eigent/backend/camel/models/litellm_model.py

# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
import os
import uuid
from typing import Any, Dict, List, Optional, Type, Union

from pydantic import BaseModel

from camel.configs import LiteLLMConfig
from camel.messages import OpenAIMessage
from camel.models import BaseModelBackend
from camel.types import ChatCompletion, ModelType
from camel.utils import (
    BaseTokenCounter,
    LiteLLMTokenCounter,
    dependencies_required,
    update_current_observation,
)

if os.environ.get("LANGFUSE_ENABLED", "False").lower() == "true":
    try:
        from langfuse.decorators import observe
    except ImportError:
        from camel.utils import observe
else:
    from camel.utils import observe


class LiteLLMModel(BaseModelBackend):
    r"""Constructor for LiteLLM backend with OpenAI compatibility.

    Args:
        model_type (Union[ModelType, str]): Model for which a backend is
            created, such as GPT-3.5-turbo, Claude-2, etc.
        model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
            that will be fed into:obj:`completion()`. If:obj:`None`,
            :obj:`LiteLLMConfig().as_dict()` will be used.
            (default: :obj:`None`)
        api_key (Optional[str], optional): The API key for authenticating with
            the model service. (default: :obj:`None`)
        url (Optional[str], optional): The url to the model service.
            (default: :obj:`None`)
        token_counter (Optional[BaseTokenCounter], optional): Token counter to
            use for the model. If not provided, :obj:`LiteLLMTokenCounter` will
            be used. (default: :obj:`None`)
        timeout (Optional[float], optional): The timeout value in seconds for
            API calls. If not provided, will fall back to the MODEL_TIMEOUT
            environment variable or default to 180 seconds.
            (default: :obj:`None`)
        **kwargs (Any): Additional arguments to pass to the client
            initialization.
    """

    # NOTE: Currently stream mode is not supported.

    @dependencies_required('litellm')
    def __init__(
        self,
        model_type: Union[ModelType, str],
        model_config_dict: Optional[Dict[str, Any]] = None,
        api_key: Optional[str] = None,
        url: Optional[str] = None,
        token_counter: Optional[BaseTokenCounter] = None,
        timeout: Optional[float] = None,
        **kwargs: Any,
    ) -> None:
        from litellm import completion

        if model_config_dict is None:
            model_config_dict = LiteLLMConfig().as_dict()
        timeout = timeout or float(os.environ.get("MODEL_TIMEOUT", 180))
        super().__init__(
            model_type, model_config_dict, api_key, url, token_counter, timeout
        )
        self.client = completion
        self.kwargs = kwargs

    def _convert_response_from_litellm_to_openai(
        self, response
    ) -> ChatCompletion:
        r"""Converts a response from the LiteLLM format to the OpenAI format.

        Parameters:
            response (LiteLLMResponse): The response object from LiteLLM.

        Returns:
            ChatCompletion: The response object in OpenAI's format.
        """

        converted_choices = []
        for choice in response.choices:
            # Build the assistant message dict
            msg_dict: Dict[str, Any] = {
                "role": choice.message.role,
                "content": choice.message.content,
            }

            if getattr(choice.message, "tool_calls", None):
                msg_dict["tool_calls"] = choice.message.tool_calls

            elif getattr(choice.message, "function_call", None):
                func_call = choice.message.function_call
                msg_dict["tool_calls"] = [
                    {
                        "id": f"call_{uuid.uuid4().hex[:24]}",
                        "type": "function",
                        "function": {
                            "name": getattr(func_call, "name", None),
                            "arguments": getattr(func_call, "arguments", "{}"),
                        },
                    }
                ]

            converted_choices.append(
                {
                    "index": choice.index,
                    "message": msg_dict,
                    "finish_reason": choice.finish_reason,
                }
            )

        return ChatCompletion.construct(
            id=response.id,
            choices=converted_choices,
            created=getattr(response, "created", None),
            model=getattr(response, "model", None),
            object=getattr(response, "object", None),
            system_fingerprint=getattr(response, "system_fingerprint", None),
            usage=getattr(response, "usage", None),
        )

    @property
    def token_counter(self) -> BaseTokenCounter:
        r"""Initialize the token counter for the model backend.

        Returns:
            BaseTokenCounter: The token counter following the model's
                tokenization style.
        """
        if not self._token_counter:
            self._token_counter = LiteLLMTokenCounter(self.model_type)
        return self._token_counter

    async def _arun(self) -> None:  # type: ignore[override]
        raise NotImplementedError

    @observe(as_type='generation')
    def _run(
        self,
        messages: List[OpenAIMessage],
        response_format: Optional[Type[BaseModel]] = None,
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> ChatCompletion:
        r"""Runs inference of LiteLLM chat completion.

        Args:
            messages (List[OpenAIMessage]): Message list with the chat history
                in OpenAI format.

        Returns:
            ChatCompletion
        """

        request_config = self.model_config_dict.copy()
        if tools:
            request_config['tools'] = tools
        if response_format:
            request_config['response_format'] = response_format

        update_current_observation(
            input={
                "messages": messages,
                "tools": tools,
            },
            model=str(self.model_type),
            model_parameters=self.model_config_dict,
        )
        self._log_and_trace()

        response = self.client(
            timeout=self._timeout,
            api_key=self._api_key,
            base_url=self._url,
            model=self.model_type,
            messages=messages,
            **request_config,
            **self.kwargs,
        )
        response = self._convert_response_from_litellm_to_openai(response)

        update_current_observation(
            usage=response.usage,
        )
        return response