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mayooear/ai-pdf-chatbot-langchain

A customizable AI chatbot template that ingests PDF documents, stores embeddings, and answers user queries using LLMs, orchestrated by LangChain and LangGraph.

Core Features

Document Ingestion Graph for parsing PDFs and storing vector embeddings.

Retrieval Graph for intelligent question answering with document references.

Real-time streaming of partial responses to the client UI.

LangGraph integration for orchestrating agentic workflows and debugging.

Next.js frontend for file uploads, real-time chat, and easy extensibility.

Detailed Introduction

This project serves as a customizable template for building an AI chatbot agent capable of processing PDF documents. It leverages LangChain and LangGraph to orchestrate the ingestion of PDFs, store their embeddings in a vector database like Supabase, and then utilize large language models (LLMs) such as OpenAI to answer user queries with relevant document references. The monorepo structure, coupled with a Next.js frontend, provides a robust foundation for developing sophisticated RAG (Retrieval Augmented Generation) applications, making it an ideal learning resource and starting point for AI-powered document interaction.