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How to Build an Agentic RAG Pipeline with Real-Time Web Search

Marcus ma 2026年08月26日 11:39 0 次阅读 来源:Dev.to

TL;DR An agentic RAG pipeline treats retrieval as a tool the AI agent can call, evaluate, and call again rather than as a fixed step. The pipeline can search an internal knowledge base first, then use real-time web search when the available evidence is missing, weak, or outdated. Internal documents and web results should be converted into a shared evidence format before the model generates an answer. A reliable system must preserve URLs, publication dates, document identifiers, and the claims supported by each source. Retrieval quality, web-search precision, citation correctness, latency, cost, and stopping behaviour should all be evaluated. A basic RAG pipeline works well until the answer is not in the knowledge base. Imagine an enterprise copilot that can answer questions about internal product documentation. It performs semantic search against a vector database, retrieves several relevant passages, and passes them to a language model. For questions covered by the indexed documents, the system may work remarkably well. Then a user asks about a release announced yesterday, a recently changed regulation, or how the company’s product compares with a new competitor. The vector database cannot retrieve information it has never indexed. A conventional pipeline may return no answer, but it may also produce a confident response from incomplete or outdated context. Adding a Web Search API helps solve the freshness problem, but it introduces another decision: when should the system trust its internal knowledge, and when should it search the open web? An agentic RAG pipeline places that decision inside the retrieval workflow. What Makes a RAG Pipeline Agentic? A traditional RAG pipeline usually follows a fixed path: transform the question into a search query, retrieve the most similar passages, add those passages to the prompt, and generate an answer. An agentic RAG pipeline allows the model to make decisions between those stages. Retrieval becomes a tool rather than a manda

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