Create a Custom AI Assistant + API in 10 Mins

Introduction

OpenAI has introduced a new service—the Assistance API—which makes it incredibly easy for users to build their own custom AI agents. This AI assistant can leverage the latest GPT models, a code interpreter, and any additional instructions or knowledge supplied by you. In this article, we'll walk you through the steps of creating your very own AI assistant to answer questions using global economic data provided in a PDF file.

Getting Started

Before diving in, make sure you have an OpenAI account. Once you're set up, navigate to platform.openai.com/assistant and click on the 'Create' button. In the resulting dialog, enter a name and an instruction or prompt for your assistant. For this tutorial, we’ll build an assistant to answer questions about economic and cost-of-living data from various cities worldwide.

Next, choose the language model (LLM) you want your assistant to utilize. Pricing is based on pay-as-you-go rates, so select a model that suits your needs. For our purposes, we'll choose GPT-4. After saving your settings, test your assistant using the provided interface.

Providing Data to Your Assistant

Initially, your assistant may struggle to provide relevant answers, as it lacks specific data. To enhance its capabilities, you'll want to upload custom knowledge. This might involve PDFs, text files, or CSV tables.

For this application, we'll be using the 2023 Global Livability Index report from The Economist, which contains valuable data about cities’ infrastructure, healthcare, culture, and environment. If you want to follow along, you can find a link to download the report in the video description.

1. Go to the tools section in your assistant settings.
2. Enable the information retrieval feature and save your changes.
3. Upload your PDF file, and click save again.

Now, let's test our assistant. When prompted, it should provide more useful responses, such as indicating that Vienna is the most livable city according to the report. Hovering over corresponding annotations will reveal snippets for data sourcing.

Integrating External Functions and APIs

If you wish for your assistant to gather additional data or perform calculations using external APIs, you can integrate custom functions.

Custom functions are external code snippets defined using JSON schema, specifying input parameters that your assistant can utilize. For example, if you want to calculate the cost of living in a city:

1. Define the function in the tools tab by providing its name, description, and parameters.
2. Save the changes.

You can now ask your assistant questions like "What is the cost of living in Melbourne?" which will prompt it to use your defined function. Although the assistant accesses this function, you’ll need to handle the actual calculations outside the assistant’s interface.

Utilizing the Code Interpreter

There’s another tool available—enabling the code interpreter gives your AI assistant the ability to run calculations and create visual representations by executing Python code.

After enabling the code interpreter:

1. Prompt your assistant to generate a graph from your PDF data.
2. View the output graph, comprising valuable insights visualized directly in the interface.

You can also view the Python code it used to create the visualization, showcasing the power of having data at your fingertips.

Using the API Endpoint

Once you've built and tested your assistant, you have the option to utilize it via an API endpoint, enabling integration within custom applications or platforms. Follow these steps to get started:

1. Ensure you have your OpenAI API key stored in an environment variable.
2. Copy your assistant ID and integrate it into your Python code.
3. Use the OpenAI Python SDK to interact with your assistant.

Set up the SDK and create a thread to start a conversation with the assistant. You will be able to monitor the progress of your requests and retrieve responses for further use in your applications.

Conclusion

By following these straightforward steps, you can build a customized AI assistant that leverages specific data sources and functions, bringing robust utility to your applications and enhancing the way you interact with data. Consider diving deeper into the OpenAI documentation or exploring Streamlit for building interactive applications with Python.

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Keyword

Custom AI Assistant, API, OpenAI, GPT Models, Knowledge Upload, Code Interpreter, Global Livability Index, Economic Data, Cost of Living, JSON Schema, Python SDK.

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FAQ

Q: What is the Assistance API?
A: The Assistance API from OpenAI allows you to create custom AI agents that utilize various models and data sources.

Q: How can I upload custom knowledge to my assistant?
A: You can upload PDFs, text files, and CSV tables through the tools section in your assistant settings.

Q: What is the purpose of custom functions?
A: Custom functions allow your assistant to call external APIs or perform calculations using specified input parameters.

Q: Can my assistant generate visual data representations?
A: Yes, by enabling the code interpreter, your assistant can execute Python code to visualize data through graphs and charts.

Q: How do I use my assistant via an API endpoint?
A: To use your assistant via an API, ensure you have an API key and assistant ID, and use the OpenAI Python SDK to create and manage threads for interaction.