Trace LLM Provider Operations with OpenTelemetry
Python example demonstrating OpenTelemetry integration with Promptfoo to trace LLM provider operations during evaluations using protobuf format.
Why it matters
Integrate OpenTelemetry with Python to trace and analyze the internal operations of your LLM providers during Promptfoo evaluations. Optimize your LLM performance by understanding execution flow and identifying bottlenecks.
Outcomes
What it gets done
Set up OpenTelemetry tracing for Python LLM provider interactions.
Export trace data using the efficient protobuf format.
Analyze LLM evaluation performance with detailed operational insights.
Debug and optimize LLM provider execution within Promptfoo.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/pfoo-python | bash Capabilities
What this chain does
Writes source code or scripts from a description.
Traces errors to their root cause and suggests fixes.
Analyzes code for bugs, style issues, and improvements.
Creates unit, integration, or end-to-end test cases.
Overview
Python
What it does
A Python implementation example showing OpenTelemetry integration with Promptfoo for tracing LLM provider operations using protobuf export format.
How it connects
Use this when you want to add OpenTelemetry tracing to your Promptfoo evaluations in Python and need to trace internal operations of LLM providers using the efficient protobuf format.
Source README
This example demonstrates how to use OpenTelemetry with Python to trace the internal operations of your LLM providers during Promptfoo evaluations. It uses the protobuf format for trace export, which is the default and most efficient format for the Python OpenTelemetry SDK.
Discussion
Questions & comments · 0
Sign In Sign in to leave a comment.