Function Calling with Mistral AI

Mistral AI
26 Feb 202406:03

TLDRIn this informative video, Sophia Yang from mral AI introduces a four-step process for function calling with mral AI, demonstrating how to integrate external tools and APIs. The tutorial uses a payment-related query as an example, guiding users through defining tools, generating function arguments, executing functions, and obtaining results. By leveraging a JSON schema to outline function specifications, the video showcases the flexibility and potential of mral AI in handling complex user queries and data extraction tasks.

Takeaways

  • 📘 Function calling with Mral AI allows models to connect with external tools and APIs.
  • 🔍 There are four steps involved in function calling: specifying tools and queries, generating function arguments, executing functions, and generating final answers.
  • 🛠️ Users define the tools they want to use, such as payment status tracking and payment date tracking tools.
  • 📝 Users provide a query, like 'what is the status of my payment?', which the model uses to determine the appropriate tool and function.
  • 🤖 Mral AI models generate function arguments based on the user's query, identifying the correct function and parameters needed.
  • 🔄 The model uses previous conversations to generate a final answer based on the information available.
  • 📚 Before starting, users need to install all required packages and create a pandas data frame for payment transactions.
  • 📋 The function specs for each tool are outlined using a JSON schema, including the function name, description, and parameters.
  • 🔑 Users need an API key from Mral AI's website to use the Mral Python client for function calling.
  • 📊 Users execute the function with the provided function name and parameters to obtain results.
  • 📝 The final step is to pass all messages and function results to the Mral model to get a customized response for the user's query.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is how to perform function calling with Mr. AI, allowing models to connect to external tools and APIs.

  • How many steps are involved in function calling with Mr. AI?

    -There are four steps involved in function calling with Mr. AI.

  • What is the first step in function calling?

    -The first step is for the user to specify the tools and queries they want to use, such as payment status tracking tools.

  • What does the user need to provide in the first step?

    -The user needs to provide the tools they want to use and a specific query, like the status of a payment.

  • What happens in the second step of function calling?

    -In the second step, the Mr. AI model generates function arguments based on the user's query.

  • How does the Mr. AI model determine which function to run?

    -The model figures out which function to run and the necessary parameters based on the user's query and the defined tool specifications.

  • What is required for the user to execute the function?

    -The user needs to provide the transaction ID or other necessary information for the function to be executed.

  • How does the Mr. AI model generate a final answer?

    -The model takes all previous conversations, the executed function results, and generates a final answer based on the available information.

  • What is an example of a tool defined in the video?

    -An example of a tool defined in the video is a payment status tracking tool that retrieves the status of a payment based on a transaction ID.

  • How are the function specifications outlined for Mr. AI models?

    -The function specifications are outlined using a JSON schema, which includes the tool type, function name, description, and required parameters.

  • What is the purpose of the 'messages' list in the script?

    -The 'messages' list is used to store all the messages exchanged between the user, the assistant, and the model during the function calling process.

Outlines

00:00

📘 Introduction to Function Calling with Mr. AI

This paragraph introduces Sophia Yang from Mr. AI, who explains the concept of function calling in Mr. AI. It outlines a four-step process that allows Mr. AI models to connect with external tools, such as user-defined functions and APIs. The video aims to demonstrate a simple example of answering payment-related questions using these tools. The steps include specifying tools and queries, generating function arguments, executing functions, and obtaining results. The example involves tracking payment status and dates using defined tools.

05:01

🛠️ Setting Up and Executing Function Calls

The second paragraph delves into the practical aspects of setting up function calls in Mr. AI. It guides users through the process of defining tools and queries, using a JSON schema to outline function specifications. The example continues with creating a pandas data frame for payment transactions and defining two functions: 'retrieve payment status' and 'retrieve payment date'. The paragraph also explains how to use the Mr. AI Python client to generate function arguments and how the model interacts with users to gather necessary information, such as transaction IDs. Finally, it describes how users execute the chosen function to obtain results and how the model generates a customized response based on the user's query and the function's output.

Mindmap

Keywords

💡Function Calling

Function calling is a programming concept where a function, which is a reusable piece of code, is invoked or 'called' to perform a specific task. In the context of the video, it refers to the process of connecting Mr. AI models to external tools, such as user-defined functions or APIs, to perform tasks like retrieving payment status. The video demonstrates how to execute a function to get the status of a payment, illustrating the practical application of function calling in AI interactions.

💡Mr. AI Models

Mr. AI Models are the artificial intelligence systems discussed in the video that can interact with users and perform tasks by connecting to various external tools. These models are designed to understand user queries, generate function arguments, and execute functions to provide relevant information. For example, they can determine the appropriate function to call based on a user's question about payment status and then execute that function to retrieve the information.

💡User-Defined Functions

User-defined functions are custom functions created by users to perform specific tasks. In the video, these functions are used to interact with Mr. AI models, allowing users to extract information from data, such as payment transactions. The video provides an example of two user-defined functions: 'retrieve payment status' and 'retrieve payment date,' which are used to answer payment-related questions.

💡External APIs

External APIs (Application Programming Interfaces) are sets of rules and protocols that allow different software applications to communicate with each other. In the video, external APIs are mentioned as potential tools that Mr. AI models can connect to, enabling them to access and utilize external services or data sources. This integration is part of the function calling process, where the AI model interacts with these APIs to perform tasks like checking payment statuses.

💡Payment Status Tracking

Payment status tracking is the process of monitoring the progress of a payment, from initiation to completion. In the video, this concept is demonstrated through the use of a user-defined function that retrieves the status of a payment based on a transaction ID. The AI model uses this function to inform the user whether their payment has been processed or not, providing a practical example of how AI can assist with transaction management.

💡Transaction ID

A transaction ID is a unique identifier assigned to a specific financial transaction, such as a payment or a bank transfer. In the video, the transaction ID is used as a parameter in the user-defined function 'retrieve payment status' to fetch the status of a particular payment. The AI model requests this ID from the user to execute the function and provide an accurate response regarding the payment status.

💡JSON Schema

JSON Schema is a standard for describing the structure of JSON data. In the video, it is used to outline the specifications of user-defined functions, including the function name, description, and parameters. This schema helps the Mr. AI model understand how to interact with these functions, ensuring that the correct parameters are passed and the expected results are obtained.

💡DataFrame

A DataFrame is a two-dimensional, table-like data structure used for data analysis in programming languages like Python. In the video, a DataFrame of payment transactions is created to simulate a data source that users can query using the Mr. AI model and user-defined functions. This allows users to extract specific information, such as payment status, from the data.

💡API Key

An API key is a unique code that allows authorized access to an API. In the video, users are instructed to obtain an API key from the Mr. AI website to enable the Python client to call the Mr. AI API. This key is essential for the function calling process, as it authenticates the user and allows the AI model to execute the necessary functions.

💡Chat Message Method

The chat message method refers to the way messages are formatted and exchanged between the user and the AI model. In the video, this method is used to store and manage the conversation history, including user queries, AI responses, and the results of function calls. This structured communication is crucial for the AI model to understand the context of the conversation and provide accurate and relevant answers.

Highlights

Sophia Yang introduces a video on function calling with Mral AI.

Function calling allows Mral models to connect to external tools like user-defined functions and APIs.

The video demonstrates how to answer payment-related questions using defined tools.

There are four steps involved in function calling: specifying tools and queries, generating function arguments, executing functions, and generating final answers.

Users can define any tools they like, such as payment status tracking and payment date tracking tools.

The user query is essential for the Mral model to understand what information is needed.

The Mral model generates function arguments based on the user query, identifying the correct tool and function parameters.

The model uses previous conversations to generate a final answer based on available information.

Before starting, users need to install all the required packages.

A pandas data frame of payment transactions is created for users to extract information using tools.

Function specs are outlined with a JSON schema, defining the type, name, description, and required parameters of each tool.

Users provide a user query, which is formatted and stored in a list called 'messages'.

The Mral Python client is used to call the API, requiring an API key from the Mral website.

The model asks for the transaction ID to check the payment status, as it is a required parameter.

The user provides the transaction ID, and the model generates a message asking for it.

The model identifies the function to run and its arguments, then the user executes the function to obtain results.

The final response is customized based on the user's transaction ID and the function's result.

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