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Current state: "Under Discussion"

ISSUE: #1924 #3199 #4201 #4430 #4810 #5603

PRs:

Keywords: string tries hybrid search

Released:


Summary


Motivation

The data types currently supported by Milvus do not include the String type. According to the feedback in the previous issue list, the support of the String data type is expected by many users. One of the most urgent requirements of the String type is to support the primary key of the custom String type. Take the image search application as an example. In actual needs, users need to uniquely identify a picture with a string type value. The string type value can be the name of the picture, the md5 value of the picture, and so on. Since Milvus does not support the string data type, users need to make an additional int64 to string mapping externally, which reduces the efficiency and increases the maintenance cost of the entire application.

In addition to vectors, Milvus2.0 supports data types such as Boolean, integers, floating-point numbers, and more. A collection in Milvus can hold multiple fields for accommodating different data features or properties. Milvus pairs scalar filtering with powerful vector similarity search to offer a modern, flexible platform for analyzing unstructured data. Obviously, scalar filtering should support attributes of type String.

Public Interfaces

When users create a Collection, they can specify a String type Field in the Schema. The Field of the String type can of course be designated as the primary field at the same time.

In the system design, the type of string field is a variable-length character string, but a fixed size limit is set for the character string, such as 64KB, 256KB, etc. If the storage size of the string exceeds the limit value, the insertion fails.

Users can retrieve the previous field of string type according to the search/query interface. Users can add scalar filtering operations for string type Fields in search/query. The filtering operations include: "==", "!=" "<" ,"<=" ,">",">="

A piece of sample code is as follows：


from pymilvus_orm import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> import random
>>> schema = CollectionSchema([
... FieldSchema("film_name", DataType.String, is_primary=True),
... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("film_collection", schema)
>>> # insert
>>> data = [
... ["film_%d"+str(i) for i in range(10)],
... [[random.random() for _ in range(2)] for _ in range(10)],
... ]
>>> collection.insert(data)
>>> # search
>>> res = collection.search(data=[1.0,1.0], 
anns_field="films",
param = {"metric_type":"L2"},
limit=2,
expr = "film_name != 'film_1'")
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