large language models (LLMs)大语言模型在语义理解和交互式决策方面有着不错的表现。ReAct在一次交互中循环使用推理和行动两个操作解决复杂问题，推理即利用模型自身语义理解能力，行动则利用模型以外的能力（如计算、搜索最新消息，用户自定义的行动）。

一个使用 ReAct 的例子

Question What is the elevation range for the area that the eastern sector of the
Colorado orogeny extends into?
Thought 1 I need to search Colorado orogeny, find the area that the eastern sector
of the Colorado orogeny extends into, then find the elevation range of the
area.
Action 1 Search[Colorado orogeny]
Observation 1 The Colorado orogeny was an episode of mountain building (an orogeny) in
Colorado and surrounding areas.
Thought 2 It does not mention the eastern sector. So I need to look up eastern
sector.
Action 2 Lookup[eastern sector]
Observation 2 (Result 1 / 1) The eastern sector extends into the High Plains and is called
the Central Plains orogeny.
Thought 3 The eastern sector of Colorado orogeny extends into the High Plains. So I
need to search High Plains and find its elevation range.
Action 3 Search[High Plains]
Observation 3 High Plains refers to one of two distinct land regions
Thought 4 I need to instead search High Plains (United States).
Action 4 Search[High Plains (United States)]
Observation 4 The High Plains are a subregion of the Great Plains. From east to west, the
High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130
m).[3]
Thought 5 High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer
is 1,800 to 7,000 ft.
Action 5 Finish[1,800 to 7,000 ft]
...

Thought：模型利用已有信息进行推理
Action：采取的行动，如：搜索Search，匹配最相近结果Lookup
Observation：可以理解为从模型以外获取的信息（如查询的信息、计算的信息）

参考：
ReAct Prompting
ReAct: Synergizing Reasoning and Acting in Language Models