Detailed Explanation and Insights on the ReAct Framework

Background

Various papers propose multiple reasoning frameworks for intelligent agents (also called frameworks): CoT, ToT, LLM+P, etc. Among them, the ReAct framework is used as the reasoning engine by several AI application development tools such as LangChain and LlamaIndex.

ReAct Framework

ReAct: Synergizing Reasoning and Acting in Language Models

PromptTemplate(
	input_variables = ['agent_scratchpad', 'input', 'tool_name', 'tools'],
	template = 'Answer the folllowing questions as best you can.
		You have access to the folowing tools: \n\n{tools}\n\n
		Use the following format: \n\n
		Question: the input question you must answer \n
		Thought: you should always think about what to do \n
		Action: the action to take, should be one of [{tool_names}] \n
		Action input: the input to the action \n...\n
		Observation: the result of the action \n...\n
		(this Thought/ Action/ Action Input/ Observation can repeat 3 times) \n
		Thought: I now know the final answer \n
		Final Answer: the final answer to the original input questions \n\n Begain! \n\n
		Questions: {input} \n
		Thought: {agent_scratchpad}'
)

During instantiation, this prompt guides a large language model to answer questions in a specific format that includes Thought, Action, Action Input, and Observation. This cycle may repeat up to 3 times as needed to arrive at the final answer.

References

LangChain official site: hwchase17/react

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