Build a rag legal research assistant that drafts briefs in under 10 minutes

Description and use

Build an automated legal research assistant that retrieves recent case law, summarizes it, and drafts a concise brief in under ten minutes using LangChain, OpenAI, and Pinecone.

Problem

Legal professionals need a fast way to retrieve relevant case law, summarize it, and draft concise briefs without manual research.

Outcome

A command‑line tool that, in under ten minutes, retrieves relevant case law, summarizes it, and drafts a concise legal brief using public APIs and OpenAI models.

Workflow

• Set up a Python 3.11 virtual environment and install langchain, openai, pinecone-client, tqdm, requests.
• Obtain API credentials for OpenAI, Pinecone, and configure CourtListener usage (no key but rate‑limit compliance).
• Create a .env file with OPENAI_API_KEY, PINECONE_API_KEY, PINECONE_ENV and load them in code.
• Write ingest_cases.py to fetch the latest 200 opinions from CourtListener, embed each with OpenAI’s text‑embedding‑ada‑002, and upsert into a Pinecone index named legal‑cases.
• Write brief.py to accept a legal question, query Pinecone for top‑5 relevant cases, summarize each with OpenAI, and prompt the model to draft a 300‑word brief.
• Run the script from the command line: python brief.py "Your legal question".
• Optionally wrap the tool in a Flask app or n8n workflow for internal use.

Evidence

You can spin up a Retrieval-Augmented Generation (RAG) legal research assistant in a few hours, hook it up to public case-law APIs, and have it return a concise brief in roughly ten minutes of work.

Source

The script below fetches the latest 200 opinions from CourtListener (via the /search/ endpoint), extracts the plain_text, embeds each with OpenAI's text-embedding-ada-002, and upserts into Pinecone.

Source