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LogAttributesValue

The allowed value type for trace and span attributes: str, int, float, or bool.

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Overview

LogAttributesValue is a oneOf union that accepts a single primitive value. It is used as Dict[str, LogAttributesValue] for the attributes parameter when creating or updating traces and spans. 
from adaline_api.models.log_attributes_value import LogAttributesValue

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Accepted Types

Python TypeDescription
strString attribute value
intInteger attribute value (validated as float internally)
floatFloating-point attribute value
boolBoolean attribute value
Values are wrapped via the actual_instance parameter or passed as a positional argument: 
LogAttributesValue(actual_instance="some string")
LogAttributesValue(actual_instance=42)
LogAttributesValue(actual_instance=3.14)
LogAttributesValue(actual_instance=True)

# Positional shorthand
LogAttributesValue("some string")
LogAttributesValue(42)

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Examples

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Setting Trace Attributes

from adaline_api.models.log_attributes_value import LogAttributesValue

trace = monitor.log_trace(
    name="chat-request",
    attributes={
        "user_id": LogAttributesValue("user_abc123"),
        "session_id": LogAttributesValue("sess_xyz"),
        "premium_user": LogAttributesValue(True),
        "request_count": LogAttributesValue(42),
    },
)

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Setting Span Attributes

from adaline_api.models.log_attributes_value import LogAttributesValue

span = trace.log_span(
    name="llm-call",
    attributes={
        "model": LogAttributesValue("gpt-4o"),
        "temperature": LogAttributesValue(0.7),
        "max_tokens": LogAttributesValue(1024),
        "stream": LogAttributesValue(False),
    },
)

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Updating Attributes

from adaline_api.models.log_attributes_value import LogAttributesValue

span.update({
    "attributes": {
        "latency_ms": LogAttributesValue(320.5),
        "cached": LogAttributesValue(True),
    },
})

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Building Attributes Dynamically

from adaline_api.models.log_attributes_value import LogAttributesValue
from typing import Dict

def build_attributes(
    user_id: str,
    environment: str,
    retries: int,
    is_test: bool = False,
) -> Dict[str, LogAttributesValue]:
    return {
        "user_id": LogAttributesValue(user_id),
        "environment": LogAttributesValue(environment),
        "retries": LogAttributesValue(retries),
        "is_test": LogAttributesValue(is_test),
    }

attrs = build_attributes("user_123", "production", 0)
trace = monitor.log_trace(name="request", attributes=attrs)

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Serialization

from adaline_api.models.log_attributes_value import LogAttributesValue

val = LogAttributesValue("hello")

d = val.to_dict()   # 'hello'
j = val.to_json()   # '"hello"'

restored = LogAttributesValue.from_dict(d)
restored = LogAttributesValue.from_json(j)