Refugee Law AI Copilot (ConHacks2026)

RAG assistant for Canadian refugee law using:

• FastAPI backend
• LangChain orchestration
• Snowflake Cortex Search retrieval
• Gemini generation + embeddings
• Next.js + Tailwind chat frontend

Project Structure

• backend/main.py: FastAPI app and /api/chat endpoint
• backend/services/rag.py: startup-initialized LangChain + Snowflake retrieval service
• backend/schemas/chat.py: request/response models
• frontend/: Next.js chat UI with citation cards

Environment Variables

Copy .env.example to .env and fill in:

• GEMINI_API_KEY
• SNOWFLAKE_ACCOUNT
• SNOWFLAKE_USER
• SNOWFLAKE_PASSWORD
• SNOWFLAKE_CORTEX_SEARCH_SERVICE

Recommended to also set:

• SNOWFLAKE_WAREHOUSE, SNOWFLAKE_DATABASE, SNOWFLAKE_SCHEMA, SNOWFLAKE_ROLE
• SNOWFLAKE_CORTEX_CONTENT_FIELD, SNOWFLAKE_CORTEX_CASE_NAME_FIELD, SNOWFLAKE_CORTEX_SOURCE_URL_FIELD
• RAG_TOP_K
• NEXT_PUBLIC_API_BASE_URL

Backend Run

python -m venv .venv
# Activate venv, then:
pip install -r requirements.txt
uvicorn backend.main:app --reload --host 127.0.0.1 --port 8000

Frontend Run

cd frontend
npm install
npm run dev

Open http://localhost:3000.

API Contract

POST /api/chat

Request:

{
  "query": "What is the legal test for X?"
}

Response:

{
  "answer": "string",
  "citations": [
    {
      "case_name": "string",
      "url": "string",
      "relevance_score": 0.92
    }
  ]
}