docs: Added Airflow integration docs (#620)

* docs: Added Airflow integration docs

* docs: Update airflow.md

qdrant-landing/content/documentation/_index.md

@@ -11,7 +11,7 @@ weight: 10
 There are three ways to use Qdrant:
 
 1. [**Run a Docker image**](quick-start/) if you don't have a Python development environment. Setup a local Qdrant server and storage in a few moments. 
-2. [**Get the Python client**](https://github.com/qdrant/qdrant-client) if you're familiar with Python. Just `pip install qdrant-client`. The client uses an in-memory database.
+2. [**Get the Python client**](https://github.com/qdrant/qdrant-client) if you're familiar with Python. Just `pip install qdrant-client`. The client also supports an in-memory database.
 3. [**Spin up a Qdrant Cloud cluster:**](cloud/) the recommended method to run Qdrant in production. Read [Quickstart](cloud/quickstart-cloud/) to setup your first instance.
 
 ### Recommended Workflow:

qdrant-landing/content/documentation/frameworks/airflow.md

@@ -0,0 +1,83 @@
+---
+title: Apache Airflow
+weight: 2100
+---
+
+# Airflow
+
+[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.
+
+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.
+
+## Prerequisites
+
+1. A Qdrant instance to connect to. You can refer to our [installation guide](https://qdrant.tech/documentation/guides/installation).
+
+2. A running Airflow instance. You can find the installation instructions [here](https://airflow.apache.org/docs/apache-airflow/stable/start.html).
+
+## Setting up a connection
+
+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).
+
+
+![Qdrant connection](/documentation/frameworks/airflow/connection.png)
+
+A connection can also be set up 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).
+
+## Qdrant hook
+
+An Airflow hook is an abstraction of a specific API that allows Airflow to interact with an external system.
+
+```python
+from airflow.providers.qdrant.hooks.qdrant import QdrantHook
+
+hook = QdrantHook(conn_id="qdrant_connection")
+
+hook.verify_connection()
+```
+
+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.
+
+```python
+from qdrant_client import models
+
+hook.conn.count("<COLLECTION_NAME>")
+
+hook.conn.upsert(
+    "<COLLECTION_NAME>",
+    points=[
+        models.PointStruct(id=32, vector=[0.32, 0.12, 0.123], payload={"color": "red"})
+    ],
+)
+
+```
+
+## Qdrant Ingest Operator
+
+The Qdrant provider also provides a convenience operator for uploading data to a Qdrant collection that internally uses the Qdrant hook.
+
+```python
+from airflow.providers.qdrant.operators.qdrant import QdrantIngestOperator
+
+vectors = [
+    [0.11, 0.22, 0.33, 0.44],
+    [0.55, 0.66, 0.77, 0.88],
+    [0.88, 0.11, 0.12, 0.13],
+]
+ids = [32, 21, "b626f6a9-b14d-4af9-b7c3-43d8deb719a6"]
+payload = [{"meta": "data"}, {"meta": "data_2"}, {"meta": "data_3", "extra": "data"}]
+
+QdrantIngestOperator(
+    conn_id="qdrant_connection"
+    task_id="qdrant_ingest",
+    collection_name="<COLLECTION_NAME>",
+    vectors=vectors,
+    ids=ids,
+    payload=payload,
+)
+```
+
+## Reference
+
+- 📦 [Provider package PyPI](https://pypi.org/project/apache-airflow-providers-qdrant/)
+- 📚 [Provider docs](https://airflow.apache.org/docs/apache-airflow-providers-qdrant/stable/index.html)