How to Build a Local Chatbot with Web Search Using DeepSeek, Ollama, and Dify

0x0 Introduction

DeepSeek's service has become popular, but free access often queues and web search can be unavailable. This article shows how to build a local chat assistant with DeepSeek R1 32b that can perform web searches.

0x1 Set Up Local Model Environment

Install Ollama on a Mac Mini, download DeepSeek R1 32b, optionally use LM Studio, but Ollama has lower overhead. Build a Docker environment and deploy Dify via docker‑compose, adjusting parameters (increase ARRAY max, timeout). After deployment, access Dify UI and add the Ollama model.

0x2 Build Search Capability

Design logic to decide whether a user query needs web search. If the model can answer from its knowledge, respond directly; otherwise generate a search keyword, perform a web search using a plugin (e.g., SEARXNG), download the result pages, extract main content, score relevance, and feed the relevant excerpts back to the model.

0x3 Test the Chat Assistant

Run the assembled pipeline; the assistant can answer questions using both its internal knowledge and the retrieved web information, though performance and token limits may require further optimization.

0x4 Optimizations

Potential improvements include using more powerful hardware, refining keyword generation, limiting the number of retrieved pages, and employing RAG with a local knowledge base to avoid token overflow.

0x5 Summary

The guide demonstrates end‑to‑end construction of a locally hosted LLM chat assistant with web‑search capability, covering model deployment, search logic, content extraction, and integration, while noting current limitations and future enhancement paths.