---
id: apache-airflow-providers-pgvector
version: "1.7.3"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# apache-airflow-providers-pgvector — Provider package apache-airflow-providers-pgvector for Apache Airflow

License: permissive · Maintenance: active · Downloads: 203.0K/mo

## What it is and what it does

This is an Apache Airflow provider package that integrates pgvector—PostgreSQL's vector extension—into Airflow's task orchestration framework. It supplies operators, hooks, and integration utilities so you can build data pipelines that work with vector embeddings stored in PostgreSQL as part of scheduled workflows.

The package depends on Apache Airflow (>=2.11.0), apache-airflow-providers-postgres (>=6.6.0), pgvector (>=0.3.1), and apache-airflow-providers-common-compat (>=1.8.0). It supports Python 3.10, 3.11, 3.12, 3.13, and 3.14, is marked Production/Stable, and installs with low friction via pip. An optional common.sql extra is available for additional SQL-based vector operations.

Use it for:

- Schedule regular jobs to insert or update vector embeddings into PostgreSQL as part of a data pipeline.
- Build ETL pipelines that compute vector similarity searches and store results in a DAG.
- Orchestrate multi-step workflows combining vector operations with other data transformations.
- Automate vector index maintenance tasks on PostgreSQL pgvector tables on a recurring schedule.
- Integrate vector search results into downstream analytics pipelines managed by Airflow.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Integrates pgvector operations into Apache Airflow workflows, providing operators and hooks to manage vector embeddings in PostgreSQL.

Yes. This is a stable, actively maintained provider for a specific use case: if you are already running Apache Airflow and need to orchestrate pgvector operations in PostgreSQL, this package is the standard integration. Low install friction, permissive license, no known vulnerabilities, and recent releases make it a safe choice. Install it only if you actually need pgvector integration in Airflow.

## Install

pip install apache-airflow-providers-pgvector
uv add apache-airflow-providers-pgvector
poetry add apache-airflow-providers-pgvector

## Installing apache-airflow-providers-pgvector

Before you install: Low friction installation with a pure-Python wheel. Actively maintained with a release 17 days ago. Requires Apache Airflow >=2.11.0, apache-airflow-providers-postgres >=6.6.0, and pgvector >=0.3.1.

License in practice: Licensed under Apache-2.0 (permissive), allowing use in commercial and proprietary projects without restriction.

Quickstart:

pip install apache-airflow-providers-pgvector

from airflow.providers.pgvector.operators import PgVectorOperator
from airflow import DAG

with DAG('vector_dag') as dag:
    task = PgVectorOperator(task_id='vector_task')

Requires Apache Airflow >=2.11.0 and Python >=3.10

Verify before relying:

- Specific operators and hooks provided by the package for pgvector operations.
- Performance characteristics when handling large-scale vector datasets in Airflow DAGs.
- Whether optional common.sql integration provides additional SQL-based vector capabilities.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 203.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

airflow pgvector integration, vector database airflow provider, pgvector operators airflow, postgres vector search airflow, airflow postgres plugin, vector embedding orchestration, airflow provider postgres, airflow-provider, vector-database, postgres-integration

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