Technology-机器学习-向量数据库 Zvec

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阿里开源的轻量级向量数据库 Zvec.

安装

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pip install zvec

案例

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import zvec

# Define collection schema
schema = zvec.CollectionSchema(
    name="example",
    vectors=zvec.VectorSchema("embedding", zvec.DataType.VECTOR_FP32, 4),
)

# Create collection
collection = zvec.create_and_open(path="./zvec_example", schema=schema)

# Insert documents
collection.insert([
    zvec.Doc(id="doc_1", vectors={"embedding": [0.1, 0.2, 0.3, 0.4]}),
    zvec.Doc(id="doc_2", vectors={"embedding": [0.2, 0.3, 0.4, 0.1]}),
])

# Search by vector similarity
results = collection.query(
    zvec.Query(field_name="embedding", vector=[0.4, 0.3, 0.3, 0.1]),
    topk=10
)

# Results: list of {'id': str, 'score': float, ...}, sorted by relevance
print(results)

测试

用了 3 千多条向量数据，每条数据长度为 1028 位的 INT8 长度，性能测试结果为：10 - 30ms 之间，速度还行。

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Search time: 0.01824737548828125 seconds


[{
  "id": "4488e47a4f2011ea8b746b720afafec6",
  "score": 0.9999998807907104,
  "fields": {},
  "vectors": {}
}, {
  "id": "f6b642064c0411eaac9ef1ec564ea6d8",
  "score": 0.4397549033164978,
  "fields": {},
  "vectors": {}
}, {
  "id": "aeb2daa04f1f11ea8b746b720afafec6",
  "score": 0.36075806617736816,
  "fields": {},
  "vectors": {}
}]