How can I monitor and log queries in Haystack?

To monitor and log queries in Haystack, you can use built-in components, custom code, and integrations with external tools. Haystack’s Pipeline class allows you to inject logging logic at specific points in your workflow. For example, the DocumentLogger component can record documents during indexing, but you’ll need to adapt similar techniques for query logging. If you’re using Haystack’s REST API, middleware (like FastAPI or Django middleware) can intercept HTTP requests to log queries and responses. For custom pipelines, adding a lightweight node to capture query data before processing and results afterward is a common approach.

A practical method is to create a custom component in your query pipeline. For instance, design a QueryLogger node that accepts a query, logs it (e.g., to a file or database), and passes it to the next step. Here’s a simplified example:

class QueryLogger(BaseComponent):
 def run(self, query: str):
 logging.info(f"Query: {query}")
 return {"query": query}, "output_1"

Add this node at the start of your pipeline to log inputs. To capture results, add another node after your retriever or reader. You could also use Python’s logging module directly in pipeline steps or wrap Pipeline.run() to log details like query text, timestamps, or result counts. For structured logging, serialize data to JSON and send it to systems like Elasticsearch.

For monitoring, integrate with tools like Prometheus and Grafana to track metrics such as query latency or error rates. Use a decorator to measure function execution time:

from prometheus_client import Summary
QUERY_TIME = Summary('query_processing_seconds', 'Time spent processing queries')

@QUERY_TIME.time()
def process_query(query):
 # Pipeline execution logic

Centralize logs using the ELK Stack (Elasticsearch, Logstash, Kibana) or cloud services like AWS CloudWatch. If using Haystack’s REST API, configure middleware to log request data automatically. For larger systems, consider distributed tracing (e.g., OpenTelemetry) to track queries across microservices. Separate logging for debugging (detailed results) and monitoring (aggregate metrics) to balance visibility with performance.