To track and log query performance in LlamaIndex, you can leverage its built-in logging capabilities along with custom instrumentation in your code. LlamaIndex allows you to monitor how long queries take to execute and log various performance metrics. To do this, you typically need to integrate logging into your application where LlamaIndex is utilized. By wrapping your query calls with timing functions, you can capture the start and end times, allowing you to calculate the duration of each query.
One effective approach is to use Python's built-in time module or a more advanced logging framework like logging. You would start by logging the time just before sending the query to LlamaIndex and log again after the response is received. For example:
import time
import logging
from llamindex import LlamaIndex
# Set up logging
logging.basicConfig(level=logging.INFO)
def query_llama_index(query):
start_time = time.time()
# Perform the query
response = LlamaIndex.query(query)
end_time = time.time()
duration = end_time - start_time
logging.info(f"Query: '{query}' executed in {duration:.2f} seconds.")
return response
In this code snippet, a simple logging setup captures the execution time for each query. You can further enhance this by logging additional information, such as the size of the dataset queried and the nature or type of query being processed. This data can provide insights into performance trends over time, such as whether certain types of queries consistently take longer than others. Collecting this information can help you optimize your indexing and querying strategies, ensuring that you deploy efficient methods for utilizing LlamaIndex in your applications.
Regularly reviewing the logs can help you identify performance bottlenecks and areas for improvement. Consider aggregating log data and analyzing it to get a broader view of query performance and to make informed decisions about optimizations or settings adjustments. For instance, if you notice that certain complex queries are taking significantly longer than average, you might investigate whether optimizing the underlying data structure or querying technique could lead to performance gains.