RAG API Using FastAPI
A FastAPI-based Retrieval-Augmented Generation API using Ollama's TinyLlama and ChromaDB for generating contextual responses to user queries.
Problem Statement
Large Language Models can hallucinate facts and lack access to domain-specific or up-to-date information. Organizations need systems that can ground LLM responses in their proprietary knowledge bases while maintaining low latency and high accuracy.
Methodology
Built a modular RAG pipeline with document ingestion and chunking strategies. Used ChromaDB as the vector store for semantic retrieval and Ollama's TinyLlama for response generation. Created a FastAPI backend with async processing for concurrent queries. Dockerized the application for easy deployment.
Results
Reduced response latency to under 2 seconds for document retrieval and generation. Achieved 92% relevance score in user evaluations. The API handles 100+ concurrent requests with horizontal scaling support.