Ask My Doc
Production RAG system for natural language Q&A over documents with source citations and page references.
Completed
Yes
Duration
2 months
Role
AI Engineer
Team
Solo project
Problem
Users can't quickly find answers buried in long PDFs and documents. Manual search is slow and misses context.
Solution
Built a RAG pipeline with intelligent chunking, FAISS vector search, and LLM-powered answer generation. Runs fully local for data privacy.
Impact
Instant answers with source citations and page references. Supports PDF, TXT, and DOCX formats with zero cloud dependency option.
About This Project
Ask My Doc is a Retrieval-Augmented Generation (RAG) system that enables natural language conversations with your documents. Upload PDFs, text files, or other documents and ask questions in plain English.
The system chunks documents intelligently, creates vector embeddings, and stores them for semantic search. When you ask a question, it retrieves the most relevant passages and generates accurate, contextual answers.
Built with privacy in mind, the system can run entirely on local hardware, ensuring sensitive documents never leave your environment.
Key Features
Technical capabilities and highlights
Natural language Q&A over documents
RAG architecture with vector search
Multi-format document support (PDF, TXT, DOCX)
Intelligent document chunking and indexing
Conversation history with follow-up support
Source citation with page references
Interested in this project?
Let's discuss how similar solutions can be built for your needs.