Class LangchainAgent (1.49.0)

Version 1.49.0

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LangchainAgent(
    model: str,
    *,
    prompt: typing.Optional[RunnableSerializable] = None,
    tools: typing.Optional[
        typing.Sequence[typing.Union[typing.Callable, BaseTool]]
    ] = None,
    output_parser: typing.Optional[RunnableSerializable] = None,
    chat_history: typing.Optional[GetSessionHistoryCallable] = None,
    model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    agent_executor_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None
)

A Langchain Agent.

Reference:

• Agent: https://python.langchain.com/docs/modules/agents/concepts
• Memory: https://python.langchain.com/docs/expression_language/how_to/message_history

Methods

LangchainAgent

LangchainAgent(
    model: str,
    *,
    prompt: typing.Optional[RunnableSerializable] = None,
    tools: typing.Optional[
        typing.Sequence[typing.Union[typing.Callable, BaseTool]]
    ] = None,
    output_parser: typing.Optional[RunnableSerializable] = None,
    chat_history: typing.Optional[GetSessionHistoryCallable] = None,
    model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    agent_executor_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None
)

Initializes the LangchainAgent.

Under-the-hood, assuming .set_up() is called, this will correspond to

from langchain import agents
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model_name=model, **model_kwargs)
agent_executor = agents.AgentExecutor(
    agent=prompt | llm.bind(functions=tools) | output_parser,
    tools=tools,
    **agent_executor_kwargs,
)
runnable = RunnableWithMessageHistory(
    runnable=agent_executor,
    get_session_history=chat_history,
    **runnable_kwargs,
)

Parameters
Name	Description
model	str Optional. The name of the model (e.g. "gemini-1.0-pro").
prompt	langchain_core.runnables.RunnableSerializable Optional. The prompt template for the model. Defaults to a ChatPromptTemplate.
tools	Sequence[langchain_core.tools.BaseTool, Callable] Optional. The tools for the agent to be able to use. All input callables (e.g. function or class method) will be converted to a langchain.tools.base.StructuredTool. Defaults to None.
output_parser	langchain_core.runnables.RunnableSerializable Optional. The output parser for the model. Defaults to an output parser that works with Gemini function-calling.
chat_history	langchain_core.runnables.history.GetSessionHistoryCallable Optional. Callable that returns a new BaseChatMessageHistory. Defaults to None, i.e. chat_history is not preserved.
model_kwargs	Mapping[str, Any] Optional. Additional keyword arguments for the constructor of chat_models.ChatVertexAI. An example would be { # temperature (float): Sampling temperature, it controls the # degree of randomness in token selection. "temperature": 0.28, # max_output_tokens (int): Token limit determines the # maximum amount of text output from one prompt. "max_output_tokens": 1000, # top_p (float): Tokens are selected from most probable to # least, until the sum of their probabilities equals the # top_p value. "top_p": 0.95, # top_k (int): How the model selects tokens for output, the # next token is selected from among the top_k most probable # tokens. "top_k": 40, }
agent_executor_kwargs	Mapping[str, Any] Optional. Additional keyword arguments for the constructor of langchain.agents.AgentExecutor. An example would be { # Whether to return the agent's trajectory of intermediate # steps at the end in addition to the final output. "return_intermediate_steps": False, # The maximum number of steps to take before ending the # execution loop. "max_iterations": 15, # The method to use for early stopping if the agent never # returns AgentFinish. Either 'force' or 'generate'. "early_stopping_method": "force", # How to handle errors raised by the agent's output parser. # Defaults to False, which raises the error. "handle_parsing_errors": False, }
runnable_kwargs	Mapping[str, Any] Optional. Additional keyword arguments for the constructor of langchain.runnables.history.RunnableWithMessageHistory if chat_history is specified. If chat_history is None, this will be ignored.

Exceptions
Type	Description
TypeError	If there is an invalid tool (e.g. function with an input
tha	did not specify its type).:

query

query(
    *,
    input: typing.Union[str, typing.Mapping[str, typing.Any]],
    config: typing.Optional[RunnableConfig] = None,
    **kwargs: typing.Any
) -> typing.Dict[str, typing.Any]

Queries the Agent with the given input and config.

Parameters
Name	Description
input	Union[str, Mapping[str, Any]] Required. The input to be passed to the Agent.
config	langchain_core.runnables.RunnableConfig Optional. The config (if any) to be used for invoking the Agent.

set_up

set_up()

Sets up the agent for execution of queries at runtime.

It initializes the model, binds the model with tools, and connects it with the prompt template and output parser.

This method should not be called for an object that being passed to the ReasoningEngine service for deployment, as it initializes clients that can not be serialized.