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Google AI Text Embeddings 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 Google AI Studio and Google Vertex AI APIs allows you to access their models' capabilities directly from Weaviate.

Configure a Weaviate vector index to use an Google AI embedding model, and Weaviate will generate embeddings for various operations using the specified model and your Google AI API key. This feature is called the vectorizer.

At import time, Weaviate generates text object embeddings and saves them into the index. For vector and hybrid search operations, Weaviate converts text queries into embeddings.

Embedding integration illustration

AI Studio availability

At the time of writing (November 2023), AI Studio is not available in all regions. See this page for the latest information.

Requirements

Weaviate configuration

Your Weaviate instance must be configured with the Google AI vectorizer integration (text2vec-palm) module.

For Weaviate Cloud (WCD) users

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

For self-hosted users

API credentials

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

AI Studio

Go to Google AI Studio to sign up and obtain an API key.

Vertex AI

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

Token expiry for Vertex AI users

Important

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:
credentials = get_credentials()
token = credentials.token

client = weaviate.connect_to_wcs( # e.g. if you use the Weaviate Cloud Service
cluster_url="https://WEAVIATE_INSTANCE_URL", # Replace WEAVIATE_INSTANCE_URL with the URL
auth_credentials=weaviate.auth.AuthApiKey(os.getenv("WCS_DEMO_RO_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.

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

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.

import weaviate
from weaviate.auth import AuthApiKey
import os

# Recommended: save sensitive data as environment variables
vertex_key = os.getenv("VERTEX_APIKEY")
studio_key = os.getenv("STUDIO_APIKEY")
headers = {
"X-Google-Vertex-Api-Key": vertex_key,
"X-Google-Studio-Api-Key": studio_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()

Configure the vectorizer

Configure a Weaviate index to use an Google AI embedding model by setting the vectorizer as follows:

from weaviate.classes.config import Configure

client.collections.create(
"DemoCollection",
vectorizer_config=[
Configure.NamedVectors.text2vec_palm(
name="title_vector",
source_properties=["title"],
# project_id="<google-cloud-project-id>" # Required for Vertex AI
)
],
# Additional parameters not shown
)

You can specify one of the available models for the vectorizer to use. The default model (textembedding-gecko@001 for Vertex AI, embedding-001 for AI Studio) is used if no model is specified.

Data import

After configuring the vectorizer, import data into Weaviate. Weaviate generates embeddings for text objects using the specified model.

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

with collection.batch.dynamic() as batch:
for src_obj in source_objects:
weaviate_obj = {
"title": src_obj["title"],
"description": src_obj["description"],
}

# The model provider integration will automatically vectorize the object
batch.add_object(
properties=weaviate_obj,
# vector=vector # Optionally provide a pre-obtained vector
)
Re-use existing vectors

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 AI model.

Embedding integration at search illustration

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.

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"])
What is a 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.

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"])

References

Vectorizer parameters

The following examples show how to configure Google AI-specific options.

  • 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. textembedding-gecko@001 (Vertex AI) or embedding-001 (AI Studio)
from weaviate.classes.config import Configure

client.collections.create(
"DemoCollection",
vectorizer_config=[
Configure.NamedVectors.text2vec_palm(
name="title_vector",
source_properties=["title"],
# project_id="<google-cloud-project-id>", # Required for Vertex AI
# # Further options
# model_id="<google-model-id>",
# api_endpoint="<google-api-endpoint>",
)
],
# Additional parameters not shown
)

Available models

Vertex AI:

  • textembedding-gecko@001 (default)
  • textembedding-gecko@002
  • textembedding-gecko@003
  • textembedding-gecko@latest
  • textembedding-gecko-multilingual@001
  • textembedding-gecko-multilingual@latest
  • text-embedding-preview-0409
  • text-multilingual-embedding-preview-0409

AI Studio:

  • embedding-001 (default)
  • text-embedding-004

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. Accordingly, please refer to the following examples, which are model-agnostic:

  • 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

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