This project demonstrates the development of an advanced AI-powered document intelligence system using Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs).
The system is designed to allow users to upload documents (PDFs, reports, research papers) and interact with them through a conversational AI interface. It enables accurate question-answering by retrieving relevant information from the documents and generating context-aware responses.
🔹 Key Features
- Intelligent document understanding using LLMs
- Retrieval-Augmented Generation (RAG) pipeline
- Support for PDFs, Word files, and text data
- Semantic search using vector databases
- Context-aware question answering
- Scalable and efficient architecture
🔹 Technologies Used
- Python
- OpenAI / LLM APIs
- LangChain
- Vector Databases (FAISS / Pinecone)
- NLP & Transformers
🔹 My Role
- Designed end-to-end system architecture
- Implemented RAG pipeline for document retrieval
- Developed backend logic for query processing
- Optimized prompts for accurate responses
- Ensured scalability and performance
🔹 Use Cases
- Business knowledge assistants
- Research paper analysis
- Legal and medical document Q&A
- Internal company knowledge systems
🔹 Outcome / Impact
- Improved information retrieval accuracy
- Reduced manual document review time
- Enabled natural interaction with complex data