To implement logging for semantic search queries, start by capturing the essential components of each search interaction. Create a logging system that records the query text, returned results, and relevant metadata. Use a structured format like JSON to store logs, making them easy to analyze later. For example, in Python, you could use the built-in logging module or a dedicated service like Elasticsearch. Include timestamps, user identifiers (if applicable), and the search parameters used (e.g., model version, similarity thresholds). Ensure logs are written to a persistent storage system, such as a database or cloud storage, to prevent data loss during restarts or failures.
Next, enrich logs with contextual details to support debugging and analysis. Track metrics like response time, number of results returned, and the confidence scores of top matches. If your semantic search uses embeddings or vector databases, log the model version used to generate embeddings, as changes in models can affect search quality. For instance, if a user searches for “affordable wireless headphones,” your log might include the embedding model’s ID, the top five product IDs returned, and their similarity scores. Add error handling to capture failed queries or exceptions, such as timeouts or invalid input. Avoid logging sensitive data—anonymize user information or exclude it entirely unless necessary for compliance.
Finally, set up tools to analyze and monitor the logged data. Use dashboards (e.g., Grafana, Kibana) to visualize trends, like frequently searched terms or slow-performing queries. Create alerts for anomalies, such as a sudden drop in results returned, which could indicate issues with your search index or embedding model. Regularly review logs to identify opportunities for improvement, such as adding synonyms for commonly searched terms or adjusting similarity thresholds. For example, if logs show users often search for “cell phone” but your system uses “mobile phone” in product data, you could update the model’s training data to align with user language. Keep logs retained for a defined period to balance usefulness with storage costs and privacy requirements.