Added LiteLLM to the stack

Development/litellm/tests/llm_translation/test_llm_response_utils/test_convert_dict_to_chat_completion.py

@@ -0,0 +1,905 @@
+import json
+import os
+import sys
+from datetime import datetime
+
+sys.path.insert(
+    0, os.path.abspath("../../../")
+)  # Adds the parent directory to the system path
+
+import litellm
+import pytest
+from datetime import timedelta
+
+from litellm.types.utils import (
+    ModelResponse,
+    Message,
+    Choices,
+    PromptTokensDetailsWrapper,
+    CompletionTokensDetailsWrapper,
+    Usage,
+)
+
+from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
+    convert_to_model_response_object,
+)
+
+
+def test_convert_to_model_response_object_basic():
+    """Test basic conversion with all fields present."""
+    response_object = {
+        "id": "chatcmpl-123456",
+        "object": "chat.completion",
+        "created": 1728933352,
+        "model": "gpt-4o-2024-08-06",
+        "choices": [
+            {
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": "Hi there! How can I assist you today?",
+                    "refusal": None,
+                },
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {
+            "prompt_tokens": 19,
+            "completion_tokens": 10,
+            "total_tokens": 29,
+            "prompt_tokens_details": {"cached_tokens": 0},
+            "completion_tokens_details": {"reasoning_tokens": 0},
+        },
+        "system_fingerprint": "fp_6b68a8204b",
+    }
+
+    result = convert_to_model_response_object(
+        model_response_object=ModelResponse(),
+        response_object=response_object,
+        stream=False,
+        start_time=datetime.now(),
+        end_time=datetime.now(),
+        hidden_params=None,
+        _response_headers=None,
+        convert_tool_call_to_json_mode=False,
+    )
+
+    assert isinstance(result, ModelResponse)
+    assert result.id == "chatcmpl-123456"
+    assert len(result.choices) == 1
+    assert isinstance(result.choices[0], Choices)
+
+    # Model details
+    assert result.model == "gpt-4o-2024-08-06"
+    assert result.object == "chat.completion"
+    assert result.created == 1728933352
+
+    # Choices assertions
+    choice = result.choices[0]
+    print("choice[0]", choice)
+    assert choice.index == 0
+    assert isinstance(choice.message, Message)
+    assert choice.message.role == "assistant"
+    assert choice.message.content == "Hi there! How can I assist you today?"
+    assert choice.finish_reason == "stop"
+
+    # Usage assertions
+    assert result.usage.prompt_tokens == 19
+    assert result.usage.completion_tokens == 10
+    assert result.usage.total_tokens == 29
+    assert result.usage.prompt_tokens_details == PromptTokensDetailsWrapper(
+        cached_tokens=0
+    )
+    assert result.usage.completion_tokens_details == CompletionTokensDetailsWrapper(
+        reasoning_tokens=0
+    )
+
+    # Other fields
+    assert result.system_fingerprint == "fp_6b68a8204b"
+
+    # hidden params
+    assert result._hidden_params is not None
+
+
+def test_convert_image_input_dict_response_to_chat_completion_response():
+    """Test conversion on a response with an image input."""
+    response_object = {
+        "id": "chatcmpl-123",
+        "object": "chat.completion",
+        "created": 1677652288,
+        "model": "gpt-4o-mini",
+        "system_fingerprint": "fp_44709d6fcb",
+        "choices": [
+            {
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": "\n\nThis image shows a wooden boardwalk extending through a lush green marshland.",
+                },
+                "logprobs": None,
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {
+            "prompt_tokens": 9,
+            "completion_tokens": 12,
+            "total_tokens": 21,
+            "completion_tokens_details": {"reasoning_tokens": 0},
+        },
+    }
+
+    result = convert_to_model_response_object(
+        model_response_object=ModelResponse(),
+        response_object=response_object,
+        stream=False,
+        start_time=datetime.now(),
+        end_time=datetime.now(),
+        hidden_params=None,
+        _response_headers=None,
+        convert_tool_call_to_json_mode=False,
+    )
+
+    assert isinstance(result, ModelResponse)
+    assert result.id == "chatcmpl-123"
+    assert result.object == "chat.completion"
+    assert result.created == 1677652288
+    assert result.model == "gpt-4o-mini"
+    assert result.system_fingerprint == "fp_44709d6fcb"
+
+    assert len(result.choices) == 1
+    choice = result.choices[0]
+    assert choice.index == 0
+    assert isinstance(choice.message, Message)
+    assert choice.message.role == "assistant"
+    assert (
+        choice.message.content
+        == "\n\nThis image shows a wooden boardwalk extending through a lush green marshland."
+    )
+    assert choice.finish_reason == "stop"
+
+    assert result.usage.prompt_tokens == 9
+    assert result.usage.completion_tokens == 12
+    assert result.usage.total_tokens == 21
+    assert result.usage.completion_tokens_details == CompletionTokensDetailsWrapper(
+        reasoning_tokens=0
+    )
+
+    assert result._hidden_params is not None
+
+
+def test_convert_to_model_response_object_tool_calls_invalid_json_arguments():
+    """
+    Critical test - this is a basic response from OpenAI API
+
+    Test conversion with tool calls.
+
+    """
+    response_object = {
+        "id": "chatcmpl-AK1uqisVA9OjUNkEuE53GJc8HPYlz",
+        "choices": [
+            {
+                "index": 0,
+                "finish_reason": "length",
+                "logprobs": None,
+                "message": {
+                    "content": None,
+                    "refusal": None,
+                    "role": "assistant",
+                    "audio": None,
+                    "function_call": None,
+                    "tool_calls": [
+                        {
+                            "id": "call_GED1Xit8lU7cNsjVM6dt2fTq",
+                            "function": {
+                                "arguments": '{"location":"Boston, MA","unit":"fahren',
+                                "name": "get_current_weather",
+                            },
+                            "type": "function",
+                        }
+                    ],
+                },
+            }
+        ],
+        "created": 1729337288,
+        "model": "gpt-4o-2024-08-06",
+        "object": "chat.completion",
+        "service_tier": None,
+        "system_fingerprint": "fp_45c6de4934",
+        "usage": {
+            "completion_tokens": 10,
+            "prompt_tokens": 92,
+            "total_tokens": 102,
+            "completion_tokens_details": {"audio_tokens": None, "reasoning_tokens": 0},
+            "prompt_tokens_details": {"audio_tokens": None, "cached_tokens": 0},
+        },
+    }
+    result = convert_to_model_response_object(
+        model_response_object=ModelResponse(),
+        response_object=response_object,
+        stream=False,
+        start_time=datetime.now(),
+        end_time=datetime.now(),
+        hidden_params=None,
+        _response_headers=None,
+        convert_tool_call_to_json_mode=False,
+    )
+
+    assert isinstance(result, ModelResponse)
+    assert result.id == "chatcmpl-AK1uqisVA9OjUNkEuE53GJc8HPYlz"
+    assert len(result.choices) == 1
+    assert result.choices[0].message.content is None
+    assert len(result.choices[0].message.tool_calls) == 1
+    assert (
+        result.choices[0].message.tool_calls[0].function.name == "get_current_weather"
+    )
+    assert (
+        result.choices[0].message.tool_calls[0].function.arguments
+        == '{"location":"Boston, MA","unit":"fahren'
+    )
+    assert result.choices[0].finish_reason == "length"
+    assert result.model == "gpt-4o-2024-08-06"
+    assert result.created == 1729337288
+    assert result.usage.completion_tokens == 10
+    assert result.usage.prompt_tokens == 92
+    assert result.usage.total_tokens == 102
+    assert result.system_fingerprint == "fp_45c6de4934"
+
+
+def test_convert_to_model_response_object_tool_calls_valid_json_arguments():
+    """
+    Critical test - this is a basic response from OpenAI API
+
+    Test conversion with tool calls.
+
+    """
+    response_object = {
+        "id": "chatcmpl-AK1uqisVA9OjUNkEuE53GJc8HPYlz",
+        "choices": [
+            {
+                "index": 0,
+                "finish_reason": "length",
+                "logprobs": None,
+                "message": {
+                    "content": None,
+                    "refusal": None,
+                    "role": "assistant",
+                    "audio": None,
+                    "function_call": None,
+                    "tool_calls": [
+                        {
+                            "id": "call_GED1Xit8lU7cNsjVM6dt2fTq",
+                            "function": {
+                                "arguments": '{"location":"Boston, MA","unit":"fahrenheit"}',
+                                "name": "get_current_weather",
+                            },
+                            "type": "function",
+                        }
+                    ],
+                },
+            }
+        ],
+        "created": 1729337288,
+        "model": "gpt-4o-2024-08-06",
+        "object": "chat.completion",
+        "service_tier": None,
+        "system_fingerprint": "fp_45c6de4934",
+        "usage": {
+            "completion_tokens": 10,
+            "prompt_tokens": 92,
+            "total_tokens": 102,
+            "completion_tokens_details": {"audio_tokens": None, "reasoning_tokens": 0},
+            "prompt_tokens_details": {"audio_tokens": None, "cached_tokens": 0},
+        },
+    }
+    result = convert_to_model_response_object(
+        model_response_object=ModelResponse(),
+        response_object=response_object,
+        stream=False,
+        start_time=datetime.now(),
+        end_time=datetime.now(),
+        hidden_params=None,
+        _response_headers=None,
+        convert_tool_call_to_json_mode=False,
+    )
+
+    assert isinstance(result, ModelResponse)
+    assert result.id == "chatcmpl-AK1uqisVA9OjUNkEuE53GJc8HPYlz"
+    assert len(result.choices) == 1
+    assert result.choices[0].message.content is None
+    assert len(result.choices[0].message.tool_calls) == 1
+    assert (
+        result.choices[0].message.tool_calls[0].function.name == "get_current_weather"
+    )
+    assert (
+        result.choices[0].message.tool_calls[0].function.arguments
+        == '{"location":"Boston, MA","unit":"fahrenheit"}'
+    )
+    assert result.choices[0].finish_reason == "length"
+    assert result.model == "gpt-4o-2024-08-06"
+    assert result.created == 1729337288
+    assert result.usage.completion_tokens == 10
+    assert result.usage.prompt_tokens == 92
+    assert result.usage.total_tokens == 102
+    assert result.system_fingerprint == "fp_45c6de4934"
+
+
+def test_convert_to_model_response_object_json_mode():
+    """
+    This test is verifying that when convert_tool_call_to_json_mode is True, a single tool call's arguments are correctly converted into the message content of the response.
+    """
+    model_response_object = ModelResponse(model="gpt-3.5-turbo")
+    from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
+
+    response_object = {
+        "choices": [
+            {
+                "message": {
+                    "role": "assistant",
+                    "tool_calls": [
+                        {
+                            "function": {
+                                "arguments": '{"key": "value"}',
+                                "name": RESPONSE_FORMAT_TOOL_NAME,
+                            }
+                        }
+                    ],
+                },
+                "finish_reason": None,
+            }
+        ],
+        "usage": {"total_tokens": 10, "prompt_tokens": 5, "completion_tokens": 5},
+        "model": "gpt-3.5-turbo",
+    }
+
+    # Call the function
+    result = convert_to_model_response_object(
+        model_response_object=model_response_object,
+        response_object=response_object,
+        stream=False,
+        start_time=datetime.now(),
+        end_time=datetime.now(),
+        hidden_params=None,
+        _response_headers=None,
+        convert_tool_call_to_json_mode=True,
+    )
+
+    # Assertions
+    assert isinstance(result, ModelResponse)
+    assert len(result.choices) == 1
+    assert result.choices[0].message.content == '{"key": "value"}'
+    assert result.choices[0].finish_reason == "stop"
+    assert result.model == "gpt-3.5-turbo"
+    assert result.usage.total_tokens == 10
+    assert result.usage.prompt_tokens == 5
+    assert result.usage.completion_tokens == 5
+
+
+def test_convert_to_model_response_object_function_output():
+    """
+    Test conversion with function output.
+
+    From here: https://platform.openai.com/docs/api-reference/chat/create
+
+    """
+    response_object = {
+        "id": "chatcmpl-abc123",
+        "object": "chat.completion",
+        "created": 1699896916,
+        "model": "gpt-4o-mini",
+        "choices": [
+            {
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": None,
+                    "tool_calls": [
+                        {
+                            "id": "call_abc123",
+                            "type": "function",
+                            "function": {
+                                "name": "get_current_weather",
+                                "arguments": '{\n"location": "Boston, MA"\n}',
+                            },
+                        }
+                    ],
+                },
+                "logprobs": None,
+                "finish_reason": "tool_calls",
+            }
+        ],
+        "usage": {
+            "prompt_tokens": 82,
+            "completion_tokens": 17,
+            "total_tokens": 99,
+            "completion_tokens_details": {"reasoning_tokens": 0},
+        },
+    }
+
+    result = convert_to_model_response_object(
+        model_response_object=ModelResponse(),
+        response_object=response_object,
+        stream=False,
+        start_time=datetime.now(),
+        end_time=datetime.now(),
+        hidden_params=None,
+        _response_headers=None,
+        convert_tool_call_to_json_mode=False,
+    )
+
+    assert isinstance(result, ModelResponse)
+    assert result.id == "chatcmpl-abc123"
+    assert result.object == "chat.completion"
+    assert result.created == 1699896916
+    assert result.model == "gpt-4o-mini"
+
+    assert len(result.choices) == 1
+    choice = result.choices[0]
+    assert choice.index == 0
+    assert isinstance(choice.message, Message)
+    assert choice.message.role == "assistant"
+    assert choice.message.content is None
+    assert choice.finish_reason == "tool_calls"
+
+    assert len(choice.message.tool_calls) == 1
+    tool_call = choice.message.tool_calls[0]
+    assert tool_call.id == "call_abc123"
+    assert tool_call.type == "function"
+    assert tool_call.function.name == "get_current_weather"
+    assert tool_call.function.arguments == '{\n"location": "Boston, MA"\n}'
+
+    assert result.usage.prompt_tokens == 82
+    assert result.usage.completion_tokens == 17
+    assert result.usage.total_tokens == 99
+    assert result.usage.completion_tokens_details == CompletionTokensDetailsWrapper(
+        reasoning_tokens=0
+    )
+
+    assert result._hidden_params is not None
+
+
+def test_convert_to_model_response_object_with_logprobs():
+    """
+
+    Test conversion with logprobs in the response.
+
+    From here: https://platform.openai.com/docs/api-reference/chat/create
+
+    """
+    response_object = {
+        "id": "chatcmpl-123",
+        "object": "chat.completion",
+        "created": 1702685778,
+        "model": "gpt-4o-mini",
+        "choices": [
+            {
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": "Hello! How can I assist you today?",
+                },
+                "logprobs": {
+                    "content": [
+                        {
+                            "token": "Hello",
+                            "logprob": -0.31725305,
+                            "bytes": [72, 101, 108, 108, 111],
+                            "top_logprobs": [
+                                {
+                                    "token": "Hello",
+                                    "logprob": -0.31725305,
+                                    "bytes": [72, 101, 108, 108, 111],
+                                },
+                                {
+                                    "token": "Hi",
+                                    "logprob": -1.3190403,
+                                    "bytes": [72, 105],
+                                },
+                            ],
+                        },
+                        {
+                            "token": "!",
+                            "logprob": -0.02380986,
+                            "bytes": [33],
+                            "top_logprobs": [
+                                {"token": "!", "logprob": -0.02380986, "bytes": [33]},
+                                {
+                                    "token": " there",
+                                    "logprob": -3.787621,
+                                    "bytes": [32, 116, 104, 101, 114, 101],
+                                },
+                            ],
+                        },
+                        {
+                            "token": " How",
+                            "logprob": -0.000054669687,
+                            "bytes": [32, 72, 111, 119],
+                            "top_logprobs": [
+                                {
+                                    "token": " How",
+                                    "logprob": -0.000054669687,
+                                    "bytes": [32, 72, 111, 119],
+                                },
+                                {
+                                    "token": "<|end|>",
+                                    "logprob": -10.953937,
+                                    "bytes": None,
+                                },
+                            ],
+                        },
+                        {
+                            "token": " can",
+                            "logprob": -0.015801601,
+                            "bytes": [32, 99, 97, 110],
+                            "top_logprobs": [
+                                {
+                                    "token": " can",
+                                    "logprob": -0.015801601,
+                                    "bytes": [32, 99, 97, 110],
+                                },
+                                {
+                                    "token": " may",
+                                    "logprob": -4.161023,
+                                    "bytes": [32, 109, 97, 121],
+                                },
+                            ],
+                        },
+                        {
+                            "token": " I",
+                            "logprob": -3.7697225e-6,
+                            "bytes": [32, 73],
+                            "top_logprobs": [
+                                {
+                                    "token": " I",
+                                    "logprob": -3.7697225e-6,
+                                    "bytes": [32, 73],
+                                },
+                                {
+                                    "token": " assist",
+                                    "logprob": -13.596657,
+                                    "bytes": [32, 97, 115, 115, 105, 115, 116],
+                                },
+                            ],
+                        },
+                        {
+                            "token": " assist",
+                            "logprob": -0.04571125,
+                            "bytes": [32, 97, 115, 115, 105, 115, 116],
+                            "top_logprobs": [
+                                {
+                                    "token": " assist",
+                                    "logprob": -0.04571125,
+                                    "bytes": [32, 97, 115, 115, 105, 115, 116],
+                                },
+                                {
+                                    "token": " help",
+                                    "logprob": -3.1089056,
+                                    "bytes": [32, 104, 101, 108, 112],
+                                },
+                            ],
+                        },
+                        {
+                            "token": " you",
+                            "logprob": -5.4385737e-6,
+                            "bytes": [32, 121, 111, 117],
+                            "top_logprobs": [
+                                {
+                                    "token": " you",
+                                    "logprob": -5.4385737e-6,
+                                    "bytes": [32, 121, 111, 117],
+                                },
+                                {
+                                    "token": " today",
+                                    "logprob": -12.807695,
+                                    "bytes": [32, 116, 111, 100, 97, 121],
+                                },
+                            ],
+                        },
+                        {
+                            "token": " today",
+                            "logprob": -0.0040071653,
+                            "bytes": [32, 116, 111, 100, 97, 121],
+                            "top_logprobs": [
+                                {
+                                    "token": " today",
+                                    "logprob": -0.0040071653,
+                                    "bytes": [32, 116, 111, 100, 97, 121],
+                                },
+                                {"token": "?", "logprob": -5.5247097, "bytes": [63]},
+                            ],
+                        },
+                        {
+                            "token": "?",
+                            "logprob": -0.0008108172,
+                            "bytes": [63],
+                            "top_logprobs": [
+                                {"token": "?", "logprob": -0.0008108172, "bytes": [63]},
+                                {
+                                    "token": "?\n",
+                                    "logprob": -7.184561,
+                                    "bytes": [63, 10],
+                                },
+                            ],
+                        },
+                    ]
+                },
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {
+            "prompt_tokens": 9,
+            "completion_tokens": 9,
+            "total_tokens": 18,
+            "completion_tokens_details": {"reasoning_tokens": 0},
+        },
+        "system_fingerprint": None,
+    }
+
+    print("ENTERING CONVERT")
+    try:
+        result = convert_to_model_response_object(
+            model_response_object=ModelResponse(),
+            response_object=response_object,
+            stream=False,
+            start_time=datetime.now(),
+            end_time=datetime.now(),
+            hidden_params=None,
+            _response_headers=None,
+            convert_tool_call_to_json_mode=False,
+        )
+    except Exception as e:
+        print(f"ERROR: {e}")
+        raise e
+
+    assert isinstance(result, ModelResponse)
+    assert result.id == "chatcmpl-123"
+    assert result.object == "chat.completion"
+    assert result.created == 1702685778
+    assert result.model == "gpt-4o-mini"
+
+    assert len(result.choices) == 1
+    choice = result.choices[0]
+    assert choice.index == 0
+    assert isinstance(choice.message, Message)
+    assert choice.message.role == "assistant"
+    assert choice.message.content == "Hello! How can I assist you today?"
+    assert choice.finish_reason == "stop"
+
+    # Check logprobs
+    assert choice.logprobs is not None
+    assert len(choice.logprobs.content) == 9
+
+    # Check each logprob entry
+    expected_tokens = [
+        "Hello",
+        "!",
+        " How",
+        " can",
+        " I",
+        " assist",
+        " you",
+        " today",
+        "?",
+    ]
+    for i, logprob in enumerate(choice.logprobs.content):
+        assert logprob.token == expected_tokens[i]
+        assert isinstance(logprob.logprob, float)
+        assert isinstance(logprob.bytes, list)
+        assert len(logprob.top_logprobs) == 2
+        assert isinstance(logprob.top_logprobs[0].token, str)
+        assert isinstance(logprob.top_logprobs[0].logprob, float)
+        assert isinstance(logprob.top_logprobs[0].bytes, (list, type(None)))
+
+    assert result.usage.prompt_tokens == 9
+    assert result.usage.completion_tokens == 9
+    assert result.usage.total_tokens == 18
+    assert result.usage.completion_tokens_details == CompletionTokensDetailsWrapper(
+        reasoning_tokens=0
+    )
+
+    assert result.system_fingerprint is None
+    assert result._hidden_params is not None
+
+
+def test_convert_to_model_response_object_error():
+    """Test error handling for None response object."""
+    with pytest.raises(Exception, match="Error in response object format"):
+        convert_to_model_response_object(
+            model_response_object=None,
+            response_object=None,
+            stream=False,
+            start_time=None,
+            end_time=None,
+            hidden_params=None,
+            _response_headers=None,
+            convert_tool_call_to_json_mode=False,
+        )
+
+
+def test_image_generation_openai_with_pydantic_warning(caplog):
+    try:
+        import logging
+        from litellm.types.utils import ImageResponse, ImageObject
+
+        convert_response_args = {
+            "response_object": {
+                "created": 1729709945,
+                "data": [
+                    {
+                        "b64_json": None,
+                        "revised_prompt": "Generate an image of a baby sea otter. It should look incredibly cute, with big, soulful eyes and a fluffy, wet fur coat. The sea otter should be on its back, as sea otters often do, with its tiny hands holding onto a shell as if it is its precious toy. The background should be a tranquil sea under a clear sky, with soft sunlight reflecting off the waters. The color palette should be soothing with blues, browns, and white.",
+                        "url": "https://oaidalleapiprodscus.blob.core.windows.net/private/org-ikDc4ex8NB5ZzfTf8m5WYVB7/user-JpwZsbIXubBZvan3Y3GchiiB/img-LL0uoOv4CFJIvNYxoNCKB8oc.png?st=2024-10-23T17%3A59%3A05Z&se=2024-10-23T19%3A59%3A05Z&sp=r&sv=2024-08-04&sr=b&rscd=inline&rsct=image/png&skoid=d505667d-d6c1-4a0a-bac7-5c84a87759f8&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2024-10-22T19%3A26%3A22Z&ske=2024-10-23T19%3A26%3A22Z&sks=b&skv=2024-08-04&sig=Hl4wczJ3H2vZNdLRt/7JvNi6NvQGDnbNkDy15%2Bl3k5s%3D",
+                    }
+                ],
+            },
+            "model_response_object": ImageResponse(
+                created=1729709929,
+                data=[],
+            ),
+            "response_type": "image_generation",
+            "stream": False,
+            "start_time": None,
+            "end_time": None,
+            "hidden_params": None,
+            "_response_headers": None,
+            "convert_tool_call_to_json_mode": None,
+        }
+
+        resp: ImageResponse = convert_to_model_response_object(**convert_response_args)
+        assert resp is not None
+        assert resp.data is not None
+        assert len(resp.data) == 1
+        assert isinstance(resp.data[0], ImageObject)
+    except Exception as e:
+        pytest.fail(f"Test failed with exception: {e}")
+
+
+def test_convert_to_model_response_object_with_empty_str():
+    """Test that convert_to_model_response_object handles empty strings correctly."""
+
+    args = {
+        "response_object": {
+            "id": "chatcmpl-B0b1BmxhH4iSoRvFVbBJdLbMwr346",
+            "choices": [
+                {
+                    "finish_reason": "stop",
+                    "index": 0,
+                    "logprobs": None,
+                    "message": {
+                        "content": "",
+                        "refusal": None,
+                        "role": "assistant",
+                        "audio": None,
+                        "function_call": None,
+                        "tool_calls": None,
+                    },
+                }
+            ],
+            "created": 1739481997,
+            "model": "gpt-4o-mini-2024-07-18",
+            "object": "chat.completion",
+            "service_tier": "default",
+            "system_fingerprint": "fp_bd83329f63",
+            "usage": {
+                "completion_tokens": 1,
+                "prompt_tokens": 121,
+                "total_tokens": 122,
+                "completion_tokens_details": {
+                    "accepted_prediction_tokens": 0,
+                    "audio_tokens": 0,
+                    "reasoning_tokens": 0,
+                    "rejected_prediction_tokens": 0,
+                },
+                "prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0},
+            },
+        },
+        "model_response_object": ModelResponse(
+            id="chatcmpl-9f9e5ad2-d570-46fe-a5e0-4983e9774318",
+            created=1739481997,
+            model=None,
+            object="chat.completion",
+            system_fingerprint=None,
+            choices=[
+                Choices(
+                    finish_reason="stop",
+                    index=0,
+                    message=Message(
+                        content=None,
+                        role="assistant",
+                        tool_calls=None,
+                        function_call=None,
+                        provider_specific_fields=None,
+                    ),
+                )
+            ],
+            usage=Usage(
+                completion_tokens=0,
+                prompt_tokens=0,
+                total_tokens=0,
+                completion_tokens_details=None,
+                prompt_tokens_details=None,
+            ),
+        ),
+        "response_type": "completion",
+        "stream": False,
+        "start_time": None,
+        "end_time": None,
+        "hidden_params": None,
+        "_response_headers": {
+            "date": "Thu, 13 Feb 2025 21:26:37 GMT",
+            "content-type": "application/json",
+            "transfer-encoding": "chunked",
+            "connection": "keep-alive",
+            "access-control-expose-headers": "X-Request-ID",
+            "openai-organization": "reliablekeystest",
+            "openai-processing-ms": "297",
+            "openai-version": "2020-10-01",
+            "x-ratelimit-limit-requests": "30000",
+            "x-ratelimit-limit-tokens": "150000000",
+            "x-ratelimit-remaining-requests": "29999",
+            "x-ratelimit-remaining-tokens": "149999846",
+            "x-ratelimit-reset-requests": "2ms",
+            "x-ratelimit-reset-tokens": "0s",
+            "x-request-id": "req_651030cbda2c80353086eba8fd0a54ec",
+            "strict-transport-security": "max-age=31536000; includeSubDomains; preload",
+            "cf-cache-status": "DYNAMIC",
+            "set-cookie": "__cf_bm=0ihEMDdqKfEr0I8iP4XZ7C6xEA5rJeAc11XFXNxZgyE-1739481997-1.0.1.1-v5jbjAWhMUZ0faO8q2izQljUQC.R85Vexb18A2MCyS895bur5eRxcguP0.WGY6EkxXSaOKN55VL3Pg3NOdq_xA; path=/; expires=Thu, 13-Feb-25 21:56:37 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None, _cfuvid=jrNMSOBRrxUnGgJ62BltpZZSNImfnEqPX9Uu8meGFLY-1739481997919-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None",
+            "x-content-type-options": "nosniff",
+            "server": "cloudflare",
+            "cf-ray": "9117e5d4caa1f7b5-LAX",
+            "content-encoding": "gzip",
+            "alt-svc": 'h3=":443"; ma=86400',
+        },
+        "convert_tool_call_to_json_mode": None,
+    }
+
+    resp: ModelResponse = convert_to_model_response_object(**args)
+    assert resp is not None
+    assert resp.choices[0].message.content is not None
+
+
+def test_convert_to_model_response_object_with_thinking_content():
+    """Test that convert_to_model_response_object handles thinking content correctly."""
+
+    args = {
+        "response_object": {
+            "id": "chatcmpl-8cc87354-70f3-4a14-b71b-332e965d98d2",
+            "created": 1741057687,
+            "model": "claude-3-7-sonnet-20250219",
+            "object": "chat.completion",
+            "system_fingerprint": None,
+            "choices": [
+                {
+                    "finish_reason": "stop",
+                    "index": 0,
+                    "message": {
+                        "content": "# LiteLLM\n\nLiteLLM is an open-source library that provides a unified interface for working with various Large Language Models (LLMs). It acts as an abstraction layer that lets developers interact with multiple LLM providers through a single, consistent API.\n\n## Key features:\n\n- **Universal API**: Standardizes interactions with models from OpenAI, Anthropic, Cohere, Azure, and many other providers\n- **Simple switching**: Easily swap between different LLM providers without changing your code\n- **Routing capabilities**: Manage load balancing, fallbacks, and cost optimization\n- **Prompt templates**: Handle different model-specific prompt formats automatically\n- **Logging and observability**: Track usage, performance, and costs across providers\n\nLiteLLM is particularly useful for teams who want flexibility in their LLM infrastructure without creating custom integration code for each provider.",
+                        "role": "assistant",
+                        "tool_calls": None,
+                        "function_call": None,
+                        "reasoning_content": "The person is asking about \"litellm\" and included what appears to be a UUID or some form of identifier at the end of their message (fffffe14-7991-43d0-acd8-d3e606db31a8).\n\nLiteLLM is an open-source library/project that provides a unified interface for working with various Large Language Models (LLMs). It's essentially a lightweight package that standardizes the way developers can work with different LLM APIs like OpenAI, Anthropic, Cohere, etc. through a consistent interface.\n\nSome key features and aspects of LiteLLM:\n\n1. Unified API for multiple LLM providers (OpenAI, Anthropic, Azure, etc.)\n2. Standardized input/output formats\n3. Handles routing, fallbacks, and load balancing\n4. Provides logging and observability\n5. Can help with cost tracking across different providers\n6. Makes it easier to switch between different LLM providers\n\nThe UUID-like string they included doesn't seem directly related to the question, unless it's some form of identifier they're including for tracking purposes.",
+                        "thinking_blocks": [
+                            {
+                                "type": "thinking",
+                                "thinking": "The person is asking about \"litellm\" and included what appears to be a UUID or some form of identifier at the end of their message (fffffe14-7991-43d0-acd8-d3e606db31a8).\n\nLiteLLM is an open-source library/project that provides a unified interface for working with various Large Language Models (LLMs). It's essentially a lightweight package that standardizes the way developers can work with different LLM APIs like OpenAI, Anthropic, Cohere, etc. through a consistent interface.\n\nSome key features and aspects of LiteLLM:\n\n1. Unified API for multiple LLM providers (OpenAI, Anthropic, Azure, etc.)\n2. Standardized input/output formats\n3. Handles routing, fallbacks, and load balancing\n4. Provides logging and observability\n5. Can help with cost tracking across different providers\n6. Makes it easier to switch between different LLM providers\n\nThe UUID-like string they included doesn't seem directly related to the question, unless it's some form of identifier they're including for tracking purposes.",
+                                "signature": "ErUBCkYIARgCIkCf+r0qMSOMYkjlFERM00IxsY9I/m19dQGEF/Zv1E0AtvdZjKGnr+nr5vXUldmb/sUCgrQRH4YUyV0X3MoMrsNnEgxDqhUFcUTg1vM0CroaDEY1wKJ0Ca0EZ6S1jCIwF8ATum3xiF/mRSIIjoD6Virh0hFcOfH3Sz6Chtev9WUwwYMAVP4/hyzbrUDnsUlmKh0CfTayaXm6o63/6Kelr6pzLbErjQx2xZRnRjCypw==",
+                            }
+                        ],
+                    },
+                }
+            ],
+            "usage": {
+                "completion_tokens": 460,
+                "prompt_tokens": 65,
+                "total_tokens": 525,
+                "completion_tokens_details": None,
+                "prompt_tokens_details": {"audio_tokens": None, "cached_tokens": 0},
+                "cache_creation_input_tokens": 0,
+                "cache_read_input_tokens": 0,
+            },
+        },
+        "model_response_object": ModelResponse(),
+    }
+
+    resp: ModelResponse = convert_to_model_response_object(**args)
+    assert resp is not None
+    assert resp.choices[0].message.reasoning_content is not None

Development/litellm/tests/llm_translation/test_llm_response_utils/test_get_headers.py

@@ -0,0 +1,79 @@
+import json
+import os
+import sys
+from datetime import datetime
+
+sys.path.insert(
+    0, os.path.abspath("../../")
+)  # Adds the parent directory to the system path
+
+import litellm
+import pytest
+
+from litellm.litellm_core_utils.llm_response_utils.get_headers import (
+    get_response_headers,
+    _get_llm_provider_headers,
+)
+
+
+def test_get_response_headers_empty():
+    result = get_response_headers()
+    assert result == {}, "Expected empty dictionary for no input"
+
+
+def test_get_response_headers_with_openai_headers():
+    """
+    OpenAI headers are forwarded as is
+    Other headers are prefixed with llm_provider-
+    """
+    input_headers = {
+        "x-ratelimit-limit-requests": "100",
+        "x-ratelimit-remaining-requests": "50",
+        "x-ratelimit-limit-tokens": "1000",
+        "x-ratelimit-remaining-tokens": "500",
+        "other-header": "value",
+    }
+    expected_output = {
+        "x-ratelimit-limit-requests": "100",
+        "x-ratelimit-remaining-requests": "50",
+        "x-ratelimit-limit-tokens": "1000",
+        "x-ratelimit-remaining-tokens": "500",
+        "llm_provider-x-ratelimit-limit-requests": "100",
+        "llm_provider-x-ratelimit-remaining-requests": "50",
+        "llm_provider-x-ratelimit-limit-tokens": "1000",
+        "llm_provider-x-ratelimit-remaining-tokens": "500",
+        "llm_provider-other-header": "value",
+    }
+    result = get_response_headers(input_headers)
+    assert result == expected_output, "Unexpected output for OpenAI headers"
+
+
+def test_get_response_headers_without_openai_headers():
+    """
+    Non-OpenAI headers are prefixed with llm_provider-
+    """
+    input_headers = {"custom-header-1": "value1", "custom-header-2": "value2"}
+    expected_output = {
+        "llm_provider-custom-header-1": "value1",
+        "llm_provider-custom-header-2": "value2",
+    }
+    result = get_response_headers(input_headers)
+    assert result == expected_output, "Unexpected output for non-OpenAI headers"
+
+
+def test_get_llm_provider_headers():
+    """
+    If non OpenAI headers are already prefixed with llm_provider- they are not prefixed with llm_provider- again
+    """
+    input_headers = {
+        "header1": "value1",
+        "header2": "value2",
+        "llm_provider-existing": "existing_value",
+    }
+    expected_output = {
+        "llm_provider-header1": "value1",
+        "llm_provider-header2": "value2",
+        "llm_provider-existing": "existing_value",
+    }
+    result = _get_llm_provider_headers(input_headers)
+    assert result == expected_output, "Unexpected output for _get_llm_provider_headers"