camel/models/qwen_model.py

# ========= Copyright 2023-2024 @ 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 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========

import os
from typing import Any, Dict, List, Optional, Union
import httpx
timeout = httpx.Timeout(
    connect=5000,  # max time to establish TCP connection
    write=5000,   # max time per chunk sent
    read=5000,    # max time per chunk received
    pool=5000      # max time to get a connection from the pool
)

from openai import OpenAI, Stream

from camel.configs import QWEN_API_PARAMS, QwenConfig
from camel.messages import OpenAIMessage
from camel.models import BaseModelBackend
from camel.types import (
    ChatCompletion,
    ChatCompletionChunk,
    ModelType,
)
from camel.utils import (
    BaseTokenCounter,
    OpenAITokenCounter,
    api_keys_required,
)


class QwenModel(BaseModelBackend):
    r"""Qwen API in a unified BaseModelBackend interface.

    Args:
        model_type (Union[ModelType, str]): Model for which a backend is
            created, one of Qwen series.
        model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
            that will be fed into:obj:`openai.ChatCompletion.create()`. If
            :obj:`None`, :obj:`QwenConfig().as_dict()` will be used.
            (default: :obj:`None`)
        api_key (Optional[str], optional): The API key for authenticating with
            the Qwen service. (default: :obj:`None`)
        url (Optional[str], optional): The url to the Qwen service.
            (default: :obj:`https://dashscope.aliyuncs.com/compatible-mode/v1`)
        token_counter (Optional[BaseTokenCounter], optional): Token counter to
            use for the model. If not provided, :obj:`OpenAITokenCounter(
            ModelType.GPT_4O_MINI)` will be used.
            (default: :obj:`None`)
    """

    @api_keys_required(
        [
            ("api_key", "QWEN_API_KEY"),
        ]
    )
    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,
    ) -> None:
        if model_config_dict is None:
            model_config_dict = QwenConfig().as_dict()
        api_key = api_key or os.environ.get("QWEN_API_KEY")
        url = url or os.environ.get(
            "QWEN_API_BASE_URL",
            "https://dashscope.aliyuncs.com/compatible-mode/v1",
        )
        super().__init__(
            model_type, model_config_dict, api_key, url, token_counter
        )
        self._client = OpenAI(
            timeout=httpx.Timeout(
                connect=5000,   # DNS + TCP + TLS
                read=5000,     # waiting for the model
                write=5000, 
                pool=5000   # letting you push up to ~512 MB slowly
            ),
            max_retries=3,
            api_key=self._api_key,
            base_url=self._url,
        )

    def run(
        self,
        messages: List[OpenAIMessage],
    ) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
        r"""Runs inference of Qwen chat completion.

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

        Returns:
            Union[ChatCompletion, Stream[ChatCompletionChunk]]:
                `ChatCompletion` in the non-stream mode, or
                `Stream[ChatCompletionChunk]` in the stream mode.
        """
        response = self._client.chat.completions.create(
            messages=messages,
            model=self.model_type,
            **self.model_config_dict,
        )
        return response

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

        Returns:
            OpenAITokenCounter: The token counter following the model's
                tokenization style.
        """

        if not self._token_counter:
            self._token_counter = OpenAITokenCounter(ModelType.GPT_4O_MINI)
        return self._token_counter

    def check_model_config(self):
        r"""Check whether the model configuration contains any
        unexpected arguments to Qwen API.

        Raises:
            ValueError: If the model configuration dictionary contains any
                unexpected arguments to Qwen API.
        """
        for param in self.model_config_dict:
            if param not in QWEN_API_PARAMS:
                raise ValueError(
                    f"Unexpected argument `{param}` is "
                    "input into Qwen model backend."
                )

    @property
    def stream(self) -> bool:
        r"""Returns whether the model is in stream mode, which sends partial
        results each time.

        Returns:
            bool: Whether the model is in stream mode.
        """
        return self.model_config_dict.get('stream', False)
    

class DeepInfraQwenModel(BaseModelBackend):
    r"""Qwen API in a unified BaseModelBackend interface.

    Args:
        model_type (Union[ModelType, str]): Model for which a backend is
            created, one of Qwen series.
        model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
            that will be fed into:obj:`openai.ChatCompletion.create()`. If
            :obj:`None`, :obj:`QwenConfig().as_dict()` will be used.
            (default: :obj:`None`)
        api_key (Optional[str], optional): The API key for authenticating with
            the Qwen service. (default: :obj:`None`)
        url (Optional[str], optional): The url to the Qwen service.
            (default: :obj:`https://dashscope.aliyuncs.com/compatible-mode/v1`)
        token_counter (Optional[BaseTokenCounter], optional): Token counter to
            use for the model. If not provided, :obj:`OpenAITokenCounter(
            ModelType.GPT_4O_MINI)` will be used.
            (default: :obj:`None`)
    """

    @api_keys_required(
        [
            ("api_key", "DEEPINFRA_API_KEY"),
        ]
    )
    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,
    ) -> None:
        if model_config_dict is None:
            model_config_dict = QwenConfig().as_dict()
        api_key = api_key or os.environ.get("DEEPINFRA_API_KEY")
        url = url or os.environ.get(
            "QWEN_API_BASE_URL",
            "https://api.deepinfra.com/v1/openai",
        )
        super().__init__(
            model_type, model_config_dict, api_key, url, token_counter
        )
        self._client = OpenAI(
            timeout=180,
            max_retries=3,
            api_key=self._api_key,
            base_url=self._url,
        )

    def run(
        self,
        messages: List[OpenAIMessage],
    ) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
        r"""Runs inference of Qwen chat completion.

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

        Returns:
            Union[ChatCompletion, Stream[ChatCompletionChunk]]:
                `ChatCompletion` in the non-stream mode, or
                `Stream[ChatCompletionChunk]` in the stream mode.
        """
        response = self._client.chat.completions.create(
            messages=messages,
            model=self.model_type,
            **self.model_config_dict,
        )
        return response

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

        Returns:
            OpenAITokenCounter: The token counter following the model's
                tokenization style.
        """

        if not self._token_counter:
            self._token_counter = OpenAITokenCounter(ModelType.GPT_4O_MINI)
        return self._token_counter

    def check_model_config(self):
        r"""Check whether the model configuration contains any
        unexpected arguments to Qwen API.

        Raises:
            ValueError: If the model configuration dictionary contains any
                unexpected arguments to Qwen API.
        """
        for param in self.model_config_dict:
            if param not in QWEN_API_PARAMS:
                raise ValueError(
                    f"Unexpected argument `{param}` is "
                    "input into Qwen model backend."
                )

    @property
    def stream(self) -> bool:
        r"""Returns whether the model is in stream mode, which sends partial
        results each time.

        Returns:
            bool: Whether the model is in stream mode.
        """
        return self.model_config_dict.get('stream', False)



class DeepInfraPhi4Model(BaseModelBackend):
    @api_keys_required(
        [
            ("api_key", "DEEPINFRA_API_KEY"),
        ]
    )
    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,
    ) -> None:
        if model_config_dict is None:
            model_config_dict = QwenConfig().as_dict()
        api_key = api_key or os.environ.get("DEEPINFRA_API_KEY")
        url = url or os.environ.get(
            "PHI4_API_BASE_URL",
            "https://api.deepinfra.com/v1/openai",
        )
        super().__init__(
            model_type, model_config_dict, api_key, url, token_counter
        )
        self._client = OpenAI(
            timeout=5000,
            max_retries=3,
            api_key=self._api_key,
            base_url=self._url,
        )

    def run(
        self,
        messages: List[OpenAIMessage],
    ) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
        r"""Runs inference of Qwen chat completion.

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

        Returns:
            Union[ChatCompletion, Stream[ChatCompletionChunk]]:
                `ChatCompletion` in the non-stream mode, or
                `Stream[ChatCompletionChunk]` in the stream mode.
        """
        response = self._client.chat.completions.create(
            messages=messages,
            model=self.model_type,
            **self.model_config_dict,
        )
        return response

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

        Returns:
            OpenAITokenCounter: The token counter following the model's
                tokenization style.
        """

        if not self._token_counter:
            self._token_counter = OpenAITokenCounter(ModelType.GPT_4O_MINI)
        return self._token_counter

    def check_model_config(self):
        r"""Check whether the model configuration contains any
        unexpected arguments to Qwen API.

        Raises:
            ValueError: If the model configuration dictionary contains any
                unexpected arguments to Qwen API.
        """
        for param in self.model_config_dict:
            if param not in QWEN_API_PARAMS:
                raise ValueError(
                    f"Unexpected argument `{param}` is "
                    "input into Qwen model backend."
                )

    @property
    def stream(self) -> bool:
        r"""Returns whether the model is in stream mode, which sends partial
        results each time.

        Returns:
            bool: Whether the model is in stream mode.
        """
        return self.model_config_dict.get('stream', False)


# class DeepInfraGeminiModel(BaseModelBackend):
#     @api_keys_required(
#         [
#             ("api_key", "DEEPINFRA_API_KEY"),
#         ]
#     )
#     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,
#     ) -> None:
#         if model_config_dict is None:
#             model_config_dict = QwenConfig().as_dict()
#         api_key = api_key or os.environ.get("DEEPINFRA_API_KEY")
#         url = url or os.environ.get(
#             "GEMINI_API_BASE_URL",
#             "https://api.deepinfra.com/v1/openai",
#         )
#         super().__init__(
#             model_type, model_config_dict, api_key, url, token_counter
#         )
#         self._client = OpenAI(
#             timeout=5000,
#             max_retries=3,
#             api_key=self._api_key,
#             base_url=self._url,
#         )

#     def run(
#         self,
#         messages: List[OpenAIMessage],
#     ) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
#         r"""Runs inference of Qwen chat completion.

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

#         Returns:
#             Union[ChatCompletion, Stream[ChatCompletionChunk]]:
#                 `ChatCompletion` in the non-stream mode, or
#                 `Stream[ChatCompletionChunk]` in the stream mode.
#         """
#         response = self._client.chat.completions.create(
#             messages=messages,
#             model=self.model_type,
#             **self.model_config_dict,
#         )
#         return response

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

#         Returns:
#             OpenAITokenCounter: The token counter following the model's
#                 tokenization style.
#         """

#         if not self._token_counter:
#             self._token_counter = OpenAITokenCounter(ModelType.GPT_4O_MINI)
#         return self._token_counter

#     def check_model_config(self):
#         r"""Check whether the model configuration contains any
#         unexpected arguments to Qwen API.

#         Raises:
#             ValueError: If the model configuration dictionary contains any
#                 unexpected arguments to Qwen API.
#         """
#         for param in self.model_config_dict:
#             if param not in QWEN_API_PARAMS:
#                 raise ValueError(
#                     f"Unexpected argument `{param}` is "
#                     "input into Qwen model backend."
#                 )

#     @property
#     def stream(self) -> bool:
#         r"""Returns whether the model is in stream mode, which sends partial
#         results each time.

#         Returns:
#             bool: Whether the model is in stream mode.
#         """
#         return self.model_config_dict.get('stream', False)