Add Resource Limit to LangChain Tools

In today's world, Artificial Intelligence (AI) is no longer a distant concept but has permeated every corner of our daily lives. From smart speakers to autonomous vehicles, AI has revolutionized the way we live. Despite the numerous conveniences AI brings us, we must acknowledge that the resources required to run AI are finite. Much like our lives, we need to plan our activities within the constraints of our resources. Hence, managing resources for AI systems, limiting their resource consumption during task execution, is of utmost importance.

Today, I want to share with you a method of introducing resource constraints into AI systems. It can help AI execute tasks more efficiently, reduce irrelevant tasks, and achieve the goal of saving resources. We use LangChain Tools, a powerful toolkit that AI can call upon to complete various complex tasks.

Purpose and Significance of the Program

Let's first understand the purpose and significance of this program. The aim of this program is to add a resource consumption metric to LangChain Tools. In this way, when AI performs tasks, it can evaluate the resources it consumes. This doesn't mean that AI has self-awareness, but it can evaluate the efficiency of its task execution based on predetermined rules and indicators.

Why do we need to add a resource consumption metric to AI? Because even in a network or code environment, AI consumes various resources when executing tasks. For example, performing a search may require payment, and even the AI's own response will consume certain computing resources, such as the tokens used by the OpenAI API. These resources are finite and costly. Therefore, AI needs to understand the resources that each step might consume when planning and executing tasks, then complete as many tasks as possible within limited resources, or minimize resource consumption when completing the same tasks.

In this way, we can not only make AI work more efficiently but also prevent AI from diverging too much and producing a large number of irrelevant tasks. This is the purpose and significance of adding resource constraints to LangChain Tools.

Implementation of the Program

Next, let's see how this program is implemented.

First, we define a global variable autogpt_resources, representing the total resources we can use. Then, we define a decorator function named resource_limiter. Decorators are a syntax in Python that can add functionality to a function without modifying the original function.

This decorator accepts a parameter resource_cost, which indicates the execution

# Here's the function signature and the first part of the decorator function

def resource_limiter(resource_cost):
    global autogpt_resources

    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            global autogpt_resources
            print(f"Autogpt resources: {autogpt_resources}")
            if autogpt_resources < resource_cost:
                raise ValueError(f"Not enough resources. Available: {autogpt_resources}, required: {resource_cost}")
            autogpt_resources -= resource_cost
            return func(*args, **kwargs)

        return wrapper

    return decorator

This function first checks whether we have enough resources to execute a specific function. If resources are insufficient, it throws an error. If resources are sufficient, it subtracts the consumed resources and then executes the function.

Next, we define a function named constrain_tools. This function takes in two parameters: tool_costs, a dictionary where the keys represent the names of the tools and the values represent the resources required to use these tools; and llm, an optional parameter indicating the model we wish to use.

def constrain_tools(tool_costs, llm=None):
    tool_names = list(tool_costs.keys())
    resource_costs = list(tool_costs.values())
    tools = load_tools(tool_names, llm=llm)

    for i, tool in enumerate(tools):
        tool.func = resource_limiter(resource_costs[i])(tool.func)

    return tools

This function initially loads all the specified tools, then applies the resource_limiter decorator we defined earlier to each tool function, thereby adding resource constraints. Finally, it returns these modified tools.

In the main program, we first define the tools we want to use and their resource costs. For instance, using serpapi consumes 5 units of resources, and invoking llm-math for mathematical computations requires 3 units. Then, we call the constrain_tools function to add resource constraints to these tools. Finally, we initialize an agent and run it to perform tasks.

# Configuring the tool names and corresponding resource consumption
tool_costs = {"serpapi": 5, "llm-math": 3}

llm = ChatOpenAI(temperature=0)
tools = constrain_tools(tool_costs, llm=llm)
tools[0].name = "Google Search"

agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")

All in all, the implementation process of this program is quite clear and intuitive. We first define the rules for resource constraints, then apply these rules to the tools we want to use, and finally use these tools to execute tasks.

The detailed code is listed on this gist.

Conclusion

By adding resource constraints to LangChain Tools, we can enable AI to perform tasks more efficiently, avoid the generation of a large number of irrelevant tasks, and thus save resources. This is a vital concept, not only helping us manage AI systems better, but also driving the development of AI technology to become more in line with practical needs and constraints. I hope this article helps you understand this concept and apply it to your projects.

I would like to conclude my sharing here. If you have any questions or ideas, feel free to leave a comment. I hope everyone can benefit from the development of AI. Let's look forward to the future of AI together!

--This article was assisted by gpt-4-code-interpreter