A Beginner’s Guide of ReAct Framework and Its LangChain Application
As LLM strategies evolve, it’s widely known that prompting a chain-of-thought (CoT), or a sequence of intermediate reasoning steps, can improve the reasoning capabilities of language fashions. However, this method has limitations because it operates as a static and closed system that depends solely on the mannequin’s inner representations and doesn’t think about exterior stimuli.
To deal with this, a brand new framework referred to as ReAct (Reasoning + Acting) has been proposed lately on this paper. The ReAct framework extends the chain-of-thought method to chain-of-thought-action-observation with further intermediate motion and commentary steps. This extension permits the language mannequin to carry out dynamic reasoning, and shortly adapt its performing plan primarily based on exterior data. By doing so, the ReAct framework creates an open system that permits for extra versatile and efficient reasoning, enhancing the power of language fashions to make selections and resolve issues.
The ReAct Framework includes an iterative course of that generates thought, motion, and commentary in an interleaved method. At every iteration, it integrates thought and motion era from the language mannequin with commentary era by executing the motion name.
Thought and Action Generation: The language mannequin generates thought and the following motion primarily based on its context historical past, which incorporates the earlier thought-action-observation sequence.
Observation Generation: The ReAct Framework then parses the textual content output from the language mannequin to extract the motion, executes the motion to acquire an commentary, and integrates this commentary into the context for the following iteration of the language mannequin.
The iteration continues till the end situation is generated from the motion and recognized by the parser.
Use the instance from LangChain RaAct Agent to resolve a multi-hop query with a number of search actions:
The diagram under illustrates the move of thought, motion, and commentary era in every step, in addition to the evolution of context throughout steps, within the LangChain ReAct Agent for fixing this drawback.
As demonstrated within the diagram, in step 1, the LLM generates “Thought1” and the corresponding “Action1” primarily based on the query and some shot examples (omitted within the diagram). The exterior search device then executes “Action1” to generate “Observation1”. “Observation1”, together with “Thought1” and “Action1”, is then used to assemble the context for the following LLM name in step 2. This step continues to generate “Thought2”, “Action2”, and “Observation2”.
In step 3, “Thought3” and “Action3” are generated, and the LangChain ReAct Agent identifies the “Finish” situation (“Finish[Bill Clinton]”) in “Action3”. This signifies that the ultimate reply is “Bill Clinton”, and the agent concludes the chain, returning the outcome.
The objective of this put up is to offer a concise and high-level introduction to the ReAct Framework and aid you shortly perceive the way it works and its utility in LangChain.
If you want to delve deeper into the ReAct Framework and discover its implementation intimately, I like to recommend referring to the LangChain codebase. This will allow you to realize a extra complete understanding of the framework and the way it may be utilized in varied contexts to totally different issues.
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