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Project description
LLM Tracekit
This library is a modified version of the OpenTelemetry instrumentaion for OpenAI, designed to simplify LLM application development and production tracing and debugging.
Installation
pip install llm-tracekit
Usage
This library serves as a replacement for the OpenTelemetry OpenAI instrumentation - to set up OpenAI instrumentation, follow the manual instrumentation process detailed in the OpenTelemetry OpenAI instrumentation docs and replace opentelemetry.instrumentation.openai_v2 with llm_tracekit.
Activation
from llm_tracekit import OpenAIInstrumentor
OpenAIInstrumentor().instrument()
Full Example
from llm_tracekit import OpenAIInstrumentor, setup_export_to_coralogix
from openai import OpenAI
# Optional: Configure sending spans to Coralogix
# Reads Coralogix connection details from the following environment variables:
# - CX_TOKEN
# - CX_ENDPOINT
# To use, install llm-tracekit[export]
setup_export_to_coralogix(
service_name="ai-service",
application_name="ai-application",
subsystem_name="ai-subsystem",
capture_content=True,
)
# Activate instrumentation
OpenAIInstrumentor().instrument()
# Example OpenAI Usage
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "user", "content": "Write a short poem on open telemetry."},
],
)
Enabling message content
Message content such as the contents of the prompt, completion, function arguments and return values are not captured by default. To capture message content as span attributes, set the environment variable OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT to true.
Most Coralogix AI evaluations will not work without message contents, so it is highly recommended to enable capturing.
Changes from OpenTelemetry
- The
userparameter in the OpenAI Chat Completions API is now recorded in the span as thegen_ai.openai.request.userattribute - The
toolsparameter in the OpenAI Chat Completions API is now recorded in the span as thegen_ai.openai.request.toolsattributes. - User prompts and model responses are captured as span attributes instead of log events (see Semantic Conventions below)
Semantic Conventions
| Attribute | Type | Description | Examples |
|---|---|---|---|
gen_ai.openai.request.user |
string | A unique identifier representing the end-user | user@company.com |
gen_ai.openai.request.tools.<tool_number>.type |
string | Type of tool entry in tools list | function |
gen_ai.openai.request.tools.<tool_number>.function.name |
string | The name of the function to use in tool calls | get_current_weather |
gen_ai.openai.request.tools.<tool_number>.function.description |
string | Description of the function | Get the current weather in a given location |
gen_ai.openai.request.tools.<tool_number>.function.parameters |
string | JSON describing the schema of the function parameters | {"type": "object", "properties": {"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}}, "required": ["location"]} |
gen_ai.prompt.<message_number>.role |
string | Role of message author for user message <message_number> | system, user, assistant, tool |
gen_ai.prompt.<message_number>.content |
string | Contents of user message <message_number> | What's the weather in Paris? |
gen_ai.prompt.<message_number>.tool_calls.<tool_call_number>.id |
string | ID of tool call in user message <message_number> | call_O8NOz8VlxosSASEsOY7LDUcP |
gen_ai.prompt.<message_number>.tool_calls.<tool_call_number>.type |
string | Type of tool call in user message <message_number> | function |
gen_ai.prompt.<message_number>.tool_calls.<tool_call_number>.function.name |
string | The name of the function used in tool call within user message <message_number> | get_current_weather |
gen_ai.prompt.<message_number>.tool_calls.<tool_call_number>.function.arguments |
string | Arguments passed to the function used in tool call within user message <message_number> | {"location": "Seattle, WA"} |
gen_ai.prompt.<message_number>.tool_call_id |
string | Tool call ID in user message <message_number> | call_mszuSIzqtI65i1wAUOE8w5H4 |
gen_ai.completion.<choice_number>.role |
string | Role of message author for choice <choice_number> in model response | assistant |
gen_ai.completion.<choice_number>.finish_reason |
string | Finish reason for choice <choice_number> in model response | stop, tool_calls, error |
gen_ai.completion.<choice_number>.content |
string | Contents of choice <choice_number> in model response | The weather in Paris is rainy and overcast, with temperatures around 57°F |
gen_ai.completion.<choice_number>.tool_calls.<tool_call_number >.id |
string | ID of tool call in choice <choice_number> | call_O8NOz8VlxosSASEsOY7LDUcP |
gen_ai.completion.<choice_number>.tool_calls.<tool_call_number >.type |
string | Type of tool call in choice <choice_number> | function |
gen_ai.completion.<choice_number>.tool_calls.<tool_call_number >.function.name |
string | The name of the function used in tool call within choice <choice_number> | get_current_weather |
gen_ai.completion.<choice_number>.tool_calls.<tool_call_number >.function.arguments |
string | Arguments passed to the function used in tool call within choice <choice_number> | {"location": "Seattle, WA"} |
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