Chain that takes in an input and produces an action and action input.
Iterator for AgentExecutor.
Tool that is run when invalid tool name is encountered by agent.
Abstract base class for creating structured sequences of calls to components.
Chains should be used to encode a sequence of calls to components like models, document retrievers, other chains, etc., and provide a simple interface to this sequence.
Base Single Action Agent class.
Base Multi Action Agent class.
Base class for parsing agent output into agent action/finish.
Base class for parsing agent output into agent actions/finish.
This is used for agents that can return multiple actions.
Agent powered by Runnables.
Agent powered by Runnables.
Tool that just returns the query.
Agent that is using tools.
An enum for agent types.
Base class for single action agents.
Agent that calls the language model and deciding the action.
This is driven by a LLMChain. The prompt in the LLMChain MUST include a variable called "agent_scratchpad" where the agent can put its intermediary work.
Chain to run queries against LLMs.
This class is deprecated. See below for an example implementation using LangChain runnables:
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
model = OpenAI()
chain = prompt | model | StrOutputParser()
chain.invoke("your adjective here")