102 lines
3.6 KiB
Python
102 lines
3.6 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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# data = []
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# measure_data = {}
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# vector = [0.61865162262130161] * 1536
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# measure_data['vector'] = vector
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# measure_data['table_num'] = int(2)
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# measure_data['table_index'] = int(2)
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# measure_data['measure_name'] = "234234"
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# measure_data['measure_value'] = "23432"
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# measure_data['measure_unit'] = "123423"
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# measure_data['file_id'] = "100000"
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#
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# data.append(measure_data)
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# res = client.insert(
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# collection_name=collection_name,
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# data=data,
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# partition_name=partition_name
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# )
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# filter_str = 'file_id == "'+"2122"+'"'
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# res = client.search(
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# collection_name=collection_name, # Replace with the actual name of your collection
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# # Replace with your query vector
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# data=data,
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# limit=3, # Max. number of search results to return
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# search_params={"metric_type": "COSINE", "params": {}}, # Search parameters
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# output_fields=["measure_name", "measure_value", "table_num", "table_index", "measure_unit"],
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# filter=filter_str,
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# partition_name=partition_name
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# )
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# print(f"============================={res}")
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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='1.94.60.103', 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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#
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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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#
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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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#
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# # 创建集合
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# collection = Collection(name="pdf_measure_v4", schema=schema)
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#
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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() |