Skip to main content

​
LogSpanModelStreamContent

Content type for streaming LLM inference spans.

​
Overview

LogSpanModelStreamContent captures streaming model calls where individual chunks are collected alongside an aggregated final output. It is wrapped in a LogSpanContent union via the actual_instance pattern. 
from adaline_api.models.log_span_model_stream_content import LogSpanModelStreamContent

​
Fields

​
type
str
required
Must be "ModelStream".
​
provider
str
required
The provider name (e.g., "openai", "anthropic"). 1–512 characters.
​
model
str
required
The model identifier (e.g., "gpt-4o", "claude-sonnet-4-20250514"). 1–512 characters.
​
input
str
required
The input payload as a JSON string. Must be valid, parseable JSON (the result of json.dumps()).
​
output
str
required
The raw streamed output. Typically the concatenated chunk payloads collected during streaming.
​
aggregate_output
str
required
The aggregated final output as a JSON string. Must be valid, parseable JSON.
​
variables
LogSpanVariable | None
Variable associated with this span for evaluation tracking. See LogSpanVariable.
​
cost
float | None
Cost of inference in USD. Overrides the automatic cost calculated by Adaline. Minimum: 0.

​
Construction Pattern

All span content is wrapped in LogSpanContent using the actual_instance parameter: 
from adaline_api.models.log_span_content import LogSpanContent
from adaline_api.models.log_span_model_stream_content import LogSpanModelStreamContent

content = LogSpanContent(
    actual_instance=LogSpanModelStreamContent(
        type="ModelStream",
        provider="anthropic",
        model="claude-sonnet-4-20250514",
        input=json.dumps(params),
        output=raw_chunks,
        aggregate_output=json.dumps(aggregated),
    )
)

​
Example

import json
from adaline_api.models.log_span_content import LogSpanContent
from adaline_api.models.log_span_model_stream_content import LogSpanModelStreamContent

params = {
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Write a haiku."}],
    "stream": True,
}

span.update({
    "status": "success",
    "content": LogSpanContent(
        actual_instance=LogSpanModelStreamContent(
            type="ModelStream",
            provider="openai",
            model="gpt-4o",
            input=json.dumps(params),
            output=collected_chunks,
            aggregate_output=json.dumps({"role": "assistant", "content": full_text}),
            cost=0.003,
        )
    ),
})