Integrate pgvector (Vector Database) with Claude, Cursor, Chatbots & AI Agents MCP Server

Q: Which distance metrics can I use for similarity search?

pgvector supports three operators: (L2/Euclidean distance), (cosine distance), and (negative inner product). The agent uses cosine distance by default, which works best for normalized embeddings like those from OpenAI.

Run vector similarity searches, manage embedding tables, and build AI-powered retrieval pipelines — all directly inside your existing PostgreSQL database.

GDPR Free for Subscribers

Compatible with every major AI agent and IDE

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

create

Create index on pgvector (Vector Database)

Create vector index

create

Create table on pgvector (Vector Database)

Create vector table

delete

Delete vector on pgvector (Vector Database)

Delete a vector

insert

Insert vector on pgvector (Vector Database)

Insert a vector

list

List tables on pgvector (Vector Database)

List tables

Search vectors on pgvector (Vector Database)

Vector similarity search

Security & Code Integrity Audit

Every tool in the pgvector (Vector Database) MCP Server is continuously audited by the Vinkius Security Engine. We guarantee zero-trust payload isolation, strict data boundaries, and deterministic execution for enterprise-grade AI agents.

A+Score: 100

How Vinkius protects your data

Is there a risk of the AI "going crazy" and deleting important company data?

No. With Vinkius, the AI operates on "rails". It can only make the exact moves you authorized in the tool's settings. It cannot invent routes, access other networks in your company, or decide to delete random files. If the action isn't in the approved catalog, the attempt is blocked instantly.

Can I audit what my AI agents are doing with this integration?

Yes, Vinkius provides an immutable, HMAC-chained audit log. Every tool execution, payload, and response is tracked in real-time on your dashboard, giving you complete visibility into your agent's actions.

Which distance metrics can I use for similarity search?

pgvector supports three operators: <-> (L2/Euclidean distance), <=> (cosine distance), and <#> (negative inner product). The agent uses cosine distance by default, which works best for normalized embeddings like those from OpenAI.

Does the AI train on my tools or API data?

No. Vinkius enforces a strict Zero-Retention policy. Your data simply passes through our secure servers to complete the requested action and is instantly forgotten. Nothing you do here is ever stored, logged, or used to train any artificial intelligence.

What can AI Agents do with pgvector (Vector Database)?

Build automated workflows with Cursor and Claude Code by connecting to the pgvector (Vector Database) MCP server.

Autonomous embeddings via AI

Build automated workflows involving embeddings by connecting pgvector (Vector Database). It provides Claude and ChatGPT with direct API hooks into your loved by devs ecosystem.

Connecting similarity search with Cursor

Add similarity search functionality to your custom chatbots. The pgvector (Vector Database) MCP handles the payload formatting required for ChatGPT and Claude to interface with loved by devs endpoints.