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RAG-Driven Generative AI

You're reading from   RAG-Driven Generative AI Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781836200918
Length 334 pages
Edition 1st Edition
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (14) Chapters Close

Preface 1. Why Retrieval Augmented Generation? FREE CHAPTER 2. RAG Embedding Vector Stores with Deep Lake and OpenAI 3. Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI 4. Multimodal Modular RAG for Drone Technology 5. Boosting RAG Performance with Expert Human Feedback 6. Scaling RAG Bank Customer Data with Pinecone 7. Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex 8. Dynamic RAG with Chroma and Hugging Face Llama 9. Empowering AI Models: Fine-Tuning RAG Data and Human Feedback 10. RAG for Video Stock Production with Pinecone and OpenAI 11. Other Books You May Enjoy
12. Index
Appendix

Pipeline 2: The Vector Store Administrator

The Vector Store Administrator AI agent performs the tasks we implemented in Chapter 6, Scaling RAG Bank Customer Data with Pinecone. The novelty in this section relies on the fact that all the data we upsert for RAG is AI-generated. Let’s open Pipeline_2_The_Vector_Store_Administrator.ipynb in the GitHub repository. We will build the Vector Store Administrator on top of the Generator and the Commentator AI agents in four steps, as illustrated in the following figure:

Figure 10.7: Workflow of the Vector Store Administrator from processing to querying video frame comments

  1. Processing the video comments: The Vector Store Administrator will load and prepare the comments for chunking as in the Pipeline 2: Scaling a Pinecone Index (vector store) section of Chapter 6. Since we are processing one video at a time in a pipeline, the system deletes the files processed, which keeps disk space constant. You can enhance the functionality...
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