main
HuangHai 1 month ago
parent e9de960359
commit a145af2490

@ -0,0 +1,32 @@
from elasticsearch import Elasticsearch
from Config.Config import ES_CONFIG
from Util.EsMappingUtil import create_vector_index, delete_index # 导入工具函数
# 初始化ES连接
es = Elasticsearch(
hosts=ES_CONFIG["hosts"],
basic_auth=ES_CONFIG["basic_auth"],
verify_certs=ES_CONFIG["verify_certs"],
ssl_show_warn=ES_CONFIG["ssl_show_warn"]
)
def manage_index(action, index_name="knowledge_base", dims=200):
"""管理Elasticsearch索引
:param action: 'create''delete'
:param index_name: 索引名称
:param dims: 向量维度(仅创建时有效)
"""
if action == "create":
return create_vector_index(index_name, dims)
elif action == "delete":
return delete_index(index_name)
else:
raise ValueError("action参数必须是'create''delete'")
# 使用示例
if __name__ == "__main__":
# 删除索引
manage_index("delete")
# 创建索引
manage_index("create", dims=200)

@ -1,54 +0,0 @@
import datetime
from elasticsearch import Elasticsearch
from Config.Config import ES_CONFIG
from T2_Txt2Vec import text_to_embedding
from Util.EsMappingUtil import create_vector_index # 导入工具函数
# 初始化ES连接
es = Elasticsearch(
hosts=ES_CONFIG["hosts"],
basic_auth=ES_CONFIG["basic_auth"],
verify_certs=ES_CONFIG["verify_certs"],
ssl_show_warn=ES_CONFIG["ssl_show_warn"]
)
def save_to_es(text, index_name="knowledge_base"):
"""将文本向量化后保存到ES"""
# 检查是否已存在相同文本
query = {
"query": {
"term": {
"text.keyword": {
"value": text
}
}
}
}
exists = es.search(index=index_name, body=query)
if exists["hits"]["total"]["value"] > 0:
print(f"文档已存在,跳过保存: {text}")
return exists["hits"]["hits"][0]["_id"] # 返回现有文档ID
# 保存新文档
vector = text_to_embedding(text)
doc = {
"text": text,
"vector": vector,
"timestamp": datetime.datetime.now().isoformat()
}
try:
res = es.index(index=index_name, document=doc)
print(f"文档已保存ID: {res['_id']}")
return res["_id"]
except Exception as e:
print(f"保存到ES失败: {str(e)}")
raise
# 使用示例
if __name__ == "__main__":
create_vector_index(dims=200) # 使用工具函数创建索引
save_to_es("如何更换支付宝绑定银行卡")

@ -0,0 +1,20 @@
import datetime
from elasticsearch import Elasticsearch
from Config.Config import ES_CONFIG
from T2_Txt2Vec import text_to_embedding
from Util.EsMappingUtil import create_vector_index # 导入工具函数
# 初始化ES连接
es = Elasticsearch(
hosts=ES_CONFIG["hosts"],
basic_auth=ES_CONFIG["basic_auth"],
verify_certs=ES_CONFIG["verify_certs"],
ssl_show_warn=ES_CONFIG["ssl_show_warn"]
)
# 使用示例
if __name__ == "__main__":
create_vector_index(dims=200) # 使用工具函数创建索引

@ -46,4 +46,19 @@ def create_vector_index(index_name="knowledge_base", dims=200):
return True
except Exception as e:
print(f"操作索引失败: {str(e)}")
raise
def delete_index(index_name):
"""删除Elasticsearch索引"""
try:
if es.indices.exists(index=index_name):
es.indices.delete(index=index_name)
print(f"索引 {index_name} 删除成功")
return True
else:
print(f"索引 {index_name} 不存在")
return False
except Exception as e:
print(f"删除索引失败: {str(e)}")
raise
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