Tools usage

Large language models (LLMs) are powerful for natural language understanding and response generation, but they have limitations, especially when it comes to accessing real-time information or performing specific computations. By incorporating external tools, LLMs can extend their capabilities, enabling them to retrieve up-to-date information, perform complex calculations, and interact with structured systems like databases and APIs.

The Role of Tool Calling

Many AI applications require interactions with both humans and external systems. While LLMs naturally excel at human communication in natural language, they also need to interact with systems that have structured data requirements. For instance, APIs often require specific input schemas or payload formats, which LLMs must generate to complete the request. This is where tool calling becomes crucial: it allows the model to call external tools and request responses in a format that aligns with the requirements of a particular system.

AI Agents Frameworks

Through tool calling, LLMs can:

  • Access Real-Time Data: Retrieve current information from search engines, databases, or other real-time sources.

  • Perform Specialized Calculations: Use tools like code interpreters to execute complex calculations or data transformations.

  • Format Responses for Structured Systems: Generate responses in specific schemas required by APIs or databases, ensuring smooth communication with these systems.

Exercise: Tools Usage - Practical Example

Let’s see the Tools Usage in Action!

From the agentic-workshop/lab-materials/02 folder, please open the notebook called 02.1-tools-calling.ipynb and follow the instructions.

Benefits of Using Tools with LLMs

Incorporating tools into LLM workflows enables AI systems to handle a broader range of tasks, increasing both accuracy and flexibility. For example:

  • Enhanced Decision-Making: LLMs can access real-time information to make more informed decisions.

  • Reduced Limitations: By relying on external tools, LLMs can overcome knowledge cutoffs and perform actions beyond their internal capabilities.

  • Greater Utility in Workflows: Tool integration makes LLMs suitable for complex applications, such as customer support, technical troubleshooting, and data-driven insights.

In summary, tools extend the potential of LLMs by providing them with external resources to support decision-making and data processing, making them far more effective and adaptable in real-world applications.