A customizable AI chatbot agent that ingests PDF documents, stores embeddings in a vector database, and answers user queries using LangChain and LangGraph.
GPT4 PDF Chatbot LangChain is an open-source AI chatbot agent template designed to answer questions from PDF documents. It processes uploaded PDFs by extracting text, generating vector embeddings stored in a database like Supabase, and using LangChain and LangGraph to orchestrate retrieval and response generation with LLMs like OpenAI. It solves the problem of making static PDF content interactively queryable through a conversational interface.
Developers and engineers building AI-powered document question-answering systems, particularly those learning or implementing LangChain and LangGraph for retrieval-augmented generation (RAG) applications.
It provides a fully functional, customizable template with a modern stack (Next.js, Supabase) and graph-based orchestration, making it easier to build and deploy production-ready PDF chatbots without starting from scratch. The integration with LangGraph offers visual workflow debugging and a state-machine approach for complex agentic behaviors.
AI PDF chatbot agent built with LangChain & LangGraph
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Uses LangGraph's state machine approach to modularize ingestion and retrieval workflows, allowing visual debugging and customization, as detailed in the architecture overview and LangGraph integration.
Integrates Server-Sent Events in the Next.js frontend to stream LLM responses live, providing a modern chat experience similar to commercial AI tools, with references to streaming in the features.
Serves as a practical example for the 'Learning LangChain' book, offering hands-on insights into building AI agents with LangChain and LangGraph, explicitly mentioned in the README's purpose.
Includes a pre-built Next.js frontend and LangGraph backend, reducing initial development time for deploying a customizable PDF chatbot, as shown in the local development setup.
The README explicitly states it's not actively maintained, meaning users must handle bug fixes, updates, and compatibility issues independently, which risks stability for production use.
Requires configuring multiple external services like Supabase with specific tables, OpenAI API keys, and optional LangChain keys, creating a barrier for quick prototyping, as outlined in the prerequisites.
While customizable, documentation for advanced changes like swapping vector databases or modifying prompts is minimal, forcing developers to rely on code exploration and trial-and-error.