Build a Multi-Agent RAG Legal Assistant with LangGraph, FastAPI, and Streamlit (Beginner Guide)
Retrieval-Augmented Generation (RAG) sounds complex, but the core concept is straightforward: instead of asking an AI model to answer purely from memory, you hand it specific reference documents and tell it to answer using only that text. In this guide, you will build an end-to-end legal assistant tailored for UAE Federal Law. Although we use UAE legal documents in this tutorial, the same…
The article titled "Build a Multi-Agent RAG Legal Assistant with LangGraph, FastAPI, and Streamlit (Beginner Guide)" provides a step-by-step guide on creating a legal assistant tailored for UAE Federal Law using Retrieval-Augmented Generation (RAG) with Pinecone, OpenRouter, LangGraph, FastAPI, and Streamlit. The guide covers the entire process, from setting up the infrastructure and installing dependencies to ingesting the legal document into a vector database and building a multi-agent system for verifying the assistant's responses.
The key steps include creating a project structure, setting up Pinecone and OpenRouter API keys, installing required dependencies, and ingesting the PDF document into Pinecone. The ingestion process involves loading the PDF using PyPDFLoader, chunking the text, generating embeddings using the HuggingFaceEmbeddings model, and uploading the vectors to the Pinecone vector store.
The final product is a Streamlit user interface that allows users to ask questions, which are then processed by the multi-agent system. This system retrieves relevant legal text chunks from Pinecone, drafts an answer using only the retrieved text, and verifies the response against the original legal document. If the assistant detects unsupported claims, it loops back to the synthesizer node to rewrite the answer until it achieves 100% support from the raw legal text.
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