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PaLMChat

This module implements a wrapper for the Google PaLM chat API, using BaseChatLLM as a base class.

PaLMChat

Bases: BaseChatLLM

A class for interacting with the Google PaLM chat API.

Inherits from BaseChatLLM.

Uses the specified Google PaLM model and parameters for interacting with the Google PaLM chat API.

Source code in llmflows/llms/palm_chat.py 
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class PaLMChat(BaseChatLLM):
    """
    A class for interacting with the Google PaLM chat API.

    Inherits from BaseChatLLM.

    Uses the specified Google PaLM model and parameters for interacting with the Google
    PaLM chat API.
    """

    def __init__(
        self,
        api_key: str,
        model: str = "models/chat-bison-001",
        temperature: float = 0.7,
        max_retries: int = 3,
    ):
        super().__init__(model)
        self.temperature = temperature
        self.max_retries = max_retries
        self._api_key = api_key
        if not self._api_key:
            raise ValueError("You must provide Google API key")
        palm.configure(api_key=self._api_key)

    def _format_results(
            self, model_outputs, retries, message_history
    ) -> tuple[str, dict, dict]:
        """
        Formats results after generation.

        Args:
            model_outputs: The model outputs.
            retries: The number of retries.
            message_history: The message history.

        Returns:
            A tuple containing the generated text, the raw response data, and the model
                configuration.
        """
        text_result = model_outputs.last

        if text_result is None:
            raise ValueError(
                "No text result returned from PaLM API. The API might have blocked "
                f"the response.\n Filter values: {model_outputs.filters}")

        call_data = {
            "raw_outputs":{
                "model": model_outputs.model,
                "context": model_outputs.context,
                "examples": model_outputs.examples,
                "messages": model_outputs.messages,
                "filters": model_outputs.filters,
                "top_p": model_outputs.top_p,
                "top_k": model_outputs.top_k,
            },
            retries: retries
        }

        model_config = {
            "model": self.model,
            "temperature": self.temperature,
            "max_retries": self.max_retries,
            "messages": message_history.messages,
        }

        return text_result, call_data, model_config

    def _convert_message_history(self, message_history: MessageHistory) -> list[dict]:
        """
            Converts a MessageHistory object into PaLM conversation history format:

            [
                {'author': '0', 'content': 'Hello'},
                {'author': '1', 'content': 'Hi there! How can I help you today?'},
                {'author': '0', 'content': "Just chillin'"},
                {'author': '1', ....
            ]

            Args:
                message_history: The MessageHistory object.

            Returns:
                A list of dictionaries representing the conversation history.
        """
        if not message_history.messages:
            raise ValueError("Message history must have at least one user message.")

        history = []

        for message in message_history.messages:
            if message["role"] == "user":
                history.append({"author": "0", "content": message["content"]})
            elif message["role"] == "assistant":
                history.append({"author": "1", "content": message["content"]})

        return history


    def generate(self, message_history: MessageHistory) -> tuple[str, dict, dict]:
        """
        Generates text from the Google PaLM Chat API.

        Args:
            message_history: A `MessageHistory` object representing the conversation
                history.

        Returns:
            A tuple containing the generated text, the raw response data, and the model
                configuration.
        """

        conversation_history = self._convert_message_history(message_history)

        completion, retries = call_with_retry(
            func=palm.chat,
            exceptions_to_retry=(),
            max_retries=self.max_retries,
            model=self.model,
            messages=conversation_history,
            temperature=self.temperature,
        )

        str_message, call_data, model_config = self._format_results(
            completion, retries, message_history
        )

        return str_message, call_data, model_config

    async def generate_async(self, message_history: MessageHistory) -> tuple[str, dict, dict]:
        """
        Generates text from the Google PaLM Chat API asynchronously.

        Args:
            message_history: A `MessageHistory` object representing the conversation
                history.

        Returns:
            A tuple containing the generated text, the raw response data, and the model
                configuration.
        """

        conversation_history = self._convert_message_history(message_history)

        completion, retries = await async_call_with_retry(
            async_func=palm.chat_async,
            exceptions_to_retry=(),
            max_retries=self.max_retries,
            model=self.model,
            messages=conversation_history,
            temperature=self.temperature,
        )

        str_message, call_data, model_config = self._format_results(
            completion, retries, message_history
        )

        return str_message, call_data, model_config

generate(message_history)

Generates text from the Google PaLM Chat API.

Parameters:

Name Type Description Default
message_history MessageHistory

A MessageHistory object representing the conversation history.

 required

Returns:

Type Description
tuple[str, dict, dict]

A tuple containing the generated text, the raw response data, and the model configuration.
Source code in llmflows/llms/palm_chat.py 
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def generate(self, message_history: MessageHistory) -> tuple[str, dict, dict]:
    """
    Generates text from the Google PaLM Chat API.

    Args:
        message_history: A `MessageHistory` object representing the conversation
            history.

    Returns:
        A tuple containing the generated text, the raw response data, and the model
            configuration.
    """

    conversation_history = self._convert_message_history(message_history)

    completion, retries = call_with_retry(
        func=palm.chat,
        exceptions_to_retry=(),
        max_retries=self.max_retries,
        model=self.model,
        messages=conversation_history,
        temperature=self.temperature,
    )

    str_message, call_data, model_config = self._format_results(
        completion, retries, message_history
    )

    return str_message, call_data, model_config

generate_async(message_history) async

Generates text from the Google PaLM Chat API asynchronously.

Parameters:

Name Type Description Default
message_history MessageHistory

A MessageHistory object representing the conversation history.

 required

Returns:

Type Description
tuple[str, dict, dict]

A tuple containing the generated text, the raw response data, and the model configuration.
Source code in llmflows/llms/palm_chat.py 
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async def generate_async(self, message_history: MessageHistory) -> tuple[str, dict, dict]:
    """
    Generates text from the Google PaLM Chat API asynchronously.

    Args:
        message_history: A `MessageHistory` object representing the conversation
            history.

    Returns:
        A tuple containing the generated text, the raw response data, and the model
            configuration.
    """

    conversation_history = self._convert_message_history(message_history)

    completion, retries = await async_call_with_retry(
        async_func=palm.chat_async,
        exceptions_to_retry=(),
        max_retries=self.max_retries,
        model=self.model,
        messages=conversation_history,
        temperature=self.temperature,
    )

    str_message, call_data, model_config = self._format_results(
        completion, retries, message_history
    )

    return str_message, call_data, model_config