RAG is dead, long live agentic retrieval

Retrieval-Augmented Generation (RAG) has evolved significantly from basic chunk retrieval to sophisticated agentic strategies, requiring AI engineers to master various techniques such as hybrid search and multi-modal embeddings. LlamaCloud's Retrieval services abstract these advanced techniques into an API, simplifying their use through top-level hyper-parameters. The blog outlines how to progress from naive top-k retrieval, where document chunks are stored in a vector database, to a comprehensive agentic retrieval system capable of querying multiple knowledge bases intelligently. It explains various retrieval modes, including auto_routed, which dynamically selects the appropriate retrieval method based on the query. The system can also handle multiple indices through a Composite Retrieval API, optimizing search paths with a lightweight agent layer that uses LLM-based classification. This approach ensures precise and relevant data retrieval, essential for modern agent-based systems, and positions agentic retrieval as the future of data retrieval systems.

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