Integration · Vector Database

pgvector

Run vector similarity search inside your existing PostgreSQL database. pgvector integration enables semantic search without a separate vector store.

pgvector
SchedulerWorkflow

What you can do

What you can do with pgvector.

  • Store document embeddings and retrieve similar content by meaning

  • Build recommendation engines using vector similarity queries

  • Search product catalogs by semantic similarity rather than keywords

Operations

pgvector operations.

Search

Insert

List Tables

Create Table

How it works

How it works.

  1. Add it
    Drop the integration onto the Workflow canvas or attach it to a Scheduler job — no code.
  2. Configure
    Pick the operation and fill in the fields; credentials are stored encrypted and resolved only at run time.
  3. Run
    Execute it inline as part of an automation, or on a cron or interval at fleet scale.

Related

More Vector Database integrations.

Annoy

Annoy

Build and query approximate nearest neighbor indexes using the Annoy library

Chroma

Chroma

Store, query, and manage vector embeddings in ChromaDB collections

DocArray

DocArray

Store, query, and manage document vectors and embeddings with DocArray

FAISS

FAISS

Perform vector similarity searches and manage indexes using FAISS library

Haystack

Haystack

Build document search and question-answering pipelines with Haystack

Jina AI

Jina AI

Generate embeddings and run vector search queries using Jina AI services

Wire pgvector into your operation.

AccuOSS deploys AccuOps and builds the automations that put integrations like this to work.

Talk to our teamBrowse the catalog