The innovation of Local Deep Research lies in the clever fusion of local LLM computing and intelligent search systems working in a synergistic mode. In terms of technical implementation, the tool manages local model resources through the Ollama framework, deploying 12b parameter gemma3 models by default, and allowing users to extend the support for vLLM and other inference engines through the config.py configuration file. The localized operation of the model completely avoids the risk of data outflow and meets the requirements of GDPR and other strict privacy regulations.
The search system adopts intelligent routing mechanism, which can automatically identify the problem domain and assign the appropriate retrieval source: academic query priority access to arXiv/PubMed, encyclopedic questions directed to Wikipedia, and technical support for SearXNG's self-built search engine to achieve de-commercialization of web crawling. Multi-language processing module can automatically identify Chinese and other non-English queries, cross-lingual integration of search results.
As a typical example, when researching "Quantum Computing Breakthroughs", the system will search the Chinese and English paper databases in parallel, and generate a comprehensive report containing 20-30 authoritative citations through local model de-weighting and abstract extraction. This integrated architecture improves efficiency by about 3-5 times compared with traditional research methods.
This answer comes from the articleLocal Deep Research: a locally run tool for generating in-depth research reportsThe
