Developers Can Build an AI Legal Research Tool Using RAG in Under 6 Hours

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A technical guide published on DEV Community outlines how developers can build a Retrieval-Augmented Generation (RAG) legal research assistant using Python, LangChain, OpenAI, and Pinecone. The system connects to the free CourtListener API to retrieve public court opinions, embeds them into a vector store, and uses an LLM to generate a concise legal brief. The estimated build time is four to six hours for a developer familiar with Python and basic HTTP authentication. Key tools include LangChain for orchestration, OpenAI's GPT-3.5-turbo for text generation, and either Pinecone or a local Chroma database for storing case embeddings. The resulting command-line tool can later be integrated into a Flask app or workflow automation platform for use within law firms.

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