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Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition

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Unlocking Data with Generative AI and RAG, Second Edition, published by Packt

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<h1 align="center">
Unlocking Data with Generative AI and RAG, Second Edition</h1>
This is the code repository for <a href ="unlocking-data-with-generative-ai-and-rag-second-edition"> Unlocking Data with Generative AI and RAG, Second Edition</a>, published by Packt.

<h2 align="center">
Learn the fundamentals and build Agents with RAG-powered memory, GraphRAG, and intelligent recall
</h2>

Keith Bourne

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<details open>
 <summary><h2>About the book</summary>
<a href="https://www.packtpub.com/product/unity-cookbook-fifth-edition/9781805123026">
<img src="https://content.packt.com/B34736/cover_image_small.jpg" alt="Unity Cookbook, Fifth Edition" height="256px" align="right">
</a>

Developing AI agents that remember, adapt, and reason over complex knowledge is no longer a distant goal- it’s now possible with Retrieval-Augmented Generation (RAG). This second edition of the bestselling guide expands into the future of agentic systems, showing how to build intelligent, explainable, and context-aware applications powered by RAG pipelines.
You’ll explore the building blocks of agentic memory, including semantic caches, procedural learning via LangMem, and the emerging CoALA framework for cognitive agents. You’ll also learn to integrate GraphRAG with tools like Neo4j to create deeply contextualized AI responses grounded in ontology-driven data.
This book walks you through real implementations of working, episodic, semantic, and procedural memory using vector stores, prompting strategies, and feedback loops. With hands-on code and production-ready patterns, you’ll gain the skills to build advanced AI systems that don’t just generate answers- they learn, recall, and evolve.
Written by a seasoned AI educator and engineer, this book blends theoretical clarity with deep practical insight, offering both foundational knowledge and cutting-edge tools for modern AI development.
</details>
<details open>
 <summary><h2>Key Learnings</summary>
<ul>

<li>Architect graph-powered RAG agents with ontology-driven knowledge bases</li>

<li>Build semantic caches to improve response speed and reduce hallucinations</li>

<li>Code memory pipelines for working, episodic, semantic, and procedural recall</li>

<li>Implement agentic learning using LangMem and prompt optimization strategies</li>

<li>Integrate retrieval, generation, and consolidation for self-improving agents</li>

<li>Design caching and memory schemas for scalable, adaptive AI systems</li>

<li>Use Neo4j, LangChain, and vector databases in production-ready RAG pipelines</li>

</ul>

</details>

<details open>
 <summary><h2>Chapters</summary>

| Chapters | Colab | Kaggle | Gradient | Studio Lab |
| :-------- | :-------- | :------- | :-------- | :-------- |
| **Chapter 1: What is Retrieval-Augmented Generation (RAG)** | | | | |
| **Chapter 2: Code Lab - An Entire RAG Pipeline** | | | | |
| <ul><li>CHAPTER2-1_RAG_PIPELINE.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_02/CHAPTER2-1_RAG_PIPELINE.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_02/CHAPTER2-1_RAG_PIPELINE.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_02/CHAPTER2-1_RAG_PIPELINE.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_02/CHAPTER2-1_RAG_PIPELINE.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 3: Practical Applications of RAG** | | | | |
| <ul><li>CHAPTER3-1_SOURCES.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_03/CHAPTER3-1_SOURCES.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_03/CHAPTER3-1_SOURCES.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_03/CHAPTER3-1_SOURCES.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_03/CHAPTER3-1_SOURCES.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 4: Components of a RAG System** | | | | |
| <ul><li>CHAPTER4-1_RAG_PIPELINE-REVIEW.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_04/CHAPTER4-1_RAG_PIPELINE-REVIEW.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_04/CHAPTER4-1_RAG_PIPELINE-REVIEW.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_04/CHAPTER4-1_RAG_PIPELINE-REVIEW.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_04/CHAPTER4-1_RAG_PIPELINE-REVIEW.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 5: Managing Security in RAG Applications** | | | | |
| <ul><li>CHAPTER5-1_SECURING_YOUR_KEYS.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_05/CHAPTER5-1_SECURING_YOUR_KEYS.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_05/CHAPTER5-1_SECURING_YOUR_KEYS.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_05/CHAPTER5-1_SECURING_YOUR_KEYS.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_05/CHAPTER5-1_SECURING_YOUR_KEYS.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| <ul><li>CHAPTER5-2_RED_TEAM_ATTACK.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_05/CHAPTER5-2_RED_TEAM_ATTACK.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_05/CHAPTER5-2_RED_TEAM_ATTACK.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_05/CHAPTER5-2_RED_TEAM_ATTACK.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_05/CHAPTER5-2_RED_TEAM_ATTACK.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| <ul><li>CHAPTER5-3_BLUE_TEAM_DEFEND.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_05/CHAPTER5-3_BLUE_TEAM_DEFEND.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_05/CHAPTER5-3_BLUE_TEAM_DEFEND.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_05/CHAPTER5-3_BLUE_TEAM_DEFEND.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_05/CHAPTER5-3_BLUE_TEAM_DEFEND.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 6: Interfacing with RAG and Gradio** | | | | |
| <ul><li>CHAPTER6-1_GRADIO.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_06/CHAPTER6-1_GRADIO.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_06/CHAPTER6-1_GRADIO.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_06/CHAPTER6-1_GRADIO.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_06/CHAPTER6-1_GRADIO.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 7: The Key Role Vectors and Vector Stores Play in RAG** | | | | |
| <ul><li>CHAPTER7-1_COMMON_VECTORIZATION_TECHNIQUES.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_07/CHAPTER7-1_COMMON_VECTORIZATION_TECHNIQUES.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_07/CHAPTER7-1_COMMON_VECTORIZATION_TECHNIQUES.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_07/CHAPTER7-1_COMMON_VECTORIZATION_TECHNIQUES.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_07/CHAPTER7-1_COMMON_VECTORIZATION_TECHNIQUES.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 8: Similarity Searching with Vectors** | | | | |
| <ul><li>CHAPTER8-1_DISTANCEMETRICS.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_08/CHAPTER8-1_DISTANCEMETRICS.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_08/CHAPTER8-1_DISTANCEMETRICS.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_08/CHAPTER8-1_DISTANCEMETRICS.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_08/CHAPTER8-1_DISTANCEMETRICS.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| <ul><li>CHAPTER8-2_HYBRID_CUSTOM.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_08/CHAPTER8-2_HYBRID_CUSTOM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_08/CHAPTER8-2_HYBRID_CUSTOM.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_08/CHAPTER8-2_HYBRID_CUSTOM.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_08/CHAPTER8-2_HYBRID_CUSTOM.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| <ul><li>CHAPTER8-3_HYBRID-ENSEMBLE.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_08/CHAPTER8-3_HYBRID-ENSEMBLE.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_08/CHAPTER8-3_HYBRID-ENSEMBLE.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_08/CHAPTER8-3_HYBRID-ENSEMBLE.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_08/CHAPTER8-3_HYBRID-ENSEMBLE.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 9: Evaluating RAG Quantitatively and with Visualizations** | | | | |
| <ul><li>CHAPTER9-1_RAGA_EVAL.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_09/CHAPTER9-1_RAGA_EVAL.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_09/CHAPTER9-1_RAGA_EVAL.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_09/CHAPTER9-1_RAGA_EVAL.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_09/CHAPTER9-1_RAGA_EVAL.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 10: Key RAG Components in LangChain** | | | | |
| <ul><li>CHAPTER10-1_VECTORSTORES.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_10/CHAPTER10-1_VECTORSTORES.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_10/CHAPTER10-1_VECTORSTORES.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_10/CHAPTER10-1_VECTORSTORES.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_10/CHAPTER10-1_VECTORSTORES.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| <ul><li>CHAPTER10-2_RETRIEVERS.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_10/CHAPTER10-2_RETRIEVERS.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_10/CHAPTER10-2_RETRIEVERS.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_10/CHAPTER10-2_RETRIEVERS.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_10/CHAPTER10-2_RETRIEVERS.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| <ul><li>CHAPTER10-3_LLM.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_10/CHAPTER10-3_LLM.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_10/CHAPTER10-3_LLM.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_10/CHAPTER10-3_LLM.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_10/CHAPTER10-3_LLM.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 11: Using LangChain to get More from RAG** | | | | |
| <ul><li>CHAPTER11-1_DOCUMENT_LOADERS.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_11/CHAPTER11-1_DOCUMENT_LOADERS.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_11/CHAPTER11-1_DOCUMENT_LOADERS.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_11/CHAPTER11-1_DOCUMENT_LOADERS.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_11/CHAPTER11-1_DOCUMENT_LOADERS.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| <ul><li>CHAPTER11-2_TEXT_SPLITTERS.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_11/CHAPTER11-2_TEXT_SPLITTERS.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_11/CHAPTER11-2_TEXT_SPLITTERS.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_11/CHAPTER11-2_TEXT_SPLITTERS.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_11/CHAPTER11-2_TEXT_SPLITTERS.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| <ul><li>CHAPTER11-3_OUTPUT_PARSERS.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_11/CHAPTER11-3_OUTPUT_PARSERS.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_11/CHAPTER11-3_OUTPUT_PARSERS.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_11/CHAPTER11-3_OUTPUT_PARSERS.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_11/CHAPTER11-3_OUTPUT_PARSERS.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 12: Combining RAG with the Power of AI Agents and LangGraph** | | | | |
| <ul><li>CHAPTER12-1_AGENTS.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_12/CHAPTER12-1_AGENTS.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_12/CHAPTER12-1_AGENTS.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_12/CHAPTER12-1_AGENTS.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_12/CHAPTER12-1_AGENTS.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 13: Graph-Based RAG** | | | | |
| **Chapter 14: Semantic Caches** | | | | |
| <ul><li>CHAPTER14-1_GRAPHS.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_14/CHAPTER14-1_GRAPHS.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_14/CHAPTER14-1_GRAPHS.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_14/CHAPTER14-1_GRAPHS.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_14/CHAPTER14-1_GRAPHS.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 15: Agentic Memory - The Secret Power Behind the Best Agents** | | | | |
| <ul><li>CHAPTER15-1_SEMANTIC_CACHE.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_15/CHAPTER15-1_SEMANTIC_CACHE.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_15/CHAPTER15-1_SEMANTIC_CACHE.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_15/CHAPTER15-1_SEMANTIC_CACHE.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_15/CHAPTER15-1_SEMANTIC_CACHE.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 16: RAG-Based Memory Components in Code** | | | | |
| **Chapter 17: Prompt Optimization and Self-Improvement with LangMem** | | | | |
| <ul><li>CHAPTER17-1_STARTER_AGENT.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_17/CHAPTER17-1_STARTER_AGENT.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_17/CHAPTER17-1_STARTER_AGENT.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_17/CHAPTER17-1_STARTER_AGENT.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_17/CHAPTER17-1_STARTER_AGENT.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| <ul><li>CHAPTER17-2_EPISODIC.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_17/CHAPTER17-2_EPISODIC.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_17/CHAPTER17-2_EPISODIC.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_17/CHAPTER17-2_EPISODIC.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_17/CHAPTER17-2_EPISODIC.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| <ul><li>CHAPTER17-3_SEMANTIC.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_17/CHAPTER17-3_SEMANTIC.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_17/CHAPTER17-3_SEMANTIC.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_17/CHAPTER17-3_SEMANTIC.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_17/CHAPTER17-3_SEMANTIC.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 18: Additional chapter 1** | | | | |
| <ul><li>CHAPTER18-1_BASELINE_PROC.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_18/CHAPTER18-1_BASELINE_PROC.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_18/CHAPTER18-1_BASELINE_PROC.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_18/CHAPTER18-1_BASELINE_PROC.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_18/CHAPTER18-1_BASELINE_PROC.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| **Chapter 19: Additional Chapter 2** | | | | |
| <ul><li>CHAPTER19-1_FULL_COALA.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_19/CHAPTER19-1_FULL_COALA.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_19/CHAPTER19-1_FULL_COALA.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_19/CHAPTER19-1_FULL_COALA.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_19/CHAPTER19-1_FULL_COALA.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |
| <ul><li>CHAPTER19_BONUS_ALGO_SELECTION.ipynb</li></ul> | <a href="https://colab.research.google.com/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_19/CHAPTER19_BONUS_ALGO_SELECTION.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://www.kaggle.com/kernels/welcome?src=https://github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_19/CHAPTER19_BONUS_ALGO_SELECTION.ipynb"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | <a href="https://gradient.run/notebook/github.com/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_19/CHAPTER19_BONUS_ALGO_SELECTION.ipynb"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Open In Gradient"></a> | <a href="https://studiolab.sagemaker.aws/import/github/PacktPublishing/Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition/blob/main/CHAPTER_19/CHAPTER19_BONUS_ALGO_SELECTION.ipynb"><img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In Studio Lab"></a> |

</details>

<details open>
 <summary><h2>Requirements for this book</summary>

## Software and Hardware Requirements

### Prerequisites
To get the most out of this book, you should have:
- A basic understanding of **Python programming**
- Familiarity with **machine learning concepts**
- Basic knowledge of **natural language processing (NLP)** and **large language models (LLMs)** (beneficial but not mandatory)
- Experience with **data processing** and **database management**
- Comfort working with **APIs** and **AI development environments**
- Prior experience using **Jupyter Notebook**

---

### Software Requirements
The following software and tools are used throughout the book and are supported on **Windows, macOS, and Linux**:

| Software / Tool     | Version / Requirement |
|---------------------|-----------------------|
| Python              | Python 3.x            |
| LangChain           | Latest stable version |
| Jupyter Notebook    | Required              |
| OpenAI API          | API key required      |

Additional API keys (such as **Tavily** or **Together AI**) may be required in certain chapters. Instructions for setting these up are provided where needed.

---

### Hardware Requirements
- **Minimum RAM:** 8 GB (recommended)
- A machine capable of running a **Python development environment with Jupyter Notebook**
- Internet access for API-based examples and model interactions

---

### Notes for Digital Readers
If you are using the digital version of this book, it is recommended to:
- **Type the code manually**, or
- **Access the source code from the book’s GitHub repository** (link provided in the next section)

This approach helps avoid errors that may occur when copying and pasting code.

</details>

<details>
 <summary><h2>Get to know Author</h2></summary>

_Keith Bourne_ Keith Bourne is a senior Generative AI data scientist at Johnson & Johnson. He has over a decade of experience in machine learning and AI working across diverse projects in companies that range in size from start-ups to Fortune 500 companies. With an MBA from Babson College and a master's in applied data science from the University of Michigan, he has developed several sophisticated modular Generative AI platforms from the ground up, using numerous advanced techniques, including RAG, AI agents, and foundational model fine-tuning. Keith seeks to share his knowledge with a broader audience, aiming to demystify the complexities of RAG for organizations looking to leverage this promising technology.

</details>
<details>
 <summary><h2>Other Related Books</h2></summary>
<ul>

<li><a href="https://www.packtpub.com/en-us/product/building-ai-agents-with-llms-rag-and-knowledge-graphs-first-edition/9781835087060">Building AI Agents with LLMs, RAG, and Knowledge Graphs, First Edition</a></li>

<li><a href="https://www.packtpub.com/en-us/product/llms-in-enterprise-first-edition/9781836203070">LLMs in Enterprise, First Edition</a></li>

</ul>

</details>

Unlocking-Data-with-Generative-AI-and-RAG-Second-Edition

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