Feat/langgraph rag agent

[mayooear]

This monorepo is a customizable template example of an AI agent chatbot that ingests PDF documents, stores embeddings in a vector database (Supabase), and then answers user queries using OpenAI (or another LLM provider) utilising LangChain and LangGraph as orchestration frameworks.

This template is also an example template associated with the book Learning LangChain (O'Reilly): Building AI and LLM applications with LangChain and LangGraph.

Features

• Document Ingestion Graph: Upload and parse PDFs into Document objects, then store vector embeddings into a vector database (we use Supabase in this example).
• Retrieval Graph: Handle user questions, decide whether to retrieve documents or give a direct answer, then generate concise responses with references to the retrieved documents.
• Streaming Responses: Real-time streaming of partial responses from the server to the client UI.
• LangGraph Integration: Built using LangGraph’s state machine approach to orchestrate ingestion and retrieval, visualise your agentic workflow, and debug each step of the graph.
• Next.js Frontend: Allows file uploads, real-time chat, and easy extension with React components and Tailwind.