Create a collection

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Creating a collection

The topk_sdk.schema module contains the schema definition for the fields in a collection.

from topk_sdk.schema import int, text, vector, vector_index, keyword_index, semantic_index

To create a collection, you need to pass the collection name and the schema to the create method.

client.collections().create(
    "books",
    schema={
        "title": text().required().index(keyword_index()),
        "title_embedding": vector(1536)
            .required()
            .index(vector_index(metric="euclidean")),
        "published_year": int().required(),
    },
)

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Schema

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Data types

TopK supports the following data types. There are more on our roadmap and we are working effortlessly to bring them to you as soon as possible.

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int()

int() is used to define an integer field in the schema.

"published_year": int()

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float()

float() is used to define a float field in the schema.

"price": float()

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bool()

bool() is used to define a boolean field in the schema.

"is_published": bool()

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text()

text() is used to define a text field in the schema.

"title": text()

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f32_vector()

f32_vector() is used to define a vector field with f32 values. You can pass vector dimension as a parameter (required, greater than 0) which will be validated when upserting documents.

"title_embedding": f32_vector(dimension=1536)

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u8_vector()

u8_vector() is used to define a vector field with u8 values. You can pass vector dimension as a parameter (required, greater than 0) which will be validated when upserting documents.

"title_embedding": u8_vector(dimension=1536)

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binary_vector()

binary_vector() is used to define a binary vector packed into u8 values. You can pass vector dimension as a parameter (required, greater than 0) which will be validated when upserting documents.

"title_embedding": binary_vector(dimension=128)

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bytes()

bytes() is used to define a bytes field in the schema.

"image": bytes()

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Properties

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required()

required() is used to mark a field as required. All fields are optional by default.

"title": text().required()

The above example shows how to mark a field title as required.

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Methods

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index()

index() is used to create an index on a field.

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Semantic index

The semantic_index() method is used to create both a vector and a keyword index on a given field. This allows you to do both semantic search and keyword search over the same field. Note that semantic_index() can only be called over text() data type.

"title": text().index(semantic_index())

Optionally, you can pass a model parameter to the semantic_index() method. Supported models are:

• cohere/embed-english-v3
• cohere/embed-multilingual-v3 (default)

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Vector index

The vector_index() method is used to create a vector index. Only fields with f32_vector(), u8_vector(), or binary_vector() data types can be indexed with a vector index.

"title_embedding": vector(dimension=1536).index(vector_index(metric="cosine"))

The above example shows how to create a vector index on a field title_embedding with the cosine similarity metric. However, there are more metrics available:

• euclidean
• cosine
• dot_product
• hamming (only supported for binary_vector() type)

Need support or want to give some feedback? You can join our community or drop us an email at support@topk.io.

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Keyword index

The keyword_index() method is used to create a keyword index on a text() field. index(keyword_index()) can only be called over text() data type.

"title": text().index(keyword_index())