فهرست مطالب

• Preface
• 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
• 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
• 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
• 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
• Boosting RAG Performance with Expert Human Feedback
• Adaptive RAG
• Building hybrid adaptive RAG in Python
• Summary
• Questions
• References
• Further reading
• 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
• 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
• 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
• 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
• 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
• Other Books You May Enjoy
• Index