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.
Build custom systems that enhance AI generation using data retrieval tools like LlamaIndex and Pinecone.
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Free · no card · unsubscribe anytimeThis 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.
RAG-Driven-Generative-AI has 616 stars on GitHub. It has been forked 213 times. RAG-Driven-Generative-AI is written mainly in Jupyter Notebook. It has been in active development since 2024. RAG-Driven-Generative-AI is available under the MIT license. Its main topics are advanced-rag, chroma, chromadb, embedding-models.
What is 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.
Is RAG-Driven-Generative-AI open source?
RAG-Driven-Generative-AI is an open-source project. It is released under the MIT license.
Is RAG-Driven-Generative-AI free?
Yes. RAG-Driven-Generative-AI is free and open source — you can use, modify and self-host it.
What license does RAG-Driven-Generative-AI use?
RAG-Driven-Generative-AI is available under the MIT license.
What language is RAG-Driven-Generative-AI written in?
RAG-Driven-Generative-AI is written mainly in Jupyter Notebook.
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