How do I create custom filters and ranking algorithms in Deepseek?

To create custom filters and ranking algorithms in Deepseek, you’ll work with its extensible architecture designed for modifying search and recommendation behaviors. Start by identifying the data attributes or patterns you want to prioritize. For filters, define criteria to include or exclude items from results (e.g., filtering outdated content). For ranking, implement scoring logic that reflects your priorities, such as boosting newer content or penalizing low-quality items. Deepseek typically provides APIs or hooks to inject custom logic into its processing pipeline, often through configuration files or code-based plugins. Check the documentation for extension points like post-process hooks or scoring modules where your code can intercept and modify results.

For example, if building a news recommendation system, you might create a filter that excludes articles older than 30 days. This could involve subclassing a base Filter class and overriding its apply method to check publication dates. Similarly, a ranking algorithm could combine relevance scores with freshness metrics. You might write a CustomRanker class that calculates a weighted score: 0.7 * relevance_score + 0.3 * freshness_score, where freshness_score is computed as 1 / (days_since_publication + 1). Deepseek’s API would let you register this class in the pipeline, ensuring it’s applied during result generation. Use unit tests to validate logic—for instance, verify that older articles receive lower scores or are excluded entirely.

Deploying custom components requires integrating them into Deepseek’s workflow. If using a configuration-driven approach, update YAML/JSON files to reference your new classes or scripts. For code-based setups, ensure your modules are imported and registered during initialization. Performance is critical: optimize filters to run early in the pipeline (reducing downstream processing) and cache frequently used data. Test with A/B experiments to measure impact—for example, track user engagement metrics before and after applying your ranking changes. Use logging to debug edge cases, like unexpected filter exclusions or scoring anomalies. If your logic depends on external data (e.g., user preferences), ensure those inputs are fetched efficiently to avoid latency spikes.