How do I monitor the performance of a Haystack-based search system?

To monitor the performance of a Haystack-based search system, focus on tracking key metrics across its components—retrieval, ranking, and answer generation—while observing system-level behavior like latency and error rates. Start by measuring retrieval accuracy using metrics like recall (the percentage of relevant documents retrieved) and precision (the percentage of retrieved documents that are relevant). For example, if your system retrieves 100 documents for a query and 80 are relevant, precision is 80%. Pair this with user feedback or labeled test data to validate relevance. Next, evaluate the ranking stage using metrics like Mean Reciprocal Rank (MRR), which measures how high the first relevant result appears in the ranked list. If the first correct answer is in position 3 for a query, its reciprocal rank is 1/3. Average this across queries to assess ranking quality.

For answer generation (if using a reader component), track accuracy metrics like Exact Match (EM) and F1 score against ground-truth answers. For instance, if the system answers “Paris” and the correct answer is “Paris, France,” EM would score 0, but F1 might credit partial correctness. Additionally, monitor response latency and throughput to ensure the system meets performance requirements. For example, track the 95th percentile latency to identify slow queries affecting user experience. Log errors like timeouts or failed API calls to detect infrastructure or model issues. Tools like Prometheus for metrics collection and Grafana for visualization can help track these in real time.

Finally, implement user feedback loops and A/B testing. Capture explicit feedback (e.g., thumbs-up/down buttons) or implicit signals (e.g., click-through rates) to gauge satisfaction. For example, if users frequently skip the top-ranked result, it may indicate poor ranking. Use A/B testing to compare model versions—deploy a new retriever to a subset of users and measure improvements in recall or user engagement. Regularly audit the system for biases or degraded performance by re-evaluating it on updated test datasets. By combining technical metrics, system observability, and user feedback, you can maintain a robust, high-performing search system.