google-cloud-vectorsearch
Google Cloud Vectorsearch API client library
Decision gist · record as of 2026-08-14
Yes, if you are building on Google Cloud and need a managed vector database. The library is actively maintained, has low install friction, and integrates cleanly with Google Cloud authentication. However, you must have a Google Cloud project with Vector Search enabled and be prepared for API costs; this is not a local or offline solution.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Google Cloud project with Vector Search API enabled, valid authentication credentials, and Python >= 3.10.
- Low install friction with a pure-Python wheel.
- Actively maintained as of 2026-06-25 with recent commits.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
last release 2026-06-25 (50 days) · last repo commit 2026-08-14 · 5,373 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,694,386 downloads/mo, #2,525 on PyPI
Alternatives
Verify before relying
pip install google-cloud-vectorsearch
from google.cloud import vectorsearch
client = vectorsearch.IndexClient(project='your-project', region='us-central1')
# Use client to query or manage vector indexes- Whether the library supports local/offline vector operations or requires live API connectivity
- Performance characteristics and latency for typical query workloads
- Cost implications and pricing model for Vector Search API usage
What it is and what it does
This is Google's official Python client for Vector Search, a managed vector database service on Google Cloud. It abstracts the complexity of connecting to and querying the Vector Search API, allowing you to work with vector embeddings and perform approximate nearest neighbor searches programmatically. The library handles authentication, RPC communication, and data serialization through its dependencies on google-api-core, google-auth, and protobuf.
You use it when you need to integrate semantic search, recommendation systems, or generative AI retrieval into Python applications running on or connecting to Google Cloud. The library is in Beta status and actively maintained, supporting current Python versions (3.10 through 3.14). It requires a live Google Cloud project with the Vector Search API enabled and proper authentication set up before you can make any queries.
Use it for
- Build semantic search features that find similar documents or items by querying stored embeddings at scale
- Implement recommendation engines that use vector similarity to suggest related products or content
- Power retrieval-augmented generation (RAG) pipelines by querying relevant embeddings for LLM context
- Store and query custom vector embeddings from your own embedding models or third-party services
- Perform fast approximate nearest neighbor searches across large datasets of high-dimensional vectors
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building on Google Cloud and need a managed vector database.
The library is actively maintained, has low install friction, and integrates cleanly with Google Cloud authentication. However, you must have a Google Cloud project with Vector Search enabled and be prepared for API costs; this is not a local or offline solution.
Install
google-cloud-vectorsearch on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained as of 2026-06-25 with recent commits. Requires Python >= 3.10 and depends on standard Google Cloud libraries (google-api-core, google-auth, grpcio, proto-plus, protobuf).
Requires Google Cloud project with Vector Search API enabled, valid authentication credentials, and Python >= 3.10.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
Quickstart
pip install google-cloud-vectorsearch
from google.cloud import vectorsearch
client = vectorsearch.IndexClient(project='your-project', region='us-central1')
# Use client to query or manage vector indexesVerify before relying
- Whether the library supports local/offline vector operations or requires live API connectivity
- Performance characteristics and latency for typical query workloads
- Cost implications and pricing model for Vector Search API usage
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesgoogle-api-coregoogle-authgrpcioproto-plusprotobuf |
| Maintenance | Actively maintained 50 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,694,386 / month, #2,525 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Internet |
Evidence: google_cloud_vectorsearch-0.11.1-py3-none-any.whl
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See also google-cloud-bigquery-storage · google-cloud-recommendations-ai · google-cloud-notebooks · google-cloud-tasks · google-cloud-recommender · google-cloud-filestore · google-cloud-discoveryengine · google-cloud-firestore · google-cloud-monitoring · google-cloud-functions