add embeddings

qdrant-landing/content/documentation/embeddings/databricks.md

@@ -0,0 +1,42 @@
+
+---
+title: Databricks Embeddings
+weight: 1500
+aliases:
+  - /documentation/examples/databricks-embeddings-search/
+  - /documentation/tutorials/databricks-embeddings-search/
+  - /documentation/integrations/databricks-embeddings/
+---
+
+# Using Databricks Embeddings with Qdrant
+
+Databricks offers an advanced platform for generating embeddings, especially within large-scale data environments. You can use the following Python code to integrate Databricks-generated embeddings with Qdrant.
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from databricks import sql
+
+# Connect to Databricks SQL endpoint
+connection = sql.connect(server_hostname='your_hostname',
+                         http_path='your_http_path',
+                         access_token='your_access_token')
+
+# Execute a query to get embeddings
+query = "SELECT embedding FROM your_table WHERE id = 1"
+cursor = connection.cursor()
+cursor.execute(query)
+embedding = cursor.fetchone()[0]
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="DatabricksEmbeddings",
+    points=Batch(
+        ids=[1],  # Unique ID for the data point
+        vectors=[embedding],  # Embedding fetched from Databricks
+    )
+)
+```