Stop Building Vanilla RAG: Embrace Agentic RAG with DeepSearcher

This post introduces DeepSearcher, an open-source project that implements agentic RAG, inspired by OpenAI's Deep Research. It details the shift from traditional RAG to agentic RAG, highlighting the latter's proactive, multi-step, and iterative approach to problem-solving. The architecture of DeepSearcher is presented, consisting of a Data Ingestion Module (leveraging Milvus for vector storage) and an Online Reasoning and Query Module that implements diverse agent strategies. Use cases like overview-type queries are illustrated with examples of DeepSearcher's iterative query generation and report synthesis. The post emphasizes the transparency provided by logging search and reasoning steps, which is crucial for debugging and optimizing RAG systems.