Chat With PDF
Instant natural language document intelligence powered by RAG and vector embeddings.
Context & Problem
Extracting precise insights, citations, and summaries from lengthy PDFs (academic papers, legal documents, technical specs) requires manual searching, reading through hundreds of pages, and cross-referencing disjointed sections.
Solution & Approach
Built an end-to-end Retrieval-Augmented Generation (RAG) system using LangChain, Google GenAI embeddings, and vector storage. Users can upload any PDF and ask natural language questions to receive grounded responses with exact context extraction.
Key Features & Highlights
- Smart PDF text extraction and semantic chunking with optimal overlap management.
- High-dimensional vector embedding generation powered by Google GenAI models.
- Context-restricted prompt assembly preventing hallucinations and ensuring grounded answers.
- Interactive Streamlit web interface for effortless drag-and-drop document exploration.
Results & Impact
Substantially reduced document review times for complex technical papers with sub-second answer retrieval accuracy.