Implementing monitoring for semantic search systems requires tracking performance, accuracy, and user interactions. Start by defining metrics that reflect how well the system retrieves relevant results. Common technical metrics include latency (how quickly results are returned), throughput (requests handled per second), and error rates. For relevance, use metrics like Normalized Discounted Cumulative Gain (NDCG) to evaluate ranking quality or precision/recall for binary relevance checks. Logging query-response pairs is essential—capture the input query, returned results, and user interactions (e.g., clicks or dwell time). Tools like Prometheus for metrics and Elasticsearch/Kibana for logging can help aggregate and visualize this data. For example, if a user searches for “affordable laptops” but the top results are high-end models, your logs should flag this mismatch for later analysis.
Next, monitor the quality of embeddings and model drift. Semantic search relies on embeddings to represent text meaning, so track embedding stability over time. Compute cosine similarity between embeddings of sample queries and their results to detect unexpected shifts. For instance, if the similarity score for “best hiking boots” and its results drops from 0.8 to 0.5 over a month, investigate whether the embedding model or data pipeline changed. Retrain or update models if drift exceeds a threshold. Additionally, implement A/B testing when deploying new models—compare the new version’s performance against the current system using a subset of live traffic. Tools like MLflow can help track model versions and their performance metrics. For example, after updating an embedding model, verify that the new version maintains or improves click-through rates for common queries like “how to fix a leaky faucet.”
Finally, focus on user feedback and data quality. Add mechanisms for explicit feedback, such as thumbs-up/down buttons or surveys, to capture user satisfaction. Analyze patterns in negative feedback—if users consistently rate “weather in Tokyo” results poorly, check whether the system confuses “Tokyo” with other locations. Monitor input data for anomalies, such as sudden spikes in non-English queries or malformed text, which could indicate bot activity or input pipeline issues. Use data validation tools like Great Expectations to ensure inputs match expected formats. For security, audit logs to detect injection attacks (e.g., maliciously crafted queries) and anonymize sensitive data in logs. For example, if a query contains personal information like “my credit card number is…,” ensure it’s masked or excluded from storage. Regularly review monitoring dashboards to spot trends and prioritize fixes—like adjusting ranking rules for frequently misranked queries.