Create Your "ownGPT" in a Protected Way and Advance Its Potential (Part 1) - a simple web chat exper
Motivation You want your "ChatGPT" to operate in a secure environment, ensuring all data and interactions with your "bot" remain private. Your prompts (inputs)
The text discusses how to create a protected environment for your "ChatGPT" using Azure OpenAI Service, ensuring data privacy. The process ensures your inputs, outputs, embeddings, and training data stay confidential, and won't be used to improve OpenAI models or strengthen any Microsoft products. Microsoft controls this service and it doesn't interact with other OpenAI services. The interface can be set up using some experience in PowerShell and Azure Administration, and if you have more knowledge in Node.js, Python, Azure CLI, you can do more.
- Building the foundation
- Incorporating your own data to create unique experiences (Part 2)
- Adding the ability to take action on your data, not just discuss or interact with it (Part 3)
- Integrating a voice channel for spoken language usage (Part 4)
To operate the model, you need to note down specific details like resource, model, key, and model name types. The chat interface is created and deployed to an Azure App Service, which can be cloned from Microsoft's prebuilt GitHub repository. From here, you can create a copy of the "env.sample" file and update it using previously saved information. Once it's up and running locally, the interface can be customized as per your needs. Azure allows hosting via the web app on the Azure portal, where you can add identity providers for additional security before finally reaching the first version of your secure "ownGPT".
Creating a protected environment for your "ChatGPT"
Creating a protected environment for your "ChatGPT" also involves creating your business' "chatGPT" and deploying an Azure OpenAI Service resource. This involves using Azure OpenAI Studio and choosing models like GPT-35-TURBO or GPT-4. Following model deployment, either successfully or not, retrieving the elements needed to access the model is the next step. Specific details from each section must be remembered for seamless model operation. Eventually, the chat interface is created and deployed to an Azure App Service. Figuring out the application settings occurs next, where details from the ".env" file are saved and any defaults are set up. Once the web app has been restarted, an identity provider is added for security reasons. The end result is a secure "ownGPT" integrated with an identity provider.
Conclusion
In conclusion, creating a protected version of your ownGPT can greatly help secure your business operations without compromising security aspects. Along with this, the Azure OpenAI Service lets you enhance business operations without risking privacy, ensuring the information stays confidential. Other applications of creating a protected "KindGPT" involve adding one's own data to provide unique experiences, enabling the potential to act on your data rather than just discussing or interacting with it, and integrating voice channels to offer a personalized and secure AI chatbot experience.
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Keywords
Microsoft OpenAI Expert, Azure OpenAI Tutorial, Building chatGPT on Azure, Secure OpenAI Deployment, Azure OpenAI model creation guide