apache-airflow-providers-pgvector
Provider package apache-airflow-providers-pgvector for Apache Airflow
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Airflow >=2.11.0 and Python >=3.10
- Low friction installation with a pure-Python wheel.
- Actively maintained with a release 17 days ago.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing use in commercial and proprietary projects without restriction.
last release 2026-07-28 (17 days) · last repo commit 2026-08-14 · 46,490 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 203,029 downloads/mo, #9,639 on PyPI
Alternatives
Verify before relying
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')- 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.
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 on this page. Verify before relying on it.
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
apache-airflow-providers-pgvector on PyPI
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.
Requires Apache Airflow >=2.11.0 and Python >=3.10
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')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 the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesapache-airflowapache-airflow-providers-common-compatapache-airflow-providers-postgrespgvector |
| Maintenance | Actively maintained 17 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 203,029 / month, #9,639 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentFramework :: Apache AirflowFramework :: Apache Airflow :: ProviderIntended Audience :: DevelopersIntended Audience :: System AdministratorsProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: System :: Monitoring |
Evidence: apache_airflow_providers_pgvector-1.7.3-py3-none-any.whl
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See also apache-airflow-providers-pinecone · apache-airflow-providers-informatica · apache-airflow-providers-qdrant · apache-airflow-providers-postgres · apache-airflow-providers-apache-drill · apache-airflow-providers-apache-pinot · pgserver · apache-airflow-providers-github · pg0-embedded · apache-airflow-providers-vertica