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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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Toc

Table of Contents (14) Chapters Close

Preface 1. Why Retrieval Augmented Generation? 2. RAG Embedding Vector Stores with Deep Lake and OpenAI FREE CHAPTER 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

Total session time

The following code measures the time between the beginning of the session and immediately after the Installing the environment section:

end_time = time.time() - session_start_time  # Measure response time
print(f"Session preparation time: {response_time:.2f} seconds")  # Print response time

The output can have two meanings:

  • It can measure the time we worked on the preparation of the dynamic RAG scenario with the daily dataset for the Chroma collection, querying, and summarizing by Llama.
  • It can measure the time it took to run the whole notebook without intervening at all.

In this case, the session time is the result of a full run with no human intervention:

Session preparation time: 780.35 seconds

The whole process takes less than 15 minutes, which fits the constraints of the preparation time in a dynamic RAG scenario. It leaves room for a few runs to tweak the system before the meeting. With that, we have successfully...

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