Palm

PaLM

Bases: CustomLLM

PaLM LLM。

示例

pip install llama-index-llms-palm

import google.generativeai as palm

# PaLM的API密钥
palm_api_key = "YOUR_API_KEY_HERE"

# 列出所有支持文本生成的模型
models = [
    m
    for m in palm.list_models()
    if "generateText" in m.supported_generation_methods
]
model = models[0].name
print(model)

# 开始使用我们的PaLM LLM抽象
from llama_index.llms.palm import PaLM

# 使用API密钥创建PaLM类的实例
llm = PaLM(model_name=model, api_key=palm_api_key)

# 使用complete方法基于提示生成文本
response = llm.complete("Your prompt text here.")
print(str(response))

Source code in llama_index/llms/palm/base.py

class PaLM(CustomLLM):
    """PaLM LLM。

    示例:
        `pip install llama-index-llms-palm`

        ```python
        import google.generativeai as palm

        # PaLM的API密钥
        palm_api_key = "YOUR_API_KEY_HERE"

        # 列出所有支持文本生成的模型
        models = [
            m
            for m in palm.list_models()
            if "generateText" in m.supported_generation_methods
        ]
        model = models[0].name
        print(model)

        # 开始使用我们的PaLM LLM抽象
        from llama_index.llms.palm import PaLM

        # 使用API密钥创建PaLM类的实例
        llm = PaLM(model_name=model, api_key=palm_api_key)

        # 使用complete方法基于提示生成文本
        response = llm.complete("Your prompt text here.")
        print(str(response))
        ```"""

    model_name: str = Field(
        default=DEFAULT_PALM_MODEL, description="The PaLM model to use."
    )
    num_output: int = Field(
        default=DEFAULT_NUM_OUTPUTS,
        description="The number of tokens to generate.",
        gt=0,
    )
    generate_kwargs: dict = Field(
        default_factory=dict, description="Kwargs for generation."
    )

    _model: Any = PrivateAttr()

    def __init__(
        self,
        api_key: Optional[str] = None,
        model_name: Optional[str] = DEFAULT_PALM_MODEL,
        num_output: Optional[int] = None,
        callback_manager: Optional[CallbackManager] = None,
        system_prompt: Optional[str] = None,
        messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
        completion_to_prompt: Optional[Callable[[str], str]] = None,
        pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
        output_parser: Optional[BaseOutputParser] = None,
        **generate_kwargs: Any,
    ) -> None:
        """初始化参数。"""
        api_key = api_key or os.environ.get("PALM_API_KEY")
        palm.configure(api_key=api_key)

        models = palm.list_models()
        models_dict = {m.name: m for m in models}
        if model_name not in models_dict:
            raise ValueError(
                f"Model name {model_name} not found in {models_dict.keys()}"
            )

        model_name = model_name
        self._model = models_dict[model_name]

        # get num_output
        num_output = num_output or self._model.output_token_limit

        generate_kwargs = generate_kwargs or {}
        super().__init__(
            model_name=model_name,
            num_output=num_output,
            generate_kwargs=generate_kwargs,
            callback_manager=callback_manager,
            system_prompt=system_prompt,
            messages_to_prompt=messages_to_prompt,
            completion_to_prompt=completion_to_prompt,
            pydantic_program_mode=pydantic_program_mode,
            output_parser=output_parser,
        )

    @classmethod
    def class_name(cls) -> str:
        return "PaLM_llm"

    @property
    def metadata(self) -> LLMMetadata:
        """获取LLM元数据。"""
        # TODO: google palm actually separates input and output token limits
        total_tokens = self._model.input_token_limit + self.num_output
        return LLMMetadata(
            context_window=total_tokens,
            num_output=self.num_output,
            model_name=self.model_name,
        )

    @llm_completion_callback()
    def complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        """预测对查询的答案。

Args:
    prompt (str): 用于预测的提示。

Returns:
    Tuple[str, str]: 预测的答案和格式化后的提示的元组。
"""
        completion = palm.generate_text(
            model=self.model_name,
            prompt=prompt,
            **kwargs,
        )
        return CompletionResponse(text=completion.result, raw=completion.candidates[0])

    @llm_completion_callback()
    def stream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseGen:
        """流式传输查询的答案。

注意：这是一个测试功能。将尝试构建或使用更好的抽象来处理响应。

Args:
    prompt（str）：用于预测的提示。

Returns:
    str：预测的答案。
"""
        raise NotImplementedError(
            "PaLM does not support streaming completion in LlamaIndex currently."
        )

metadata property

metadata: LLMMetadata

获取LLM元数据。

complete

complete(
    prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse

预测对查询的答案。

Parameters:

Name	Type	Description	Default
prompt	str	用于预测的提示。	required

Returns:

Type	Description
CompletionResponse	Tuple[str, str]: 预测的答案和格式化后的提示的元组。

Source code in llama_index/llms/palm/base.py

    @llm_completion_callback()
    def complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        """预测对查询的答案。

Args:
    prompt (str): 用于预测的提示。

Returns:
    Tuple[str, str]: 预测的答案和格式化后的提示的元组。
"""
        completion = palm.generate_text(
            model=self.model_name,
            prompt=prompt,
            **kwargs,
        )
        return CompletionResponse(text=completion.result, raw=completion.candidates[0])

stream_complete

stream_complete(
    prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen

流式传输查询的答案。

注意：这是一个测试功能。将尝试构建或使用更好的抽象来处理响应。

Returns:

Type	Description
CompletionResponseGen	str：预测的答案。

Source code in llama_index/llms/palm/base.py

    @llm_completion_callback()
    def stream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseGen:
        """流式传输查询的答案。

注意：这是一个测试功能。将尝试构建或使用更好的抽象来处理响应。

Args:
    prompt（str）：用于预测的提示。

Returns:
    str：预测的答案。
"""
        raise NotImplementedError(
            "PaLM does not support streaming completion in LlamaIndex currently."
        )