pyflink.dataframe.read_milvus

read_milvus(endpoint: str, *, schema: Dict[str, DataType], columns: Optional[List[str]] = None, username: str, password: str, database_name: str, collection_name: str, port: int = 19530, partition_name: str = '_default', partition_key_enabled: bool = False, max_retries: int = 3, search_metric: Literal['L2', 'IP', 'COSINE'] = 'L2', extra_options: Optional[Dict[str, Any]] = None) → pyflink.dataframe.dataframe.DataFrame

Read a Milvus vector search source into a DataFrame.

Parameters

• endpoint – Milvus service endpoint URL.

• schema – Dict of {column_name: DataType} describing the source schema. Required and must not be empty.

• columns – Optional list of columns to expose from the Milvus vector search source. If specified, the Milvus descriptor is created with only these columns so vector search does not add an unsupported search-side projection.

• username – Milvus user name for authentication.

• password – Milvus password for authentication.

• database_name – Source database name.

• collection_name – Source collection name.

• port – Milvus service port. Defaults to 19530.

• partition_name – Source partition name. Defaults to "_default".

• partition_key_enabled – Whether partition-key routing is enabled.

• max_retries – Maximum retry count for connector operations.

• search_metric – Vector search metric, one of "L2", "IP", or "COSINE".

• extra_options – Additional connector options. Values override named options except "connector", which is reserved.

Returns

A new DataFrame backed by the configured Milvus source.