KubeAI Generative AI with Weaviate

Weaviate's integration with OpenAI-style APIs allows you to access KubeAI models' directly from Weaviate.

Configure a Weaviate collection to use KubeAI, and Weaviate will perform retrieval augmented generation (RAG) using the specified model.

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

Requirements

KubeAI configuration

KubeAI must be deployed in a Kubernetes cluster with an embedding model. For more specific instructions, see this KubeAI deployment guide.

Weaviate configuration

Your Weaviate instance must be configured with the 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

The OpenAI integration requires an API key value. To use KubeAI, provide any value for the API key, as this value is not used by KubeAI.

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

• Set the OPENAI_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.classes.init import Auth
import os

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

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=weaviate_url,                       # `weaviate_url`: your Weaviate URL
    auth_credentials=Auth.api_key(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 openaiApiKey = process.env.OPENAI_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-OpenAI-Api-Key': openaiApiKey,
    }
  }
)

// Work with Weaviate

client.close()

Configure collection

Configure Weaviate to use a KubeAI generative AI model:

Python API v4

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.openai(
        # Setting the model and base_url is required
        model="gpt-3.5-turbo",
        base_url="http://kubeai/openai", # Your private KubeAI API endpoint
        # These parameters are optional
        # frequency_penalty=0,
        # max_tokens=500,
        # presence_penalty=0,
        # temperature=0.7,
        # top_p=0.7,
    )
    # Additional parameters not shown
)

JS/TS API v3

await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.openAI({
    // Setting the model and base_url is required
    model: 'gpt-3.5-turbo',
    baseURL: 'http://kubeai/openai',
    // These parameters are optional
    // frequencyPenaltyProperty: 0,
    // maxTokensProperty: 500,
    // presencePenaltyProperty: 0,
    // temperatureProperty: 0.7,
    // topPProperty: 0.7,
  }),
  // Additional parameters not shown
});

Any model that is supported by vLLM or Ollama can be used with KubeAI.

Refer to the KubeAI docs on model management for more information on available models and how to configure them.

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']);
}

Further resources

Other integrations

• KubeAI 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.

External resources

• KubeAI documentation
• KubeAI documentation on model management

Questions and feedback

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

• Technical questions
• Documentation feedback