[LEADS-349] Calculate aggregated score from key metrics

config/system.yaml

@@ -104,6 +104,16 @@ api:
   # Legacy authentication (fallback when mcp_headers is not configured or disabled)
   # Authentication via API_KEY environment variable only for MCP server (without Server name)
 
+# Quality Score Configuration
+# Aggregated score from selected metrics for overall system quality assessment
+quality_score:
+  metrics:
+    - "ragas:faithfulness"
+    - "ragas:context_precision_with_reference"
+    - "custom:tool_eval"
+    - "custom:answer_correctness"
+  default: true # If true, all metrics in this list get default: true
+
 # Default metrics metadata
 metrics_metadata:
   # Turn-level metrics metadata

docs/configuration.md

@@ -335,6 +335,37 @@ metrics_metadata:
       description: "How completely the conversation addresses user intentions"
 ```
 
+## Quality Score
+
+Compute an aggregated quality score from selected metrics using weighted averaging.
+
+| Setting (quality_score.) | Default | Description |
+|--------------------------|---------|-------------|
+| metrics | required | List of metric identifiers (must be defined in metrics_metadata) |
+| default | `false` | If `true`, auto-enable all listed metrics globally |
+
+**Validation**: Metrics must exist in `default_turn_metrics_metadata` or `default_conversation_metrics_metadata`. Invalid metrics fail at config load time.
+
+**Calculation**: Weighted average where each metric's weight = its sample_count / total_samples.
+
+### Example
+```yaml
+# Define metrics
+metrics_metadata:
+  turn_level:
+    "ragas:faithfulness":
+      threshold: 0.7
+    "ragas:answer_relevancy":
+      threshold: 0.8
+
+# Configure quality score
+quality_score:
+  metrics:
+    - "ragas:faithfulness"
+    - "ragas:answer_relevancy"
+  default: true  # Auto-enable these metrics
+```
+
 ## Storage
 Lightspeed Evaluation can persist results to files and/or databases. The `storage` section configures one or more storage backends.
 

src/lightspeed_evaluation/core/models/quality.py

@@ -0,0 +1,181 @@
+"""Quality score models for aggregated quality assessment.
+
+Provides Pydantic models for computing and reporting an aggregated quality score
+from selected metrics using weighted averaging based on sample sizes.
+"""
+
+import logging
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+from lightspeed_evaluation.core.models.summary import MetricStats, ScoreStatistics
+
+logger = logging.getLogger(__name__)
+
+
+class QualityMetricResult(BaseModel):
+    """Quality metric result using composition to add weight to score statistics."""
+
+    statistics: ScoreStatistics = Field(
+        description="Score statistics for this quality metric"
+    )
+    weight: float = Field(
+        default=0.0,
+        description="Weight proportion (sample_size / total_samples) used in weighted average",
+    )
+
+
+class QualityReport(BaseModel):
+    """Aggregated quality score from selected metrics."""
+
+    aggregated_quality_score: float = Field(
+        default=0.0, description="Weighted average of quality score metrics"
+    )
+    quality_metrics: dict[str, QualityMetricResult] = Field(
+        default_factory=dict,
+        description="Individual metrics used in quality score calculation",
+    )
+    extra_metrics: dict[str, ScoreStatistics] = Field(
+        default_factory=dict,
+        description="Other evaluated metrics calculated, not used for quality score calculation",
+    )
+    warnings: list[str] = Field(
+        default_factory=list,
+        description="Warnings about quality metrics configuration or usage",
+    )
+    api_latency: float = Field(
+        default=0.0, description="[Placeholder] Average API response time in seconds"
+    )
+    api_tokens: int = Field(
+        default=0,
+        description="[Placeholder] Total number of tokens consumed across all API calls",
+    )
+
+    @staticmethod
+    def create_report(
+        by_metric: dict[str, MetricStats],
+        quality_score_metrics: list[str],
+    ) -> Optional["QualityReport"]:
+        """Creates a quality report with aggregated quality score from selected metrics.
+
+        Separates metrics into quality metrics (used for quality score calculation) and
+        extra metrics (evaluated but not included in quality score).
+
+        Args:
+            by_metric: Dictionary mapping metric identifiers to their computed statistics.
+            quality_score_metrics: Metric identifiers to include in quality score calculation.
+                All specified metrics must exist in by_metric.
+
+        Returns:
+            QualityReport with aggregated quality score and separated quality/extra metrics,
+            or None if all quality score metrics have zero samples.
+
+        Raises:
+            ValueError: If any quality_score_metrics are not found in by_metric.
+        """
+        warnings: list[str] = []
+
+        # Validate all quality score metrics exist in computed metrics (by_metric)
+        missing_metrics = [m for m in quality_score_metrics if m not in by_metric]
+        if missing_metrics:
+            warning_msg = (
+                "WARNING: "
+                f"Quality score metrics {missing_metrics} were excluded from "
+                "quality score computation. "
+                f"Reason: Not found in evaluation results."
+            )
+            warnings.append(warning_msg)
+            logger.warning(warning_msg)
+
+            quality_score_metrics = list(
+                set(quality_score_metrics) - set(missing_metrics)
+            )
+
+        # Calculate total samples from quality score metrics only
+        total_samples = 0
+        for metric_id in quality_score_metrics:
+            score_stats = by_metric[metric_id].score_statistics
+            if score_stats is not None:
+                total_samples += score_stats.count
+        if total_samples == 0:
+            logger.warning(
+                "CRITICAL: Quality score computation failed. "
+                "All configured quality metrics have zero evaluation results."
+            )
+            return None
+
+        quality_metrics: dict[str, QualityMetricResult] = {}
+        extra_metrics: dict[str, ScoreStatistics] = {}
+
+        # Separate quality metrics from extra metrics
+        for metric_id in by_metric:
+            if metric_id in quality_score_metrics:
+                score_stats = by_metric[metric_id].score_statistics
+
+                # Skip if score_statistics is None
+                if score_stats is None:
+                    warning_msg = (
+                        f"WARNING: Quality score metric '{metric_id}' "
+                        "was excluded from quality score computation. "
+                        "Reason: Missing score statistics data."
+                    )
+                    warnings.append(warning_msg)
+                    logger.warning(warning_msg)
+                    continue
+
+                sample_size = score_stats.count
+
+                # Skip metrics with zero samples
+                if sample_size == 0:
+                    warning_msg = (
+                        f"WARNING: Quality score metric '{metric_id}' "
+                        "was excluded from quality score computation. "
+                        "Reason: Zero evaluation results for this metric."
+                    )
+                    warnings.append(warning_msg)
+                    logger.warning(warning_msg)
+                    continue
+
+                weight = sample_size / total_samples
+
+                quality_metrics[metric_id] = QualityMetricResult(
+                    statistics=score_stats,
+                    weight=weight,
+                )
+            else:
+                stats = by_metric[metric_id].score_statistics
+                if stats is not None:
+                    extra_metrics[metric_id] = stats
+
+        # Calculate aggregated quality score
+        aggregated_score = QualityReport._calculate_quality_score(quality_metrics)
+
+        return QualityReport(
+            aggregated_quality_score=aggregated_score,
+            quality_metrics=quality_metrics,
+            extra_metrics=extra_metrics,
+            warnings=warnings,
+        )
+
+    @staticmethod
+    def _calculate_quality_score(
+        quality_metrics: dict[str, QualityMetricResult],
+    ) -> float:
+        """Calculate weighted average quality score from quality metrics.
+
+        Computes a weighted average where each metric's weight is proportional to its
+        sample size relative to the total samples across all quality metrics.
+
+        Args:
+            quality_metrics: Dictionary of quality metric results with statistics and
+                weights. Each metric contains statistics with a mean score and a weight
+                (sample_size / total_samples).
+
+        Returns:
+            Weighted average quality score computed as sum of (mean * weight) for all metrics.
+        """
+        weighted_sum = 0.0
+        for metric in quality_metrics.values():
+            weighted_sum += metric.statistics.mean * metric.weight
+        return weighted_sum

src/lightspeed_evaluation/core/models/system.py

@@ -1,3 +1,4 @@
+# pylint: disable=too-many-lines
 """System configuration models."""
 
 import os
@@ -804,6 +805,39 @@ def from_metadata(cls, raw: dict[str, Any]) -> "GEvalConfig":
         return cls.model_validate(data)
 
 
+class QualityScoreConfig(BaseModel):
+    """Quality score configuration."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    metrics: list[str] = Field(
+        default_factory=list,
+        description="List of metric identifiers to use for quality score computation",
+    )
+    default: bool = Field(
+        default=False,
+        description="If true, set default: true for all metrics in the list",
+    )
+
+    @field_validator("metrics")
+    @classmethod
+    def validate_metrics(cls, v: list[str]) -> list[str]:
+        """Ensure metrics list is not empty and contains no duplicates."""
+        if len(v) == 0:
+            raise ValueError(
+                "Quality score metrics list cannot be empty. "
+                "Either specify at least one metric or "
+                "remove the quality_score section from configuration."
+            )
+        if len(v) != len(set(v)):
+            duplicates = [m for m in v if v.count(m) > 1]
+            raise ValueError(
+                f"Quality score metrics contains duplicates: {set(duplicates)}. "
+                "Each metric should appear only once."
+            )
+        return v
+
+
 class SystemConfig(BaseModel):
     """System configuration using individual config models."""
 
@@ -848,6 +882,11 @@ class SystemConfig(BaseModel):
         default_factory=VisualizationConfig, description="Visualization configuration"
     )
 
+    # Quality score configuration
+    quality_score: Optional[QualityScoreConfig] = Field(
+        default=None, description="Quality score configuration"
+    )
+
     # Default metrics metadata from system config
     default_turn_metrics_metadata: dict[str, dict[str, Any]] = Field(
         default_factory=dict, description="Default turn metrics metadata"
@@ -889,6 +928,33 @@ def validate_default_metrics_metadata_geval(
                     ) from e
         return v
 
+    @model_validator(mode="after")
+    def validate_quality_score_metrics(self) -> "SystemConfig":
+        """Validate quality_score metrics exist in metrics_metadata.
+
+        Raises:
+            ConfigurationError: When quality_score contains metrics not defined
+                in turn or conversation level metrics_metadata.
+        """
+        if not self.quality_score:
+            return self
+
+        # Combine all available metrics from both turn and conversation level metadata
+        all_metrics = set(self.default_turn_metrics_metadata.keys()) | set(
+            self.default_conversation_metrics_metadata.keys()
+        )
+
+        # Check for invalid metrics
+        invalid = [m for m in self.quality_score.metrics if m not in all_metrics]
+        if invalid:
+            raise ConfigurationError(
+                f"Invalid quality_score metrics: {invalid}. "
+                "Must be defined in default_turn_metrics_metadata or "
+                "default_conversation_metrics_metadata."
+            )
+
+        return self
+
     @property
     def turn_level_metric_names(self) -> set[str]:
         """Return turn-level metric names derived from metadata keys."""