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91 lines
3.2 KiB
Markdown
91 lines
3.2 KiB
Markdown
---
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title: Apache Airflow
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weight: 2100
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---
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# Apache Airflow
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[Apache Airflow](https://airflow.apache.org/) is an open-source platform for authoring, scheduling and monitoring data and computing workflows. Airflow uses Python to create workflows that can be easily scheduled and monitored.
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Qdrant is available as a [provider](https://airflow.apache.org/docs/apache-airflow-providers-qdrant/stable/index.html) in Airflow to interface with the database.
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## Prerequisites
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Before configuring Airflow, you need:
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1. A Qdrant instance to connect to. You can set one up in our [installation guide](/documentation/guides/installation/).
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2. A running Airflow instance. You can use their [Quick Start Guide](https://airflow.apache.org/docs/apache-airflow/stable/start.html).
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## Installation
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You can install the Qdrant provider by running `pip install apache-airflow-providers-qdrant` in your Airflow shell.
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**NOTE**: You'll have to restart your Airflow session for the provider to be available.
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## Setting up a connection
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Open the `Admin-> Connections` section of the Airflow UI. Click the `Create` link to create a new [Qdrant connection](https://airflow.apache.org/docs/apache-airflow-providers-qdrant/stable/connections.html).
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You can also set up a connection using [environment variables](https://airflow.apache.org/docs/apache-airflow/stable/howto/connection.html#environment-variables-connections) or an [external secret backend](https://airflow.apache.org/docs/apache-airflow/stable/security/secrets/secrets-backend/index.html).
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## Qdrant hook
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An Airflow hook is an abstraction of a specific API that allows Airflow to interact with an external system.
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```python
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from airflow.providers.qdrant.hooks.qdrant import QdrantHook
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hook = QdrantHook(conn_id="qdrant_connection")
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hook.verify_connection()
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```
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A [`qdrant_client#QdrantClient`](https://pypi.org/project/qdrant-client/) instance is available via `@property conn` of the `QdrantHook` instance for use within your Airflow workflows.
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```python
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from qdrant_client import models
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hook.conn.count("<COLLECTION_NAME>")
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hook.conn.upsert(
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"<COLLECTION_NAME>",
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points=[
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models.PointStruct(id=32, vector=[0.32, 0.12, 0.123], payload={"color": "red"})
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],
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)
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```
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## Qdrant Ingest Operator
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The Qdrant provider also provides a convenience operator for uploading data to a Qdrant collection that internally uses the Qdrant hook.
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```python
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from airflow.providers.qdrant.operators.qdrant import QdrantIngestOperator
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vectors = [
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[0.11, 0.22, 0.33, 0.44],
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[0.55, 0.66, 0.77, 0.88],
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[0.88, 0.11, 0.12, 0.13],
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]
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ids = [32, 21, "b626f6a9-b14d-4af9-b7c3-43d8deb719a6"]
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payload = [{"meta": "data"}, {"meta": "data_2"}, {"meta": "data_3", "extra": "data"}]
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QdrantIngestOperator(
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conn_id="qdrant_connection",
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task_id="qdrant_ingest",
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collection_name="<COLLECTION_NAME>",
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vectors=vectors,
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ids=ids,
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payload=payload,
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)
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```
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## Reference
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- 📦 [Provider package PyPI](https://pypi.org/project/apache-airflow-providers-qdrant/)
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- 📚 [Provider docs](https://airflow.apache.org/docs/apache-airflow-providers-qdrant/stable/index.html)
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- 📄 [Source Code](https://github.com/apache/airflow/tree/main/airflow/providers/qdrant)
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