Designing and managing controlled, reliable multimodal generative AI pipelines is complex. RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and Generative AI systems that will balance performance and costs.

From foundational concepts to complex implementations, this book offers a detailed exploration of how RAG can control and enhance AI systems by tracing each output to its source document. RAG’s traceable process allows human feedback for continual improvements, minimizing inaccuracies, hallucinations, and bias. This AI book shows you how to build a RAG framework from scratch, providing practical knowledge on vector stores, chunking, indexing, and ranking. You’ll discover techniques to optimize performance and costs, improving model accuracy by combining with human feedback, managing costs with when to fine-tune, and improving accuracy and retrieval speed by combining with embedded-indexed knowledge graphs.

Experience a blend of theory and practice using frameworks like LlamaIndex, LangChain, Pinecone, and Deep Lake and models from Hugging Face, OpenAI, and Google Vertex AI.

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.

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Spis treści

RAG-Driven Generative AI. Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone eBook -- spis treści

• 1. Why Retrieval Augmented Generation?
• 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