bhaskatripathi/pdfGPT
PdfGPT
pdfGPT is a retrieval augmented generation application and chatbot designed to analyze PDF documents. It functions as a document analyzer and vector search interface, using large language models to answer questions grounded in the content of uploaded files.
The system implements a pipeline that extracts text from PDFs, splits content into overlapping segments, and uses vector-based semantic search to retrieve relevant context. This process allows the application to provide responses with verifiable source citations, including page number references to the original document.
The project also includes session-based conversation memory and chat history tracking to maintain context across multi-turn dialogues.
Features
- RAG Pipelines - Implements a full RAG pipeline that retrieves relevant document snippets to ground language model responses.
- Conversational Document Querying - Provides an interactive chatbot interface for querying indexed PDF documents with citations.
- Retrieval Augmented Generation - Grounds language model responses in external PDF data by indexing information for context-aware retrieval.
- General PDF Analysis - Extracts and queries diverse information from PDF documents using language models.
- Text Chunks - Divides large PDF documents into overlapping segments to optimize content for LLM context windows.
- PDF Chatbots - Provides a conversational interface that uses LLMs to answer questions based on uploaded PDF contents.
- PDF Content Querying - Provides the ability to extract text from PDF files to answer questions using a language model.
- PDF Document Analyzers - Extracts text from PDF files and performs semantic search to locate specific information.
- Semantic Search - Identifies relevant sections within large PDFs using vector search to capture conceptual meaning.
- Source Citations - Generates responses with verifiable page number references to the original PDF source.
- PDF Text Extraction - Parses binary PDF files into raw text strings to prepare data for vectorization.
- Semantic Search - Utilizes vector encoding and neighbor search to find the most relevant text chunks for AI responses.
- Vector Similarity Search - Uses numerical embeddings and similarity search to retrieve the most relevant context chunks from documents.
- Conversation Context Tracking - Implements mechanisms to store and retrieve historical query-response pairs for continuity in multi-turn dialogues.
- Conversation Memory Managers - Manages sequences of interaction history to maintain context across multi-turn generative sessions.
- Vector Retrieval Systems - Indexes and searches document embeddings to provide relevant context for generative AI responses.
- Page Index Mapping - Tracks original page indices of text chunks to enable verifiable source citations in generated outputs.
- Document & Data Assistants - Open-source solution for chatting with PDF documents.
- Document Q&A - Open-source solution for chatting with PDF documents.
- Natural Language Processing - Listed in the “Natural Language Processing” section of the FunNLP awesome list.
- Web Applications - Tool for interactive conversation with PDF document contents.
- Document Tools - Tool for conversational interaction with PDF content.
Star history
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