Building An AI Agent That Can Use Tools Without LangChain (Part 2)
In the previous part, I introduced the concept of an AI agent that could leverage tools to enhance its capabilities. Inspired by the potential of such an approach, I decided to continue this journey and build upon the groundwork. Here, I’ll discuss the key improvements and modifications I made, specifically introducing more tools and using a more robust language model — GPT-4.
Incorporation of More Tools
The initial AI agent was somewhat limited in its range of functionalities. To make it more versatile, I introduced two new tools:
WikipediaSearch Tool: Recognizing the importance of reliable and diverse information sources, I added a tool that searches Wikipedia and retrieves content based on a given query. With the vast database of Wikipedia at its disposal, the agent can now answer a wide range of questions with factual information. The addition of this tool was relatively straightforward; I used the wikipedia Python module to extract summaries of queries. For ambiguous searches that return multiple results, I've coded the tool to simply opt for the first option.
CurrentDateTime Tool: A basic but often essential tool, this allows the agent to retrieve the current date and time. I designed this feature to provide the information in a standard format, ensuring clarity and precision for the user.
Upgrading to GPT-4
When designing AI-based solutions, the choice of the language model plays a pivotal role in determining its efficacy. Initially, my agent was using an older version of the GPT model. However, I wanted my agent to be more adept at reasoning and handling complex queries. Given the advancements and capabilities offered by GPT-4, it seemed like the natural choice for an upgrade.
Switching to GPT-4 has offered a host of advantages:
- Improved Reasoning: GPT-4’s more extensive training data and refined architecture enable it to handle complex reasoning tasks with ease.
- More Contextual Understanding: The model exhibits a better grasp of context, leading to more accurate and relevant outputs.
- Enhanced Dialogue Management: With GPT-4, the agent can manage extended interactions more coherently, making it more user-friendly.
Reflecting on The Journey
With the added tools and the switch to GPT-4, the agent has transformed into a robust assistant, adept at using tools to solve user queries. The modular design, where tools can be added or removed, makes it incredibly scalable. Additionally, the agent’s ability to reflect on its steps adds a layer of transparency that’s often missing in AI interactions.
In the future, I envision further enhancing the agent with more diverse tools, maybe even venturing into specialized domains. But for now, I’m satisfied with its progress and eager to see it in action!
In conclusion, building an AI agent that can seamlessly integrate tools and provide reasoned outputs has been a rewarding experience. It’s a testament to the evolving landscape of AI, where models like GPT-4 and modular designs can come together to create truly powerful systems.
