litellm/tests/test_litellm/caching/test_redis_semantic_cache.py

import os
import sys
from unittest.mock import AsyncMock, MagicMock, patch

import pytest

sys.path.insert(
    0, os.path.abspath("../../..")
)  # Adds the parent directory to the system path


# Tests for RedisSemanticCache
def test_redis_semantic_cache_initialization(monkeypatch):
    # Mock the redisvl import
    semantic_cache_mock = MagicMock()
    with patch.dict(
        "sys.modules",
        {
            "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
            "redisvl.utils.vectorize": MagicMock(CustomTextVectorizer=MagicMock()),
        },
    ):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        # Set environment variables
        monkeypatch.setenv("REDIS_HOST", "localhost")
        monkeypatch.setenv("REDIS_PORT", "6379")
        monkeypatch.setenv("REDIS_PASSWORD", "test_password")

        # Initialize the cache with a similarity threshold
        redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)

        # Verify the semantic cache was initialized with correct parameters
        assert redis_semantic_cache.similarity_threshold == 0.8

        # Use pytest.approx for floating point comparison to handle precision issues
        assert redis_semantic_cache.distance_threshold == pytest.approx(0.2, abs=1e-10)
        assert redis_semantic_cache.embedding_model == "text-embedding-ada-002"

        # Test initialization with missing similarity_threshold
        with pytest.raises(ValueError, match="similarity_threshold must be provided"):
            RedisSemanticCache()


def test_redis_semantic_cache_get_cache(monkeypatch):
    # Mock the redisvl import and embedding function
    semantic_cache_mock = MagicMock()
    custom_vectorizer_mock = MagicMock()

    with patch.dict(
        "sys.modules",
        {
            "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
            "redisvl.utils.vectorize": MagicMock(
                CustomTextVectorizer=custom_vectorizer_mock
            ),
        },
    ):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        # Set environment variables
        monkeypatch.setenv("REDIS_HOST", "localhost")
        monkeypatch.setenv("REDIS_PORT", "6379")
        monkeypatch.setenv("REDIS_PASSWORD", "test_password")

        # Initialize cache
        redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)

        # Mock the llmcache.check method to return a result
        mock_result = [
            {
                "prompt": "What is the capital of France?",
                "response": '{"content": "Paris is the capital of France."}',
                "vector_distance": 0.1,  # Distance of 0.1 means similarity of 0.9
                RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key",
            }
        ]
        redis_semantic_cache.llmcache.check = MagicMock(return_value=mock_result)

        # Mock the embedding function
        with (
            patch(
                "litellm.embedding",
                return_value={"data": [{"embedding": [0.1, 0.2, 0.3]}]},
            ),
            patch.object(
                redis_semantic_cache,
                "_get_cache_key_filter_expression",
                return_value="cache-key-filter",
            ),
        ):
            # Test get_cache with a message
            metadata = {}
            result = redis_semantic_cache.get_cache(
                key="test_key",
                messages=[{"content": "What is the capital of France?"}],
                metadata=metadata,
            )

            # Verify result is properly parsed
            assert result == {"content": "Paris is the capital of France."}
            assert metadata["semantic-similarity"] == pytest.approx(0.9)

            # Verify llmcache.check was called
            redis_semantic_cache.llmcache.check.assert_called_once_with(
                prompt="What is the capital of France?",
                vector=[0.1, 0.2, 0.3],
                filter_expression="cache-key-filter",
            )


def test_redis_semantic_cache_rejects_unscoped_cache_hit(monkeypatch):
    semantic_cache_mock = MagicMock()
    custom_vectorizer_mock = MagicMock()

    with patch.dict(
        "sys.modules",
        {
            "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
            "redisvl.utils.vectorize": MagicMock(
                CustomTextVectorizer=custom_vectorizer_mock
            ),
        },
    ):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        monkeypatch.setenv("REDIS_HOST", "localhost")
        monkeypatch.setenv("REDIS_PORT", "6379")
        monkeypatch.setenv("REDIS_PASSWORD", "test_password")

        redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)
        redis_semantic_cache.llmcache.check = MagicMock(
            return_value=[
                {
                    "prompt": "What is the capital of France?",
                    "response": '{"content": "Paris"}',
                    "vector_distance": 0.1,
                }
            ]
        )

        with (
            patch(
                "litellm.embedding",
                return_value={"data": [{"embedding": [0.1, 0.2, 0.3]}]},
            ),
            patch.object(
                redis_semantic_cache,
                "_get_cache_key_filter_expression",
                return_value="cache-key-filter",
            ),
        ):
            metadata = {}
            result = redis_semantic_cache.get_cache(
                key="test_key",
                messages=[{"content": "What is the capital of France?"}],
                metadata=metadata,
            )

        assert result is None
        assert metadata["semantic-similarity"] == 0.0


def test_redis_semantic_cache_set_cache_stores_cache_key_filter(monkeypatch):
    semantic_cache_mock = MagicMock()
    custom_vectorizer_mock = MagicMock()

    with patch.dict(
        "sys.modules",
        {
            "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
            "redisvl.utils.vectorize": MagicMock(
                CustomTextVectorizer=custom_vectorizer_mock
            ),
        },
    ):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        monkeypatch.setenv("REDIS_HOST", "localhost")
        monkeypatch.setenv("REDIS_PORT", "6379")
        monkeypatch.setenv("REDIS_PASSWORD", "test_password")

        redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)
        redis_semantic_cache.llmcache.store = MagicMock()

        with patch(
            "litellm.embedding",
            return_value={"data": [{"embedding": [0.1, 0.2, 0.3]}]},
        ):
            redis_semantic_cache.set_cache(
                key="test_key",
                value={"content": "Paris"},
                messages=[{"content": "What is the capital of France?"}],
                ttl=60,
            )

        redis_semantic_cache.llmcache.store.assert_called_once_with(
            "What is the capital of France?",
            "{'content': 'Paris'}",
            vector=[0.1, 0.2, 0.3],
            filters={RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key"},
            ttl=60,
        )


def test_redis_semantic_cache_uses_isolated_index_for_old_schema(monkeypatch):
    fallback_cache_mock = MagicMock()
    semantic_cache_mock = MagicMock(
        side_effect=[
            ValueError("stored index schema differs from requested fields"),
            fallback_cache_mock,
        ]
    )
    custom_vectorizer_mock = MagicMock()

    with patch.dict(
        "sys.modules",
        {
            "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
            "redisvl.utils.vectorize": MagicMock(
                CustomTextVectorizer=custom_vectorizer_mock
            ),
        },
    ):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        monkeypatch.setenv("REDIS_HOST", "localhost")
        monkeypatch.setenv("REDIS_PORT", "6379")
        monkeypatch.setenv("REDIS_PASSWORD", "test_password")

        redis_semantic_cache = RedisSemanticCache(
            similarity_threshold=0.8,
            index_name="existing_index",
        )

        assert redis_semantic_cache.llmcache is fallback_cache_mock
        assert semantic_cache_mock.call_args_list[0].kwargs["name"] == "existing_index"
        assert (
            semantic_cache_mock.call_args_list[1].kwargs["name"]
            == "existing_index_isolated"
        )
        assert semantic_cache_mock.call_args_list[1].kwargs["filterable_fields"] == [
            RedisSemanticCache._cache_key_filterable_field()
        ]


def test_redis_semantic_cache_overwrites_stale_isolated_index(monkeypatch):
    fallback_cache_mock = MagicMock()
    semantic_cache_mock = MagicMock(
        side_effect=[
            ValueError("Existing index schema does not match"),
            ValueError("Existing index schema does not match"),
            fallback_cache_mock,
        ]
    )
    custom_vectorizer_mock = MagicMock()

    with patch.dict(
        "sys.modules",
        {
            "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
            "redisvl.utils.vectorize": MagicMock(
                CustomTextVectorizer=custom_vectorizer_mock
            ),
        },
    ):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        monkeypatch.setenv("REDIS_HOST", "localhost")
        monkeypatch.setenv("REDIS_PORT", "6379")
        monkeypatch.setenv("REDIS_PASSWORD", "test_password")

        redis_semantic_cache = RedisSemanticCache(
            similarity_threshold=0.8,
            index_name="existing_index",
        )

        assert redis_semantic_cache.llmcache is fallback_cache_mock
        assert (
            semantic_cache_mock.call_args_list[2].kwargs["name"]
            == "existing_index_isolated"
        )
        assert semantic_cache_mock.call_args_list[2].kwargs["overwrite"] is True
        assert semantic_cache_mock.call_args_list[2].kwargs["filterable_fields"] == [
            RedisSemanticCache._cache_key_filterable_field()
        ]


def test_redis_semantic_cache_reraises_unexpected_isolated_index_error(monkeypatch):
    semantic_cache_mock = MagicMock(
        side_effect=[
            ValueError("Existing index schema does not match"),
            ValueError("connection failed"),
        ]
    )
    custom_vectorizer_mock = MagicMock()

    with patch.dict(
        "sys.modules",
        {
            "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
            "redisvl.utils.vectorize": MagicMock(
                CustomTextVectorizer=custom_vectorizer_mock
            ),
        },
    ):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        monkeypatch.setenv("REDIS_HOST", "localhost")
        monkeypatch.setenv("REDIS_PORT", "6379")
        monkeypatch.setenv("REDIS_PASSWORD", "test_password")

        with pytest.raises(ValueError, match="connection failed"):
            cache = RedisSemanticCache(
                similarity_threshold=0.8,
                index_name="existing_index",
            )
            _ = cache.llmcache


def test_redis_semantic_cache_reraises_unexpected_index_error():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    redis_semantic_cache = RedisSemanticCache.__new__(RedisSemanticCache)
    redis_semantic_cache.distance_threshold = 0.2
    semantic_cache_mock = MagicMock(side_effect=ValueError("connection failed"))

    with pytest.raises(ValueError, match="connection failed"):
        redis_semantic_cache._init_semantic_cache(
            semantic_cache_cls=semantic_cache_mock,
            index_name="existing_index",
            redis_url="redis://localhost:6379",
            cache_vectorizer=MagicMock(),
        )


def test_redis_semantic_cache_matches_bytes_cache_key():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    redis_semantic_cache = RedisSemanticCache.__new__(RedisSemanticCache)

    assert redis_semantic_cache._cache_hit_matches_key(
        cache_hit={RedisSemanticCache.CACHE_KEY_FIELD_NAME: b"test_key"},
        key="test_key",
    )


def test_redis_semantic_cache_rejects_pre_isolation_unscoped_hit():
    """Pre-isolation entries with no cache-key field cannot be safely
    reassigned to a caller's scope and are treated as misses."""
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    redis_semantic_cache = RedisSemanticCache.__new__(RedisSemanticCache)

    cache_hit = {
        "prompt": "What is the capital of France?",
        "response": '{"content": "Paris"}',
        "vector_distance": 0.1,
    }
    assert not redis_semantic_cache._cache_hit_matches_key(
        cache_hit=cache_hit,
        key="test_key",
    )


def test_redis_semantic_cache_builds_filter_expression(monkeypatch):
    class FakeTag:
        def __init__(self, field_name):
            self.field_name = field_name

        def __eq__(self, value):
            return (self.field_name, value)

    with patch.dict("sys.modules", {"redisvl.query.filter": MagicMock(Tag=FakeTag)}):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        redis_semantic_cache = RedisSemanticCache.__new__(RedisSemanticCache)

        assert redis_semantic_cache._get_cache_key_filter_expression("test_key") == (
            RedisSemanticCache.CACHE_KEY_FIELD_NAME,
            "test_key",
        )


@pytest.mark.asyncio
async def test_redis_semantic_cache_async_get_cache(monkeypatch):
    # Mock the redisvl import
    semantic_cache_mock = MagicMock()
    custom_vectorizer_mock = MagicMock()

    with patch.dict(
        "sys.modules",
        {
            "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
            "redisvl.utils.vectorize": MagicMock(
                CustomTextVectorizer=custom_vectorizer_mock
            ),
        },
    ):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        # Set environment variables
        monkeypatch.setenv("REDIS_HOST", "localhost")
        monkeypatch.setenv("REDIS_PORT", "6379")
        monkeypatch.setenv("REDIS_PASSWORD", "test_password")

        # Initialize cache
        redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)

        # Mock the async methods
        mock_result = [
            {
                "prompt": "What is the capital of France?",
                "response": '{"content": "Paris is the capital of France."}',
                "vector_distance": 0.1,  # Distance of 0.1 means similarity of 0.9
                RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key",
            }
        ]

        redis_semantic_cache.llmcache.acheck = AsyncMock(return_value=mock_result)
        redis_semantic_cache._get_async_embedding = AsyncMock(
            return_value=[0.1, 0.2, 0.3]
        )

        with patch.object(
            redis_semantic_cache,
            "_get_cache_key_filter_expression",
            return_value="cache-key-filter",
        ):
            # Test async_get_cache with a message
            result = await redis_semantic_cache.async_get_cache(
                key="test_key",
                messages=[{"content": "What is the capital of France?"}],
                metadata={},
            )

        # Verify result is properly parsed
        assert result == {"content": "Paris is the capital of France."}

        # Verify methods were called
        redis_semantic_cache._get_async_embedding.assert_called_once()
        redis_semantic_cache.llmcache.acheck.assert_called_once_with(
            prompt="What is the capital of France?",
            vector=[0.1, 0.2, 0.3],
            filter_expression="cache-key-filter",
        )


@pytest.mark.asyncio
async def test_redis_semantic_cache_async_get_cache_rejects_unscoped_hit(monkeypatch):
    semantic_cache_mock = MagicMock()
    custom_vectorizer_mock = MagicMock()

    with patch.dict(
        "sys.modules",
        {
            "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
            "redisvl.utils.vectorize": MagicMock(
                CustomTextVectorizer=custom_vectorizer_mock
            ),
        },
    ):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        monkeypatch.setenv("REDIS_HOST", "localhost")
        monkeypatch.setenv("REDIS_PORT", "6379")
        monkeypatch.setenv("REDIS_PASSWORD", "test_password")

        redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)
        redis_semantic_cache.llmcache.acheck = AsyncMock(
            return_value=[
                {
                    "prompt": "What is the capital of France?",
                    "response": '{"content": "Paris"}',
                    "vector_distance": 0.1,
                }
            ]
        )
        redis_semantic_cache._get_async_embedding = AsyncMock(
            return_value=[0.1, 0.2, 0.3]
        )

        with patch.object(
            redis_semantic_cache,
            "_get_cache_key_filter_expression",
            return_value="cache-key-filter",
        ):
            result = await redis_semantic_cache.async_get_cache(
                key="test_key",
                messages=[{"content": "What is the capital of France?"}],
                metadata={},
            )

        assert result is None


@pytest.mark.asyncio
async def test_redis_semantic_cache_async_set_cache_stores_cache_key_filter(
    monkeypatch,
):
    semantic_cache_mock = MagicMock()
    custom_vectorizer_mock = MagicMock()

    with patch.dict(
        "sys.modules",
        {
            "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
            "redisvl.utils.vectorize": MagicMock(
                CustomTextVectorizer=custom_vectorizer_mock
            ),
        },
    ):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        monkeypatch.setenv("REDIS_HOST", "localhost")
        monkeypatch.setenv("REDIS_PORT", "6379")
        monkeypatch.setenv("REDIS_PASSWORD", "test_password")

        redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)
        redis_semantic_cache.llmcache.astore = AsyncMock()
        redis_semantic_cache._get_async_embedding = AsyncMock(
            return_value=[0.1, 0.2, 0.3]
        )

        await redis_semantic_cache.async_set_cache(
            key="test_key",
            value={"content": "Paris"},
            messages=[{"content": "What is the capital of France?"}],
            ttl=60,
        )

        redis_semantic_cache.llmcache.astore.assert_called_once_with(
            "What is the capital of France?",
            "{'content': 'Paris'}",
            vector=[0.1, 0.2, 0.3],
            filters={RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key"},
            ttl=60,
        )


def test_redis_semantic_cache_set_cache_uses_responses_string_input():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    redis_semantic_cache = RedisSemanticCache.__new__(RedisSemanticCache)
    redis_semantic_cache.llmcache = MagicMock()
    redis_semantic_cache._get_cache_filters = MagicMock(
        return_value={RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key"}
    )
    redis_semantic_cache._get_ttl = MagicMock(return_value=None)
    redis_semantic_cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])

    redis_semantic_cache.set_cache(
        key="test_key",
        value={"content": "Paris"},
        input="What is the capital of France?",
    )

    redis_semantic_cache.llmcache.store.assert_called_once_with(
        "What is the capital of France?",
        "{'content': 'Paris'}",
        vector=[0.1, 0.2, 0.3],
        filters={RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key"},
    )


def test_redis_semantic_cache_get_cache_uses_responses_string_input():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    redis_semantic_cache = RedisSemanticCache.__new__(RedisSemanticCache)
    redis_semantic_cache.similarity_threshold = 0.8
    redis_semantic_cache.llmcache = MagicMock()
    redis_semantic_cache.llmcache.check = MagicMock(
        return_value=[
            {
                "prompt": "What is the capital of France?",
                "response": '{"content": "Paris"}',
                "vector_distance": 0.1,
                RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key",
            }
        ]
    )
    redis_semantic_cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])

    with patch.object(
        redis_semantic_cache,
        "_get_cache_key_filter_expression",
        return_value="cache-key-filter",
    ):
        metadata = {}
        result = redis_semantic_cache.get_cache(
            key="test_key",
            input="What is the capital of France?",
            metadata=metadata,
        )

    assert result == {"content": "Paris"}
    assert metadata["semantic-similarity"] == pytest.approx(0.9)
    redis_semantic_cache.llmcache.check.assert_called_once_with(
        prompt="What is the capital of France?",
        vector=[0.1, 0.2, 0.3],
        filter_expression="cache-key-filter",
    )


def test_redis_semantic_cache_set_cache_flattens_structured_responses_input():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    redis_semantic_cache = RedisSemanticCache.__new__(RedisSemanticCache)
    redis_semantic_cache.llmcache = MagicMock()
    redis_semantic_cache._get_cache_filters = MagicMock(
        return_value={RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key"}
    )
    redis_semantic_cache._get_ttl = MagicMock(return_value=None)
    redis_semantic_cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])

    redis_semantic_cache.set_cache(
        key="test_key",
        value={"content": "Paris"},
        input=[
            {
                "role": "user",
                "content": [
                    {"type": "input_text", "text": "What is the capital of France?"},
                    {"type": "input_text", "text": "Answer briefly."},
                    {
                        "type": "input_image",
                        "image_url": "https://example.com/paris.png",
                    },
                ],
            }
        ],
    )

    redis_semantic_cache.llmcache.store.assert_called_once_with(
        "What is the capital of France?\nAnswer briefly.",
        "{'content': 'Paris'}",
        vector=[0.1, 0.2, 0.3],
        filters={RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key"},
    )


def test_redis_semantic_cache_prompt_extraction_prefers_messages():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    prompt = RedisSemanticCache._get_prompt_from_kwargs(
        messages=[{"content": "message prompt"}],
        input="responses prompt",
    )

    assert prompt == "message prompt"


def test_redis_semantic_cache_prompt_extraction_handles_model_objects():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    class ModelDumpInput:
        def model_dump(self):
            return {"content": [{"text": "model dump prompt"}]}

    class DictInput:
        def dict(self):
            return {"content": [{"output_text": "dict prompt"}]}

    prompt = RedisSemanticCache._get_prompt_from_kwargs(
        input=[
            ModelDumpInput(),
            DictInput(),
            {"content": [{"input_text": "inline prompt"}]},
            {"content": [{"type": "input_image", "image_url": "https://example.com"}]},
        ]
    )

    assert prompt == "model dump prompt\ndict prompt\ninline prompt"


def test_redis_semantic_cache_prompt_extraction_returns_none_without_text():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    assert RedisSemanticCache._get_prompt_from_kwargs() is None
    assert RedisSemanticCache._get_prompt_from_kwargs(input=None) is None
    assert RedisSemanticCache._get_prompt_from_kwargs(input="   ") is None
    assert (
        RedisSemanticCache._get_prompt_from_kwargs(
            input=[{"type": "input_image", "image_url": "https://example.com"}]
        )
        is None
    )


def test_redis_semantic_cache_prompt_extraction_skips_blank_dict_text_keys():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    prompt = RedisSemanticCache._get_prompt_from_kwargs(
        input={"text": "   ", "input_text": "fallback prompt"}
    )

    assert prompt == "fallback prompt"


def test_redis_semantic_cache_prompt_extraction_skips_blank_object_text_keys():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    class ResponseInput:
        text = "   "
        input_text = "fallback prompt"

    prompt = RedisSemanticCache._get_prompt_from_kwargs(input=ResponseInput())

    assert prompt == "fallback prompt"


def test_redis_semantic_cache_prompt_extraction_handles_object_content():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    class ResponseInput:
        content = [{"text": "object content prompt"}]

    prompt = RedisSemanticCache._get_prompt_from_kwargs(input=ResponseInput())

    assert prompt == "object content prompt"


def test_redis_semantic_cache_set_cache_skips_blank_responses_input():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    redis_semantic_cache = RedisSemanticCache.__new__(RedisSemanticCache)
    redis_semantic_cache.llmcache = MagicMock()

    redis_semantic_cache.set_cache(
        key="test_key",
        value={"content": "Paris"},
        input="   ",
    )

    redis_semantic_cache.llmcache.store.assert_not_called()


def test_redis_semantic_cache_get_cache_sets_similarity_on_blank_responses_input():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    redis_semantic_cache = RedisSemanticCache.__new__(RedisSemanticCache)
    redis_semantic_cache.llmcache = MagicMock()
    metadata = {}

    result = redis_semantic_cache.get_cache(
        key="test_key",
        input="   ",
        metadata=metadata,
    )

    assert result is None
    assert metadata["semantic-similarity"] == 0.0
    redis_semantic_cache.llmcache.check.assert_not_called()


def test_redis_semantic_cache_get_cache_sets_similarity_when_no_results():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    redis_semantic_cache = RedisSemanticCache.__new__(RedisSemanticCache)
    redis_semantic_cache.llmcache = MagicMock()
    redis_semantic_cache.llmcache.check = MagicMock(return_value=[])
    redis_semantic_cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])

    with patch.object(
        redis_semantic_cache,
        "_get_cache_key_filter_expression",
        return_value="cache-key-filter",
    ):
        metadata = {}
        result = redis_semantic_cache.get_cache(
            key="test_key",
            input="What is the capital of France?",
            metadata=metadata,
        )

    assert result is None
    assert metadata["semantic-similarity"] == 0.0
    redis_semantic_cache.llmcache.check.assert_called_once_with(
        prompt="What is the capital of France?",
        vector=[0.1, 0.2, 0.3],
        filter_expression="cache-key-filter",
    )


@pytest.mark.asyncio
async def test_redis_semantic_cache_async_paths_use_responses_string_input():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    redis_semantic_cache = RedisSemanticCache.__new__(RedisSemanticCache)
    redis_semantic_cache.similarity_threshold = 0.8
    redis_semantic_cache.llmcache = MagicMock()
    redis_semantic_cache.llmcache.astore = AsyncMock()
    redis_semantic_cache.llmcache.acheck = AsyncMock(
        return_value=[
            {
                "prompt": "What is the capital of France?",
                "response": '{"content": "Paris"}',
                "vector_distance": 0.1,
                RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key",
            }
        ]
    )
    redis_semantic_cache._get_cache_filters = MagicMock(
        return_value={RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key"}
    )
    redis_semantic_cache._get_ttl = MagicMock(return_value=None)
    redis_semantic_cache._get_async_embedding = AsyncMock(return_value=[0.1, 0.2, 0.3])

    await redis_semantic_cache.async_set_cache(
        key="test_key",
        value={"content": "Paris"},
        input="What is the capital of France?",
    )

    with patch.object(
        redis_semantic_cache,
        "_get_cache_key_filter_expression",
        return_value="cache-key-filter",
    ):
        metadata = {}
        result = await redis_semantic_cache.async_get_cache(
            key="test_key",
            input="What is the capital of France?",
            metadata=metadata,
        )

    redis_semantic_cache.llmcache.astore.assert_called_once_with(
        "What is the capital of France?",
        "{'content': 'Paris'}",
        vector=[0.1, 0.2, 0.3],
        filters={RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key"},
    )
    assert result == {"content": "Paris"}
    assert metadata["semantic-similarity"] == pytest.approx(0.9)
    redis_semantic_cache.llmcache.acheck.assert_called_once_with(
        prompt="What is the capital of France?",
        vector=[0.1, 0.2, 0.3],
        filter_expression="cache-key-filter",
    )


@pytest.mark.asyncio
async def test_redis_semantic_cache_async_paths_set_similarity_on_misses():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    redis_semantic_cache = RedisSemanticCache.__new__(RedisSemanticCache)
    redis_semantic_cache.llmcache = MagicMock()
    redis_semantic_cache.llmcache.astore = AsyncMock()
    redis_semantic_cache.llmcache.acheck = AsyncMock(return_value=[])
    redis_semantic_cache._get_async_embedding = AsyncMock(return_value=[0.1, 0.2, 0.3])

    await redis_semantic_cache.async_set_cache(
        key="test_key",
        value={"content": "Paris"},
        input="   ",
    )

    redis_semantic_cache.llmcache.astore.assert_not_called()
    redis_semantic_cache._get_async_embedding.assert_not_called()

    blank_metadata = {}
    blank_result = await redis_semantic_cache.async_get_cache(
        key="test_key",
        input="   ",
        metadata=blank_metadata,
    )

    assert blank_result is None
    assert blank_metadata["semantic-similarity"] == 0.0
    redis_semantic_cache.llmcache.acheck.assert_not_called()
    redis_semantic_cache._get_async_embedding.assert_not_called()

    with patch.object(
        redis_semantic_cache,
        "_get_cache_key_filter_expression",
        return_value="cache-key-filter",
    ):
        miss_metadata = {}
        miss_result = await redis_semantic_cache.async_get_cache(
            key="test_key",
            input="What is the capital of France?",
            metadata=miss_metadata,
        )

    assert miss_result is None
    assert miss_metadata["semantic-similarity"] == 0.0
    redis_semantic_cache.llmcache.acheck.assert_called_once_with(
        prompt="What is the capital of France?",
        vector=[0.1, 0.2, 0.3],
        filter_expression="cache-key-filter",
    )


def test_redis_get_embedding_routes_through_router(monkeypatch):
    import sys
    import types

    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    cache = RedisSemanticCache.__new__(RedisSemanticCache)
    cache.embedding_model = "sem-embed"

    router = MagicMock()
    router.embedding = MagicMock(return_value={"data": [{"embedding": [0.5, 0.6]}]})
    fake_proxy = types.ModuleType("litellm.proxy.proxy_server")
    fake_proxy.llm_router = router
    fake_proxy.llm_model_list = [{"model_name": "sem-embed"}]
    monkeypatch.setitem(sys.modules, "litellm.proxy.proxy_server", fake_proxy)

    with patch("litellm.embedding") as direct_embed:
        vec = cache._get_embedding("hello", metadata={"user_api_key": "sk-x"})

    assert vec == [0.5, 0.6]
    router.embedding.assert_called_once()
    assert router.embedding.call_args.kwargs["model"] == "sem-embed"
    assert router.embedding.call_args.kwargs["input"] == "hello"
    assert router.embedding.call_args.kwargs["cache"] == {
        "no-store": True,
        "no-cache": True,
    }
    assert router.embedding.call_args.kwargs["metadata"] == {
        "user_api_key": "sk-x",
        "semantic-cache-embedding": True,
    }
    direct_embed.assert_not_called()


def test_redis_get_embedding_falls_back_to_direct(monkeypatch):
    import sys
    import types

    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    cache = RedisSemanticCache.__new__(RedisSemanticCache)
    cache.embedding_model = "text-embedding-ada-002"

    fake_proxy = types.ModuleType("litellm.proxy.proxy_server")
    fake_proxy.llm_router = None
    fake_proxy.llm_model_list = None
    monkeypatch.setitem(sys.modules, "litellm.proxy.proxy_server", fake_proxy)

    with patch(
        "litellm.embedding", return_value={"data": [{"embedding": [0.1, 0.2]}]}
    ) as direct_embed:
        vec = cache._get_embedding("hello")

    assert vec == [0.1, 0.2]
    direct_embed.assert_called_once()


def test_cache_get_cache_passes_responses_input_to_backend_cache():
    from litellm.caching.caching import Cache

    cache = Cache.__new__(Cache)
    cache.cache = MagicMock()
    cache.cache.get_cache = MagicMock(return_value=None)
    cache.should_use_cache = MagicMock(return_value=True)
    cache.get_cache_key = MagicMock(return_value="test_key")

    metadata = {}
    cache.get_cache(
        input="What is the capital of France?",
        metadata=metadata,
        cache={},
    )

    cache.cache.get_cache.assert_called_once_with(
        "test_key",
        input="What is the capital of France?",
        metadata=metadata,
    )


def test_cache_get_cache_filters_non_lookup_kwargs_from_backend_cache():
    from litellm.caching.caching import Cache

    cache = Cache.__new__(Cache)
    cache.cache = MagicMock()
    cache.should_use_cache = MagicMock(return_value=True)
    cache.get_cache_key = MagicMock(return_value="test_key")
    cache._get_cache_logic = MagicMock(return_value={"content": "Paris"})

    def _cache_hit(_cache_key, **cache_kwargs):
        cache_kwargs["metadata"]["semantic-similarity"] = 0.7
        return {"content": "Paris"}

    cache.cache.get_cache = MagicMock(side_effect=_cache_hit)

    metadata = {"user_api_key": "sk-secret", "trace_id": "trace-id"}
    result = cache.get_cache(
        input="What is the capital of France?",
        metadata=metadata,
        cache={"s-maxage": 10},
        api_key="sk-secret",
        headers={"authorization": "Bearer sk-secret"},
    )

    assert result == {"content": "Paris"}
    assert metadata == {
        "user_api_key": "sk-secret",
        "trace_id": "trace-id",
        "semantic-similarity": 0.7,
    }

    forwarded_kwargs = cache.cache.get_cache.call_args.kwargs
    assert forwarded_kwargs == {
        "input": "What is the capital of France?",
        "metadata": {
            "user_api_key": "sk-secret",
            "trace_id": "trace-id",
            "semantic-similarity": 0.7,
        },
    }
    assert forwarded_kwargs["metadata"] is not metadata
    cache._get_cache_logic.assert_called_once_with(
        cached_result={"content": "Paris"},
        max_age=10,
    )


def test_cache_get_cache_filters_sensitive_kwargs_without_metadata():
    from litellm.caching.caching import Cache

    cache = Cache.__new__(Cache)
    cache.cache = MagicMock()
    cache.cache.get_cache = MagicMock(return_value={"content": "Paris"})
    cache.should_use_cache = MagicMock(return_value=True)
    cache.get_cache_key = MagicMock(return_value="test_key")
    cache._get_cache_logic = MagicMock(return_value={"content": "Paris"})

    result = cache.get_cache(
        input="What is the capital of France?",
        cache={"s-maxage": 10},
        api_key="sk-secret",
        headers={"authorization": "Bearer sk-secret"},
    )

    assert result == {"content": "Paris"}
    cache.cache.get_cache.assert_called_once_with(
        "test_key",
        input="What is the capital of France?",
    )


def test_cache_get_cache_passes_responses_input_to_dynamic_cache():
    from litellm.caching.caching import Cache

    cache = Cache.__new__(Cache)
    cache.should_use_cache = MagicMock(return_value=True)
    cache.get_cache_key = MagicMock(return_value="test_key")
    cache._get_cache_logic = MagicMock(return_value={"content": "Paris"})
    dynamic_cache_object = MagicMock()
    dynamic_cache_object.get_cache = MagicMock(return_value={"content": "Paris"})

    metadata = {}
    result = cache.get_cache(
        dynamic_cache_object=dynamic_cache_object,
        input="What is the capital of France?",
        metadata=metadata,
        cache={},
    )

    assert result == {"content": "Paris"}
    dynamic_cache_object.get_cache.assert_called_once_with(
        "test_key",
        input="What is the capital of France?",
        metadata=metadata,
    )
    cache._get_cache_logic.assert_called_once_with(
        cached_result={"content": "Paris"},
        max_age=float("inf"),
    )


def test_redis_sync_set_cache_passes_precomputed_vector():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    cache = RedisSemanticCache.__new__(RedisSemanticCache)
    cache.llmcache = MagicMock()
    cache._get_cache_filters = MagicMock(
        return_value={RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key"}
    )
    cache._get_ttl = MagicMock(return_value=None)
    cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])

    cache.set_cache(
        key="test_key",
        value={"content": "Paris"},
        messages=[{"content": "What is the capital of France?"}],
    )

    cache._get_embedding.assert_called_once()
    cache.llmcache.store.assert_called_once_with(
        "What is the capital of France?",
        "{'content': 'Paris'}",
        vector=[0.1, 0.2, 0.3],
        filters={RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key"},
    )


def test_redis_sync_get_cache_passes_precomputed_vector():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    cache = RedisSemanticCache.__new__(RedisSemanticCache)
    cache.similarity_threshold = 0.8
    cache.llmcache = MagicMock()
    cache.llmcache.check = MagicMock(
        return_value=[
            {
                "prompt": "What is the capital of France?",
                "response": '{"content": "Paris"}',
                "vector_distance": 0.1,
                RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key",
            }
        ]
    )
    cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])

    with patch.object(
        cache, "_get_cache_key_filter_expression", return_value="cache-key-filter"
    ):
        result = cache.get_cache(
            key="test_key",
            messages=[{"content": "What is the capital of France?"}],
            metadata={},
        )

    assert result == {"content": "Paris"}
    cache._get_embedding.assert_called_once()
    cache.llmcache.check.assert_called_once_with(
        prompt="What is the capital of France?",
        vector=[0.1, 0.2, 0.3],
        filter_expression="cache-key-filter",
    )


@pytest.mark.asyncio
async def test_redis_async_embedding_forwards_full_metadata(monkeypatch):
    import sys
    import types

    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    cache = RedisSemanticCache.__new__(RedisSemanticCache)
    cache.embedding_model = "sem-embed"

    router = MagicMock()
    router.aembedding = AsyncMock(return_value={"data": [{"embedding": [0.1, 0.2]}]})
    fake_proxy = types.ModuleType("litellm.proxy.proxy_server")
    fake_proxy.llm_router = router
    fake_proxy.llm_model_list = [{"model_name": "sem-embed"}]
    monkeypatch.setitem(sys.modules, "litellm.proxy.proxy_server", fake_proxy)

    await cache._get_async_embedding(
        "hello",
        metadata={"user_api_key": "sk-x", "user_api_key_team_id": "team-1"},
    )

    md = router.aembedding.call_args.kwargs["metadata"]
    assert md["user_api_key"] == "sk-x"
    assert md["user_api_key_team_id"] == "team-1"  # FAILS today: team_id is dropped
    assert md["semantic-cache-embedding"] is True


def test_redis_init_defers_redisvl_construction(monkeypatch):
    semantic_cache_mock = MagicMock()
    custom_vectorizer_mock = MagicMock()

    with patch.dict(
        "sys.modules",
        {
            "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
            "redisvl.utils.vectorize": MagicMock(
                CustomTextVectorizer=custom_vectorizer_mock
            ),
        },
    ):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        monkeypatch.setenv("REDIS_HOST", "localhost")
        monkeypatch.setenv("REDIS_PORT", "6379")
        monkeypatch.setenv("REDIS_PASSWORD", "test_password")

        cache = RedisSemanticCache(similarity_threshold=0.8)

        semantic_cache_mock.assert_not_called()
        custom_vectorizer_mock.assert_not_called()

        first = cache.llmcache
        semantic_cache_mock.assert_called_once()
        custom_vectorizer_mock.assert_called_once()

        second = cache.llmcache
        assert first is second
        semantic_cache_mock.assert_called_once()


def test_redis_failed_llmcache_build_is_not_memoized(monkeypatch):
    built_cache = MagicMock()
    semantic_cache_mock = MagicMock(
        side_effect=[ConnectionError("redis down"), built_cache]
    )
    custom_vectorizer_mock = MagicMock()

    with patch.dict(
        "sys.modules",
        {
            "redisvl.extensions.llmcache": MagicMock(SemanticCache=semantic_cache_mock),
            "redisvl.utils.vectorize": MagicMock(
                CustomTextVectorizer=custom_vectorizer_mock
            ),
        },
    ):
        from litellm.caching.redis_semantic_cache import RedisSemanticCache

        monkeypatch.setenv("REDIS_HOST", "localhost")
        monkeypatch.setenv("REDIS_PORT", "6379")
        monkeypatch.setenv("REDIS_PASSWORD", "test_password")

        cache = RedisSemanticCache(similarity_threshold=0.8)

        with pytest.raises(ConnectionError, match="redis down"):
            _ = cache.llmcache

        assert cache.llmcache is built_cache
        assert semantic_cache_mock.call_count == 2


def test_redis_llmcache_setter_supported():
    from litellm.caching.redis_semantic_cache import RedisSemanticCache

    cache = RedisSemanticCache.__new__(RedisSemanticCache)
    sentinel = MagicMock()
    cache.llmcache = sentinel
    assert cache.llmcache is sentinel