mayooear/gpt4-pdf-chatbot-langchainArchived
Gpt4 Pdf Chatbot Langchain
This project is a framework for building custom AI chatbots capable of PDF document analysis. It implements Retrieval Augmented Generation to connect a large language model to private document data.
The system utilizes graph-based agent orchestration to control conversation flow and decision logic. It maintains context across interactions through thread-based state management and delivers AI responses to the user interface via real-time streaming.
The project covers PDF document ingestion through chunk-based processing and vector-store retrieval. It includes mechanisms for query-based data retrieval to extract relevant excerpts from ingested documents to ground the model's answers.
Features
- Knowledge Base Retrieval - Implements a retrieval system that grounds AI responses in uploaded document data via a knowledge base.
- Graph-Based State Orchestrations - Uses a stateful directed graph to orchestrate agent decision logic and conversation flow.
- Conversation State Management - Tracks multi-turn interaction history and maintains state within unique session threads.
- Conversation State Persistence - Provides mechanisms to persist conversation history and context across multiple user sessions.
- Document Chunking Strategies - Employs specific segmentation strategies to split PDF documents into chunks that fit within LLM context windows.
- PDF Knowledge Extraction - Extracts relevant excerpts from indexed PDF documents to provide grounding context for AI responses.
- General PDF Analysis - Enables querying and extraction of specific information from uploaded PDF files using an LLM.
- RAG Frameworks - Implements a RAG framework to connect large language models to private document data.
- Vector Retrieval Systems - Indexes document embeddings in a vector store to perform semantic searches for AI context.
- Vector Indexing Pipelines - Converts PDF files into a searchable vector database to enable efficient information retrieval.
- AI Chatbots - Provides a framework for building custom conversational AI assistants with complex workflow orchestration.
- Real-Time Conversational AI Frameworks - Developed with a focus on low-latency delivery of AI-generated responses to the user.
- RAG Query Analyzers - Analyzes user queries to determine whether to retrieve document chunks or generate a direct answer.
- Streaming Response Architectures - Utilizes an architectural pattern to stream partial AI tokens to the UI in real time.
- Response Streaming Interfaces - Implements a user interface capable of handling real-time data delivery of AI responses from the backend.
- Document & Data Assistants - LangChain-based chatbot for large PDF documentation.
- Bots - Chatbot for querying large PDF documents.
- Document Tools - Chatbot for querying multiple large PDF documents.
