Local Deep Research utilizes a layered interaction design to meet diverse user needs. The core interface includes: a web GUI based on the Flask framework, which provides a visual query builder and report editor for non-technical users; a full-featured Command Line Interface (CLI) that supports pipeline operations and redirection of results, making it easy for developers to integrate into automated workflows; and an upcoming REST API layer that will enable in-depth interfacing with enterprise knowledge management systems.
The optimization of human-computer interaction is reflected in the following: the web page provides a template for query suggestions (e.g., "biomedical" questions automatically recommend PubMed sources); the CLI supports batch mode, which can continuously process multiple topics in research_questions.txt; the interface language adapts to the system environment, and the language switching between English and Chinese is already realized. realize the switching between Chinese and English. Feedback from users in educational institutions indicates that this multimodal interaction design allows the tool to be used for both classroom teaching demonstrations and embedded in large-scale research projects for graduate students.
For special demand scenarios, the tool retains the bottom configuration portal. Advanced users can adjust more than 20 professional parameters such as LLM temperature parameters and search depth by modifying config.py, and even replace the default BM25 search algorithm with a custom semantic matching engine. This balance of flexibility and ease of use is a key factor in its widespread adoption by all types of users.
This answer comes from the articleLocal Deep Research: a locally run tool for generating in-depth research reportsThe
