7d4722b62c
- 实现QA数据 - 优化现在创建知识库的体验 - 优化文件处理与加载方法 - 整体的格式检查与优化 - 优化 Embedding model 的加载逻辑，修复并行问题 - 优化整体的颜色布局 - 移除未使用的接口（前后端） - 优化知识库的 chunk 逻辑 - 添加新的 Embedding模型支持 - 修复新建知识库后，Agent无法reload的问题
429 lines
16 KiB
Python
429 lines
16 KiB
Python
import os
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import time
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import traceback
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import json
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from pathlib import Path
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from typing import Any
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from datetime import datetime
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from functools import partial
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from pymilvus import (
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connections, utility, Collection, CollectionSchema,
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FieldSchema, DataType, db
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)
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from src import config
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from src.models.embedding import OtherEmbedding
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from src.knowledge.knowledge_base import KnowledgeBase
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from src.knowledge.kb_utils import split_text_into_chunks, split_text_into_qa_chunks, prepare_item_metadata, get_embedding_config
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from src.utils import logger, hashstr
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MILVUS_AVAILABLE = True
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class MilvusKB(KnowledgeBase):
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"""基于 Milvus 的生产级向量知识库实现"""
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def __init__(self, work_dir: str, **kwargs):
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"""
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初始化 Milvus 知识库
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Args:
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work_dir: 工作目录
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**kwargs: 其他配置参数
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"""
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super().__init__(work_dir)
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if not MILVUS_AVAILABLE:
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raise ImportError("pymilvus is not installed. Please install it with: pip install pymilvus")
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# Milvus 配置
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# self.milvus_host = kwargs.get('milvus_host', os.getenv('MILVUS_HOST', 'localhost'))
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# self.milvus_port = kwargs.get('milvus_port', int(os.getenv('MILVUS_PORT', '19530')))
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self.milvus_token = kwargs.get('milvus_token', os.getenv('MILVUS_TOKEN', ''))
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self.milvus_uri = kwargs.get('milvus_uri', os.getenv('MILVUS_URI', 'http://localhost:19530'))
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self.milvus_db = kwargs.get('milvus_db', 'yuxi_know')
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# 连接名称
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self.connection_alias = f"milvus_{hashstr(work_dir, 6)}"
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# 存储集合映射 {db_id: Collection}
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self.collections: dict[str, Any] = {}
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# 分块配置
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self.chunk_size = kwargs.get('chunk_size', 1000)
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self.chunk_overlap = kwargs.get('chunk_overlap', 200)
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# 初始化连接
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self._init_connection()
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logger.info("MilvusKB initialized")
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@property
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def kb_type(self) -> str:
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"""知识库类型标识"""
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return "milvus"
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def _init_connection(self):
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"""初始化 Milvus 连接"""
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try:
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# 连接到 Milvus
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connections.connect(
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alias=self.connection_alias,
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uri=self.milvus_uri,
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token=self.milvus_token
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)
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# 创建数据库（如果不存在）
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try:
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if self.milvus_db not in db.list_database():
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db.create_database(self.milvus_db)
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db.using_database(self.milvus_db)
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except Exception as e:
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logger.warning(f"Database operation failed, using default: {e}")
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logger.info(f"Connected to Milvus at {self.milvus_uri}")
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except Exception as e:
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logger.error(f"Failed to connect to Milvus: {e}")
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raise
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async def _create_kb_instance(self, db_id: str, kb_config: dict) -> Any:
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"""创建 Milvus 集合"""
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logger.info(f"Creating Milvus collection for {db_id}")
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if db_id not in self.databases_meta:
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raise ValueError(f"Database {db_id} not found")
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embed_info = self.databases_meta[db_id].get("embed_info", {})
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collection_name = f"kb_{db_id}"
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try:
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# 检查集合是否存在
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if utility.has_collection(collection_name, using=self.connection_alias):
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collection = Collection(
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name=collection_name,
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using=self.connection_alias
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)
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# 检查嵌入模型是否匹配
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description = collection.description
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expected_model = embed_info.get("name") if embed_info else "default"
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if expected_model not in description:
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logger.warning(f"Collection {collection_name} model mismatch, recreating...")
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utility.drop_collection(collection_name, using=self.connection_alias)
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raise Exception("Model mismatch, recreating collection")
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logger.info(f"Retrieved existing collection: {collection_name}")
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else:
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raise Exception("Collection not found, creating new one")
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except Exception:
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# 创建新集合
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embedding_dim = embed_info.get("dimension", 1024) if embed_info else 1024
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model_name = embed_info.get("name", "default") if embed_info else "default"
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# 定义集合Schema
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fields = [
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FieldSchema(name="id", dtype=DataType.VARCHAR, max_length=100, is_primary=True),
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FieldSchema(name="content", dtype=DataType.VARCHAR, max_length=65535),
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FieldSchema(name="source", dtype=DataType.VARCHAR, max_length=500),
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FieldSchema(name="chunk_id", dtype=DataType.VARCHAR, max_length=100),
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FieldSchema(name="file_id", dtype=DataType.VARCHAR, max_length=100),
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FieldSchema(name="chunk_index", dtype=DataType.INT64),
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FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=embedding_dim)
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]
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schema = CollectionSchema(
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fields=fields,
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description=f"Knowledge base collection for {db_id} using {model_name}"
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)
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# 创建集合
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collection = Collection(
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name=collection_name,
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schema=schema,
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using=self.connection_alias
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)
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# 创建索引
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index_params = {
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"metric_type": "COSINE",
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"index_type": "IVF_FLAT",
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"params": {"nlist": 1024}
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}
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collection.create_index("embedding", index_params)
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logger.info(f"Created new Milvus collection: {collection_name}")
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return collection
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async def _initialize_kb_instance(self, instance: Any) -> None:
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"""初始化 Milvus 集合（加载到内存）"""
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try:
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instance.load()
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logger.info("Milvus collection loaded into memory")
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except Exception as e:
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logger.warning(f"Failed to load collection into memory: {e}")
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def _get_async_embedding_function(self, embed_info: dict):
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"""获取 embedding 函数"""
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config_dict = get_embedding_config(embed_info)
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embedding_model = OtherEmbedding(
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model=config_dict.get("model"),
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base_url=config_dict.get("base_url"),
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api_key=config_dict.get("api_key"),
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)
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return partial(embedding_model.abatch_encode, batch_size=40)
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def _get_embedding_function(self, embed_info: dict):
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"""获取 embedding 函数"""
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config_dict = get_embedding_config(embed_info)
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embedding_model = OtherEmbedding(
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model=config_dict.get("model"),
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base_url=config_dict.get("base_url"),
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api_key=config_dict.get("api_key"),
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)
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return partial(embedding_model.batch_encode, batch_size=40)
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async def _get_milvus_collection(self, db_id: str):
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"""获取或创建 Milvus 集合"""
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if db_id in self.collections:
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return self.collections[db_id]
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if db_id not in self.databases_meta:
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return None
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try:
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# 创建集合
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collection = await self._create_kb_instance(db_id, {})
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await self._initialize_kb_instance(collection)
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self.collections[db_id] = collection
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return collection
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except Exception as e:
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logger.error(f"Failed to create Milvus collection for {db_id}: {e}")
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logger.error(f"Traceback: {traceback.format_exc()}")
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return None
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def _split_text_into_chunks(self, text: str, file_id: str, filename: str, params: dict) -> list[dict]:
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"""将文本分割成块"""
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# 检查是否使用QA分割模式
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use_qa_split = params.get('use_qa_split', False)
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if use_qa_split:
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# 使用QA分割模式
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qa_separator = params.get('qa_separator', '\n\n\n')
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return split_text_into_qa_chunks(text, file_id, filename, qa_separator, params)
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else:
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# 使用传统分割模式
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return split_text_into_chunks(text, file_id, filename, params)
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async def add_content(self, db_id: str, items: list[str],
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params: dict | None = {}) -> list[dict]:
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"""添加内容（文件/URL）"""
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if db_id not in self.databases_meta:
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raise ValueError(f"Database {db_id} not found")
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collection = await self._get_milvus_collection(db_id)
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if not collection:
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raise ValueError(f"Failed to get Milvus collection for {db_id}")
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embed_info = self.databases_meta[db_id].get("embed_info", {})
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embedding_function = self._get_async_embedding_function(embed_info)
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content_type = params.get('content_type', 'file') if params else 'file'
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processed_items_info = []
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for item in items:
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# 准备文件元数据
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metadata = prepare_item_metadata(item, content_type, db_id)
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file_id = metadata["file_id"]
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filename = metadata["filename"]
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# 添加文件记录
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file_record = metadata.copy()
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del file_record["file_id"] # 从记录中移除file_id，因为它是key
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self.files_meta[file_id] = file_record
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self._save_metadata()
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# 添加 file_id 到返回数据
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file_record = file_record.copy()
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file_record["file_id"] = file_id
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try:
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# 根据内容类型处理内容
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if content_type == "file":
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markdown_content = await self._process_file_to_markdown(item, params=params)
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else: # URL
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markdown_content = await self._process_url_to_markdown(item, params=params)
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# 分割文本成块
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chunks = self._split_text_into_chunks(markdown_content, file_id, filename, params)
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logger.info(f"Split {filename} into {len(chunks)} chunks")
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# 准备 Milvus 插入的数据
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if chunks:
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# 生成嵌入向量
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texts = [chunk["content"] for chunk in chunks]
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embeddings = await embedding_function(texts)
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# 准备插入数据
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entities = [
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[chunk["id"] for chunk in chunks], # id
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[chunk["content"] for chunk in chunks], # content
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[chunk["source"] for chunk in chunks], # source
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[chunk["chunk_id"] for chunk in chunks], # chunk_id
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[chunk["file_id"] for chunk in chunks], # file_id
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[chunk["chunk_index"] for chunk in chunks], # chunk_index
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embeddings # embedding
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]
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# 插入到 Milvus
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collection.insert(entities)
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collection.flush()
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logger.info(f"Inserted {content_type} {item} into Milvus. Done.")
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# 更新状态为完成
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self.files_meta[file_id]["status"] = "done"
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self._save_metadata()
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file_record['status'] = "done"
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except Exception as e:
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logger.error(f"处理{content_type} {item} 失败: {e}, {traceback.format_exc()}")
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self.files_meta[file_id]["status"] = "failed"
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self._save_metadata()
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file_record['status'] = "failed"
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processed_items_info.append(file_record)
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return processed_items_info
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async def aquery(self, query_text: str, db_id: str, **kwargs) -> str:
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"""异步查询知识库"""
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collection = await self._get_milvus_collection(db_id)
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if not collection:
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raise ValueError(f"Database {db_id} not found")
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try:
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# 设置查询参数 - Milvus 知识库特有的参数
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top_k = kwargs.get("top_k", 10)
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similarity_threshold = kwargs.get("similarity_threshold", 0.0) # 相似度阈值
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include_distances = kwargs.get("include_distances", True) # 是否包含距离信息
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metric_type = kwargs.get("metric_type", "COSINE") # 距离度量类型
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# 生成查询向量
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embed_info = self.databases_meta[db_id].get("embed_info", {})
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embedding_function = self._get_embedding_function(embed_info)
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query_embedding = embedding_function([query_text])
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# 执行相似性搜索
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search_params = {"metric_type": metric_type, "params": {"nprobe": 10}}
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results = collection.search(
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data=query_embedding,
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anns_field="embedding",
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param=search_params,
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limit=top_k,
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output_fields=["content", "source", "chunk_id", "file_id", "chunk_index"]
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)
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# 处理结果
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if results and len(results) > 0 and len(results[0]) > 0:
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contexts = []
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for i, hit in enumerate(results[0]):
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# 计算相似度
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similarity = 1 - hit.distance if metric_type == "COSINE" else 1 / (1 + hit.distance)
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# 应用相似度阈值过滤
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if similarity < similarity_threshold:
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continue
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entity = hit.entity
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content = entity.get("content", "")
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source = entity.get("source", "未知来源")
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chunk_id = entity.get("chunk_id", f"chunk_{i}")
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context = f"[文档片段 {i+1}]:\n{content}\n"
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context += f"来源: {source} ({chunk_id})\n"
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if include_distances:
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context += f"相似度: {similarity:.3f}\n"
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contexts.append(context)
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response = "\n".join(contexts)
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logger.debug(f"Milvus query response: {len(contexts)} chunks found (after similarity filtering)")
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return response
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return ""
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except Exception as e:
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logger.error(f"Milvus query error: {e}, {traceback.format_exc()}")
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return ""
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async def delete_file(self, db_id: str, file_id: str) -> None:
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"""删除文件"""
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collection = await self._get_milvus_collection(db_id)
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if collection:
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try:
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# 删除所有相关chunks
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expr = f'file_id == "{file_id}"'
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collection.delete(expr)
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collection.flush()
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logger.info(f"Deleted chunks for file {file_id} from Milvus")
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except Exception as e:
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logger.error(f"Error deleting file {file_id} from Milvus: {e}")
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# 删除文件记录
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if file_id in self.files_meta:
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del self.files_meta[file_id]
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self._save_metadata()
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async def get_file_info(self, db_id: str, file_id: str) -> dict:
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"""获取文件信息和chunks"""
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if file_id not in self.files_meta:
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raise Exception(f"File not found: {file_id}")
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# 使用 Milvus 获取chunks
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collection = await self._get_milvus_collection(db_id)
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if collection:
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try:
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# 查询文档的所有chunks
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expr = f'file_id == "{file_id}"'
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results = collection.query(
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expr=expr,
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output_fields=["content", "chunk_id", "chunk_index"],
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limit=10000 # 假设单个文件不会超过10000个chunks
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)
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# 构建chunks数据
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doc_chunks = []
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for result in results:
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chunk_data = {
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"id": result.get("chunk_id", ""),
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"content": result.get("content", ""),
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"chunk_order_index": result.get("chunk_index", 0)
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}
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doc_chunks.append(chunk_data)
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# 按 chunk_order_index 排序
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doc_chunks.sort(key=lambda x: x.get("chunk_order_index", 0))
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return {"lines": doc_chunks}
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except Exception as e:
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logger.error(f"Error getting chunks for file {file_id}: {e}")
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return {"lines": []}
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def __del__(self):
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"""清理连接"""
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try:
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if hasattr(self, 'connection_alias'):
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connections.disconnect(self.connection_alias)
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except Exception:
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pass
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