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A sophisticated RAG (Retrieval-Augmented Generation) Telegram bot that transforms articles and documents into interactive knowledge bases. Upload PDFs/URLs and get AI-powered answers with source citations.
AI-First Full-Stack Engineer building production LLM systems. 3 years shipping RAG architecture, multi-model orchestration, real-time AI. Open to remote roles.
Transform your legal document review from days to minutes with LegalRAG. Our AI assistant understands complex legal questions, searches thousands of documents with semantic precision, and delivers professional answers with source citations. Experience faster case prep and thorough due diligence.
The project is designed as a practical introduction to RAG systems, showing how retrieval and generation can be orchestrated to answer user queries using only relevant document chunks. It is well-suited for learners and portfolio use, highlighting modern AI patterns like embeddings, vector search, and LLM integration.
Intelligent RAG-based AI Agent. Built with Llama-3 (Groq), Redis Vector Search for context retrieval, BullMQ for asynchronous job processing, and Socket.io for real-time streaming.
A Green AI Knowledge Governance Engine for Enterprise RAG. Features FinOps-driven ingestion, PII redaction middleware, and strict lifecycle management to ensure zero-trust, cost-optimized AI. Built with FastAPI, React, Qdrant, and GPT-4o.
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