sk-... pnpm docs:chat:serve
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```
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## Pipeline Integration
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CI rebuilds the keyword index whenever docs change so PRs keep
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`scripts/docs-chat/search-index.json` in sync. For production deployments with
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RAG, run `pnpm docs:chat:index:vector` during deploy.
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## Mintlify widget
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Mintlify loads any `.js` in the docs content directory on every page.
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`docs/assets/docs-chat-widget.js` injects a floating "Ask Molty" button and
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calls the API at:
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```
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window.DOCS_CHAT_API_URL || "http://localhost:3001"
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```
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To use a deployed API, set `window.DOCS_CHAT_API_URL` before the widget runs
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(for example by adding another small `.js` file in `docs/assets/` that sets it).
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## Architecture
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```
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docs/**/*.md
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│
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▼
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┌─────────────────┐
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│ build-vector- │ Chunking + OpenAI Embeddings
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│ index.ts │
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└────────┬────────┘
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│
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▼
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┌─────────────────┐
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│ .lance-db/ │ LanceDB Vector Store
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└────────┬────────┘
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│
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▼
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┌─────────────────┐
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│ serve.ts │ Hybrid Retrieval (Vector + Keyword Boost)
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│ │ → GPT-4o-mini Streaming Response
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└─────────────────┘
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```
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