Construct the scratchpad that lets the agent continue its thought process.
Create an agent that uses ReAct prompting.
Based on paper "ReAct: Synergizing Reasoning and Acting in Language Models" (https://arxiv.org/abs/2210.03629)
This implementation is based on the foundational ReAct paper but is older and not well-suited for production applications.
For a more robust and feature-rich implementation, we recommend using the
create_agent function from the langchain library.
See the reference doc for more information.
Base class for parsing agent output into agent action/finish.
Parses ReAct-style LLM calls that have a single tool input.
Expects output to be in one of two formats.
If the output signals that an action should be taken, should be in the below format. This will result in an AgentAction being returned.
Thought: agent thought here
Action: search
Action Input: what is the temperature in SF?
If the output signals that a final answer should be given, should be in the below format. This will result in an AgentFinish being returned.
Thought: agent thought here
Final Answer: The temperature is 100 degrees