DEV Community
Follow
How to Build a Local Agentic Search Pipeline That Actually Gets Facts Right
The text discusses the limitations of basic Retrieval-Augmented Generation (RAG) for local LLMs, particularly regarding factual accuracy. Agentic search is presented as a superior approach, employing a tool-use loop where the model searches, refines queries, and synthesizes answers. This method allows the LLM to verify information, addressing the shortcomings of single-pass retrieval. The article explains how to set up an agentic search system on a single RTX 3090 using model quantization to fit a large model. It outlines crucial components including model serving with llama.cpp or vllm, search backends like SearXNG, and effective system prompt engineering. The key to success involves precise prompts, controlling search iterations, and monitoring resource usage. The article highlights the advancements in open-source models, quantization techniques, and serving stacks that make local agentic search viable. The author emphasizes the shift from model size to engineering as the primary challenge and concludes this is a significant step towards enabling private, offline, and cost-effective use of LLMs.
