🤖Semantic Search & RAG with LangChain: A Practical Guide

This guide outlines building a semantic search system using LangChain for corporate documents. It explains how to use embeddings and Retrieval-Augmented Generation (RAG) to find information by meaning, not just keywords, using LLMs from OpenAI, Groq, or DeepSeek. It demonstrates the setup and selection of vector databases like FAISS and Pinecone.

The guide includes a Streamlit app for user interaction and details of deploying the system on AWS, including automated document indexing and user authentication via AWS Cognito. The purpose is to show how to create a scalable, secure, and ethically compliant enterprise search solution.

🔍 Module 3: Semantic Mastery with Embeddings & RAG