LLM-powered Retrieval-Augmented Generation (RAG) System
Enterprise-scale document retrieval and insight generation system deployed on Azure
Key Features
- Scalable indexing of scientific documents
- Slack integration for seamless access
- GPT-based contextual response generation
- Enterprise-grade security and compliance
Technical Stack
- Azure Web Apps for deployment
- Pinecone vector stores for document indexing
- Langchain for LLM integration
- Slack API for user interaction
Impact & Scale
- Serving 100+ scientific users across research teams
- Processing tens of thousands of scientific documents
- Significant reduction in information retrieval time
- Enhanced decision-making through contextual insights
This enterprise-level RAG system demonstrates the successful implementation of LLM technology in a production environment. By combining Azure's cloud infrastructure with advanced language models and vector search capabilities, the system provides researchers with instant access to relevant information from vast document repositories.
The integration with Slack makes the system easily accessible within existing workflows, while the use of Pinecone vector stores ensures efficient and accurate document retrieval. The Langchain-powered GPT model generates contextually accurate responses, making complex scientific information more accessible and actionable.
LLM-powered RAG System Demo
Interactive demonstration of scientific document retrieval and response generation