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AI PDF Chatbot LangChain is a full-stack template for building conversational agents that can ingest and answer questions about PDF documents. The project demonstrates how to combine LangChain and LangGraph with a vector database to enable retrieval-augmented question answering over user-provided files. It includes both frontend and backend components, making it suitable as a production starting point rather than just a minimal demo. The system parses uploaded PDFs into document chunks, generates embeddings, and stores them for semantic retrieval during chat interactions. It also supports real-time streaming responses and configurable LLM providers, giving developers flexibility in deployment. The repository is designed to be customizable and extensible so teams can adapt it to their own document intelligence workflows. Overall, it functions as a practical reference architecture for building document-aware AI assistants.
Features
- PDF ingestion and embedding pipeline
- LangChain and LangGraph orchestration
- Vector database retrieval workflow
- Streaming chat response support
- Full-stack frontend and backend template
- Configurable multi-LLM integration
Project Samples
