"""
Stub for Automated Evaluation Pipeline.

In a full FAANG-level production setup, this script would run in your CI/CD pipeline
using a framework like RAGAS or TruLens to evaluate the agent responses against a
golden dataset of queries.

To implement:
1. Define a golden dataset of prompts and expected contexts/answers.
2. Run the graph locally.
3. Score responses on metrics: Context Precision, Faithfulness, Answer Relevance.
4. Assert scores > threshold (e.g., 0.85) to pass the build.
"""
import os
import pytest

# Placeholder for evaluation logic
def test_evaluation_pipeline():
    enable_eval = os.getenv("ENABLE_EVAL", "false").lower() == "true"
    if not enable_eval:
        pytest.skip("Evaluation disabled in environment.")
        
    threshold = float(os.getenv("EVAL_THRESHOLD", "0.7"))
    
    # 1. Load dataset (mock)
    golden_dataset = [
        {"query": "Write a python loop", "expected_agent": "coding_agent"}
    ]
    
    # 2. Invoke graph and collect scores (mock)
    scores = {"context_precision": 0.9, "faithfulness": 0.88, "answer_relevance": 0.95}
    
    # 3. Assertions
    for metric, score in scores.items():
        assert score >= threshold, f"Metric {metric} failed with score {score} < {threshold}"