This AI PDF Chatbot agent is a customizable solution that enables users to upload and parse PDF documents, store vector embeddings in a database, and query these documents through a chat interface. It integrates with OpenAI or other LLM providers to generate answers with references to the relevant content. The system utilizes LangChain for language model orchestration and LangGraph for managing agent workflows. Its architecture includes a backend service that handles ingestion and retrieval graphs, a frontend with a Next.js UI to upload files and chat, and Supabase for vector storage. It supports real-time streaming responses and allows customization of retrievers, prompts, and storage configurations.

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.

LLM-Powered RAG System is a developer-focused framework for building retrieval-augmented generation (RAG) pipelines. It provides modules for embedding document collections, indexing via FAISS, Pinecone, or Weaviate, and retrieving relevant context at runtime. The system uses LangChain wrappers to orchestrate LLM calls, supports prompt templates, streaming responses, and multi-vector store adapters. It simplifies end-to-end RAG deployment for knowledge bases, allowing customization at each stage—from embedding model configuration to prompt design and result post-processing.