Function calling is a critical capability for large language models (LLMs) to interact with the real world and be useful beyond a simple chat assistant. LLMs may not have the internal knowledge to perform certain tasks, such as retrieving current weather conditions. However, they can leverage external APIs or functions to retrieve and process the necessary information.

The flow of function calling works as follows:

1. The LLM first determines whether it can perform the operation based on its internal training knowledge or if it needs to use external tools or functions.
2. If external tools are required, the LLM will analyze the user query and select the appropriate functions to execute.
3. The LLM will then use a Python compiler to make the function calls, retrieve the results, and feed them back into the LLM to generate the final response.

This capability allows LLMs to extend their functionality and interact with the real world, making them more useful and versatile. Function calling enables LLMs to perform tasks such as weather forecasting, stock market analysis, and device control, which are not possible with their internal knowledge alone.

In this section, we will explore the model's ability to handle multi-function calls and nested function calls. The goal is to test the model's capability to decompose complex queries and execute multiple functions sequentially to provide a comprehensive response.

First, we will look at an example of multi-function calling, where the model needs to execute two separate functions to address the user's query. The model should be able to identify the relevant functions, make the necessary function calls, and combine the results to generate the final response.

Next, we will explore the concept of nested function calls. In this scenario, the output of one function call becomes the input for another function call. The model needs to recognize this dependency, execute the functions in the correct order, and provide the final result to the user.

To ensure the model's responses are accurate and well-formatted, we will utilize a system message that guides the model on how to handle dependent function calls. This approach helps the model maintain the integrity of the data throughout the multi-stage function execution process.

Finally, we will test a more complex example that combines both multi-function calling and nested function calls. This will demonstrate the model's ability to handle intricate queries and coordinate the execution of multiple functions to deliver a comprehensive and accurate response.

By exploring these examples, we can assess the model's versatility and its capacity to interact with real-world scenarios that require the integration of external tools and functions.

Nested function calling is a powerful feature that allows language models to perform complex, multi-step operations by chaining together the outputs of multiple functions. This capability is critical for enabling language models to interact with the real world and tackle advanced use cases beyond simple chatbots.

In this section, we'll explore how the Mistol 7B version 3 model can handle nested function calls, where the output of one function call is used as the input to another function call. This allows the model to break down complex user queries into a series of interdependent steps, each executed by a separate function.

The key steps involved in implementing nested function calling are:

1. Identifying Dependent Functions: The model must recognize when a user query requires the output of one function to be used as the input to another function. This requires the model to have a comprehensive understanding of the available functions and their input/output relationships.

2. Executing Function Calls in Sequence: Once the dependent functions have been identified, the model must execute them in the correct order, passing the relevant data from one function call to the next.

3. Formatting Output Correctly: To ensure the final output is accurate and meaningful, the model should use a system message to guide the formatting of the intermediate function call results.

By mastering these techniques, language models can tackle increasingly complex real-world tasks, seamlessly integrating multiple external tools and APIs to provide users with comprehensive and tailored responses. This level of functionality is a key step towards making language models truly useful and indispensable in a wide range of applications.

When dealing with multi-stage or nested function calls, it's crucial to ensure that the input and output formatting is correct. Uncle Code recommends using a system message to guide the assistant in selecting the appropriate tools and handling the dependencies between them.

The system message suggested is:

You're a helpful assistant. Your job is to select tools relevant to the user query. In the case of multiple tools, if the tools are dependent on each other and one tool's input parameter comes from another function, use @followed by the function name for the parameter value. This ensures the value is correctly formatted.

This system message serves two key purposes:

1. Tool Selection: It instructs the assistant to select the relevant tools based on the user's query, ensuring that the necessary functionality is covered.

2. Dependency Handling: For cases where the tools are interdependent, and the output of one function is required as the input for another, the system message guides the assistant to use the @function_name syntax to correctly reference the previous function's output.

By using this system message, the assistant can ensure that the input and output formatting is correct, enabling seamless execution of the multi-stage or nested function calls. This approach helps to maintain the integrity of the data flow and provides a reliable way to leverage the model's function calling capabilities.

The Mistol V3 model has demonstrated impressive capabilities in handling multi-function calls and nested function calls. The key takeaways are:

1. The model can decompose complex user queries into separate function calls and execute them in parallel, as seen in the example of getting the current weather in Paris and the current time in San Francisco.

2. For nested function calls, where the output of one function is the input to another, the model can handle this seamlessly. It uses a system message to ensure the output of the first function call is in the correct format for the second function call.

3. The model's ability to handle a combination of multi-function calls and nested function calls, as shown in the final example, highlights its flexibility and robustness in real-world scenarios.

Overall, the Mistol V3 model's function calling capabilities make it a powerful tool for building interactive applications that can leverage external APIs and services. By integrating this model into your projects, you can create intelligent assistants that can perform a wide range of tasks beyond simple chat interactions.