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mayooear/ai-pdf-chatbot-langchain

A customizable AI chatbot template built with LangChain and LangGraph, enabling users to ingest PDF documents, store embeddings, and answer queries using an LLM.

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 responses from server to 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 provides a robust, customizable template for building an AI chatbot agent capable of processing PDF documents. Leveraging LangChain and LangGraph, it orchestrates complex workflows for document ingestion, vector embedding storage (e.g., Supabase), and intelligent retrieval-augmented generation using large language models (e.g., OpenAI). The template features a Next.js frontend for a seamless user experience, offering real-time chat and document upload functionalities. It serves as an excellent starting point for developers looking to create their own RAG-based AI applications.