DB-AI Backend (Ollama + SQL)

A small, DB-aware chatbot backend with an Ollama LLM, schema-aware SQL generation, and an optional RAG layer.

Quick start

1. Create a virtual environment and install dependencies:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

2. Copy .env.example to .env and update values.

3. Start the API:

uvicorn app.main:app --host 0.0.0.0 --port 8000

Endpoints

• GET /health – basic health check
• GET /schema – current schema catalog (tables, columns, foreign keys)
• POST /chat – ask a question and get an answer
• POST /rag/ingest – add a document to the local vector store (when RAG_ENABLED=true)

Example request

curl -X POST http://localhost:8000/chat \
  -H 'Content-Type: application/json' \
  -d '{"question":"Top 5 customers by revenue last month?","include_debug":true}'

Notes

• SQL is constrained to read-only SELECT and limited to allowed tables.
• For complex databases, tune SCHEMA_MAX_CANDIDATES and allow/deny lists.
• The local vector store is for small datasets. For production, replace it with pgvector, Qdrant, or another vector DB.
• To switch databases, set DB_DIALECT and driver, or provide DB_URL directly.