mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
core[patch]: docstrings langchain_core/ files update (#24285)
Added missed docstrings. Formatted docstrings to the consistent form.
This commit is contained in:
Leonid Ganeline
GitHub
parent
7aeaa1974d
commit
198b85334f
@ -65,12 +65,15 @@ class AgentAction(Serializable):
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@classmethod
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def is_lc_serializable(cls) -> bool:
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"""Return whether or not the class is serializable."""
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"""Return whether or not the class is serializable.
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Default is True.
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"""
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return True
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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"""Get the namespace of the langchain object.
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Default is ["langchain", "schema", "agent"]."""
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return ["langchain", "schema", "agent"]
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@property
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@ -80,7 +83,7 @@ class AgentAction(Serializable):
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class AgentActionMessageLog(AgentAction):
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"""A representation of an action to be executed by an agent.
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"""Representation of an action to be executed by an agent.
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This is similar to AgentAction, but includes a message log consisting of
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chat messages. This is useful when working with ChatModels, and is used
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@ -102,7 +105,7 @@ class AgentActionMessageLog(AgentAction):
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class AgentStep(Serializable):
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"""The result of running an AgentAction."""
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"""Result of running an AgentAction."""
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action: AgentAction
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"""The AgentAction that was executed."""
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@ -111,12 +114,12 @@ class AgentStep(Serializable):
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@property
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def messages(self) -> Sequence[BaseMessage]:
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"""Return the messages that correspond to this observation."""
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"""Messages that correspond to this observation."""
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return _convert_agent_observation_to_messages(self.action, self.observation)
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class AgentFinish(Serializable):
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"""The final return value of an ActionAgent.
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"""Final return value of an ActionAgent.
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Agents return an AgentFinish when they have reached a stopping condition.
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"""
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@ -148,7 +151,7 @@ class AgentFinish(Serializable):
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@property
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def messages(self) -> Sequence[BaseMessage]:
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"""Return the messages that correspond to this observation."""
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"""Messages that correspond to this observation."""
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return [AIMessage(content=self.log)]
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@ -180,6 +183,7 @@ def _convert_agent_observation_to_messages(
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Args:
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agent_action: Agent action to convert.
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observation: Observation to convert to a message.
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Returns:
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AIMessage that corresponds to the original tool invocation.
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@ -196,11 +200,11 @@ def _create_function_message(
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"""Convert agent action and observation into a function message.
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Args:
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agent_action: the tool invocation request from the agent
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observation: the result of the tool invocation
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agent_action: the tool invocation request from the agent.
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observation: the result of the tool invocation.
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Returns:
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FunctionMessage that corresponds to the original tool invocation
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FunctionMessage that corresponds to the original tool invocation.
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"""
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if not isinstance(observation, str):
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try:
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@ -44,7 +44,7 @@ class BaseCache(ABC):
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The default implementation of the async methods is to run the synchronous
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method in an executor. It's recommended to override the async methods
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and provide an async implementations to avoid unnecessary overhead.
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and provide async implementations to avoid unnecessary overhead.
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"""
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@abstractmethod
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@ -56,8 +56,8 @@ class BaseCache(ABC):
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Args:
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prompt: a string representation of the prompt.
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In the case of a Chat model, the prompt is a non-trivial
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serialization of the prompt into the language model.
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In the case of a Chat model, the prompt is a non-trivial
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serialization of the prompt into the language model.
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llm_string: A string representation of the LLM configuration.
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This is used to capture the invocation parameters of the LLM
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(e.g., model name, temperature, stop tokens, max tokens, etc.).
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@ -78,8 +78,8 @@ class BaseCache(ABC):
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Args:
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prompt: a string representation of the prompt.
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In the case of a Chat model, the prompt is a non-trivial
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serialization of the prompt into the language model.
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In the case of a Chat model, the prompt is a non-trivial
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serialization of the prompt into the language model.
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llm_string: A string representation of the LLM configuration.
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This is used to capture the invocation parameters of the LLM
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(e.g., model name, temperature, stop tokens, max tokens, etc.).
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@ -101,8 +101,8 @@ class BaseCache(ABC):
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Args:
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prompt: a string representation of the prompt.
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In the case of a Chat model, the prompt is a non-trivial
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serialization of the prompt into the language model.
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In the case of a Chat model, the prompt is a non-trivial
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serialization of the prompt into the language model.
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llm_string: A string representation of the LLM configuration.
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This is used to capture the invocation parameters of the LLM
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(e.g., model name, temperature, stop tokens, max tokens, etc.).
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@ -125,8 +125,8 @@ class BaseCache(ABC):
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Args:
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prompt: a string representation of the prompt.
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In the case of a Chat model, the prompt is a non-trivial
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serialization of the prompt into the language model.
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In the case of a Chat model, the prompt is a non-trivial
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serialization of the prompt into the language model.
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llm_string: A string representation of the LLM configuration.
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This is used to capture the invocation parameters of the LLM
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(e.g., model name, temperature, stop tokens, max tokens, etc.).
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@ -152,6 +152,10 @@ class InMemoryCache(BaseCache):
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maxsize: The maximum number of items to store in the cache.
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If None, the cache has no maximum size.
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If the cache exceeds the maximum size, the oldest items are removed.
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Default is None.
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Raises:
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ValueError: If maxsize is less than or equal to 0.
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"""
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self._cache: Dict[Tuple[str, str], RETURN_VAL_TYPE] = {}
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if maxsize is not None and maxsize <= 0:
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@ -116,7 +116,7 @@ class BaseChatMessageHistory(ABC):
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This method may be deprecated in a future release.
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Args:
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message: The human message to add
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message: The human message to add to the store.
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"""
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if isinstance(message, HumanMessage):
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self.add_message(message)
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@ -200,22 +200,38 @@ class BaseChatMessageHistory(ABC):
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class InMemoryChatMessageHistory(BaseChatMessageHistory, BaseModel):
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"""In memory implementation of chat message history.
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Stores messages in an in memory list.
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Stores messages in a memory list.
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"""
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messages: List[BaseMessage] = Field(default_factory=list)
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"""A list of messages stored in memory."""
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async def aget_messages(self) -> List[BaseMessage]:
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"""Async version of getting messages."""
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"""Async version of getting messages.
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Can over-ride this method to provide an efficient async implementation.
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In general, fetching messages may involve IO to the underlying
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persistence layer.
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Returns:
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List of messages.
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"""
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return self.messages
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def add_message(self, message: BaseMessage) -> None:
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"""Add a self-created message to the store."""
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"""Add a self-created message to the store.
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Args:
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message: The message to add.
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"""
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self.messages.append(message)
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async def aadd_messages(self, messages: Sequence[BaseMessage]) -> None:
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"""Async add messages to the store"""
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"""Async add messages to the store.
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Args:
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messages: The messages to add.
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"""
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self.add_messages(messages)
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def clear(self) -> None:
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@ -4,7 +4,11 @@ from functools import lru_cache
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@lru_cache(maxsize=1)
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def get_runtime_environment() -> dict:
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"""Get information about the LangChain runtime environment."""
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"""Get information about the LangChain runtime environment.
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Returns:
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A dictionary with information about the runtime environment.
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"""
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# Lazy import to avoid circular imports
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from langchain_core import __version__
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@ -22,13 +22,14 @@ class OutputParserException(ValueError, LangChainException):
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Parameters:
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error: The error that's being re-raised or an error message.
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observation: String explanation of error which can be passed to a
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model to try and remediate the issue.
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model to try and remediate the issue. Defaults to None.
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llm_output: String model output which is error-ing.
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Defaults to None.
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send_to_llm: Whether to send the observation and llm_output back to an Agent
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after an OutputParserException has been raised. This gives the underlying
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model driving the agent the context that the previous output was improperly
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structured, in the hopes that it will update the output to the correct
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format.
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format. Defaults to False.
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"""
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def __init__(
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@ -18,7 +18,11 @@ _llm_cache: Optional["BaseCache"] = None
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def set_verbose(value: bool) -> None:
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"""Set a new value for the `verbose` global setting."""
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"""Set a new value for the `verbose` global setting.
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Args:
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value: The new value for the `verbose` global setting.
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"""
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try:
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import langchain # type: ignore[import]
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@ -46,7 +50,11 @@ def set_verbose(value: bool) -> None:
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def get_verbose() -> bool:
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"""Get the value of the `verbose` global setting."""
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"""Get the value of the `verbose` global setting.
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Returns:
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The value of the `verbose` global setting.
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"""
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try:
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import langchain # type: ignore[import]
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@ -79,7 +87,11 @@ def get_verbose() -> bool:
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def set_debug(value: bool) -> None:
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"""Set a new value for the `debug` global setting."""
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"""Set a new value for the `debug` global setting.
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Args:
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value: The new value for the `debug` global setting.
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"""
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try:
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import langchain # type: ignore[import]
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@ -105,7 +117,11 @@ def set_debug(value: bool) -> None:
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def get_debug() -> bool:
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"""Get the value of the `debug` global setting."""
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"""Get the value of the `debug` global setting.
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Returns:
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The value of the `debug` global setting.
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"""
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try:
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import langchain # type: ignore[import]
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@ -168,7 +184,11 @@ def set_llm_cache(value: Optional["BaseCache"]) -> None:
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def get_llm_cache() -> "BaseCache":
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"""Get the value of the `llm_cache` global setting."""
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"""Get the value of the `llm_cache` global setting.
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Returns:
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The value of the `llm_cache` global setting.
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"""
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try:
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import langchain # type: ignore[import]
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@ -62,13 +62,20 @@ class BaseMemory(Serializable, ABC):
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"""Return key-value pairs given the text input to the chain.
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Args:
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inputs: The inputs to the chain."""
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inputs: The inputs to the chain.
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Returns:
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A dictionary of key-value pairs.
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"""
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async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
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"""Async return key-value pairs given the text input to the chain.
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Args:
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inputs: The inputs to the chain.
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Returns:
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A dictionary of key-value pairs.
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"""
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return await run_in_executor(None, self.load_memory_variables, inputs)
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@ -24,17 +24,20 @@ class PromptValue(Serializable, ABC):
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"""Base abstract class for inputs to any language model.
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PromptValues can be converted to both LLM (pure text-generation) inputs and
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ChatModel inputs.
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ChatModel inputs.
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"""
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@classmethod
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def is_lc_serializable(cls) -> bool:
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"""Return whether this class is serializable."""
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"""Return whether this class is serializable. Defaults to True."""
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return True
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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"""Get the namespace of the langchain object.
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This is used to determine the namespace of the object when serializing.
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Defaults to ["langchain", "schema", "prompt"].
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"""
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return ["langchain", "schema", "prompt"]
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@abstractmethod
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@ -55,7 +58,10 @@ class StringPromptValue(PromptValue):
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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"""Get the namespace of the langchain object.
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This is used to determine the namespace of the object when serializing.
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Defaults to ["langchain", "prompts", "base"].
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"""
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return ["langchain", "prompts", "base"]
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def to_string(self) -> str:
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@ -86,7 +92,10 @@ class ChatPromptValue(PromptValue):
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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"""Get the namespace of the langchain object.
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This is used to determine the namespace of the object when serializing.
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Defaults to ["langchain", "prompts", "chat"].
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"""
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return ["langchain", "prompts", "chat"]
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@ -94,7 +103,8 @@ class ImageURL(TypedDict, total=False):
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"""Image URL."""
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detail: Literal["auto", "low", "high"]
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"""Specifies the detail level of the image."""
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"""Specifies the detail level of the image. Defaults to "auto".
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Can be "auto", "low", or "high"."""
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url: str
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"""Either a URL of the image or the base64 encoded image data."""
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@ -127,5 +137,8 @@ class ChatPromptValueConcrete(ChatPromptValue):
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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"""Get the namespace of the langchain object.
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This is used to determine the namespace of the object when serializing.
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Defaults to ["langchain", "prompts", "chat"].
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"""
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return ["langchain", "prompts", "chat"]
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@ -53,14 +53,13 @@ RetrieverOutputLike = Runnable[Any, RetrieverOutput]
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class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
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"""Abstract base class for a Document retrieval system.
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A retrieval system is defined as something that can take string queries and return
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the most 'relevant' Documents from some source.
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Usage:
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A retriever follows the standard Runnable interface, and should be used
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via the standard runnable methods of `invoke`, `ainvoke`, `batch`, `abatch`.
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via the standard Runnable methods of `invoke`, `ainvoke`, `batch`, `abatch`.
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Implementation:
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@ -89,7 +88,7 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
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\"\"\"(Optional) async native implementation.\"\"\"
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return self.docs[:self.k]
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Example: A simple retriever based on a scitkit learn vectorizer
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Example: A simple retriever based on a scikit-learn vectorizer
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.. code-block:: python
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@ -178,12 +177,12 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
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Main entry point for synchronous retriever invocations.
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Args:
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input: The query string
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config: Configuration for the retriever
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**kwargs: Additional arguments to pass to the retriever
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input: The query string.
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config: Configuration for the retriever. Defaults to None.
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**kwargs: Additional arguments to pass to the retriever.
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Returns:
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List of relevant documents
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List of relevant documents.
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Examples:
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@ -237,12 +236,12 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
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Main entry point for asynchronous retriever invocations.
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Args:
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input: The query string
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config: Configuration for the retriever
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**kwargs: Additional arguments to pass to the retriever
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input: The query string.
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config: Configuration for the retriever. Defaults to None.
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**kwargs: Additional arguments to pass to the retriever.
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Returns:
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List of relevant documents
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List of relevant documents.
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Examples:
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@ -292,10 +291,10 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
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"""Get documents relevant to a query.
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Args:
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query: String to find relevant documents for
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run_manager: The callback handler to use
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query: String to find relevant documents for.
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run_manager: The callback handler to use.
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Returns:
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List of relevant documents
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List of relevant documents.
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"""
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async def _aget_relevant_documents(
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@ -333,18 +332,21 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
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`get_relevant_documents directly`.
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Args:
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query: string to find relevant documents for
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callbacks: Callback manager or list of callbacks
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tags: Optional list of tags associated with the retriever. Defaults to None
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query: string to find relevant documents for.
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callbacks: Callback manager or list of callbacks. Defaults to None.
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tags: Optional list of tags associated with the retriever.
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These tags will be associated with each call to this retriever,
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and passed as arguments to the handlers defined in `callbacks`.
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metadata: Optional metadata associated with the retriever. Defaults to None
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Defaults to None.
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metadata: Optional metadata associated with the retriever.
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This metadata will be associated with each call to this retriever,
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and passed as arguments to the handlers defined in `callbacks`.
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run_name: Optional name for the run.
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Defaults to None.
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run_name: Optional name for the run. Defaults to None.
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**kwargs: Additional arguments to pass to the retriever.
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Returns:
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List of relevant documents
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List of relevant documents.
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"""
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config: RunnableConfig = {}
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if callbacks:
|
||||
@ -374,18 +376,21 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
|
||||
`aget_relevant_documents directly`.
|
||||
|
||||
Args:
|
||||
query: string to find relevant documents for
|
||||
callbacks: Callback manager or list of callbacks
|
||||
tags: Optional list of tags associated with the retriever. Defaults to None
|
||||
query: string to find relevant documents for.
|
||||
callbacks: Callback manager or list of callbacks.
|
||||
tags: Optional list of tags associated with the retriever.
|
||||
These tags will be associated with each call to this retriever,
|
||||
and passed as arguments to the handlers defined in `callbacks`.
|
||||
metadata: Optional metadata associated with the retriever. Defaults to None
|
||||
Defaults to None.
|
||||
metadata: Optional metadata associated with the retriever.
|
||||
This metadata will be associated with each call to this retriever,
|
||||
and passed as arguments to the handlers defined in `callbacks`.
|
||||
run_name: Optional name for the run.
|
||||
Defaults to None.
|
||||
run_name: Optional name for the run. Defaults to None.
|
||||
**kwargs: Additional arguments to pass to the retriever.
|
||||
|
||||
Returns:
|
||||
List of relevant documents
|
||||
List of relevant documents.
|
||||
"""
|
||||
config: RunnableConfig = {}
|
||||
if callbacks:
|
||||
|
||||
@ -150,7 +150,6 @@ class BaseStore(Generic[K, V], ABC):
|
||||
|
||||
Yields:
|
||||
Iterator[K | str]: An iterator over keys that match the given prefix.
|
||||
|
||||
This method is allowed to return an iterator over either K or str
|
||||
depending on what makes more sense for the given store.
|
||||
"""
|
||||
@ -165,7 +164,6 @@ class BaseStore(Generic[K, V], ABC):
|
||||
|
||||
Yields:
|
||||
Iterator[K | str]: An iterator over keys that match the given prefix.
|
||||
|
||||
This method is allowed to return an iterator over either K or str
|
||||
depending on what makes more sense for the given store.
|
||||
"""
|
||||
|
||||
@ -10,10 +10,12 @@ from langchain_core.pydantic_v1 import BaseModel
|
||||
|
||||
|
||||
class Visitor(ABC):
|
||||
"""Defines interface for IR translation using visitor pattern."""
|
||||
"""Defines interface for IR translation using a visitor pattern."""
|
||||
|
||||
allowed_comparators: Optional[Sequence[Comparator]] = None
|
||||
"""Allowed comparators for the visitor."""
|
||||
allowed_operators: Optional[Sequence[Operator]] = None
|
||||
"""Allowed operators for the visitor."""
|
||||
|
||||
def _validate_func(self, func: Union[Operator, Comparator]) -> None:
|
||||
if isinstance(func, Operator) and self.allowed_operators is not None:
|
||||
@ -31,15 +33,27 @@ class Visitor(ABC):
|
||||
|
||||
@abstractmethod
|
||||
def visit_operation(self, operation: Operation) -> Any:
|
||||
"""Translate an Operation."""
|
||||
"""Translate an Operation.
|
||||
|
||||
Args:
|
||||
operation: Operation to translate.
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def visit_comparison(self, comparison: Comparison) -> Any:
|
||||
"""Translate a Comparison."""
|
||||
"""Translate a Comparison.
|
||||
|
||||
Args:
|
||||
comparison: Comparison to translate.
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def visit_structured_query(self, structured_query: StructuredQuery) -> Any:
|
||||
"""Translate a StructuredQuery."""
|
||||
"""Translate a StructuredQuery.
|
||||
|
||||
Args:
|
||||
structured_query: StructuredQuery to translate.
|
||||
"""
|
||||
|
||||
|
||||
def _to_snake_case(name: str) -> str:
|
||||
@ -60,10 +74,10 @@ class Expr(BaseModel):
|
||||
"""Accept a visitor.
|
||||
|
||||
Args:
|
||||
visitor: visitor to accept
|
||||
visitor: visitor to accept.
|
||||
|
||||
Returns:
|
||||
result of visiting
|
||||
result of visiting.
|
||||
"""
|
||||
return getattr(visitor, f"visit_{_to_snake_case(self.__class__.__name__)}")(
|
||||
self
|
||||
@ -98,7 +112,13 @@ class FilterDirective(Expr, ABC):
|
||||
|
||||
|
||||
class Comparison(FilterDirective):
|
||||
"""Comparison to a value."""
|
||||
"""Comparison to a value.
|
||||
|
||||
Parameters:
|
||||
comparator: The comparator to use.
|
||||
attribute: The attribute to compare.
|
||||
value: The value to compare to.
|
||||
"""
|
||||
|
||||
comparator: Comparator
|
||||
attribute: str
|
||||
@ -113,7 +133,12 @@ class Comparison(FilterDirective):
|
||||
|
||||
|
||||
class Operation(FilterDirective):
|
||||
"""Llogical operation over other directives."""
|
||||
"""Logical operation over other directives.
|
||||
|
||||
Parameters:
|
||||
operator: The operator to use.
|
||||
arguments: The arguments to the operator.
|
||||
"""
|
||||
|
||||
operator: Operator
|
||||
arguments: List[FilterDirective]
|
||||
|
||||
@ -6,7 +6,11 @@ from typing import Sequence
|
||||
|
||||
|
||||
def print_sys_info(*, additional_pkgs: Sequence[str] = tuple()) -> None:
|
||||
"""Print information about the environment for debugging purposes."""
|
||||
"""Print information about the environment for debugging purposes.
|
||||
|
||||
Args:
|
||||
additional_pkgs: Additional packages to include in the output.
|
||||
"""
|
||||
import pkgutil
|
||||
import platform
|
||||
import sys
|
||||
|
||||
@ -249,7 +249,16 @@ def _infer_arg_descriptions(
|
||||
|
||||
|
||||
class _SchemaConfig:
|
||||
"""Configuration for the pydantic model."""
|
||||
"""Configuration for the pydantic model.
|
||||
|
||||
This is used to configure the pydantic model created from
|
||||
a function's signature.
|
||||
|
||||
Parameters:
|
||||
extra: Whether to allow extra fields in the model.
|
||||
arbitrary_types_allowed: Whether to allow arbitrary types in the model.
|
||||
Defaults to True.
|
||||
"""
|
||||
|
||||
extra: Any = Extra.forbid
|
||||
arbitrary_types_allowed: bool = True
|
||||
@ -265,15 +274,18 @@ def create_schema_from_function(
|
||||
) -> Type[BaseModel]:
|
||||
"""Create a pydantic schema from a function's signature.
|
||||
Args:
|
||||
model_name: Name to assign to the generated pydandic schema
|
||||
func: Function to generate the schema from
|
||||
filter_args: Optional list of arguments to exclude from the schema
|
||||
model_name: Name to assign to the generated pydantic schema.
|
||||
func: Function to generate the schema from.
|
||||
filter_args: Optional list of arguments to exclude from the schema.
|
||||
Defaults to FILTERED_ARGS.
|
||||
parse_docstring: Whether to parse the function's docstring for descriptions
|
||||
for each argument.
|
||||
error_on_invalid_docstring: if ``parse_docstring`` is provided, configures
|
||||
for each argument. Defaults to False.
|
||||
error_on_invalid_docstring: if ``parse_docstring`` is provided, configure
|
||||
whether to raise ValueError on invalid Google Style docstrings.
|
||||
Defaults to False.
|
||||
|
||||
Returns:
|
||||
A pydantic model with the same arguments as the function
|
||||
A pydantic model with the same arguments as the function.
|
||||
"""
|
||||
# https://docs.pydantic.dev/latest/usage/validation_decorator/
|
||||
validated = validate_arguments(func, config=_SchemaConfig) # type: ignore
|
||||
@ -348,8 +360,9 @@ class ChildTool(BaseTool):
|
||||
args_schema: Optional[Type[BaseModel]] = None
|
||||
"""Pydantic model class to validate and parse the tool's input arguments."""
|
||||
return_direct: bool = False
|
||||
"""Whether to return the tool's output directly. Setting this to True means
|
||||
"""Whether to return the tool's output directly.
|
||||
|
||||
Setting this to True means
|
||||
that after the tool is called, the AgentExecutor will stop looping.
|
||||
"""
|
||||
verbose: bool = False
|
||||
@ -360,13 +373,13 @@ class ChildTool(BaseTool):
|
||||
callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
|
||||
"""Deprecated. Please use callbacks instead."""
|
||||
tags: Optional[List[str]] = None
|
||||
"""Optional list of tags associated with the tool. Defaults to None
|
||||
"""Optional list of tags associated with the tool. Defaults to None.
|
||||
These tags will be associated with each call to this tool,
|
||||
and passed as arguments to the handlers defined in `callbacks`.
|
||||
You can use these to eg identify a specific instance of a tool with its use case.
|
||||
"""
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
"""Optional metadata associated with the tool. Defaults to None
|
||||
"""Optional metadata associated with the tool. Defaults to None.
|
||||
This metadata will be associated with each call to this tool,
|
||||
and passed as arguments to the handlers defined in `callbacks`.
|
||||
You can use these to eg identify a specific instance of a tool with its use case.
|
||||
@ -383,7 +396,7 @@ class ChildTool(BaseTool):
|
||||
"""Handle the content of the ValidationError thrown."""
|
||||
|
||||
response_format: Literal["content", "content_and_artifact"] = "content"
|
||||
"""The tool response format.
|
||||
"""The tool response format. Defaults to 'content'.
|
||||
|
||||
If "content" then the output of the tool is interpreted as the contents of a
|
||||
ToolMessage. If "content_and_artifact" then the output is expected to be a
|
||||
@ -414,7 +427,14 @@ class ChildTool(BaseTool):
|
||||
def get_input_schema(
|
||||
self, config: Optional[RunnableConfig] = None
|
||||
) -> Type[BaseModel]:
|
||||
"""The tool's input schema."""
|
||||
"""The tool's input schema.
|
||||
|
||||
Args:
|
||||
config: The configuration for the tool.
|
||||
|
||||
Returns:
|
||||
The input schema for the tool.
|
||||
"""
|
||||
if self.args_schema is not None:
|
||||
return self.args_schema
|
||||
else:
|
||||
@ -441,7 +461,11 @@ class ChildTool(BaseTool):
|
||||
# --- Tool ---
|
||||
|
||||
def _parse_input(self, tool_input: Union[str, Dict]) -> Union[str, Dict[str, Any]]:
|
||||
"""Convert tool input to pydantic model."""
|
||||
"""Convert tool input to a pydantic model.
|
||||
|
||||
Args:
|
||||
tool_input: The input to the tool.
|
||||
"""
|
||||
input_args = self.args_schema
|
||||
if isinstance(tool_input, str):
|
||||
if input_args is not None:
|
||||
@ -460,7 +484,14 @@ class ChildTool(BaseTool):
|
||||
|
||||
@root_validator(pre=True)
|
||||
def raise_deprecation(cls, values: Dict) -> Dict:
|
||||
"""Raise deprecation warning if callback_manager is used."""
|
||||
"""Raise deprecation warning if callback_manager is used.
|
||||
|
||||
Args:
|
||||
values: The values to validate.
|
||||
|
||||
Returns:
|
||||
The validated values.
|
||||
"""
|
||||
if values.get("callback_manager") is not None:
|
||||
warnings.warn(
|
||||
"callback_manager is deprecated. Please use callbacks instead.",
|
||||
@ -514,7 +545,28 @@ class ChildTool(BaseTool):
|
||||
tool_call_id: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""Run the tool."""
|
||||
"""Run the tool.
|
||||
|
||||
Args:
|
||||
tool_input: The input to the tool.
|
||||
verbose: Whether to log the tool's progress. Defaults to None.
|
||||
start_color: The color to use when starting the tool. Defaults to 'green'.
|
||||
color: The color to use when ending the tool. Defaults to 'green'.
|
||||
callbacks: Callbacks to be called during tool execution. Defaults to None.
|
||||
tags: Optional list of tags associated with the tool. Defaults to None.
|
||||
metadata: Optional metadata associated with the tool. Defaults to None.
|
||||
run_name: The name of the run. Defaults to None.
|
||||
run_id: The id of the run. Defaults to None.
|
||||
config: The configuration for the tool. Defaults to None.
|
||||
tool_call_id: The id of the tool call. Defaults to None.
|
||||
kwargs: Additional arguments to pass to the tool
|
||||
|
||||
Returns:
|
||||
The output of the tool.
|
||||
|
||||
Raises:
|
||||
ToolException: If an error occurs during tool execution.
|
||||
"""
|
||||
callback_manager = CallbackManager.configure(
|
||||
callbacks,
|
||||
self.callbacks,
|
||||
@ -600,7 +652,28 @@ class ChildTool(BaseTool):
|
||||
tool_call_id: Optional[str] = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""Run the tool asynchronously."""
|
||||
"""Run the tool asynchronously.
|
||||
|
||||
Args:
|
||||
tool_input: The input to the tool.
|
||||
verbose: Whether to log the tool's progress. Defaults to None.
|
||||
start_color: The color to use when starting the tool. Defaults to 'green'.
|
||||
color: The color to use when ending the tool. Defaults to 'green'.
|
||||
callbacks: Callbacks to be called during tool execution. Defaults to None.
|
||||
tags: Optional list of tags associated with the tool. Defaults to None.
|
||||
metadata: Optional metadata associated with the tool. Defaults to None.
|
||||
run_name: The name of the run. Defaults to None.
|
||||
run_id: The id of the run. Defaults to None.
|
||||
config: The configuration for the tool. Defaults to None.
|
||||
tool_call_id: The id of the tool call. Defaults to None.
|
||||
kwargs: Additional arguments to pass to the tool
|
||||
|
||||
Returns:
|
||||
The output of the tool.
|
||||
|
||||
Raises:
|
||||
ToolException: If an error occurs during tool execution.
|
||||
"""
|
||||
callback_manager = AsyncCallbackManager.configure(
|
||||
callbacks,
|
||||
self.callbacks,
|
||||
@ -709,7 +782,11 @@ class Tool(BaseTool):
|
||||
|
||||
@property
|
||||
def args(self) -> dict:
|
||||
"""The tool's input arguments."""
|
||||
"""The tool's input arguments.
|
||||
|
||||
Returns:
|
||||
The input arguments for the tool.
|
||||
"""
|
||||
if self.args_schema is not None:
|
||||
return self.args_schema.schema()["properties"]
|
||||
# For backwards compatibility, if the function signature is ambiguous,
|
||||
@ -788,7 +865,23 @@ class Tool(BaseTool):
|
||||
] = None, # This is last for compatibility, but should be after func
|
||||
**kwargs: Any,
|
||||
) -> Tool:
|
||||
"""Initialize tool from a function."""
|
||||
"""Initialize tool from a function.
|
||||
|
||||
Args:
|
||||
func: The function to create the tool from.
|
||||
name: The name of the tool.
|
||||
description: The description of the tool.
|
||||
return_direct: Whether to return the output directly. Defaults to False.
|
||||
args_schema: The schema of the tool's input arguments. Defaults to None.
|
||||
coroutine: The asynchronous version of the function. Defaults to None.
|
||||
**kwargs: Additional arguments to pass to the tool.
|
||||
|
||||
Returns:
|
||||
The tool.
|
||||
|
||||
Raises:
|
||||
ValueError: If the function is not provided.
|
||||
"""
|
||||
if func is None and coroutine is None:
|
||||
raise ValueError("Function and/or coroutine must be provided")
|
||||
return cls(
|
||||
@ -893,25 +986,34 @@ class StructuredTool(BaseTool):
|
||||
A classmethod that helps to create a tool from a function.
|
||||
|
||||
Args:
|
||||
func: The function from which to create a tool
|
||||
coroutine: The async function from which to create a tool
|
||||
name: The name of the tool. Defaults to the function name
|
||||
description: The description of the tool. Defaults to the function docstring
|
||||
return_direct: Whether to return the result directly or as a callback
|
||||
args_schema: The schema of the tool's input arguments
|
||||
infer_schema: Whether to infer the schema from the function's signature
|
||||
func: The function from which to create a tool.
|
||||
coroutine: The async function from which to create a tool.
|
||||
name: The name of the tool. Defaults to the function name.
|
||||
description: The description of the tool.
|
||||
Defaults to the function docstring.
|
||||
return_direct: Whether to return the result directly or as a callback.
|
||||
Defaults to False.
|
||||
args_schema: The schema of the tool's input arguments. Defaults to None.
|
||||
infer_schema: Whether to infer the schema from the function's signature.
|
||||
Defaults to True.
|
||||
response_format: The tool response format. If "content" then the output of
|
||||
the tool is interpreted as the contents of a ToolMessage. If
|
||||
"content_and_artifact" then the output is expected to be a two-tuple
|
||||
corresponding to the (content, artifact) of a ToolMessage.
|
||||
Defaults to "content".
|
||||
parse_docstring: if ``infer_schema`` and ``parse_docstring``, will attempt
|
||||
to parse parameter descriptions from Google Style function docstrings.
|
||||
error_on_invalid_docstring: if ``parse_docstring`` is provided, configures
|
||||
Defaults to False.
|
||||
error_on_invalid_docstring: if ``parse_docstring`` is provided, configure
|
||||
whether to raise ValueError on invalid Google Style docstrings.
|
||||
Defaults to False.
|
||||
**kwargs: Additional arguments to pass to the tool
|
||||
|
||||
Returns:
|
||||
The tool
|
||||
The tool.
|
||||
|
||||
Raises:
|
||||
ValueError: If the function is not provided.
|
||||
|
||||
Examples:
|
||||
|
||||
@ -989,19 +1091,27 @@ def tool(
|
||||
Args:
|
||||
*args: The arguments to the tool.
|
||||
return_direct: Whether to return directly from the tool rather
|
||||
than continuing the agent loop.
|
||||
args_schema: optional argument schema for user to specify
|
||||
than continuing the agent loop. Defaults to False.
|
||||
args_schema: optional argument schema for user to specify.
|
||||
Defaults to None.
|
||||
infer_schema: Whether to infer the schema of the arguments from
|
||||
the function's signature. This also makes the resultant tool
|
||||
accept a dictionary input to its `run()` function.
|
||||
Defaults to True.
|
||||
response_format: The tool response format. If "content" then the output of
|
||||
the tool is interpreted as the contents of a ToolMessage. If
|
||||
"content_and_artifact" then the output is expected to be a two-tuple
|
||||
corresponding to the (content, artifact) of a ToolMessage.
|
||||
Defaults to "content".
|
||||
parse_docstring: if ``infer_schema`` and ``parse_docstring``, will attempt to
|
||||
parse parameter descriptions from Google Style function docstrings.
|
||||
error_on_invalid_docstring: if ``parse_docstring`` is provided, configures
|
||||
Defaults to False.
|
||||
error_on_invalid_docstring: if ``parse_docstring`` is provided, configure
|
||||
whether to raise ValueError on invalid Google Style docstrings.
|
||||
Defaults to True.
|
||||
|
||||
Returns:
|
||||
The tool.
|
||||
|
||||
Requires:
|
||||
- Function must be of type (str) -> str
|
||||
@ -1230,9 +1340,11 @@ def create_retriever_tool(
|
||||
so should be unique and somewhat descriptive.
|
||||
description: The description for the tool. This will be passed to the language
|
||||
model, so should be descriptive.
|
||||
document_prompt: The prompt to use for the document. Defaults to None.
|
||||
document_separator: The separator to use between documents. Defaults to "\n\n".
|
||||
|
||||
Returns:
|
||||
Tool class to pass to an agent
|
||||
Tool class to pass to an agent.
|
||||
"""
|
||||
document_prompt = document_prompt or PromptTemplate.from_template("{page_content}")
|
||||
func = partial(
|
||||
@ -1262,6 +1374,12 @@ ToolsRenderer = Callable[[List[BaseTool]], str]
|
||||
def render_text_description(tools: List[BaseTool]) -> str:
|
||||
"""Render the tool name and description in plain text.
|
||||
|
||||
Args:
|
||||
tools: The tools to render.
|
||||
|
||||
Returns:
|
||||
The rendered text.
|
||||
|
||||
Output will be in the format of:
|
||||
|
||||
.. code-block:: markdown
|
||||
@ -1284,6 +1402,12 @@ def render_text_description(tools: List[BaseTool]) -> str:
|
||||
def render_text_description_and_args(tools: List[BaseTool]) -> str:
|
||||
"""Render the tool name, description, and args in plain text.
|
||||
|
||||
Args:
|
||||
tools: The tools to render.
|
||||
|
||||
Returns:
|
||||
The rendered text.
|
||||
|
||||
Output will be in the format of:
|
||||
|
||||
.. code-block:: markdown
|
||||
@ -1444,7 +1568,18 @@ def convert_runnable_to_tool(
|
||||
description: Optional[str] = None,
|
||||
arg_types: Optional[Dict[str, Type]] = None,
|
||||
) -> BaseTool:
|
||||
"""Convert a Runnable into a BaseTool."""
|
||||
"""Convert a Runnable into a BaseTool.
|
||||
|
||||
Args:
|
||||
runnable: The runnable to convert.
|
||||
args_schema: The schema for the tool's input arguments. Defaults to None.
|
||||
name: The name of the tool. Defaults to None.
|
||||
description: The description of the tool. Defaults to None.
|
||||
arg_types: The types of the arguments. Defaults to None.
|
||||
|
||||
Returns:
|
||||
The tool.
|
||||
"""
|
||||
if args_schema:
|
||||
runnable = runnable.with_types(input_type=args_schema)
|
||||
description = description or _get_description_from_runnable(runnable)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user