How We Built Agentic Retrieval at Ragie

The text explores the limitations of traditional Retrieval-Augmented Generation (RAG) systems in handling complex queries and introduces agentic retrieval as a solution that incorporates reasoning into the retrieval process. Agentic retrieval breaks down complex questions into sub-queries, dynamically chooses search strategies, and continuously evaluates the retrieved results to ensure accuracy and completeness. The approach is exemplified by Ragie's deep-search, which uses a multi-agent architecture to manage context and tasks, execute complex operations, and synthesize well-supported answers from verifiable sources. This system adapts to different levels of question complexity and supports API integration using OpenAI's schema. The text highlights the superior performance of deep-search over traditional RAG systems, citing its ability to handle complex datasets and provide transparent, structured outputs that facilitate debugging and auditing.

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