Source code for camel.models.vllm_model

# =========== Copyright 2023 @ 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 @ CAMEL-AI.org. All Rights Reserved. ===========
import os
import subprocess
from typing import Any, Dict, List, Optional, Union

from openai import OpenAI, Stream

from camel.configs import VLLM_API_PARAMS
from camel.messages import OpenAIMessage
from camel.types import ChatCompletion, ChatCompletionChunk, ModelType
from camel.utils import BaseTokenCounter, OpenAITokenCounter


# flake8: noqa: E501


class VLLMModel:
    r"""vLLM service interface."""

    def __init__(
        self,
        model_type: str,
        model_config_dict: Dict[str, Any],
        url: Optional[str] = None,
        api_key: Optional[str] = None,
        token_counter: Optional[BaseTokenCounter] = None,
    ) -> None:
        r"""Constructor for vLLM backend with OpenAI compatibility.

        # Reference: https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html

        Args:
            model_type (str): Model for which a backend is created.
            model_config_dict (Dict[str, Any]): A dictionary that will
                be fed into openai.ChatCompletion.create().
            url (Optional[str]): The url to the model service. (default:
                :obj:`"http://localhost:8000/v1"`)
            api_key (Optional[str]): The API key for authenticating with the
                model service.
            token_counter (Optional[BaseTokenCounter]): Token counter to use
                for the model. If not provided, `OpenAITokenCounter(ModelType.
                GPT_4O_MINI)` will be used.
        """
        self.model_type = model_type
        self.model_config_dict = model_config_dict
        self._url = (
            url
            or os.environ.get("VLLM_BASE_URL")
            or "http://localhost:8000/v1"
        )
        if not url and not os.environ.get("VLLM_BASE_URL"):
            self._start_server()
        # Use OpenAI cilent as interface call vLLM
        self._client = OpenAI(
            timeout=60,
            max_retries=3,
            base_url=self._url,
            api_key=api_key,
        )
        self._token_counter = token_counter
        self.check_model_config()

    def _start_server(self) -> None:
        r"""Starts the vllm server in a subprocess."""
        try:
            subprocess.Popen(
                ["vllm", "server", "--port", "8000"],
                stdout=subprocess.PIPE,
                stderr=subprocess.PIPE,
            )
            print(
                f"vllm server started on http://localhost:8000/v1 "
                f"for {self.model_type} model."
            )
        except Exception as e:
            print(f"Failed to start vllm server: {e}.")

    @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 = 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 vLLM API.

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




    def run(
        self,
        messages: List[OpenAIMessage],
    ) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
        r"""Runs inference of OpenAI 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_limit(self) -> int:
        r"""Returns the maximum token limit for the given model.

        Returns:
            int: The maximum token limit for the given model.
        """
        max_tokens = self.model_config_dict.get("max_tokens")
        if isinstance(max_tokens, int):
            return max_tokens
        print(
            "Must set `max_tokens` as an integer in `model_config_dict` when"
            " setting up the model. Using 4096 as default value."
        )
        return 4096

    @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)