Azure OpenAI Generative AI with Weaviate

New Documentation

The model provider integration pages are new and still undergoing improvements. We appreciate any feedback on this forum thread.

Weaviate's integration with Azure OpenAI's APIs allows you to access their models' capabilities directly from Weaviate.

Configure a Weaviate collection to use an Azure OpenAI generative AI model, and Weaviate will perform retrieval augmented generation (RAG) using the specified model and your Azure OpenAI API key.

More specifically, Weaviate will perform a search, retrieve the most relevant objects, and then pass them to the Azure OpenAI generative model to generate outputs.

Requirements

Weaviate configuration

Your Weaviate instance must be configured with the Azure OpenAI generative AI integration (generative-openai) module.

For Weaviate Cloud (WCD) users

This integration is enabled by default on Weaviate Cloud (WCD) serverless instances.

For self-hosted users

• Check the cluster metadata to verify if the module is enabled.
• Follow the how-to configure modules guide to enable the module in Weaviate.

API credentials

You must provide a valid Azure OpenAI API key to Weaviate for this integration. Go to Azure OpenAI to sign up and obtain an API key.

Provide the API key to Weaviate using one of the following methods:

• Set the AZURE_APIKEY environment variable that is available to Weaviate.
• Provide the API key at runtime, as shown in the examples below.

Python API v4

import weaviate
from weaviate.auth import AuthApiKey
import os

# Recommended: save sensitive data as environment variables
azure_key = os.getenv("AZURE_APIKEY")
headers = {
    "X-Azure-Api-Key": azure_key,
}

client = weaviate.connect_to_wcs(
    cluster_url=weaviate_url,                       # `weaviate_url`: your Weaviate URL
    auth_credentials=AuthApiKey(weaviate_key),      # `weaviate_key`: your Weaviate API key
    headers=headers
)

# Work with Weaviate

client.close()

JS/TS API v3

import weaviate from 'weaviate-client'

const azureApiKey = process.env.AZURE_APIKEY || '';  // Replace with your inference API key

const client = await weaviate.connectToWeaviateCloud(
  'WEAVIATE_INSTANCE_URL',  // Replace with your instance URL
  {
    authCredentials: new weaviate.ApiKey('WEAVIATE_INSTANCE_APIKEY'),
    headers: {
      'X-Azure-Api-Key': azureApiKey,
    }
  }
)

// Work with Weaviate

client.close()

Configure collection

Configure a Weaviate collection to use an OpenAI generative AI model as follows:

Select the model to be used by specifying the Azure resource name.

Python API v4

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.azure_openai(
        resource_name="<azure-resource-name>",
        deployment_id="<azure-deployment-id>",
    )
    # Additional parameters not shown
)

JS/TS API v3

await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.azureOpenAI({
    resourceName: '<azure-resource-name>',
    deploymentId: '<azure-deployment-id>',
  }),
  // Additional parameters not shown
});

Retrieval augmented generation

After configuring the generative AI integration, perform RAG operations, either with the single prompt or grouped task method.

Single prompt

To generate text for each object in the search results, use the single prompt method.

The example below generates outputs for each of the n search results, where n is specified by the limit parameter.

When creating a single prompt query, use braces {} to interpolate the object properties you want Weaviate to pass on to the language model. For example, to pass on the object's title property, include {title} in the query.

Python API v4

collection = client.collections.get("DemoCollection")

response = collection.generate.near_text(
    query="A holiday film",  # The model provider integration will automatically vectorize the query
    single_prompt="Translate this into French: {title}",
    limit=2
)

for obj in response.objects:
    print(obj.properties["title"])
    print(f"Generated output: {obj.generated}")  # Note that the generated output is per object

JS/TS API v3

let myCollection = client.collections.get('DemoCollection');

const singlePromptResults = await myCollection.generate.nearText(
  ['A holiday film'],
  {
    singlePrompt: `Translate this into French: {title}`,
  },
  {
    limit: 2,
  }
);

for (const obj of singlePromptResults.objects) {
  console.log(obj.properties['title']);
  console.log(`Generated output: ${obj.generated}`);  // Note that the generated output is per object
}

Grouped task

To generate one text for the entire set of search results, use the grouped task method.

In other words, when you have n search results, the generative model generates one output for the entire group.

Python API v4

collection = client.collections.get("DemoCollection")

response = collection.generate.near_text(
    query="A holiday film",  # The model provider integration will automatically vectorize the query
    grouped_task="Write a fun tweet to promote readers to check out these films.",
    limit=2
)

print(f"Generated output: {response.generated}")  # Note that the generated output is per query
for obj in response.objects:
    print(obj.properties["title"])

JS/TS API v3

let myCollection = client.collections.get('DemoCollection');
const groupedTaskResults = await myCollection.generate.nearText(
  ['A holiday film'],
  {
    groupedTask: `Write a fun tweet to promote readers to check out these films.`,
  },
  {
    limit: 2,
  }
);

console.log(`Generated output: ${groupedTaskResults.generated}`);  // Note that the generated output is per query
for (const obj of groupedTaskResults.objects) {
  console.log(obj.properties['title']);
}

References

Generative parameters

Configure the following generative parameters to customize the model behavior.

Python API v4

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.azure_openai(
        resource_name="<azure-resource-name>",
        deployment_id="<azure-deployment-id>",
        # # These parameters are optional
        # frequency_penalty=0,
        # max_tokens=500,
        # presence_penalty=0,
        # temperature=0.7,
        # top_p=0.7,
        # base_url="<custom-azure-url>"
    )
    # Additional parameters not shown
)

JS/TS API v3

await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.azureOpenAI({
    resourceName: '<azure-resource-name>',
    deploymentId: '<azure-deployment-id>',
    // // These parameters are optional
    // frequencyPenaltyProperty: 0,
    // maxTokensProperty: 500,
    // presencePenaltyProperty: 0,
    // temperatureProperty: 0.7,
    // topPProperty: 0.7,
  }),
  // Additional parameters not shown
});

For further details on these parameters, see consult the Azure OpenAI API documentation.

Available models

See the Azure OpenAI documentation for a list of available models and their regional availability.

Further resources

Other integrations

• Azure OpenAI embedding models + Weaviate.

Code examples

Once the integrations are configured at the collection, the data management and search operations in Weaviate work identically to any other collection. See the following model-agnostic examples:

• The how-to: manage data guides show how to perform data operations (i.e. create, update, delete).
• The how-to: search guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation.

References

• Azure OpenAI API documentation
• Azure OpenAI models and availability

If you have any questions or feedback, let us know in the user forum.

• Technical questions
• Documentation feedback