From 5eeaa77e22ff72de588f3c46d5ee0835ba4342be Mon Sep 17 00:00:00 2001 From: wangmeihua <13383952685@163.com> Date: Tue, 29 Jul 2025 17:51:01 +0800 Subject: [PATCH] =?UTF-8?q?=E8=B0=83=E7=94=A8neo4j=E6=9C=8D=E5=8A=A1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- rag/milvus_connection.py | 298 ++++++++++++++------------------------- 1 file changed, 104 insertions(+), 194 deletions(-) diff --git a/rag/milvus_connection.py b/rag/milvus_connection.py index 9d08f31..8f552b5 100644 --- a/rag/milvus_connection.py +++ b/rag/milvus_connection.py @@ -1,10 +1,8 @@ -from appPublic.jsonConfig import getConfig import os from appPublic.log import debug, error, info -import yaml -from threading import Lock from llmengine.base_connection import connection_register from typing import Dict, List, Any +import numpy as np import aiohttp from aiohttp import ClientSession, ClientTimeout from langchain_core.documents import Document @@ -12,49 +10,52 @@ from langchain_text_splitters import RecursiveCharacterTextSplitter import uuid from datetime import datetime from filetxt.loader import fileloader -from llmengine.kgc import KnowledgeGraph -import numpy as np -from py2neo import Graph -from scipy.spatial.distance import cosine import time from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type import traceback import asyncio import re - # 嵌入缓存 EMBED_CACHE = {} class MilvusConnection: - _instance = None - _lock = Lock() - - def __new__(cls): - with cls._lock: - if cls._instance is None: - cls._instance = super(MilvusConnection, cls).__new__(cls) - cls._instance._initialized = False - return cls._instance - def __init__(self): - if self._initialized: - return + pass + + @retry(stop = stop_after_attempt(3)) + async def _make_neo4japi_request(self, action: str, params: Dict[str, Any]) -> Dict[str, Any]: + debug(f"开始API请求：action={action}, params={params}") try: - config = getConfig() - self.neo4j_uri = config['neo4j']['uri'] - self.neo4j_user = config['neo4j']['user'] - self.neo4j_password = config['neo4j']['password'] - except KeyError as e: - error(f"配置文件缺少必要字段: {str(e)}") - raise RuntimeError(f"配置文件缺少必要字段: {str(e)}") - self._initialized = True - info("Neo4jConnection initialized") + async with ClientSession(timeout=ClientTimeout(total=300)) as session: + url = f"http://localhost:8885/v1/{action}" + debug(f"发起POST请求：{url}") + async with session.post( + url, + headers={'Content-Type': 'application/json'}, + json=params + ) as response: + debug(f"收到相应: status={response.status}, headers={response.headers}") + respose_text = await response.text() + debug(f"响应内容: {respose_text}") + result = await response.json() + debug(f"API响应内容: {result}") + if response.status == 400: + debug(f"客户端错误，状态码: {response.status},返回响应: {result}") + return result + if response.status != 200: + error(f"API 调用失败，动作: {action}, 状态码: {response.status}, 响应: {response_text}") + raise RuntimeError(f"API 调用失败: {response.status}") + debug(f"API 调用成功: {action}, 响应: {result}") + return result + except Exception as e: + error(f"API 调用失败: {action}, 错误: {str(e)}, 堆栈: {traceback.format_exc()}") + raise RuntimeError(f"API 调用失败: {str(e)}") @retry(stop=stop_after_attempt(3)) async def _make_api_request(self, action: str, params: Dict[str, Any]) -> Dict[str, Any]: debug(f"开始 API 请求: action={action}, params={params}") try: - async with ClientSession(timeout=ClientTimeout(total=10)) as session: + async with ClientSession(timeout=ClientTimeout(total=300)) as session: url = f"http://localhost:8886/v1/{action}" debug(f"发起 POST 请求: {url}") async with session.post( @@ -377,15 +378,28 @@ class MilvusConnection: f"三元组抽取耗时: {timings['extract_triples']:.2f} 秒, 抽取到 {len(unique_triples)} 个三元组: {unique_triples[:5]}") # Neo4j 插入 - debug(f"抽取到 {len(unique_triples)} 个三元组，插入 Neo4j") + debug(f"抽取到 {len(unique_triples)} 个三元组，调用Neo4j服务插入") start_neo4j = time.time() if unique_triples: - kg = KnowledgeGraph(triples=unique_triples, document_id=document_id, - knowledge_base_id=knowledge_base_id, userid=userid) - kg.create_graphnodes() - kg.create_graphrels() - kg.export_data() - info(f"文件 {file_path} 三元组成功插入 Neo4j") + neo4j_result = await self._make_neo4japi_request("inserttriples", { + "triples":unique_triples, + "document_id": document_id, + "knowledge_base_id": knowledge_base_id, + "userid": userid + }) + debug(f"Neo4j服务响应: {neo4j_result}") + if neo4j_result.get("status") != "success": + timings["insert_neo4j"] = time.time() - start_neo4j + timings["total"] = time.time() - start_total + return{ + "status": "error", + "document_id": document_id, + "collection_name": collection_name, + "timings": timings, + "message": f"Neo4j 三元组插入失败: {neo4j_result.get('message', '未知错误')}", + "status_code": 400 + } + info(f"文件 {file_path} 三元组成功插入 Neo4j: {neo4j_result.get('message')}") else: debug(f"文件 {file_path} 未抽取到三元组") timings["insert_neo4j"] = time.time() - start_neo4j @@ -519,33 +533,28 @@ class MilvusConnection: document_ids = milvus_result.get("document_id", "").split(",") if milvus_result.get("document_id") else [] + # 调用 Neo4j 删除端点 neo4j_deleted_nodes = 0 neo4j_deleted_rels = 0 - - # 删除 Neo4j 数据 for doc_id in document_ids: if not doc_id: continue try: - graph = Graph(self.neo4j_uri, auth=(self.neo4j_user, self.neo4j_password)) - query = """ - MATCH (n {document_id: $document_id}) - OPTIONAL MATCH (n)-[r {document_id: $document_id}]->() - WITH collect(r) AS rels, collect(n) AS nodes - FOREACH (r IN rels | DELETE r) - FOREACH (n IN nodes | DELETE n) - RETURN size(nodes) AS node_count, size(rels) AS rel_count, [r IN rels | type(r)] AS rel_types - """ - result = graph.run(query, document_id=doc_id).data() - nodes_deleted = result[0]['node_count'] if result else 0 - rels_deleted = result[0]['rel_count'] if result else 0 - rel_types = result[0]['rel_types'] if result else [] - info( - f"成功删除 document_id={doc_id} 的 {nodes_deleted} 个 Neo4j 节点和 {rels_deleted} 个关系，关系类型: {rel_types}") + debug(f"调用 Neo4j 删除文档端点: document_id={doc_id}") + neo4j_result = await self._make_neo4japi_request("deletedocument", { + "document_id": doc_id + }) + if neo4j_result.get("status") != "success": + error( + f"Neo4j 删除文档失败: document_id={doc_id}, 错误: {neo4j_result.get('message', '未知错误')}") + continue + nodes_deleted = neo4j_result.get("nodes_deleted", 0) + rels_deleted = neo4j_result.get("rels_deleted", 0) neo4j_deleted_nodes += nodes_deleted neo4j_deleted_rels += rels_deleted + info(f"成功删除 document_id={doc_id} 的 {nodes_deleted} 个 Neo4j 节点和 {rels_deleted} 个关系") except Exception as e: - error(f"删除 document_id={doc_id} 的 Neo4j 三元组失败: {str(e)}") + error(f"删除 document_id={doc_id} 的 Neo4j 数据失败: {str(e)}") continue return { @@ -584,30 +593,30 @@ class MilvusConnection: deleted_files = milvus_result.get("deleted_files", []) - # 删除 Neo4j 数据 + # 新增：调用 Neo4j 删除知识库端点 neo4j_deleted_nodes = 0 neo4j_deleted_rels = 0 try: - debug(f"尝试连接 Neo4j: uri={self.neo4j_uri}, user={self.neo4j_user}") - graph = Graph(self.neo4j_uri, auth=(self.neo4j_user, self.neo4j_password)) - debug("Neo4j 连接成功") - query = """ - MATCH (n {userid: $userid, knowledge_base_id: $knowledge_base_id}) - OPTIONAL MATCH (n)-[r {userid: $userid, knowledge_base_id: $knowledge_base_id}]->() - WITH collect(r) AS rels, collect(n) AS nodes - FOREACH (r IN rels | DELETE r) - FOREACH (n IN nodes | DELETE n) - RETURN size(nodes) AS node_count, size(rels) AS rel_count, [r IN rels | type(r)] AS rel_types - """ - result = graph.run(query, userid=userid, knowledge_base_id=knowledge_base_id).data() - nodes_deleted = result[0]['node_count'] if result else 0 - rels_deleted = result[0]['rel_count'] if result else 0 - rel_types = result[0]['rel_types'] if result else [] - neo4j_deleted_nodes += nodes_deleted - neo4j_deleted_rels += rels_deleted - info(f"成功删除 {nodes_deleted} 个 Neo4j 节点和 {rels_deleted} 个关系，关系类型: {rel_types}") + debug(f"调用 Neo4j 删除知识库端点: userid={userid}, knowledge_base_id={knowledge_base_id}") + neo4j_result = await self._make_neo4japi_request("deleteknowledgebase", { + "userid": userid, + "knowledge_base_id": knowledge_base_id + }) + if neo4j_result.get("status") == "success": + neo4j_deleted_nodes = neo4j_result.get("nodes_deleted", 0) + neo4j_deleted_rels = neo4j_result.get("rels_deleted", 0) + info(f"成功删除 {neo4j_deleted_nodes} 个 Neo4j 节点和 {neo4j_deleted_rels} 个关系") + else: + error(f"Neo4j 删除知识库失败: {neo4j_result.get('message', '未知错误')}") + return { + "status": "success", + "collection_name": collection_name, + "deleted_files": deleted_files, + "message": f"成功删除 Milvus 知识库，{neo4j_deleted_nodes} 个 Neo4j 节点，{neo4j_deleted_rels} 个 Neo4j 关系，但 Neo4j 删除失败: {neo4j_result.get('message')}", + "status_code": 200 + } except Exception as e: - error(f"删除 Neo4j 数据失败: {str(e)}") + error(f"Neo4j 删除知识库失败: {str(e)}") return { "status": "success", "collection_name": collection_name, @@ -672,119 +681,6 @@ class MilvusConnection: error(f"实体识别服务调用失败: {str(e)}") return [] - async def _match_triplets(self, query: str, query_entities: List[str], userid: str, knowledge_base_id: str) -> List[Dict]: - """匹配查询实体与 Neo4j 中的三元组""" - start_time = time.time() # 记录开始时间 - matched_triplets = [] - ENTITY_SIMILARITY_THRESHOLD = 0.8 - - try: - graph = Graph(self.neo4j_uri, auth=(self.neo4j_user, self.neo4j_password)) - debug(f"已连接到 Neo4j: {self.neo4j_uri}") - neo4j_connect_time = time.time() - start_time - debug(f"Neo4j 连接耗时: {neo4j_connect_time:.3f} 秒") - - matched_names = set() - entity_match_start = time.time() - for entity in query_entities: - normalized_entity = entity.lower().strip() - query = """ - MATCH (n {userid: $userid, knowledge_base_id: $knowledge_base_id}) - WHERE toLower(n.name) CONTAINS $entity - OR apoc.text.levenshteinSimilarity(toLower(n.name), $entity) > 0.7 - RETURN n.name, apoc.text.levenshteinSimilarity(toLower(n.name), $entity) AS sim - ORDER BY sim DESC - LIMIT 100 - """ - try: - results = graph.run(query, userid=userid, knowledge_base_id=knowledge_base_id, entity=normalized_entity).data() - for record in results: - matched_names.add(record['n.name']) - debug(f"实体 {entity} 匹配节点: {record['n.name']} (Levenshtein 相似度: {record['sim']:.2f})") - except Exception as e: - debug(f"模糊匹配实体 {entity} 失败: {str(e)}") - continue - entity_match_time = time.time() - entity_match_start - debug(f"实体匹配耗时: {entity_match_time:.3f} 秒") - - triplets = [] - if matched_names: - triplet_query_start = time.time() - query = """ - MATCH (h {userid: $userid, knowledge_base_id: $knowledge_base_id})-[r {userid: $userid, knowledge_base_id: $knowledge_base_id}]->(t {userid: $userid, knowledge_base_id: $knowledge_base_id}) - WHERE h.name IN $matched_names OR t.name IN $matched_names - RETURN h.name AS head, r.name AS type, t.name AS tail - LIMIT 100 - """ - try: - results = graph.run(query, userid=userid, knowledge_base_id=knowledge_base_id, matched_names=list(matched_names)).data() - seen = set() - for record in results: - head, type_, tail = record['head'], record['type'], record['tail'] - triplet_key = (head.lower(), type_.lower(), tail.lower()) - if triplet_key not in seen: - seen.add(triplet_key) - triplets.append({ - 'head': head, - 'type': type_, - 'tail': tail, - 'head_type': '', - 'tail_type': '' - }) - debug(f"从 Neo4j 加载三元组: knowledge_base_id={knowledge_base_id}, 数量={len(triplets)}") - except Exception as e: - error(f"检索三元组失败: knowledge_base_id={knowledge_base_id}, 错误: {str(e)}") - return [] - triplet_query_time = time.time() - triplet_query_start - debug(f"Neo4j 三元组查询耗时: {triplet_query_time:.3f} 秒") - - if not triplets: - debug(f"知识库 knowledge_base_id={knowledge_base_id} 无匹配三元组") - return [] - - embedding_start = time.time() - texts_to_embed = query_entities + [t['head'] for t in triplets] + [t['tail'] for t in triplets] - embeddings = await self._get_embeddings(texts_to_embed) - entity_vectors = {entity: embeddings[i] for i, entity in enumerate(query_entities)} - head_vectors = {t['head']: embeddings[len(query_entities) + i] for i, t in enumerate(triplets)} - tail_vectors = {t['tail']: embeddings[len(query_entities) + len(triplets) + i] for i, t in enumerate(triplets)} - debug(f"成功获取 {len(embeddings)} 个嵌入向量（{len(query_entities)} entities + {len(triplets)} heads + {len(triplets)} tails）") - embedding_time = time.time() - embedding_start - debug(f"嵌入向量生成耗时: {embedding_time:.3f} 秒") - - similarity_start = time.time() - for entity in query_entities: - entity_vec = entity_vectors[entity] - for d_triplet in triplets: - d_head_vec = head_vectors[d_triplet['head']] - d_tail_vec = tail_vectors[d_triplet['tail']] - head_similarity = 1 - cosine(entity_vec, d_head_vec) - tail_similarity = 1 - cosine(entity_vec, d_tail_vec) - - if head_similarity >= ENTITY_SIMILARITY_THRESHOLD or tail_similarity >= ENTITY_SIMILARITY_THRESHOLD: - matched_triplets.append(d_triplet) - debug(f"匹配三元组: {d_triplet['head']} - {d_triplet['type']} - {d_triplet['tail']} " - f"(entity={entity}, head_sim={head_similarity:.2f}, tail_sim={tail_similarity:.2f})") - similarity_time = time.time() - similarity_start - debug(f"相似度计算耗时: {similarity_time:.3f} 秒") - - unique_matched = [] - seen = set() - for t in matched_triplets: - identifier = (t['head'].lower(), t['type'].lower(), t['tail'].lower()) - if identifier not in seen: - seen.add(identifier) - unique_matched.append(t) - - total_time = time.time() - start_time - debug(f"_match_triplets 总耗时: {total_time:.3f} 秒") - info(f"找到 {len(unique_matched)} 个匹配的三元组") - return unique_matched - - except Exception as e: - error(f"匹配三元组失败: {str(e)}") - return [] - async def _rerank_results(self, query: str, results: List[Dict], top_n: int) -> List[Dict]: """调用重排序服务""" try: @@ -936,14 +832,28 @@ class MilvusConnection: timing_stats["entity_extraction"] = time.time() - entity_extract_start debug(f"提取实体: {query_entities}, 耗时: {timing_stats['entity_extraction']:.3f} 秒") - # 匹配三元组 + # 调用 Neo4j 服务进行三元组匹配 all_triplets = [] triplet_match_start = time.time() for kb_id in knowledge_base_ids: - debug(f"处理知识库: {kb_id}") - matched_triplets = await self._match_triplets(query, query_entities, userid, kb_id) - debug(f"知识库 {kb_id} 匹配三元组: {len(matched_triplets)} 条") - all_triplets.extend(matched_triplets) + debug(f"调用 Neo4j 三元组匹配: knowledge_base_id={kb_id}") + try: + neo4j_result = await self._make_neo4japi_request("matchtriplets", { + "query": query, + "query_entities": query_entities, + "userid": userid, + "knowledge_base_id": kb_id + }) + if neo4j_result.get("status") == "success": + triplets = neo4j_result.get("triplets", []) + all_triplets.extend(triplets) + debug(f"知识库 {kb_id} 匹配到 {len(triplets)} 个三元组: {triplets[:5]}") + else: + error( + f"Neo4j 三元组匹配失败: knowledge_base_id={kb_id}, 错误: {neo4j_result.get('message', '未知错误')}") + except Exception as e: + error(f"Neo4j 三元组匹配失败: knowledge_base_id={kb_id}, 错误: {str(e)}") + continue timing_stats["triplet_matching"] = time.time() - triplet_match_start debug(f"三元组匹配总耗时: {timing_stats['triplet_matching']:.3f} 秒") @@ -977,7 +887,7 @@ class MilvusConnection: # 调用融合搜索端点 search_start = time.time() - result = await self._make_api_request("searchquery", { # 注意：使用 searchquery 端点 + result = await self._make_api_request("searchquery", { "query_vector": query_vector.tolist(), "userid": userid, "knowledge_base_ids": knowledge_base_ids,