evaluation = client.spans.evaluate(span_id, options)
# Async
evaluation = await client.spans.aevaluate(span_id, options)
Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
span_id | str | Yes | The span ID to evaluate |
options | EvaluateOptions | Yes | Evaluation options |
options.evaluator | str | Yes | The evaluator key. Currently only quality is supported. |
options.value | int | float | Yes | The evaluation score. Use Eval.POSITIVE (1) or Eval.NEGATIVE (0) |
options.note | str | No | Optional note explaining the evaluation |
Response
class EvaluateResult(BaseModel):
id: str
evaluator_key: str # alias: "evaluatorKey"
evaluator_name: str # alias: "evaluatorName"
value: float
source: str
note: str | None
created_at: datetime # alias: "createdAt"
Examples
Binary Evaluation (Thumbs Up/Down)
from tracia import Tracia, Eval, EvaluateOptions
client = Tracia(api_key="tr_your_api_key")
# Positive evaluation
client.spans.evaluate(
"sp_abc123def456",
EvaluateOptions(evaluator="quality", value=Eval.POSITIVE),
)
# Negative evaluation
client.spans.evaluate(
"sp_abc123def456",
EvaluateOptions(evaluator="quality", value=Eval.NEGATIVE),
)
Adding a Note
client.spans.evaluate(
"sp_abc123def456",
EvaluateOptions(
evaluator="quality",
value=Eval.NEGATIVE,
note="Response was off-topic and did not address the user question",
),
)
User Feedback Integration
def submit_user_feedback(span_id: str, is_helpful: bool, comment: str | None = None):
client.spans.evaluate(
span_id,
EvaluateOptions(
evaluator="quality",
value=Eval.POSITIVE if is_helpful else Eval.NEGATIVE,
note=comment,
),
)
Error Handling
from tracia import TraciaError, TraciaErrorCode, Eval, EvaluateOptions
try:
client.spans.evaluate(
"sp_abc123def456",
EvaluateOptions(evaluator="quality", value=Eval.POSITIVE),
)
except TraciaError as error:
if error.code == TraciaErrorCode.NOT_FOUND:
print("Span not found")
elif error.code == TraciaErrorCode.INVALID_REQUEST:
print("Invalid evaluation value")