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  • RAG-Driven Generative AI
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RAG-Driven Generative AI

RAG-Driven Generative AI

By : Denis Rothman
4.3 (18)
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RAG-Driven Generative AI

RAG-Driven Generative AI

4.3 (18)
By: Denis Rothman

Overview of this book

RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs. This 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 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. You’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 from production to customer service across any project.
Table of Contents (14 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Why Retrieval Augmented Generation?
Why Retrieval Augmented Generation?
What is RAG?
Naïve, advanced, and modular RAG configurations
RAG versus fine-tuning
The RAG ecosystem
Naïve, advanced, and modular RAG in code
Summary
Questions
References
Further reading
2
RAG Embedding Vector Stores with Deep Lake and OpenAI
RAG Embedding Vector Stores with Deep Lake and OpenAI
From raw data to embeddings in vector stores
Organizing RAG in a pipeline
A RAG-driven generative AI pipeline
Building a RAG pipeline
Evaluating the output with cosine similarity
Summary
Questions
References
Further reading
3
Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI
Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI
Why use index-based RAG?
Building a semantic search engine and generative agent for drone technology
Vector store index query engine
Tree index query engine
List index query engine
Keyword index query engine
Summary
Questions
References
Further reading
4
Multimodal Modular RAG for Drone Technology
Multimodal Modular RAG for Drone Technology
What is multimodal modular RAG?
Building a multimodal modular RAG program for drone technology
Summary
Questions
References
Further reading
5
Boosting RAG Performance with Expert Human Feedback
Boosting RAG Performance with Expert Human Feedback
Adaptive RAG
Building hybrid adaptive RAG in Python
Summary
Questions
References
Further reading
6
Scaling RAG Bank Customer Data with Pinecone
Scaling RAG Bank Customer Data with Pinecone
Scaling with Pinecone
Pipeline 1: Collecting and preparing the dataset
Pipeline 2: Scaling a Pinecone index (vector store)
Pipeline 3: RAG generative AI
Summary
Questions
References
Further reading
7
Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex
Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex
The architecture of RAG for knowledge-graph-based semantic search
Pipeline 1: Collecting and preparing the documents
Pipeline 2: Creating and populating the Deep Lake vector store
Pipeline 3: Knowledge graph index-based RAG
Summary
Questions
References
Further reading
8
Dynamic RAG with Chroma and Hugging Face Llama
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Dynamic RAG with Chroma and Hugging Face Llama
The architecture of dynamic RAG
Installing the environment
Activating session time
Downloading and preparing the dataset
Embedding and upserting the data in a Chroma collection
Querying the collection
Prompt and retrieval
RAG with Llama
Total session time
Summary
Questions
References
Further reading
9
Empowering AI Models: Fine-Tuning RAG Data and Human Feedback
Empowering AI Models: Fine-Tuning RAG Data and Human Feedback
The architecture of fine-tuning static RAG data
Installing the environment
1. Preparing the dataset for fine-tuning
2. Fine-tuning the model
3. Using the fine-tuned OpenAI model
Metrics
Summary
Questions
References
Further reading
10
RAG for Video Stock Production with Pinecone and OpenAI
RAG for Video Stock Production with Pinecone and OpenAI
The architecture of RAG for video production
The environment of the video production ecosystem
Pipeline 1: Generator and Commentator
Pipeline 2: The Vector Store Administrator
Pipeline 3: The Video Expert
Summary
Questions
References
Further reading
11
Other Books You May Enjoy
Other Books You May Enjoy
12
Index
Index
Appendix
Appendix

RAG with Llama

We initialized meta-llama/Llama-2-7b-chat-hf in the Installing the environment section. We must now create a function to configure Llama 2’s behavior:

def LLaMA2(prompt):
    sequences = pipeline(
        prompt,
        do_sample=True,
        top_k=10,
        num_return_sequences=1,
        eos_token_id=tokenizer.eos_token_id,
        max_new_tokens=100, # Control the output length more granularly
        temperature=0.5,  # Slightly higher for more diversity
        repetition_penalty=2.0,  # Adjust based on experimentation
        truncation=True
    )
    return sequences

You can tweak each parameter to your expectations:

  • prompt: The input text that the model uses to generate the output. It’s the starting point for the model’s response.
  • do_sample: A Boolean value (True or False). When set to True, it enables stochastic sampling, meaning the model will pick tokens randomly based on their probability distribution, allowing...
End of Section 9
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