{"data":{"slug":"denis2054-rag-driven-generative-ai","name":"RAG-Driven-Generative-AI","tagline":"Builds Retrieval Augmented Generation AI using LlamaIndex with support from Deep Lake and Pinecone","github_url":"https://github.com/Denis2054/RAG-Driven-Generative-AI","owner":"Denis2054","repo":"RAG-Driven-Generative-AI","owner_avatar_url":"https://avatars.githubusercontent.com/u/30811222?v=4","primary_language":"Jupyter Notebook","stars":621,"forks":215,"topics":["advanced-rag","chroma","chromadb","embedding-models","fine-tuning","gpt-4o-mini","gpt4-omni","grok","huggingface","indexing-querying","llama","llama-index","multimodal","openai-api","pinecone","rag","scaling","vision-transformer","xai-grok"],"archived":false,"github_pushed_at":"2025-09-23T15:31:25+00:00","maintenance_label":"Slowing","stars_delta_30d":5,"url":"https://www.graphcanon.com/tools/denis2054-rag-driven-generative-ai","markdown_url":"https://www.graphcanon.com/tools/denis2054-rag-driven-generative-ai.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/denis2054-rag-driven-generative-ai","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=denis2054-rag-driven-generative-ai","description":"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.","homepage_url":null,"license":"MIT","open_issues":0,"watchers":9,"ai_summary":"The repository facilitates the development of Generative AI models enhanced by Retrieval Augmentation (RAG) through an integration of tools like LlamaIndex, Deep Lake, and Pinecone. It also leverages OpenAI and Hugging Face for generation and evaluation.","readme_excerpt":"<h1 align=\"center\">\nRAG-driven Generative AI, First Edition</h1>\n\n<p align=\"center\">\n    This is the code repository for <a href =\"https://www.packtpub.com/en-us/product/rag-driven-generative-ai-9781836200918\"> RAG Driven GenAI, First Edition</a>, published by Packt.\n</p>\n    \n<p align=\"center\">Last updated: September 23, 2025.</p>\n\n<p align=\"center\">\n  See the <a href=\"https://github.com/Denis2054/RAG-Driven-Generative-AI/blob/main/CHANGELOG.md\">CHANGELOG.md</a> for details.\n</p>\n\n\n\n\n<h2 align=\"center\">\nBuild custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone\n</h2>\n<p align=\"center\">\nDenis Rothman</p>\n\n<p align=\"center\">\n   <a href=\"https://packt.link/genai\" alt=\"Discord\" title=\"Learn more on the Discord server\"><img width=\"32px\" src=\"https://cliply.co/wp-content/uploads/2021/08/372108630_DISCORD_LOGO_400.gif\"/></a>\n  &#8287;&#8287;&#8287;&#8287;&#8287;\n  <a href=\"https://packt.link/free-ebook/9781836200918\"><img width=\"32px\" alt=\"Free PDF\" title=\"Free PDF\" src=\"https://cdn-icons-png.flaticon.com/512/4726/4726010.png\"/></a>\n &#8287;&#8287;&#8287;&#8287;&#8287;\n  <a href=\"https://packt.link/gbp/9781836200918\"><img width=\"32px\" alt=\"Graphic Bundle\" title=\"Graphic Bundle\" src=\"https://cdn-icons-png.flaticon.com/512/2659/2659360.png\"/></a>\n  &#8287;&#8287;&#8287;&#8287;&#8287;\n   <a href=\"https://www.amazon.com/RAG-Driven-Generative-retrieval-generation-LlamaIndex-ebook/dp/B0CW18RC6F\"><img width=\"32px\" alt=\"Amazon\" title=\"Get your copy\" src=\"https://cdn-icons-png.flaticon.com/512/15466/15466027.png\"/></a>\n  &#8287;&#8287;&#8287;&#8287;&#8287;\n</p>\n<details open> \n  <summary><h2>About the book</summary>\n<a href=\"https://www.packtpub.com/en-us/product/rag-driven-generative-ai-9781836200918\">\n<img src=\"https://content.packt.com/B31169/cover_image_small.jpg\" alt=\"RAG for GenAI, First Edition\" height=\"256px\" align=\"right\">\n</a>\n\nRAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs.\nThis book offers a detailed exploration of RAG and how to design, manage, and control multimodal AI pipelines. By connecting outputs to traceable source documents, RAG improves output accuracy and contextual relevance, offering a dynamic approach to managing large volumes of information. This AI book also shows you how to build a RAG framework, providing practical knowledge on vector stores, chunking, indexing, and ranking. You'll discover techniques to optimize your project's performance and better understand your data, including using adaptive RAG and human feedback to refine retrieval accuracy, balancing RAG with fine-tuning, implementing dynamic RAG to enhance real-time decision-making, and visualizing complex data with knowledge graphs.\nYou'll be exposed to a hands-on blend of frameworks like LlamaIndex and Deep Lake, vector databases such as Pinecone and Chroma, and models from Hugging Face and OpenAI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields ranging from production to customer service across any project.\n\n</details>\n<details open> \n  <summary><h2>Key Learnings</summary>\n<ul>\n\n<li>Scale RAG pipelines to handle large datasets efficiently</li>\n<li>Employ techniques that minimize hallucinations and ensure accurate responses</li>\n<li>Implement indexing techniques to improve AI accuracy with traceable and transparent outputs</li>\n<li>Customize and scale RAG-driven generative AI systems across domains</li>\n<li>Find out how to use Deep Lake and Pinecone for efficient and fast data retrieval</li>\n<li>Control and build robust generative AI systems grounded in real-world data</li>\n<li>Combine text and image data for richer, more informative AI responses</li>\n</ul>\n\n  </details>\n\n<details open> \n  <summary><h2>Chapters</summary>\n\n**This repo is continually updated and upgraded.**    \n📝 For details on updates and improvements, see the [Changel","github_created_at":"2024-04-19T17:40:16+00:00","created_at":"2026-07-11T11:40:48.638305+00:00","updated_at":"2026-08-24T06:01:27.672244+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"}],"tags":[{"slug":"advanced-rag","name":"advanced-rag"},{"slug":"chroma","name":"chroma"},{"slug":"embedding-models","name":"embedding-models"},{"slug":"fine-tuning","name":"fine-tuning"},{"slug":"huggingface","name":"huggingface"},{"slug":"indexing-querying","name":"indexing-querying"},{"slug":"llama-index","name":"llama-index"},{"slug":"multimodal","name":"multimodal"}],"trust":{"provenance":{"is_fork":false,"github_id":789098173,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-24T06:01:26.970Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":334,"last_release_at":null,"stars_delta_30d":5,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:40:49.948Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-24T06:01:27.405Z"},"languages":{"value":["jupyter notebook"],"source":"github.language","observed_at":"2026-08-24T06:01:27.405Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-08-24T06:01:27.405Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need advanced RAG capabilities with LlamaIndex's specific toolset","For projects that require tight integration of vector databases like Deep Lake and Pinecone"],"when_not_to_use":["If your project strictly requires customization beyond the offered models from OpenAI and Hugging Face","When you prefer alternative database integrations not including Deep Lake or Pinecone"],"source":"enrich:decision_facts","observed_at":"2026-07-16T20:32:02.062Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"RAG-Driven-Generative-AI uses LlamaIndex with Deep Lake and Pinecone for retrieval augmentation, integrating OpenAI and Hugging Face models."}]}}