How do I monitor the performance and accuracy of searches in LlamaIndex?

To monitor the performance and accuracy of searches in LlamaIndex, you need to track both quantitative metrics and qualitative feedback. Start by defining key metrics such as hit rate (the percentage of queries returning relevant results), latency (time taken to return results), and precision/recall (measuring result relevance against ground truth). For example, calculate hit rate by comparing the number of search results matching expected outcomes in a test dataset. Use tools like Python’s time module to measure latency, or integrate logging to capture response times across queries. Precision and recall can be evaluated by comparing retrieved documents against a labeled dataset to determine if the system surfaces the most accurate information.

Next, implement evaluation frameworks specific to retrieval-augmented generation (RAG) systems. LlamaIndex provides utilities like QueryEngine and ResponseEvaluator to assess response quality. For instance, use ResponseEvaluator to check if generated answers align with source documents or if retrieved contexts are sufficient. You can also generate synthetic test queries (e.g., using GPT-4) to simulate user inputs and validate performance. Additionally, set up automated tests to run periodic evaluations against a validation dataset, flagging drops in accuracy or speed. For custom needs, extend these tools by adding domain-specific checks, such as verifying that medical searches prioritize peer-reviewed sources.

Finally, use logging and visualization tools to track trends over time. Log metrics like query success rates, latency distributions, and user feedback to a database (e.g., PostgreSQL) or monitoring service (Prometheus/Grafana). Tools like Weights & Biases or TensorBoard can visualize embedding quality or retrieval patterns. For example, log user-reported inaccuracies to identify recurring issues, such as poor performance on ambiguous queries. Continuously refine your index by adjusting parameters like chunk size or embedding models based on these insights. Regularly audit the system by comparing its outputs against human-reviewed benchmarks to ensure alignment with real-world requirements.