"""
Test FAISS memory system with deduplication and semantic search.

Run with: python test_memory.py
"""

import os
import sys
import logging
import tempfile
import shutil

# Setup logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

logger = logging.getLogger(__name__)


def test_basic_memory():
    """Test basic add/search functionality."""
    from core.memory import AgentMemory
    from core.embeddings import EmbeddingModel

    logger.info("=== Test 1: Basic Memory Operations ===")

    embedder = EmbeddingModel()
    memory = AgentMemory(embedder=embedder)

    # Add some tasks
    tasks = [
        ("Calculate the sum of 2 and 2", "4"),
        ("What is 5 multiplied by 3?", "15"),
        ("Convert 100 Fahrenheit to Celsius", "37.78"),
        ("List the first 5 prime numbers", "2, 3, 5, 7, 11"),
    ]

    for task, result in tasks:
        added = memory.add(task, result)
        assert added, f"Failed to add: {task}"

    # Check stats
    stats = memory.get_stats()
    logger.info(f"Memory stats: {stats}")
    assert stats["total_items"] == len(tasks), f"Expected {len(tasks)}, got {stats['total_items']}"

    # Search for similar task
    results = memory.search("What is 2+2?", k=2)
    logger.info(f"Search results for '2+2': {len(results)} found")

    if results:
        logger.info(f"Top result: {results[0]['task']} -> {results[0]['result']} (similarity={results[0]['similarity']:.3f})")
        # Should find the "2 and 2" task
        assert results[0]['similarity'] > 0.5, "Expected high similarity"

    logger.info("✓ Basic memory operations passed\n")


def test_deduplication():
    """Test deduplication functionality."""
    from core.memory import AgentMemory
    from core.embeddings import EmbeddingModel

    logger.info("=== Test 2: Deduplication ===")

    embedder = EmbeddingModel()
    memory = AgentMemory(embedder=embedder, dedup_threshold=0.95)

    # Add original task
    task1 = "Calculate the square root of 16"
    result1 = "4"
    added1 = memory.add(task1, result1)
    assert added1, "Failed to add original task"
    logger.info(f"Added original: {task1}")

    # Try to add very similar task (should be detected as duplicate)
    task2 = "Calculate the square root of 16"  # Exact duplicate
    result2 = "4.0"
    added2 = memory.add(task2, result2)
    logger.info(f"Duplicate detection for exact match: {'Blocked' if not added2 else 'Added'}")

    # Try slightly different task (might not be duplicate)
    task3 = "What is the square root of 16?"
    result3 = "4"
    added3 = memory.add(task3, result3)
    logger.info(f"Similar but different: {'Blocked' if not added3 else 'Added'}")

    # Check total items
    stats = memory.get_stats()
    logger.info(f"Total items after dedup test: {stats['total_items']}")
    assert stats['total_items'] <= 2, f"Deduplication failed, expected <=2, got {stats['total_items']}"

    # Explicit duplicate check
    is_dup = memory.is_duplicate("Calculate the square root of 16")
    logger.info(f"Explicit duplicate check: {is_dup}")
    assert is_dup, "Should detect duplicate"

    logger.info("✓ Deduplication passed\n")


def test_semantic_search():
    """Test semantic similarity search."""
    from core.memory import AgentMemory
    from core.embeddings import EmbeddingModel

    logger.info("=== Test 3: Semantic Search ===")

    embedder = EmbeddingModel()
    memory = AgentMemory(embedder=embedder, similarity_threshold=0.5)

    # Add tasks with different topics
    tasks = [
        ("What is the capital of France?", "Paris"),
        ("What is the capital of Germany?", "Berlin"),
        ("How do I bake a chocolate cake?", "Mix flour, sugar, eggs, cocoa..."),
        ("What's the recipe for cookies?", "Mix butter, sugar, flour..."),
        ("Solve the equation x + 5 = 10", "x = 5"),
        ("What is 15 divided by 3?", "5"),
    ]

    for task, result in tasks:
        memory.add(task, result)

    # Search for capital city query (should find similar tasks)
    results = memory.search("What is the capital of Spain?", k=3)
    logger.info(f"Search 'capital of Spain' found {len(results)} results:")
    for r in results:
        logger.info(f"  - {r['task'][:50]} (sim={r['similarity']:.3f})")

    # Should find other capital queries
    if results:
        assert "capital" in results[0]["task"].lower(), "Should find capital-related tasks"

    # Search for math query
    results = memory.search("Solve x + 10 = 20", k=3)
    logger.info(f"Search 'solve equation' found {len(results)} results:")
    for r in results:
        logger.info(f"  - {r['task'][:50]} (sim={r['similarity']:.3f})")

    # Search for baking query
    results = memory.search("How to make brownies?", k=3)
    logger.info(f"Search 'make brownies' found {len(results)} results:")
    for r in results:
        logger.info(f"  - {r['task'][:50]} (sim={r['similarity']:.3f})")

    logger.info("✓ Semantic search passed\n")


def test_persistence():
    """Test save/load functionality."""
    from core.memory import AgentMemory
    from core.embeddings import EmbeddingModel

    logger.info("=== Test 4: Persistence (Save/Load) ===")

    # Create temporary directory
    temp_dir = tempfile.mkdtemp()
    try:
        save_path = os.path.join(temp_dir, "test_memory")

        # Create and populate memory
        embedder = EmbeddingModel()
        memory1 = AgentMemory(embedder=embedder)

        tasks = [
            ("Task 1", "Result 1"),
            ("Task 2", "Result 2"),
            ("Task 3", "Result 3"),
        ]

        for task, result in tasks:
            memory1.add(task, result, metadata={"source": "test"})

        # Save to disk
        memory1.save(save_path)
        logger.info(f"Saved memory to {save_path}")
        assert os.path.exists(f"{save_path}.index"), "Index file not created"
        assert os.path.exists(f"{save_path}.meta"), "Metadata file not created"

        # Create new memory and load
        memory2 = AgentMemory(embedder=embedder)
        memory2.load(save_path)
        logger.info(f"Loaded memory from {save_path}")

        # Verify loaded data
        stats1 = memory1.get_stats()
        stats2 = memory2.get_stats()
        assert stats1["total_items"] == stats2["total_items"], "Item count mismatch"
        assert stats1["dimension"] == stats2["dimension"], "Dimension mismatch"
        logger.info(f"Loaded {stats2['total_items']} items with dim={stats2['dimension']}")

        # Search in loaded memory
        results = memory2.search("Task 1", k=1)
        assert len(results) > 0, "Search in loaded memory failed"
        assert "Task 1" in results[0]["task"], "Loaded data doesn't match"
        logger.info(f"Search in loaded memory: {results[0]['task']}")

        logger.info("✓ Persistence passed\n")

    finally:
        # Cleanup
        shutil.rmtree(temp_dir)
        logger.info(f"Cleaned up {temp_dir}")


def test_threshold_behavior():
    """Test threshold filtering."""
    from core.memory import AgentMemory
    from core.embeddings import EmbeddingModel

    logger.info("=== Test 5: Threshold Behavior ===")

    embedder = EmbeddingModel()

    # Test with strict threshold
    memory_strict = AgentMemory(embedder=embedder, similarity_threshold=0.9)
    memory_strict.add("Python programming language", "A high-level language")

    results_strict = memory_strict.search("Java programming", k=5)
    logger.info(f"Strict threshold (0.9): {len(results_strict)} results")

    # Test with lenient threshold
    memory_lenient = AgentMemory(embedder=embedder, similarity_threshold=0.3)
    memory_lenient.add("Python programming language", "A high-level language")

    results_lenient = memory_lenient.search("Java programming", k=5)
    logger.info(f"Lenient threshold (0.3): {len(results_lenient)} results")

    # Lenient should find more (or equal) results
    assert len(results_lenient) >= len(results_strict), "Lenient threshold should find more results"

    logger.info("✓ Threshold behavior passed\n")


def test_metadata():
    """Test metadata storage and retrieval."""
    from core.memory import AgentMemory
    from core.embeddings import EmbeddingModel

    logger.info("=== Test 6: Metadata ===")

    embedder = EmbeddingModel()
    memory = AgentMemory(embedder=embedder)

    # Add with metadata
    memory.add(
        task="Complex calculation",
        result="42",
        metadata={
            "execution_time": 1.5,
            "tokens": 100,
            "model": "test-model"
        }
    )

    # Search and verify metadata
    results = memory.search("calculation", k=1)
    assert len(results) > 0, "Search failed"

    meta = results[0]["metadata"]
    logger.info(f"Retrieved metadata: {meta}")
    assert "execution_time" in meta, "Metadata missing"
    assert meta["execution_time"] == 1.5, "Metadata value incorrect"

    logger.info("✓ Metadata passed\n")


def test_clear():
    """Test memory clearing."""
    from core.memory import AgentMemory
    from core.embeddings import EmbeddingModel

    logger.info("=== Test 7: Clear Memory ===")

    embedder = EmbeddingModel()
    memory = AgentMemory(embedder=embedder)

    # Add items
    for i in range(5):
        memory.add(f"Task {i}", f"Result {i}")

    stats_before = memory.get_stats()
    logger.info(f"Before clear: {stats_before['total_items']} items")
    assert stats_before["total_items"] == 5

    # Clear
    memory.clear()

    stats_after = memory.get_stats()
    logger.info(f"After clear: {stats_after['total_items']} items")
    assert stats_after["total_items"] == 0, "Memory not cleared"

    logger.info("✓ Clear passed\n")


def run_all_tests():
    """Run all memory tests."""
    logger.info("Starting FAISS Memory System Tests\n")

    try:
        test_basic_memory()
        test_deduplication()
        test_semantic_search()
        test_persistence()
        test_threshold_behavior()
        test_metadata()
        test_clear()

        logger.info("=" * 50)
        logger.info("All tests passed! ✓")
        logger.info("=" * 50)

    except Exception as e:
        logger.error(f"Test failed: {e}", exc_info=True)
        sys.exit(1)


if __name__ == "__main__":
    run_all_tests()