Enhance Observability: Implement Logging and Metrics

[nansravn]

To be discussed.

[duboc]

Enhance Observability: Implement Comprehensive Logging and Metrics for LLM Interactions

What would you like to be added?

Implement a robust observability system for PromptWeaver, focusing on logging and metrics specifically tailored for LLM interactions. This system should provide insights into content generation, response times, and overall performance of the LLM workflow.

Why is this needed?

As PromptWeaver is designed to streamline prompt development and management in Generative AI workflows, having detailed insights into its operation is crucial. Enhanced observability will allow developers to optimize performance, debug issues more effectively, and gain valuable insights into LLM behavior and performance.

Motivation

PromptWeaver users need better visibility into the LLM interaction process to:

1. Optimize prompt engineering
2. Monitor and reduce costs associated with LLM API usage
3. Identify and resolve performance bottlenecks
4. Ensure data privacy and security
5. Compare performance across different LLMs or prompt versions

Goals

• Implement structured logging for all LLM interactions, including prompts, responses, and metadata
• Add metrics collection for token usage, latency, and error rates
• Create a system for tracking and versioning prompt templates
• Develop a mechanism for monitoring content quality and potential hallucinations
• Implement privacy-preserving logging practices to handle sensitive information

Non-Goals

• Building a full-fledged analytics dashboard is not within the scope of this enhancement
• Implementing automated prompt optimization based on collected data (this could be a future enhancement)

Risks and Mitigations

1. Risk: Increased computational overhead due to logging and metrics collection Mitigation: Implement efficient logging practices and consider sampling for high-volume scenarios

2. Risk: Potential exposure of sensitive information in logs Mitigation: Develop robust PII redaction mechanisms and ensure secure storage of logs

3. Risk: Complexity increase in the codebase Mitigation: Design a modular observability system that can be easily maintained and extended

Design Details

1. Structured Logging:

   • Use JSON format for logs to enable easy parsing and analysis
   • Include fields for prompt ID, template version, input/output tokens, latency, etc.

2. Metrics Collection:

   • Implement counters for total requests, errors, and token usage
   • Create histograms for latency measurements
   • Set up gauges for concurrent requests and queue lengths

3. Prompt Tracking:

   • Develop a system to version and track prompt templates
   • Log prompt variations and their performance metrics

4. Privacy and Security:

   • Implement configurable PII redaction for both prompts and responses
   • Ensure all logged data is stored securely and access is properly controlled

5. Integration:

   • Design the observability system to be modular and easily integrable with various LLM clients
   • Provide hooks for custom metrics and logging as needed by users

6. Output and Storage:

   • Allow configuration of log output (file, stdout, centralized logging system)
   • Provide options for metrics exposition (e.g., Prometheus endpoint)

Next Steps:

1. Gather feedback from the community on the proposed enhancement
2. Create a detailed technical design document
3. Implement a proof of concept
4. Review and iterate based on community feedback
5. Develop full implementation
6. Update documentation and provide examples for users