structure saas with tools

.venv/lib/python3.10/site-packages/litellm/llms/openai/chat/gpt_audio_transformation.py

@@ -0,0 +1,48 @@
+"""
+Support for GPT-4o audio Family
+
+OpenAI Doc: https://platform.openai.com/docs/guides/audio/quickstart?audio-generation-quickstart-example=audio-in&lang=python
+"""
+
+import litellm
+
+from .gpt_transformation import OpenAIGPTConfig
+
+
+class OpenAIGPTAudioConfig(OpenAIGPTConfig):
+    """
+    Reference: https://platform.openai.com/docs/guides/audio
+    """
+
+    @classmethod
+    def get_config(cls):
+        return super().get_config()
+
+    def get_supported_openai_params(self, model: str) -> list:
+        """
+        Get the supported OpenAI params for the `gpt-audio` models
+
+        """
+
+        all_openai_params = super().get_supported_openai_params(model=model)
+        audio_specific_params = ["audio"]
+        return all_openai_params + audio_specific_params
+
+    def is_model_gpt_audio_model(self, model: str) -> bool:
+        if model in litellm.open_ai_chat_completion_models and "audio" in model:
+            return True
+        return False
+
+    def _map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        return super()._map_openai_params(
+            non_default_params=non_default_params,
+            optional_params=optional_params,
+            model=model,
+            drop_params=drop_params,
+        )

.venv/lib/python3.10/site-packages/litellm/llms/openai/chat/gpt_transformation.py

@@ -0,0 +1,419 @@
+"""
+Support for gpt model family 
+"""
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    AsyncIterator,
+    Iterator,
+    List,
+    Optional,
+    Union,
+    cast,
+)
+
+import httpx
+
+import litellm
+from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
+from litellm.llms.base_llm.base_utils import BaseLLMModelInfo
+from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionFileObject,
+    ChatCompletionFileObjectFile,
+    ChatCompletionImageObject,
+    ChatCompletionImageUrlObject,
+)
+from litellm.types.utils import ModelResponse, ModelResponseStream
+from litellm.utils import convert_to_model_response_object
+
+from ..common_utils import OpenAIError
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+
+class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
+    """
+    Reference: https://platform.openai.com/docs/api-reference/chat/create
+
+    The class `OpenAIConfig` provides configuration for the OpenAI's Chat API interface. Below are the parameters:
+
+    - `frequency_penalty` (number or null): Defaults to 0. Allows a value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, thereby minimizing repetition.
+
+    - `function_call` (string or object): This optional parameter controls how the model calls functions.
+
+    - `functions` (array): An optional parameter. It is a list of functions for which the model may generate JSON inputs.
+
+    - `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion.
+
+    - `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion.
+
+    - `n` (integer or null): This optional parameter helps to set how many chat completion choices to generate for each input message.
+
+    - `presence_penalty` (number or null): Defaults to 0. It penalizes new tokens based on if they appear in the text so far, hence increasing the model's likelihood to talk about new topics.
+
+    - `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens.
+
+    - `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2.
+
+    - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling.
+    """
+
+    frequency_penalty: Optional[int] = None
+    function_call: Optional[Union[str, dict]] = None
+    functions: Optional[list] = None
+    logit_bias: Optional[dict] = None
+    max_tokens: Optional[int] = None
+    n: Optional[int] = None
+    presence_penalty: Optional[int] = None
+    stop: Optional[Union[str, list]] = None
+    temperature: Optional[int] = None
+    top_p: Optional[int] = None
+    response_format: Optional[dict] = None
+
+    def __init__(
+        self,
+        frequency_penalty: Optional[int] = None,
+        function_call: Optional[Union[str, dict]] = None,
+        functions: Optional[list] = None,
+        logit_bias: Optional[dict] = None,
+        max_tokens: Optional[int] = None,
+        n: Optional[int] = None,
+        presence_penalty: Optional[int] = None,
+        stop: Optional[Union[str, list]] = None,
+        temperature: Optional[int] = None,
+        top_p: Optional[int] = None,
+        response_format: Optional[dict] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return super().get_config()
+
+    def get_supported_openai_params(self, model: str) -> list:
+        base_params = [
+            "frequency_penalty",
+            "logit_bias",
+            "logprobs",
+            "top_logprobs",
+            "max_tokens",
+            "max_completion_tokens",
+            "modalities",
+            "prediction",
+            "n",
+            "presence_penalty",
+            "seed",
+            "stop",
+            "stream",
+            "stream_options",
+            "temperature",
+            "top_p",
+            "tools",
+            "tool_choice",
+            "function_call",
+            "functions",
+            "max_retries",
+            "extra_headers",
+            "parallel_tool_calls",
+            "audio",
+        ]  # works across all models
+
+        model_specific_params = []
+        if (
+            model != "gpt-3.5-turbo-16k" and model != "gpt-4"
+        ):  # gpt-4 does not support 'response_format'
+            model_specific_params.append("response_format")
+
+        if (
+            model in litellm.open_ai_chat_completion_models
+        ) or model in litellm.open_ai_text_completion_models:
+            model_specific_params.append(
+                "user"
+            )  # user is not a param supported by all openai-compatible endpoints - e.g. azure ai
+        return base_params + model_specific_params
+
+    def _map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        """
+        If any supported_openai_params are in non_default_params, add them to optional_params, so they are use in API call
+
+        Args:
+            non_default_params (dict): Non-default parameters to filter.
+            optional_params (dict): Optional parameters to update.
+            model (str): Model name for parameter support check.
+
+        Returns:
+            dict: Updated optional_params with supported non-default parameters.
+        """
+        supported_openai_params = self.get_supported_openai_params(model)
+        for param, value in non_default_params.items():
+            if param in supported_openai_params:
+                optional_params[param] = value
+        return optional_params
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        return self._map_openai_params(
+            non_default_params=non_default_params,
+            optional_params=optional_params,
+            model=model,
+            drop_params=drop_params,
+        )
+
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str
+    ) -> List[AllMessageValues]:
+        """OpenAI no longer supports image_url as a string, so we need to convert it to a dict"""
+        for message in messages:
+            message_content = message.get("content")
+            if message_content and isinstance(message_content, list):
+                for content_item in message_content:
+                    litellm_specific_params = {"format"}
+                    if content_item.get("type") == "image_url":
+                        content_item = cast(ChatCompletionImageObject, content_item)
+                        if isinstance(content_item["image_url"], str):
+                            content_item["image_url"] = {
+                                "url": content_item["image_url"],
+                            }
+                        elif isinstance(content_item["image_url"], dict):
+                            new_image_url_obj = ChatCompletionImageUrlObject(
+                                **{  # type: ignore
+                                    k: v
+                                    for k, v in content_item["image_url"].items()
+                                    if k not in litellm_specific_params
+                                }
+                            )
+                            content_item["image_url"] = new_image_url_obj
+                    elif content_item.get("type") == "file":
+                        content_item = cast(ChatCompletionFileObject, content_item)
+                        file_obj = content_item["file"]
+                        new_file_obj = ChatCompletionFileObjectFile(
+                            **{  # type: ignore
+                                k: v
+                                for k, v in file_obj.items()
+                                if k not in litellm_specific_params
+                            }
+                        )
+                        content_item["file"] = new_file_obj
+        return messages
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        """
+        Transform the overall request to be sent to the API.
+
+        Returns:
+            dict: The transformed request. Sent as the body of the API call.
+        """
+        messages = self._transform_messages(messages=messages, model=model)
+        return {
+            "model": model,
+            "messages": messages,
+            **optional_params,
+        }
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+        """
+        Transform the response from the API.
+
+        Returns:
+            dict: The transformed response.
+        """
+
+        ## LOGGING
+        logging_obj.post_call(
+            input=messages,
+            api_key=api_key,
+            original_response=raw_response.text,
+            additional_args={"complete_input_dict": request_data},
+        )
+
+        ## RESPONSE OBJECT
+        try:
+            completion_response = raw_response.json()
+        except Exception as e:
+            response_headers = getattr(raw_response, "headers", None)
+            raise OpenAIError(
+                message="Unable to get json response - {}, Original Response: {}".format(
+                    str(e), raw_response.text
+                ),
+                status_code=raw_response.status_code,
+                headers=response_headers,
+            )
+        raw_response_headers = dict(raw_response.headers)
+        final_response_obj = convert_to_model_response_object(
+            response_object=completion_response,
+            model_response_object=model_response,
+            hidden_params={"headers": raw_response_headers},
+            _response_headers=raw_response_headers,
+        )
+
+        return cast(ModelResponse, final_response_obj)
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        return OpenAIError(
+            status_code=status_code,
+            message=error_message,
+            headers=cast(httpx.Headers, headers),
+        )
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        api_key: Optional[str],
+        model: str,
+        optional_params: dict,
+        litellm_params: dict,
+        stream: Optional[bool] = None,
+    ) -> str:
+        """
+        Get the complete URL for the API call.
+
+        Returns:
+            str: The complete URL for the API call.
+        """
+        if api_base is None:
+            api_base = "https://api.openai.com"
+        endpoint = "chat/completions"
+
+        # Remove trailing slash from api_base if present
+        api_base = api_base.rstrip("/")
+
+        # Check if endpoint is already in the api_base
+        if endpoint in api_base:
+            return api_base
+
+        return f"{api_base}/{endpoint}"
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        if api_key is not None:
+            headers["Authorization"] = f"Bearer {api_key}"
+
+        # Ensure Content-Type is set to application/json
+        if "content-type" not in headers and "Content-Type" not in headers:
+            headers["Content-Type"] = "application/json"
+
+        return headers
+
+    def get_models(
+        self, api_key: Optional[str] = None, api_base: Optional[str] = None
+    ) -> List[str]:
+        """
+        Calls OpenAI's `/v1/models` endpoint and returns the list of models.
+        """
+
+        if api_base is None:
+            api_base = "https://api.openai.com"
+        if api_key is None:
+            api_key = get_secret_str("OPENAI_API_KEY")
+
+        response = litellm.module_level_client.get(
+            url=f"{api_base}/v1/models",
+            headers={"Authorization": f"Bearer {api_key}"},
+        )
+
+        if response.status_code != 200:
+            raise Exception(f"Failed to get models: {response.text}")
+
+        models = response.json()["data"]
+        return [model["id"] for model in models]
+
+    @staticmethod
+    def get_api_key(api_key: Optional[str] = None) -> Optional[str]:
+        return (
+            api_key
+            or litellm.api_key
+            or litellm.openai_key
+            or get_secret_str("OPENAI_API_KEY")
+        )
+
+    @staticmethod
+    def get_api_base(api_base: Optional[str] = None) -> Optional[str]:
+        return (
+            api_base
+            or litellm.api_base
+            or get_secret_str("OPENAI_API_BASE")
+            or "https://api.openai.com/v1"
+        )
+
+    @staticmethod
+    def get_base_model(model: Optional[str] = None) -> Optional[str]:
+        return model
+
+    def get_model_response_iterator(
+        self,
+        streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
+        sync_stream: bool,
+        json_mode: Optional[bool] = False,
+    ) -> Any:
+        return OpenAIChatCompletionStreamingHandler(
+            streaming_response=streaming_response,
+            sync_stream=sync_stream,
+            json_mode=json_mode,
+        )
+
+
+class OpenAIChatCompletionStreamingHandler(BaseModelResponseIterator):
+    def chunk_parser(self, chunk: dict) -> ModelResponseStream:
+        try:
+            return ModelResponseStream(
+                id=chunk["id"],
+                object="chat.completion.chunk",
+                created=chunk["created"],
+                model=chunk["model"],
+                choices=chunk["choices"],
+            )
+        except Exception as e:
+            raise e

.venv/lib/python3.10/site-packages/litellm/llms/openai/chat/o_series_handler.py

@@ -0,0 +1,3 @@
+"""
+LLM Calling done in `openai/openai.py`
+"""

.venv/lib/python3.10/site-packages/litellm/llms/openai/chat/o_series_transformation.py

@@ -0,0 +1,159 @@
+"""
+Support for o1/o3 model family 
+
+https://platform.openai.com/docs/guides/reasoning
+
+Translations handled by LiteLLM:
+- modalities: image => drop param (if user opts in to dropping param)  
+- role: system ==> translate to role 'user' 
+- streaming => faked by LiteLLM 
+- Tools, response_format =>  drop param (if user opts in to dropping param) 
+- Logprobs => drop param (if user opts in to dropping param) 
+"""
+
+from typing import List, Optional
+
+import litellm
+from litellm import verbose_logger
+from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
+from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage
+from litellm.utils import (
+    supports_function_calling,
+    supports_parallel_function_calling,
+    supports_response_schema,
+    supports_system_messages,
+)
+
+from .gpt_transformation import OpenAIGPTConfig
+
+
+class OpenAIOSeriesConfig(OpenAIGPTConfig):
+    """
+    Reference: https://platform.openai.com/docs/guides/reasoning
+    """
+
+    @classmethod
+    def get_config(cls):
+        return super().get_config()
+
+    def translate_developer_role_to_system_role(
+        self, messages: List[AllMessageValues]
+    ) -> List[AllMessageValues]:
+        """
+        O-series models support `developer` role.
+        """
+        return messages
+
+    def get_supported_openai_params(self, model: str) -> list:
+        """
+        Get the supported OpenAI params for the given model
+
+        """
+
+        all_openai_params = super().get_supported_openai_params(model=model)
+        non_supported_params = [
+            "logprobs",
+            "top_p",
+            "presence_penalty",
+            "frequency_penalty",
+            "top_logprobs",
+        ]
+
+        o_series_only_param = ["reasoning_effort"]
+
+        all_openai_params.extend(o_series_only_param)
+
+        try:
+            model, custom_llm_provider, api_base, api_key = get_llm_provider(
+                model=model
+            )
+        except Exception:
+            verbose_logger.debug(
+                f"Unable to infer model provider for model={model}, defaulting to openai for o1 supported param check"
+            )
+            custom_llm_provider = "openai"
+
+        _supports_function_calling = supports_function_calling(
+            model, custom_llm_provider
+        )
+        _supports_response_schema = supports_response_schema(model, custom_llm_provider)
+        _supports_parallel_tool_calls = supports_parallel_function_calling(
+            model, custom_llm_provider
+        )
+
+        if not _supports_function_calling:
+            non_supported_params.append("tools")
+            non_supported_params.append("tool_choice")
+            non_supported_params.append("function_call")
+            non_supported_params.append("functions")
+
+        if not _supports_parallel_tool_calls:
+            non_supported_params.append("parallel_tool_calls")
+
+        if not _supports_response_schema:
+            non_supported_params.append("response_format")
+
+        return [
+            param for param in all_openai_params if param not in non_supported_params
+        ]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ):
+        if "max_tokens" in non_default_params:
+            optional_params["max_completion_tokens"] = non_default_params.pop(
+                "max_tokens"
+            )
+        if "temperature" in non_default_params:
+            temperature_value: Optional[float] = non_default_params.pop("temperature")
+            if temperature_value is not None:
+                if temperature_value == 1:
+                    optional_params["temperature"] = temperature_value
+                else:
+                    ## UNSUPPORTED TOOL CHOICE VALUE
+                    if litellm.drop_params is True or drop_params is True:
+                        pass
+                    else:
+                        raise litellm.utils.UnsupportedParamsError(
+                            message="O-series models don't support temperature={}. Only temperature=1 is supported. To drop unsupported openai params from the call, set `litellm.drop_params = True`".format(
+                                temperature_value
+                            ),
+                            status_code=400,
+                        )
+
+        return super()._map_openai_params(
+            non_default_params, optional_params, model, drop_params
+        )
+
+    def is_model_o_series_model(self, model: str) -> bool:
+        if model in litellm.open_ai_chat_completion_models and (
+            "o1" in model
+            or "o3" in model
+            or "o4"
+            in model  # [TODO] make this a more generic check (e.g. using `openai-o-series` as provider like gemini)
+        ):
+            return True
+        return False
+
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str
+    ) -> List[AllMessageValues]:
+        """
+        Handles limitations of O-1 model family.
+        - modalities: image => drop param (if user opts in to dropping param)
+        - role: system ==> translate to role 'user'
+        """
+        _supports_system_messages = supports_system_messages(model, "openai")
+        for i, message in enumerate(messages):
+            if message["role"] == "system" and not _supports_system_messages:
+                new_message = ChatCompletionUserMessage(
+                    content=message["content"], role="user"
+                )
+                messages[i] = new_message  # Replace the old message with the new one
+
+        messages = super()._transform_messages(messages, model)
+        return messages