Overview
A sophisticated document-based Q&A system that uses Retrieval-Augmented Generation to provide accurate, contextual responses with source citations. Implements semantic search across multiple documents.
Technology Stack
Pinecone
OpenAI
LangChain
FastAPI
React
Key Features
▸Multi-document ingestion and processing
▸Semantic search using vector embeddings
▸Contextual response generation with GPT-4
▸Source citation and highlighting
▸Conversation memory and context retention
▸Confidence scoring for responses
▸Multi-language document support
▸Document summarization capabilities
Desktop Screenshots
Mobile Screenshots
Project Structure
Learning & Development
Key learning outcomes and technical insights gained from this project:
▸Vector database design and optimization strategies
▸Retrieval-Augmented Generation (RAG) implementation patterns
▸Embedding generation and similarity search algorithms
▸Document chunking and preprocessing techniques
▸Context window management for large documents
Implementation Status
Coming soon...