Create a Custom AI Assistant + API in 10 Mins

pixegami
25 Dec 202310:28

TLDRThis video tutorial guides viewers on creating a custom AI assistant using OpenAI's new Assistants API. The assistant, powered by GPT models, can access uploaded data and external APIs to provide detailed answers. The process involves setting up an account, defining the assistant's capabilities, and integrating custom data and functions. The assistant can run Python code for calculations and data visualization. The video also demonstrates how to use the assistant via an API endpoint for integration into custom applications.

Takeaways

  • 🚀 The Assistants API from OpenAI enables users to create custom AI agents with access to the latest GPT models, a code interpreter, and uploaded knowledge.
  • 📄 Custom AI agents can be created on the OpenAI website and used via an API endpoint for integration into Python or JavaScript applications.
  • 📈 To enhance the AI's capabilities, users can upload custom data such as PDFs or CSV files containing specialized knowledge.
  • 🔍 The AI assistant can retrieve and utilize data from uploaded documents to provide more informed responses.
  • 📊 The assistant can also call custom functions defined by the user, allowing for integration with external APIs or data sources.
  • 📝 Custom functions are defined using a JSON schema, which the AI uses to understand the input parameters required for the function.
  • 📱 The code interpreter feature enables the AI to run Python code for calculations and data visualization, such as generating graphs from data.
  • 🔗 After building and testing the AI assistant in the OpenAI playground, it can be accessed via an API for use in custom applications.
  • 🔑 Users need an OpenAI account and API key to use the Assistants API and integrate it into their applications.
  • 📚 The OpenAI documentation provides detailed instructions and examples for using the Assistants API effectively.
  • 🛠️ For further development, users can explore tools like Streamlit for building interactive custom UIs with Python.

Q & A

  • What is the purpose of the Assistants API from OpenAI?

    -The Assistants API allows users to build custom AI agents that can access the latest GPT models, a code interpreter, and any additional instructions or knowledge uploaded to it.

  • How can I create a custom AI assistant using the OpenAI website?

    -To create a custom AI assistant, you need an OpenAI account and should go to platform.openai.com/assistants, click on the create button, fill in the name and instructions, choose an LLM model, and save the settings.

  • What type of data can I provide to my AI assistant to enhance its capabilities?

    -You can provide custom data such as text files, PDFs, or CSV tables that contain specialized knowledge relevant to your application.

  • How does the AI assistant retrieve data from the provided documents?

    -The AI assistant can retrieve data by enabling the information retrieval feature, uploading the file, and then saving it. The assistant can then access this data to answer questions.

  • What are custom functions in the context of the AI assistant?

    -Custom functions are external functions that the AI assistant can call. They are defined using a JSON schema that specifies the input parameters for the function.

  • How can I test the AI assistant's ability to fetch data and call custom functions?

    -You can test the AI assistant by asking it questions that require the use of the uploaded data or custom functions. You can also use the test button on the OpenAI website to interact with the assistant.

  • What is the code interpreter feature of the AI assistant?

    -The code interpreter feature allows the AI assistant to run calculations and plot graphs by writing and executing Python code, enabling it to analyze and visualize data.

  • How can I use the AI assistant via an API endpoint?

    -To use the AI assistant via an API endpoint, you need an OpenAI account and API key. You then create a thread, submit messages to the assistant, and wait for the asynchronous run to complete before retrieving the response.

  • What is the recommended next step after building and testing the AI assistant in the OpenAI playground?

    -The recommended next step is to read the documentation for the Assistants API to understand how to use it in detail, or to watch a tutorial on building a custom UI using Python for a more interactive experience.

  • How can I integrate the AI assistant into my own custom application?

    -You can integrate the AI assistant into your custom application by using the API endpoint to call the assistant and then processing the response within your application's logic.

  • What is the role of the Python OpenAI SDK in interacting with the AI assistant?

    -The Python OpenAI SDK is used to interact with the AI assistant programmatically. It allows you to create threads, submit messages, and retrieve responses from the assistant within your Python code.

Outlines

00:00

🤖 Building a Custom AI Assistant

This paragraph introduces the Assistants API from OpenAI, a service that simplifies the creation of a custom AI agent. It explains the AI's capabilities, such as access to GPT models, code interpretation, and the use of additional data or instructions. The video demonstrates how to build an AI assistant that uses global economic data, specifically focusing on the cost of living in various cities. The process includes setting up an OpenAI account, creating an assistant with a name and prompt, choosing a model, and testing the assistant. It also highlights the need for custom data to enhance the assistant's functionality beyond standard ChatGPT capabilities.

05:01

📈 Integrating Custom Data and Functions

The second paragraph delves into the integration of custom data and functions to improve the AI assistant's utility. It guides through the process of adding a custom data source, such as a PDF report on global liveability, and how to test the assistant's ability to retrieve and use this data. The paragraph also explains how to add custom functions, which are external functions that the assistant can call, and how to test these functions. It demonstrates the assistant's ability to fetch additional information and call APIs, and introduces the code interpreter feature, which allows the assistant to run Python code for calculations and data visualization.

10:02

🔗 Using the Assistant via API

The final paragraph focuses on how to use the custom AI assistant via an API endpoint, enabling its integration into custom applications. It outlines the steps to obtain an OpenAI account and API key, and how to use these to interact with the assistant programmatically. The paragraph provides a Python code example for creating a thread and submitting messages to the assistant, as well as how to wait for and retrieve the response. It concludes with suggestions for further exploration, such as building a custom UI with Python and Streamlit, and encourages viewers to read the documentation for more detailed instructions.

Mindmap

Keywords

💡Assistants API

The Assistants API is a service provided by OpenAI that allows users to create custom AI agents. It is the central tool discussed in the video, enabling the creation of AI assistants with access to various functionalities. In the context of the video, it is used to build an AI assistant that can answer questions using global economic data.

💡Custom AI Agent

A custom AI agent refers to an AI model that is personalized or modified to perform specific tasks or answer questions based on the user's requirements. The video demonstrates how to build such an agent using the Assistants API, which can access additional data and instructions provided by the user.

💡GPT Models

GPT (Generative Pre-trained Transformer) models are a series of language processing AI models developed by OpenAI. These models are capable of understanding and generating human-like text. In the video, the AI assistant has access to the latest GPT models, which form the basis of its language understanding and generation capabilities.

💡Code Interpreter

A code interpreter is a feature that allows an AI assistant to execute code, such as Python, to perform calculations or generate visualizations. In the video, the code interpreter is enabled to allow the AI assistant to run Python code for tasks like plotting graphs from data.

💡Information Retrieval

Information retrieval is the process of obtaining relevant information from a collection of data. In the context of the video, the AI assistant's information retrieval feature is used to access and utilize custom data uploaded by the user, such as the global economic data PDF.

💡Custom Functions

Custom functions are user-defined external functions that an AI assistant can call to perform specific tasks. These functions are defined using a JSON schema and allow the AI to interact with external systems or APIs.

💡API Endpoint

An API (Application Programming Interface) endpoint is a URL that allows a program to interact with a web service. In the video, once the AI assistant is created, it can be accessed via an API endpoint, enabling its integration into custom applications or services.

💡Global Economic Data

Global economic data refers to statistical information about the economies of various countries or regions worldwide. This data can include indicators such as GDP, cost of living, infrastructure, healthcare, and more. In the video, the AI assistant is designed to answer questions using this type of data.

💡Cost of Living

The cost of living is an index or measurement of the amount of money needed to sustain a certain standard of living in a particular city or country. It is often used to compare the affordability of different locations. In the video, the AI assistant is capable of providing information on the cost of living in various cities.

💡Liveability Index

The liveability index is a quality of life measure that assesses factors such as infrastructure, healthcare, culture, and environment to determine the overall desirability of a city for living. The video uses the '2023 Global Liveability Index Report' as a source of data for the AI assistant.

💡Streamlit

Streamlit is a Python library used for creating fast, interactive web applications. It is mentioned in the video as a tool that can be used to build a custom UI for the AI assistant, making it more user-friendly and accessible.

Highlights

The Assistants API from OpenAI simplifies the process of creating a custom AI assistant.

AI assistants have access to the latest GPT models, code interpreters, and uploaded knowledge.

Custom AI assistants can be built using global economic data and Python for calculations.

The assistant can be accessed via an API endpoint for integration into custom applications.

Creating an assistant requires an OpenAI account and access to platform.openai.com/assistants.

Custom data, like PDFs or CSV files, can be uploaded to enhance the assistant's knowledge base.

The assistant can retrieve and utilize data from uploaded reports, like the 2023 Global Liveability Index Report.

Custom functions can be defined and called by the assistant to fetch additional information or interact with APIs.

The code interpreter feature allows the assistant to run Python code for calculations and data visualization.

The assistant can plot graphs and visualize data directly within the chat interface.

After testing in the OpenAI playground, the assistant can be used via an API endpoint for production applications.

An OpenAI account and API key are required to use the Assistants API in a custom application.

The Python OpenAI SDK is needed to interact with the assistant programmatically.

Threads are created to initiate asynchronous interactions with the assistant.

The assistant's responses can be retrieved and used in custom applications or services.

The video provides a tutorial on building a custom AI assistant and integrating it into applications.

The video also suggests using Streamlit for building interactive custom UIs for chatbots.

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