README.md

metadata

title: LexRAG
emoji: ⚖️
colorFrom: indigo
colorTo: blue
sdk: docker
app_port: 7860
pinned: false
license: apache-2.0

LexRAG — Hybrid-Search Legal RAG

🔗 Live demo: https://huggingface.co/spaces/RV302001/lexrag

Retrieval-augmented Q&A over real Indian court judgments (opennyaiorg/InJudgements_dataset, Apache-2.0). Answers are grounded in retrieved judgments and cite their sources; hallucinated citations are stripped before display.

How it works

1. Hybrid retrieval — one SQL CTE fuses two arms with Reciprocal Rank Fusion:
   • Vector: pgvector cosine (<=>) over bge-small-en-v1.5 embeddings (384-dim), HNSW index.
   • Keyword: Postgres full-text ts_rank_cd over a generated tsvector, GIN index.
2. Generation — Groq llama-3.1-8b-instant, temp 0.1, strict "answer only from context, cite [case_name]" prompt.
3. Validation — citations not matching a retrieved case name are stripped.

Data lives in an external Neon Postgres (pgvector), so it persists across Space restarts. The Space holds no data — only the app + embedding model.

Stack

FastAPI · SQLAlchemy 2.0 + Alembic · Postgres 15 + pgvector (Neon) · sentence-transformers · Groq · plain HTML/JS. No Celery/Redis.

Configuration

Set these as Space secrets (or local .env, see .env.example):

Secret	Description
DATABASE_URL	Neon connection string (pgvector enabled)
GROQ_API_KEY	Groq API key

One-time setup (offline, run locally)

pip install -r requirements.txt
alembic upgrade head                 # create extension, table, HNSW + GIN indexes
python -m ingest.build_index         # load 300 judgments -> chunk -> embed -> insert

Then deploy: push this repo to a Hugging Face Space (Docker SDK) with the two secrets set.

Local run

uvicorn app.main:app --host 0.0.0.0 --port 7860

Endpoints: GET / (UI), POST /ask {question}, GET /health.