pygsvector

A python SDK for GaussDB Multimodal Store (Vector Store / Full Text Search), based on SQLAlchemy, compatible with Milvus API.

Installation

• git clone this repo, then install with:

poetry install

• install with pip (already been released):

pip install pygsvector-0.1.0-py3-none-any.whl

Build Doc

You can build document locally with sphinx:

mkdir build
make html

Usage

pygsvector supports two modes:

• Milvus compatible mode: You can use the MilvusLikeClient class to use vector storage in a way similar to the Milvus API
• SQLAlchemy hybrid mode: You can use the vector storage function provided by the GsVecClient class and execute the relational database statement with the SQLAlchemy library. In this mode, you can regard pygsvector as an extension of SQLAlchemy.

Milvus compatible mode

Refer to tests/test_milvus_like_client.py for more examples.

A simple workflow to perform ANN search with GaussDB Vector Store:

• setup a client:

from pygsvector import *

client = MilvusLikeClient(uri="127.0.0.1:2881", user="test@test")

• create a collection with vector index:

test_collection_name = "ann_test"
# define the schema of collection with optional partitions
range_part = GsRangePartition(False, range_part_infos = [
    RangeListPartInfo('p0', 100),
    RangeListPartInfo('p1', 'maxvalue'),
], range_expr='id')
schema = client.create_schema(partitions=range_part)
# define field schema of collection
schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=3)
schema.add_field(field_name="meta", datatype=DataType.JSON, nullable=True)
# define index parameters
idx_params = client.prepare_index_params()
idx_params.add_index(
    field_name='embedding',
    index_type=VecIndexType.GSDISKANN,
    index_name='vidx',
    local_index=True,
    metric_type="L2",
    params={},
)
# create collection
client.create_collection(
    collection_name=test_collection_name,
    schema=schema,
    index_params=idx_params,
)

• insert data to your collection:

# prepare
vector_value1 = [0.748479,0.276979,0.555195]
vector_value2 = [0, 0, 0]
data1 = [{'id': i, 'embedding': vector_value1} for i in range(10)]
data1.extend([{'id': i, 'embedding': vector_value2} for i in range(10, 13)])
data1.extend([{'id': i, 'embedding': vector_value2} for i in range(111, 113)])
# insert data
client.insert(collection_name=test_collection_name, data=data1)

• do ann search:

res = client.search(collection_name=test_collection_name, data=[0,0,0], anns_field='embedding', limit=5, output_fields=['id'])
# For example, the result will be:
# [{'id': 112}, {'id': 111}, {'id': 10}, {'id': 11}, {'id': 12}]

SQLAlchemy hybrid mode

Refer to tests/test_milvus_like_client.py for more examples.

• setup a client:

from pygsvector import *
from sqlalchemy import Column, Integer, JSON
from sqlalchemy import func

client = GsVecClient(uri="127.0.0.1:2881", user="test@test")

• create a partitioned table with vector index:

# create partitioned table
range_part = GsRangePartition(False, range_part_infos=[
    RangeListPartInfo('p0', 100),
    RangeListPartInfo('p1', 'maxvalue'),
], range_expr='id')
test_collection_name = "ann_search_test"
cols = [
    Column('id', Integer, primary_key=True, autoincrement=False),
    Column('embedding', FLOATVECTOR(3)),
    Column('meta', JSON)
]
client.create_table(test_collection_name, columns=cols, partitions=range_part)

# create vector index
client.create_index(
    test_collection_name,
    is_vec_index=True,
    index_name="vidx13",
    column_names=["embedding"],
    index_type="GSDISKANN",
    metric_type="l2",
    local_index=True,
    vidx_params="enable_pq=false",
)

• insert data to your collection:

# insert data
vector_value1 = [0.748479, 0.276979, 0.555195]
vector_value2 = [0, 0, 0]
data1 = [{'id': i, 'embedding': vector_value1} for i in range(10)]
data1.extend([{'id': i, 'embedding': vector_value2} for i in range(10, 13)])
data1.extend([{'id': i, 'embedding': vector_value2} for i in range(111, 113)])
client.insert(test_collection_name, data=data1)

• do ann search:

# perform ann search with basic column selection
res = client.ann_search(
    test_collection_name,
    vec_data=[0, 0, 0],
    vec_column_name='embedding',
    distance_func=l2_distance,
    topk=5,
    output_column_names=['id']  # Legacy parameter
)
# For example, the result will be:
# [(112,), (111,), (10,), (11,), (12,)]

# perform ann search with SQLAlchemy expressions (recommended)
from sqlalchemy import Table, text, func

table = Table(test_collection_name, client.metadata_obj, autoload_with=client.engine)
res = client.ann_search(
    test_collection_name,
    vec_data=[0, 0, 0],
    vec_column_name='embedding',
    distance_func=l2_distance,
    topk=5,
    output_columns=[
        table.c.id,
        table.c.meta,
        (table.c.id + 1000).label('id_plus_1000'),
        text("JSON_EXTRACT(meta, '$.key') as extracted_key")
    ]
)
# For example, the result will be:
# [(112, '{"key": "value"}', 1112, 'value'), ...]

# perform ann search with distance threshold (filter results by distance)
res = client.ann_search(
    test_collection_name,
    vec_data=[0, 0, 0],
    vec_column_name='embedding',
    distance_func=l2_distance,
    with_dist=True,
    topk=10,
    output_column_names=['id'],
    distance_threshold=0.5  # Only return results where distance <= 0.5
)
# Only returns results with distance <= 0.5
# For example, the result will be:
# [(10, 0.0), (11, 0.0), ...]  # Only includes results with distance <= 0.5

ann_search Parameters

The ann_search method supports flexible output column selection through the output_columns parameter:

• output_columns (recommended): Accepts SQLAlchemy Column objects, expressions, or a mix of both
• Column objects: table.c.id, table.c.name
  • Expressions: (table.c.age + 10).label('age_plus_10')
  • String functions: func.concat(table.c.name, ' (', table.c.age, ')').label('name_age')
• output_column_names (legacy): Accepts list of column name strings
• Example: ['id', 'name', 'meta']
• Parameter Priority: output_columns takes precedence over output_column_names when both are provided
• distance_threshold (optional): Filter results by distance threshold
• Type: Optional[float]
  • Only returns results where distance <= threshold
  • Example: distance_threshold=0.5 returns only results with distance <= 0.5
  • Use case: Quality control for similarity search, only return highly similar results
• If you want to use pure SQLAlchemy API with GaussDB dialect, you can just get an SQLAlchemy.engine via client.engine. The engine can also be created as following:

import pygsvector
from sqlalchemy.dialects import registry
from sqlalchemy import create_engine

uri: str = "127.0.0.1:2881"
user: str = "root@test"
password: str = ""
db_name: str = "test"
registry.register("postgresql.gaussdb", "pygsvector", "GaussDBDialect")
connection_str = (
    f"postgresql+gaussdb://{user}:{password}@{uri}/{db_name}"
)
engine = create_engine(connection_str, **kwargs)

• Async engine is also supported:

import pygsvector
from sqlalchemy.dialects import registry
from sqlalchemy.ext.asyncio import create_async_engine

uri: str = "127.0.0.1:2881"
user: str = "root@test"
password: str = ""
db_name: str = "test"
registry.register("postgresql.asyncgaussdb", "pygsvector", "AsyncGaussDBDialect")
connection_str = (
    f"postgresql+asyncgaussdb://{user}:{password}@{uri}/{db_name}"
)
engine = create_async_engine(connection_str)

• For further usage in pure SQLAlchemy mode, please refer to SQLAlchemy