structure saas with tools

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

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+"""
+Calls handled in openai/
+
+as mistral is an openai-compatible endpoint.
+"""

.venv/lib/python3.10/site-packages/litellm/llms/mistral/embedding.py

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+"""
+Calls handled in openai/
+
+as mistral is an openai-compatible endpoint.
+"""

.venv/lib/python3.10/site-packages/litellm/llms/mistral/mistral_chat_transformation.py

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+"""
+Transformation logic from OpenAI /v1/chat/completion format to Mistral's /chat/completion format.
+
+Why separate file? Make it easy to see how transformation works
+
+Docs - https://docs.mistral.ai/api/
+"""
+
+from typing import List, Literal, Optional, Tuple, Union
+
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    handle_messages_with_content_list_to_str_conversion,
+    strip_none_values_from_message,
+)
+from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.mistral import MistralToolCallMessage
+from litellm.types.llms.openai import AllMessageValues
+
+
+class MistralConfig(OpenAIGPTConfig):
+    """
+    Reference: https://docs.mistral.ai/api/
+
+    The class `MistralConfig` provides configuration for the Mistral's Chat API interface. Below are the parameters:
+
+    - `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2. API Default - 0.7.
+
+    - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling. API Default - 1.
+
+    - `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion. API Default - null.
+
+    - `tools` (list or null): A list of available tools for the model. Use this to specify functions for which the model can generate JSON inputs.
+
+    - `tool_choice` (string - 'auto'/'any'/'none' or null): Specifies if/how functions are called. If set to none the model won't call a function and will generate a message instead. If set to auto the model can choose to either generate a message or call a function. If set to any the model is forced to call a function. Default - 'auto'.
+
+    - `stop` (string or array of strings): Stop generation if this token is detected. Or if one of these tokens is detected when providing an array
+
+    - `random_seed` (integer or null): The seed to use for random sampling. If set, different calls will generate deterministic results.
+
+    - `safe_prompt` (boolean): Whether to inject a safety prompt before all conversations. API Default - 'false'.
+
+    - `response_format` (object or null): An object specifying the format that the model must output. Setting to { "type": "json_object" } enables JSON mode, which guarantees the message the model generates is in JSON. When using JSON mode you MUST also instruct the model to produce JSON yourself with a system or a user message.
+    """
+
+    temperature: Optional[int] = None
+    top_p: Optional[int] = None
+    max_tokens: Optional[int] = None
+    tools: Optional[list] = None
+    tool_choice: Optional[Literal["auto", "any", "none"]] = None
+    random_seed: Optional[int] = None
+    safe_prompt: Optional[bool] = None
+    response_format: Optional[dict] = None
+    stop: Optional[Union[str, list]] = None
+
+    def __init__(
+        self,
+        temperature: Optional[int] = None,
+        top_p: Optional[int] = None,
+        max_tokens: Optional[int] = None,
+        tools: Optional[list] = None,
+        tool_choice: Optional[Literal["auto", "any", "none"]] = None,
+        random_seed: Optional[int] = None,
+        safe_prompt: Optional[bool] = None,
+        response_format: Optional[dict] = None,
+        stop: Optional[Union[str, list]] = 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[str]:
+        return [
+            "stream",
+            "temperature",
+            "top_p",
+            "max_tokens",
+            "max_completion_tokens",
+            "tools",
+            "tool_choice",
+            "seed",
+            "stop",
+            "response_format",
+        ]
+
+    def _map_tool_choice(self, tool_choice: str) -> str:
+        if tool_choice == "auto" or tool_choice == "none":
+            return tool_choice
+        elif tool_choice == "required":
+            return "any"
+        else:  # openai 'tool_choice' object param not supported by Mistral API
+            return "any"
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        for param, value in non_default_params.items():
+            if param == "max_tokens":
+                optional_params["max_tokens"] = value
+            if (
+                param == "max_completion_tokens"
+            ):  # max_completion_tokens should take priority
+                optional_params["max_tokens"] = value
+            if param == "tools":
+                optional_params["tools"] = value
+            if param == "stream" and value is True:
+                optional_params["stream"] = value
+            if param == "temperature":
+                optional_params["temperature"] = value
+            if param == "top_p":
+                optional_params["top_p"] = value
+            if param == "stop":
+                optional_params["stop"] = value
+            if param == "tool_choice" and isinstance(value, str):
+                optional_params["tool_choice"] = self._map_tool_choice(
+                    tool_choice=value
+                )
+            if param == "seed":
+                optional_params["extra_body"] = {"random_seed": value}
+            if param == "response_format":
+                optional_params["response_format"] = value
+        return optional_params
+
+    def _get_openai_compatible_provider_info(
+        self, api_base: Optional[str], api_key: Optional[str]
+    ) -> Tuple[Optional[str], Optional[str]]:
+        # mistral is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.mistral.ai
+        api_base = (
+            api_base
+            or get_secret_str("MISTRAL_AZURE_API_BASE")  # for Azure AI Mistral
+            or "https://api.mistral.ai/v1"
+        )  # type: ignore
+
+        # if api_base does not end with /v1 we add it
+        if api_base is not None and not api_base.endswith(
+            "/v1"
+        ):  # Mistral always needs a /v1 at the end
+            api_base = api_base + "/v1"
+        dynamic_api_key = (
+            api_key
+            or get_secret_str("MISTRAL_AZURE_API_KEY")  # for Azure AI Mistral
+            or get_secret_str("MISTRAL_API_KEY")
+        )
+        return api_base, dynamic_api_key
+
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str
+    ) -> List[AllMessageValues]:
+        """
+        - handles scenario where content is list and not string
+        - content list is just text, and no images
+        - if image passed in, then just return as is (user-intended)
+        - if `name` is passed, then drop it for mistral API: https://github.com/BerriAI/litellm/issues/6696
+
+        Motivation: mistral api doesn't support content as a list
+        """
+        ## 1. If 'image_url' in content, then return as is
+        for m in messages:
+            _content_block = m.get("content")
+            if _content_block and isinstance(_content_block, list):
+                for c in _content_block:
+                    if c.get("type") == "image_url":
+                        return messages
+
+        ## 2. If content is list, then convert to string
+        messages = handle_messages_with_content_list_to_str_conversion(messages)
+
+        ## 3. Handle name in message
+        new_messages: List[AllMessageValues] = []
+        for m in messages:
+            m = MistralConfig._handle_name_in_message(m)
+            m = MistralConfig._handle_tool_call_message(m)
+            m = strip_none_values_from_message(m)  # prevents 'extra_forbidden' error
+            new_messages.append(m)
+
+        return new_messages
+
+    @classmethod
+    def _handle_name_in_message(cls, message: AllMessageValues) -> AllMessageValues:
+        """
+        Mistral API only supports `name` in tool messages
+
+        If role == tool, then we keep `name`
+        Otherwise, we drop `name`
+        """
+        _name = message.get("name")  # type: ignore
+        if _name is not None and message["role"] != "tool":
+            message.pop("name", None)  # type: ignore
+
+        return message
+
+    @classmethod
+    def _handle_tool_call_message(cls, message: AllMessageValues) -> AllMessageValues:
+        """
+        Mistral API only supports tool_calls in Messages in `MistralToolCallMessage` spec
+        """
+        _tool_calls = message.get("tool_calls")
+        mistral_tool_calls: List[MistralToolCallMessage] = []
+        if _tool_calls is not None and isinstance(_tool_calls, list):
+            for _tool in _tool_calls:
+                _tool_call_message = MistralToolCallMessage(
+                    id=_tool.get("id"),
+                    type="function",
+                    function=_tool.get("function"),  # type: ignore
+                )
+                mistral_tool_calls.append(_tool_call_message)
+            message["tool_calls"] = mistral_tool_calls  # type: ignore
+        return message