README.md

            `# Dify Documentation Expert (RAG Chatbot)

A Retrieval-Augmented Generation (RAG) chatbot for Dify documentation, using MiniLM for semantic search and DistilBERT for answer generation. Built with Python, Flask, LangChain, and FAISS. Deployable on Vercel.

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Features

• Scrapes and processes Dify documentation
• Builds a FAISS vector store with MiniLM embeddings
• Uses DistilBERT for question answering
• Fast API with /ask and /health endpoints
• Ready for serverless deployment (Vercel)

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Project Structure

Dify-Expert/
├── api/
│   └── dify_api.py         # Flask API (entry point for Vercel)
├── dify_docs_scraper/
│   ├── dify_rag.py         # RAG logic (vector store + QA)
│   ├── dify_faiss_index/   # FAISS index files
│   └── ...                 # Scraper, processing scripts, etc.
├── requirements.txt        # Python dependencies
├── vercel.json             # Vercel config
├── README.md               # This file
└── futurescope.md          # Project roadmap/ideas

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Quickstart

1. Install dependencies

pip install -r requirements.txt

2. Build the vector store (if not already built)

python3 dify_docs_scraper/build_vector_store.py

3. Run locally (API)

python3 api/dify_api.py

4. Query the API

curl -X POST http://localhost:8000/ask \
  -H "Content-Type: application/json" \
  -d '{"question": "What is Dify?"}'

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Deployment (Vercel)

1. Push to GitHub.
2. Connect repo to Vercel.
3. Deploy (Vercel auto-detects Python from requirements.txt).
4. Endpoints:
   • /ask (POST)
   • /health (GET)

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API Endpoints

• POST /ask — { "question": "..." } → { "answer": ..., "score": ..., "sources": [...] }
• GET /health — { "status": "ok" }

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Roadmap

See futurescope.md for planned features and ideas.

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License

MIT