DeepResearch offers several customization settings that help developers tailor search results to their needs, including safe search and source preference options. These features are designed to give users control over the type of content they encounter and the sources prioritized in their results. While the exact implementation depends on the platform’s API or interface, the core functionality focuses on filtering and ranking mechanisms that align with user-defined parameters. For example, safe search can exclude explicit or non-technical content, while source preferences let users emphasize results from specific repositories, documentation hubs, or academic databases.
One key customization is the ability to enforce strict content filtering through safe search. This isn’t limited to blocking explicit material—it can also filter out low-quality or irrelevant technical content. For instance, a developer working on a medical imaging project might enable a filter to exclude results from social media or forums, ensuring only peer-reviewed papers or official documentation appear. Similarly, source preferences allow users to prioritize domains like GitHub, arXiv, or Stack Overflow. This is often implemented via API parameters such as source_weights, where developers assign higher scores to trusted sources. A user could configure the tool to rank Python’s official documentation higher than personal blogs when searching for language-specific syntax.
Advanced settings include regex-based filters for file types, date ranges, or code repositories. For example, a developer might exclude test directories (**/tests/) in GitHub searches or limit results to Markdown files (.md) for documentation. Date filters are useful for prioritizing recent research—like excluding papers published before 2020. These options are typically accessible through configuration files or API headers, such as exclude_patterns: ["*.log"] or min_date: "2022-01-01". By combining these settings, developers can create highly specific search workflows, reducing noise and improving the relevance of results for tasks like debugging or literature reviews. The flexibility of these tools makes DeepResearch adaptable to both broad exploratory searches and tightly scoped technical inquiries.