Make your AI work with Vertex AI Vector Search
Connect your account once and let the AI you already use work with it, without building another integration or switching to a different AI. Manage Google Cloud vector indexes and perform semantic searches directly in chat.
6 live capabilities. One account. Your AI. Real work.
- Step 01
Connect
Link your account through Vinkius.
- Step 02
Authorize
You decide what your AI can access.
- Step 03
Pick your AI
Use it with the AI application you already use.
- Step 04
Get things done
Ask your AI to work with your connected account.
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Works with
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Waiting for input…
Why people use Vertex AI Vector Search
Vertex AI Vector Search for Managing Cloud Vector Infrastructure
This Connector changes that by bringing the data to you. You just ask your agent to list your indexes or check a deployment's status. You get the information you need in a few seconds without ever leaving your code editor.
What Vinkius changes
You get direct, conversational control over Google's vector search infrastructure without leaving your workspace.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 7,800+ Connectors
- Real-world use case 01
Monitoring long-running index builds
An MLOps engineer is worried a 5TB index build failed.
- Real-world use case 02
Testing RAG search precision
A data scientist wants to see if a new embedding model is working.
- Real-world use case 03
Auditing cloud infrastructure
A backend dev needs to audit the cloud setup.
Complete set · 6capabilities
The complete Vertex AI Vector Search capability set.
These are the exact actions your AI can choose when you ask it to work with Vertex AI Vector Search.
01—03
3 capabilities in this set.
Part of 6 available through Vertex AI Vector Search.
- 01 Capability
List deployed indexes
See every index currently deployed to a specific endpoint. It's the fastest way to check what's live.
- 02 Capability
List index endpoints
View all index endpoints in your project. Use this to map out your production infrastructure.
- 03 Capability
List vector operations
Check the status of long-running tasks. It's perfect for tracking multi-terabyte index builds.
04—06
3 capabilities in this set.
Part of 6 available through Vertex AI Vector Search.
- 04 Capability
Get index details
Get the configuration and metadata for a specific vector index. This helps you verify dimensions and settings quickly.
- 05 Capability
List vector indexes
List every vector index in your project. This gives you a full bird's eye view of your data assets.
- 06 Capability
Search nearest neighbors
Perform a similarity search using a query vector and an endpoint ID. This is the core capability for RAG applications.
Where the request belongs
Work Vertex AI Vector Search can move forward.
This is for the MLOps engineer who's tired of clicking through the Cloud Console at 2am to check on index builds or the RAG data scientist who needs to test vector proximity on the fly.
MLOps Engineer
Monitors multi-hour index deployments and checks for errors while staying in the terminal.
RAG Data Scientist
Pushes experimental float arrays to production endpoints to gauge search precision quickly.
Backend Architect
Verifies infrastructure shards and node counts for organization-wide vector databases.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsVald
Power your agent with Vald. query, insert, and manage dense vectors on a highly scalable, distributed nearest-neighbor engine.
Pinecone
Equip your AI agent to manage your Pinecone vector databases. Query embeddings, fetch metrics, manage collections, and run stats natively via chat.
Chroma (Vector DB)
Manage vector embeddings via Chroma. list collections, query embeddings, and audit document counts directly from any AI agent.
Oracle Vector DB
Run vector similarity searches on Oracle 23ai. execute VECTOR_DISTANCE queries, inspect schemas, list vector indexes, and query tables from any AI agent.
MongoDB Atlas Vector Search
Manage vector storage via MongoDB Atlas. perform similarity searches, query MQL documents, and audit collections.
pgvector (Vector Database)
Run vector similarity searches, manage embedding tables, and build AI-powered retrieval pipelines. all directly inside your existing PostgreSQL database.
Bring your own AI
Change the model, client or framework. Keep Vertex AI Vector Search connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Vertex AI Vector Search.
The practical details behind the request, access and result.
Can I use the Vertex AI Vector Search MCP to see my index status?
Yes, you can ask your agent to check the active state and configuration of any index in your project. It will return the dimensionality, configuration details, and current status instantly.
How does this Connector help with RAG applications?
It allows your agent to perform semantic searches by pulling the nearest neighbors for any query vector you provide. This helps ground your agent's responses in your own data.
Can I check if my vector deployments are actually live?
Yes, you can ask the agent to list all deployed indexes for a specific endpoint. This confirms which specific versions are currently live and receiving production traffic.
What happens if a multi-terabyte index build takes a long time?
You can use the Connector to query the operation logs and see the exact progress percentage and timeline. This lets you monitor large-scale builds without refreshing the cloud console.
Does this Connector work with other cloud providers?
No, this Connector is specifically built for Google Cloud's Vertex AI Vector Search service. It connects directly to Google Cloud infrastructure.
Can I see the dimensions of my embeddings?
Yes, the Connector can pull the metadata for any index to show you the dimensionality and other core settings. This is useful for ensuring your embedding models match your index configurations.
How do I perform a nearest-neighbor similarity test via chat?
Just write: Search my endpoint '1xxx' against index 'deployed_abc_1' looking for 3 nearest neighbors to the vector [0.015, -0.042, 0.111]. The queryIndexTool bridges to Vertex and returns the IDs and distances of your geometrical matches instantly.
Can I query a status for indices that take hours to build on GCP?
Absolutely. Use the prompt: Check my google cloud vector operations. The listOperationsTool reveals all in-flight Cloud operations indicating completion percentages and precise timestamps, allowing you to sidestep the Google Console completely.
Where do I easily find the short-lived VERTEX_ACCESS_TOKEN?
On your terminal with gcloud installed and logged in, simply type gcloud auth print-access-token. Copy the output stream starting with ya29... into your configurations and the integration is ready for connection.
One connection away
Give your agent a direct line to Vertex AI Vector Search.
Connect Vertex AI Vector Search once. Keep it beside 7,800+ managed Connectors when the next task needs more.
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