litellm/tests/test_litellm/caching/test_caching_handler.py

import asyncio
import json
import os
import sys
import time
from unittest.mock import MagicMock, patch

import httpx
import pytest
import respx
from fastapi.testclient import TestClient

sys.path.insert(
    0, os.path.abspath("../../..")
)  # Adds the parent directory to the system path
from datetime import datetime
from unittest.mock import AsyncMock, MagicMock

from litellm.caching.caching_handler import LLMCachingHandler


@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 test_is_chat_completion_cached_dict():
    from litellm.caching.caching_handler import _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 _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_aresponses_bridge_chat_completion_stream():
    """openai/responses chat-completions bridge: streaming cache hit replays as chat stream."""
    from litellm import aresponses
    from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
    from litellm.types.utils import CallTypes

    caching_handler = LLMCachingHandler(
        original_function=aresponses, request_kwargs={}, 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=_build_logging_obj(CallTypes.aresponses.value, stream=True),
        model="gpt-5.4",
        args=(),
    )

    assert isinstance(result, CustomStreamWrapper)


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)