Page Comparison

- [Introduction](#introduction)
- [Prerequisites](#prerequisites)
- [Configure the Azure OpenAI Models](#configure-the-azure-openai-models)

# Introduction
This guide describes how to configure Azure to generate the prerequisites required to enable the Ayfie Personal Assistant feature. It is assumed that one already has an Azure subscription and access to Azure OpenAI as described in [Azure OpenAI Service Documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview).

# Prerequisites
The prerequisites are to provide the following entities:
- **Deployment Name**
- **API Address**
- **API Key**

for each of the following 3 Azure OpenAI models:
- **Main Model**
- **High Quality Model**
- **Embeddings Model**

The intended difference between the Main Model and the High Quality Model is the quality of the chat responses when users toggle between the two in the Personal Assistant UI. The normal approach is to set the Main Model up with GPT-3.5 and the High Quality Model with GPT-4. However, it is fully possibly to set both of them up with for instance GPT 3.5 which would mean that toggling between the two in the UI would have no effect

There are two GPT-4 alternatives for the High Quality model: The lightweight model *gpt-4* that is suitable for resource-constrained scenarios where cost and response time are critical, or alternatively the more resource rich *gpt-4-32k* model that offers higher performance, accuracy, and the ability to handle larger sets of documents. Consider your specific requirements, performance needs, and cost constraints to choose the most appropriate model.

The third model is for creating embeddings. Embeddings are numerical representations of words that are learned from large amounts of text data. Currently, there is only one supported model.

Each of the 3 models requires an API address and an API key. However, unless one chose to spread the models across geographical regions, all 3 models will be reached via the same API address and an API key.

Given the limited options for each setting, the configuration of the prerequisites in Azure described in the next section, has a very predictable outcome:
- Main Model Deployment Name: **gpt-35-turbo**
- High Quality Model Deployment Name: **gpt-4** or **gpt-4-32k**
- Embeddings Model Deployment Name: **text-embedding-ada-002**
- API Address: the same one for all three
- API Key: the same one for all three

# Configure the Azure OpenAI Models
For more information, consult [Azure OpenAI Service Documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview).

Follow these steps to set up the deployments for the 3 Azure OpenAI models:
- Log in to the Azure portal at [portal.azure.com](https://portal.azure.com/)
- Make sure the account that is logged in has at least one subscription
- Go to *Azure OpenAI*
  - Click *Create*, to create a Resource
    - In *Project Details*, select *Subscription*
    - In *Project Details*, select *Resource Group*
    - In *Instance Details*, select *Region*. Not all models are available in all regions. Choose a region that has the models one wants to use
    - In *Instance Details*, set the *Name*
    - In *Instance Details*, select *Pricing Tier*
  - When Resource is created, select the resource in *Azure OpenAI*
  - Click *Click here to view endpoints*.
    - **Copy the value of *Language APIs*, it will be required later as the API Address**
    - **Copy the value of *KEY 1*, it will be required later as the API Key** (optionally *KEY 2*, both keys are valid)
  - Click *Go to Azure OpenAI Studio* (this will open a new portal)
  - Click *Management->Deployments*
  - Click *Create new deployment* to create Main model
    - Select the model (typically *gpt-35-turbo*)
    - Set the *Deployment Name* (must be same as model name)
      - **Copy the *Deployment Name*, it will be required later**
    - In *Advanced Options*, set *Tokens per Minute Rate Limit (thousands)* to maximum value.
  - Click *Create new deployment* to create High Quality model
    - Select the model
    - Set the *Deployment Name* (must be same as model name).
      - **Copy the *Deployment Name*, it will be required later**
    - In *Advanced Options*, set *Tokens per Minute Rate Limit (thousands)* to maximum value.
  - Click *Create new deployment* to create Embeddings model
    - Select the model (must be *text-embedding-ada-002*)
    - Set the *Deployment Name* (must be same as model name)
      - **Copy the *Deployment Name*, it will be required later**
    - In *Advanced Options*, set *Tokens per Minute Rate Limit (thousands)* to maximum value