Creating a Custom OpenAI GPT-3 API with Azure Functions
Table of Contents
- Introduction to OpenAI GPT-3
- Building a Custom OpenAI API
- Developing the API with Azure Functions
- Deploying and Testing the API
- Conclusion and Next Steps
Introduction to OpenAI GPT-3: Capabilities and Applications
OpenAI GPT-3 is an extremely advanced natural language processing model that has opened up many new applications. It can generate human-like text and respond to prompts with remarkable coherence. In this post, we will explore GPT-3's capabilities and how to leverage them by building a custom API.
The key benefit of having a custom API is that we can mask sensitive API keys and add custom prompts to optimize GPT-3's output for our specific use cases. By the end, we will have a scalable and secure way to integrate GPT-3's abilities into any application.
What is OpenAI GPT-3?
GPT-3 stands for Generative Pretrained Transformer 3 and is the third generation model developed by OpenAI for advanced natural language processing tasks. It uses deep learning to analyze enormous datasets of text and uncover the statistical patterns in language. This allows GPT-3 to generate remarkably human-like writing as well as understand and respond to natural language prompts with great accuracy. It can answer questions, summarize text, write code, poetry and more based on example data it has processed.
OpenAI GPT-3 Capabilities
As demonstrated through numerous examples, GPT-3 has exceptional skills in text generation, classification, summarization and other language tasks. It can parse context, follow logical reasoning and plan multi-step operations. Use cases span from creating content like articles, emails and code to chatbots, search refinement and language translation. The possibilities are rapidly expanding as more build on top of this powerful model.
Building a Custom OpenAI API
While OpenAI offers an API to access GPT-3, creating a custom wrapper provides key advantages. We can secure our secret API key, tailor prompts specifically for our application's domain and simplify how clients interact with the AI.
We will build this API using Azure functions for serverless scaling, though the principles apply for any platform.
Masking API Keys
Exposing API keys publicly can lead to unwanted charges or misuse if compromised. By wrapping OpenAI calls in our own API, the keys stay securely on the server side. Clients only see inputs and outputs without needing access behind the scenes.
Adding Custom Prompts
Prepending domain-specific details to prompts can help steer GPT-3 to more accurate, relevant responses. For a legal blog, we might prepend context like "Provide legal analysis in essay format..." before each user query. This stays hidden from public view.
Developing the API with Azure Functions
Azure functions provide serverless, event-driven execution that can scale seamlessly without needing to manage infrastructure. Combined with Python for rapid development, they are perfect for exposing AI through APIs.
We will configure OpenAI access, create our API endpoint and test querying GPT-3 via HTTP requests.
Setting Up the Azure Function
First, we initialize a new function app using the Azure CLI or web portal. After setting runtime stack and authentication, dependencies like OpenAI library are installed. Base code is scaffolded out handling HTTP triggers directed to our API endpoint.
Configuring OpenAI Access
The OpenAI secret key needs stored securely, here in a separate file imported only on the server side. We instantiate an OpenAI object with this key so all API requests route through this client.
Creating the API Endpoint
Our API exposes a single endpoint called "/prompt" that accepts user text queries. After preprocessing these prompts, we pass them into GPT-3 client and return the full text response through the API.
Deploying and Testing the API
Publishing the Azure Function
With code finalized, we link up to our cloud function app and deploy. This packages code and dependencies to be run in a serverless container. We are given a public HTTPS endpoint to access the API.
Sending Queries to the API
As a serverless API, we can now send requests programatically from any client. Passing prompt text in the URL, our API handles routing requests to GPT-3 and returning responses. We've successfully built an extensible AI interface!
Conclusion and Next Steps
Wrapping GPT-3 access through a custom API unlocks key benefits like security, customization and simplifies integration. Azure functions provide a scalable serverless foundation on top of which AI capabilities can be exposed.
From here, the possibilities are endless for embedding GPT-3 into applications - smarter search, analytical tools, content generation and far more. We've only begun unlocking the potential of this incredibly capable AI model!
FAQ
Q: What is GPT-3 used for?
A: GPT-3 can be used for natural language processing tasks like text generation, classification, summarization and more.
Q: Why create a custom API for GPT-3?
A: Creating a custom API allows you to hide API keys, customize prompts, and integrate GPT-3 into your own applications.
Q: How do Azure functions help when building a GPT-3 API?
A: Azure functions provide serverless compute to easily create and deploy the API without managing infrastructure.
Q: What code/languages can be used with Azure functions?
A: Azure functions support multiple languages like C#, JavaScript, Java, Python and more.
Q: How can the deployed API be used?
A: The deployed API creates an endpoint that allows sending text prompts and receiving AI-generated responses programmatically.
Q: Can this API be made publicly accessible?
A: The OpenAI API has usage charges so the custom API would need to implement authentication and limits for public use.
Q: What are some next steps after creating the initial API?
A: Next steps include adding authentication, request limits, caching, integrating with applications, and expanding capabilities.
Q: What are some use cases for this type of API?
A: Use cases include writing assistants, classification/tagging tools, chatbots, summarization programs, and more AI-enhanced applications.
Q: Can multiple models be used with this API approach?
A: Yes, the API could be extended to support different OpenAI models like Codex, DALL-E, and others.
Q: What programming skills are required to build custom APIs like this?
A: Some experience with a backend language like Python and cloud platforms like Azure functions is needed.