Skip to main content

Azure

Azure OpenAI Service offers REST API access to advanced language models like GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo, and Embeddings models. Both GPT-4 and GPT-3.5-Turbo models are now generally available, empowering users with capabilities such as content generation, summarization, image understanding, semantic search, and natural language to code translation. Accessible through REST APIs, Python SDK, or the Azure OpenAI Studio web interface, these models can be seamlessly integrated into various applications.

Deploying your model​

Azure OpenAI Studio, offers the capability to deploy both Chat Models(LLM) as well as Embedding Models from the console.

Chat Model​

Go through the Azure documentation and start deploying your model. You can test out the model in Azure OpenAI Studio > Chat Playground. Here is a Quick Start to help you in the process.

Once your model is deployed successfully, you can use it to serve the LLM purpose in the MAAP framework.

Usage with MAAP​

To use Azure OpenAI model with MAAP framework, you would need to feed below values.

  • Config File :​

    Provided below are the values required to be added in config.yaml file in LLM section.

    llms:
        class_name: AzureOpenAI

    MAAP now provides the option to choose if you want to use LlamaIndex as your main framework to deploy your LLM models.

    This can be done by adding the 'framework' configuration to the config.yaml file

    llms:
        class_name: AzureOpenAI
        model_name: <check_references_below>
        framework: llamaindex

    Llamaindex requires a provided model name.

  • Environment Variable :​

    Below value(s) are to be added in .env file, present at builder/partnerproduct/.

    AZURE_OPENAI_API_KEY=<check_references_below>
    AZURE_OPENAI_API_INSTANCE_NAME=<check_references_below>
    AZURE_OPENAI_API_VERSION=<check_references_below>
    AZURE_OPENAI_API_DEPLOYMENT_NAME=<check_references_below>

Embedding Model​

You can follow the same steps as above to deploy the embedding model as well. The process is documented here.

Usage with MAAP​

To use Azure OpenAI embedding with MAAP framework, you would need to feed below values.

  • Config File :​

    Provided below are the values required to be added in config.yaml file in embedding section.

    embedding:
        class_name: Azure-OpenAI-Embeddings
        model_name: <model_selected>
        max_tokens: <integer_value>
        temperature: <integer_value>

    Model name specified should be one of the below listed:

    • text-embedding-ada-002
    • text-embedding-3-small
    • text-embedding-3-large

  • Environment Variable :​

    Below value(s) are to be added in .env file, present at builder/partnerproduct/.

    AZURE_OPENAI_API_KEY=<check_references_below>
    AZURE_OPENAI_API_INSTANCE_NAME=<check_references_below>
    AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=<check_references_below>
    AZURE_OPENAI_API_VERSION=<check_references_below>

Deploying your model using the LlamaIndex framework​

Chat Model​

MAAP now provides the option to choose if you want to use LlamaIndex as your main framework to deploy your LLM models.

This can be done by adding the 'framework' configuration to the config.yaml file

  • Config File​

    llms:
      class_name: AzureOpenAI
      model_name: <check_references_below>
      framework: 'llamaindex'

Embedding Model​

MAAP now provides the option to choose if you want to use LlamaIndex as your main framework to deploy your embeddings.

This can be done by adding the 'framework' configuration to the config.yaml file

  • Config File​

    embedding:
      class_name: Azure-OpenAI-Embeddings
      model_name: <model_selected>
      max_tokens: <integer_value>
      temperature: <integer_value>
      framework: 'llamaindex'

    Model name specified should be one of the below listed:

    • text-embedding-ada-002
    • text-embedding-3-small
    • text-embedding-3-large

  • Environment Variable :​

    Below value(s) are to be added in .env file, present at builder/partnerproduct/.

    AZURE_OPENAI_API_KEY=<check_references_below>
    AZURE_OPENAI_API_INSTANCE_NAME=<check_references_below>
    AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=<check_references_below>
    AZURE_OPENAI_API_VERSION=<check_references_below>

References​

Provided below are the instructions on how to procure the right values for building your MAAP framework.

  • Deployment Name​

    You can pick the deployment name for AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME and AZURE_OPENAI_API_DEPLOYMENT_NAME as shown below from your console.

    Console Image

    For Llamaindex you can pick the model name from the column of the same name.

  • API Key and Instance Name​

    To retrieve the key and instance name, you can go to Resource Management in Azure Portal for your service and copy,

    • KEY 1 or KEY 2 for AZURE_OPENAI_API_KEY
    • Endpoint for azure_openai_api_instance_name

    The instance name can be retrieved from the endpoint itself. For example, Instance name for https://maap-demo.openai.azure.com/ is maap-demo.

    End point and Key

    Refer here for further details.

  • API Version​

    Check the docs here to pass the right api-version.