Openai legacy

ContextRetrieverOpenAIAgent

Bases: BaseOpenAIAgent

ContextRetriever OpenAI Agent.

This agent performs retrieval from BaseRetriever before calling the LLM. Allows it to augment user message with context.

NOTE: this is a beta feature, function interfaces might change.

Parameters:

Name	Type	Description	Default
tools	List[BaseTool]	A list of tools.	required
retriever	BaseRetriever	A retriever.	required
qa_prompt	Optional[PromptTemplate]	A QA prompt.	required
context_separator	str	A context separator.	required
llm	Optional[OpenAI]	An OpenAI LLM.	required
chat_history	Optional[List[ChatMessage]]	A chat history.	required
prefix_messages	List[ChatMessage]	List[ChatMessage]: A list of prefix messages.	required
verbose	bool	Whether to print debug statements.	False
max_function_calls	int	Maximum number of function calls.	DEFAULT_MAX_FUNCTION_CALLS
callback_manager	Optional[CallbackManager]	A callback manager.	None

Source code in llama-index-integrations/agent/llama-index-agent-openai-legacy/llama_index/agent/openai_legacy/context_retriever_agent.py

class ContextRetrieverOpenAIAgent(BaseOpenAIAgent):
    """ContextRetriever OpenAI Agent.

    This agent performs retrieval from BaseRetriever before
    calling the LLM. Allows it to augment user message with context.

    NOTE: this is a beta feature, function interfaces might change.

    Args:
        tools (List[BaseTool]): A list of tools.
        retriever (BaseRetriever): A retriever.
        qa_prompt (Optional[PromptTemplate]): A QA prompt.
        context_separator (str): A context separator.
        llm (Optional[OpenAI]): An OpenAI LLM.
        chat_history (Optional[List[ChatMessage]]): A chat history.
        prefix_messages: List[ChatMessage]: A list of prefix messages.
        verbose (bool): Whether to print debug statements.
        max_function_calls (int): Maximum number of function calls.
        callback_manager (Optional[CallbackManager]): A callback manager.

    """

    def __init__(
        self,
        tools: List[BaseTool],
        retriever: BaseRetriever,
        qa_prompt: PromptTemplate,
        context_separator: str,
        llm: OpenAI,
        memory: BaseMemory,
        prefix_messages: List[ChatMessage],
        verbose: bool = False,
        max_function_calls: int = DEFAULT_MAX_FUNCTION_CALLS,
        callback_manager: Optional[CallbackManager] = None,
    ) -> None:
        super().__init__(
            llm=llm,
            memory=memory,
            prefix_messages=prefix_messages,
            verbose=verbose,
            max_function_calls=max_function_calls,
            callback_manager=callback_manager,
        )
        self._tools = tools
        self._qa_prompt = qa_prompt
        self._retriever = retriever
        self._context_separator = context_separator

    @classmethod
    def from_tools_and_retriever(
        cls,
        tools: List[BaseTool],
        retriever: BaseRetriever,
        qa_prompt: Optional[PromptTemplate] = None,
        context_separator: str = "\n",
        llm: Optional[LLM] = None,
        chat_history: Optional[List[ChatMessage]] = None,
        memory: Optional[BaseMemory] = None,
        memory_cls: Type[BaseMemory] = ChatMemoryBuffer,
        verbose: bool = False,
        max_function_calls: int = DEFAULT_MAX_FUNCTION_CALLS,
        callback_manager: Optional[CallbackManager] = None,
        system_prompt: Optional[str] = None,
        prefix_messages: Optional[List[ChatMessage]] = None,
    ) -> "ContextRetrieverOpenAIAgent":
        """Create a ContextRetrieverOpenAIAgent from a retriever.

        Args:
            retriever (BaseRetriever): A retriever.
            qa_prompt (Optional[PromptTemplate]): A QA prompt.
            context_separator (str): A context separator.
            llm (Optional[OpenAI]): An OpenAI LLM.
            chat_history (Optional[ChatMessageHistory]): A chat history.
            verbose (bool): Whether to print debug statements.
            max_function_calls (int): Maximum number of function calls.
            callback_manager (Optional[CallbackManager]): A callback manager.

        """
        qa_prompt = qa_prompt or DEFAULT_QA_PROMPT
        chat_history = chat_history or []
        llm = llm or Settings.llm
        if not isinstance(llm, OpenAI):
            raise ValueError("llm must be a OpenAI instance")
        if callback_manager is not None:
            llm.callback_manager = callback_manager

        memory = memory or memory_cls.from_defaults(chat_history=chat_history, llm=llm)

        if not llm.metadata.is_function_calling_model:
            raise ValueError(
                f"Model name {llm.model} does not support function calling API."
            )
        if system_prompt is not None:
            if prefix_messages is not None:
                raise ValueError(
                    "Cannot specify both system_prompt and prefix_messages"
                )
            prefix_messages = [ChatMessage(content=system_prompt, role="system")]

        prefix_messages = prefix_messages or []

        return cls(
            tools=tools,
            retriever=retriever,
            qa_prompt=qa_prompt,
            context_separator=context_separator,
            llm=llm,
            memory=memory,
            prefix_messages=prefix_messages,
            verbose=verbose,
            max_function_calls=max_function_calls,
            callback_manager=callback_manager,
        )

    def _get_tools(self, message: str) -> List[BaseTool]:
        """Get tools."""
        return self._tools

    def _build_formatted_message(self, message: str) -> str:
        # augment user message
        retrieved_nodes_w_scores: List[NodeWithScore] = self._retriever.retrieve(
            message
        )
        retrieved_nodes = [node.node for node in retrieved_nodes_w_scores]
        retrieved_texts = [node.get_content() for node in retrieved_nodes]

        # format message
        context_str = self._context_separator.join(retrieved_texts)
        return self._qa_prompt.format(context_str=context_str, query_str=message)

    def chat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Union[str, dict] = "auto",
    ) -> AgentChatResponse:
        """Chat."""
        formatted_message = self._build_formatted_message(message)
        if self._verbose:
            print_text(formatted_message + "\n", color="yellow")

        return super().chat(
            formatted_message, chat_history=chat_history, tool_choice=tool_choice
        )

    async def achat(
        self,
        message: str,
        chat_history: Optional[List[ChatMessage]] = None,
        tool_choice: Union[str, dict] = "auto",
    ) -> AgentChatResponse:
        """Chat."""
        formatted_message = self._build_formatted_message(message)
        if self._verbose:
            print_text(formatted_message + "\n", color="yellow")

        return await super().achat(
            formatted_message, chat_history=chat_history, tool_choice=tool_choice
        )

    def get_tools(self, message: str) -> List[BaseTool]:
        """Get tools."""
        return self._get_tools(message)

from_tools_and_retriever classmethod

from_tools_and_retriever(tools: List[BaseTool], retriever: BaseRetriever, qa_prompt: Optional[PromptTemplate] = None, context_separator: str = '\n', llm: Optional[LLM] = None, chat_history: Optional[List[ChatMessage]] = None, memory: Optional[BaseMemory] = None, memory_cls: Type[BaseMemory] = ChatMemoryBuffer, verbose: bool = False, max_function_calls: int = DEFAULT_MAX_FUNCTION_CALLS, callback_manager: Optional[CallbackManager] = None, system_prompt: Optional[str] = None, prefix_messages: Optional[List[ChatMessage]] = None) -> ContextRetrieverOpenAIAgent

Create a ContextRetrieverOpenAIAgent from a retriever.

Parameters:

Name	Type	Description	Default
retriever	BaseRetriever	A retriever.	required
qa_prompt	Optional[PromptTemplate]	A QA prompt.	None
context_separator	str	A context separator.	'\n'
llm	Optional[OpenAI]	An OpenAI LLM.	None
chat_history	Optional[ChatMessageHistory]	A chat history.	None
verbose	bool	Whether to print debug statements.	False
max_function_calls	int	Maximum number of function calls.	DEFAULT_MAX_FUNCTION_CALLS
callback_manager	Optional[CallbackManager]	A callback manager.	None

Source code in llama-index-integrations/agent/llama-index-agent-openai-legacy/llama_index/agent/openai_legacy/context_retriever_agent.py

@classmethod
def from_tools_and_retriever(
    cls,
    tools: List[BaseTool],
    retriever: BaseRetriever,
    qa_prompt: Optional[PromptTemplate] = None,
    context_separator: str = "\n",
    llm: Optional[LLM] = None,
    chat_history: Optional[List[ChatMessage]] = None,
    memory: Optional[BaseMemory] = None,
    memory_cls: Type[BaseMemory] = ChatMemoryBuffer,
    verbose: bool = False,
    max_function_calls: int = DEFAULT_MAX_FUNCTION_CALLS,
    callback_manager: Optional[CallbackManager] = None,
    system_prompt: Optional[str] = None,
    prefix_messages: Optional[List[ChatMessage]] = None,
) -> "ContextRetrieverOpenAIAgent":
    """Create a ContextRetrieverOpenAIAgent from a retriever.

    Args:
        retriever (BaseRetriever): A retriever.
        qa_prompt (Optional[PromptTemplate]): A QA prompt.
        context_separator (str): A context separator.
        llm (Optional[OpenAI]): An OpenAI LLM.
        chat_history (Optional[ChatMessageHistory]): A chat history.
        verbose (bool): Whether to print debug statements.
        max_function_calls (int): Maximum number of function calls.
        callback_manager (Optional[CallbackManager]): A callback manager.

    """
    qa_prompt = qa_prompt or DEFAULT_QA_PROMPT
    chat_history = chat_history or []
    llm = llm or Settings.llm
    if not isinstance(llm, OpenAI):
        raise ValueError("llm must be a OpenAI instance")
    if callback_manager is not None:
        llm.callback_manager = callback_manager

    memory = memory or memory_cls.from_defaults(chat_history=chat_history, llm=llm)

    if not llm.metadata.is_function_calling_model:
        raise ValueError(
            f"Model name {llm.model} does not support function calling API."
        )
    if system_prompt is not None:
        if prefix_messages is not None:
            raise ValueError(
                "Cannot specify both system_prompt and prefix_messages"
            )
        prefix_messages = [ChatMessage(content=system_prompt, role="system")]

    prefix_messages = prefix_messages or []

    return cls(
        tools=tools,
        retriever=retriever,
        qa_prompt=qa_prompt,
        context_separator=context_separator,
        llm=llm,
        memory=memory,
        prefix_messages=prefix_messages,
        verbose=verbose,
        max_function_calls=max_function_calls,
        callback_manager=callback_manager,
    )

chat

chat(message: str, chat_history: Optional[List[ChatMessage]] = None, tool_choice: Union[str, dict] = 'auto') -> AgentChatResponse

Chat.

Source code in llama-index-integrations/agent/llama-index-agent-openai-legacy/llama_index/agent/openai_legacy/context_retriever_agent.py

def chat(
    self,
    message: str,
    chat_history: Optional[List[ChatMessage]] = None,
    tool_choice: Union[str, dict] = "auto",
) -> AgentChatResponse:
    """Chat."""
    formatted_message = self._build_formatted_message(message)
    if self._verbose:
        print_text(formatted_message + "\n", color="yellow")

    return super().chat(
        formatted_message, chat_history=chat_history, tool_choice=tool_choice
    )

achat async

achat(message: str, chat_history: Optional[List[ChatMessage]] = None, tool_choice: Union[str, dict] = 'auto') -> AgentChatResponse

Chat.

Source code in llama-index-integrations/agent/llama-index-agent-openai-legacy/llama_index/agent/openai_legacy/context_retriever_agent.py

async def achat(
    self,
    message: str,
    chat_history: Optional[List[ChatMessage]] = None,
    tool_choice: Union[str, dict] = "auto",
) -> AgentChatResponse:
    """Chat."""
    formatted_message = self._build_formatted_message(message)
    if self._verbose:
        print_text(formatted_message + "\n", color="yellow")

    return await super().achat(
        formatted_message, chat_history=chat_history, tool_choice=tool_choice
    )

get_tools

get_tools(message: str) -> List[BaseTool]

Get tools.

Source code in llama-index-integrations/agent/llama-index-agent-openai-legacy/llama_index/agent/openai_legacy/context_retriever_agent.py

def get_tools(self, message: str) -> List[BaseTool]:
    """Get tools."""
    return self._get_tools(message)

FnRetrieverOpenAIAgent

Bases: OpenAIAgent

Function Retriever OpenAI Agent.

Uses our object retriever module to retrieve openai agent.

NOTE: This is deprecated, you can just use the base OpenAIAgent class by specifying the following:

agent = OpenAIAgent.from_tools(tool_retriever=retriever, ...)

Source code in llama-index-integrations/agent/llama-index-agent-openai-legacy/llama_index/agent/openai_legacy/retriever_openai_agent.py

class FnRetrieverOpenAIAgent(OpenAIAgent):
    """Function Retriever OpenAI Agent.

    Uses our object retriever module to retrieve openai agent.

    NOTE: This is deprecated, you can just use the base `OpenAIAgent` class by
    specifying the following:
    ```
    agent = OpenAIAgent.from_tools(tool_retriever=retriever, ...)
    ```

    """

    @classmethod
    def from_retriever(
        cls, retriever: ObjectRetriever[BaseTool], **kwargs: Any
    ) -> "FnRetrieverOpenAIAgent":
        return cast(
            FnRetrieverOpenAIAgent, cls.from_tools(tool_retriever=retriever, **kwargs)
        )