How to Implement the ReAct Logic Using an Agent in LangChain?

LangChain is the framework for building language models and chatbots that can understand and generate text in human language. The model’s performance is based on the understanding of the language and its complexities during the training phase of the process. The next phase is generating the data or text in the natural language that can be easily readable and understandable. These phases are referred to as “Reasoning” and “Acting” respectively in the Natural Language Processing domain.

Quick Outline

This post will demonstrate:

• How to Implement the ReAct Logic Using an Agent in LangChain
• Installing Frameworks
• Loading Language Model
• Method 1: Using LandChain Expression Language
• Building Agent
• Method 2: Using ZeroShotReactAgent
• Method 3: Using Chat Models
• Building the Agent
• Method 4: Using ChatZeroShotReactAgent
• Conclusion

How to Implement the ReAct Logic Using an Agent in LangChain?

The “ReAct” is the combination of the Reason and Action phases to enhance the performance of the language model as “Re” refers to reason and “Act” to action. A ReAct logic is often considered the most optimized for building the LLMs or chatbots to understand and generate text. The agent is the decision maker who decides which actions to perform and in what order will be performed.

To learn the process of implementing the ReAct logic using an agent in LangChain, simply go through the following guide:

Step 1: Installing Frameworks

First of all, install the LangChain to get started with the process of implementing ReAct logic:

Install the google-search-results module to get the dependencies for building the agent that can get the search results from the google:

After that, install the OpenAI modules which can be used to load the Large Language Models or LLMs to implement ReAct logic:

After getting all the required modules, simply set up the OpenAI environment for building LLM and SerpAPI environment for using the agent in the model:

Step 2: Loading Language Model

The next step is loading the language models by importing the required libraries using the LangChain framework:

Use the OpenAI() method to build the Language Model (llm) and then configure the tools for the agents using SerpAPI:

Method 1: Using LandChain Expression Language

LCEL is the process of integrating or composing chains together while building the language models in LangChain. Start the process by installing the LangChainHub to get its dependencies for building and using ReAct logic with agents in LangChain:

Importing the libraries from the LangChain like ReActSingleInputOutputParser to build the model that can use the ReAct model:

Load the language model to design the ReAct logic using the pull() method and store it in the prompt variable. Define some tools that can be used to implement the logic on the loaded dataset to implement its working with an agent:

Building Agent

Now, configure the agent and its tools by integrating it with the language model loaded in step 2 of this guide:

Define an agent variable to provide the arguments and tools to configure the agent that explain its working. The previously configured parts like llm_with_stop and ReActSingleInputOutputParser() method are being integrated with the agent:

Get the AgentExecutor library and configure its method using arguments like agent, tools, and verbose to get a more readable output:

Invoke the agent_executor() method with the string query as the input which will trigger the agent to extract the output:

Output

The following screenshot displays that the agent has extracted the information using the ReAct logic and generated the text in natural language:

Method 2: Using ZeroShotReactAgent

The ReAct logic can also be implemented by using another agent like ZeroShotReactAgent while configuring the agent_executor variable. After that, simply call the agent_executor variable with the question as the input to invoke the agent:

Output

The agent has extracted the information based on the input asked while invoking the agent_executor:

Method 3: Using Chat Models

Another process that can be used to implement the ReAct logic by using the chat models after importing the ChatOpenAI library:

Build the chat model using the ChatOpenAI() method with the value of temperature equal to 0 that can control the randomness in the results of the model:

Load the model on which the user can implement the ReAct logic to store it in the prompt variable and configure the tools to be used in the process:

Building the Agent

Use the chat model to store the observations or recent messages when the model stops generating the text:

Get the ReActJsonSingleInputOutputParser library to implement the ReAct logic and produce the results in the JSON format:

Build and configure the agent using the chat_model variable and method to produce results by calling the agent:

Configure the agent_executor and run it to get the results based on the query provided in the input variable:

Output

The agent has extracted the output as displayed in the following screenshot:

Method 4: Using ChatZeroShotReactAgent

Changing the value of the agent can also be used to implement the ReAct logic with the LangChain framework:

Output

The output structure contains detailed information about the working of the agent from the tokens and model used to extract the information:

That’s all about the process of implementing the ReAct logic using an agent in LangChain.

Conclusion

To implement the ReAct logic with an agent using the LangChain framework, install modules like google-search-results to configure the agent. After that, set up the environment using the OpenAI and SerpAPI credentials from their accounts to start using the model. The ReAct logic can be implemented using the LCEL and chat models with multiple agents offered by the LangChain module. This guide has elaborated on implementing the ReAct logic using the agent in LangChain.