Google Multimodal Embeddings with Weaviate
Weaviate's integration with Google Vertex AI APIs allows you to access their models' capabilities directly from Weaviate.
Multimodal embeddings are currently not available to Google AI Studio users.
Configure a Weaviate vector index to use a Google embedding model, and Weaviate will generate embeddings for various operations using the specified model and your Google API key. This feature is called the vectorizer.
At import time, Weaviate generates multimodal object embeddings and saves them into the index. For vector and hybrid search operations, Weaviate converts queries of one or more modalities into embeddings. Multimodal search operations are also supported.
Requirements
Weaviate configuration
Your Weaviate instance must be configured with the Google multimodal vectorizer integration (multi2vec-google
) module.
multi2vec-google
was called multi2vec-palm
in Weaviate versions prior to v1.27
.
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 valid API credentials to Weaviate for the appropriate integration.
Vertex AI
This is called an access token
in Google Cloud.
Automatic token generation
From Weaviate versions 1.24.16
, 1.25.3
and 1.26
.
This feature is not available on Weaviate cloud instances.
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 totrue
. - 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
.
Search locations for Google Vertex AI credentials
Once USE_GOOGLE_AUTH
is set to true
, Weaviate will look for credentials in the following places, preferring the first location found:
- A JSON file whose path is specified by the
GOOGLE_APPLICATION_CREDENTIALS
environment variable. For workload identity federation, refer to this link on how to generate the JSON configuration file for on-prem/non-Google cloud platforms. - A JSON file in a location known to the
gcloud
command-line tool. On Windows, this is%APPDATA%/gcloud/application_default_credentials.json
. On other systems,$HOME/.config/gcloud/application_default_credentials.json
. - On Google App Engine standard first generation runtimes (<= Go 1.9) it uses the appengine.AccessToken function.
- On Google Compute Engine, Google App Engine standard second generation runtimes (>= Go 1.11), and Google App Engine flexible environment, it fetches credentials from the metadata server.
Manual token retrieval
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
Token expiry for Vertex AI users
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:
With Google Cloud CLI
If you are using the Google Cloud CLI, write a script to periodically update the token and extract the results.
Python code to extract the token looks like this:
client = re_instantiate_weaviate()
This is the re_instantiate_weaviate
function:
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-Google-Vertex-Api-Key": token,
}
)
return client
# Run this every ~60 minutes
client = re_instantiate_weaviate()
With google-auth
Another way is through Google's own authentication library google-auth
.
See the links to google-auth
in Python and Node.js libraries.
You can, then, periodically the refresh
function (see Python docs) to obtain a renewed token, and re-instantiate the Weaviate client.
For example, you could periodically run:
client = re_instantiate_weaviate()
Where re_instantiate_weaviate
is something like:
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["WCD_DEMO_RO_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-Google-Vertex-Api-Key": token,
},
)
return client
# Run this every ~60 minutes
client = re_instantiate_weaviate()
The service account key shown above can be generated by following this guide.
Provide the API key
Provide the API key to Weaviate at runtime, as shown in the examples below.
API key headers
Starting from v1.25.1
and v1.24.14
, there are separate headers X-Google-Vertex-Api-Key
and X-Google-Studio-Api-Key
for Vertex AI users and AI Studio respectively.
Prior to Weaviate v1.25.1
or v1.24.14
, there was one header for both Vertex AI users and AI Studio, specified with either X-Google-Api-Key
or X-PaLM-Api-Key
. We recommend using the new headers for clarity and future compatibility.
- Python API v4
- JS/TS API v3
import weaviate
from weaviate.classes.init import Auth
import os
# Recommended: save sensitive data as environment variables
vertex_key = os.getenv("VERTEX_APIKEY")
headers = {
"X-Google-Vertex-Api-Key": vertex_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()
import weaviate from 'weaviate-client'
const vertexApiKey = process.env.VERTEX_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-Vertex-Api-Key': vertexApiKey,
}
}
)
// Work with Weaviate
client.close()
Configure the vectorizer
Configure a Weaviate index as follows to use a Google embedding model:
- Python API v4
- JS/TS API v3
from weaviate.classes.config import Property, DataType, Configure, Multi2VecField
client.collections.create(
"DemoCollection",
properties=[
Property(name="title", data_type=DataType.TEXT),
Property(name="poster", data_type=DataType.BLOB),
],
vectorizer_config=[
Configure.NamedVectors.multi2vec_palm(
name="title_vector",
# Define the fields to be used for the vectorization - using image_fields, text_fields, video_fields
image_fields=[
Multi2VecField(name="poster", weight=0.9)
],
text_fields=[
Multi2VecField(name="title", weight=0.1)
],
# video_fields=[],
project_id="<google-cloud-project-id>", # Required for Vertex AI
location="<google-cloud-location>", # Required for Vertex AI
)
],
# Additional parameters not shown
)
await client.collections.create({
name: 'DemoCollection',
properties: [
{
name: 'title',
dataType: 'text' as const,
},
{
name: 'poster',
dataType: 'blob' as const,
},
],
vectorizers: [
weaviate.configure.vectorizer.multi2VecGoogle({
name: 'title_vector',
location: '<google-cloud-location>',
projectId: '<google-cloud-project-id>',
imageFields: [
{
name: 'poster',
weight: 0.9,
},
],
textFields: [
{
name: 'title',
weight: 0.1,
},
],
}),
],
// Additional parameters not shown
});
You can specify one of the available models for the vectorizer to use. Currently, multimodalembedding@001
is the only available model.
Vectorization behavior
Weaviate follows the collection configuration and a set of predetermined rules to vectorize objects.
Unless specified otherwise in the collection definition, the default behavior is to:
- Only vectorize properties that use the
text
ortext[]
data type (unless skipped) - Sort properties in alphabetical (a-z) order before concatenating values
- If
vectorizePropertyName
istrue
(false
by default) prepend the property name to each property value - Join the (prepended) property values with spaces
- Prepend the class name (unless
vectorizeClassName
isfalse
) - Convert the produced string to lowercase
Vectorizer parameters
The following examples show how to configure Google-specific options.
location
(Required): e.g."us-central1"
projectId
(Only required if using Vertex AI): e.g.cloud-large-language-models
apiEndpoint
(Optional): e.g.us-central1-aiplatform.googleapis.com
modelId
(Optional): e.g.multimodalembedding@001
dimensions
(Optional): Must be one of:128
,256
,512
,1408
. Default is1408
.
- Python API v4
- JS/TS API v3
from weaviate.classes.config import Configure
client.collections.create(
"DemoCollection",
properties=[
Property(name="title", data_type=DataType.TEXT),
Property(name="description", data_type=DataType.TEXT),
Property(name="poster", data_type=DataType.BLOB),
],
vectorizer_config=[
Configure.NamedVectors.multi2vec_palm(
name="title_vector",
# project_id="<google-cloud-project-id>" # Required for Vertex AI
# model_id="<google-model-id>",
# location="us-central1",
# dimensions=512,
image_fields=[
Multi2VecField(name="poster", weight=0.9)
],
text_fields=[
Multi2VecField(name="title", weight=0.1)
],
# video_fields=[]
# video_interval_seconds=20
)
],
# Additional parameters not shown
)
await client.collections.create({
name: 'DemoCollection',
properties: [
{
name: 'title',
dataType: 'text' as const,
},
{
name: 'description',
dataType: 'text' as const,
},
{
name: 'poster',
dataType: 'blob' as const,
},
],
vectorizers: [
weaviate.configure.vectorizer.multi2VecGoogle({
name: 'title_vector',
projectId: '<google-cloud-project-id>',
modelId: '<google-model-id>',
location: '<google-cloud-location>',
dimensions: 512,
imageFields: [
{
name: 'poster',
weight: 0.9,
},
],
textFields: [
{
name: 'title',
weight: 0.1,
},
],
// videoFields: []
// video_interval_seconds: 20
}),
],
// Additional parameters not shown
});
Data import
After configuring the vectorizer, import data into Weaviate. Weaviate generates embeddings for the objects using the specified model.
- Python API v4
- JS/TS API v3
collection = client.collections.get("DemoCollection")
with collection.batch.dynamic() as batch:
for src_obj in source_objects:
poster_b64 = url_to_base64(src_obj["poster_path"])
weaviate_obj = {
"title": src_obj["title"],
"poster": poster_b64 # Add the image in base64 encoding
}
# The model provider integration will automatically vectorize the object
batch.add_object(
properties=weaviate_obj,
# vector=vector # Optionally provide a pre-obtained vector
)
const collectionName = 'DemoCollection'
const myCollection = client.collections.get(collectionName)
let multiModalObjects = []
for (let mmSrcObject of mmSrcObjects) {
multiModalObjects.push({
title: mmSrcObject.title,
poster: mmSrcObject.poster, // Add the image in base64 encoding
});
}
// The model provider integration will automatically vectorize the object
const mmInsertResponse = await myCollection.data.insertMany(dataObject);
console.log(mmInsertResponse);
If you already have a compatible model vector available, you can provide it directly to Weaviate. This can be useful if you have already generated embeddings using the same model and want to use them in Weaviate, such as when migrating data from another system.
Searches
Once the vectorizer is configured, Weaviate will perform vector and hybrid search operations using the specified Google model.
Vector (near text) search
When you perform a vector search, Weaviate converts the text query into an embedding using the specified model and returns the most similar objects from the database.
The query below returns the n
most similar objects from the database, set by limit
.
- Python API v4
- JS/TS API v3
collection = client.collections.get("DemoCollection")
response = collection.query.near_text(
query="A holiday film", # The model provider integration will automatically vectorize the query
limit=2
)
for obj in response.objects:
print(obj.properties["title"])
const collectionName = 'DemoCollection'
const myCollection = client.collections.get(collectionName)
let result;
result = await myCollection.query.nearText(
'A holiday film', // The model provider integration will automatically vectorize the query
{
limit: 2,
}
)
console.log(JSON.stringify(result.objects, null, 2));
Hybrid search
A hybrid search performs a vector search and a keyword (BM25) search, before combining the results to return the best matching objects from the database.
When you perform a hybrid search, Weaviate converts the text query into an embedding using the specified model and returns the best scoring objects from the database.
The query below returns the n
best scoring objects from the database, set by limit
.
- Python API v4
- JS/TS API v3
collection = client.collections.get("DemoCollection")
response = collection.query.hybrid(
query="A holiday film", # The model provider integration will automatically vectorize the query
limit=2
)
for obj in response.objects:
print(obj.properties["title"])
const collectionName = 'DemoCollection'
const myCollection = client.collections.get(collectionName)
result = await myCollection.query.hybrid(
'A holiday film', // The model provider integration will automatically vectorize the query
{
limit: 2,
}
)
console.log(JSON.stringify(result.objects, null, 2));
Vector (near media) search
When you perform a media search such as a near image search, Weaviate converts the query into an embedding using the specified model and returns the most similar objects from the database.
To perform a near media search such as near image search, convert the media query into a base64 string and pass it to the search query.
The query below returns the n
most similar objects to the input image from the database, set by limit
.
- Python API v4
- JS/TS API v3
def url_to_base64(url):
import requests
import base64
image_response = requests.get(url)
content = image_response.content
return base64.b64encode(content).decode("utf-8")
collection = client.collections.get("DemoCollection")
query_b64 = url_to_base64(src_img_path)
response = collection.query.near_image(
near_image=query_b64,
limit=2,
return_properties=["title", "release_date", "tmdb_id", "poster"] # To include the poster property in the response (`blob` properties are not returned by default)
)
for obj in response.objects:
print(obj.properties["title"])
const base64String = 'SOME_BASE_64_REPRESENTATION';
result = await myCollection.query.nearImage(
base64String, // The model provider integration will automatically vectorize the query
{
limit: 2,
}
)
console.log(JSON.stringify(result.objects, null, 2));
References
Available models
multimodalembedding@001
(default)
Further resources
Other integrations
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
Questions and feedback
If you have any questions or feedback, let us know in the user forum.