Create a streaming AI assistant with ChatGPT, FastAPI, WebSockets and React βœ¨πŸ€–πŸš€

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • SurveyJS - Open-Source JSON Form Builder to Create Dynamic Forms Right in Your App
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  • ai-assistant

    Streaming AI assistant with ChatGPT, FastAPI, WebSockets and React βœ¨πŸ€–πŸš€

  • ⭐️ The complete source code referenced in this guide is available on GitHub https://github.com/dpills/ai-assistant

  • com.openai.unity

    A Non-Official OpenAI Rest Client for Unity (UPM)

  • A Generative Pre-Trained Transformer (GPT) is a type of Large Language Model (LLM) and they are the hot topic in the technology world this year and many companies are scrambling to add this technology to their products. Creating and training these large models can be a very complex, time consuming and expensive. You may think that you cannot use this technology since it is so complex and expensive but companies like OpenAI have done a ton of work to create useful models and setup platforms exposing APIs to use them. If you have ever used an API where you send some data in, it does some magic behind the scenes and you get some data to use in a response, then you can integrate this cutting edge technology into your application. Let’s take a look at how we can setup a Full stack web app which lets us ask questions sent to OpenAI and stream the response.

  • SurveyJS

    Open-Source JSON Form Builder to Create Dynamic Forms Right in Your App. With SurveyJS form UI libraries, you can build and style forms in a fully-integrated drag & drop form builder, render them in your JS app, and store form submission data in any backend, inc. PHP, ASP.NET Core, and Node.js.

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  • material-ui-docs

    ⚠️ Please don't submit PRs here as they will be closed. To edit the docs or source code, please use the main repository:

  • Now create the index.html file using the CDN development version of React, transpiled with the standalone Babel version and the React Material UI library. The app connects to the web socket and submits new questions to the WebSocket API which then streams the response as it is generated and renders the markdown.

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