Source code for langchain_community.llms.bananadev

import logging
from typing import Any, Dict, List, Mapping, Optional, cast

from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.pydantic_v1 import Extra, Field, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

from langchain_community.llms.utils import enforce_stop_tokens

logger = logging.getLogger(__name__)


class Banana(LLM):
    """香蕉大型语言模型。

    要使用，您应该已安装``banana-dev`` python包，并设置环境变量``BANANA_API_KEY``为您的API密钥。
    这是在香蕉仪表板中提供的团队API密钥。

    任何可以传递给调用的有效参数都可以传递进来，即使在这个类中没有明确保存。

    示例：
        .. code-block:: python

            from langchain_community.llms import Banana
            banana = Banana(model_key="", model_url_slug="")
    """

    model_key: str = ""
    """要使用的模型密钥"""

    model_url_slug: str = ""
    """使用的模型端点"""

    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """包含在`create`调用中有效但未明确指定的任何模型参数。"""

    banana_api_key: Optional[SecretStr] = None

    class Config:
        """这是用于pydantic配置的设置。"""

        extra = Extra.forbid

    @root_validator(pre=True)
    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """从传入的额外参数构建额外的kwargs。"""
        all_required_field_names = {field.alias for field in cls.__fields__.values()}

        extra = values.get("model_kwargs", {})
        for field_name in list(values):
            if field_name not in all_required_field_names:
                if field_name in extra:
                    raise ValueError(f"Found {field_name} supplied twice.")
                logger.warning(
                    f"""{field_name} was transferred to model_kwargs.
                    Please confirm that {field_name} is what you intended."""
                )
                extra[field_name] = values.pop(field_name)
        values["model_kwargs"] = extra
        return values

    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """验证环境中是否存在API密钥和Python包。"""
        banana_api_key = convert_to_secret_str(
            get_from_dict_or_env(values, "banana_api_key", "BANANA_API_KEY")
        )
        values["banana_api_key"] = banana_api_key
        return values

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """获取识别参数。"""
        return {
            **{"model_key": self.model_key},
            **{"model_url_slug": self.model_url_slug},
            **{"model_kwargs": self.model_kwargs},
        }

    @property
    def _llm_type(self) -> str:
        """llm的返回类型。"""
        return "bananadev"

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """调用香蕉端点。"""
        try:
            from banana_dev import Client
        except ImportError:
            raise ImportError(
                "Could not import banana-dev python package. "
                "Please install it with `pip install banana-dev`."
            )
        params = self.model_kwargs or {}
        params = {**params, **kwargs}
        api_key = cast(SecretStr, self.banana_api_key)
        model_key = self.model_key
        model_url_slug = self.model_url_slug
        model_inputs = {
            # a json specific to your model.
            "prompt": prompt,
            **params,
        }
        model = Client(
            # Found in main dashboard
            api_key=api_key.get_secret_value(),
            # Both found in model details page
            model_key=model_key,
            url=f"https://{model_url_slug}.run.banana.dev",
        )
        response, meta = model.call("/", model_inputs)
        try:
            text = response["outputs"]
        except (KeyError, TypeError):
            raise ValueError(
                "Response should be of schema: {'outputs': 'text'}."
                "\nTo fix this:"
                "\n- fork the source repo of the Banana model"
                "\n- modify app.py to return the above schema"
                "\n- deploy that as a custom repo"
            )
        if stop is not None:
            # I believe this is required since the stop tokens
            # are not enforced by the model parameters
            text = enforce_stop_tokens(text, stop)
        return text