)
    except NotImplementedError as e:
        if event_name == "on_chat_model_start":
            message_strings = [get_buffer_string(m) for m in args[1]]
            await _ahandle_event_for_handler(
                handler,
                "on_llm_start",
                "ignore_llm",
                args[0],
                message_strings,
                *args[2:],
                **kwargs,
            )
        else:
            logger.warning(
                f"NotImplementedError in {handler.__class__.__name__}.{event_name}"
                f" callback: {repr(e)}"
            )
    except Exception as e:
        logger.warning(
            f"Error in {handler.__class__.__name__}.{event_name} callback:"
            f" {repr(e)}"
        )
        if handler.raise_error:
            raise e
[docs]async def ahandle_event(
    handlers: List[BaseCallbackHandler],
    event_name: str,
    ignore_condition_name: Optional[str],
    *args: Any,
    **kwargs: Any,
) -> None:
    """Generic event handler for AsyncCallbackManager.
    Note: This function is used by langserve to handle events.
    Args:
        handlers: The list of handlers that will handle the event
        event_name: The name of the event (e.g., "on_llm_start")
        ignore_condition_name: Name of the attribute defined on handler
            that if True will cause the handler to be skipped for the given event
        *args: The arguments to pass to the event handler
        **kwargs: The keyword arguments to pass to the event handler
    """
    for handler in [h for h in handlers if h.run_inline]:
        await _ahandle_event_for_handler(


await _ahandle_event_for_handler(
            handler, event_name, ignore_condition_name, *args, **kwargs
        )
    await asyncio.gather(
        *(
            _ahandle_event_for_handler(
                handler, event_name, ignore_condition_name, *args, **kwargs
            )
            for handler in handlers
            if not handler.run_inline
        )
    )
BRM = TypeVar("BRM", bound="BaseRunManager")
[docs]class BaseRunManager(RunManagerMixin):
    """Base class for run manager (a bound callback manager)."""
[docs]    def __init__(
        self,
        *,
        run_id: UUID,
        handlers: List[BaseCallbackHandler],
        inheritable_handlers: List[BaseCallbackHandler],
        parent_run_id: Optional[UUID] = None,
        tags: Optional[List[str]] = None,
        inheritable_tags: Optional[List[str]] = None,
        metadata: Optional[Dict[str, Any]] = None,
        inheritable_metadata: Optional[Dict[str, Any]] = None,
    ) -> None:
        """Initialize the run manager.
        Args:
            run_id (UUID): The ID of the run.
            handlers (List[BaseCallbackHandler]): The list of handlers.
            inheritable_handlers (List[BaseCallbackHandler]):
                The list of inheritable handlers.
            parent_run_id (UUID, optional): The ID of the parent run.
                Defaults to None.
            tags (Optional[List[str]]): The list of tags.
            inheritable_tags (Optional[List[str]]): The list of inheritable tags.
            metadata (Optional[Dict[str, Any]]): The metadata.
            inheritable_metadata (Optional[Dict[str, Any]]): The inheritable metadata.


inheritable_metadata (Optional[Dict[str, Any]]): The inheritable metadata.
        """
        self.run_id = run_id
        self.handlers = handlers
        self.inheritable_handlers = inheritable_handlers
        self.parent_run_id = parent_run_id
        self.tags = tags or []
        self.inheritable_tags = inheritable_tags or []
        self.metadata = metadata or {}
        self.inheritable_metadata = inheritable_metadata or {}
[docs]    @classmethod
    def get_noop_manager(cls: Type[BRM]) -> BRM:
        """Return a manager that doesn't perform any operations.
        Returns:
            BaseRunManager: The noop manager.
        """
        return cls(
            run_id=uuid.uuid4(),
            handlers=[],
            inheritable_handlers=[],
            tags=[],
            inheritable_tags=[],
            metadata={},
            inheritable_metadata={},
        )
[docs]class RunManager(BaseRunManager):
    """Sync Run Manager."""
[docs]    def on_text(
        self,
        text: str,
        **kwargs: Any,
    ) -> Any:
        """Run when text is received.
        Args:
            text (str): The received text.
        Returns:
            Any: The result of the callback.
        """
        handle_event(
            self.handlers,
            "on_text",
            None,
            text,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    def on_retry(
        self,
        retry_state: RetryCallState,
        **kwargs: Any,
    ) -> None:
        handle_event(
            self.handlers,
            "on_retry",


handle_event(
            self.handlers,
            "on_retry",
            "ignore_retry",
            retry_state,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]class ParentRunManager(RunManager):
    """Sync Parent Run Manager."""
[docs]    def get_child(self, tag: Optional[str] = None) -> CallbackManager:
        """Get a child callback manager.
        Args:
            tag (str, optional): The tag for the child callback manager.
                Defaults to None.
        Returns:
            CallbackManager: The child callback manager.
        """
        manager = CallbackManager(handlers=[], parent_run_id=self.run_id)
        manager.set_handlers(self.inheritable_handlers)
        manager.add_tags(self.inheritable_tags)
        manager.add_metadata(self.inheritable_metadata)
        if tag is not None:
            manager.add_tags([tag], False)
        return manager
[docs]class AsyncRunManager(BaseRunManager):
    """Async Run Manager."""
[docs]    async def on_text(
        self,
        text: str,
        **kwargs: Any,
    ) -> Any:
        """Run when text is received.
        Args:
            text (str): The received text.
        Returns:
            Any: The result of the callback.
        """
        await ahandle_event(
            self.handlers,
            "on_text",
            None,
            text,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    async def on_retry(
        self,
        retry_state: RetryCallState,


self,
        retry_state: RetryCallState,
        **kwargs: Any,
    ) -> None:
        await ahandle_event(
            self.handlers,
            "on_retry",
            "ignore_retry",
            retry_state,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]class AsyncParentRunManager(AsyncRunManager):
    """Async Parent Run Manager."""
[docs]    def get_child(self, tag: Optional[str] = None) -> AsyncCallbackManager:
        """Get a child callback manager.
        Args:
            tag (str, optional): The tag for the child callback manager.
                Defaults to None.
        Returns:
            AsyncCallbackManager: The child callback manager.
        """
        manager = AsyncCallbackManager(handlers=[], parent_run_id=self.run_id)
        manager.set_handlers(self.inheritable_handlers)
        manager.add_tags(self.inheritable_tags)
        manager.add_metadata(self.inheritable_metadata)
        if tag is not None:
            manager.add_tags([tag], False)
        return manager
[docs]class CallbackManagerForLLMRun(RunManager, LLMManagerMixin):
    """Callback manager for LLM run."""
[docs]    def on_llm_new_token(
        self,
        token: str,
        *,
        chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None,
        **kwargs: Any,
    ) -> None:
        """Run when LLM generates a new token.
        Args:
            token (str): The new token.
        """
        handle_event(
            self.handlers,
            "on_llm_new_token",
            "ignore_llm",


"on_llm_new_token",
            "ignore_llm",
            token=token,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            chunk=chunk,
            **kwargs,
        )
[docs]    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
        """Run when LLM ends running.
        Args:
            response (LLMResult): The LLM result.
        """
        handle_event(
            self.handlers,
            "on_llm_end",
            "ignore_llm",
            response,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    def on_llm_error(
        self,
        error: BaseException,
        **kwargs: Any,
    ) -> None:
        """Run when LLM errors.
        Args:
            error (Exception or KeyboardInterrupt): The error.
            kwargs (Any): Additional keyword arguments.
                - response (LLMResult): The response which was generated before
                    the error occurred.
        """
        handle_event(
            self.handlers,
            "on_llm_error",
            "ignore_llm",
            error,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin):
    """Async callback manager for LLM run."""
[docs]    async def on_llm_new_token(
        self,
        token: str,
        *,


self,
        token: str,
        *,
        chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None,
        **kwargs: Any,
    ) -> None:
        """Run when LLM generates a new token.
        Args:
            token (str): The new token.
        """
        await ahandle_event(
            self.handlers,
            "on_llm_new_token",
            "ignore_llm",
            token,
            chunk=chunk,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
        """Run when LLM ends running.
        Args:
            response (LLMResult): The LLM result.
        """
        await ahandle_event(
            self.handlers,
            "on_llm_end",
            "ignore_llm",
            response,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    async def on_llm_error(
        self,
        error: BaseException,
        **kwargs: Any,
    ) -> None:
        """Run when LLM errors.
        Args:
            error (Exception or KeyboardInterrupt): The error.
            kwargs (Any): Additional keyword arguments.
                - response (LLMResult): The response which was generated before
                    the error occurred.
        """
        await ahandle_event(
            self.handlers,
            "on_llm_error",
            "ignore_llm",
            error,
            run_id=self.run_id,


"ignore_llm",
            error,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]class CallbackManagerForChainRun(ParentRunManager, ChainManagerMixin):
    """Callback manager for chain run."""
[docs]    def on_chain_end(self, outputs: Union[Dict[str, Any], Any], **kwargs: Any) -> None:
        """Run when chain ends running.
        Args:
            outputs (Union[Dict[str, Any], Any]): The outputs of the chain.
        """
        handle_event(
            self.handlers,
            "on_chain_end",
            "ignore_chain",
            outputs,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    def on_chain_error(
        self,
        error: BaseException,
        **kwargs: Any,
    ) -> None:
        """Run when chain errors.
        Args:
            error (Exception or KeyboardInterrupt): The error.
        """
        handle_event(
            self.handlers,
            "on_chain_error",
            "ignore_chain",
            error,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
        """Run when agent action is received.
        Args:
            action (AgentAction): The agent action.
        Returns:
            Any: The result of the callback.
        """
        handle_event(
            self.handlers,
            "on_agent_action",


"""
        handle_event(
            self.handlers,
            "on_agent_action",
            "ignore_agent",
            action,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
        """Run when agent finish is received.
        Args:
            finish (AgentFinish): The agent finish.
        Returns:
            Any: The result of the callback.
        """
        handle_event(
            self.handlers,
            "on_agent_finish",
            "ignore_agent",
            finish,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]class AsyncCallbackManagerForChainRun(AsyncParentRunManager, ChainManagerMixin):
    """Async callback manager for chain run."""
[docs]    async def on_chain_end(
        self, outputs: Union[Dict[str, Any], Any], **kwargs: Any
    ) -> None:
        """Run when chain ends running.
        Args:
            outputs (Union[Dict[str, Any], Any]): The outputs of the chain.
        """
        await ahandle_event(
            self.handlers,
            "on_chain_end",
            "ignore_chain",
            outputs,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    async def on_chain_error(
        self,
        error: BaseException,
        **kwargs: Any,
    ) -> None:
        """Run when chain errors.
        Args:


) -> None:
        """Run when chain errors.
        Args:
            error (Exception or KeyboardInterrupt): The error.
        """
        await ahandle_event(
            self.handlers,
            "on_chain_error",
            "ignore_chain",
            error,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    async def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
        """Run when agent action is received.
        Args:
            action (AgentAction): The agent action.
        Returns:
            Any: The result of the callback.
        """
        await ahandle_event(
            self.handlers,
            "on_agent_action",
            "ignore_agent",
            action,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
        """Run when agent finish is received.
        Args:
            finish (AgentFinish): The agent finish.
        Returns:
            Any: The result of the callback.
        """
        await ahandle_event(
            self.handlers,
            "on_agent_finish",
            "ignore_agent",
            finish,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]class CallbackManagerForToolRun(ParentRunManager, ToolManagerMixin):
    """Callback manager for tool run."""
[docs]    def on_tool_end(
        self,
        output: str,


[docs]    def on_tool_end(
        self,
        output: str,
        **kwargs: Any,
    ) -> None:
        """Run when tool ends running.
        Args:
            output (str): The output of the tool.
        """
        handle_event(
            self.handlers,
            "on_tool_end",
            "ignore_agent",
            output,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    def on_tool_error(
        self,
        error: BaseException,
        **kwargs: Any,
    ) -> None:
        """Run when tool errors.
        Args:
            error (Exception or KeyboardInterrupt): The error.
        """
        handle_event(
            self.handlers,
            "on_tool_error",
            "ignore_agent",
            error,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]class AsyncCallbackManagerForToolRun(AsyncParentRunManager, ToolManagerMixin):
    """Async callback manager for tool run."""
[docs]    async def on_tool_end(self, output: str, **kwargs: Any) -> None:
        """Run when tool ends running.
        Args:
            output (str): The output of the tool.
        """
        await ahandle_event(
            self.handlers,
            "on_tool_end",
            "ignore_agent",
            output,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    async def on_tool_error(
        self,


)
[docs]    async def on_tool_error(
        self,
        error: BaseException,
        **kwargs: Any,
    ) -> None:
        """Run when tool errors.
        Args:
            error (Exception or KeyboardInterrupt): The error.
        """
        await ahandle_event(
            self.handlers,
            "on_tool_error",
            "ignore_agent",
            error,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]class CallbackManagerForRetrieverRun(ParentRunManager, RetrieverManagerMixin):
    """Callback manager for retriever run."""
[docs]    def on_retriever_end(
        self,
        documents: Sequence[Document],
        **kwargs: Any,
    ) -> None:
        """Run when retriever ends running."""
        handle_event(
            self.handlers,
            "on_retriever_end",
            "ignore_retriever",
            documents,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    def on_retriever_error(
        self,
        error: BaseException,
        **kwargs: Any,
    ) -> None:
        """Run when retriever errors."""
        handle_event(
            self.handlers,
            "on_retriever_error",
            "ignore_retriever",
            error,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]class AsyncCallbackManagerForRetrieverRun(
    AsyncParentRunManager,


[docs]class AsyncCallbackManagerForRetrieverRun(
    AsyncParentRunManager,
    RetrieverManagerMixin,
):
    """Async callback manager for retriever run."""
[docs]    async def on_retriever_end(
        self, documents: Sequence[Document], **kwargs: Any
    ) -> None:
        """Run when retriever ends running."""
        await ahandle_event(
            self.handlers,
            "on_retriever_end",
            "ignore_retriever",
            documents,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]    async def on_retriever_error(
        self,
        error: BaseException,
        **kwargs: Any,
    ) -> None:
        """Run when retriever errors."""
        await ahandle_event(
            self.handlers,
            "on_retriever_error",
            "ignore_retriever",
            error,
            run_id=self.run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            **kwargs,
        )
[docs]class CallbackManager(BaseCallbackManager):
    """Callback manager that handles callbacks from LangChain."""
[docs]    def on_llm_start(
        self,
        serialized: Dict[str, Any],
        prompts: List[str],
        **kwargs: Any,
    ) -> List[CallbackManagerForLLMRun]:
        """Run when LLM starts running.
        Args:
            serialized (Dict[str, Any]): The serialized LLM.
            prompts (List[str]): The list of prompts.
            run_id (UUID, optional): The ID of the run. Defaults to None.
        Returns:


Returns:
            List[CallbackManagerForLLMRun]: A callback manager for each
                prompt as an LLM run.
        """
        managers = []
        for prompt in prompts:
            run_id_ = uuid.uuid4()
            handle_event(
                self.handlers,
                "on_llm_start",
                "ignore_llm",
                serialized,
                [prompt],
                run_id=run_id_,
                parent_run_id=self.parent_run_id,
                tags=self.tags,
                metadata=self.metadata,
                **kwargs,
            )
            managers.append(
                CallbackManagerForLLMRun(
                    run_id=run_id_,
                    handlers=self.handlers,
                    inheritable_handlers=self.inheritable_handlers,
                    parent_run_id=self.parent_run_id,
                    tags=self.tags,
                    inheritable_tags=self.inheritable_tags,
                    metadata=self.metadata,
                    inheritable_metadata=self.inheritable_metadata,
                )
            )
        return managers
[docs]    def on_chat_model_start(
        self,
        serialized: Dict[str, Any],
        messages: List[List[BaseMessage]],
        **kwargs: Any,
    ) -> List[CallbackManagerForLLMRun]:
        """Run when LLM starts running.
        Args:
            serialized (Dict[str, Any]): The serialized LLM.
            messages (List[List[BaseMessage]]): The list of messages.
            run_id (UUID, optional): The ID of the run. Defaults to None.
        Returns:
            List[CallbackManagerForLLMRun]: A callback manager for each
                list of messages as an LLM run.
        """
        managers = []
        for message_list in messages:
            run_id_ = uuid.uuid4()
            handle_event(


run_id_ = uuid.uuid4()
            handle_event(
                self.handlers,
                "on_chat_model_start",
                "ignore_chat_model",
                serialized,
                [message_list],
                run_id=run_id_,
                parent_run_id=self.parent_run_id,
                tags=self.tags,
                metadata=self.metadata,
                **kwargs,
            )
            managers.append(
                CallbackManagerForLLMRun(
                    run_id=run_id_,
                    handlers=self.handlers,
                    inheritable_handlers=self.inheritable_handlers,
                    parent_run_id=self.parent_run_id,
                    tags=self.tags,
                    inheritable_tags=self.inheritable_tags,
                    metadata=self.metadata,
                    inheritable_metadata=self.inheritable_metadata,
                )
            )
        return managers
[docs]    def on_chain_start(
        self,
        serialized: Dict[str, Any],
        inputs: Union[Dict[str, Any], Any],
        run_id: Optional[UUID] = None,
        **kwargs: Any,
    ) -> CallbackManagerForChainRun:
        """Run when chain starts running.
        Args:
            serialized (Dict[str, Any]): The serialized chain.
            inputs (Union[Dict[str, Any], Any]): The inputs to the chain.
            run_id (UUID, optional): The ID of the run. Defaults to None.
        Returns:
            CallbackManagerForChainRun: The callback manager for the chain run.
        """
        if run_id is None:
            run_id = uuid.uuid4()
        handle_event(
            self.handlers,
            "on_chain_start",
            "ignore_chain",
            serialized,
            inputs,
            run_id=run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,


parent_run_id=self.parent_run_id,
            tags=self.tags,
            metadata=self.metadata,
            **kwargs,
        )
        return CallbackManagerForChainRun(
            run_id=run_id,
            handlers=self.handlers,
            inheritable_handlers=self.inheritable_handlers,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
            metadata=self.metadata,
            inheritable_metadata=self.inheritable_metadata,
        )
[docs]    def on_tool_start(
        self,
        serialized: Dict[str, Any],
        input_str: str,
        run_id: Optional[UUID] = None,
        parent_run_id: Optional[UUID] = None,
        **kwargs: Any,
    ) -> CallbackManagerForToolRun:
        """Run when tool starts running.
        Args:
            serialized (Dict[str, Any]): The serialized tool.
            input_str (str): The input to the tool.
            run_id (UUID, optional): The ID of the run. Defaults to None.
            parent_run_id (UUID, optional): The ID of the parent run. Defaults to None.
        Returns:
            CallbackManagerForToolRun: The callback manager for the tool run.
        """
        if run_id is None:
            run_id = uuid.uuid4()
        handle_event(
            self.handlers,
            "on_tool_start",
            "ignore_agent",
            serialized,
            input_str,
            run_id=run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            metadata=self.metadata,
            **kwargs,
        )
        return CallbackManagerForToolRun(
            run_id=run_id,
            handlers=self.handlers,


run_id=run_id,
            handlers=self.handlers,
            inheritable_handlers=self.inheritable_handlers,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
            metadata=self.metadata,
            inheritable_metadata=self.inheritable_metadata,
        )
[docs]    def on_retriever_start(
        self,
        serialized: Dict[str, Any],
        query: str,
        run_id: Optional[UUID] = None,
        parent_run_id: Optional[UUID] = None,
        **kwargs: Any,
    ) -> CallbackManagerForRetrieverRun:
        """Run when retriever starts running."""
        if run_id is None:
            run_id = uuid.uuid4()
        handle_event(
            self.handlers,
            "on_retriever_start",
            "ignore_retriever",
            serialized,
            query,
            run_id=run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            metadata=self.metadata,
            **kwargs,
        )
        return CallbackManagerForRetrieverRun(
            run_id=run_id,
            handlers=self.handlers,
            inheritable_handlers=self.inheritable_handlers,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
            metadata=self.metadata,
            inheritable_metadata=self.inheritable_metadata,
        )
[docs]    @classmethod
    def configure(
        cls,
        inheritable_callbacks: Callbacks = None,
        local_callbacks: Callbacks = None,
        verbose: bool = False,
        inheritable_tags: Optional[List[str]] = None,
        local_tags: Optional[List[str]] = None,


local_tags: Optional[List[str]] = None,
        inheritable_metadata: Optional[Dict[str, Any]] = None,
        local_metadata: Optional[Dict[str, Any]] = None,
    ) -> CallbackManager:
        """Configure the callback manager.
        Args:
            inheritable_callbacks (Optional[Callbacks], optional): The inheritable
                callbacks. Defaults to None.
            local_callbacks (Optional[Callbacks], optional): The local callbacks.
                Defaults to None.
            verbose (bool, optional): Whether to enable verbose mode. Defaults to False.
            inheritable_tags (Optional[List[str]], optional): The inheritable tags.
                Defaults to None.
            local_tags (Optional[List[str]], optional): The local tags.
                Defaults to None.
            inheritable_metadata (Optional[Dict[str, Any]], optional): The inheritable
                metadata. Defaults to None.
            local_metadata (Optional[Dict[str, Any]], optional): The local metadata.
                Defaults to None.
        Returns:
            CallbackManager: The configured callback manager.
        """
        return _configure(
            cls,
            inheritable_callbacks,
            local_callbacks,
            verbose,
            inheritable_tags,
            local_tags,
            inheritable_metadata,
            local_metadata,
        )
[docs]class CallbackManagerForChainGroup(CallbackManager):
    """Callback manager for the chain group."""
[docs]    def __init__(
        self,
        handlers: List[BaseCallbackHandler],
        inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,
        parent_run_id: Optional[UUID] = None,
        *,
        parent_run_manager: CallbackManagerForChainRun,
        **kwargs: Any,
    ) -> None:
        super().__init__(
            handlers,


) -> None:
        super().__init__(
            handlers,
            inheritable_handlers,
            parent_run_id,
            **kwargs,
        )
        self.parent_run_manager = parent_run_manager
        self.ended = False
[docs]    def copy(self) -> CallbackManagerForChainGroup:
        return self.__class__(
            handlers=self.handlers,
            inheritable_handlers=self.inheritable_handlers,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
            metadata=self.metadata,
            inheritable_metadata=self.inheritable_metadata,
            parent_run_manager=self.parent_run_manager,
        )
[docs]    def on_chain_end(self, outputs: Union[Dict[str, Any], Any], **kwargs: Any) -> None:
        """Run when traced chain group ends.
        Args:
            outputs (Union[Dict[str, Any], Any]): The outputs of the chain.
        """
        self.ended = True
        return self.parent_run_manager.on_chain_end(outputs, **kwargs)
[docs]    def on_chain_error(
        self,
        error: BaseException,
        **kwargs: Any,
    ) -> None:
        """Run when chain errors.
        Args:
            error (Exception or KeyboardInterrupt): The error.
        """
        self.ended = True
        return self.parent_run_manager.on_chain_error(error, **kwargs)
[docs]class AsyncCallbackManager(BaseCallbackManager):
    """Async callback manager that handles callbacks from LangChain."""
    @property
    def is_async(self) -> bool:
        """Return whether the handler is async."""
        return True
[docs]    async def on_llm_start(
        self,
        serialized: Dict[str, Any],


self,
        serialized: Dict[str, Any],
        prompts: List[str],
        **kwargs: Any,
    ) -> List[AsyncCallbackManagerForLLMRun]:
        """Run when LLM starts running.
        Args:
            serialized (Dict[str, Any]): The serialized LLM.
            prompts (List[str]): The list of prompts.
            run_id (UUID, optional): The ID of the run. Defaults to None.
        Returns:
            List[AsyncCallbackManagerForLLMRun]: The list of async
                callback managers, one for each LLM Run corresponding
                to each prompt.
        """
        tasks = []
        managers = []
        for prompt in prompts:
            run_id_ = uuid.uuid4()
            tasks.append(
                ahandle_event(
                    self.handlers,
                    "on_llm_start",
                    "ignore_llm",
                    serialized,
                    [prompt],
                    run_id=run_id_,
                    parent_run_id=self.parent_run_id,
                    tags=self.tags,
                    metadata=self.metadata,
                    **kwargs,
                )
            )
            managers.append(
                AsyncCallbackManagerForLLMRun(
                    run_id=run_id_,
                    handlers=self.handlers,
                    inheritable_handlers=self.inheritable_handlers,
                    parent_run_id=self.parent_run_id,
                    tags=self.tags,
                    inheritable_tags=self.inheritable_tags,
                    metadata=self.metadata,
                    inheritable_metadata=self.inheritable_metadata,
                )
            )
        await asyncio.gather(*tasks)
        return managers
[docs]    async def on_chat_model_start(
        self,
        serialized: Dict[str, Any],
        messages: List[List[BaseMessage]],
        **kwargs: Any,


messages: List[List[BaseMessage]],
        **kwargs: Any,
    ) -> List[AsyncCallbackManagerForLLMRun]:
        """Run when LLM starts running.
        Args:
            serialized (Dict[str, Any]): The serialized LLM.
            messages (List[List[BaseMessage]]): The list of messages.
            run_id (UUID, optional): The ID of the run. Defaults to None.
        Returns:
            List[AsyncCallbackManagerForLLMRun]: The list of
                async callback managers, one for each LLM Run
                corresponding to each inner  message list.
        """
        tasks = []
        managers = []
        for message_list in messages:
            run_id_ = uuid.uuid4()
            tasks.append(
                ahandle_event(
                    self.handlers,
                    "on_chat_model_start",
                    "ignore_chat_model",
                    serialized,
                    [message_list],
                    run_id=run_id_,
                    parent_run_id=self.parent_run_id,
                    tags=self.tags,
                    metadata=self.metadata,
                    **kwargs,
                )
            )
            managers.append(
                AsyncCallbackManagerForLLMRun(
                    run_id=run_id_,
                    handlers=self.handlers,
                    inheritable_handlers=self.inheritable_handlers,
                    parent_run_id=self.parent_run_id,
                    tags=self.tags,
                    inheritable_tags=self.inheritable_tags,
                    metadata=self.metadata,
                    inheritable_metadata=self.inheritable_metadata,
                )
            )
        await asyncio.gather(*tasks)
        return managers
[docs]    async def on_chain_start(
        self,
        serialized: Dict[str, Any],
        inputs: Union[Dict[str, Any], Any],
        run_id: Optional[UUID] = None,


run_id: Optional[UUID] = None,
        **kwargs: Any,
    ) -> AsyncCallbackManagerForChainRun:
        """Run when chain starts running.
        Args:
            serialized (Dict[str, Any]): The serialized chain.
            inputs (Union[Dict[str, Any], Any]): The inputs to the chain.
            run_id (UUID, optional): The ID of the run. Defaults to None.
        Returns:
            AsyncCallbackManagerForChainRun: The async callback manager
                for the chain run.
        """
        if run_id is None:
            run_id = uuid.uuid4()
        await ahandle_event(
            self.handlers,
            "on_chain_start",
            "ignore_chain",
            serialized,
            inputs,
            run_id=run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            metadata=self.metadata,
            **kwargs,
        )
        return AsyncCallbackManagerForChainRun(
            run_id=run_id,
            handlers=self.handlers,
            inheritable_handlers=self.inheritable_handlers,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
            metadata=self.metadata,
            inheritable_metadata=self.inheritable_metadata,
        )
[docs]    async def on_tool_start(
        self,
        serialized: Dict[str, Any],
        input_str: str,
        run_id: Optional[UUID] = None,
        parent_run_id: Optional[UUID] = None,
        **kwargs: Any,
    ) -> AsyncCallbackManagerForToolRun:
        """Run when tool starts running.
        Args:
            serialized (Dict[str, Any]): The serialized tool.
            input_str (str): The input to the tool.


input_str (str): The input to the tool.
            run_id (UUID, optional): The ID of the run. Defaults to None.
            parent_run_id (UUID, optional): The ID of the parent run.
                Defaults to None.
        Returns:
            AsyncCallbackManagerForToolRun: The async callback manager
                for the tool run.
        """
        if run_id is None:
            run_id = uuid.uuid4()
        await ahandle_event(
            self.handlers,
            "on_tool_start",
            "ignore_agent",
            serialized,
            input_str,
            run_id=run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            metadata=self.metadata,
            **kwargs,
        )
        return AsyncCallbackManagerForToolRun(
            run_id=run_id,
            handlers=self.handlers,
            inheritable_handlers=self.inheritable_handlers,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
            metadata=self.metadata,
            inheritable_metadata=self.inheritable_metadata,
        )
[docs]    async def on_retriever_start(
        self,
        serialized: Dict[str, Any],
        query: str,
        run_id: Optional[UUID] = None,
        parent_run_id: Optional[UUID] = None,
        **kwargs: Any,
    ) -> AsyncCallbackManagerForRetrieverRun:
        """Run when retriever starts running."""
        if run_id is None:
            run_id = uuid.uuid4()
        await ahandle_event(
            self.handlers,
            "on_retriever_start",
            "ignore_retriever",
            serialized,
            query,
            run_id=run_id,


serialized,
            query,
            run_id=run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            metadata=self.metadata,
            **kwargs,
        )
        return AsyncCallbackManagerForRetrieverRun(
            run_id=run_id,
            handlers=self.handlers,
            inheritable_handlers=self.inheritable_handlers,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
            metadata=self.metadata,
            inheritable_metadata=self.inheritable_metadata,
        )
[docs]    @classmethod
    def configure(
        cls,
        inheritable_callbacks: Callbacks = None,
        local_callbacks: Callbacks = None,
        verbose: bool = False,
        inheritable_tags: Optional[List[str]] = None,
        local_tags: Optional[List[str]] = None,
        inheritable_metadata: Optional[Dict[str, Any]] = None,
        local_metadata: Optional[Dict[str, Any]] = None,
    ) -> AsyncCallbackManager:
        """Configure the async callback manager.
        Args:
            inheritable_callbacks (Optional[Callbacks], optional): The inheritable
                callbacks. Defaults to None.
            local_callbacks (Optional[Callbacks], optional): The local callbacks.
                Defaults to None.
            verbose (bool, optional): Whether to enable verbose mode. Defaults to False.
            inheritable_tags (Optional[List[str]], optional): The inheritable tags.
                Defaults to None.
            local_tags (Optional[List[str]], optional): The local tags.
                Defaults to None.
            inheritable_metadata (Optional[Dict[str, Any]], optional): The inheritable
                metadata. Defaults to None.
            local_metadata (Optional[Dict[str, Any]], optional): The local metadata.


local_metadata (Optional[Dict[str, Any]], optional): The local metadata.
                Defaults to None.
        Returns:
            AsyncCallbackManager: The configured async callback manager.
        """
        return _configure(
            cls,
            inheritable_callbacks,
            local_callbacks,
            verbose,
            inheritable_tags,
            local_tags,
            inheritable_metadata,
            local_metadata,
        )
[docs]class AsyncCallbackManagerForChainGroup(AsyncCallbackManager):
    """Async callback manager for the chain group."""
[docs]    def __init__(
        self,
        handlers: List[BaseCallbackHandler],
        inheritable_handlers: Optional[List[BaseCallbackHandler]] = None,
        parent_run_id: Optional[UUID] = None,
        *,
        parent_run_manager: AsyncCallbackManagerForChainRun,
        **kwargs: Any,
    ) -> None:
        super().__init__(
            handlers,
            inheritable_handlers,
            parent_run_id,
            **kwargs,
        )
        self.parent_run_manager = parent_run_manager
        self.ended = False
[docs]    def copy(self) -> AsyncCallbackManagerForChainGroup:
        return self.__class__(
            handlers=self.handlers,
            inheritable_handlers=self.inheritable_handlers,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
            metadata=self.metadata,
            inheritable_metadata=self.inheritable_metadata,
            parent_run_manager=self.parent_run_manager,
        )
[docs]    async def on_chain_end(
        self, outputs: Union[Dict[str, Any], Any], **kwargs: Any
    ) -> None:
        """Run when traced chain group ends.
        Args:


) -> None:
        """Run when traced chain group ends.
        Args:
            outputs (Union[Dict[str, Any], Any]): The outputs of the chain.
        """
        self.ended = True
        await self.parent_run_manager.on_chain_end(outputs, **kwargs)
[docs]    async def on_chain_error(
        self,
        error: BaseException,
        **kwargs: Any,
    ) -> None:
        """Run when chain errors.
        Args:
            error (Exception or KeyboardInterrupt): The error.
        """
        self.ended = True
        await self.parent_run_manager.on_chain_error(error, **kwargs)
T = TypeVar("T", CallbackManager, AsyncCallbackManager)
H = TypeVar("H", bound=BaseCallbackHandler, covariant=True)
def _configure(
    callback_manager_cls: Type[T],
    inheritable_callbacks: Callbacks = None,
    local_callbacks: Callbacks = None,
    verbose: bool = False,
    inheritable_tags: Optional[List[str]] = None,
    local_tags: Optional[List[str]] = None,
    inheritable_metadata: Optional[Dict[str, Any]] = None,
    local_metadata: Optional[Dict[str, Any]] = None,
) -> T:
    """Configure the callback manager.
    Args:
        callback_manager_cls (Type[T]): The callback manager class.
        inheritable_callbacks (Optional[Callbacks], optional): The inheritable
            callbacks. Defaults to None.
        local_callbacks (Optional[Callbacks], optional): The local callbacks.
            Defaults to None.
        verbose (bool, optional): Whether to enable verbose mode. Defaults to False.
        inheritable_tags (Optional[List[str]], optional): The inheritable tags.
            Defaults to None.


Defaults to None.
        local_tags (Optional[List[str]], optional): The local tags. Defaults to None.
        inheritable_metadata (Optional[Dict[str, Any]], optional): The inheritable
            metadata. Defaults to None.
        local_metadata (Optional[Dict[str, Any]], optional): The local metadata.
            Defaults to None.
    Returns:
        T: The configured callback manager.
    """
    from langchain_core.tracers.context import (
        _configure_hooks,
        _get_tracer_project,
        _tracing_v2_is_enabled,
        tracing_callback_var,
        tracing_v2_callback_var,
    )
    run_tree = get_run_tree_context()
    parent_run_id = None if run_tree is None else getattr(run_tree, "id")
    callback_manager = callback_manager_cls(handlers=[], parent_run_id=parent_run_id)
    if inheritable_callbacks or local_callbacks:
        if isinstance(inheritable_callbacks, list) or inheritable_callbacks is None:
            inheritable_callbacks_ = inheritable_callbacks or []
            callback_manager = callback_manager_cls(
                handlers=inheritable_callbacks_.copy(),
                inheritable_handlers=inheritable_callbacks_.copy(),
                parent_run_id=parent_run_id,
            )
        else:
            callback_manager = callback_manager_cls(
                handlers=inheritable_callbacks.handlers.copy(),
                inheritable_handlers=inheritable_callbacks.inheritable_handlers.copy(),
                parent_run_id=inheritable_callbacks.parent_run_id,
                tags=inheritable_callbacks.tags.copy(),
                inheritable_tags=inheritable_callbacks.inheritable_tags.copy(),
                metadata=inheritable_callbacks.metadata.copy(),
                inheritable_metadata=inheritable_callbacks.inheritable_metadata.copy(),
            )
        local_handlers_ = (
            local_callbacks
            if isinstance(local_callbacks, list)


local_handlers_ = (
            local_callbacks
            if isinstance(local_callbacks, list)
            else (local_callbacks.handlers if local_callbacks else [])
        )
        for handler in local_handlers_:
            callback_manager.add_handler(handler, False)
    if inheritable_tags or local_tags:
        callback_manager.add_tags(inheritable_tags or [])
        callback_manager.add_tags(local_tags or [], False)
    if inheritable_metadata or local_metadata:
        callback_manager.add_metadata(inheritable_metadata or {})
        callback_manager.add_metadata(local_metadata or {}, False)
    tracer = tracing_callback_var.get()
    tracing_enabled_ = (
        env_var_is_set("LANGCHAIN_TRACING")
        or tracer is not None
        or env_var_is_set("LANGCHAIN_HANDLER")
    )
    tracer_v2 = tracing_v2_callback_var.get()
    tracing_v2_enabled_ = _tracing_v2_is_enabled()
    tracer_project = _get_tracer_project()
    debug = _get_debug()
    if verbose or debug or tracing_enabled_ or tracing_v2_enabled_:
        from langchain_core.tracers.langchain import LangChainTracer
        from langchain_core.tracers.langchain_v1 import LangChainTracerV1
        from langchain_core.tracers.stdout import ConsoleCallbackHandler
        if verbose and not any(
            isinstance(handler, StdOutCallbackHandler)
            for handler in callback_manager.handlers
        ):
            if debug:
                pass
            else:
                callback_manager.add_handler(StdOutCallbackHandler(), False)
        if debug and not any(
            isinstance(handler, ConsoleCallbackHandler)
            for handler in callback_manager.handlers
        ):
            callback_manager.add_handler(ConsoleCallbackHandler(), True)
        if tracing_enabled_ and not any(
            isinstance(handler, LangChainTracerV1)


isinstance(handler, LangChainTracerV1)
            for handler in callback_manager.handlers
        ):
            if tracer:
                callback_manager.add_handler(tracer, True)
            else:
                handler = LangChainTracerV1()
                handler.load_session(tracer_project)
                callback_manager.add_handler(handler, True)
        if tracing_v2_enabled_ and not any(
            isinstance(handler, LangChainTracer)
            for handler in callback_manager.handlers
        ):
            if tracer_v2:
                callback_manager.add_handler(tracer_v2, True)
            else:
                try:
                    handler = LangChainTracer(project_name=tracer_project)
                    callback_manager.add_handler(handler, True)
                except Exception as e:
                    logger.warning(
                        "Unable to load requested LangChainTracer."
                        " To disable this warning,"
                        " unset the LANGCHAIN_TRACING_V2 environment variables.",
                        e,
                    )
    for var, inheritable, handler_class, env_var in _configure_hooks:
        create_one = (
            env_var is not None
            and env_var_is_set(env_var)
            and handler_class is not None
        )
        if var.get() is not None or create_one:
            var_handler = var.get() or cast(Type[BaseCallbackHandler], handler_class)()
            if handler_class is None:
                if not any(
                    handler is var_handler  # direct pointer comparison
                    for handler in callback_manager.handlers
                ):
                    callback_manager.add_handler(var_handler, inheritable)
            else:
                if not any(
                    isinstance(handler, handler_class)
                    for handler in callback_manager.handlers
                ):
                    callback_manager.add_handler(var_handler, inheritable)
    return callback_manager


Source code for langchain_core.language_models.base
from __future__ import annotations
from abc import ABC, abstractmethod
from functools import lru_cache
from typing import (
    TYPE_CHECKING,
    Any,
    List,
    Optional,
    Sequence,
    Set,
    TypeVar,
    Union,
)
from typing_extensions import TypeAlias
from langchain_core.messages import AnyMessage, BaseMessage, get_buffer_string
from langchain_core.prompt_values import PromptValue
from langchain_core.runnables import RunnableSerializable
from langchain_core.utils import get_pydantic_field_names
if TYPE_CHECKING:
    from langchain_core.callbacks import Callbacks
    from langchain_core.outputs import LLMResult
@lru_cache(maxsize=None)  # Cache the tokenizer
def get_tokenizer() -> Any:
    try:
        from transformers import GPT2TokenizerFast  # type: ignore[import]
    except ImportError:
        raise ImportError(
            "Could not import transformers python package. "
            "This is needed in order to calculate get_token_ids. "
            "Please install it with `pip install transformers`."
        )
    # create a GPT-2 tokenizer instance
    return GPT2TokenizerFast.from_pretrained("gpt2")
def _get_token_ids_default_method(text: str) -> List[int]:
    """Encode the text into token IDs."""
    # get the cached tokenizer
    tokenizer = get_tokenizer()
    # tokenize the text using the GPT-2 tokenizer
    return tokenizer.encode(text)
LanguageModelInput = Union[PromptValue, str, List[BaseMessage]]
LanguageModelOutput = TypeVar("LanguageModelOutput")
[docs]class BaseLanguageModel(
    RunnableSerializable[LanguageModelInput, LanguageModelOutput], ABC
):


RunnableSerializable[LanguageModelInput, LanguageModelOutput], ABC
):
    """Abstract base class for interfacing with language models.
    All language model wrappers inherit from BaseLanguageModel.
    Exposes three main methods:
    - generate_prompt: generate language model outputs for a sequence of prompt
        values. A prompt value is a model input that can be converted to any language
        model input format (string or messages).
    - predict: pass in a single string to a language model and return a string
        prediction.
    - predict_messages: pass in a sequence of BaseMessages (corresponding to a single
        model call) to a language model and return a BaseMessage prediction.
    Each of these has an equivalent asynchronous method.
    """
    @property
    def InputType(self) -> TypeAlias:
        """Get the input type for this runnable."""
        from langchain_core.prompt_values import (
            ChatPromptValueConcrete,
            StringPromptValue,
        )
        # This is a version of LanguageModelInput which replaces the abstract
        # base class BaseMessage with a union of its subclasses, which makes
        # for a much better schema.
        return Union[
            str,
            Union[StringPromptValue, ChatPromptValueConcrete],
            List[AnyMessage],
        ]
[docs]    @abstractmethod
    def generate_prompt(
        self,
        prompts: List[PromptValue],
        stop: Optional[List[str]] = None,
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> LLMResult:
        """Pass a sequence of prompts to the model and return model generations.
        This method should make use of batched calls for models that expose a batched
        API.
        Use this method when you want to:


API.
        Use this method when you want to:
            1. take advantage of batched calls,
            2. need more output from the model than just the top generated value,
            3. are building chains that are agnostic to the underlying language model
                type (e.g., pure text completion models vs chat models).
        Args:
            prompts: List of PromptValues. A PromptValue is an object that can be
                converted to match the format of any language model (string for pure
                text generation models and BaseMessages for chat models).
            stop: Stop words to use when generating. Model output is cut off at the
                first occurrence of any of these substrings.
            callbacks: Callbacks to pass through. Used for executing additional
                functionality, such as logging or streaming, throughout generation.
            **kwargs: Arbitrary additional keyword arguments. These are usually passed
                to the model provider API call.
        Returns:
            An LLMResult, which contains a list of candidate Generations for each input
                prompt and additional model provider-specific output.
        """
[docs]    @abstractmethod
    async def agenerate_prompt(
        self,
        prompts: List[PromptValue],
        stop: Optional[List[str]] = None,
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> LLMResult:
        """Asynchronously pass a sequence of prompts and return model generations.
        This method should make use of batched calls for models that expose a batched
        API.
        Use this method when you want to:
            1. take advantage of batched calls,
            2. need more output from the model than just the top generated value,
            3. are building chains that are agnostic to the underlying language model


3. are building chains that are agnostic to the underlying language model
                type (e.g., pure text completion models vs chat models).
        Args:
            prompts: List of PromptValues. A PromptValue is an object that can be
                converted to match the format of any language model (string for pure
                text generation models and BaseMessages for chat models).
            stop: Stop words to use when generating. Model output is cut off at the
                first occurrence of any of these substrings.
            callbacks: Callbacks to pass through. Used for executing additional
                functionality, such as logging or streaming, throughout generation.
            **kwargs: Arbitrary additional keyword arguments. These are usually passed
                to the model provider API call.
        Returns:
            An LLMResult, which contains a list of candidate Generations for each input
                prompt and additional model provider-specific output.
        """
[docs]    @abstractmethod
    def predict(
        self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
    ) -> str:
        """Pass a single string input to the model and return a string prediction.
         Use this method when passing in raw text. If you want to pass in specific
            types of chat messages, use predict_messages.
        Args:
            text: String input to pass to the model.
            stop: Stop words to use when generating. Model output is cut off at the
                first occurrence of any of these substrings.
            **kwargs: Arbitrary additional keyword arguments. These are usually passed
                to the model provider API call.
        Returns:
            Top model prediction as a string.
        """
[docs]    @abstractmethod
    def predict_messages(
        self,
        messages: List[BaseMessage],
        *,


self,
        messages: List[BaseMessage],
        *,
        stop: Optional[Sequence[str]] = None,
        **kwargs: Any,
    ) -> BaseMessage:
        """Pass a message sequence to the model and return a message prediction.
        Use this method when passing in chat messages. If you want to pass in raw text,
            use predict.
        Args:
            messages: A sequence of chat messages corresponding to a single model input.
            stop: Stop words to use when generating. Model output is cut off at the
                first occurrence of any of these substrings.
            **kwargs: Arbitrary additional keyword arguments. These are usually passed
                to the model provider API call.
        Returns:
            Top model prediction as a message.
        """
[docs]    @abstractmethod
    async def apredict(
        self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
    ) -> str:
        """Asynchronously pass a string to the model and return a string prediction.
        Use this method when calling pure text generation models and only the top
            candidate generation is needed.
        Args:
            text: String input to pass to the model.
            stop: Stop words to use when generating. Model output is cut off at the
                first occurrence of any of these substrings.
            **kwargs: Arbitrary additional keyword arguments. These are usually passed
                to the model provider API call.
        Returns:
            Top model prediction as a string.
        """
[docs]    @abstractmethod
    async def apredict_messages(
        self,
        messages: List[BaseMessage],
        *,
        stop: Optional[Sequence[str]] = None,
        **kwargs: Any,
    ) -> BaseMessage:


**kwargs: Any,
    ) -> BaseMessage:
        """Asynchronously pass messages to the model and return a message prediction.
        Use this method when calling chat models and only the top
            candidate generation is needed.
        Args:
            messages: A sequence of chat messages corresponding to a single model input.
            stop: Stop words to use when generating. Model output is cut off at the
                first occurrence of any of these substrings.
            **kwargs: Arbitrary additional keyword arguments. These are usually passed
                to the model provider API call.
        Returns:
            Top model prediction as a message.
        """
[docs]    def get_token_ids(self, text: str) -> List[int]:
        """Return the ordered ids of the tokens in a text.
        Args:
            text: The string input to tokenize.
        Returns:
            A list of ids corresponding to the tokens in the text, in order they occur
                in the text.
        """
        return _get_token_ids_default_method(text)
[docs]    def get_num_tokens(self, text: str) -> int:
        """Get the number of tokens present in the text.
        Useful for checking if an input will fit in a model's context window.
        Args:
            text: The string input to tokenize.
        Returns:
            The integer number of tokens in the text.
        """
        return len(self.get_token_ids(text))
[docs]    def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
        """Get the number of tokens in the messages.
        Useful for checking if an input will fit in a model's context window.
        Args:
            messages: The message inputs to tokenize.
        Returns:
            The sum of the number of tokens across the messages.


Returns:
            The sum of the number of tokens across the messages.
        """
        return sum([self.get_num_tokens(get_buffer_string([m])) for m in messages])
    @classmethod
    def _all_required_field_names(cls) -> Set:
        """DEPRECATED: Kept for backwards compatibility.
        Use get_pydantic_field_names.
        """
        return get_pydantic_field_names(cls)


Source code for langchain_core.language_models.llms
"""Base interface for large language models to expose."""
from __future__ import annotations
import asyncio
import functools
import inspect
import json
import logging
import warnings
from abc import ABC, abstractmethod
from functools import partial
from pathlib import Path
from typing import (
    Any,
    AsyncIterator,
    Callable,
    Dict,
    Iterator,
    List,
    Mapping,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
    cast,
)
import yaml
from tenacity import (
    RetryCallState,
    before_sleep_log,
    retry,
    retry_base,
    retry_if_exception_type,
    stop_after_attempt,
    wait_exponential,
)
from langchain_core.callbacks import (
    AsyncCallbackManager,
    AsyncCallbackManagerForLLMRun,
    BaseCallbackManager,
    CallbackManager,
    CallbackManagerForLLMRun,
    Callbacks,
)
from langchain_core.globals import get_llm_cache
from langchain_core.language_models.base import BaseLanguageModel, LanguageModelInput
from langchain_core.load import dumpd
from langchain_core.messages import AIMessage, BaseMessage, get_buffer_string
from langchain_core.outputs import Generation, GenerationChunk, LLMResult, RunInfo
from langchain_core.prompt_values import ChatPromptValue, PromptValue, StringPromptValue
from langchain_core.pydantic_v1 import Field, root_validator, validator
from langchain_core.runnables import RunnableConfig, get_config_list
logger = logging.getLogger(__name__)
def _get_verbosity() -> bool:
    from langchain_core.globals import get_verbose
    return get_verbose()
@functools.lru_cache


return get_verbose()
@functools.lru_cache
def _log_error_once(msg: str) -> None:
    """Log an error once."""
    logger.error(msg)
[docs]def create_base_retry_decorator(
    error_types: List[Type[BaseException]],
    max_retries: int = 1,
    run_manager: Optional[
        Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
    ] = None,
) -> Callable[[Any], Any]:
    """Create a retry decorator for a given LLM and provided list of error types."""
    _logging = before_sleep_log(logger, logging.WARNING)
    def _before_sleep(retry_state: RetryCallState) -> None:
        _logging(retry_state)
        if run_manager:
            if isinstance(run_manager, AsyncCallbackManagerForLLMRun):
                coro = run_manager.on_retry(retry_state)
                try:
                    loop = asyncio.get_event_loop()
                    if loop.is_running():
                        loop.create_task(coro)
                    else:
                        asyncio.run(coro)
                except Exception as e:
                    _log_error_once(f"Error in on_retry: {e}")
            else:
                run_manager.on_retry(retry_state)
        return None
    min_seconds = 4
    max_seconds = 10
    # Wait 2^x * 1 second between each retry starting with
    # 4 seconds, then up to 10 seconds, then 10 seconds afterwards
    retry_instance: "retry_base" = retry_if_exception_type(error_types[0])
    for error in error_types[1:]:
        retry_instance = retry_instance | retry_if_exception_type(error)
    return retry(
        reraise=True,


return retry(
        reraise=True,
        stop=stop_after_attempt(max_retries),
        wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
        retry=retry_instance,
        before_sleep=_before_sleep,
    )
[docs]def get_prompts(
    params: Dict[str, Any], prompts: List[str]
) -> Tuple[Dict[int, List], str, List[int], List[str]]:
    """Get prompts that are already cached."""
    llm_string = str(sorted([(k, v) for k, v in params.items()]))
    missing_prompts = []
    missing_prompt_idxs = []
    existing_prompts = {}
    llm_cache = get_llm_cache()
    for i, prompt in enumerate(prompts):
        if llm_cache is not None:
            cache_val = llm_cache.lookup(prompt, llm_string)
            if isinstance(cache_val, list):
                existing_prompts[i] = cache_val
            else:
                missing_prompts.append(prompt)
                missing_prompt_idxs.append(i)
    return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts
[docs]def update_cache(
    existing_prompts: Dict[int, List],
    llm_string: str,
    missing_prompt_idxs: List[int],
    new_results: LLMResult,
    prompts: List[str],
) -> Optional[dict]:
    """Update the cache and get the LLM output."""
    llm_cache = get_llm_cache()
    for i, result in enumerate(new_results.generations):
        existing_prompts[missing_prompt_idxs[i]] = result
        prompt = prompts[missing_prompt_idxs[i]]
        if llm_cache is not None:


if llm_cache is not None:
            llm_cache.update(prompt, llm_string, result)
    llm_output = new_results.llm_output
    return llm_output
[docs]class BaseLLM(BaseLanguageModel[str], ABC):
    """Base LLM abstract interface.
    It should take in a prompt and return a string."""
    cache: Optional[bool] = None
    verbose: bool = Field(default_factory=_get_verbosity)
    """Whether to print out response text."""
    callbacks: Callbacks = Field(default=None, exclude=True)
    callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
    tags: Optional[List[str]] = Field(default=None, exclude=True)
    """Tags to add to the run trace."""
    metadata: Optional[Dict[str, Any]] = Field(default=None, exclude=True)
    """Metadata to add to the run trace."""
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
    @root_validator()
    def raise_deprecation(cls, values: Dict) -> Dict:
        """Raise deprecation warning if callback_manager is used."""
        if values.get("callback_manager") is not None:
            warnings.warn(
                "callback_manager is deprecated. Please use callbacks instead.",
                DeprecationWarning,
            )
            values["callbacks"] = values.pop("callback_manager", None)
        return values
    @validator("verbose", pre=True, always=True)
    def set_verbose(cls, verbose: Optional[bool]) -> bool:
        """If verbose is None, set it.
        This allows users to pass in None as verbose to access the global setting.
        """
        if verbose is None:
            return _get_verbosity()
        else:


if verbose is None:
            return _get_verbosity()
        else:
            return verbose
    # --- Runnable methods ---
    @property
    def OutputType(self) -> Type[str]:
        """Get the input type for this runnable."""
        return str
    def _convert_input(self, input: LanguageModelInput) -> PromptValue:
        if isinstance(input, PromptValue):
            return input
        elif isinstance(input, str):
            return StringPromptValue(text=input)
        elif isinstance(input, list):
            return ChatPromptValue(messages=input)
        else:
            raise ValueError(
                f"Invalid input type {type(input)}. "
                "Must be a PromptValue, str, or list of BaseMessages."
            )
[docs]    def invoke(
        self,
        input: LanguageModelInput,
        config: Optional[RunnableConfig] = None,
        *,
        stop: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> str:
        config = config or {}
        return (
            self.generate_prompt(
                [self._convert_input(input)],
                stop=stop,
                callbacks=config.get("callbacks"),
                tags=config.get("tags"),
                metadata=config.get("metadata"),
                run_name=config.get("run_name"),
                **kwargs,
            )
            .generations[0][0]
            .text
        )
[docs]    async def ainvoke(
        self,
        input: LanguageModelInput,
        config: Optional[RunnableConfig] = None,
        *,
        stop: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> str:
        config = config or {}
        llm_result = await self.agenerate_prompt(


config = config or {}
        llm_result = await self.agenerate_prompt(
            [self._convert_input(input)],
            stop=stop,
            callbacks=config.get("callbacks"),
            tags=config.get("tags"),
            metadata=config.get("metadata"),
            run_name=config.get("run_name"),
            **kwargs,
        )
        return llm_result.generations[0][0].text
[docs]    def batch(
        self,
        inputs: List[LanguageModelInput],
        config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any,
    ) -> List[str]:
        if not inputs:
            return []
        config = get_config_list(config, len(inputs))
        max_concurrency = config[0].get("max_concurrency")
        if max_concurrency is None:
            try:
                llm_result = self.generate_prompt(
                    [self._convert_input(input) for input in inputs],
                    callbacks=[c.get("callbacks") for c in config],
                    tags=[c.get("tags") for c in config],
                    metadata=[c.get("metadata") for c in config],
                    run_name=[c.get("run_name") for c in config],
                    **kwargs,
                )
                return [g[0].text for g in llm_result.generations]
            except Exception as e:
                if return_exceptions:
                    return cast(List[str], [e for _ in inputs])
                else:
                    raise e
        else:
            batches = [
                inputs[i : i + max_concurrency]
                for i in range(0, len(inputs), max_concurrency)
            ]


for i in range(0, len(inputs), max_concurrency)
            ]
            config = [{**c, "max_concurrency": None} for c in config]  # type: ignore[misc]
            return [
                output
                for i, batch in enumerate(batches)
                for output in self.batch(
                    batch,
                    config=config[i * max_concurrency : (i + 1) * max_concurrency],
                    return_exceptions=return_exceptions,
                    **kwargs,
                )
            ]
[docs]    async def abatch(
        self,
        inputs: List[LanguageModelInput],
        config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any,
    ) -> List[str]:
        if not inputs:
            return []
        config = get_config_list(config, len(inputs))
        max_concurrency = config[0].get("max_concurrency")
        if max_concurrency is None:
            try:
                llm_result = await self.agenerate_prompt(
                    [self._convert_input(input) for input in inputs],
                    callbacks=[c.get("callbacks") for c in config],
                    tags=[c.get("tags") for c in config],
                    metadata=[c.get("metadata") for c in config],
                    run_name=[c.get("run_name") for c in config],
                    **kwargs,
                )
                return [g[0].text for g in llm_result.generations]
            except Exception as e:
                if return_exceptions:
                    return cast(List[str], [e for _ in inputs])
                else:
                    raise e
        else:
            batches = [


else:
                    raise e
        else:
            batches = [
                inputs[i : i + max_concurrency]
                for i in range(0, len(inputs), max_concurrency)
            ]
            config = [{**c, "max_concurrency": None} for c in config]  # type: ignore[misc]
            return [
                output
                for i, batch in enumerate(batches)
                for output in await self.abatch(
                    batch,
                    config=config[i * max_concurrency : (i + 1) * max_concurrency],
                    return_exceptions=return_exceptions,
                    **kwargs,
                )
            ]
[docs]    def stream(
        self,
        input: LanguageModelInput,
        config: Optional[RunnableConfig] = None,
        *,
        stop: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> Iterator[str]:
        if type(self)._stream == BaseLLM._stream:
            # model doesn't implement streaming, so use default implementation
            yield self.invoke(input, config=config, stop=stop, **kwargs)
        else:
            prompt = self._convert_input(input).to_string()
            config = config or {}
            params = self.dict()
            params["stop"] = stop
            params = {**params, **kwargs}
            options = {"stop": stop}
            callback_manager = CallbackManager.configure(
                config.get("callbacks"),
                self.callbacks,
                self.verbose,
                config.get("tags"),
                self.tags,
                config.get("metadata"),
                self.metadata,
            )
            (run_manager,) = callback_manager.on_llm_start(
                dumpd(self),
                [prompt],
                invocation_params=params,
                options=options,


[prompt],
                invocation_params=params,
                options=options,
                name=config.get("run_name"),
                batch_size=1,
            )
            generation: Optional[GenerationChunk] = None
            try:
                for chunk in self._stream(
                    prompt, stop=stop, run_manager=run_manager, **kwargs
                ):
                    yield chunk.text
                    if generation is None:
                        generation = chunk
                    else:
                        generation += chunk
                assert generation is not None
            except BaseException as e:
                run_manager.on_llm_error(
                    e,
                    response=LLMResult(
                        generations=[[generation]] if generation else []
                    ),
                )
                raise e
            else:
                run_manager.on_llm_end(LLMResult(generations=[[generation]]))
[docs]    async def astream(
        self,
        input: LanguageModelInput,
        config: Optional[RunnableConfig] = None,
        *,
        stop: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> AsyncIterator[str]:
        if type(self)._astream == BaseLLM._astream:
            # model doesn't implement streaming, so use default implementation
            yield await self.ainvoke(input, config=config, stop=stop, **kwargs)
        else:
            prompt = self._convert_input(input).to_string()
            config = config or {}
            params = self.dict()
            params["stop"] = stop
            params = {**params, **kwargs}
            options = {"stop": stop}
            callback_manager = AsyncCallbackManager.configure(
                config.get("callbacks"),
                self.callbacks,
                self.verbose,
                config.get("tags"),
                self.tags,
                config.get("metadata"),


config.get("tags"),
                self.tags,
                config.get("metadata"),
                self.metadata,
            )
            (run_manager,) = await callback_manager.on_llm_start(
                dumpd(self),
                [prompt],
                invocation_params=params,
                options=options,
                name=config.get("run_name"),
                batch_size=1,
            )
            generation: Optional[GenerationChunk] = None
            try:
                async for chunk in self._astream(
                    prompt, stop=stop, run_manager=run_manager, **kwargs
                ):
                    yield chunk.text
                    if generation is None:
                        generation = chunk
                    else:
                        generation += chunk
                assert generation is not None
            except BaseException as e:
                await run_manager.on_llm_error(
                    e,
                    response=LLMResult(
                        generations=[[generation]] if generation else []
                    ),
                )
                raise e
            else:
                await run_manager.on_llm_end(LLMResult(generations=[[generation]]))
    # --- Custom methods ---
    @abstractmethod
    def _generate(
        self,
        prompts: List[str],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> LLMResult:
        """Run the LLM on the given prompts."""
    async def _agenerate(
        self,
        prompts: List[str],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> LLMResult:
        """Run the LLM on the given prompts."""


) -> LLMResult:
        """Run the LLM on the given prompts."""
        return await asyncio.get_running_loop().run_in_executor(
            None, partial(self._generate, **kwargs), prompts, stop, run_manager
        )
    def _stream(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[GenerationChunk]:
        raise NotImplementedError()
    def _astream(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> AsyncIterator[GenerationChunk]:
        raise NotImplementedError()
[docs]    def generate_prompt(
        self,
        prompts: List[PromptValue],
        stop: Optional[List[str]] = None,
        callbacks: Optional[Union[Callbacks, List[Callbacks]]] = None,
        **kwargs: Any,
    ) -> LLMResult:
        prompt_strings = [p.to_string() for p in prompts]
        return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
[docs]    async def agenerate_prompt(
        self,
        prompts: List[PromptValue],
        stop: Optional[List[str]] = None,
        callbacks: Optional[Union[Callbacks, List[Callbacks]]] = None,
        **kwargs: Any,
    ) -> LLMResult:
        prompt_strings = [p.to_string() for p in prompts]
        return await self.agenerate(
            prompt_strings, stop=stop, callbacks=callbacks, **kwargs
        )


prompt_strings, stop=stop, callbacks=callbacks, **kwargs
        )
    def _generate_helper(
        self,
        prompts: List[str],
        stop: Optional[List[str]],
        run_managers: List[CallbackManagerForLLMRun],
        new_arg_supported: bool,
        **kwargs: Any,
    ) -> LLMResult:
        try:
            output = (
                self._generate(
                    prompts,
                    stop=stop,
                    # TODO: support multiple run managers
                    run_manager=run_managers[0] if run_managers else None,
                    **kwargs,
                )
                if new_arg_supported
                else self._generate(prompts, stop=stop)
            )
        except BaseException as e:
            for run_manager in run_managers:
                run_manager.on_llm_error(e, response=LLMResult(generations=[]))
            raise e
        flattened_outputs = output.flatten()
        for manager, flattened_output in zip(run_managers, flattened_outputs):
            manager.on_llm_end(flattened_output)
        if run_managers:
            output.run = [
                RunInfo(run_id=run_manager.run_id) for run_manager in run_managers
            ]
        return output
[docs]    def generate(
        self,
        prompts: List[str],
        stop: Optional[List[str]] = None,
        callbacks: Optional[Union[Callbacks, List[Callbacks]]] = None,
        *,
        tags: Optional[Union[List[str], List[List[str]]]] = None,
        metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
        run_name: Optional[Union[str, List[str]]] = None,
        **kwargs: Any,


**kwargs: Any,
    ) -> LLMResult:
        """Run the LLM on the given prompt and input."""
        if not isinstance(prompts, list):
            raise ValueError(
                "Argument 'prompts' is expected to be of type List[str], received"
                f" argument of type {type(prompts)}."
            )
        # Create callback managers
        if (
            isinstance(callbacks, list)
            and callbacks
            and (
                isinstance(callbacks[0], (list, BaseCallbackManager))
                or callbacks[0] is None
            )
        ):
            # We've received a list of callbacks args to apply to each input
            assert len(callbacks) == len(prompts)
            assert tags is None or (
                isinstance(tags, list) and len(tags) == len(prompts)
            )
            assert metadata is None or (
                isinstance(metadata, list) and len(metadata) == len(prompts)
            )
            assert run_name is None or (
                isinstance(run_name, list) and len(run_name) == len(prompts)
            )
            callbacks = cast(List[Callbacks], callbacks)
            tags_list = cast(List[Optional[List[str]]], tags or ([None] * len(prompts)))
            metadata_list = cast(
                List[Optional[Dict[str, Any]]], metadata or ([{}] * len(prompts))
            )
            run_name_list = run_name or cast(
                List[Optional[str]], ([None] * len(prompts))
            )
            callback_managers = [
                CallbackManager.configure(
                    callback,
                    self.callbacks,
                    self.verbose,
                    tag,
                    self.tags,
                    meta,
                    self.metadata,
                )


self.tags,
                    meta,
                    self.metadata,
                )
                for callback, tag, meta in zip(callbacks, tags_list, metadata_list)
            ]
        else:
            # We've received a single callbacks arg to apply to all inputs
            callback_managers = [
                CallbackManager.configure(
                    cast(Callbacks, callbacks),
                    self.callbacks,
                    self.verbose,
                    cast(List[str], tags),
                    self.tags,
                    cast(Dict[str, Any], metadata),
                    self.metadata,
                )
            ] * len(prompts)
            run_name_list = [cast(Optional[str], run_name)] * len(prompts)
        params = self.dict()
        params["stop"] = stop
        options = {"stop": stop}
        (
            existing_prompts,
            llm_string,
            missing_prompt_idxs,
            missing_prompts,
        ) = get_prompts(params, prompts)
        disregard_cache = self.cache is not None and not self.cache
        new_arg_supported = inspect.signature(self._generate).parameters.get(
            "run_manager"
        )
        if get_llm_cache() is None or disregard_cache:
            if self.cache is not None and self.cache:
                raise ValueError(
                    "Asked to cache, but no cache found at `langchain.cache`."
                )
            run_managers = [
                callback_manager.on_llm_start(
                    dumpd(self),
                    [prompt],
                    invocation_params=params,
                    options=options,
                    name=run_name,
                    batch_size=len(prompts),
                )[0]
                for callback_manager, prompt, run_name in zip(
                    callback_managers, prompts, run_name_list
                )
            ]
            output = self._generate_helper(


)
            ]
            output = self._generate_helper(
                prompts, stop, run_managers, bool(new_arg_supported), **kwargs
            )
            return output
        if len(missing_prompts) > 0:
            run_managers = [
                callback_managers[idx].on_llm_start(
                    dumpd(self),
                    [prompts[idx]],
                    invocation_params=params,
                    options=options,
                    name=run_name_list[idx],
                    batch_size=len(missing_prompts),
                )[0]
                for idx in missing_prompt_idxs
            ]
            new_results = self._generate_helper(
                missing_prompts, stop, run_managers, bool(new_arg_supported), **kwargs
            )
            llm_output = update_cache(
                existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts
            )
            run_info = (
                [RunInfo(run_id=run_manager.run_id) for run_manager in run_managers]
                if run_managers
                else None
            )
        else:
            llm_output = {}
            run_info = None
        generations = [existing_prompts[i] for i in range(len(prompts))]
        return LLMResult(generations=generations, llm_output=llm_output, run=run_info)
    async def _agenerate_helper(
        self,
        prompts: List[str],
        stop: Optional[List[str]],
        run_managers: List[AsyncCallbackManagerForLLMRun],
        new_arg_supported: bool,
        **kwargs: Any,
    ) -> LLMResult:
        try:
            output = (
                await self._agenerate(
                    prompts,
                    stop=stop,


await self._agenerate(
                    prompts,
                    stop=stop,
                    run_manager=run_managers[0] if run_managers else None,
                    **kwargs,
                )
                if new_arg_supported
                else await self._agenerate(prompts, stop=stop)
            )
        except BaseException as e:
            await asyncio.gather(
                *[
                    run_manager.on_llm_error(e, response=LLMResult(generations=[]))
                    for run_manager in run_managers
                ]
            )
            raise e
        flattened_outputs = output.flatten()
        await asyncio.gather(
            *[
                run_manager.on_llm_end(flattened_output)
                for run_manager, flattened_output in zip(
                    run_managers, flattened_outputs
                )
            ]
        )
        if run_managers:
            output.run = [
                RunInfo(run_id=run_manager.run_id) for run_manager in run_managers
            ]
        return output
[docs]    async def agenerate(
        self,
        prompts: List[str],
        stop: Optional[List[str]] = None,
        callbacks: Optional[Union[Callbacks, List[Callbacks]]] = None,
        *,
        tags: Optional[Union[List[str], List[List[str]]]] = None,
        metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
        run_name: Optional[Union[str, List[str]]] = None,
        **kwargs: Any,
    ) -> LLMResult:
        """Run the LLM on the given prompt and input."""
        # Create callback managers
        if isinstance(callbacks, list) and (
            isinstance(callbacks[0], (list, BaseCallbackManager))


isinstance(callbacks[0], (list, BaseCallbackManager))
            or callbacks[0] is None
        ):
            # We've received a list of callbacks args to apply to each input
            assert len(callbacks) == len(prompts)
            assert tags is None or (
                isinstance(tags, list) and len(tags) == len(prompts)
            )
            assert metadata is None or (
                isinstance(metadata, list) and len(metadata) == len(prompts)
            )
            assert run_name is None or (
                isinstance(run_name, list) and len(run_name) == len(prompts)
            )
            callbacks = cast(List[Callbacks], callbacks)
            tags_list = cast(List[Optional[List[str]]], tags or ([None] * len(prompts)))
            metadata_list = cast(
                List[Optional[Dict[str, Any]]], metadata or ([{}] * len(prompts))
            )
            run_name_list = run_name or cast(
                List[Optional[str]], ([None] * len(prompts))
            )
            callback_managers = [
                AsyncCallbackManager.configure(
                    callback,
                    self.callbacks,
                    self.verbose,
                    tag,
                    self.tags,
                    meta,
                    self.metadata,
                )
                for callback, tag, meta in zip(callbacks, tags_list, metadata_list)
            ]
        else:
            # We've received a single callbacks arg to apply to all inputs
            callback_managers = [
                AsyncCallbackManager.configure(
                    cast(Callbacks, callbacks),
                    self.callbacks,
                    self.verbose,
                    cast(List[str], tags),
                    self.tags,
                    cast(Dict[str, Any], metadata),
                    self.metadata,
                )
            ] * len(prompts)


self.metadata,
                )
            ] * len(prompts)
            run_name_list = [cast(Optional[str], run_name)] * len(prompts)
        params = self.dict()
        params["stop"] = stop
        options = {"stop": stop}
        (
            existing_prompts,
            llm_string,
            missing_prompt_idxs,
            missing_prompts,
        ) = get_prompts(params, prompts)
        disregard_cache = self.cache is not None and not self.cache
        new_arg_supported = inspect.signature(self._agenerate).parameters.get(
            "run_manager"
        )
        if get_llm_cache() is None or disregard_cache:
            if self.cache is not None and self.cache:
                raise ValueError(
                    "Asked to cache, but no cache found at `langchain.cache`."
                )
            run_managers = await asyncio.gather(
                *[
                    callback_manager.on_llm_start(
                        dumpd(self),
                        [prompt],
                        invocation_params=params,
                        options=options,
                        name=run_name,
                        batch_size=len(prompts),
                    )
                    for callback_manager, prompt, run_name in zip(
                        callback_managers, prompts, run_name_list
                    )
                ]
            )
            run_managers = [r[0] for r in run_managers]
            output = await self._agenerate_helper(
                prompts, stop, run_managers, bool(new_arg_supported), **kwargs
            )
            return output
        if len(missing_prompts) > 0:
            run_managers = await asyncio.gather(
                *[
                    callback_managers[idx].on_llm_start(
                        dumpd(self),
                        [prompts[idx]],
                        invocation_params=params,
                        options=options,


[prompts[idx]],
                        invocation_params=params,
                        options=options,
                        name=run_name_list[idx],
                        batch_size=len(missing_prompts),
                    )
                    for idx in missing_prompt_idxs
                ]
            )
            run_managers = [r[0] for r in run_managers]
            new_results = await self._agenerate_helper(
                missing_prompts, stop, run_managers, bool(new_arg_supported), **kwargs
            )
            llm_output = update_cache(
                existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts
            )
            run_info = (
                [RunInfo(run_id=run_manager.run_id) for run_manager in run_managers]
                if run_managers
                else None
            )
        else:
            llm_output = {}
            run_info = None
        generations = [existing_prompts[i] for i in range(len(prompts))]
        return LLMResult(generations=generations, llm_output=llm_output, run=run_info)
[docs]    def __call__(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        callbacks: Callbacks = None,
        *,
        tags: Optional[List[str]] = None,
        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> str:
        """Check Cache and run the LLM on the given prompt and input."""
        if not isinstance(prompt, str):
            raise ValueError(
                "Argument `prompt` is expected to be a string. Instead found "
                f"{type(prompt)}. If you want to run the LLM on multiple prompts, use "
                "`generate` instead."
            )
        return (


"`generate` instead."
            )
        return (
            self.generate(
                [prompt],
                stop=stop,
                callbacks=callbacks,
                tags=tags,
                metadata=metadata,
                **kwargs,
            )
            .generations[0][0]
            .text
        )
    async def _call_async(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        callbacks: Callbacks = None,
        *,
        tags: Optional[List[str]] = None,
        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> str:
        """Check Cache and run the LLM on the given prompt and input."""
        result = await self.agenerate(
            [prompt],
            stop=stop,
            callbacks=callbacks,
            tags=tags,
            metadata=metadata,
            **kwargs,
        )
        return result.generations[0][0].text
[docs]    def predict(
        self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
    ) -> str:
        if stop is None:
            _stop = None
        else:
            _stop = list(stop)
        return self(text, stop=_stop, **kwargs)
[docs]    def predict_messages(
        self,
        messages: List[BaseMessage],
        *,
        stop: Optional[Sequence[str]] = None,
        **kwargs: Any,
    ) -> BaseMessage:
        text = get_buffer_string(messages)
        if stop is None:
            _stop = None
        else:
            _stop = list(stop)


_stop = None
        else:
            _stop = list(stop)
        content = self(text, stop=_stop, **kwargs)
        return AIMessage(content=content)
[docs]    async def apredict(
        self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
    ) -> str:
        if stop is None:
            _stop = None
        else:
            _stop = list(stop)
        return await self._call_async(text, stop=_stop, **kwargs)
[docs]    async def apredict_messages(
        self,
        messages: List[BaseMessage],
        *,
        stop: Optional[Sequence[str]] = None,
        **kwargs: Any,
    ) -> BaseMessage:
        text = get_buffer_string(messages)
        if stop is None:
            _stop = None
        else:
            _stop = list(stop)
        content = await self._call_async(text, stop=_stop, **kwargs)
        return AIMessage(content=content)
    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {}
    def __str__(self) -> str:
        """Get a string representation of the object for printing."""
        cls_name = f"\033[1m{self.__class__.__name__}\033[0m"
        return f"{cls_name}\nParams: {self._identifying_params}"
    @property
    @abstractmethod
    def _llm_type(self) -> str:
        """Return type of llm."""
[docs]    def dict(self, **kwargs: Any) -> Dict:
        """Return a dictionary of the LLM."""


"""Return a dictionary of the LLM."""
        starter_dict = dict(self._identifying_params)
        starter_dict["_type"] = self._llm_type
        return starter_dict
[docs]    def save(self, file_path: Union[Path, str]) -> None:
        """Save the LLM.
        Args:
            file_path: Path to file to save the LLM to.
        Example:
        .. code-block:: python
            llm.save(file_path="path/llm.yaml")
        """
        # Convert file to Path object.
        if isinstance(file_path, str):
            save_path = Path(file_path)
        else:
            save_path = file_path
        directory_path = save_path.parent
        directory_path.mkdir(parents=True, exist_ok=True)
        # Fetch dictionary to save
        prompt_dict = self.dict()
        if save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(prompt_dict, f, indent=4)
        elif save_path.suffix == ".yaml":
            with open(file_path, "w") as f:
                yaml.dump(prompt_dict, f, default_flow_style=False)
        else:
            raise ValueError(f"{save_path} must be json or yaml")
[docs]class LLM(BaseLLM):
    """Base LLM abstract class.
    The purpose of this class is to expose a simpler interface for working
    with LLMs, rather than expect the user to implement the full _generate method.
    """
    @abstractmethod
    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,