Yandex CloudCompatible with Milvus 2.6.x

This topic describes how to configure and use Yandex Cloud embedding functions in Milvus.

Choose an embedding model

Milvus supports Yandex Cloud AI Studio text vectorization models through the yc provider. In the Function parameters, set model_name to the Yandex Cloud model URI that Milvus should call.

For example, Yandex Text Embeddings for documents use a model URI such as emb://<folder_ID>/text-search-doc/latest and return 256-dimensional vectors. For available model URIs and dimensions, refer to Text vectorization models.

Configure credentials

Milvus must know your Yandex Cloud API key before it can request embeddings. You can configure the API key in milvus.yaml or through an environment variable.

Option 1: Configuration file

Store your API key in milvus.yaml and point the Yandex Cloud provider to the credential label.

# milvus.yaml
credential:
  yandex_apikey:
    apikey: <YOUR_YC_API_KEY>

function:
  textEmbedding:
    providers:
      yc:
        credential: yandex_apikey
        # url: https://llm.api.cloud.yandex.net/foundationModels/v1/textEmbedding

Option 2: Environment variable

If no matching credential is configured in milvus.yaml, Milvus can read the Yandex Cloud API key from the following environment variable:

Variable	Required?	Description
MILVUS_YC_API_KEY	Yes	Yandex Cloud API key used by the Milvus service to call Yandex Cloud AI Studio.

Use embedding function

Once credentials are configured, define a schema with an input text field and an output vector field, then add a Yandex Cloud embedding Function to the schema.

from pymilvus import MilvusClient, DataType, Function, FunctionType

client = MilvusClient(uri="http://localhost:19530")

schema = client.create_schema()
schema.add_field("id", DataType.INT64, is_primary=True, auto_id=False)
schema.add_field("document", DataType.VARCHAR, max_length=9000)
schema.add_field("dense", DataType.FLOAT_VECTOR, dim=256)

text_embedding_function = Function(
    name="yandex_cloud_embedding",
    function_type=FunctionType.TEXTEMBEDDING,
    input_field_names=["document"],
    output_field_names=["dense"],
    params={
        "provider": "yc",
        "model_name": "emb://<folder_ID>/text-search-doc/latest",
        "credential": "yandex_apikey",
        "dim": "256",
    },
)

schema.add_function(text_embedding_function)

Yandex Cloud-specific parameters

Parameter	Required?	Description	Value / Example
provider	Yes	The embedding model provider to use.	"yc"
model_name	Yes	The Yandex Cloud model URI to call.	"emb://<folder_ID>/text-search-doc/latest"
credential	No	The label of a credential defined in the top-level credential: section of milvus.yaml.	"yandex_apikey"
dim	No	The output vector dimension. If set, the value must match the dimension of the output vector field.	"256"

Next steps

After configuring the embedding function, refer to Embedding Function Overview for guidance on creating indexes, inserting data, and running semantic search.