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Preface
Who this book is for
What this book covers
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1 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
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
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
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
Adaptive RAG
Building hybrid adaptive RAG in Python
Summary
Questions
References
Further reading
6 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
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
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
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
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
Appendix
Chapter 1, Why Retrieval Augmented Generation?
Chapter 2, RAG Embedding Vector Stores with Deep Lake and OpenAI
Chapter 3, Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI
Chapter 4, Multimodal Modular RAG for Drone Technology
Chapter 5, Boosting RAG Performance with Expert Human Feedback
Chapter 6, Scaling RAG Bank Customer Data with Pinecone
Chapter 7, Building Scalable Knowledge-Graph-based RAG with Wikipedia API and LlamaIndex
Chapter 8, Dynamic RAG with Chroma and Hugging Face Llama
Chapter 9, Empowering AI Models: Fine-Tuning RAG Data and Human Feedback
Chapter 10, RAG for Video Stock Production with Pinecone and OpenAI
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Index

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RAG-Driven Generative AI