Merge pull request #606 from davidmyriel/dave-docs-interfaces

New images to adhere to figma style guide
This commit is contained in:
Tim Visée
2024-02-13 08:50:55 +01:00
committed by GitHub
5 changed files with 13 additions and 6 deletions
@@ -4,7 +4,7 @@ short_description: "Combining our most popular features to support scalable mach
description: "Combining our most popular features to support scalable machine learning solutions."
social_preview_image: /articles_data/multitenancy/social_preview.png
preview_dir: /articles_data/multitenancy/preview
small_preview_image: /articles_data/multitenancy/scatter-graph.svg
small_preview_image: /articles_data/multitenancy/icon.svg
weight: -101
author: David Myriel
date: 2024-02-06T13:21:00.000Z
@@ -83,7 +83,7 @@ Additionally, each vector can now be allocated to a shard. You can specify the `
```python
client.upsert(
collection_name="{collection_name}",
collection_name="{tenant_data}",
points=[
models.PointStruct(
id=1,
@@ -103,7 +103,7 @@ Keep in mind that the data for each `group_id` is isolated. In the example below
```python
client.upsert(
collection_name="{collection_name}",
collection_name="{tenant_data}",
points=[
models.PointStruct(
id=3,
@@ -121,7 +121,7 @@ The access control setup is completed as you specify the criteria for data retri
```python
client.search(
collection_name="{collection_name}",
collection_name="{tenant_data}",
query_filter=models.Filter(
must=[
models.FieldCondition(
@@ -154,7 +154,7 @@ from qdrant_client import QdrantClient, models
client = QdrantClient("localhost", port=6333)
client.create_collection(
collection_name="{collection_name}",
collection_name="{tenant_data}",
vectors_config=models.VectorParams(size=768, distance=models.Distance.COSINE),
hnsw_config=models.HnswConfigDiff(
payload_m=16,
@@ -167,7 +167,7 @@ client.create_collection(
```python
client.create_payload_index(
collection_name="{collection_name}",
collection_name="{tenant_data}",
field_name="group_id",
field_schema=models.PayloadSchemaType.KEYWORD,
)