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Function search_vector_search

database_utils.py:68–102  ·  view source on GitHub ↗

level_mode: 0: 原始节点 1: 聚合节点 2: 所有节点

(working_dir,query,topk=10,level_mode=2)

Source from the content-addressed store, hash-verified

66 # )
67
68def search_vector_search(working_dir,query,topk=10,level_mode=2):
69 '''
70 level_mode: 0: 原始节点
71 1: 聚合节点
72 2: 所有节点
73 '''
74 if level_mode==0:
75 filter_filed=" level == 0 "
76 elif level_mode==1:
77 filter_filed=" level > 0 "
78 # elif level_mode==2:
79 # filter_filed=" level < 58736"
80 else:
81 filter_filed=""
82 dataset=os.path.basename(working_dir)
83 if os.path.exists(f"{working_dir}/milvus_demo.db"):
84 print(f"{working_dir}milvus_demo.db already exists, using it")
85 milvus_client = MilvusClient(uri=f"{working_dir}/milvus_demo.db")
86 else:
87 print("milvus_demo.db not found, using default")
88 milvus_client = MilvusClient(uri=f"/data/zyz/trag_ds/exp/ds_hire_cs20_top20_chunk5/{dataset}/milvus_demo.db")
89 collection_name = "entity_collection"
90 # query_embedding = emb_text(query)
91 search_results = milvus_client.search(
92 collection_name=collection_name,
93 data=query,
94 limit=topk,
95 params={"metric_type": "IP", "params": {}},
96 filter=filter_filed,
97 output_fields=["entity_name", "description","parent","level","source_id"],
98 )
99 # print(search_results)
100 extract_results=[(i['entity']['entity_name'],i["entity"]["parent"],i["entity"]["description"],i["entity"]["source_id"])for i in search_results[0]]
101 # print(extract_results)
102 return extract_results
103def create_db_table_mysql(working_dir):
104 con = pymysql.connect(host='localhost',port=4321, user='root',
105 passwd='123', charset='utf8mb4')

Callers 1

query_graphFunction · 0.90

Calls

no outgoing calls

Tested by

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