import json import os from pymilvus import ( connections, utility, FieldSchema, CollectionSchema, DataType, Collection, MilvusClient, db, Function, FunctionType, AnnSearchRequest, WeightedRanker ) from typing import List, Dict, Union, Optional, Any from pypinyin import lazy_pinyin from typing import List, Dict, Tuple, Optional class Milvus_Database: def __init__(self, user=os.getenv("Milvus_USER", "root"), password=os.getenv("Milvus_PASSWORD", "Milvus"), uri=os.getenv("Milvus_URI")): self.client = MilvusClient( uri=uri, token=f"{user}:{password}" ) # self.conn = connections.connect(host="172.18.127.124",port=19530,user=user,password=password) # database = db.create_database(database_name) # self.client.list_databases() def create_database(self, database_name) -> List[str]: database = self.client.create_database(database_name) return self.client.list_databases() def list_databases(self) -> List[str]: return self.client.list_databases() def using_database(self, database_name): self.client.use_database(database_name) def create_hierarchical_collection(self, collection_name, database_name): self.client.use_database(database_name) schema = MilvusClient.create_schema( auto_id=True, enable_dynamic_field=True, ) analyzer_params_custom = { "tokenizer": "jieba", "type": "chinese", "filter": ["cnalphanumonly"] } schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True) # 层次召回中的摘要部分 schema.add_field(field_name="text", datatype=DataType.VARCHAR, max_length=65535, enable_analyzer=True, analyzer_params=analyzer_params_custom) # 层次召回中的完整文档 schema.add_field(field_name="origin_text", datatype=DataType.VARCHAR, max_length=65535, enable_analyzer=True, analyzer_params=analyzer_params_custom) schema.add_field(field_name="bm_25", datatype=DataType.SPARSE_FLOAT_VECTOR) # Qwen3-8B-Embedding 维度4096 schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=4096, ) schema.add_field(field_name="resource", datatype=DataType.VARCHAR, max_length=1024, default_value='EMPTY'), schema.add_field(field_name="img_path", datatype=DataType.VARCHAR, max_length=4096, default_value="EMPTY") schema.add_field(field_name="page_idx", datatype=DataType.INT64, default_value=0) schema.add_field(field_name="origin_id", datatype=DataType.VARCHAR, max_length=256, default_value="EMPTY") # schema.add_field(field_name="img_information",datatype=DataType.Json,default_value="EMPTY") schema.add_field(field_name="header_1", datatype=DataType.VARCHAR, max_length=1024, default_value='EMPTY') schema.add_field(field_name="header_2", datatype=DataType.VARCHAR, max_length=1024, default_value='EMPTY') schema.add_field(field_name="header_3", datatype=DataType.VARCHAR, max_length=1024, default_value='EMPTY') bm25_function = Function( name="bm25_emb", # Function name input_field_names=["text"], # Name of the VARCHAR field containing raw text data output_field_names=["bm_25"], # Name of the SPARSE_FLOAT_VECTOR field reserved to store generated embeddings function_type=FunctionType.BM25, ) schema.add_function(bm25_function) index_params = self.client.prepare_index_params() # Add indexes index_params.add_index( field_name="vector", index_name="vector_index", index_type="HNSW", # "IVF_FLAT". AUTOINDEX metric_type="IP", # COSINE # params={"nlist": 128}, ) index_params.add_index( field_name="bm_25", index_name="bm_25_index", index_type="SPARSE_INVERTED_INDEX", # Index type for sparse vectors metric_type="BM25", # Set to `BM25` when using function to generate sparse vectors params={"inverted_index_algo": "DAAT_MAXSCORE"}, # The ratio of small vector values to be dropped during indexing ) self.client.create_collection( collection_name=collection_name, schema=schema, index_params=index_params ) self.release_collection(collection_name) return "collections created" def create_hybrid_collection(self, collection_name, database_name: str, save_title: bool = True): """ 中文专用混合检索 表 """ self.client.use_database(database_name) schema = MilvusClient.create_schema( auto_id=True, enable_dynamic_field=True, ) analyzer_params_custom = { "tokenizer": "jieba", "type": "chinese", "filter": ["cnalphanumonly"] } schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True) schema.add_field(field_name="text", datatype=DataType.VARCHAR, max_length=65535, enable_analyzer=True, analyzer_params=analyzer_params_custom) schema.add_field(field_name="origin_text", datatype=DataType.VARCHAR, max_length=65535, default_value="EMPTY") schema.add_field(field_name="bm_25", datatype=DataType.SPARSE_FLOAT_VECTOR) schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=1024, ) schema.add_field(field_name="resource", datatype=DataType.VARCHAR, max_length=1024, default_value='EMPTY') schema.add_field(field_name="img_path", datatype=DataType.VARCHAR, max_length=4096, default_value="EMPTY") schema.add_field(field_name="page_idx", datatype=DataType.INT64, default_value=0) schema.add_field(field_name="origin_id", datatype=DataType.VARCHAR, max_length=256, default_value="EMPTY") # schema.add_field(field_name="img_information",datatype=DataType.Json,default_value="EMPTY") if save_title: schema.add_field(field_name="header_1", datatype=DataType.VARCHAR, max_length=1024, default_value='EMPTY') schema.add_field(field_name="header_2", datatype=DataType.VARCHAR, max_length=1024, default_value='EMPTY') schema.add_field(field_name="header_3", datatype=DataType.VARCHAR, max_length=1024, default_value='EMPTY') bm25_function = Function( name="bm25_emb", # Function name input_field_names=["text"], # Name of the VARCHAR field containing raw text data output_field_names=["bm_25"], # Name of the SPARSE_FLOAT_VECTOR field reserved to store generated embeddings function_type=FunctionType.BM25, ) schema.add_function(bm25_function) index_params = self.client.prepare_index_params() # Add indexes index_params.add_index( field_name="vector", index_name="vector_index", index_type="HNSW", # "IVF_FLAT". AUTOINDEX metric_type="IP", # COSINE # params={"nlist": 128}, ) index_params.add_index( field_name="bm_25", index_name="bm_25_index", index_type="SPARSE_INVERTED_INDEX", # Index type for sparse vectors metric_type="BM25", # Set to `BM25` when using function to generate sparse vectors params={"inverted_index_algo": "DAAT_MAXSCORE"}, # The ratio of small vector values to be dropped during indexing ) self.client.create_collection( collection_name=collection_name, schema=schema, index_params=index_params ) self.release_collection(collection_name) return "collections created" def drop_collection(self, collection_name, database_name: str): self.client.use_database(database_name) self.load_collection(collection_name=collection_name) self.client.drop_collection(collection_name) def load_collection(self, collection_name): self.client.load_collection(collection_name=collection_name) res = self.client.get_load_state( collection_name=collection_name ) return res def release_collection(self, collection_name): self.client.release_collection(collection_name=collection_name) res = self.client.get_load_state( collection_name=collection_name ) return res def delete_resource(self, collection_name, resource, database_name): self.client.use_database(database_name) self.load_collection(collection_name) res = self.client.delete( collection_name=collection_name, filter="resource == '{}'".format(resource), ) return res def has_collection(self, collection_name, database_name): """ Before using list collection, You need to using using_database method first """ self.using_database(database_name=database_name) return self.client.has_collection(collection_name) def list_collections(self, database_name): """ Before using list collection, You need to using using_database method first """ self.client.use_database(database_name) return self.client.list_collections() def hierarchical_insert(self, params, collection_name: str, database_name: str): try: self.client.use_database(database_name) self.load_collection(collection_name) res = self.client.insert( collection_name=collection_name, data=params ) return res finally: try: self.client.release_collection(collection_name) except Exception as e: print(f"释放集合失败: {e}") def hybrid_insert(self, params, collection_name: str, database_name: str): try: self.client.use_database(database_name) self.load_collection(collection_name) res = self.client.insert( collection_name=collection_name, data=params ) return res finally: try: self.client.release_collection(collection_name) except Exception as e: print(f"释放集合失败: {e}") # def insert(self, params,collection_name:str = "MarkdownTest",database_name:str = "JXTest"): # """ # params: List[List] # 例子： [[id, header_1, header_2, header_3, text, vector]] # """ # # self.client.insert(collection_name=collection_name, data=params) # db.using_database(database_name) # collection = Collection(collection_name) # mr = collection.insert(params) # return mr def get_all_chunk_by_resource(self, collection_name, database_name, resource): """ return List : """ try: self.client.using_database(db_name=database_name) if not self.client.has_collection(collection_name): return [] else: self.client.load_collection(collection_name) iterator = self.client.query_iterator( collection_name=collection_name, batch_size=5, filter=f"resource like \"{resource}\"", output_fields=["id", "text", "resource", "Header_1", "Header_2", "Header_3"], ) results = [] while True: result = iterator.next() if not result: iterator.close() break results += result return results finally: # 3. 无论成功还是失败，最终释放集合 try: self.client.release_collection(collection_name) except Exception as e: print(f"释放集合失败: {e}") def get_chunk_by_resource(self, collection_name, database_name, resource): """ return List : """ try: self.client.using_database(db_name=database_name) if not self.client.has_collection(collection_name): return [] else: self.client.load_collection(collection_name) iterator = self.client.query_iterator( collection_name=collection_name, batch_size=5, filter=f"resource like \"{resource}\"", output_fields=["id", "text", "resource"], ) results = [] while True: result = iterator.next() if not result: iterator.close() break results += result return results finally: # 3. 无论成功还是失败，最终释放集合 try: self.client.release_collection(collection_name) except Exception as e: print(f"释放集合失败: {e}") def get_chunk_by_id(self, collection_name, database_name, id): """ return List : """ try: self.client.using_database(db_name=database_name) if not self.client.has_collection(collection_name): return [] else: self.client.load_collection(collection_name) iterator = self.client.query_iterator( collection_name=collection_name, batch_size=5, filter=f"id=={id + 1}", output_fields=["id", "text", "resource"], ) results = [] while True: result = iterator.next() if not result: iterator.close() break results += result return results finally: # 3. 无论成功还是失败，最终释放集合 try: self.client.release_collection(collection_name) except Exception as e: print(f"释放集合失败: {e}") def get_text_by_resource(self, collection_name, database_name, resource, logger): """ return List : """ # def prepare_milvus_filter( resource): # """安全构造 Milvus 过滤条件""" # escaped_resource = json.dumps(resource)[1:-1] # return f'resource="{escaped_resource}"' def prepare_milvus_filter(resource): """安全构造 Milvus 过滤条件""" # 替换资源名称中的双引号 safe_resource = resource.replace('"', '\\"') return f'resource == "{safe_resource}"' self.client.using_database(db_name=database_name) # 检查集合是否存在 if not self.client.has_collection(collection_name): logger.info(f"集合 {collection_name} 不存在") return [] else: self.client.load_collection(collection_name) try: # 2. 执行查询 filter_expr = prepare_milvus_filter(resource) iterator = self.client.query_iterator( collection_name=collection_name, batch_size=5, filter=filter_expr, output_fields=["id", "resource", "text"], ) results = [] while True: try: result = iterator.next() if not result: break results.extend(result) except StopIteration: break except Exception as e: print(f"查询迭代出错: {e}") break return results except Exception as e: print(f"查询失败: {e}") return [] finally: # 3. 无论成功还是失败，最终释放集合 try: self.client.release_collection(collection_name) except Exception as e: print(f"释放集合失败: {e}") def embedding_search(self, query_vector: List, top_k=3, collection_name: str = "MarkdownTest", database_name: str = "JXTest") -> List[dict | str]: """ 纯向量检索 """ self.client.use_database(database_name) self.load_collection(collection_name) res = self.client.search( collection_name=collection_name, anns_field="vector", limit=top_k, data=[query_vector], search_params={ "metric_type": "IP", # COSINE "params": {"ef": 64} # "params": {"nprobe": 10} }, output_fields=["id", "Header_1", "Header_2", "Header_3", "text", "resource", "img_path", "page_idx", "origin_id", "*"]) search_results = [] search_resources = [] recall_results = [] try: for i, hits in enumerate(res): for j, hit in enumerate(hits): id = hit["entity"].get("id") search_result_text = hit["entity"].get("text") distance = hit["distance"] page_idx = hit["entity"].get("page_idx") search_result_header_1 = hit["entity"].get("header_1") search_result_header_2 = hit["entity"].get("header_2") search_result_header_3 = hit["entity"].get("header_3") search_result_resource = hit["entity"].get("resource") search_result_img_path = hit["entity"].get("img_path") origin_id = hit["entity"].get("origin_id") dynamic_field_name = hit["entity"].get_properties() # search_score = hit["entity"].get("score") # search_result_img_information = hit["entity"].get("img_information") if search_result_header_1 is None: search_result_header_2 = "" else: search_result_header_1 = f"#{search_result_header_1}" if search_result_header_2 is None: search_result_header_2 = "" else: search_result_header_2 = f"##{search_result_header_2}" if search_result_header_3 is None: search_result_header_3 = "" else: search_result_header_3 = f"###{search_result_header_3}" # search_result = f"#{search_result_header_1}\n{search_result_header_2}\n{search_result_header_3}\n\n{search_result_text}" search_result = search_result_text search_results.append(search_result) search_resources.append({"resource": search_result_resource, "img_path": search_result_img_path}) # search_resources.append({"resource":search_result_resource,"img_path":search_result_img_path,"img_information":search_result_img_imformation}) recall_results.append( {"id": origin_id, "index": i + j + 1, "text": search_result, "resource": search_result_resource, "img_path": search_result_img_path, "score": distance, "page_idx": page_idx, "dynamic_field_name": dynamic_field_name}) # recall_results.append({"index":i+j+1,"text":search_result,"resource":search_result_resource,"img_path":search_result_img_path,"img_information":search_result_img_imformation}) return search_results, search_resources, recall_results finally: # 3. 无论成功还是失败，最终释放集合 try: self.client.release_collection(collection_name) except Exception as e: print(f"释放集合失败: {e}") def hierarchical_search(self, query_vector: List, query, top_k=3, collection_name: str = "MarkdownTest", database_name: str = "JXTest") -> List[dict | str]: """ 层级检索 """ try: self.client.use_database(database_name) self.load_collection(collection_name) search_param_1 = { "data": [query_vector], "anns_field": "vector", "param": { "metric_type": "IP", # COSINE "params": {"ef": 64} # "params": {"nprobe": 10} }, "limit": top_k } request_1 = AnnSearchRequest(**search_param_1) search_param_2 = { "data": [query], "anns_field": "bm_25", "param": { "metric_type": "BM25", }, "limit": top_k } request_2 = AnnSearchRequest(**search_param_2) reqs = [request_1, request_2] ranker = WeightedRanker(0.3, 0.7) res = self.client.hybrid_search( collection_name=collection_name, reqs=reqs, ranker=ranker, limit=top_k, output_fields=["id", "Header_1", "Header_2", "Header_3", "text", "resource", "img_path", "page_idx", "origin_id"] # output_fields=["Header_1","Header_2","Header_3","text","resource","img_path","img_information"] ) search_results = [] search_resources = [] recall_results = [] for i, hits in enumerate(res): for j, hit in enumerate(hits): id = hit["entity"].get("id") search_result_text = hit["entity"].get("text") distance = hit["distance"] page_idx = hit["entity"].get("page_idx") text = hit["entity"].get("text") search_result_header_1 = hit["entity"].get("header_1") search_result_header_2 = hit["entity"].get("header_2") search_result_header_3 = hit["entity"].get("header_3") search_result_resource = hit["entity"].get("resource") search_result_img_path = hit["entity"].get("img_path") origin_id = hit["entity"].get("origin_id") origin_text = hit["entity".get("origin_text")] # search_result_img_information = hit["entity"].get("img_information") if search_result_header_1 is None: search_result_header_2 = "" else: search_result_header_1 = f"#{search_result_header_1}" if search_result_header_2 is None: search_result_header_2 = "" else: search_result_header_2 = f"##{search_result_header_2}" if search_result_header_3 is None: search_result_header_3 = "" else: search_result_header_3 = f"###{search_result_header_3}" # search_result = f"#{search_result_header_1}\n{search_result_header_2}\n{search_result_header_3}\n\n{search_result_text}" search_result = search_result_text search_results.append(search_result) search_resources.append({"resource": search_result_resource, "img_path": search_result_img_path}) # search_resources.append({"resource":search_result_resource,"img_path":search_result_img_path,"img_information":search_result_img_imformation}) # recall_results.append({"index":i+j+1,"id":origin_id,"text":origin_text,"resource":search_result_resource,"img_path":search_result_img_path,"score":distance,"page_idx":page_idx}) recall_results.append( {"index": i + j + 1, "id": origin_id, "text": text, "resource": search_result_resource, "img_path": search_result_img_path, "score": distance, "page_idx": page_idx}) self.release_collection(collection_name) return search_results, search_resources, recall_results finally: try: self.client.release_collection(collection_name) except Exception as e: print(f"释放集合失败: {e}") def hybrid_search(self, query_vector: List, query, top_k=3, collection_name: str = "MarkdownTest", database_name: str = "JXTest", embedding_rate=0.3) -> List[dict | str]: """ """ try: self.client.use_database(database_name) self.load_collection(collection_name) search_param_1 = { "data": [query_vector], "anns_field": "vector", "param": { "metric_type": "IP", # COSINE "params": {"ef": 64} # "params": {"nprobe": 10} }, "limit": top_k } request_1 = AnnSearchRequest(**search_param_1) search_param_2 = { "data": [query], "anns_field": "bm_25", "param": { "metric_type": "BM25", }, "limit": top_k } request_2 = AnnSearchRequest(**search_param_2) reqs = [request_1, request_2] ranker = WeightedRanker(float(embedding_rate), 1 - float(embedding_rate)) res = self.client.hybrid_search( collection_name=collection_name, reqs=reqs, ranker=ranker, limit=top_k, output_fields=["id", "Header_1", "Header_2", "Header_3", "text", "resource", "img_path", "page_idx", "origin_id", "origin_text"] # output_fields=["Header_1","Header_2","Header_3","text","resource","img_path","img_information"] ) search_results = [] search_resources = [] recall_results = [] for i, hits in enumerate(res): for j, hit in enumerate(hits): id = hit["entity"].get("id") search_result_text = hit["entity"].get("text") distance = hit["distance"] page_idx = hit["entity"].get("page_idx") search_result_header_1 = hit["entity"].get("Header_1") search_result_header_2 = hit["entity"].get("Header_2") search_result_header_3 = hit["entity"].get("Header_3") search_result_resource = hit["entity"].get("resource") search_result_img_path = hit["entity"].get("img_path") origin_id = hit["entity"].get("origin_id") header_1_num = len(search_result_header_1) if search_result_header_1 else 0 header_2_num = len(search_result_header_2) if search_result_header_2 else 0 header_3_num = len(search_result_header_3) if search_result_header_3 else 0 origin_text = hit["entity"].get("origin_text") # search_result_img_information = hit["entity"].get("img_information") if search_result_header_1 is None: search_result_header_2 = "" else: search_result_header_1 = f"#{search_result_header_1}" if search_result_header_2 is None: search_result_header_2 = "" else: search_result_header_2 = f"##{search_result_header_2}" if search_result_header_3 is None: search_result_header_3 = "" else: search_result_header_3 = f"###{search_result_header_3}" # search_result = f"#{search_result_header_1}\n{search_result_header_2}\n{search_result_header_3}\n\n{search_result_text}" search_result = search_result_text origin_text = search_result[header_1_num + header_2_num + header_3_num + 3:] search_results.append(search_result) search_resources.append({"resource": search_result_resource, "img_path": search_result_img_path}) # 获取片段的下文，并且拼接 # next_chunk = self.get_chunk_by_id(collection_name=collection_name,database_name=database_name,id =id) # next_text = next_chunk[0].get("text") # search_result += next_text # origin_text += next_text # search_resources.append({"resource":search_result_resource,"img_path":search_result_img_path,"img_information":search_result_img_imformation}) recall_results.append( {"index": i + j + 1, "id": origin_id, "text": search_result, "resource": search_result_resource, "img_path": search_result_img_path, "score": distance, "page_idx": page_idx, "origin_text": origin_text}) # recall_results.append({"index":i+j+1,"text":search_result,"resource":search_result_resource,"img_path":search_result_img_path,"img_information":search_result_img_imformation}) self.release_collection(collection_name) return search_results, search_resources, recall_results finally: try: self.client.release_collection(collection_name) except Exception as e: print(f"释放集合失败: {e}") # return search_results def hybrid_search_batch( self, query_vectors: List[List[float]], queries: List[str], top_k: int = 3, collection_name: str = "MarkdownTest", database_name: str = "JXTest", embedding_rate: float = 0.3 ) -> Tuple[List[List[Dict]], List[List[Dict]], List[List[Dict]]]: """ 执行批量混合检索（稠密向量 + BM25 稀疏检索），对每个查询分别进行 hybrid search。 Args: query_vectors (List[List[float]]): 每个查询对应的稠密向量列表，形状为 [N, dim] queries (List[str]): 原始文本查询列表，长度为 N top_k (int): 返回每个查询的 top-k 结果 collection_name (str): 集合名称 database_name (str): 数据库名称 embedding_rate (float): 稠密向量检索结果的权重（BM25 权重 = 1 - embedding_rate） Returns: Tuple[ List[List[str]]: 每个查询对应的结果文本列表（外层 list 是 query 数量，内层是 top-k 结果）, List[List[dict]]: 每个查询对应的资源信息列表 , List[List[dict]]: 每个查询对应的完整召回详情列表（含 score、id、origin_text 等） ] """ if len(query_vectors) != len(queries): raise ValueError("query_vectors 和 queries 的数量必须一致") try: self.client.use_database(database_name) self.load_collection(collection_name) all_search_results = [] # [[res1_q1, res2_q1], [res1_q2, ...]] all_search_resources = [] all_recall_results = [] for i, (vec, text) in enumerate(zip(query_vectors, queries)): try: # 构建稠密向量请求 req1 = AnnSearchRequest( data=[vec], anns_field="vector", param={"metric_type": "IP", "params": {"ef": 64}}, limit=top_k ) # 构建稀疏向量请求（BM25） req2 = AnnSearchRequest( data=[text], anns_field="bm_25", param={"metric_type": "BM25"}, limit=top_k ) # 混合搜索 ranker = WeightedRanker(embedding_rate, 1 - embedding_rate) res = self.client.hybrid_search( collection_name=collection_name, reqs=[req1, req2], ranker=ranker, limit=top_k, output_fields=[ "id", "Header_1", "Header_2", "Header_3", "text", "resource", "img_path", "page_idx", "origin_id", "origin_text" ], timeout=30 ) # 解析单个查询的结果 search_results = [] search_resources = [] recall_results = [] for j, hit in enumerate(res[0]): # res 是 List[List[Hit]], res[0] 对应第一个 request 的 hits entity = hit["entity"] id_val = entity.get("id") origin_id = entity.get("origin_id") text_val = entity.get("text") or "" header_1 = entity.get("Header_1") or "" header_2 = entity.get("Header_2") or "" header_3 = entity.get("Header_3") or "" resource = entity.get("resource") img_path = entity.get("img_path") page_idx = entity.get("page_idx") distance = hit["distance"] origin_text = entity.get("origin_text") or text_val # 格式化标题 header_1 = f"#{header_1}" if header_1 else "" header_2 = f"##{header_2}" if header_2 else "" header_3 = f"###{header_3}" if header_3 else "" # 组合内容（可根据需要调整） combined_text = "\n".join(filter(None, [header_1, header_2, header_3, text_val])).strip() search_results.append(combined_text) search_resources.append({"resource": resource, "img_path": img_path}) recall_results.append({ "keyword": text, "index": j + 1, "id": origin_id, "text": combined_text, "resource": resource, "img_path": img_path, "score": distance, "page_idx": page_idx, "origin_text": origin_text }) all_search_results.append(search_results) all_search_resources.append(search_resources) all_recall_results.append(recall_results) except Exception as e: print(f"第 {i + 1} 个查询 '{text}' 检索失败: {e}") # 失败时返回空列表占位 all_search_results.append([]) all_search_resources.append([]) all_recall_results.append([]) return all_search_results, all_search_resources, all_recall_results except Exception as e: print(f"批量混合检索整体失败: {e}") raise finally: try: self.client.release_collection(collection_name) except Exception as e: print(f"释放集合失败: {e}") # return search_results def close(self): self.client.close() def sanitize_collection_name(name: str) -> str: """ 中文转换为拼音 """ def is_valid_milvus_collection_name(name: str) -> bool: """检查名称是否符合 Milvus 集合命名规则""" import re pattern = r'^[a-zA-Z_][a-zA-Z0-9_]*$' # 首字符必须是字母或_，后续可以是字母、数字、_ return bool(re.fullmatch(pattern, name)) if is_valid_milvus_collection_name(name): return name pinyin = "_".join(lazy_pinyin(name)) # 确保首字符是字母或下划线 if not pinyin[0].isalpha() and pinyin[0] != "_": pinyin = f"_{pinyin}" return pinyin ##应用示例 # if __name__ == '__main__': # #创建collection # database = Milvus_Database("XIAN") # result = database.create_connection("MarkdownTest",drop_=False) # print(result) # print(database.list_partitions("MarkdownTest")) # 创建db # from pymilvus import connections, db # # # conn = Milvus_Database(user='root', # password='Milvus', # uri="http://192.168.0.46:19530") # print(conn.list_collections("XIAN")) # conn = connections.connect(user='root', # password='Milvus', # uri="http://172.18.30.165:19530") # database = db.create_database("JXTest") # db.using_database("JXTest")