RAG and Generative AI. Creating Your Own RAG Pipelines with LlamaIndex, Deep Lake, and Pinecone

The book covers techniques for creating efficient large language models, computer vision systems, and generative AI that demonstrate high performance at relatively low cost. It provides a detailed examination of RAG technology, as well as approaches to designing and managing multimodal AI pipelines. By linking output to source documents, RAG enhances the accuracy and contextual relevance of results, offering a dynamic approach to managing large volumes of information.
Learn how to build RAG infrastructure while understanding vector stores, fragmentation, indexing, and ranking. Familiarize yourself with performance optimization methods and techniques for deeper data exploration, including the use of adaptive RAG and human feedback for refining searches, fine-tuning RAG, implementing dynamic RAG for real-time decision support, and visualizing complex data with knowledge graphs. You'll also see how to combine frameworks such as LlamaIndex and Deep Lake, vector databases like Pinecone and Chroma, and models offered by companies like Hugging Face and OpenAI in practice. Acquire the skills to implement intelligent solutions that will enhance your competitiveness across various domains: from production to customer service in any project.