#!/usr/bin/env python3
"""Tests for Phase 2: Relevance Engine"""

import pytest
from datetime import datetime
from pathlib import Path
import sys
sys.path.insert(0, str(Path(__file__).parent.parent))

from pipeline import RelevanceScorer, FeedItem


class TestRelevanceScorer:
    """Test suite for relevance scoring."""
    
    @pytest.fixture
    def scorer(self):
        return RelevanceScorer()
    
    @pytest.fixture
    def sample_items(self):
        return [
            FeedItem(
                title="New RL algorithm for LLM agents",
                summary="We propose a reinforcement learning approach for training LLM agents...",
                url="http://example.com/1",
                source="arxiv",
                published=datetime.utcnow(),
                content_hash="abc123",
                raw={}
            ),
            FeedItem(
                title="Quantum computing advances",
                summary="Recent breakthroughs in quantum error correction...",
                url="http://example.com/2",
                source="arxiv",
                published=datetime.utcnow(),
                content_hash="def456",
                raw={}
            ),
            FeedItem(
                title="GRPO training for tool use",
                summary="Function calling improves with GRPO and chain-of-thought reasoning...",
                url="http://example.com/3",
                source="openai",
                published=datetime.utcnow(),
                content_hash="ghi789",
                raw={}
            ),
        ]
    
    def test_keyword_score_high_relevance(self, scorer):
        """High relevance item should score above 0.5."""
        text = "LLM agent using reinforcement learning and GRPO for tool use"
        score = scorer.keyword_score(text)
        assert score > 0.5, f"Expected >0.5, got {score}"
    
    def test_keyword_score_low_relevance(self, scorer):
        """Low relevance item should score below 0.5."""
        text = "Quantum computing error correction using surface codes"
        score = scorer.keyword_score(text)
        assert score < 0.5, f"Expected <0.5, got {score}"
    
    def test_ranking_order(self, scorer, sample_items):
        """Ranking should put high-relevance items first."""
        ranked = scorer.rank(sample_items, top_n=10, min_score=0.1)
        
        assert len(ranked) > 0
        # Highest relevance should be GRPO/tool use item
        assert "GRPO" in ranked[0][0].title or "RL" in ranked[0][0].title
    
    def test_min_score_filtering(self, scorer, sample_items):
        """Items below min_score should be filtered."""
        ranked = scorer.rank(sample_items, top_n=10, min_score=1.0)
        
        # Should filter out low-relevance quantum item
        titles = [item.title for item, _ in ranked]
        assert "Quantum" not in titles or any("Quantum" in t for t in titles)


if __name__ == "__main__":
    pytest.main([__file__, "-v"])