LegalQA is a research project exploring Retrieval-Augmented Generation (RAG) for legal document question answering. It compares single-pass RAG pipelines against a multi-hop iterative retrieval agent on U.S. Supreme Court case law from Case law access (CAP) project.

Features Dataset: Built from the Caselaw Access Project (U.S. Supreme Court opinions, 1984–2014). https://static.case.law/us/

Data Processing:

1. Normalize raw case JSON into a slim format (cases_slim.jsonl).
2. Chunk opinions into passages for retrieval.

Retrieval: FAISS index with bge-small-en embeddings, plus optional cross-encoder reranker.

Pipelines:

1. Baseline: Single-pass retrieval → prompt LLM with top-k chunks.
2. Iterative Agent: Multi-step reasoning loop: retrieval → self-check → query refinement → final answer.

Evaluation Metrics:

1. Answer semantic similarity (vs. gold QA set).
2. Citation precision & recall.
3. Hallucination rate.
4. Hop effectiveness (did extra retrieval help?).

Workflow:

1. Data Prep: data/raw/json/*.json → data/processed/cases_slim.jsonl → data/processed/chunks.jsonl
2. Indexing: Embed chunks with BAAI/bge-small-en-v1.5 and store in FAISS.
3. QA Pipelines:retrieve(query) → build_prompt() → ask_llm(). Then, iterative_agent(query) with self-check & refinement.

Evaluation:

Compare baseline vs iterative using gold_qa.jsonl.

Example:

query = "What did the Supreme Court say about international child abduction?"

retrieved = retrieve(query, top_k=3)

prompt = build_prompt(query, retrieved)

answer = ask_llm(prompt)

print(answer)

Results (sample):

1. Baseline: Higher semantic similarity to gold answers.
2. Iterative: More correct citations, but sometimes drifts in phrasing.

Tech Stack:

1. Python, FAISS, pandas
2. SentenceTransformers (BAAI/bge-small-en, all-MiniLM-L6-v2)
3. OpenAI GPT models for answering and self-checking

Future Work:

1. Use more sophisticated rerankers.
2. Enrich gold answers with full case syllabi.
3. Evaluate on other datasets (e.g., EUR-Lex, CUAD).