overview.
Built MultiPDF Chat App, a Python application that allows users to chat with multiple PDF documents using natural language. Uses LangChain for document processing and OpenAI for context-aware response generation through a RAG pipeline.
about.
MultiPDF Chat App processes multiple PDF documents, chunks and embeds their content, and enables natural language querying through a Retrieval-Augmented Generation pipeline. Users upload PDFs and ask questions in a Streamlit chat interface, receiving accurate answers based on the document content.
technical implementation.
RAG Pipeline
Built a Retrieval-Augmented Generation pipeline using LangChain for document processing, OpenAI embeddings for vector representations, and FAISS for efficient similarity search across document chunks.
Document Processing
Implemented PDF text extraction with automatic chunking into semantically meaningful segments. Each chunk is embedded and indexed for retrieval, with overlap to maintain context across boundaries.
key features.
- Upload and process multiple PDF documents simultaneously
- Natural language querying across all loaded PDFs
- RAG pipeline for accurate, context-aware responses
- Streamlit-based interactive chat interface
- FAISS vector store for fast similarity search
- Automatic text chunking and embedding generation
screenshots.
Natural language querying across multiple PDF documents.
Upload multiple PDFs for simultaneous analysis.
challenges & solutions.
Challenge: Maintaining context across document boundaries
Solution: Implemented overlapping chunk boundaries with configurable overlap size. Combined with conversational history to maintain context across multiple retrieval rounds.
Challenge: Efficient similarity search across large document collections
Solution: Used FAISS for vector indexing with GPU acceleration support. Implemented incremental index updates when new documents are added without rebuilding the entire index.