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Vinkius

pgvector (Vector Database) Connector for AI agents.

6 live capabilities

Run semantic search and manage embeddings inside your PostgreSQL database.

Live agent request pgvector (Vector Database) / Connector

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AI Agent

Why people use pgvector (Vector Database)

Pgvector PostgreSQL Vector Search for RAG Pipelines

This Connector changes that by letting your agent handle the pgvector logic directly. You get the power of semantic search without the headache of extra infrastructure, keeping everything in one place.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get production-ready vector search without leaving your PostgreSQL environment.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Finding similar support tickets

    A developer needs to find the 5 most similar documents to a user's question using search_vectors.

  2. Real-world use case 02

    Improving search speed on large datasets

    An engineer needs to create a new index on a large product table to improve search speed with create_index.

  3. Real-world use case 03

    Auditing vector columns across the database

    A data scientist wants to see which tables currently hold vector data to plan a migration using list_tables.

Complete set · 6capabilities

The complete pgvector (Vector Database) capability set.

These are the exact actions your AI can choose when you ask it to work with pgvector (Vector Database).

Capability set01 / 02

01—03

3 capabilities in this set.

Part of 6 available through pgvector (Vector Database).

  1. 01 Capability

    Search vectors

    Run a similarity search to find the closest matches for a specific query. This helps you find relevant data based on meaning rather than just keywords.

  2. 02 Capability

    List tables

    View a list of all tables in your database. Use this to quickly identify which ones contain your vector data.

  3. 03 Capability

    Insert vector

    Add a new vector and its associated metadata into an existing table. This keeps your knowledge base updated with new entries.

Capability set02 / 02

04—06

3 capabilities in this set.

Part of 6 available through pgvector (Vector Database).

  1. 04 Capability

    Create index

    Set up HNSW or IVFFlat indexes on your vector columns. This makes your similarity searches much faster as your dataset grows.

  2. 05 Capability

    Create table

    Build a new table with the specific dimensions required for your embeddings. This sets up the correct structure for your vector data.

  3. 06 Capability

    Delete vector

    Remove a specific vector entry from your database. This is useful for keeping your data clean and up to date.

Set up in minutes

One URL. Then ask pgvector (Vector Database) to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use pgvector (Vector Database) from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_m3Dmr7TKd7NjkaPwx4OA0eTWiY7yo9GZdmp9NSJ4/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it pgvector (Vector Database), and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable pgvector (Vector Database) for the conversation.

Where the request belongs

Work pgvector can move forward.

Built around the request

This is for the developer who wants to keep their architecture simple and the data architect who doesn't want to manage a separate vector database.

01

AI Engineer

Building RAG pipelines that need to query production embeddings without extra API hops.

02

Data Architect

Managing vector schemas and monitoring index performance from a single chat interface.

03

Fullstack Developer

Adding semantic search to an existing app without spinning up new infrastructure.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
Supabase Vector logo
01 7 capabilities

Supabase Vector

Connect your AI to Supabase Vector. Execute pgvector semantic searches, manage embeddings, and run relational database queries directly from your terminal.

View Connector
Milvus (Open-Source Vector Database) logo
02 7 capabilities

Milvus (Open-Source Vector Database)

Manage vector storage via Milvus. perform ANN searches, query scalar entities, and audit collections.

View Connector
MyScale (SQL Vector Database API) logo
03 6 capabilities

MyScale (SQL Vector Database API)

Manage MyScale vector databases via SQL. execute queries, perform vector searches, and manage indices directly from any AI agent.

View Connector
Redis Vector logo
04 6 capabilities

Redis Vector

Equip your AI to autonomously manage embeddings, run KNN similarity searches, and administrate vector indexes natively inside your Redis stack.

View Connector
Weaviate logo
05 7 capabilities

Weaviate

Search and manage vector data on Weaviate. the AI-native database for building production-grade AI applications.

View Connector
Qdrant logo
06 7 capabilities

Qdrant

Empower your AI to interact directly with your Qdrant vector database. query clusters, perform similarity searches, and manage collections effortlessly.

View Connector

Bring your own AI

Change the model, client or framework. Keep pgvector connected.

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Before you connect

Questions about pgvector.

The practical details behind the request, access and result.

Can pgvector MCP help me build a RAG system?

Yes, it allows your agent to query embeddings directly from your PostgreSQL database. This makes it easy to build a retrieval-augmented generation pipeline without needing a separate vector database.

How do I use pgvector MCP to speed up my searches?

You can ask your agent to create HNSW or IVFFlat indexes on your vector columns. These indexes significantly reduce the time it takes to find the most similar results in large datasets.

Does pgvector MCP support different distance metrics?

Yes, it supports common metrics like cosine, L2, and inner product. You can specify which one your agent should use when performing a similarity search.

Can I manage my embeddings directly with pgvector MCP?

Absolutely. You can ask your agent to insert new embeddings, update existing ones, or delete old records directly within your PostgreSQL tables.

Is pgvector MCP better than a separate vector database?

It depends on your needs. If you already use PostgreSQL and want to keep your data in one place, this is often the simplest choice. If you need specialized high-scale vector-only features, a dedicated store might be better.

How does pgvector MCP handle large datasets?

It handles large scale data by using production-grade indexing. By creating HNSW or IVFFlat indexes, the Connector ensures your similarity searches remain fast even as your row count grows into the millions.

Does the agent connect directly to my database?

Yes. Your connection string is encrypted at rest and injected into an isolated runtime. The agent connects directly to your PostgreSQL instance. no intermediate proxies, no data copies, no third-party storage.

What vector dimensions are supported?

Any dimension supported by pgvector. from small 128-d vectors to large 3072-d embeddings (e.g., OpenAI text-embedding-3-large). Specify the dimension when creating a table and the agent handles the rest.

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.

One connection away

Give your agent a direct line to pgvector.

Connect pgvector once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available