Implementing Agentic RAG Using Claude 3.5 Sonnet, LlamaIndex, and Milvus

The concept of Compound AI Systems is introduced by Bill Zhang, Director of Engineering at Zilliz, in his talk on the evolution of LLM app architectures. This modular approach integrates multiple components to handle various tasks rather than relying on a single AI model, delivering more tailored and efficient results. The architecture development of LLM applications is discussed, along with the concepts of Retrieval Augmented Generation (RAG) and Agentic RAG. Challenges and benefits of these systems are also highlighted. An example of building an Agentic RAG using Claude 3.4 Sonnet, LlamaIndex, and Milvus vector database is provided in a step-by-step manner. The complete architecture of the agentic RAG built with Milvus, LlamaIndex, and Cluade 3.5 Sonnet is also presented.