How to Build Smarter AI Agents with Make.com + OpenAI Assistants

Key Concepts:

• AI Agents
• Make.com (formerly Integromat)
• OpenAI Assistants API
• Knowledge Retrieval
• Function Calling
• Automation
• Scenarios (Make.com)
• Modules (Make.com)
• Data Transformation
• API Integration
• Prompt Engineering
• Context Window
• Vector Embeddings

Introduction: Building AI Agents with Make.com and OpenAI Assistants

The video demonstrates how to build intelligent AI agents using Make.com (a no-code automation platform) and the OpenAI Assistants API. The core idea is to leverage the Assistants API's capabilities for knowledge retrieval and function calling, combined with Make.com's automation features, to create agents that can perform complex tasks. The presenter emphasizes the power of this combination for building practical, real-world AI applications.

Setting Up the OpenAI Assistant

The video begins by outlining the setup of an OpenAI Assistant. This involves:

1. Creating an Assistant: Within the OpenAI platform, an assistant is created and configured.
2. Defining Instructions: Clear and specific instructions are provided to the assistant, defining its role, capabilities, and desired behavior. For example, the assistant might be instructed to act as a customer support agent or a data analyst.
3. Uploading Knowledge: Relevant knowledge files (e.g., PDFs, text documents) are uploaded to the assistant. The Assistants API automatically handles indexing and retrieval of information from these files using vector embeddings. This allows the assistant to answer questions based on the provided knowledge base.
4. Enabling Tools: The "retrieval" tool is enabled, allowing the assistant to access and utilize the uploaded knowledge. Function calling can also be enabled to allow the assistant to trigger external functions.

Building the Make.com Scenario

The core of the demonstration lies in building a Make.com scenario to interact with the OpenAI Assistant. The scenario is structured as follows:

1. Trigger: The scenario is triggered by an event, such as receiving an email or a message in a chat application. The video uses a simple "Instant Trigger" for demonstration purposes, but emphasizes that this could be replaced with a real-world trigger.
2. OpenAI Module (Create a Message): The first module in the scenario uses the OpenAI "Create a Message" action. This module sends the user's input (e.g., the email body or chat message) to the OpenAI Assistant. The module requires the Assistant ID and the Thread ID.
3. OpenAI Module (Run an Assistant): The "Run an Assistant" module instructs the OpenAI Assistant to process the message and generate a response. This module also requires the Assistant ID and the Thread ID.
4. Looping and Status Check: A loop is implemented to continuously check the status of the assistant's run. The "Get Run" module retrieves the status of the assistant's run. The loop continues until the status is "completed." This ensures that the scenario waits for the assistant to finish processing the message before proceeding.
5. OpenAI Module (List Messages): Once the run is completed, the "List Messages" module retrieves all messages from the thread, including the assistant's response.
6. Data Transformation (Parsing the Response): The response from the assistant is often in a complex JSON format. Make.com's built-in functions are used to parse the JSON and extract the relevant text content. This involves using functions like map() and join() to iterate through the message array and concatenate the text content of each message.
7. Action (Send a Reply): Finally, the scenario sends a reply to the user, containing the assistant's response. This could be sending an email, posting a message to a chat channel, or updating a database. The video uses a simple "Data Store" module to store the response for demonstration purposes.

Function Calling Example: Integrating with a Weather API

The video also demonstrates how to integrate function calling into the scenario. This allows the AI agent to interact with external APIs and perform actions based on the user's request. The example uses a weather API to retrieve weather information for a specific location.

1. Defining the Function: A function is defined in the OpenAI Assistant settings, specifying the function's name, description, and parameters. For example, the function might be named "get_weather," with parameters for "location" and "unit" (e.g., Celsius or Fahrenheit).
2. Conditional Logic in Make.com: In the Make.com scenario, a conditional branch is added to check if the assistant has requested a function call. The "Get Run" module returns information about the requested function.
3. Calling the API: If a function call is requested, a separate module is added to call the weather API. The module uses the "HTTP" module to send a request to the API, passing the location and unit parameters.
4. Submitting the Function Result: The result from the weather API is then submitted back to the OpenAI Assistant using the "Submit Tool Outputs to Run" module. This allows the assistant to incorporate the weather information into its response.
5. Generating the Final Response: The assistant then generates a final response, incorporating the weather information.

Key Arguments and Perspectives

The video argues that combining the OpenAI Assistants API with Make.com provides a powerful and flexible way to build AI agents. The Assistants API handles the complex tasks of knowledge retrieval and function calling, while Make.com provides the automation and integration capabilities needed to connect the agent to real-world systems. The presenter emphasizes the ease of use and the speed of development that this approach enables.

Notable Quotes

• "The OpenAI Assistants API is a game-changer for building AI agents."
• "Make.com allows you to connect your AI agent to any application or service."
• "Function calling allows your AI agent to take actions in the real world."

Technical Terms and Concepts

• AI Agents: Software programs that can perceive their environment, make decisions, and take actions to achieve specific goals.
• Make.com (formerly Integromat): A no-code automation platform that allows users to connect different applications and services to automate workflows.
• OpenAI Assistants API: An API that allows developers to build AI assistants with knowledge retrieval and function calling capabilities.
• Knowledge Retrieval: The process of retrieving relevant information from a knowledge base to answer a user's question.
• Function Calling: The ability of an AI agent to trigger external functions or APIs to perform actions.
• Scenarios (Make.com): Automated workflows built in Make.com.
• Modules (Make.com): Individual components within a Make.com scenario that perform specific tasks.
• Data Transformation: The process of converting data from one format to another.
• API Integration: The process of connecting different APIs to exchange data and functionality.
• Prompt Engineering: The process of designing effective prompts to elicit desired responses from AI models.
• Context Window: The amount of text that an AI model can consider when generating a response.
• Vector Embeddings: Numerical representations of text that capture its semantic meaning.

Logical Connections

The video logically connects the different components of the AI agent: the OpenAI Assistant, the Make.com scenario, and the external APIs. It demonstrates how these components work together to create a functional and intelligent agent. The video also highlights the importance of data transformation and error handling in building robust AI applications.

Data, Research Findings, or Statistics

The video does not explicitly mention specific data, research findings, or statistics. However, it implicitly relies on the underlying research and development that has gone into the OpenAI Assistants API and the Make.com platform.

Conclusion

The video provides a practical and detailed guide to building AI agents using Make.com and the OpenAI Assistants API. It demonstrates how to set up an assistant, build a Make.com scenario, and integrate function calling. The key takeaway is that this combination provides a powerful and accessible way to build intelligent AI agents that can perform complex tasks and interact with real-world systems. The presenter encourages viewers to experiment with the platform and explore the possibilities of AI automation.