How do I track and log query performance in LlamaIndex?

To track and log query performance in LlamaIndex, you can use built-in tools and custom logging strategies to monitor metrics like response time, token usage, and retrieval accuracy. LlamaIndex provides utilities for capturing events during query execution, which you can extend with standard logging libraries or third-party monitoring services. The process typically involves instrumenting your code to record key performance indicators (KPIs) and storing or visualizing the data for analysis.

First, leverage LlamaIndex’s callback system to capture query-related events. The CallbackManager class allows you to define handlers for events like query start, end, and node retrieval. For example, you can create a custom handler that logs the time taken to process a query by recording timestamps at the start and end of execution. You might also track token counts using components like TokenPredictor to estimate usage for cost monitoring. A basic implementation could involve writing logs to a file or sending metrics to a service like Prometheus. For instance, you could log the duration of each query, the number of nodes retrieved, or the tokens consumed by the language model.

Second, integrate external logging frameworks for more structured analysis. Python’s built-in logging module can be combined with LlamaIndex’s event system to record performance data. For example, you might configure a logger to capture debug-level events during query execution, including retrieval latency or API errors. Additionally, tools like Weights & Biases (W&B) or TensorBoard can be used for visualization. By wrapping query execution in a context manager, you can log metrics like response time and token counts to W&B runs, enabling dashboards for trend analysis. If you’re using OpenAI models, you could also parse the API response headers to log precise token usage and costs directly.

Finally, consider tracking domain-specific metrics to evaluate quality. For instance, log the relevance of retrieved nodes by comparing them to ground-truth data or calculating precision/recall scores. This helps identify whether performance bottlenecks stem from retrieval accuracy or model limitations. To automate this, you might write a post-processing script that aggregates logs, computes averages for latency or token usage, and flags outliers. By combining these approaches—using LlamaIndex’s built-in hooks, standard logging tools, and third-party services—you can create a comprehensive performance monitoring pipeline tailored to your application’s needs.