Logging¶
Hayhooks provides comprehensive logging capabilities for monitoring, debugging, and auditing pipeline execution and server operations.
Log Levels¶
Available Levels¶
- TRACE: Detailed information for debugging
- DEBUG: Detailed information for debugging
- INFO: General information about server operations
- SUCCESS: Success messages
- WARNING: Warning messages that don't stop execution
- ERROR: Error messages that affect functionality
- CRITICAL: Critical errors that may cause server failure
Setting Log Level¶
export LOG=debug # or LOG=DEBUG
hayhooks run
Or in your .env file:
LOG=info # or LOG=INFO
Or inline:
LOG=debug hayhooks run
Log Configuration¶
Environment Variables¶
LOG¶
- Default:
info - Description: Minimum log level to display (consumed by Loguru)
- Options:
debug,info,warning,error
Note: Hayhooks does not expose
HAYHOOKS_LOG_FORMATorHAYHOOKS_LOG_FILEenv vars; formatting/handlers are configured internally in the code.
Custom Log Format¶
If you need custom formatting, handle it in your host app via Loguru sinks.
File Logging¶
Basic File Logging¶
Configure file sinks in your host app using
log.add(...).
Rotating File Logs¶
If you embed Hayhooks programmatically and want custom logging, set up logging in your host app and direct Hayhooks logs there.
Pipeline Logging¶
The log object in Hayhooks is a Loguru logger instance. You can use all Loguru features and capabilities in your pipeline code.
Basic Usage¶
from hayhooks import log
class PipelineWrapper(BasePipelineWrapper):
def setup(self) -> None:
log.info("Setting up pipeline")
# ... setup code
def run_api(self, query: str) -> str:
log.debug(f"Processing query: {query}")
try:
result = self.pipeline.run({"prompt": {"query": query}})
log.info("Pipeline execution completed successfully")
return result["llm"]["replies"][0]
except Exception as e:
log.error(f"Pipeline execution failed: {e}")
raise
Execution Time Logging¶
import time
from hayhooks import log
class PipelineWrapper(BasePipelineWrapper):
def run_api(self, query: str) -> str:
start_time = time.time()
result = self.pipeline.run({"prompt": {"query": query}})
execution_time = time.time() - start_time
log.info(f"Pipeline executed in {execution_time:.2f} seconds")
return result["llm"]["replies"][0]
For more advanced logging patterns (structured logging, custom sinks, formatting, etc.), refer to the Loguru documentation.
Next Steps¶
- API Reference - Complete API documentation
- Environment Variables - Configuration options