🧠 Legal Document Q&A System (RAG with Cohere)

This project is a Retrieval-Augmented Generation (RAG) based Legal Document Question Answering System built using:

• 🦜 LangChain
• 📄 PDF loaders
• ⚡ Cohere Embeddings & LLM
• 📚 Chroma vector store

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📌 Features

• ✅ Upload legal PDF documents
• ✅ Chunk and embed text using Cohere's embed-english-v3.0
• ✅ Store document vectors in a local Chroma DB
• ✅ Ask natural language questions about the PDF
• ✅ Get AI-generated answers with source references
• ✅ Streamlit UI for easy interaction

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📷 Demo

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🏗️ Tech Stack

Tool	Purpose
LangChain	Orchestrate RAG pipeline
Cohere	Embeddings + LLM for Q&A
Chroma	Vector DB for semantic search
Streamlit	Web UI
dotenv	Local secret handling
PyPDF2	PDF parsing

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