Generative AI

Weaviate's integration with Google Gemini API and Google Vertex AI APIs allows you to access their models' capabilities directly from Weaviate.

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

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

Your Weaviate instance must be configured with the Google generative AI integration (generative-google) module.

You must provide valid API credentials to Weaviate for the appropriate integration.

Go to Google Gemini API to sign up and obtain an API key.

This is called an access token in Google Cloud.

You can save your Google Vertex AI credentials and have Weaviate generate the necessary tokens for you. This enables use of IAM service accounts in private deployments that can hold Google credentials.

To do so:

• Set USE_GOOGLE_AUTH environment variable to true.
• Have the credentials available in one of the following locations.

Once appropriate credentials are found, Weaviate uses them to generate an access token and authenticates itself against Vertex AI. Upon token expiry, Weaviate generates a replacement access token.

In a containerized environment, you can mount the credentials file to the container. For example, you can mount the credentials file to the /etc/weaviate/ directory and set the GOOGLE_APPLICATION_CREDENTIALS environment variable to /etc/weaviate/google_credentials.json.

This is called an access token in Google Cloud.

If you have the Google Cloud CLI tool installed and set up, you can view your token by running the following command:

gcloud auth print-access-token

By default, Google Cloud's OAuth 2.0 access tokens have a lifetime of 1 hour. You can create tokens that last up to 12 hours. To create longer lasting tokens, follow the instructions in the Google Cloud IAM Guide.

Since the OAuth token is only valid for a limited time, you must periodically replace the token with a new one. After you generate the new token, you have to re-instantiate your Weaviate client to use it.

You can update the OAuth token manually, but manual updates may not be appropriate for your use case.

You can also automate the OAth token update. Weaviate does not control the OAth token update procedure. However, here are some automation options:

client = re_instantiate_weaviate()

import subprocess
import weaviate

def refresh_token() -> str:
    result = subprocess.run(["gcloud", "auth", "print-access-token"], capture_output=True, text=True)
    if result.returncode != 0:
        print(f"Error refreshing token: {result.stderr}")
        return None
    return result.stdout.strip()

def re_instantiate_weaviate() -> weaviate.Client:
    token = refresh_token()

    client = weaviate.Client(
      url = "https://WEAVIATE_INSTANCE_URL",  # Replace WEAVIATE_INSTANCE_URL with the URL
      additional_headers = {
        "X-Goog-Vertex-Api-Key": token,
      }
    )
    return client

# Run this every ~60 minutes
client = re_instantiate_weaviate()

client = re_instantiate_weaviate()

from google.auth.transport.requests import Request
from google.oauth2.service_account import Credentials
import weaviate
import os


def get_credentials() -> Credentials:
    credentials = Credentials.from_service_account_file(
        "path/to/your/service-account.json",
        scopes=[
            "https://www.googleapis.com/auth/generative-language",
            "https://www.googleapis.com/auth/cloud-platform",
        ],
    )
    request = Request()
    credentials.refresh(request)
    return credentials


def re_instantiate_weaviate() -> weaviate.Client:
    from weaviate.classes.init import Auth

    weaviate_api_key = os.environ["WEAVIATE_API_KEY"]
    credentials = get_credentials()
    token = credentials.token

    client = weaviate.connect_to_weaviate_cloud(  # e.g. if you use the Weaviate Cloud Service
        cluster_url="https://WEAVIATE_INSTANCE_URL",  # Replace WEAVIATE_INSTANCE_URL with the URL
        auth_credentials=Auth.api_key(weaviate_api_key),  # Replace with your Weaviate Cloud key
        headers={
            "X-Goog-Vertex-Api-Key": token,
        },
    )
    return client


# Run this every ~60 minutes
client = re_instantiate_weaviate()

Provide the API key to Weaviate at runtime, as shown in the examples below.

Note the separate headers that are available for Gemini API and Vertex AI users.

# Recommended: save sensitive data as environment variables
vertex_key = os.getenv("VERTEX_API_KEY")
studio_key = os.getenv("STUDIO_API_KEY")

const vertexApiKey = process.env.VERTEX_API_KEY || '';  // Replace with your inference API key
const studioApiKey = process.env.STUDIO_API_KEY || '';  // Replace with your inference API key

Configure a Weaviate index as follows to use a Google generative AI model as follows:

Note that the required parameters differ between Vertex AI and Gemini API.

You can specify one of the available models for Weaviate to use. The default model is used if no model is specified.

Vertex AI users must provide the Google Cloud project ID in the collection configuration.

from weaviate.classes.config import Configureclient.collections.create(    "DemoCollection",    generative_config=Configure.Generative.google(        project_id="<google-cloud-project-id>",  # Required for Vertex AI        model_id="gemini-2.5-flash"    )    # Additional parameters not shown)

await client.collections.create({  name: 'DemoCollection',  generative: weaviate.configure.generative.google({    projectId: '<google-cloud-project-id>',  // Required for Vertex AI    modelId: 'gemini-2.5-flash'  }),  // Additional parameters not shown});

from weaviate.classes.config import Configureclient.collections.create(    "DemoCollection",    generative_config=Configure.Generative.google(        model_id="gemini-2.5-flash"    )    # Additional parameters not shown)

await client.collections.create({  name: 'DemoCollection',  generative: weaviate.configure.generative.google({    modelId: 'gemini-2.5-flash',  }),  // Additional parameters not shown});

Configure the following generative parameters to customize the model behavior.

from weaviate.classes.config import Configureclient.collections.create(    "DemoCollection",    generative_config=Configure.Generative.google(        # project_id="<google-cloud-project-id>",  # Required for Vertex AI        # model_id="<google-model-id>",        # api_endpoint="<google-api-endpoint>",        # temperature=0.7,        # top_k=5,        # top_p=0.9,        # vectorize_collection_name=False,    )    # Additional parameters not shown)

await client.collections.create({  name: 'DemoCollection',  generative: weaviate.configure.generative.google({    projectId: '<google-cloud-project-id>',  // Required for Vertex AI    modelId: '<google-model-id>',    apiEndpoint: '<google-api-endpoint>',    temperature: 0.7,    topK: 5,    topP: 0.9,  }),  // Additional parameters not shown});

Aside from setting the default model provider when creating the collection, you can also override it at query time.

from weaviate.classes.config import Configurefrom weaviate.classes.generate import GenerativeConfigcollection = client.collections.use("DemoCollection")response = collection.generate.near_text(    query="A holiday film",    limit=2,    grouped_task="Write a tweet promoting these two movies",    generative_provider=GenerativeConfig.google(        # # These parameters are optional        # project_id="<google-cloud-project-id>",  # Required for Vertex AI        # model_id="<google-model-id>",        # api_endpoint="<google-api-endpoint>",        # temperature=0.7,        # top_k=5,        # top_p=0.9,        # vectorize_collection_name=False,    ),    # Additional parameters not shown)

import { generativeParameters } from 'weaviate-client';

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

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.

collection = client.collections.use("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

let response;
const myCollection = client.collections.use("DemoCollection");

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.

collection = client.collections.use("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.generative.text}")  # Note that the generated output is per queryfor obj in response.objects:    print(obj.properties["title"])

let response;
const myCollection = client.collections.use("DemoCollection");

You can also supply images as a part of the input when performing retrieval augmented generation in both single prompts and grouped tasks.

import base64import requestsfrom weaviate.classes.generate import GenerativeConfig, GenerativeParameterssrc_img_path = "https://upload.wikimedia.org/wikipedia/commons/thumb/b/b0/Winter_forest_silver.jpg/960px-Winter_forest_silver.jpg"base64_image = base64.b64encode(requests.get(src_img_path).content).decode('utf-8')prompt = GenerativeParameters.grouped_task(    prompt="Which movie is closest to the image in terms of atmosphere",    images=[base64_image],      # A list of base64 encoded strings of the image bytes    # image_properties=["img"], # Properties containing images in Weaviate)jeopardy = client.collections.use("DemoCollection")response = jeopardy.generate.near_text(    query="Movies",    limit=5,    grouped_task=prompt,    generative_provider=GenerativeConfig.google(        max_tokens=1000    ),)# Print the source property and the generated responsefor o in response.objects:    print(f"Title property: {o.properties['title']}")print(f"Grouped task result: {response.generative.text}")

import { generativeParameters } from 'weaviate-client';

Vertex AI:

• gemini-2.5-pro
• gemini-2.5-flash
• gemini-2.0-flash
• gemini-1.5-pro
• gemini-1.5-flash

Gemini API:

• gemini-2.5-flash
• gemini-2.5-pro
• gemini-2.0-flash
• gemini-1.5-pro
• gemini-1.5-flash

• Google text embedding models + Weaviate.
• Google multimodal embedding models + Weaviate.

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 collections and How-to: Manage objects guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them).
• The How-to: Query & Search guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation.

• Google Vertex AI
• Google Gemini API

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