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Merge pull request #606 from davidmyriel/dave-docs-interfaces
New images to adhere to figma style guide
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@@ -4,7 +4,7 @@ short_description: "Combining our most popular features to support scalable mach
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description: "Combining our most popular features to support scalable machine learning solutions."
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social_preview_image: /articles_data/multitenancy/social_preview.png
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preview_dir: /articles_data/multitenancy/preview
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small_preview_image: /articles_data/multitenancy/scatter-graph.svg
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small_preview_image: /articles_data/multitenancy/icon.svg
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weight: -101
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author: David Myriel
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date: 2024-02-06T13:21:00.000Z
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@@ -83,7 +83,7 @@ Additionally, each vector can now be allocated to a shard. You can specify the `
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```python
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client.upsert(
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collection_name="{collection_name}",
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collection_name="{tenant_data}",
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points=[
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models.PointStruct(
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id=1,
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@@ -103,7 +103,7 @@ Keep in mind that the data for each `group_id` is isolated. In the example below
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```python
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client.upsert(
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collection_name="{collection_name}",
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collection_name="{tenant_data}",
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points=[
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models.PointStruct(
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id=3,
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@@ -121,7 +121,7 @@ The access control setup is completed as you specify the criteria for data retri
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```python
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client.search(
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collection_name="{collection_name}",
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collection_name="{tenant_data}",
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query_filter=models.Filter(
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must=[
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models.FieldCondition(
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@@ -154,7 +154,7 @@ from qdrant_client import QdrantClient, models
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client = QdrantClient("localhost", port=6333)
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client.create_collection(
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collection_name="{collection_name}",
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collection_name="{tenant_data}",
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vectors_config=models.VectorParams(size=768, distance=models.Distance.COSINE),
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hnsw_config=models.HnswConfigDiff(
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payload_m=16,
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@@ -167,7 +167,7 @@ client.create_collection(
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```python
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client.create_payload_index(
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collection_name="{collection_name}",
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collection_name="{tenant_data}",
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field_name="group_id",
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field_schema=models.PayloadSchemaType.KEYWORD,
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)
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