How to Use GPT 3.5 on Azure?

GPT 3.5 is the latest version of OpenAI’s powerful language model that can generate human-like text. Some key capabilities of GPT 3.5 include:

• More accurate text generation compared to previous GPT versions
• Better comprehension of context and more relevant responses
• Improved logical reasoning and common sense
• Ability to answer follow up questions more consistently
• Higher level of output quality while generating long form text

GPT 3.5 has over 175 billion parameters, making it one of the largest language models today. The model has been trained on a huge dataset of text from the web, books, articles etc. This allows it to have broad knowledge on a variety of topics and conversations.

Overview of Azure OpenAI Service

Microsoft Azure became one of the first major cloud platforms to offer GPT-3 based models via its Azure OpenAI service. This makes it easy for developers to integrate advanced language capabilities within their applications using simple REST API calls.

Some of the key capabilities offered under Azure OpenAI include:

• GPT 3.5 model – Generate human-like text on diverse topics
• Codex model – Translate natural language to code in multiple languages
• Embedding model – Get vector representations of text for semantic search
• Image generation models – Create images from text descriptions

By provisioning OpenAI service on Azure, you get access to stable and scalable API endpoints for text generation, classification and embedding scenarios.

Deploying GPT 3.5 on Azure

Deploying GPT 3.5 on Azure involves a few simple steps:

Step 1 – Create Azure OpenAI Service Resource

First, login to your Azure portal and create a new Azure OpenAI service resource. Keep note of the location, pricing tier, resource group etc. that you select during creation.

The pricing tier determines how many tokens you can process through the API per month. For example, the S0 tier allows up to 1 million tokens per month.

Step 2 – Add API Key

Once the resource is created, go to the Keys and Endpoint section to grab your OpenAI account API key.

This secret key will be used to authenticate and authorize your API requests. Keep this key secure and do not share it publicly.

Step 3 – Install Azure SDK

Install the Azure OpenAI Python SDK in your environment using:

pip install azure-ai-textanalytics azure-cognitiveservices-language-textanalytics

This SDK allows you to easily call the text generation, embedding and other OpenAI endpoints from Python code.

Step 4 – Generate Tokens

Now you are ready to start generating text from GPT 3.5 model on Azure.

Import the SDK:

from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential

Create a TextAnalyticsClient by passing in the API key:

“`python
client = TextAnalyticsClient(endpoint=”https://.openai.azure.com/”,
credential=AzureKeyCredential())

Then call the generate method to get output from GPT 3.5:

python
response = client.generate(
documents=[“Human: Hello, how are you doing today?”],
use_staging=False,
enable_pagination=True
)

print(response.documents[0].content)
“`

The text you provide as prompt will be sent to GPT 3.5 to continue the conversation and give a human-like response.

There are additional parameters to customize model, temperature, response length etc. Refer to the SDK docs for more options.

Integrating GPT 3.5 with Applications

Once you are able to generate output with the Python SDK, you can wrap this into a client library or web application.

Here are some tips for integrating GPT 3.5 into apps:

Expose it via REST API Layer

Create a REST API layer that handles calling the Azure OpenAI endpoint. This keeps your secret API key secure on the server.

Your client apps can call the custom REST API to get AI generated text without exposing the keys.

Queue Background Jobs

If you need a lot text generation or embedding jobs, adding them to a queue处理 that runs in the background is better.

This ensures good response time for user requests.

Cache Common Requests

Cache frequent queries and embeddings to reduce API calls and cost. This saves tokens for your pricing tier.

Monitor Costs

Keep track of the tokens consumed through dashboards. This helps plan and control spend based on usage.

Set alerts on high burn rates to avoid unintentional overspend if application traffic spikes.

Follow Best Practices

Refer to Azure recommendations on security, reliability, performance and cost optimization best practices. This ensures you build a robust GPT-3 application.

Use Cases for GPT 3.5 on Azure

Here are some examples of applications that can be built with GPT 3.5 API on Azure:

Chatbots and Virtual Assistants

The conversational ability of GPT-3 makes it great for powering chatbots, virtual assistants or social bots. It can understand contexts and provide relevant and human-like responses.

Content Generation

Automatically generate blogs, articles, stories, tweets, ads based on prompts and high level guidance. GPT-3 can create long form high quality content that resonates with target users.

Text Summarization

Summarize long news articles, research papers, legal documents etc. into concise overviews retaining key points.

Semantic Search

By encoding search queries and document corpus into vector embeddings, semantic search retrieval can be greatly improved over just keywords.

Sentiment Analysis

Classify sentiment of product reviews, survey responses or tweets to determine public perception of brands, events etc. This can drive marketing and PR strategy.

Predict Code Completions

The Codex model can suggest completions for Python, JavaScript, Bash and other languages. This helps accelerate software development.

Analyze Questions

Classify incoming questions to right support teams or identify those that can be answered by chatbots vs. humans.

As you can see GPT 3.5 opens up many possibilities to build smart applications! With the Azure platform and Python SDK, tapping into its advanced AI capabilities is quick and convenient.

Summary

• GPT 3.5 provides powerful text generation and comprehension capabilities
• Azure OpenAI service offers easy access to GPT-3 models via REST APIs
• Steps to deploy – create resource, get API key, install SDK, generate text
• Can be integrated into apps via REST layer, background jobs, caching etc.
• Enables many use cases like chatbots, content generation, search, code assistants etc.

With this guide, you should be able to get started with consuming GPT 3.5 from your Azure subscription today to create intelligent applications!

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