litellm/tests/unit/caching/test_caching_handler.py

import time
import traceback
from litellm._uuid import uuid

from dotenv import load_dotenv

load_dotenv()
import asyncio
import hashlib
import random

import pytest

import litellm
from litellm import aembedding, completion, embedding, aresponses, responses
from litellm.caching.caching import Cache
from litellm.responses.streaming_iterator import CachedResponsesAPIStreamingIterator

from unittest.mock import AsyncMock, patch, MagicMock
from litellm.caching.caching_handler import (
    LLMCachingHandler,
    CachingHandlerResponse,
    _is_chat_completion_cached_dict,
    _should_defer_streaming_cache_hit_callbacks,
)
from litellm.caching.caching import LiteLLMCacheType
from litellm.types.utils import CallTypes
from litellm.types.rerank import RerankResponse
from litellm.types.utils import (
    ModelResponse,
    EmbeddingResponse,
    TextCompletionResponse,
    TranscriptionResponse,
    Embedding,
)
from litellm.types.llms.openai import ResponsesAPIResponse
from datetime import timedelta, datetime
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
from litellm._logging import verbose_logger
import logging
import json
import httpx
import respx
from fastapi.testclient import TestClient
from litellm._internal_context import in_post_response_phase
from litellm.caching.caching_handler import _PENDING_CACHE_WRITES


def setup_cache():
    # Set up the cache
    cache = Cache(type=LiteLLMCacheType.LOCAL)
    litellm.cache = cache
    return cache


chat_completion_response = litellm.ModelResponse(
    id=str(uuid.uuid4()),
    choices=[
        litellm.Choices(
            message=litellm.Message(
                role="assistant", content="Hello, how can I help you today?"
            )
        )
    ],
)

text_completion_response = litellm.TextCompletionResponse(
    id=str(uuid.uuid4()),
    choices=[litellm.utils.TextChoices(text="Hello, how can I help you today?")],
)


@pytest.mark.asyncio
@pytest.mark.parametrize(
    "response", [chat_completion_response, text_completion_response]
)
async def test_async_set_get_cache(response):
    litellm.set_verbose = True
    setup_cache()
    verbose_logger.setLevel(logging.DEBUG)
    caching_handler = LLMCachingHandler(
        original_function=completion, request_kwargs={}, start_time=datetime.now()
    )

    messages = [{"role": "user", "content": f"Unique message {datetime.now()}"}]

    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.completion.value,
        model="gpt-3.5-turbo",
        messages=messages,
        function_id=str(uuid.uuid4()),
        stream=False,
        start_time=datetime.now(),
    )

    result = response
    print("result", result)

    original_function = (
        litellm.acompletion
        if isinstance(response, litellm.ModelResponse)
        else litellm.atext_completion
    )
    if isinstance(response, litellm.ModelResponse):
        kwargs = {"messages": messages}
        call_type = CallTypes.acompletion.value
    else:
        kwargs = {"prompt": f"Hello, how can I help you today? {datetime.now()}"}
        call_type = CallTypes.atext_completion.value

    await caching_handler.async_set_cache(
        result=result, original_function=original_function, kwargs=kwargs
    )

    await asyncio.sleep(2)

    # Verify the result was cached
    cached_response = await caching_handler._async_get_cache(
        model="gpt-3.5-turbo",
        original_function=original_function,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=call_type,
        kwargs=kwargs,
    )

    assert cached_response.cached_result is not None
    assert cached_response.cached_result.id == result.id


@pytest.mark.asyncio
async def test_async_log_cache_hit_on_callbacks():
    """
    Assert logging callbacks are called after a cache hit
    """
    # Setup
    caching_handler = LLMCachingHandler(
        original_function=completion, request_kwargs={}, start_time=datetime.now()
    )

    mock_logging_obj = MagicMock()
    mock_logging_obj.async_success_handler = AsyncMock()
    mock_logging_obj.success_handler = MagicMock()
    mock_logging_obj.handle_sync_success_callbacks_for_async_calls = MagicMock()

    cached_result = "Mocked cached result"
    start_time = datetime.now()
    end_time = start_time + timedelta(seconds=1)
    cache_hit = True

    # Call the method
    caching_handler._async_log_cache_hit_on_callbacks(
        logging_obj=mock_logging_obj,
        cached_result=cached_result,
        start_time=start_time,
        end_time=end_time,
        cache_hit=cache_hit,
    )

    # Wait for the async task to complete
    await asyncio.sleep(0.5)

    print("mock logging obj methods called", mock_logging_obj.mock_calls)

    # Assertions
    mock_logging_obj.async_success_handler.assert_called_once_with(
        result=cached_result,
        start_time=start_time,
        end_time=end_time,
        cache_hit=cache_hit,
    )

    # Wait for the thread to complete
    await asyncio.sleep(0.5)

    mock_logging_obj.handle_sync_success_callbacks_for_async_calls.assert_called_once_with(
        result=cached_result,
        start_time=start_time,
        end_time=end_time,
        cache_hit=cache_hit,
    )


@pytest.mark.parametrize(
    "call_type, cached_result, expected_type",
    [
        (
            CallTypes.completion.value,
            {
                "id": "test",
                "choices": [{"message": {"role": "assistant", "content": "Hello"}}],
            },
            ModelResponse,
        ),
        (
            CallTypes.text_completion.value,
            {"id": "test", "choices": [{"text": "Hello"}]},
            TextCompletionResponse,
        ),
        (
            CallTypes.embedding.value,
            {"data": [{"embedding": [0.1, 0.2, 0.3]}]},
            EmbeddingResponse,
        ),
        (
            CallTypes.rerank.value,
            {"id": "test", "results": [{"index": 0, "relevance_score": 0.9}]},
            RerankResponse,
        ),
        (
            CallTypes.transcription.value,
            {"text": "Hello, world!"},
            TranscriptionResponse,
        ),
    ],
)
def test_convert_cached_result_to_model_response(
    call_type, cached_result, expected_type
):
    """
    Assert that the cached result is converted to the correct type
    """
    caching_handler = LLMCachingHandler(
        original_function=lambda: None, request_kwargs={}, start_time=datetime.now()
    )
    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=call_type,
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Hello, how can I help you today?"}],
        function_id=str(uuid.uuid4()),
        stream=False,
        start_time=datetime.now(),
    )

    result = caching_handler._convert_cached_result_to_model_response(
        cached_result=cached_result,
        call_type=call_type,
        kwargs={},
        logging_obj=logging_obj,
        model="test-model",
        args=(),
    )

    assert isinstance(result, expected_type)
    assert result is not None


def test_combine_cached_embedding_response_with_api_result():
    """
    If the cached response has [cache_hit, None, cache_hit]
    result should be [cache_hit, api_result, cache_hit]
    """
    # Setup
    caching_handler = LLMCachingHandler(
        original_function=lambda: None, request_kwargs={}, start_time=datetime.now()
    )

    start_time = datetime.now()
    end_time = start_time + timedelta(seconds=1)

    # Create a CachingHandlerResponse with some cached and some None values
    cached_response = EmbeddingResponse(
        data=[
            Embedding(embedding=[0.1, 0.2, 0.3], index=0, object="embedding"),
            None,
            Embedding(embedding=[0.7, 0.8, 0.9], index=2, object="embedding"),
        ]
    )
    caching_handler_response = CachingHandlerResponse(
        final_embedding_cached_response=cached_response
    )

    # Create an API EmbeddingResponse for the missing value
    api_response = EmbeddingResponse(
        data=[Embedding(embedding=[0.4, 0.5, 0.6], index=1, object="embedding")]
    )

    # Call the method
    result = caching_handler._combine_cached_embedding_response_with_api_result(
        _caching_handler_response=caching_handler_response,
        embedding_response=api_response,
        start_time=start_time,
        end_time=end_time,
    )

    # Assertions
    assert isinstance(result, EmbeddingResponse)
    assert len(result.data) == 3
    assert result.data[0].embedding == [0.1, 0.2, 0.3]
    assert result.data[1].embedding == [0.4, 0.5, 0.6]
    assert result.data[2].embedding == [0.7, 0.8, 0.9]
    assert result._hidden_params["cache_hit"] == True
    assert isinstance(result._response_ms, float)
    assert result._response_ms > 0


def test_combine_cached_embedding_response_multiple_missing_values():
    """
    If the cached response has [cache_hit, None, None, cache_hit, None]
    result should be            [cache_hit, api_result, api_result, cache_hit, api_result]
    """

    # Setup
    caching_handler = LLMCachingHandler(
        original_function=lambda: None, request_kwargs={}, start_time=datetime.now()
    )

    start_time = datetime.now()
    end_time = start_time + timedelta(seconds=1)

    # Create a CachingHandlerResponse with some cached and some None values
    cached_response = EmbeddingResponse(
        data=[
            Embedding(embedding=[0.1, 0.2, 0.3], index=0, object="embedding"),
            None,
            None,
            Embedding(embedding=[0.7, 0.8, 0.9], index=3, object="embedding"),
            None,
        ]
    )

    caching_handler_response = CachingHandlerResponse(
        final_embedding_cached_response=cached_response
    )

    # Create an API EmbeddingResponse for the missing values
    api_response = EmbeddingResponse(
        data=[
            Embedding(embedding=[0.4, 0.5, 0.6], index=1, object="embedding"),
            Embedding(embedding=[0.4, 0.5, 0.6], index=2, object="embedding"),
            Embedding(embedding=[0.4, 0.5, 0.6], index=4, object="embedding"),
        ]
    )

    # Call the method
    result = caching_handler._combine_cached_embedding_response_with_api_result(
        _caching_handler_response=caching_handler_response,
        embedding_response=api_response,
        start_time=start_time,
        end_time=end_time,
    )

    # Assertions
    assert isinstance(result, EmbeddingResponse)
    assert len(result.data) == 5
    assert result.data[0].embedding == [0.1, 0.2, 0.3]
    assert result.data[1].embedding == [0.4, 0.5, 0.6]
    assert result.data[2].embedding == [0.4, 0.5, 0.6]
    assert result.data[3].embedding == [0.7, 0.8, 0.9]


@pytest.mark.asyncio
async def test_embedding_cache_model_field_consistency():
    """
    Test that the model field is consistently preserved in cached embedding responses.
    This ensures that cache hits return the same model field as the original API response.
    """
    # Setup cache
    setup_cache()

    caching_handler = LLMCachingHandler(
        original_function=aembedding, request_kwargs={}, start_time=datetime.now()
    )

    # Create a mock embedding response with a specific model
    original_model = "text-embedding-005"
    embedding_response = EmbeddingResponse(
        model=original_model,
        data=[
            Embedding(embedding=[0.1, 0.2, 0.3], index=0, object="embedding"),
            Embedding(embedding=[0.4, 0.5, 0.6], index=1, object="embedding"),
        ],
    )

    # Mock logging object
    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.aembedding.value,
        model=original_model,
        messages=[],  # Not used for embeddings
        function_id=str(uuid.uuid4()),
        stream=False,
        start_time=datetime.now(),
    )

    # Test parameters
    kwargs = {
        "model": original_model,
        "input": ["test input 1", "test input 2"],
        "caching": True,
    }

    # Step 1: Cache the embedding response
    await caching_handler.async_set_cache(
        result=embedding_response, original_function=aembedding, kwargs=kwargs
    )

    # Step 2: Retrieve from cache
    cached_response = await caching_handler._async_get_cache(
        model=original_model,
        original_function=aembedding,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=CallTypes.aembedding.value,
        kwargs=kwargs,
    )

    # Step 3: Verify the model field is preserved
    assert cached_response.final_embedding_cached_response is not None
    assert cached_response.final_embedding_cached_response.model == original_model
    assert len(cached_response.final_embedding_cached_response.data) == 2
    assert cached_response.final_embedding_cached_response.data[0].embedding == [
        0.1,
        0.2,
        0.3,
    ]
    assert cached_response.final_embedding_cached_response.data[0].index == 0
    assert cached_response.final_embedding_cached_response.data[1].embedding == [
        0.4,
        0.5,
        0.6,
    ]
    assert cached_response.final_embedding_cached_response.data[1].index == 1

    # Verify cache hit flag is set
    assert (
        cached_response.final_embedding_cached_response._hidden_params["cache_hit"]
        == True
    )


@pytest.mark.asyncio
async def test_embedding_cache_model_field_with_vendor_prefix():
    """
    Test that the model field is preserved even when using vendor-prefixed model names.
    This simulates the real-world scenario where models might be prefixed with vendor names.
    """
    # Setup cache
    setup_cache()

    caching_handler = LLMCachingHandler(
        original_function=aembedding, request_kwargs={}, start_time=datetime.now()
    )

    # Test with vendor-prefixed model name (like vertex_ai/text-embedding-005)
    vendor_model = "vertex_ai/text-embedding-005"
    actual_model = "text-embedding-005"  # What the provider actually returns

    # Create embedding response with the actual model name (as returned by provider)
    embedding_response = EmbeddingResponse(
        model=actual_model,  # Provider returns this
        data=[
            Embedding(embedding=[0.1, 0.2, 0.3], index=0, object="embedding"),
        ],
    )

    # Mock logging object
    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.aembedding.value,
        model=vendor_model,
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=False,
        start_time=datetime.now(),
    )

    # Test parameters with vendor-prefixed model
    kwargs = {
        "model": vendor_model,  # Request uses vendor prefix
        "input": ["test input"],
        "caching": True,
    }

    # Cache the response
    await caching_handler.async_set_cache(
        result=embedding_response, original_function=aembedding, kwargs=kwargs
    )

    # Retrieve from cache
    cached_response = await caching_handler._async_get_cache(
        model=vendor_model,
        original_function=aembedding,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=CallTypes.aembedding.value,
        kwargs=kwargs,
    )

    # Verify the model field matches the original provider response, not the request
    assert cached_response.final_embedding_cached_response is not None
    assert (
        cached_response.final_embedding_cached_response.model == actual_model
    )  # Should be the provider's model name
    assert (
        cached_response.final_embedding_cached_response.model != vendor_model
    )  # Should NOT be the vendor-prefixed name


def test_extract_model_from_cached_results():
    """
    Test the helper method that extracts model names from cached results.
    """
    caching_handler = LLMCachingHandler(
        original_function=aembedding, request_kwargs={}, start_time=datetime.now()
    )

    # Test with valid cached results
    non_null_list = [
        (
            0,
            {
                "embedding": [0.1, 0.2],
                "index": 0,
                "object": "embedding",
                "model": "text-embedding-005",
            },
        ),
        (
            1,
            {
                "embedding": [0.3, 0.4],
                "index": 1,
                "object": "embedding",
                "model": "text-embedding-005",
            },
        ),
    ]

    model_name = caching_handler._extract_model_from_cached_results(non_null_list)
    assert model_name == "text-embedding-005"

    # Test with missing model field
    non_null_list_no_model = [
        (0, {"embedding": [0.1, 0.2], "index": 0, "object": "embedding"}),
        (1, {"embedding": [0.3, 0.4], "index": 1, "object": "embedding"}),
    ]

    model_name = caching_handler._extract_model_from_cached_results(
        non_null_list_no_model
    )
    assert model_name is None

    # Test with empty list
    model_name = caching_handler._extract_model_from_cached_results([])
    assert model_name is None


@pytest.mark.asyncio
async def test_async_responses_api_caching():
    """
    Test that responses API calls are properly cached and retrieved.
    This verifies the full cache lifecycle for ResponsesAPIResponse objects.
    """
    # Setup cache
    setup_cache()

    caching_handler = LLMCachingHandler(
        original_function=aresponses, request_kwargs={}, start_time=datetime.now()
    )

    # Create a mock ResponsesAPIResponse
    original_model = "gpt-4o"
    responses_api_response = ResponsesAPIResponse(
        id="resp_test123",
        created_at=int(time.time()),
        status="completed",
        model=original_model,
        object="response",
        output=[
            {
                "type": "message",
                "id": "msg_123",
                "status": "completed",
                "role": "assistant",
                "content": [
                    {
                        "type": "output_text",
                        "text": "This is a test response from the responses API.",
                        "annotations": [],
                    }
                ],
            }
        ],
    )

    # Mock logging object
    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.aresponses.value,
        model=original_model,
        messages=[],  # Responses API uses input, not messages
        function_id=str(uuid.uuid4()),
        stream=False,
        start_time=datetime.now(),
    )

    # Test parameters
    kwargs = {
        "model": original_model,
        "input": "Tell me a short story",
        "max_output_tokens": 100,
        "caching": True,
    }

    # Step 1: Cache the responses API response
    await caching_handler.async_set_cache(
        result=responses_api_response, original_function=aresponses, kwargs=kwargs
    )

    await asyncio.sleep(0.5)

    # Step 2: Retrieve from cache
    cached_response = await caching_handler._async_get_cache(
        model=original_model,
        original_function=aresponses,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=CallTypes.aresponses.value,
        kwargs=kwargs,
    )

    # Step 3: Verify the response is properly cached and retrieved
    assert cached_response.cached_result is not None
    assert isinstance(cached_response.cached_result, ResponsesAPIResponse)
    assert cached_response.cached_result.id == responses_api_response.id
    assert cached_response.cached_result.model == original_model
    assert cached_response.cached_result.status == "completed"
    assert len(cached_response.cached_result.output) == 1

    # Verify cache hit flag is set
    assert cached_response.cached_result._hidden_params["cache_hit"] == True


@pytest.mark.asyncio
async def test_async_get_cache_updates_request_kwargs_for_streaming_responses():
    """
    Ensure streamed responses retain the normalized lookup kwargs so a later
    cache write can reuse the exact cache key from the read path.
    """
    setup_cache()

    caching_handler = LLMCachingHandler(
        original_function=aresponses,
        request_kwargs={"stale": True},
        start_time=datetime.now(),
    )

    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.aresponses.value,
        model="gpt-4o",
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=True,
        start_time=datetime.now(),
    )

    kwargs = {
        "model": "gpt-4o",
        "input": "hello",
        "stream": True,
        "caching": True,
    }

    await caching_handler._async_get_cache(
        model="gpt-4o",
        original_function=aresponses,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=CallTypes.aresponses.value,
        kwargs=kwargs,
    )

    assert "stale" not in caching_handler.request_kwargs
    assert caching_handler.request_kwargs["model"] == "gpt-4o"
    assert caching_handler.request_kwargs["input"] == "hello"
    assert caching_handler.request_kwargs["stream"] is True
    assert caching_handler.request_kwargs["cache_key"] == litellm.cache.get_cache_key(
        **caching_handler.request_kwargs
    )


def test_sync_responses_api_caching():
    """
    Test that synchronous responses API calls are properly cached and retrieved.
    """
    # Setup cache
    setup_cache()

    caching_handler = LLMCachingHandler(
        original_function=responses, request_kwargs={}, start_time=datetime.now()
    )

    # Create a mock ResponsesAPIResponse
    original_model = "gpt-4o"
    responses_api_response = ResponsesAPIResponse(
        id="resp_sync_test456",
        created_at=int(time.time()),
        status="completed",
        model=original_model,
        object="response",
        output=[
            {
                "type": "message",
                "id": "msg_456",
                "status": "completed",
                "role": "assistant",
                "content": [
                    {
                        "type": "output_text",
                        "text": "Sync response test.",
                        "annotations": [],
                    }
                ],
            }
        ],
    )

    # Mock logging object
    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.responses.value,
        model=original_model,
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=False,
        start_time=datetime.now(),
    )

    # Test parameters
    kwargs = {
        "model": original_model,
        "input": "Tell me another story",
        "max_output_tokens": 100,
        "caching": True,
    }

    # Step 1: Cache the responses API response
    caching_handler.sync_set_cache(result=responses_api_response, kwargs=kwargs)

    # Step 2: Retrieve from cache
    cached_response = caching_handler._sync_get_cache(
        model=original_model,
        original_function=responses,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=CallTypes.responses.value,
        kwargs=kwargs,
    )

    # Step 3: Verify the response is properly cached and retrieved
    assert cached_response.cached_result is not None
    assert isinstance(cached_response.cached_result, ResponsesAPIResponse)
    assert cached_response.cached_result.id == responses_api_response.id
    assert cached_response.cached_result.model == original_model
    assert cached_response.cached_result.status == "completed"

    # Verify cache hit flag is set
    assert cached_response.cached_result._hidden_params["cache_hit"] == True


def test_convert_cached_responses_api_result_to_model_response():
    """
    Test that cached ResponsesAPIResponse results are properly converted back
    to ResponsesAPIResponse objects with correct structure.
    """
    caching_handler = LLMCachingHandler(
        original_function=responses, request_kwargs={}, start_time=datetime.now()
    )

    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.responses.value,
        model="gpt-4o",
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=False,
        start_time=datetime.now(),
    )

    # Simulate cached result as a dictionary
    cached_result = {
        "id": "resp_convert_test789",
        "created_at": int(time.time()),
        "status": "completed",
        "model": "gpt-4o",
        "object": "response",
        "output": [
            {
                "type": "message",
                "id": "msg_789",
                "status": "completed",
                "role": "assistant",
                "content": [
                    {
                        "type": "output_text",
                        "text": "Conversion test response.",
                        "annotations": [],
                    }
                ],
            }
        ],
    }

    # Convert cached result to ResponsesAPIResponse
    result = caching_handler._convert_cached_result_to_model_response(
        cached_result=cached_result,
        call_type=CallTypes.responses.value,
        kwargs={"model": "gpt-4o", "input": "test"},
        logging_obj=logging_obj,
        model="gpt-4o",
        args=(),
    )

    # Verify conversion
    assert isinstance(result, ResponsesAPIResponse)
    assert result.id == "resp_convert_test789"
    assert result.model == "gpt-4o"
    assert result.status == "completed"
    assert len(result.output) == 1


def test_sync_get_cache_does_not_eagerly_log_streaming_responses_hits():
    litellm.set_verbose = True
    setup_cache()
    caching_handler = LLMCachingHandler(
        original_function=responses, request_kwargs={}, start_time=datetime.now()
    )

    original_model = "gpt-4o"
    responses_api_response = ResponsesAPIResponse(
        id="resp_stream_sync_hit",
        created_at=int(time.time()),
        status="completed",
        model=original_model,
        object="response",
        output=[
            {
                "type": "message",
                "id": "msg_stream_sync_hit",
                "status": "completed",
                "role": "assistant",
                "content": [
                    {
                        "type": "output_text",
                        "text": "Sync streamed cache hit response.",
                        "annotations": [],
                    }
                ],
            }
        ],
    )

    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.responses.value,
        model=original_model,
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=True,
        start_time=datetime.now(),
    )
    logging_obj.handle_sync_success_callbacks_for_async_calls = MagicMock()

    kwargs = {
        "model": original_model,
        "input": "Tell me a cached story",
        "stream": True,
        "caching": True,
    }

    caching_handler.sync_set_cache(result=responses_api_response, kwargs=kwargs)

    cached_response = caching_handler._sync_get_cache(
        model=original_model,
        original_function=responses,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=CallTypes.responses.value,
        kwargs=kwargs,
    )

    assert cached_response.cached_result is not None
    assert isinstance(
        cached_response.cached_result, CachedResponsesAPIStreamingIterator
    )
    logging_obj.handle_sync_success_callbacks_for_async_calls.assert_not_called()


def test_sync_get_cache_defers_streaming_completion_hit_callbacks():
    litellm.set_verbose = True
    setup_cache()
    caching_handler = LLMCachingHandler(
        original_function=completion, request_kwargs={}, start_time=datetime.now()
    )

    original_model = "gpt-4o"
    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.completion.value,
        model=original_model,
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=True,
        start_time=datetime.now(),
    )
    logging_obj.handle_sync_success_callbacks_for_async_calls = MagicMock()

    kwargs = {
        "model": original_model,
        "messages": [{"role": "user", "content": "Tell me a cached joke"}],
        "stream": True,
        "caching": True,
    }

    caching_handler.sync_set_cache(result=chat_completion_response, kwargs=kwargs)

    cached_response = caching_handler._sync_get_cache(
        model=original_model,
        original_function=completion,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=CallTypes.completion.value,
        kwargs=kwargs,
    )

    assert cached_response.cached_result is not None
    logging_obj.handle_sync_success_callbacks_for_async_calls.assert_not_called()


def test_should_defer_streaming_cache_hit_callbacks_for_any_streaming_request():
    logging_obj = MagicMock()
    logging_obj.model_call_details = {}
    stream_replay = CustomStreamWrapper(
        completion_stream=iter(()), model="gpt-4o", logging_obj=logging_obj
    )
    assert _should_defer_streaming_cache_hit_callbacks(cached_result=stream_replay) is True
    assert _should_defer_streaming_cache_hit_callbacks(cached_result=ModelResponse()) is False
    assert _should_defer_streaming_cache_hit_callbacks(cached_result={"id": "msg_1"}) is False


@pytest.mark.asyncio
async def test_async_get_cache_defers_streaming_completion_hit_callbacks():
    litellm.set_verbose = True
    setup_cache()
    caching_handler = LLMCachingHandler(
        original_function=completion, request_kwargs={}, start_time=datetime.now()
    )

    original_model = "gpt-4o"
    kwargs = {
        "model": original_model,
        "messages": [{"role": "user", "content": "Tell me a cached joke"}],
        "stream": True,
        "caching": True,
    }

    await caching_handler.async_set_cache(
        result=chat_completion_response,
        original_function=litellm.acompletion,
        kwargs=kwargs,
    )
    await asyncio.sleep(0.2)

    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.acompletion.value,
        model=original_model,
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=True,
        start_time=datetime.now(),
    )
    caching_handler._async_log_cache_hit_on_callbacks = MagicMock()

    cached_response = await caching_handler._async_get_cache(
        model=original_model,
        original_function=litellm.acompletion,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=CallTypes.acompletion.value,
        kwargs=kwargs,
    )

    assert cached_response is not None
    assert cached_response.cached_result is not None
    caching_handler._async_log_cache_hit_on_callbacks.assert_not_called()


def test_convert_cached_streaming_responses_result_to_iterator():
    """
    Test that cached streaming Responses results are replayed through a synthetic
    streaming iterator instead of being returned as a full response object.
    """
    caching_handler = LLMCachingHandler(
        original_function=responses, request_kwargs={}, start_time=datetime.now()
    )

    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.responses.value,
        model="gpt-4o",
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=True,
        start_time=datetime.now(),
    )

    cached_result = {
        "id": "resp_stream_cache_test",
        "created_at": int(time.time()),
        "status": "completed",
        "model": "gpt-4o",
        "object": "response",
        "output": [
            {
                "type": "message",
                "id": "msg_stream_cache_test",
                "status": "completed",
                "role": "assistant",
                "content": [
                    {
                        "type": "output_text",
                        "text": "Streaming cache replay test.",
                        "annotations": [],
                    }
                ],
            }
        ],
    }

    result = caching_handler._convert_cached_result_to_model_response(
        cached_result=cached_result,
        call_type=CallTypes.responses.value,
        kwargs={"model": "gpt-4o", "input": "test", "stream": True},
        logging_obj=logging_obj,
        model="gpt-4o",
        args=(),
    )

    assert isinstance(result, CachedResponsesAPIStreamingIterator)
    assert result.completed_response is not None
    assert result.completed_response.response.id == cached_result["id"]

    streamed_events = list(result)
    assert streamed_events[0].type == "response.created"
    assert streamed_events[1].type == "response.in_progress"
    assert streamed_events[2].type == "response.output_item.added"
    assert streamed_events[3].type == "response.content_part.added"
    assert streamed_events[-4].type == "response.output_text.done"
    assert streamed_events[-3].type == "response.content_part.done"
    assert streamed_events[-2].type == "response.output_item.done"
    assert streamed_events[-1].type == "response.completed"
    assert streamed_events[-1].response.id == cached_result["id"]
    assert streamed_events[-1].response.output[0].content[0].text == (
        "Streaming cache replay test."
    )


def test_is_chat_completion_cached_dict():
    assert _is_chat_completion_cached_dict(
        {"id": "chatcmpl-abc", "object": "chat.completion", "choices": []}
    )
    assert _is_chat_completion_cached_dict(
        {"id": "other", "object": "chat.completion.chunk", "choices": []}
    )
    assert _is_chat_completion_cached_dict(
        {"id": "no-object", "choices": [{"index": 0}]}
    )
    assert not _is_chat_completion_cached_dict(
        {"id": "resp_abc", "object": "response", "output": []}
    )


def test_convert_cached_aresponses_bridge_chat_completion_stream():
    """
    openai/responses chat-completions bridge caches ModelResponse JSON on aresponses
    cache keys; replay must not call ResponsesAPIResponse(**chatcmpl_dict).
    """
    caching_handler = LLMCachingHandler(
        original_function=aresponses, request_kwargs={}, start_time=datetime.now()
    )
    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.aresponses.value,
        model="gpt-5.4",
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=True,
        start_time=datetime.now(),
    )
    cached_result = {
        "id": "chatcmpl-bridge-cache-test",
        "object": "chat.completion",
        "created": int(time.time()),
        "model": "gpt-5.4",
        "choices": [
            {
                "index": 0,
                "message": {"role": "assistant", "content": "Hi!"},
                "finish_reason": "stop",
            }
        ],
        "usage": {
            "prompt_tokens": 7,
            "completion_tokens": 11,
            "total_tokens": 18,
        },
    }

    result = caching_handler._convert_cached_result_to_model_response(
        cached_result=cached_result,
        call_type=CallTypes.aresponses.value,
        kwargs={
            "model": "gpt-5.4",
            "stream": True,
            "messages": [{"role": "user", "content": "hi"}],
        },
        logging_obj=logging_obj,
        model="gpt-5.4",
        args=(),
    )

    assert isinstance(result, CustomStreamWrapper)


def test_convert_cached_streaming_reasoning_result_to_iterator():
    caching_handler = LLMCachingHandler(
        original_function=responses, request_kwargs={}, start_time=datetime.now()
    )

    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.responses.value,
        model="gpt-4o",
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=True,
        start_time=datetime.now(),
    )

    cached_result = {
        "id": "resp_stream_reasoning_cache_test",
        "created_at": int(time.time()),
        "status": "completed",
        "model": "gpt-4o",
        "object": "response",
        "output": [
            {
                "type": "reasoning",
                "id": "rs_stream_cache_test",
                "summary": [
                    {
                        "type": "summary_text",
                        "text": "Cached reasoning summary.",
                    }
                ],
            }
        ],
    }

    result = caching_handler._convert_cached_result_to_model_response(
        cached_result=cached_result,
        call_type=CallTypes.responses.value,
        kwargs={"model": "gpt-4o", "input": "test", "stream": True},
        logging_obj=logging_obj,
        model="gpt-4o",
        args=(),
    )

    assert isinstance(result, CachedResponsesAPIStreamingIterator)

    streamed_events = list(result)
    streamed_event_types = [
        event.type.value if hasattr(event.type, "value") else str(event.type)
        for event in streamed_events
    ]

    assert streamed_event_types[:3] == [
        "response.created",
        "response.in_progress",
        "response.output_item.added",
    ]
    assert streamed_event_types[-4:] == [
        "response.reasoning_summary_text.done",
        "response.reasoning_summary_part.done",
        "response.output_item.done",
        "response.completed",
    ]
    assert streamed_event_types.count("response.reasoning_summary_text.delta") >= 1

    delta_events = [
        event
        for event in streamed_events
        if (event.type.value if hasattr(event.type, "value") else str(event.type))
        == "response.reasoning_summary_text.delta"
    ]
    text_done_event = streamed_events[-4]
    part_done_event = streamed_events[-3]
    output_item_done_event = streamed_events[-2]

    assert all(delta_event.summary_index == 0 for delta_event in delta_events)
    assert text_done_event.text == "Cached reasoning summary."
    assert text_done_event.summary_index == 0
    assert part_done_event.part.type == "summary_text"
    assert part_done_event.part.text == "Cached reasoning summary."
    assert output_item_done_event.item.type == "reasoning"
    assert output_item_done_event.item.summary[0]["text"] == "Cached reasoning summary."


@pytest.mark.asyncio
async def test_responses_api_cache_with_different_inputs():
    """
    Test that different inputs to the responses API result in different cache keys.
    This ensures cache isolation between different requests.
    """
    # Setup cache
    setup_cache()

    caching_handler = LLMCachingHandler(
        original_function=aresponses, request_kwargs={}, start_time=datetime.now()
    )

    original_model = "gpt-4o"

    # First request
    response_1 = ResponsesAPIResponse(
        id="resp_1",
        created_at=int(time.time()),
        status="completed",
        model=original_model,
        object="response",
        output=[
            {
                "type": "message",
                "id": "msg_1",
                "status": "completed",
                "role": "assistant",
                "content": [
                    {"type": "output_text", "text": "Response 1", "annotations": []}
                ],
            }
        ],
    )

    kwargs_1 = {"model": original_model, "input": "First unique input", "caching": True}

    await caching_handler.async_set_cache(
        result=response_1, original_function=aresponses, kwargs=kwargs_1
    )

    # Second request with different input
    response_2 = ResponsesAPIResponse(
        id="resp_2",
        created_at=int(time.time()),
        status="completed",
        model=original_model,
        object="response",
        output=[
            {
                "type": "message",
                "id": "msg_2",
                "status": "completed",
                "role": "assistant",
                "content": [
                    {"type": "output_text", "text": "Response 2", "annotations": []}
                ],
            }
        ],
    )

    kwargs_2 = {
        "model": original_model,
        "input": "Second unique input",
        "caching": True,
    }

    await caching_handler.async_set_cache(
        result=response_2, original_function=aresponses, kwargs=kwargs_2
    )

    await asyncio.sleep(0.5)

    # Retrieve both from cache
    logging_obj_1 = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.aresponses.value,
        model=original_model,
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=False,
        start_time=datetime.now(),
    )

    logging_obj_2 = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=CallTypes.aresponses.value,
        model=original_model,
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=False,
        start_time=datetime.now(),
    )

    cached_1 = await caching_handler._async_get_cache(
        model=original_model,
        original_function=aresponses,
        logging_obj=logging_obj_1,
        start_time=datetime.now(),
        call_type=CallTypes.aresponses.value,
        kwargs=kwargs_1,
    )

    cached_2 = await caching_handler._async_get_cache(
        model=original_model,
        original_function=aresponses,
        logging_obj=logging_obj_2,
        start_time=datetime.now(),
        call_type=CallTypes.aresponses.value,
        kwargs=kwargs_2,
    )

    # Verify each input gets its own cached response
    assert cached_1.cached_result is not None
    assert cached_2.cached_result is not None
    assert cached_1.cached_result.id == "resp_1"
    assert cached_2.cached_result.id == "resp_2"

    # Access output content properly (could be dict or object)
    output_1 = cached_1.cached_result.output[0]
    if isinstance(output_1, dict):
        text_1 = output_1["content"][0]["text"]
    else:
        text_1 = (
            output_1.content[0].text
            if hasattr(output_1.content[0], "text")
            else output_1.content[0]["text"]
        )

    output_2 = cached_2.cached_result.output[0]
    if isinstance(output_2, dict):
        text_2 = output_2["content"][0]["text"]
    else:
        text_2 = (
            output_2.content[0].text
            if hasattr(output_2.content[0], "text")
            else output_2.content[0]["text"]
        )

    assert text_1 == "Response 1"
    assert text_2 == "Response 2"


@pytest.mark.parametrize(
    "call_type, cached_result, expected_type",
    [
        (
            CallTypes.responses.value,
            {
                "id": "resp_param_test",
                "created_at": 1234567890,
                "status": "completed",
                "model": "gpt-4o",
                "object": "response",
                "output": [
                    {
                        "type": "message",
                        "id": "msg_param",
                        "status": "completed",
                        "role": "assistant",
                        "content": [
                            {"type": "output_text", "text": "Test", "annotations": []}
                        ],
                    }
                ],
            },
            ResponsesAPIResponse,
        ),
        (
            CallTypes.aresponses.value,
            {
                "id": "resp_async_param_test",
                "created_at": 1234567890,
                "status": "completed",
                "model": "gpt-4o",
                "object": "response",
                "output": [
                    {
                        "type": "message",
                        "id": "msg_async_param",
                        "status": "completed",
                        "role": "assistant",
                        "content": [
                            {
                                "type": "output_text",
                                "text": "Async Test",
                                "annotations": [],
                            }
                        ],
                    }
                ],
            },
            ResponsesAPIResponse,
        ),
    ],
)
def test_convert_cached_responses_result_parameterized(
    call_type, cached_result, expected_type
):
    """
    Parameterized test to verify both sync and async responses API cached results
    are converted to the correct ResponsesAPIResponse type.
    """
    caching_handler = LLMCachingHandler(
        original_function=lambda: None, request_kwargs={}, start_time=datetime.now()
    )
    logging_obj = LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=call_type,
        model="gpt-4o",
        messages=[],
        function_id=str(uuid.uuid4()),
        stream=False,
        start_time=datetime.now(),
    )

    result = caching_handler._convert_cached_result_to_model_response(
        cached_result=cached_result,
        call_type=call_type,
        kwargs={},
        logging_obj=logging_obj,
        model="gpt-4o",
        args=(),
    )

    assert isinstance(result, expected_type)
    assert result is not None
    assert result.id == cached_result["id"]
    assert result.status == cached_result["status"]


@pytest.mark.asyncio
async def test_process_async_embedding_cached_response():
    llm_caching_handler = LLMCachingHandler(
        original_function=MagicMock(),
        request_kwargs={},
        start_time=datetime.now(),
    )

    args = {
        "cached_result": [
            {
                "embedding": [-0.025122925639152527, -0.019487135112285614],
                "index": 0,
                "object": "embedding",
            }
        ]
    }

    mock_logging_obj = MagicMock()
    mock_logging_obj.async_success_handler = AsyncMock()
    response, cache_hit = llm_caching_handler._process_async_embedding_cached_response(
        final_embedding_cached_response=None,
        cached_result=args["cached_result"],
        kwargs={"model": "text-embedding-ada-002", "input": "test"},
        logging_obj=mock_logging_obj,
        start_time=datetime.now(),
        model="text-embedding-ada-002",
    )

    assert cache_hit

    print(f"response: {response}")
    assert len(response.data) == 1


@pytest.mark.asyncio
async def test_embedding_cache_preserves_prompt_tokens_details():
    """Test that prompt_tokens_details (including image_count) survives a full cache hit."""
    llm_caching_handler = LLMCachingHandler(
        original_function=MagicMock(),
        request_kwargs={},
        start_time=datetime.now(),
    )

    cached_result = [
        {
            "embedding": [-0.025, -0.019],
            "index": 0,
            "object": "embedding",
            "model": "amazon.titan-embed-image-v1",
            "prompt_tokens_details": {"image_count": 1},
        }
    ]

    mock_logging_obj = MagicMock()
    mock_logging_obj.async_success_handler = AsyncMock()
    response, cache_hit = llm_caching_handler._process_async_embedding_cached_response(
        final_embedding_cached_response=None,
        cached_result=cached_result,
        kwargs={"model": "amazon.titan-embed-image-v1", "input": "base64imagedata"},
        logging_obj=mock_logging_obj,
        start_time=datetime.now(),
        model="amazon.titan-embed-image-v1",
    )

    assert cache_hit
    assert response.usage is not None
    assert response.usage.prompt_tokens_details is not None
    assert response.usage.prompt_tokens_details.image_count == 1


@pytest.mark.asyncio
async def test_embedding_cache_backward_compat_no_prompt_tokens_details():
    """Test that old cached items without prompt_tokens_details still work."""
    llm_caching_handler = LLMCachingHandler(
        original_function=MagicMock(),
        request_kwargs={},
        start_time=datetime.now(),
    )

    # Old-format cached item — no prompt_tokens_details field
    cached_result = [
        {
            "embedding": [-0.025, -0.019],
            "index": 0,
            "object": "embedding",
            "model": "text-embedding-ada-002",
        }
    ]

    mock_logging_obj = MagicMock()
    mock_logging_obj.async_success_handler = AsyncMock()
    response, cache_hit = llm_caching_handler._process_async_embedding_cached_response(
        final_embedding_cached_response=None,
        cached_result=cached_result,
        kwargs={"model": "text-embedding-ada-002", "input": "test"},
        logging_obj=mock_logging_obj,
        start_time=datetime.now(),
        model="text-embedding-ada-002",
    )

    assert cache_hit
    assert response.usage is not None
    assert response.usage.prompt_tokens_details is None


@pytest.mark.asyncio
async def test_embedding_cache_aggregates_multiple_image_counts():
    """Test that image_count is summed correctly across multiple cached items."""
    llm_caching_handler = LLMCachingHandler(
        original_function=MagicMock(),
        request_kwargs={},
        start_time=datetime.now(),
    )

    cached_result = [
        {
            "embedding": [-0.025, -0.019],
            "index": 0,
            "object": "embedding",
            "model": "amazon.titan-embed-image-v1",
            "prompt_tokens_details": {"image_count": 1},
        },
        {
            "embedding": [0.031, 0.042],
            "index": 1,
            "object": "embedding",
            "model": "amazon.titan-embed-image-v1",
            "prompt_tokens_details": {"image_count": 1},
        },
    ]

    mock_logging_obj = MagicMock()
    mock_logging_obj.async_success_handler = AsyncMock()
    response, cache_hit = llm_caching_handler._process_async_embedding_cached_response(
        final_embedding_cached_response=None,
        cached_result=cached_result,
        kwargs={
            "model": "amazon.titan-embed-image-v1",
            "input": ["img1", "img2"],
        },
        logging_obj=mock_logging_obj,
        start_time=datetime.now(),
        model="amazon.titan-embed-image-v1",
    )

    assert cache_hit
    assert response.usage.prompt_tokens_details is not None
    assert response.usage.prompt_tokens_details.image_count == 2


def test_combine_usage_merges_prompt_tokens_details():
    """Test that combine_usage merges prompt_tokens_details from both Usage objects."""
    from litellm.types.utils import PromptTokensDetailsWrapper, Usage

    llm_caching_handler = LLMCachingHandler(
        original_function=MagicMock(),
        request_kwargs={},
        start_time=datetime.now(),
    )

    usage1 = Usage(
        prompt_tokens=10,
        completion_tokens=0,
        total_tokens=10,
        prompt_tokens_details=PromptTokensDetailsWrapper(image_count=1),
    )
    usage2 = Usage(
        prompt_tokens=20,
        completion_tokens=0,
        total_tokens=20,
        prompt_tokens_details=PromptTokensDetailsWrapper(image_count=2),
    )

    combined = llm_caching_handler.combine_usage(usage1, usage2)

    assert combined.prompt_tokens == 30
    assert combined.total_tokens == 30
    assert combined.prompt_tokens_details is not None
    assert combined.prompt_tokens_details.image_count == 3


def test_combine_usage_handles_none_details():
    """Test that combine_usage works when one or both sides have null prompt_tokens_details."""
    from litellm.types.utils import PromptTokensDetailsWrapper, Usage

    llm_caching_handler = LLMCachingHandler(
        original_function=MagicMock(),
        request_kwargs={},
        start_time=datetime.now(),
    )

    # Both null
    usage_a = Usage(prompt_tokens=10, completion_tokens=0, total_tokens=10)
    usage_b = Usage(prompt_tokens=20, completion_tokens=0, total_tokens=20)
    combined = llm_caching_handler.combine_usage(usage_a, usage_b)
    assert combined.prompt_tokens_details is None

    # Only first has details
    usage_c = Usage(
        prompt_tokens=10,
        completion_tokens=0,
        total_tokens=10,
        prompt_tokens_details=PromptTokensDetailsWrapper(image_count=1),
    )
    combined = llm_caching_handler.combine_usage(usage_c, usage_b)
    assert combined.prompt_tokens_details is not None
    assert combined.prompt_tokens_details.image_count == 1

    # Only second has details
    combined = llm_caching_handler.combine_usage(usage_a, usage_c)
    assert combined.prompt_tokens_details is not None
    assert combined.prompt_tokens_details.image_count == 1


def _build_logging_obj(call_type: str, stream: bool):
    import uuid as _uuid

    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging

    return LiteLLMLogging(
        litellm_call_id=str(datetime.now()),
        call_type=call_type,
        model="gpt-5.4",
        messages=[],
        function_id=str(_uuid.uuid4()),
        stream=stream,
        start_time=datetime.now(),
    )


def test_convert_cached_responses_bridge_chat_completion_nonstream():
    """openai/responses chat-completions bridge: non-streaming cache hit replays as ModelResponse."""
    from litellm import responses
    from litellm.types.utils import CallTypes, ModelResponse

    caching_handler = LLMCachingHandler(
        original_function=responses, request_kwargs={}, start_time=datetime.now()
    )
    cached_result = {
        "id": "chatcmpl-bridge-nonstream",
        "object": "chat.completion",
        "created": int(time.time()),
        "model": "gpt-5.4",
        "choices": [
            {
                "index": 0,
                "message": {"role": "assistant", "content": "Hi!"},
                "finish_reason": "stop",
            }
        ],
        "usage": {"prompt_tokens": 7, "completion_tokens": 11, "total_tokens": 18},
    }

    result = caching_handler._convert_cached_result_to_model_response(
        cached_result=cached_result,
        call_type=CallTypes.responses.value,
        kwargs={
            "model": "gpt-5.4",
            "stream": False,
            "messages": [{"role": "user", "content": "hi"}],
        },
        logging_obj=_build_logging_obj(CallTypes.responses.value, stream=False),
        model="gpt-5.4",
        args=(),
    )

    assert isinstance(result, ModelResponse)
    assert result.choices[0].message.content == "Hi!"


def test_convert_cached_responses_legacy_nonstream_path():
    """Genuine ResponsesAPIResponse dict (no chatcmpl/choices) falls through legacy path."""
    from litellm import responses
    from litellm.types.llms.openai import ResponsesAPIResponse
    from litellm.types.utils import CallTypes

    caching_handler = LLMCachingHandler(
        original_function=responses, request_kwargs={}, start_time=datetime.now()
    )
    cached_result = {
        "id": "resp_legacy_nonstream",
        "created_at": int(time.time()),
        "status": "completed",
        "model": "gpt-4o",
        "object": "response",
        "output": [
            {
                "type": "message",
                "id": "msg_legacy",
                "status": "completed",
                "role": "assistant",
                "content": [
                    {
                        "type": "output_text",
                        "text": "legacy response",
                        "annotations": [],
                    }
                ],
            }
        ],
    }

    result = caching_handler._convert_cached_result_to_model_response(
        cached_result=cached_result,
        call_type=CallTypes.responses.value,
        kwargs={"model": "gpt-4o", "input": "hi", "stream": False},
        logging_obj=_build_logging_obj(CallTypes.responses.value, stream=False),
        model="gpt-4o",
        args=(),
    )

    assert isinstance(result, ResponsesAPIResponse)
    assert result.id == "resp_legacy_nonstream"


def test_convert_cached_responses_legacy_stream_path():
    """Genuine ResponsesAPIResponse dict (no chatcmpl/choices) on stream falls through legacy path."""
    from litellm import responses
    from litellm.responses.streaming_iterator import (
        CachedResponsesAPIStreamingIterator,
    )
    from litellm.types.utils import CallTypes

    caching_handler = LLMCachingHandler(
        original_function=responses, request_kwargs={}, start_time=datetime.now()
    )
    cached_result = {
        "id": "resp_legacy_stream",
        "created_at": int(time.time()),
        "status": "completed",
        "model": "gpt-4o",
        "object": "response",
        "output": [
            {
                "type": "message",
                "id": "msg_legacy_stream",
                "status": "completed",
                "role": "assistant",
                "content": [
                    {
                        "type": "output_text",
                        "text": "legacy stream",
                        "annotations": [],
                    }
                ],
            }
        ],
    }

    result = caching_handler._convert_cached_result_to_model_response(
        cached_result=cached_result,
        call_type=CallTypes.responses.value,
        kwargs={"model": "gpt-4o", "input": "hi", "stream": True},
        logging_obj=_build_logging_obj(CallTypes.responses.value, stream=True),
        model="gpt-4o",
        args=(),
    )

    assert isinstance(result, CachedResponsesAPIStreamingIterator)


@pytest.mark.asyncio
async def test_embedding_cache_restores_stored_prompt_tokens_for_image_input():
    """Image-embedding cache hit restores prompt_tokens=0 from the stored value
    instead of recomputing a bogus count by tokenizing the base64 input."""
    llm_caching_handler = LLMCachingHandler(
        original_function=MagicMock(),
        request_kwargs={},
        start_time=datetime.now(),
    )

    # base64-like blob — token_counter over this would return a large nonzero count
    image_input = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNk" * 50

    cached_result = [
        {
            "embedding": [-0.025, -0.019],
            "index": 0,
            "object": "embedding",
            "model": "amazon.titan-embed-image-v1",
            "prompt_tokens": 0,
            "prompt_tokens_details": {"image_count": 1},
        }
    ]

    mock_logging_obj = MagicMock()
    mock_logging_obj.async_success_handler = AsyncMock()
    response, cache_hit = llm_caching_handler._process_async_embedding_cached_response(
        final_embedding_cached_response=None,
        cached_result=cached_result,
        kwargs={"model": "amazon.titan-embed-image-v1", "input": image_input},
        logging_obj=mock_logging_obj,
        start_time=datetime.now(),
        model="amazon.titan-embed-image-v1",
    )

    assert cache_hit
    assert response.usage is not None
    assert response.usage.prompt_tokens == 0
    assert response.usage.total_tokens == 0
    assert response.usage.prompt_tokens_details.image_count == 1


@pytest.mark.asyncio
async def test_embedding_cache_sums_stored_prompt_tokens_across_items():
    """A multi-item cache hit sums the stored per-item prompt_tokens back to the total."""
    llm_caching_handler = LLMCachingHandler(
        original_function=MagicMock(),
        request_kwargs={},
        start_time=datetime.now(),
    )

    cached_result = [
        {
            "embedding": [-0.01],
            "index": 0,
            "object": "embedding",
            "model": "text-embedding-3-small",
            "prompt_tokens": 5,
        },
        {
            "embedding": [-0.02],
            "index": 1,
            "object": "embedding",
            "model": "text-embedding-3-small",
            "prompt_tokens": 4,
        },
    ]

    mock_logging_obj = MagicMock()
    mock_logging_obj.async_success_handler = AsyncMock()
    response, cache_hit = llm_caching_handler._process_async_embedding_cached_response(
        final_embedding_cached_response=None,
        cached_result=cached_result,
        kwargs={"model": "text-embedding-3-small", "input": ["hello world", "foo bar"]},
        logging_obj=mock_logging_obj,
        start_time=datetime.now(),
        model="text-embedding-3-small",
    )

    assert cache_hit
    assert response.usage.prompt_tokens == 9
    assert response.usage.total_tokens == 9


@pytest.mark.asyncio
async def test_embedding_cache_falls_back_to_token_counter_for_legacy_entries():
    """Legacy cache entries with no stored prompt_tokens still recompute via token_counter
    for str inputs (backward compatibility)."""
    llm_caching_handler = LLMCachingHandler(
        original_function=MagicMock(),
        request_kwargs={},
        start_time=datetime.now(),
    )

    # No prompt_tokens key — pre-fix entry
    cached_result = [
        {
            "embedding": [-0.025, -0.019],
            "index": 0,
            "object": "embedding",
            "model": "text-embedding-ada-002",
        },
    ]

    mock_logging_obj = MagicMock()
    mock_logging_obj.async_success_handler = AsyncMock()
    response, cache_hit = llm_caching_handler._process_async_embedding_cached_response(
        final_embedding_cached_response=None,
        cached_result=cached_result,
        kwargs={"model": "text-embedding-ada-002", "input": "hello world"},
        logging_obj=mock_logging_obj,
        start_time=datetime.now(),
        model="text-embedding-ada-002",
    )

    assert cache_hit
    # token_counter over "hello world" yields a nonzero count — fallback path still runs
    assert response.usage.prompt_tokens > 0


@pytest.mark.asyncio
async def test_embedding_cache_hit_sets_custom_llm_provider_on_logging_obj():
    """A full embedding cache hit must stamp the resolved provider onto the logging
    obj so spend logs record the provider instead of None/unknown."""
    from litellm.types.utils import CallTypes

    llm_caching_handler = LLMCachingHandler(
        original_function=MagicMock(),
        request_kwargs={},
        start_time=datetime.now(),
    )

    cached_result = [
        {
            "embedding": [-0.025, -0.019],
            "index": 0,
            "object": "embedding",
            "model": "text-embedding-3-small",
            "prompt_tokens": 5,
        }
    ]

    logging_obj = _build_logging_obj(CallTypes.aembedding.value, stream=False)
    logging_obj.async_success_handler = AsyncMock()

    response, cache_hit = llm_caching_handler._process_async_embedding_cached_response(
        final_embedding_cached_response=None,
        cached_result=cached_result,
        kwargs={"model": "text-embedding-3-small", "input": "hello world"},
        logging_obj=logging_obj,
        start_time=datetime.now(),
        model="text-embedding-3-small",
    )

    assert cache_hit
    assert logging_obj.model_call_details["custom_llm_provider"] == "openai"


def test_sync_stream_responses_cache_hit_sets_custom_llm_provider_on_logging_obj(monkeypatch):
    import litellm
    from litellm.caching.caching import Cache
    from litellm.types.utils import CallTypes

    monkeypatch.setattr(litellm, "cache", Cache(type="local"))
    kwargs = {"model": "azure/gpt-5.4-mini", "input": "hello", "stream": True}
    cached_response = {
        "id": "resp_sync_stream",
        "created_at": int(time.time()),
        "status": "completed",
        "model": "gpt-5.4-mini",
        "object": "response",
        "output": [
            {
                "type": "message",
                "id": "msg_sync_stream",
                "status": "completed",
                "role": "assistant",
                "content": [{"type": "output_text", "text": "hi", "annotations": []}],
            }
        ],
    }
    litellm.cache.add_cache(json.dumps(cached_response), **kwargs)
    handler = LLMCachingHandler(original_function=litellm.responses, request_kwargs=kwargs, start_time=datetime.now())
    logging_obj = _build_logging_obj(CallTypes.responses.value, stream=True)

    hit = handler._sync_get_cache(
        model="azure/gpt-5.4-mini",
        original_function=litellm.responses,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=CallTypes.responses.value,
        kwargs=kwargs,
        args=(),
    )

    assert hit.cached_result is not None
    assert logging_obj.model_call_details["custom_llm_provider"] == "azure"
    assert logging_obj.model_call_details["litellm_params"]["custom_llm_provider"] == "azure"


def test_request_kwargs_does_not_retain_logging_obj():
    """
    The caching handler lives on logging_obj._llm_caching_handler, so keeping
    litellm_logging_obj inside request_kwargs closes a reference cycle
    (Logging -> LLMCachingHandler -> kwargs -> Logging). That cycle keeps the
    full request payload alive until a generational GC pass instead of being
    freed by refcount when the request finishes; under bursts of large-token
    requests this presents as stepwise RSS growth that never returns to
    baseline. Other kwargs (messages included) must be preserved.
    """
    logging_obj = MagicMock()
    kwargs = {
        "model": "gpt-4o",
        "messages": [{"role": "user", "content": "hello"}],
        "litellm_logging_obj": logging_obj,
    }

    handler = LLMCachingHandler(
        original_function=MagicMock(),
        request_kwargs=kwargs,
        start_time=datetime.now(),
    )

    assert "litellm_logging_obj" not in handler.request_kwargs
    assert handler.request_kwargs["messages"] == kwargs["messages"]
    assert handler.request_kwargs["model"] == "gpt-4o"


def test_async_cache_write_completes_when_asyncio_run_closes_the_loop(monkeypatch):
    """
    Regression test for the SDK losing async cache writes in short-lived scripts:
    async_set_cache dispatched the write as a bare fire-and-forget task, so
    asyncio.run cancelled it at loop close before the write landed (LIT-6184,
    deterministic with hiredis installed). The write must survive loop shutdown.
    """
    import litellm

    writes = []

    class _SlowWriteCache:
        supported_call_types = ["acompletion"]
        cache = None

        async def async_add_cache(self, result, dynamic_cache_object=None, **kwargs):
            await asyncio.sleep(0.2)
            writes.append(result)

    async def acompletion(**kwargs):
        return None

    handler = LLMCachingHandler(
        original_function=acompletion,
        request_kwargs={},
        start_time=datetime.now(),
    )
    monkeypatch.setattr(litellm, "cache", _SlowWriteCache())

    async def _short_lived_script():
        await handler.async_set_cache(
            result=litellm.ModelResponse(),
            original_function=acompletion,
            kwargs={},
        )

    asyncio.run(_short_lived_script())

    assert len(writes) == 1


def test_async_cache_write_runs_in_the_post_response_phase_without_leaking_it(monkeypatch):
    """The response-cache write happens after the response is handed to the caller, so the
    service spans it logs must detach from the request trace even while the server span is
    still open. The marker must stay inside the write task and not leak into the request."""
    import litellm

    phases = []

    class _PhaseRecordingCache:
        supported_call_types = ["acompletion"]
        cache = None

        async def async_add_cache(self, result, dynamic_cache_object=None, **kwargs):
            phases.append(in_post_response_phase())

    async def acompletion(**kwargs):
        return None

    handler = LLMCachingHandler(original_function=acompletion, request_kwargs={}, start_time=datetime.now())
    monkeypatch.setattr(litellm, "cache", _PhaseRecordingCache())

    async def _request():
        await handler.async_set_cache(result=litellm.ModelResponse(), original_function=acompletion, kwargs={})
        leaked = in_post_response_phase()
        await asyncio.gather(*_PENDING_CACHE_WRITES)
        return leaked

    assert asyncio.run(_request()) is False, "the phase must not leak into the request task"
    assert phases == [True], "async_add_cache must observe the post-response phase"


@pytest.mark.asyncio
async def test_cache_hit_records_the_looked_up_key_as_the_preset_cache_key(monkeypatch):
    """The spend log for a cache hit must reuse the key the lookup already computed instead of hashing again."""
    import litellm
    from litellm.caching.caching import Cache
    from litellm.types.utils import CallTypes

    async def acompletion(**kwargs):
        return None

    monkeypatch.setattr(litellm, "cache", Cache(type="local"))
    kwargs = {"model": "gpt-5.4", "messages": [{"role": "user", "content": "hello"}], "caching": True}
    await litellm.cache.async_add_cache(
        litellm.ModelResponse(choices=[{"message": {"role": "assistant", "content": "hi"}}]), **kwargs
    )
    handler = LLMCachingHandler(original_function=acompletion, request_kwargs=kwargs, start_time=datetime.now())
    logging_obj = _build_logging_obj(CallTypes.acompletion.value, stream=False)
    logging_obj.async_success_handler = AsyncMock()

    hit = await handler._async_get_cache(
        model="gpt-5.4",
        original_function=acompletion,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=CallTypes.acompletion.value,
        kwargs=kwargs,
        args=(),
    )

    assert hit is not None and hit.cached_result is not None
    assert handler.preset_cache_key is not None
    assert logging_obj.litellm_params["preset_cache_key"] == handler.preset_cache_key
    assert hit.cached_result._hidden_params["cache_key"] == handler.preset_cache_key


@pytest.mark.asyncio
async def test_converted_stream_cache_hit_replayed_as_plain_object_logs_at_hit_time(monkeypatch):
    import litellm
    from litellm.caching.caching import Cache
    from litellm.types.utils import CallTypes

    async def aanthropic_messages(**kwargs):
        return None

    monkeypatch.setattr(litellm, "cache", Cache(type="local"))
    kwargs = {
        "model": "claude-sonnet-5",
        "messages": [{"role": "user", "content": "hello"}],
        "max_tokens": 16,
        "caching": True,
        "stream": False,
        "_websearch_interception_converted_stream": True,
    }
    cached_message = {
        "id": "msg_1",
        "type": "message",
        "role": "assistant",
        "content": [{"type": "text", "text": "hi"}],
    }
    await litellm.cache.async_add_cache(cached_message, **kwargs)
    handler = LLMCachingHandler(original_function=aanthropic_messages, request_kwargs=kwargs, start_time=datetime.now())
    logging_obj = _build_logging_obj(CallTypes.aanthropic_messages.value, stream=False)
    logging_obj.async_success_handler = AsyncMock()
    logging_obj.handle_sync_success_callbacks_for_async_calls = MagicMock()

    hit = await handler._async_get_cache(
        model="claude-sonnet-5",
        original_function=aanthropic_messages,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=CallTypes.aanthropic_messages.value,
        kwargs=kwargs,
        args=(),
    )

    assert hit is not None and hit.cached_result == cached_message
    logging_obj.handle_sync_success_callbacks_for_async_calls.assert_called_once()
    assert logging_obj.handle_sync_success_callbacks_for_async_calls.call_args.kwargs["cache_hit"] is True


@pytest.mark.asyncio
async def test_agentic_loop_followup_cache_hit_with_converted_stream_marker_replays_as_plain_object(monkeypatch):
    import litellm
    from litellm.caching.caching import Cache
    from litellm.types.utils import CallTypes

    async def acompletion(**kwargs):
        return None

    monkeypatch.setattr(litellm, "cache", Cache(type="local"))
    kwargs = {
        "model": "gpt-5.6",
        "messages": [{"role": "user", "content": "run the code"}],
        "caching": True,
        "stream": False,
        "_code_interpreter_interception_converted_stream": True,
        "_agentic_loop_depth": 1,
    }
    await litellm.cache.async_add_cache(
        litellm.ModelResponse(choices=[{"message": {"role": "assistant", "content": "done"}}]), **kwargs
    )
    handler = LLMCachingHandler(original_function=acompletion, request_kwargs=kwargs, start_time=datetime.now())
    logging_obj = _build_logging_obj(CallTypes.acompletion.value, stream=False)
    logging_obj.async_success_handler = AsyncMock()
    logging_obj.handle_sync_success_callbacks_for_async_calls = MagicMock()

    hit = await handler._async_get_cache(
        model="gpt-5.6",
        original_function=acompletion,
        logging_obj=logging_obj,
        start_time=datetime.now(),
        call_type=CallTypes.acompletion.value,
        kwargs=kwargs,
        args=(),
    )

    assert hit is not None and isinstance(hit.cached_result, litellm.ModelResponse)
    assert hit.cached_result.choices[0].message.content == "done"
    logging_obj.handle_sync_success_callbacks_for_async_calls.assert_called_once()
    assert logging_obj.handle_sync_success_callbacks_for_async_calls.call_args.kwargs["cache_hit"] is True


@pytest.mark.asyncio
async def test_partial_embedding_cache_hit_sends_only_misses_and_keeps_input_order(monkeypatch):
    import litellm
    from litellm import CustomLLM
    from litellm.caching.caching import Cache
    from litellm.types.utils import Embedding, EmbeddingResponse

    class RecordingEmbedder(CustomLLM):
        provider_inputs: tuple[tuple[str, ...], ...] = ()

        async def aembedding(self, model, input, model_response, **kwargs) -> EmbeddingResponse:
            self.provider_inputs = (*self.provider_inputs, tuple(input))
            return EmbeddingResponse(
                model=model,
                data=[
                    Embedding(embedding=[float(len(text))], index=idx, object="embedding")
                    for idx, text in enumerate(input)
                ],
            )

    embedder = RecordingEmbedder()
    monkeypatch.setattr(litellm, "custom_provider_map", [{"provider": "recording-embedder", "custom_handler": embedder}])
    monkeypatch.setattr(litellm, "provider_list", [*litellm.provider_list, "recording-embedder"])
    monkeypatch.setattr(litellm, "_custom_providers", [*litellm._custom_providers, "recording-embedder"])
    monkeypatch.setattr(litellm, "cache", Cache(type="local"))

    await litellm.aembedding(model="recording-embedder/m", input=["aa", "bbbb"])
    await asyncio.gather(*_PENDING_CACHE_WRITES)
    mixed_input = ["c", "aa", "ddd", "bbbb", "eeeee"]
    response = await litellm.aembedding(model="recording-embedder/m", input=mixed_input)
    await asyncio.gather(*_PENDING_CACHE_WRITES)

    assert embedder.provider_inputs == (("aa", "bbbb"), ("c", "ddd", "eeeee")), embedder.provider_inputs
    assert [item["index"] for item in response.data] == [0, 1, 2, 3, 4]
    assert [item["embedding"] for item in response.data] == [[float(len(text))] for text in mixed_input]
    assert response._hidden_params["cache_hit"] is True, "a partial hit must still be reported as a cache hit"

    repeat = await litellm.aembedding(model="recording-embedder/m", input=mixed_input)

    assert len(embedder.provider_inputs) == 2, embedder.provider_inputs
    assert [item["embedding"] for item in repeat.data] == [[float(len(text))] for text in mixed_input]