mayooear/ai-pdf-chatbot-langchainArchived
Ai Pdf Chatbot Langchain
This project is a retrieval-augmented generation application designed to answer questions from uploaded PDF documents. It functions as a document question-answering engine and a streaming AI chat interface that provides responses backed by specific source citations.
The system utilizes a state-machine workflow orchestrator to coordinate multi-step document ingestion and retrieval pipelines. This orchestration allows for step-by-step visualization and debugging of the process as documents are parsed and processed.
The application manages the full lifecycle of document interaction, including PDF-to-text chunking, vector-based embedding for semantic search, and session-based message history to track individual conversation threads. It employs server-sent events to stream partial tokens from the language model to the client in real time.
Features
- PDF Chatbots - Functions as a conversational AI application specifically designed to interact with and answer questions from uploaded PDF documents.
- LangGraph Orchestrations - Uses LangGraph to manage the complex state transitions involved in PDF ingestion and retrieval workflows.
- Retrieval-Augmented Generation - Implements a retrieval-augmented generation pipeline to ground AI answers in uploaded PDF content with source citations.
- RAG Document Retrieval - Retrieves relevant snippets from local PDFs to provide grounded, cited context for AI responses.
- Text Chunks - Divides PDF text into smaller, overlapping segments to ensure retrieved context fits within the LLM window.
- PDF Document Analyzers - Combines text extraction and vector indexing to analyze and query information within uploaded PDF files.
- Durable Multi-Step Orchestrators - Coordinates ingestion and retrieval tasks through a durable, multi-step pipeline using a state machine.
- Document Question Answering - Enables conversational interaction with uploaded PDF documents to answer specific questions backed by source text.
- Document Embedding Stores - Stores numerical vector embeddings of PDF text chunks to enable efficient semantic similarity searching.
- Graph-Based Workflow Orchestrators - Employs a graph-based state machine to coordinate the multi-step flow of document ingestion and retrieval.
- File Chat Interfaces - Provides a chat interface that allows users to upload PDF files for AI-driven questioning and analysis.
- Streaming Chat Responses - Implements a streaming mechanism to send AI-generated text incrementally to the chat interface.
- Visual Workflow Debuggers - Provides a state-machine orchestrator with step-by-step visualization for debugging document processing pipelines.
- AI Session History - Persists conversation transcripts and dialogue state to maintain context across multiple interaction turns.
- Chat Session Managers - Manages the lifecycle of individual chat sessions to preserve conversational state for different users.
- Token Streaming - Streams AI-generated tokens to the user interface in real time for a responsive chat experience.
- Server-Sent Events - Uses server-sent events to push partial AI tokens from the backend to the client.
- Source Attribution Interfaces - Displays the specific document chunks used to generate AI responses, providing transparent source citations.
Star history
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