Skip to main content
This method evaluates either:
  1. An entire conversation stored in a Session by creating evaluations for each of its Inference Results
  2. A single Inference Result by providing its ID

​
Returns

Returns a list of Evaluation objects, one for each metric provided.

​
Usage

This method evaluates inference results using the specified metrics. It supports both Galtea-hosted metrics and self-hosted custom metrics.
You must provide either session_id or inference_result_id, but not both. For single-turn evaluations, you can also use galtea.inference_results.create_and_evaluate() which combines creating an inference result and evaluating it in one call.

​
Development Testing

For non self-hosted metrics 
evaluations = galtea.evaluations.create(
    session_id=session_id,
    metrics=[{"name": "Role Adherence"}, {"name": "Conversation Relevancy"}],
)
If you pre-computed the score 
evaluations = galtea.evaluations.create(
    session_id=session_id,
    metrics=[{"name": self_hosted_metric.name, "score": 0.85}],
)

​
Evaluating a Single Inference Result

You can evaluate a specific inference result by providing its ID instead of a session ID: 
# Evaluate a specific inference result by providing its ID
evaluations = galtea.evaluations.create(
    inference_result_id=inference_result_id,
    metrics=[{"name": "Factual Accuracy"}, {"name": "Answer Relevancy"}],
)

​
Production Monitoring

In order to monitor your product in a production environment, you can create evaluations not linked to a specific test case, but you need to set the is_production flag of the Session to True. 
production_session = galtea.sessions.create(version_id=version_id, is_production=True)

# Create an inference result for the production session first
production_inference_result = galtea.inference_results.create(
    session_id=production_session.id,
    input="Production user query",
    output="Production response",
)

evaluations = galtea.evaluations.create(
    session_id=production_session.id,
    metrics=[{"name": self_hosted_metric.name, "score": 0.85}],
)

​
Advanced Usage

You can also create evaluations using self-hosted metrics with dynamically calculated scores by utilizing the CustomScoreEvaluationMetric class, which allows for more complex evaluation scenarios. 
# First, create a session, in this case it is a production session, so we do not need a test case
session = galtea.sessions.create(version_id=version_id, is_production=True)

# Then, add some inference results to the session
galtea.inference_results.create_batch(
    session_id=session.id,
    conversation_turns=[
        {"role": "user", "content": "Hi"},
        {"role": "assistant", "content": "Hello!"},
        {"role": "user", "content": "How are you?"},
        {"role": "assistant", "content": "I am fine, thank you."},
    ],
)


# Define scoring logic as a class
class PolitenessCheck(CustomScoreEvaluationMetric):
    def __init__(self):
        super().__init__(name="politeness-check")

    def measure(self, *args, actual_output: str | None = None, **kwargs) -> float:
        if actual_output is None:
            return 0.0
        polite_words = ["please", "thank you", "you're welcome"]
        return (
            1.0 if any(word in actual_output.lower() for word in polite_words) else 0.0
        )


# Create the metric in the platform if it doesn't exist yet
# Note: This can be done via the Dashboard too
try:
    metric = galtea.metrics.get_by_name(name="politeness-check")
except Exception:
    metric = None
if metric is None:
    galtea.metrics.create(
        name="politeness-check",
        source="self_hosted",
        description="Checks if polite words appear in the output",
    )

# Now, evaluate the entire session
evaluations = galtea.evaluations.create(
    session_id=session.id,
    metrics=[
        {"name": "Role Adherence"},  # You can use galtea-hosted metrics simultaneously
        {"score": PolitenessCheck()},  # Self-hosted with dynamic scoring
        # Note: No 'name' or 'id' in dict - it comes from PolitenessCheck(name="...")
    ],
)
Both options are equally valid for self-hosted metrics. Choose based on your preference: pre-compute for simplicity, or use CustomScoreEvaluationMetric for encapsulation and reusability.
When using CustomScoreEvaluationMetric, your measure() method receives an inference_results parameter containing InferenceResult objects — all turns for session evaluations, or a single-element list for single inference result evaluations. This enables conversation-level custom scoring. See Evaluate with Custom Metrics for examples.

​
Parameters

​
session_id
string
The ID of the session containing the inference results to be evaluated.
Either session_id or inference_result_id must be provided, but not both.
​
inference_result_id
string
The ID of a specific inference result to evaluate.
Either session_id or inference_result_id must be provided, but not both.
​
specification_ids
List[str]
A list of Specification IDs. When provided, the evaluation uses the metrics linked to these specifications.Can be combined with metrics — the API merges and deduplicates by metric ID. If neither metrics nor specification_ids is provided, the API falls back to metrics from all specifications linked to the product.
This parameter allows you to evaluate against specific product specifications without manually listing all their associated metrics.
​
metrics
List[Union[str, CustomScoreEvaluationMetric, Dict]]
A list of metrics to use for the evaluation.Optional when specification_ids is provided or when the product has specifications with linked metrics (in which case those metrics are used as a fallback).The MetricInput dictionary supports the following keys:
  • id (string, optional): The ID of an existing metric
  • name (string, optional): The name of the metric
  • score (float | CustomScoreEvaluationMetric, optional): For self-hosted metrics only
    • If float: Pre-computed score (0.0 to 1.0). Requires id or name in the dict.
    • If CustomScoreEvaluationMetric: Score will be calculated dynamically. The CustomScoreEvaluationMetric instance must be initialized with name or id. Do NOT provide id or name in the dict when using this option.
For self-hosted metrics, both score options are equally valid: pre-compute as a float, or use CustomScoreEvaluationMetric for dynamic calculation. Galtea-hosted metrics automatically compute scores and should not include a score field.