AI Agents Tutorial: Build and Use Your Own AI Agents

Introduction to AI Agents

AI agents are gaining immense popularity due to their ability to autonomously solve tasks. If you've heard about projects like AutoGPT, BabyAGI, or CAMEL, you're on the right track!

What is an AI Agent?

An AI agent is a computer system built to make decisions and take actions to accomplish predefined goals without human intervention. These agents are powerful tools with a self-reliance that captures significant attention and are deemed the future of technology.

How to Use AI Agents?

Various options exist to explore AI agents. Off-the-shelf solutions like AutoGPT are available. Our team has worked with these solutions; tutorials are available on our platform. Alternatively, one can build their own AI agent. We'll dive into this in depth, utilizing LangChain—a framework designed for applications based on Large Language Models (LLMs).

Coding Part

With a solid understanding of AI agents' relevance, let’s embark on creating our own project!

Project Structure

Begin by creating a new directory and initializing your Python environment.

Dependencies

Install necessary tools:

• langchain: This aids in working with LLMs and agents.
• requests: For making requests to external APIs.
• openai SDK: To simplify using OpenAI's models.
• duckduckgo-search: For web searches.

Let’s import the libraries now.

Setting Up the Model

Now, we need to define the LLM from OpenAI. For this tutorial, GPT-3 will be utilized, and the initial prompt needs to be established to create an effective chain for this model.

Model Testing

Next, we can test our model by asking a couple of questions. Let’s inquire about lablab.ai and ask it to solve an integral.

Lablab.ai: This technology platform provides AI and ML-powered solutions that streamline business operations and enhance efficiency. Services include Natural Language Processing, Machine Vision, and other industry-specific solutions aimed at improving customer experiences, reducing costs, and boosting productivity.

Integral Solution: The response received was incorrect—stating "x^2 log(x)^3 / 3 + C" where C is an arbitrary integration constant. The accurate integral solution is: 1/4 x^2 (1 - 2 log(x) + 2 log²(x))!

These inaccuracies are partly due to the model's prior cut-off in September 2021, hence its outdated knowledge about lablab.ai. However, we can amend this with tools!

Implementing Tools

Using an internet search tool increases our model’s knowledge scope. We can leverage the DuckDuckGo Search tool, already available in LangChain. Furthermore, utilizing an API from Wolfram Alpha for mathematical problems is advisable due to its robust problem-solving capabilities.

Creating the Tool for Math Problems

Let's set up a custom class for the Wolfram Alpha API that allows handling requests in natural language and returns accurate results.

Creating an Agent and Testing Performance

Finally, it's time to create an agent with the tools we've defined and assess its performance.

Previous vs. New Answers

Previous Answers:

• Lablab.ai: Misconception regarding its offerings.
• Integral Answer: Incorrect as mentioned.

New Answers: With tools integrated:

• Lablab.ai: Described accurately, emphasizing AI tools and tutorials.
• Integral Answer: Correct resolution of the integral!

Conclusion

The introduction of tools significantly improves answer accuracy. The performance of the AI enhanced by tools showcases their critical role in complex queries. Thus, it’s crucial to invest time now in leveraging LLMs more effectively!

Potential Improvements

To further enhance applications, exploring other types of agents can be beneficial. Employing memory features and leveraging Vector Databases might provide additional advantages. Remarkably, LangChain supports these advancements!