69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection,MilvusClient
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from config import MILVUS_CLIENT
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import time
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from datetime import datetime, timedelta
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def create_partition_by_hour(current_hour):
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# 连接到 Milvus 服务器
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connections.connect("default",uri=MILVUS_CLIENT)
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# 获取集合
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collection_name = "pdf_measure_v4"
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collection = Collection(collection_name)
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# 创建当前小时的分区
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partition_name = f"partition_{current_hour}"
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if not collection.has_partition(partition_name):
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collection.create_partition(partition_name)
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print(f"Created partition: {partition_name}")
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partition = collection.partition(partition_name)
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partition.load()
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# 获取所有分区
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partitions = collection.partitions
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# 删除所有分区（除了默认分区和当前分区）
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for partition in partitions:
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name = partition.name
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if name not in ["_default", partition_name]: # 保留默认分区
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pre_partition = collection.partition(name)
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pre_partition.release()
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collection.drop_partition(name)
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print(f"Partition '{name}' deleted.")
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from pymilvus import connections, CollectionSchema, Collection,utility,FieldSchema,DataType
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# 连接到 B 服务器上的 Milvus
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# connections.connect(host='124.70.129.232', port='19530')# 测试服务器
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connections.connect(host='127.0.0.1', port='19530')# 测试服务器
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# # 获取集合列表
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utility.drop_collection("pdf_measure_v4")
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# 定义字段
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fields = [
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FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
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FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=1536),
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FieldSchema(name="table_num", dtype=DataType.INT16),
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FieldSchema(name="table_index", dtype=DataType.INT16),
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FieldSchema(name="measure_name", dtype=DataType.VARCHAR, max_length=200),
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FieldSchema(name="measure_value", dtype=DataType.VARCHAR, max_length=200),
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FieldSchema(name="file_id", dtype=DataType.VARCHAR, max_length=200),
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FieldSchema(name="measure_unit", dtype=DataType.VARCHAR, max_length=200)
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]
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# 定义集合的 schema
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schema = CollectionSchema(fields=fields, description="My Milvus collection")
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# 创建集合
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collection = Collection(name="pdf_measure_v4", schema=schema)
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collection = Collection("pdf_measure_v4")
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index_params = {
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"index_type": "IVF_FLAT",
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"metric_type": "COSINE",
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"params": {"nlist": 128}
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}
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collection.create_index(field_name="vector", index_params=index_params)
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collection.load() |