1073 lines
56 KiB
Python
1073 lines
56 KiB
Python
from appPublic.jsonConfig import getConfig
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import os
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from appPublic.log import debug, error, info
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import yaml
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from threading import Lock
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from llmengine.base_connection import connection_register
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from typing import Dict, List, Any
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import aiohttp
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from aiohttp import ClientSession, ClientTimeout
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from langchain_core.documents import Document
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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import uuid
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from datetime import datetime
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from filetxt.loader import fileloader
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from llmengine.kgc import KnowledgeGraph
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import numpy as np
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from py2neo import Graph
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from scipy.spatial.distance import cosine
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import time
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from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
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import traceback
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import asyncio
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import re
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# 嵌入缓存
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EMBED_CACHE = {}
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class MilvusConnection:
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_instance = None
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_lock = Lock()
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def __new__(cls):
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with cls._lock:
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if cls._instance is None:
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cls._instance = super(MilvusConnection, cls).__new__(cls)
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cls._instance._initialized = False
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return cls._instance
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def __init__(self):
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if self._initialized:
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return
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try:
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config = getConfig()
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self.neo4j_uri = config['neo4j']['uri']
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self.neo4j_user = config['neo4j']['user']
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self.neo4j_password = config['neo4j']['password']
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except KeyError as e:
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error(f"配置文件缺少必要字段: {str(e)}")
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raise RuntimeError(f"配置文件缺少必要字段: {str(e)}")
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self._initialized = True
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info("Neo4jConnection initialized")
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@retry(stop=stop_after_attempt(3))
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async def _make_api_request(self, action: str, params: Dict[str, Any]) -> Dict[str, Any]:
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debug(f"开始 API 请求: action={action}, params={params}")
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try:
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async with ClientSession(timeout=ClientTimeout(total=10)) as session:
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url = f"http://localhost:8886/v1/{action}"
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debug(f"发起 POST 请求: {url}")
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async with session.post(
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url,
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headers={"Content-Type": "application/json"},
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json=params
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) as response:
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debug(f"收到响应: status={response.status}, headers={response.headers}")
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response_text = await response.text()
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debug(f"响应内容: {response_text}")
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result = await response.json()
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debug(f"API 响应内容: {result}")
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if response.status == 400: # 客户端错误，直接返回
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debug(f"客户端错误，状态码: {response.status}, 返回响应: {result}")
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return result
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if response.status != 200:
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error(f"API 调用失败，动作: {action}, 状态码: {response.status}, 响应: {response_text}")
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raise RuntimeError(f"API 调用失败: {response.status}")
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debug(f"API 调用成功: {action}, 响应: {result}")
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return result
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except Exception as e:
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error(f"API 调用失败: {action}, 错误: {str(e)}, 堆栈: {traceback.format_exc()}")
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raise RuntimeError(f"API 调用失败: {str(e)}")
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async def handle_connection(self, action: str, params: Dict = None) -> Dict:
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"""处理数据库操作"""
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try:
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debug(f"处理操作: action={action}, params={params}")
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if not params:
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params = {}
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# 通用 db_type 验证
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db_type = params.get("db_type", "")
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collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
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if db_type and "_" in db_type:
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return {"status": "error", "message": "db_type 不能包含下划线", "collection_name": collection_name,
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"document_id": "", "status_code": 400}
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if db_type and len(db_type) > 100:
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return {"status": "error", "message": "db_type 的长度应小于 100", "collection_name": collection_name,
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"document_id": "", "status_code": 400}
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if action == "initialize":
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return {"status": "success", "message": "Milvus 服务已初始化"}
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elif action == "get_params":
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return {"status": "success", "params": {}}
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elif action == "create_collection":
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return await self._create_collection(db_type)
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elif action == "delete_collection":
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return await self._delete_collection(db_type)
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elif action == "insert_document":
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file_path = params.get("file_path", "")
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userid = params.get("userid", "")
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knowledge_base_id = params.get("knowledge_base_id", "")
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if not file_path or not userid or not knowledge_base_id:
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return {"status": "error", "message": "file_path、userid 和 knowledge_base_id 不能为空",
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"collection_name": collection_name, "document_id": "", "status_code": 400}
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if "_" in userid or "_" in knowledge_base_id:
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return {"status": "error", "message": "userid 和 knowledge_base_id 不能包含下划线",
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"collection_name": collection_name, "document_id": "", "status_code": 400}
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if len(knowledge_base_id) > 100:
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return {"status": "error", "message": "knowledge_base_id 的长度应小于 100",
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"collection_name": collection_name, "document_id": "", "status_code": 400}
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return await self._insert_document(file_path, userid, knowledge_base_id, db_type)
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elif action == "delete_document":
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userid = params.get("userid", "")
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filename = params.get("filename", "")
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knowledge_base_id = params.get("knowledge_base_id", "")
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if not userid or not filename or not knowledge_base_id:
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return {"status": "error", "message": "userid、filename 和 knowledge_base_id 不能为空",
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"collection_name": collection_name, "document_id": "", "status_code": 400}
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if "_" in userid or "_" in knowledge_base_id:
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return {"status": "error", "message": "userid 和 knowledge_base_id 不能包含下划线",
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"collection_name": collection_name, "document_id": "", "status_code": 400}
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if len(userid) > 100 or len(filename) > 255 or len(knowledge_base_id) > 100:
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return {"status": "error", "message": "userid、filename 或 knowledge_base_id 的长度超出限制",
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"collection_name": collection_name, "document_id": "", "status_code": 400}
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return await self._delete_document(userid, filename, knowledge_base_id, db_type)
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elif action == "delete_knowledge_base":
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userid = params.get("userid", "")
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knowledge_base_id = params.get("knowledge_base_id", "")
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if not userid or not knowledge_base_id:
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return {"status": "error", "message": "userid 和 knowledge_base_id 不能为空",
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"collection_name": collection_name, "document_id": "", "status_code": 400}
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if "_" in userid or "_" in knowledge_base_id:
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return {"status": "error", "message": "userid 和 knowledge_base_id 不能包含下划线",
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"collection_name": collection_name, "document_id": "", "status_code": 400}
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if len(userid) > 100 or len(knowledge_base_id) > 100:
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return {"status": "error", "message": "userid 或 knowledge_base_id 的长度超出限制",
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"collection_name": collection_name, "document_id": "", "status_code": 400}
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return await self._delete_knowledge_base(db_type, userid, knowledge_base_id)
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elif action == "search_query":
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query = params.get("query", "")
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userid = params.get("userid", "")
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knowledge_base_ids = params.get("knowledge_base_ids", [])
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limit = params.get("limit", 5)
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offset = params.get("offset", 0)
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db_type = params.get("db_type", "")
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use_rerank = params.get("use_rerank", True)
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if not query or not userid or not knowledge_base_ids:
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return {"status": "error", "message": "query、userid 或 knowledge_base_ids 不能为空",
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"collection_name": collection_name, "document_id": "", "status_code": 400}
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return await self._search_query(query, userid, knowledge_base_ids, limit, offset, use_rerank, db_type)
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elif action == "fused_search":
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query = params.get("query", "")
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userid = params.get("userid", "")
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knowledge_base_ids = params.get("knowledge_base_ids", [])
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limit = params.get("limit", 5)
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offset = params.get("offset", 0)
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db_type = params.get("db_type", "")
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use_rerank = params.get("use_rerank", True)
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if not query or not userid or not knowledge_base_ids:
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return {"status": "error", "message": "query、userid 或 knowledge_base_ids 不能为空",
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"collection_name": collection_name, "document_id": "", "status_code": 400}
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return await self._fused_search(query, userid, knowledge_base_ids, limit, offset, use_rerank, db_type)
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elif action == "list_user_files":
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userid = params.get("userid", "")
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if not userid:
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return {"status": "error", "message": "userid 不能为空", "collection_name": collection_name,
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"document_id": "", "status_code": 400}
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return await self._list_user_files(userid, db_type)
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elif action == "list_all_knowledge_bases":
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return await self._list_all_knowledge_bases(db_type)
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else:
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return {"status": "error", "message": f"未知的 action: {action}", "collection_name": collection_name,
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"document_id": "", "status_code": 400}
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except Exception as e:
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error(f"处理操作失败: action={action}, 错误: {str(e)}")
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return {
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"status": "error",
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"message": f"服务器错误: {str(e)}",
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"collection_name": collection_name,
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"document_id": "",
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"status_code": 400
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}
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async def _create_collection(self, db_type: str = "") -> Dict[str, Any]:
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"""创建 Milvus 集合"""
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collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
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try:
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if len(collection_name) > 255:
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raise ValueError(f"集合名称 {collection_name} 超过 255 个字符")
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if len(db_type) > 100:
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raise ValueError("db_type 的长度应小于 100")
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debug(f"调用创建集合端点: {collection_name}, 参数: {{'db_type': '{db_type}'}}")
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result = await self._make_api_request("createcollection", {"db_type": db_type})
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return result
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except Exception as e:
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error(f"创建集合失败: {str(e)}, 堆栈: {traceback.format_exc()}")
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return {
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"status": "error",
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"collection_name": collection_name,
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"message": f"创建集合失败: {str(e)}",
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"status_code": 400
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}
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async def _delete_collection(self, db_type: str = "") -> Dict:
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"""删除 Milvus 集合通过服务化端点"""
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try:
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collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
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if len(collection_name) > 255:
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raise ValueError(f"集合名称 {collection_name} 超过 255 个字符")
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if db_type and "_" in db_type:
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raise ValueError("db_type 不能包含下划线")
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if db_type and len(db_type) > 100:
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raise ValueError("db_type 的长度应小于 100")
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debug(f"调用删除集合端点: {collection_name}")
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result = await self._make_api_request("deletecollection", {"db_type": db_type})
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return result
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except Exception as e:
|
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error(f"删除集合失败: {str(e)}")
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||
return {
|
||
"status": "error",
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||
"collection_name": collection_name,
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"message": str(e),
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||
"status_code": 400
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||
}
|
||
|
||
async def _insert_document(self, file_path: str, userid: str, knowledge_base_id: str, db_type: str = "") -> Dict[
|
||
str, Any]:
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"""将文档插入 Milvus 并抽取三元组到 Neo4j"""
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document_id = str(uuid.uuid4())
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collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
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debug(
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f'Inserting document: file_path={file_path}, userid={userid}, db_type={db_type}, knowledge_base_id={knowledge_base_id}, document_id={document_id}')
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||
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timings = {}
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||
start_total = time.time()
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||
|
||
try:
|
||
# 验证参数
|
||
if not userid or not knowledge_base_id:
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||
raise ValueError("userid 和 knowledge_base_id 不能为空")
|
||
if "_" in userid or "_" in knowledge_base_id:
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||
raise ValueError("userid 和 knowledge_base_id 不能包含下划线")
|
||
if len(userid) > 100 or len(knowledge_base_id) > 100:
|
||
raise ValueError("userid 或 knowledge_base_id 的长度超出限制")
|
||
if not os.path.exists(file_path):
|
||
raise ValueError(f"文件 {file_path} 不存在")
|
||
|
||
supported_formats = {'pdf', 'docx', 'xlsx', 'pptx', 'csv', 'txt'}
|
||
ext = file_path.rsplit('.', 1)[1].lower() if '.' in file_path else ''
|
||
if ext not in supported_formats:
|
||
raise ValueError(f"不支持的文件格式: {ext}, 支持的格式: {', '.join(supported_formats)}")
|
||
|
||
info(f"生成 document_id: {document_id} for file: {file_path}")
|
||
|
||
# 文件加载
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||
debug(f"加载文件: {file_path}")
|
||
start_load = time.time()
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||
text = fileloader(file_path)
|
||
text = re.sub(r'[^\u4e00-\u9fa5a-zA-Z0-9\s.;,\n]', '', text)
|
||
timings["load_file"] = time.time() - start_load
|
||
debug(f"加载文件耗时: {timings['load_file']:.2f} 秒, 文本长度: {len(text)}")
|
||
if not text or not text.strip():
|
||
raise ValueError(f"文件 {file_path} 加载为空")
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||
|
||
# 文本分片
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||
document = Document(page_content=text)
|
||
text_splitter = RecursiveCharacterTextSplitter(
|
||
chunk_size=500,
|
||
chunk_overlap=100,
|
||
length_function=len,
|
||
)
|
||
debug("开始分片文件内容")
|
||
start_split = time.time()
|
||
chunks = text_splitter.split_documents([document])
|
||
timings["split_text"] = time.time() - start_split
|
||
debug(
|
||
f"文本分片耗时: {timings['split_text']:.2f} 秒, 分片数量: {len(chunks)}, 分片内容: {[chunk.page_content[:50] for chunk in chunks[:5]]}")
|
||
if not chunks:
|
||
raise ValueError(f"文件 {file_path} 未生成任何文档块")
|
||
|
||
filename = os.path.basename(file_path).rsplit('.', 1)[0]
|
||
upload_time = datetime.now().isoformat()
|
||
|
||
# 生成嵌入向量
|
||
debug("调用嵌入服务生成向量")
|
||
start_embedding = time.time()
|
||
texts = [chunk.page_content for chunk in chunks]
|
||
embeddings = await self._get_embeddings(texts)
|
||
if not embeddings or not all(len(vec) == 1024 for vec in embeddings):
|
||
raise ValueError("所有嵌入向量必须是长度为 1024 的浮点数列表")
|
||
timings["generate_embeddings"] = time.time() - start_embedding
|
||
debug(f"生成嵌入向量耗时: {timings['generate_embeddings']:.2f} 秒, 嵌入数量: {len(embeddings)}")
|
||
|
||
# 构造 chunks 参数（展平结构）
|
||
chunks_data = []
|
||
for i, chunk in enumerate(chunks):
|
||
chunks_data.append({
|
||
"userid": userid,
|
||
"knowledge_base_id": knowledge_base_id,
|
||
"text": chunk.page_content,
|
||
"vector": embeddings[i].tolist(),
|
||
"document_id": document_id,
|
||
"filename": filename + '.' + ext,
|
||
"file_path": file_path,
|
||
"upload_time": upload_time,
|
||
"file_type": ext,
|
||
})
|
||
|
||
# 调用 Milvus 插入端点
|
||
debug(f"调用插入文件端点: {file_path}")
|
||
start_milvus = time.time()
|
||
result = await self._make_api_request("insertdocument", {
|
||
"chunks": chunks_data,
|
||
"db_type": db_type,
|
||
})
|
||
timings["insert_milvus"] = time.time() - start_milvus
|
||
debug(f"Milvus 插入耗时: {timings['insert_milvus']:.2f} 秒")
|
||
|
||
if result.get("status") != "success":
|
||
timings["total"] = time.time() - start_total
|
||
return {
|
||
"status": "error",
|
||
"document_id": document_id,
|
||
"collection_name": collection_name,
|
||
"timings": timings,
|
||
"message": result.get("message", "未知错误"),
|
||
"status_code": 400
|
||
}
|
||
|
||
# 三元组抽取
|
||
debug("调用三元组抽取服务")
|
||
start_triples = time.time()
|
||
try:
|
||
chunk_texts = [doc.page_content for doc in chunks]
|
||
debug(f"处理 {len(chunk_texts)} 个分片进行三元组抽取")
|
||
|
||
tasks = [self._extract_triples(chunk) for chunk in chunk_texts]
|
||
results = await asyncio.gather(*tasks, return_exceptions=True)
|
||
|
||
triples = []
|
||
for i, result in enumerate(results):
|
||
if isinstance(result, list):
|
||
triples.extend(result)
|
||
debug(f"分片 {i + 1} 抽取到 {len(result)} 个三元组: {result[:5]}")
|
||
else:
|
||
error(f"分片 {i + 1} 处理失败: {str(result)}")
|
||
|
||
# 去重
|
||
unique_triples = []
|
||
seen = set()
|
||
for t in triples:
|
||
identifier = (t['head'].lower(), t['tail'].lower(), t['type'].lower())
|
||
if identifier not in seen:
|
||
seen.add(identifier)
|
||
unique_triples.append(t)
|
||
else:
|
||
for existing in unique_triples:
|
||
if (existing['head'].lower() == t['head'].lower() and
|
||
existing['tail'].lower() == t['tail'].lower() and
|
||
len(t['type']) > len(existing['type'])):
|
||
unique_triples.remove(existing)
|
||
unique_triples.append(t)
|
||
debug(f"替换三元组为更具体类型: {t}")
|
||
break
|
||
|
||
timings["extract_triples"] = time.time() - start_triples
|
||
debug(
|
||
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")
|
||
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")
|
||
else:
|
||
debug(f"文件 {file_path} 未抽取到三元组")
|
||
timings["insert_neo4j"] = time.time() - start_neo4j
|
||
debug(f"Neo4j 插入耗时: {timings['insert_neo4j']:.2f} 秒")
|
||
|
||
except Exception as e:
|
||
timings["extract_triples"] = time.time() - start_triples if "extract_triples" not in timings else \
|
||
timings["extract_triples"]
|
||
timings["insert_neo4j"] = time.time() - start_neo4j
|
||
debug(f"处理三元组或 Neo4j 插入失败: {str(e)}, 堆栈: {traceback.format_exc()}")
|
||
timings["total"] = time.time() - start_total
|
||
return {
|
||
"status": "success",
|
||
"document_id": document_id,
|
||
"collection_name": collection_name,
|
||
"timings": timings,
|
||
"unique_triples": unique_triples,
|
||
"message": f"文件 {file_path} 成功嵌入，但三元组处理或 Neo4j 插入失败: {str(e)}",
|
||
"status_code": 200
|
||
}
|
||
|
||
timings["total"] = time.time() - start_total
|
||
debug(f"总耗时: {timings['total']:.2f} 秒")
|
||
return {
|
||
"status": "success",
|
||
"document_id": document_id,
|
||
"collection_name": collection_name,
|
||
"timings": timings,
|
||
"unique_triples": unique_triples,
|
||
"message": f"文件 {file_path} 成功嵌入并处理三元组",
|
||
"status_code": 200
|
||
}
|
||
|
||
except Exception as e:
|
||
error(f"插入文档失败: {str(e)}, 堆栈: {traceback.format_exc()}")
|
||
timings["total"] = time.time() - start_total
|
||
return {
|
||
"status": "error",
|
||
"document_id": document_id,
|
||
"collection_name": collection_name,
|
||
"timings": timings,
|
||
"message": f"插入文档失败: {str(e)}",
|
||
"status_code": 400
|
||
}
|
||
|
||
@retry(
|
||
stop=stop_after_attempt(3),
|
||
wait=wait_exponential(multiplier=1, min=1, max=10),
|
||
retry=retry_if_exception_type((aiohttp.ClientError, RuntimeError)),
|
||
before_sleep=lambda retry_state: debug(f"重试三元组抽取服务，第 {retry_state.attempt_number} 次")
|
||
)
|
||
async def _get_embeddings(self, texts: List[str]) -> List[List[float]]:
|
||
"""调用嵌入服务获取文本的向量，带缓存"""
|
||
try:
|
||
# 检查缓存
|
||
uncached_texts = [text for text in texts if text not in EMBED_CACHE]
|
||
if uncached_texts:
|
||
async with aiohttp.ClientSession() as session:
|
||
async with session.post(
|
||
"http://localhost:9998/v1/embeddings",
|
||
headers={"Content-Type": "application/json"},
|
||
json={"input": uncached_texts}
|
||
) as response:
|
||
if response.status != 200:
|
||
error(f"嵌入服务调用失败，状态码: {response.status}")
|
||
raise RuntimeError(f"嵌入服务调用失败: {response.status}")
|
||
result = await response.json()
|
||
if result.get("object") != "list" or not result.get("data"):
|
||
error(f"嵌入服务响应格式错误: {result}")
|
||
raise RuntimeError("嵌入服务响应格式错误")
|
||
embeddings = [item["embedding"] for item in result["data"]]
|
||
for text, embedding in zip(uncached_texts, embeddings):
|
||
EMBED_CACHE[text] = np.array(embedding) / np.linalg.norm(embedding)
|
||
debug(f"成功获取 {len(embeddings)} 个新嵌入向量，缓存大小: {len(EMBED_CACHE)}")
|
||
# 返回缓存中的嵌入
|
||
return [EMBED_CACHE[text] for text in texts]
|
||
except Exception as e:
|
||
error(f"嵌入服务调用失败: {str(e)}")
|
||
raise RuntimeError(f"嵌入服务调用失败: {str(e)}")
|
||
|
||
async def _extract_triples(self, text: str) -> List[Dict]:
|
||
"""调用三元组抽取服务，无超时限制"""
|
||
request_id = str(uuid.uuid4()) # 为每个请求生成唯一 ID
|
||
start_time = time.time()
|
||
debug(f"Request #{request_id} started for triples extraction")
|
||
try:
|
||
async with aiohttp.ClientSession(
|
||
connector=aiohttp.TCPConnector(limit=30),
|
||
timeout=aiohttp.ClientTimeout(total=None) # 无限等待
|
||
) as session:
|
||
async with session.post(
|
||
"http://localhost:9991/v1/triples",
|
||
headers={"Content-Type": "application/json; charset=utf-8"},
|
||
json={"text": text}
|
||
) as response:
|
||
elapsed_time = time.time() - start_time
|
||
debug(f"Request #{request_id} received response, status: {response.status}, took {elapsed_time:.2f} seconds")
|
||
if response.status != 200:
|
||
error_text = await response.text()
|
||
error(f"Request #{request_id} failed, status: {response.status}, response: {error_text}")
|
||
raise RuntimeError(f"三元组抽取服务调用失败: {response.status}, {error_text}")
|
||
result = await response.json()
|
||
if result.get("object") != "list" or not result.get("data"):
|
||
error(f"Request #{request_id} invalid response format: {result}")
|
||
raise RuntimeError("三元组抽取服务响应格式错误")
|
||
triples = result["data"]
|
||
debug(f"Request #{request_id} extracted {len(triples)} triples, total time: {elapsed_time:.2f} seconds")
|
||
return triples
|
||
except Exception as e:
|
||
elapsed_time = time.time() - start_time
|
||
error(f"Request #{request_id} failed to extract triples: {str(e)}, took {elapsed_time:.2f} seconds")
|
||
debug(f"Request #{request_id} traceback: {traceback.format_exc()}")
|
||
raise RuntimeError(f"三元组抽取服务调用失败: {str(e)}")
|
||
|
||
async def _delete_document(self, userid: str, filename: str, knowledge_base_id: str, db_type: str = "") -> Dict[str, Any]:
|
||
"""删除用户指定文件数据，包括 Milvus 和 Neo4j 中的记录"""
|
||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||
try:
|
||
# 调用 Milvus 删除文件端点
|
||
debug(f"调用删除文件端点: userid={userid}, filename={filename}, knowledge_base_id={knowledge_base_id}")
|
||
milvus_result = await self._make_api_request("deletedocument", {
|
||
"userid": userid,
|
||
"filename": filename,
|
||
"knowledge_base_id": knowledge_base_id,
|
||
"db_type": db_type
|
||
})
|
||
|
||
if milvus_result.get("status") != "success":
|
||
error(f"Milvus 删除文件失败: {milvus_result.get('message', '未知错误')}")
|
||
return milvus_result
|
||
|
||
document_ids = milvus_result.get("document_id", "").split(",") if milvus_result.get("document_id") else []
|
||
|
||
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}")
|
||
neo4j_deleted_nodes += nodes_deleted
|
||
neo4j_deleted_rels += rels_deleted
|
||
except Exception as e:
|
||
error(f"删除 document_id={doc_id} 的 Neo4j 三元组失败: {str(e)}")
|
||
continue
|
||
|
||
return {
|
||
"status": "success",
|
||
"collection_name": collection_name,
|
||
"document_id": ",".join(document_ids),
|
||
"message": f"成功删除 Milvus 记录，{neo4j_deleted_nodes} 个 Neo4j 节点，{neo4j_deleted_rels} 个 Neo4j 关系",
|
||
"status_code": 200
|
||
}
|
||
|
||
except Exception as e:
|
||
error(f"删除文档失败: {str(e)}")
|
||
return {
|
||
"status": "error",
|
||
"collection_name": collection_name,
|
||
"document_id": "",
|
||
"message": f"删除文档失败: {str(e)}",
|
||
"status_code": 400
|
||
}
|
||
|
||
async def _delete_knowledge_base(self, db_type: str, userid: str, knowledge_base_id: str) -> Dict[str, Any]:
|
||
"""删除用户的整个知识库，包括 Milvus 和 Neo4j 中的记录"""
|
||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||
try:
|
||
# 调用 Milvus 删除知识库端点
|
||
debug(f"调用删除知识库端点: userid={userid}, knowledge_base_id={knowledge_base_id}")
|
||
milvus_result = await self._make_api_request("deleteknowledgebase", {
|
||
"userid": userid,
|
||
"knowledge_base_id": knowledge_base_id,
|
||
"db_type": db_type
|
||
})
|
||
|
||
if milvus_result.get("status") != "success":
|
||
error(f"Milvus 删除知识库失败: {milvus_result.get('message', '未知错误')}")
|
||
return milvus_result
|
||
|
||
deleted_files = milvus_result.get("deleted_files", [])
|
||
|
||
# 删除 Neo4j 数据
|
||
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}")
|
||
except Exception as e:
|
||
error(f"删除 Neo4j 数据失败: {str(e)}")
|
||
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: {str(e)}",
|
||
"status_code": 200
|
||
}
|
||
|
||
if not deleted_files and neo4j_deleted_nodes == 0 and neo4j_deleted_rels == 0:
|
||
debug(f"没有删除任何记录，userid={userid}, knowledge_base_id={knowledge_base_id}")
|
||
return {
|
||
"status": "success",
|
||
"collection_name": collection_name,
|
||
"deleted_files": [],
|
||
"message": f"没有找到 userid={userid}, knowledge_base_id={knowledge_base_id} 的记录，无需删除",
|
||
"status_code": 200
|
||
}
|
||
|
||
info(
|
||
f"总计删除 Milvus 记录，{neo4j_deleted_nodes} 个 Neo4j 节点，{neo4j_deleted_rels} 个 Neo4j 关系，删除文件: {deleted_files}")
|
||
return {
|
||
"status": "success",
|
||
"collection_name": collection_name,
|
||
"deleted_files": deleted_files,
|
||
"message": f"成功删除 Milvus 记录，{neo4j_deleted_nodes} 个 Neo4j 节点，{neo4j_deleted_rels} 个 Neo4j 关系，删除文件: {deleted_files}",
|
||
"status_code": 200
|
||
}
|
||
|
||
except Exception as e:
|
||
error(f"删除知识库失败: {str(e)}")
|
||
return {
|
||
"status": "error",
|
||
"collection_name": collection_name,
|
||
"deleted_files": [],
|
||
"message": f"删除知识库失败: {str(e)}",
|
||
"status_code": 400
|
||
}
|
||
|
||
async def _extract_entities(self, query: str) -> List[str]:
|
||
"""调用实体识别服务"""
|
||
try:
|
||
if not query:
|
||
raise ValueError("查询文本不能为空")
|
||
async with aiohttp.ClientSession() as session:
|
||
async with session.post(
|
||
"http://localhost:9990/v1/entities",
|
||
headers={"Content-Type": "application/json"},
|
||
json={"query": query}
|
||
) as response:
|
||
if response.status != 200:
|
||
error(f"实体识别服务调用失败，状态码: {response.status}")
|
||
raise RuntimeError(f"实体识别服务调用失败: {response.status}")
|
||
result = await response.json()
|
||
if result.get("object") != "list" or not result.get("data"):
|
||
error(f"实体识别服务响应格式错误: {result}")
|
||
raise RuntimeError("实体识别服务响应格式错误")
|
||
entities = result["data"]
|
||
unique_entities = list(dict.fromkeys(entities)) # 去重
|
||
debug(f"成功提取 {len(unique_entities)} 个唯一实体: {unique_entities}")
|
||
return unique_entities
|
||
except Exception as e:
|
||
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:
|
||
if not results:
|
||
debug("无结果需要重排序")
|
||
return results
|
||
|
||
if not isinstance(top_n, int) or top_n < 1:
|
||
debug(f"无效的 top_n 参数: {top_n}，使用 len(results)={len(results)}")
|
||
top_n = len(results)
|
||
else:
|
||
top_n = min(top_n, len(results))
|
||
debug(f"重排序 top_n={top_n}, 原始结果数={len(results)}")
|
||
|
||
documents = [result["text"] for result in results]
|
||
async with aiohttp.ClientSession() as session:
|
||
async with session.post(
|
||
"http://localhost:9997/v1/rerank",
|
||
headers={"Content-Type": "application/json"},
|
||
json={
|
||
"model": "rerank-001",
|
||
"query": query,
|
||
"documents": documents,
|
||
"top_n": top_n
|
||
}
|
||
) as response:
|
||
if response.status != 200:
|
||
error(f"重排序服务调用失败，状态码: {response.status}")
|
||
raise RuntimeError(f"重排序服务调用失败: {response.status}")
|
||
result = await response.json()
|
||
if result.get("object") != "rerank.result" or not result.get("data"):
|
||
error(f"重排序服务响应格式错误: {result}")
|
||
raise RuntimeError("重排序服务响应格式错误")
|
||
rerank_data = result["data"]
|
||
reranked_results = []
|
||
for item in rerank_data:
|
||
index = item["index"]
|
||
if index < len(results):
|
||
results[index]["rerank_score"] = item["relevance_score"]
|
||
reranked_results.append(results[index])
|
||
debug(f"成功重排序 {len(reranked_results)} 条结果")
|
||
return reranked_results[:top_n]
|
||
except Exception as e:
|
||
error(f"重排序服务调用失败: {str(e)}")
|
||
return results
|
||
|
||
async def _search_query(self, query: str, userid: str, knowledge_base_ids: List[str], limit: int = 5,
|
||
offset: int = 0, use_rerank: bool = True, db_type: str = "") -> Dict[str, Any]:
|
||
"""纯向量搜索，调用服务化端点"""
|
||
start_time = time.time()
|
||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||
timing_stats = {}
|
||
try:
|
||
info(
|
||
f"开始纯向量搜索: query={query}, userid={userid}, db_type={db_type}, knowledge_base_ids={knowledge_base_ids}, limit={limit}, offset={offset}, use_rerank={use_rerank}")
|
||
|
||
if not query:
|
||
raise ValueError("查询文本不能为空")
|
||
if not userid:
|
||
raise ValueError("userid 不能为空")
|
||
if "_" in userid or (db_type and "_" in db_type):
|
||
raise ValueError("userid 和 db_type 不能包含下划线")
|
||
if (db_type and len(db_type) > 100) or len(userid) > 100:
|
||
raise ValueError("userid 或 db_type 的长度超出限制")
|
||
if limit <= 0 or limit > 16384:
|
||
raise ValueError("limit 必须在 1 到 16384 之间")
|
||
if offset < 0:
|
||
raise ValueError("offset 不能为负数")
|
||
if limit + offset > 16384:
|
||
raise ValueError("limit + offset 不能超过 16384")
|
||
if not knowledge_base_ids:
|
||
raise ValueError("knowledge_base_ids 不能为空")
|
||
for kb_id in knowledge_base_ids:
|
||
if not isinstance(kb_id, str):
|
||
raise ValueError(f"knowledge_base_id 必须是字符串: {kb_id}")
|
||
if len(kb_id) > 100:
|
||
raise ValueError(f"knowledge_base_id 长度超出 100 个字符: {kb_id}")
|
||
if "_" in kb_id:
|
||
raise ValueError(f"knowledge_base_id 不能包含下划线: {kb_id}")
|
||
|
||
# 将查询文本转换为向量
|
||
vector_start = time.time()
|
||
query_vector = await self._get_embeddings([query])
|
||
if not query_vector or not all(len(vec) == 1024 for vec in query_vector):
|
||
raise ValueError("查询向量必须是长度为 1024 的浮点数列表")
|
||
query_vector = query_vector[0] # 取第一个向量
|
||
timing_stats["vector_generation"] = time.time() - vector_start
|
||
debug(f"生成查询向量耗时: {timing_stats['vector_generation']:.3f} 秒")
|
||
|
||
# 调用纯向量搜索端点
|
||
search_start = time.time()
|
||
result = await self._make_api_request("searchquery", {
|
||
"query_vector": query_vector.tolist(),
|
||
"userid": userid,
|
||
"knowledge_base_ids": knowledge_base_ids,
|
||
"limit": limit,
|
||
"offset": offset,
|
||
"db_type": db_type
|
||
})
|
||
timing_stats["vector_search"] = time.time() - search_start
|
||
debug(f"向量搜索耗时: {timing_stats['vector_search']:.3f} 秒")
|
||
|
||
if result.get("status") != "success":
|
||
error(f"纯向量搜索失败: {result.get('message', '未知错误')}")
|
||
return {"results": [], "timing": timing_stats}
|
||
|
||
unique_results = result.get("results", [])
|
||
if use_rerank and unique_results:
|
||
rerank_start = time.time()
|
||
debug("开始重排序")
|
||
unique_results = await self._rerank_results(query, unique_results, limit)
|
||
unique_results = sorted(unique_results, key=lambda x: x.get('rerank_score', 0), reverse=True)
|
||
timing_stats["reranking"] = time.time() - rerank_start
|
||
debug(f"重排序耗时: {timing_stats['reranking']:.3f} 秒")
|
||
debug(f"重排序分数分布: {[round(r.get('rerank_score', 0), 3) for r in unique_results]}")
|
||
else:
|
||
unique_results = [{k: v for k, v in r.items() if k != 'rerank_score'} for r in unique_results]
|
||
|
||
timing_stats["total_time"] = time.time() - start_time
|
||
info(f"纯向量搜索完成，返回 {len(unique_results)} 条结果，总耗时: {timing_stats['total_time']:.3f} 秒")
|
||
return {"results": unique_results[:limit], "timing": timing_stats}
|
||
|
||
except Exception as e:
|
||
error(f"纯向量搜索失败: {str(e)}, 堆栈: {traceback.format_exc()}")
|
||
return {"results": [], "timing": timing_stats}
|
||
|
||
async def _fused_search(self, query: str, userid: str, knowledge_base_ids: List[str], limit: int = 5,
|
||
offset: int = 0, use_rerank: bool = True, db_type: str = "") -> Dict[str, Any]:
|
||
"""融合搜索，调用服务化端点"""
|
||
start_time = time.time()
|
||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||
timing_stats = {}
|
||
try:
|
||
info(
|
||
f"开始融合搜索: query={query}, userid={userid}, db_type={db_type}, knowledge_base_ids={knowledge_base_ids}, limit={limit}, offset={offset}, use_rerank={use_rerank}")
|
||
|
||
if not query or not userid or not knowledge_base_ids:
|
||
raise ValueError("query、userid 和 knowledge_base_ids 不能为空")
|
||
if "_" in userid or (db_type and "_" in db_type):
|
||
raise ValueError("userid 和 db_type 不能包含下划线")
|
||
if (db_type and len(db_type) > 100) or len(userid) > 100:
|
||
raise ValueError("db_type 或 userid 的长度超出限制")
|
||
if limit < 1 or limit > 16384 or offset < 0:
|
||
raise ValueError("limit 必须在 1 到 16384 之间，offset 必须大于或等于 0")
|
||
|
||
# 提取实体
|
||
entity_extract_start = time.time()
|
||
query_entities = await self._extract_entities(query)
|
||
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
|
||
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)
|
||
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")
|
||
|
||
# 拼接三元组文本
|
||
triplet_text_start = time.time()
|
||
triplet_texts = []
|
||
for triplet in all_triplets:
|
||
head = triplet.get('head', '')
|
||
type_ = triplet.get('type', '')
|
||
tail = triplet.get('tail', '')
|
||
if head and type_ and tail:
|
||
triplet_texts.append(f"{head} {type_} {tail}")
|
||
else:
|
||
debug(f"无效三元组: {triplet}")
|
||
combined_text = query
|
||
if triplet_texts:
|
||
combined_text += " [三元组] " + "; ".join(triplet_texts)
|
||
debug(
|
||
f"拼接文本: {combined_text[:200]}... (总长度: {len(combined_text)}, 三元组数量: {len(triplet_texts)})")
|
||
timing_stats["triplet_text_combine"] = time.time() - triplet_text_start
|
||
debug(f"拼接三元组文本耗时: {timing_stats['triplet_text_combine']:.3f} 秒")
|
||
|
||
# 将拼接文本转换为向量
|
||
vector_start = time.time()
|
||
query_vector = await self._get_embeddings([combined_text])
|
||
if not query_vector or not all(len(vec) == 1024 for vec in query_vector):
|
||
raise ValueError("查询向量必须是长度为 1024 的浮点数列表")
|
||
query_vector = query_vector[0] # 取第一个向量
|
||
timing_stats["vector_generation"] = time.time() - vector_start
|
||
debug(f"生成查询向量耗时: {timing_stats['vector_generation']:.3f} 秒")
|
||
|
||
# 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
|
||
"query_vector": query_vector.tolist(),
|
||
"userid": userid,
|
||
"knowledge_base_ids": knowledge_base_ids,
|
||
"limit": limit,
|
||
"offset": offset,
|
||
"db_type": db_type
|
||
})
|
||
timing_stats["vector_search"] = time.time() - search_start
|
||
debug(f"向量搜索耗时: {timing_stats['vector_search']:.3f} 秒")
|
||
|
||
if result.get("status") != "success":
|
||
error(f"融合搜索失败: {result.get('message', '未知错误')}")
|
||
return {"results": [], "timing": timing_stats}
|
||
|
||
unique_results = result.get("results", [])
|
||
if use_rerank and unique_results:
|
||
rerank_start = time.time()
|
||
debug("开始重排序")
|
||
unique_results = await self._rerank_results(combined_text, unique_results, limit)
|
||
unique_results = sorted(unique_results, key=lambda x: x.get('rerank_score', 0), reverse=True)
|
||
timing_stats["reranking"] = time.time() - rerank_start
|
||
debug(f"重排序耗时: {timing_stats['reranking']:.3f} 秒")
|
||
debug(f"重排序分数分布: {[round(r.get('rerank_score', 0), 3) for r in unique_results]}")
|
||
else:
|
||
unique_results = [{k: v for k, v in r.items() if k != 'rerank_score'} for r in unique_results]
|
||
|
||
timing_stats["total_time"] = time.time() - start_time
|
||
info(f"融合搜索完成，返回 {len(unique_results)} 条结果，总耗时: {timing_stats['total_time']:.3f} 秒")
|
||
return {"results": unique_results[:limit], "timing": timing_stats}
|
||
|
||
except Exception as e:
|
||
error(f"融合搜索失败: {str(e)}, 堆栈: {traceback.format_exc()}")
|
||
return {"results": [], "timing": timing_stats}
|
||
|
||
async def _list_user_files(self, userid: str, db_type: str = "") -> Dict[str, List[Dict]]:
|
||
"""列出用户的所有知识库及其文件，按 knowledge_base_id 分组"""
|
||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||
try:
|
||
info(f"列出用户文件: userid={userid}, db_type={db_type}")
|
||
|
||
if not userid:
|
||
raise ValueError("userid 不能为空")
|
||
if "_" in userid or (db_type and "_" in db_type):
|
||
raise ValueError("userid 和 db_type 不能包含下划线")
|
||
if (db_type and len(db_type) > 100) or len(userid) > 100:
|
||
raise ValueError("userid 或 db_type 的长度超出限制")
|
||
|
||
# 调用列出用户文件端点
|
||
result = await self._make_api_request("listuserfiles", {
|
||
"userid": userid,
|
||
"db_type": db_type
|
||
})
|
||
|
||
if result.get("status") != "success":
|
||
error(f"列出用户文件失败: {result.get('message', '未知错误')}")
|
||
return {}
|
||
|
||
return result.get("files_by_knowledge_base", {})
|
||
|
||
except Exception as e:
|
||
error(f"列出用户文件失败: {str(e)}")
|
||
return {}
|
||
|
||
async def _list_all_knowledge_bases(self, db_type: str = "") -> Dict[str, Any]:
|
||
"""列出数据库中所有用户的知识库及其文件，按用户分组"""
|
||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||
try:
|
||
info(f"列出所有用户的知识库: db_type={db_type}")
|
||
|
||
if db_type and "_" in db_type:
|
||
raise ValueError("db_type 不能包含下划线")
|
||
if db_type and len(db_type) > 100:
|
||
raise ValueError("db_type 的长度应小于 100")
|
||
|
||
# 调用列出所有知识库端点
|
||
result = await self._make_api_request("listallknowledgebases", {
|
||
"db_type": db_type
|
||
})
|
||
|
||
return result
|
||
|
||
except Exception as e:
|
||
error(f"列出所有用户知识库失败: {str(e)}")
|
||
return {
|
||
"status": "error",
|
||
"users_knowledge_bases": {},
|
||
"collection_name": collection_name,
|
||
"message": f"列出所有用户知识库失败: {str(e)}",
|
||
"status_code": 400
|
||
}
|
||
|
||
connection_register('Rag', MilvusConnection)
|
||
info("MilvusConnection registered") |