Denis2054/RAG-Driven-Generative-AI
This repository provides programs to build Retrieval Augmented Generation (RAG) code for Generative AI with LlamaIndex, Deep Lake, and Pinecone leveraging the power of OpenAI and Hugging Face models for generation and evaluation.
This project provides practical guidance and code examples for building advanced Generative AI systems. It helps AI practitioners integrate their proprietary data into large language models to produce accurate, contextually relevant, and traceable outputs. You'll input your documents, images, and other data, and get out AI models that generate responses grounded in your specific information.
589 stars. No commits in the last 6 months.
Use this if you are an AI engineer, data scientist, or researcher looking to build custom generative AI solutions that minimize inaccuracies and leverage your unique datasets.
Not ideal if you are looking for a pre-built, off-the-shelf generative AI application that doesn't require custom development or data integration.
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MIT
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Last pushed
Sep 23, 2025
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