LangChain RAG Agent Chatbot sets up a pipeline that ingests documents, converts them into embeddings with OpenAI models, and stores them in a FAISS vector database. When a user query arrives, the LangChain retrieval chain fetches relevant passages, and the agent executor orchestrates between retrieval and generation tools to produce contextually rich answers. This modular architecture supports custom prompt templates, multiple LLM providers, and configurable vector stores, making it ideal for building knowledge-driven chatbots.