rag and RAG-Driven-Generative-AI
These are ecosystem siblings—both are reference implementations of RAG pipelines that demonstrate how to integrate different vector databases (Deep Lake, Pinecone) and LLM frameworks (LlamaIndex, OpenAI, Hugging Face) into production-ready systems, serving as educational blueprints rather than competing tools.
About rag
NVIDIA-AI-Blueprints/rag
This NVIDIA RAG blueprint serves as a reference solution for a foundational Retrieval Augmented Generation (RAG) pipeline.
This project helps organizations build custom AI chatbots that can answer questions using their own internal documents, data, and information. You provide it with a variety of enterprise documents (text, tables, charts, audio) and it delivers accurate, fact-based answers grounded in that data. It's designed for anyone needing to create a reliable question-answering system for their business, ensuring responses are consistent with company knowledge.
About RAG-Driven-Generative-AI
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.
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