Source code for langchain_community.chat_models.everlyai

"""EverlyAI终端聊天包装器。在ChatOpenAI上有很大依赖。"""
from __future__ import annotations

import logging
import sys
from typing import TYPE_CHECKING, Dict, Optional, Set

from langchain_core.messages import BaseMessage
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.utils import get_from_dict_or_env

from langchain_community.adapters.openai import convert_message_to_dict
from langchain_community.chat_models.openai import (
    ChatOpenAI,
    _import_tiktoken,
)

if TYPE_CHECKING:
    import tiktoken

logger = logging.getLogger(__name__)


DEFAULT_API_BASE = "https://everlyai.xyz/hosted"
DEFAULT_MODEL = "meta-llama/Llama-2-7b-chat-hf"


class ChatEverlyAI(ChatOpenAI):
    """`EverlyAI` 聊天大型语言模型。

    要使用，您应该已安装``openai`` python包，并且设置了环境变量``EVERLYAI_API_KEY``为您的API密钥。
    或者，您可以使用everlyai_api_key关键字参数。

    任何可以传递给`openai.create`调用的参数都可以传递，即使在此类中没有明确保存。

    示例:
        .. code-block:: python

            from langchain_community.chat_models import ChatEverlyAI
            chat = ChatEverlyAI(model_name="meta-llama/Llama-2-7b-chat-hf")
    """

    @property
    def _llm_type(self) -> str:
        """聊天模型的返回类型。"""
        return "everlyai-chat"

    @property
    def lc_secrets(self) -> Dict[str, str]:
        return {"everlyai_api_key": "EVERLYAI_API_KEY"}

    @classmethod
    def is_lc_serializable(cls) -> bool:
        return False

    everlyai_api_key: Optional[str] = None
    """EverlyAI 终端 API 密钥。"""
    model_name: str = Field(default=DEFAULT_MODEL, alias="model")
    """要使用的模型名称。"""
    everlyai_api_base: str = DEFAULT_API_BASE
    """API请求的基础URL路径。"""
    available_models: Optional[Set[str]] = None
    """EverlyAI API中可用的模型。"""

    @staticmethod
    def get_available_models() -> Set[str]:
        """从EverlyAI API获取可用模型。"""
        # EverlyAI doesn't yet support dynamically query for available models.
        return set(
            [
                "meta-llama/Llama-2-7b-chat-hf",
                "meta-llama/Llama-2-13b-chat-hf-quantized",
            ]
        )

    @root_validator(pre=True)
    def validate_environment_override(cls, values: dict) -> dict:
        """验证环境中是否存在API密钥和Python包。"""
        values["openai_api_key"] = get_from_dict_or_env(
            values,
            "everlyai_api_key",
            "EVERLYAI_API_KEY",
        )
        values["openai_api_base"] = DEFAULT_API_BASE

        try:
            import openai

        except ImportError as e:
            raise ImportError(
                "Could not import openai python package. "
                "Please install it with `pip install openai`.",
            ) from e
        try:
            values["client"] = openai.ChatCompletion
        except AttributeError as exc:
            raise ValueError(
                "`openai` has no `ChatCompletion` attribute, this is likely "
                "due to an old version of the openai package. Try upgrading it "
                "with `pip install --upgrade openai`.",
            ) from exc

        if "model_name" not in values.keys():
            values["model_name"] = DEFAULT_MODEL

        model_name = values["model_name"]

        available_models = cls.get_available_models()

        if model_name not in available_models:
            raise ValueError(
                f"Model name {model_name} not found in available models: "
                f"{available_models}.",
            )

        values["available_models"] = available_models

        return values

    def _get_encoding_model(self) -> tuple[str, tiktoken.Encoding]:
        tiktoken_ = _import_tiktoken()
        if self.tiktoken_model_name is not None:
            model = self.tiktoken_model_name
        else:
            model = self.model_name
        # Returns the number of tokens used by a list of messages.
        try:
            encoding = tiktoken_.encoding_for_model("gpt-3.5-turbo-0301")
        except KeyError:
            logger.warning("Warning: model not found. Using cl100k_base encoding.")
            model = "cl100k_base"
            encoding = tiktoken_.get_encoding(model)
        return model, encoding

    def get_num_tokens_from_messages(self, messages: list[BaseMessage]) -> int:
        """使用tiktoken包计算num tokens。

官方文档链接: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
"""
        if sys.version_info[1] <= 7:
            return super().get_num_tokens_from_messages(messages)
        model, encoding = self._get_encoding_model()
        tokens_per_message = 3
        tokens_per_name = 1
        num_tokens = 0
        messages_dict = [convert_message_to_dict(m) for m in messages]
        for message in messages_dict:
            num_tokens += tokens_per_message
            for key, value in message.items():
                # Cast str(value) in case the message value is not a string
                # This occurs with function messages
                num_tokens += len(encoding.encode(str(value)))
                if key == "name":
                    num_tokens += tokens_per_name
        # every reply is primed with <im_start>assistant
        num_tokens += 3
        return num_tokens