988 lines
41 KiB
Python
988 lines
41 KiB
Python
import asyncio
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import os
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import time
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import traceback
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from functools import partial
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from typing import Any
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from pymilvus import (
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AnnSearchRequest,
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Collection,
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CollectionSchema,
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DataType,
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FieldSchema,
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Function,
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FunctionType,
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WeightedRanker,
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connections,
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db,
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utility,
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)
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from yuxi import config
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from yuxi.knowledge.base import FileStatus, KnowledgeBase
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from yuxi.knowledge.chunking.ragflow_like.dispatcher import chunk_markdown
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from yuxi.knowledge.chunking.ragflow_like.presets import resolve_chunk_processing_params
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from yuxi.knowledge.utils.kb_utils import get_embedding_config
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from yuxi.models.embed import OtherEmbedding
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from yuxi.plugins.parser.unified import Parser
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from yuxi.utils import hashstr, logger
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from yuxi.utils.datetime_utils import utc_isoformat
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MILVUS_AVAILABLE = True
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CONTENT_SPARSE_FIELD = "content_sparse"
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CONTENT_ANALYZER_PARAMS = {"type": "chinese"}
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class MilvusKB(KnowledgeBase):
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"""基于 Milvus 的生产级向量库"""
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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") or "")
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self.milvus_uri = kwargs.get("milvus_uri", os.getenv("MILVUS_URI") or "http://localhost:19530")
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self.milvus_db = kwargs.get("milvus_db") or "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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# 元数据锁
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self._metadata_lock = asyncio.Lock()
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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(alias=self.connection_alias, uri=self.milvus_uri, token=self.milvus_token)
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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 not (metadata := self.databases_meta.get(db_id)):
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raise ValueError(f"Database {db_id} not found")
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# 获取嵌入模型信息
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if not (embed_info := metadata.get("embed_info")):
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logger.error(f"Embedding info not found for database {db_id}, using default model")
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embed_info = config.embed_model_names[config.embed_model]
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collection_name = 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(name=collection_name, using=self.connection_alias)
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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["name"] if embed_info else "default"
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if expected_model not in description:
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logger.warning(
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f"Collection {collection_name} model mismatch: "
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f"expected='{expected_model}', found_in_description='{description}'"
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)
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utility.drop_collection(collection_name, using=self.connection_alias)
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return self._create_new_collection(collection_name, embed_info, db_id)
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if not self._collection_supports_bm25(collection):
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logger.warning(f"Collection {collection_name} schema does not support BM25, recreating")
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utility.drop_collection(collection_name, using=self.connection_alias)
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return self._create_new_collection(collection_name, embed_info, db_id)
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logger.info(f"Retrieved existing collection: {collection_name}")
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return collection
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else:
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logger.info(f"Collection {collection_name} not found, creating new one")
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return self._create_new_collection(collection_name, embed_info, db_id)
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except (connections.MilvusException, RuntimeError) as e:
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logger.error(f"Error checking collection {collection_name}: {e}")
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raise
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except Exception as e:
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logger.error(f"Unexpected error while managing collection {collection_name}: {e}")
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logger.debug(f"Traceback: {traceback.format_exc()}")
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raise
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def _create_new_collection(self, collection_name: str, embed_info: Any, db_id: str) -> Collection:
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"""创建新的 Milvus 集合"""
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embedding_dim = embed_info.get("dimension", 1024)
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model_name = embed_info.get("name", "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(
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name="content",
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dtype=DataType.VARCHAR,
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max_length=65535,
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enable_analyzer=True,
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analyzer_params=CONTENT_ANALYZER_PARAMS,
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),
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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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FieldSchema(name=CONTENT_SPARSE_FIELD, dtype=DataType.SPARSE_FLOAT_VECTOR),
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]
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bm25_function = Function(
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name="content_bm25",
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input_field_names=["content"],
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output_field_names=[CONTENT_SPARSE_FIELD],
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function_type=FunctionType.BM25,
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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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functions=[bm25_function],
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)
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# 创建集合
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collection = Collection(name=collection_name, schema=schema, using=self.connection_alias)
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# 创建索引
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index_params = {"metric_type": "COSINE", "index_type": "IVF_FLAT", "params": {"nlist": 1024}}
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collection.create_index("embedding", index_params)
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sparse_index_params = {
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"metric_type": "BM25",
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"index_type": "SPARSE_INVERTED_INDEX",
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"params": {"inverted_index_algo": "DAAT_MAXSCORE"},
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}
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collection.create_index(CONTENT_SPARSE_FIELD, sparse_index_params)
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logger.info(f"Created new Milvus collection: {collection_name} '{model_name=}', {embedding_dim=}")
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return collection
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def _collection_supports_bm25(self, collection: Collection) -> bool:
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"""检查集合是否具备 Milvus 内置 BM25 所需的 schema。"""
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fields = {field.name: field for field in collection.schema.fields}
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content_field = fields.get("content")
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sparse_field = fields.get(CONTENT_SPARSE_FIELD)
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if not content_field or content_field.dtype != DataType.VARCHAR:
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return False
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if content_field.params.get("enable_analyzer") is not True:
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return False
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if not sparse_field or sparse_field.dtype != DataType.SPARSE_FLOAT_VECTOR:
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return False
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for function in collection.schema.functions:
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if (
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function.type == FunctionType.BM25
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and function.input_field_names == ["content"]
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and function.output_field_names == [CONTENT_SPARSE_FIELD]
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):
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return True
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return False
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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(self, embed_info: dict):
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"""获取 embedding 函数"""
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# 检查是否有 model_id 字段，优先使用 select_embedding_model
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if embed_info and "model_id" in embed_info:
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from yuxi.models.embed import select_embedding_model
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return select_embedding_model(embed_info["model_id"])
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# 使用原有的逻辑（兼容模式））
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config_dict = get_embedding_config(embed_info)
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return 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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def _get_async_embedding_function(self, embed_info: dict):
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"""获取 embedding 函数"""
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embedding_model = self._get_async_embedding(embed_info)
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batch_size = int(getattr(embedding_model, "batch_size", 40) or 40)
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return partial(embedding_model.abatch_encode, batch_size=batch_size)
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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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embedding_model = self._get_async_embedding(embed_info)
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batch_size = int(getattr(embedding_model, "batch_size", 40) or 40)
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return partial(embedding_model.batch_encode, batch_size=batch_size)
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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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return chunk_markdown(text, file_id, filename, params)
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async def index_file(self, db_id: str, file_id: str, operator_id: str | None = None, params: dict | None = None) -> dict:
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"""
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Index parsed file (Status: INDEXING -> INDEXED/ERROR_INDEXING)
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Args:
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db_id: Database ID
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file_id: File ID
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operator_id: ID of the user performing the operation
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params: Override processing params to apply during indexing (merged on top of stored params)
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Returns:
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Updated file metadata
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"""
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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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# Get/Create collection
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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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# Get file meta
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async with self._metadata_lock:
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if file_id not in self.files_meta:
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raise ValueError(f"File {file_id} not found")
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file_meta = self.files_meta[file_id]
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# Validate current status - only allow indexing from these states
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current_status = file_meta.get("status")
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allowed_statuses = {
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FileStatus.PARSED,
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FileStatus.ERROR_INDEXING,
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FileStatus.INDEXED, # For re-indexing
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"done", # Legacy status
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}
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if current_status not in allowed_statuses:
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raise ValueError(
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f"Cannot index file with status '{current_status}'. "
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f"File must be parsed first (status should be one of: {', '.join(allowed_statuses)})"
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)
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# Check markdown file exists
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if not file_meta.get("markdown_file"):
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raise ValueError("File has not been parsed yet (no markdown_file)")
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# Clear previous error if any
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if "error" in file_meta:
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self.files_meta[file_id].pop("error", None)
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# Update status and add to processing queue
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self.files_meta[file_id]["status"] = FileStatus.INDEXING
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self.files_meta[file_id]["updated_at"] = utc_isoformat()
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if operator_id:
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self.files_meta[file_id]["updated_by"] = operator_id
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# 将传入的 params 作为 request_params，确保用户指定的参数始终覆盖存储的参数
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params = resolve_chunk_processing_params(
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kb_additional_params=self.databases_meta.get(db_id, {}).get("metadata"),
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file_processing_params=file_meta.get("processing_params"),
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request_params=params,
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)
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self.files_meta[file_id]["processing_params"] = params
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await self._save_metadata()
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logger.debug(f"[index_file] file_id={file_id}, processing_params={params}")
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# Add to processing queue
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self._add_to_processing_queue(file_id)
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try:
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# Read markdown
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markdown_content = await self._read_markdown_from_minio(file_meta["markdown_file"])
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filename = file_meta.get("filename")
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# Split
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chunks = self._split_text_into_chunks(markdown_content, file_id, filename, params)
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logger.info(
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f"Split {filename} into {len(chunks)} chunks with params: "
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f"chunk_preset_id={params.get('chunk_preset_id')}, "
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f"chunk_size={params.get('chunk_size')}, "
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f"chunk_overlap={params.get('chunk_overlap')}, "
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f"qa_separator={params.get('qa_separator')}"
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)
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if chunks:
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texts = [chunk["content"] for chunk in chunks]
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embeddings = await embedding_function(texts)
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entities = [
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[chunk["id"] for chunk in chunks],
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[chunk["content"] for chunk in chunks],
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[chunk["source"] for chunk in chunks],
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[chunk["chunk_id"] for chunk in chunks],
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[chunk["file_id"] for chunk in chunks],
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[chunk["chunk_index"] for chunk in chunks],
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embeddings,
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]
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# Clean up existing chunks if any (for re-indexing)
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await self.delete_file_chunks_only(db_id, file_id)
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def _insert_records():
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collection.insert(entities)
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await asyncio.to_thread(_insert_records)
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logger.info(f"Indexed file {file_id} into Milvus")
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# Update status
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async with self._metadata_lock:
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self.files_meta[file_id]["status"] = FileStatus.INDEXED
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self.files_meta[file_id]["updated_at"] = utc_isoformat()
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if operator_id:
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self.files_meta[file_id]["updated_by"] = operator_id
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await self._persist_file(file_id)
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return self.files_meta[file_id]
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except Exception as e:
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logger.error(f"Indexing failed for {file_id}: {e}")
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async with self._metadata_lock:
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self.files_meta[file_id]["status"] = FileStatus.ERROR_INDEXING
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self.files_meta[file_id]["error"] = str(e)
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self.files_meta[file_id]["updated_at"] = utc_isoformat()
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if operator_id:
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self.files_meta[file_id]["updated_by"] = operator_id
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await self._persist_file(file_id)
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raise
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finally:
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# Remove from processing queue
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self._remove_from_processing_queue(file_id)
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async def update_content(self, db_id: str, file_ids: list[str], params: dict | None = None) -> list[dict]:
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"""更新内容 - 根据file_ids重新解析文件并更新向量库"""
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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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# 处理默认参数
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if params is None:
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params = {}
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processed_items_info = []
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for file_id in file_ids:
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# 从元数据中获取文件信息
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async with self._metadata_lock:
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if file_id not in self.files_meta:
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logger.warning(f"File {file_id} not found in metadata, skipping")
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continue
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file_meta = self.files_meta[file_id]
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file_path = file_meta.get("path")
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filename = file_meta.get("filename")
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if not file_path:
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logger.warning(f"File path not found for {file_id}, skipping")
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continue
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# 添加到处理队列
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self._add_to_processing_queue(file_id)
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try:
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# 更新状态为处理中
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async with self._metadata_lock:
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resolved_params = resolve_chunk_processing_params(
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kb_additional_params=self.databases_meta.get(db_id, {}).get("metadata"),
|
||
file_processing_params=self.files_meta[file_id].get("processing_params"),
|
||
request_params=params,
|
||
)
|
||
self.files_meta[file_id]["processing_params"] = resolved_params
|
||
self.files_meta[file_id]["status"] = "processing"
|
||
await self._persist_file(file_id)
|
||
|
||
# 重新解析文件为 markdown
|
||
params["image_bucket"] = "public"
|
||
params["image_prefix"] = f"{db_id}/kb-images"
|
||
markdown_content = await Parser.aparse(source=file_path, params=params)
|
||
|
||
# 先删除现有的 Milvus 数据（仅删除chunks，保留元数据）
|
||
await self.delete_file_chunks_only(db_id, file_id)
|
||
|
||
# 重新生成 chunks
|
||
chunks = self._split_text_into_chunks(markdown_content, file_id, filename, resolved_params)
|
||
logger.info(f"Split {filename} into {len(chunks)} chunks")
|
||
|
||
if chunks:
|
||
texts = [chunk["content"] for chunk in chunks]
|
||
embeddings = await embedding_function(texts)
|
||
|
||
entities = [
|
||
[chunk["id"] for chunk in chunks],
|
||
[chunk["content"] for chunk in chunks],
|
||
[chunk["source"] for chunk in chunks],
|
||
[chunk["chunk_id"] for chunk in chunks],
|
||
[chunk["file_id"] for chunk in chunks],
|
||
[chunk["chunk_index"] for chunk in chunks],
|
||
embeddings,
|
||
]
|
||
|
||
def _insert_records():
|
||
collection.insert(entities)
|
||
|
||
await asyncio.to_thread(_insert_records)
|
||
|
||
logger.info(f"Updated file {file_path} in Milvus. Done.")
|
||
|
||
# 更新元数据状态
|
||
async with self._metadata_lock:
|
||
self.files_meta[file_id]["status"] = "done"
|
||
await self._persist_file(file_id)
|
||
|
||
# 从处理队列中移除
|
||
self._remove_from_processing_queue(file_id)
|
||
|
||
# 返回更新后的文件信息
|
||
updated_file_meta = file_meta.copy()
|
||
updated_file_meta["status"] = "done"
|
||
updated_file_meta["file_id"] = file_id
|
||
processed_items_info.append(updated_file_meta)
|
||
|
||
except Exception as e:
|
||
logger.error(f"更新file {file_path} 失败: {e}, {traceback.format_exc()}")
|
||
async with self._metadata_lock:
|
||
self.files_meta[file_id]["status"] = "failed"
|
||
await self._persist_file(file_id)
|
||
|
||
# 从处理队列中移除
|
||
self._remove_from_processing_queue(file_id)
|
||
|
||
# 返回失败的文件信息
|
||
failed_file_meta = file_meta.copy()
|
||
failed_file_meta["status"] = "failed"
|
||
failed_file_meta["file_id"] = file_id
|
||
processed_items_info.append(failed_file_meta)
|
||
|
||
return processed_items_info
|
||
|
||
def _build_chunk_from_hit(
|
||
self,
|
||
hit: Any,
|
||
score: float,
|
||
include_distances: bool,
|
||
score_field: str | None = None,
|
||
) -> dict:
|
||
"""将 Milvus Hit 转成知识库统一返回的 Chunk 结构。"""
|
||
entity = hit.entity
|
||
metadata = {
|
||
"source": entity.get("source", "未知来源"),
|
||
"chunk_id": entity.get("chunk_id"),
|
||
"file_id": entity.get("file_id"),
|
||
"chunk_index": entity.get("chunk_index"),
|
||
}
|
||
chunk = {"content": entity.get("content", ""), "metadata": metadata, "score": float(score or 0.0)}
|
||
if score_field:
|
||
chunk[score_field] = float(score or 0.0)
|
||
if include_distances:
|
||
chunk["distance"] = hit.distance
|
||
return chunk
|
||
|
||
async def aquery(self, query_text: str, db_id: str, agent_call: bool = False, **kwargs) -> list[dict]:
|
||
"""异步查询知识库"""
|
||
collection = await self._get_milvus_collection(db_id)
|
||
if not collection:
|
||
raise ValueError(f"Database {db_id} not found")
|
||
|
||
query_params = self._get_query_params(db_id)
|
||
# 合并查询参数：kwargs（临时参数）优先级高于 query_params（持久化参数）
|
||
# 这样允许用户在单次查询中临时覆盖持久化配置
|
||
merged_kwargs = {**query_params, **kwargs}
|
||
|
||
try:
|
||
# 查询参数（从 merged_kwargs 读取）
|
||
logger.debug(f"Query params: {merged_kwargs}")
|
||
final_top_k = int(merged_kwargs.get("final_top_k", 10))
|
||
final_top_k = max(final_top_k, 1)
|
||
similarity_threshold = float(merged_kwargs.get("similarity_threshold", 0.2))
|
||
metric_type = merged_kwargs.get("metric_type", "COSINE")
|
||
include_distances = bool(merged_kwargs.get("include_distances", True))
|
||
search_mode = str(merged_kwargs.get("search_mode", "vector")).lower()
|
||
if search_mode not in {"vector", "keyword", "hybrid"}:
|
||
search_mode = "vector"
|
||
|
||
use_reranker = bool(merged_kwargs.get("use_reranker", False))
|
||
if use_reranker:
|
||
recall_top_k = int(merged_kwargs.get("recall_top_k", 50))
|
||
recall_top_k = max(recall_top_k, final_top_k)
|
||
else:
|
||
recall_top_k = final_top_k
|
||
|
||
# 构建过滤表达式（文件名）
|
||
file_expr = None
|
||
if file_name := merged_kwargs.get("file_name"):
|
||
safe_file_name = file_name.replace('"', '\\"')
|
||
if "%" not in safe_file_name:
|
||
file_expr = f'source like "%{safe_file_name}%"'
|
||
else:
|
||
file_expr = f'source like "{safe_file_name}"'
|
||
logger.debug(f"Using filter expression: {file_expr}")
|
||
|
||
output_fields = ["content", "source", "chunk_id", "file_id", "chunk_index"]
|
||
retrieved_chunks: list[dict] = []
|
||
if search_mode == "vector":
|
||
embed_info = self.databases_meta[db_id].get("embed_info", {})
|
||
embedding_function = self._get_embedding_function(embed_info)
|
||
query_embedding = embedding_function([query_text])
|
||
|
||
search_params = {"metric_type": metric_type, "params": {"nprobe": 10}}
|
||
|
||
results = collection.search(
|
||
data=query_embedding,
|
||
anns_field="embedding",
|
||
param=search_params,
|
||
limit=recall_top_k,
|
||
expr=file_expr,
|
||
output_fields=output_fields,
|
||
)
|
||
|
||
if results and len(results) > 0 and len(results[0]) > 0:
|
||
for hit in results[0]:
|
||
similarity = hit.distance if metric_type == "COSINE" else 1 / (1 + hit.distance)
|
||
if similarity < similarity_threshold:
|
||
continue
|
||
|
||
retrieved_chunks.append(self._build_chunk_from_hit(hit, similarity, include_distances))
|
||
|
||
logger.debug(
|
||
f"Milvus vector query response: {len(retrieved_chunks)} chunks found (after similarity filtering)"
|
||
)
|
||
|
||
elif search_mode == "keyword":
|
||
bm25_top_k = int(merged_kwargs.get("bm25_top_k", recall_top_k))
|
||
bm25_top_k = max(bm25_top_k, 1)
|
||
bm25_drop_ratio_search = float(merged_kwargs.get("bm25_drop_ratio_search", 0.0))
|
||
bm25_search_params = {
|
||
"metric_type": "BM25",
|
||
"params": {"drop_ratio_search": bm25_drop_ratio_search},
|
||
}
|
||
|
||
results = collection.search(
|
||
data=[query_text],
|
||
anns_field=CONTENT_SPARSE_FIELD,
|
||
param=bm25_search_params,
|
||
limit=bm25_top_k,
|
||
expr=file_expr,
|
||
output_fields=output_fields,
|
||
)
|
||
|
||
if results and len(results) > 0 and len(results[0]) > 0:
|
||
for hit in results[0]:
|
||
retrieved_chunks.append(
|
||
self._build_chunk_from_hit(hit, hit.distance, include_distances, score_field="bm25_score")
|
||
)
|
||
|
||
logger.debug(f"Milvus BM25 query response: {len(retrieved_chunks)} chunks found")
|
||
else:
|
||
embed_info = self.databases_meta[db_id].get("embed_info", {})
|
||
embedding_function = self._get_embedding_function(embed_info)
|
||
query_embedding = embedding_function([query_text])
|
||
bm25_top_k = int(merged_kwargs.get("bm25_top_k", recall_top_k))
|
||
bm25_top_k = max(bm25_top_k, 1)
|
||
bm25_drop_ratio_search = float(merged_kwargs.get("bm25_drop_ratio_search", 0.0))
|
||
vector_weight = float(merged_kwargs.get("vector_weight", 0.7))
|
||
bm25_weight = float(merged_kwargs.get("bm25_weight", 0.3))
|
||
|
||
vector_request = AnnSearchRequest(
|
||
data=query_embedding,
|
||
anns_field="embedding",
|
||
param={"metric_type": metric_type, "params": {"nprobe": 10}},
|
||
limit=recall_top_k,
|
||
expr=file_expr,
|
||
)
|
||
bm25_request = AnnSearchRequest(
|
||
data=[query_text],
|
||
anns_field=CONTENT_SPARSE_FIELD,
|
||
param={
|
||
"metric_type": "BM25",
|
||
"params": {"drop_ratio_search": bm25_drop_ratio_search},
|
||
},
|
||
limit=bm25_top_k,
|
||
expr=file_expr,
|
||
)
|
||
results = collection.hybrid_search(
|
||
reqs=[vector_request, bm25_request],
|
||
rerank=WeightedRanker(vector_weight, bm25_weight),
|
||
limit=recall_top_k,
|
||
output_fields=output_fields,
|
||
)
|
||
if results and len(results) > 0 and len(results[0]) > 0:
|
||
for hit in results[0]:
|
||
score = float(hit.distance or 0.0)
|
||
if score < similarity_threshold:
|
||
continue
|
||
retrieved_chunks.append(
|
||
self._build_chunk_from_hit(hit, score, include_distances, score_field="hybrid_score")
|
||
)
|
||
|
||
logger.debug(f"Milvus hybrid query response: {len(retrieved_chunks)} chunks found")
|
||
|
||
if not retrieved_chunks:
|
||
return []
|
||
|
||
if not use_reranker:
|
||
return retrieved_chunks[:final_top_k]
|
||
|
||
# 使用重排序模型
|
||
reranker_model = merged_kwargs.get("reranker_model")
|
||
if not reranker_model:
|
||
raise ValueError(
|
||
"Reranker model must be specified when use_reranker=True. "
|
||
"Please provide reranker_model in query parameters."
|
||
)
|
||
|
||
try:
|
||
from yuxi.models.rerank import get_reranker
|
||
|
||
reranker = get_reranker(reranker_model)
|
||
try:
|
||
rerank_start = time.time()
|
||
documents_text = [chunk["content"] for chunk in retrieved_chunks]
|
||
rerank_scores = await reranker.acompute_score([query_text, documents_text], normalize=True)
|
||
|
||
for chunk, rerank_score in zip(retrieved_chunks, rerank_scores):
|
||
chunk["rerank_score"] = float(rerank_score)
|
||
|
||
retrieved_chunks.sort(
|
||
key=lambda item: item.get("rerank_score", item.get("score", 0.0)), reverse=True
|
||
)
|
||
elapsed = time.time() - rerank_start
|
||
logger.info(f"Reranking completed for {db_id} in {elapsed:.3f}s with model {reranker_model}")
|
||
finally:
|
||
await reranker.aclose()
|
||
|
||
except Exception as exc: # noqa: BLE001
|
||
logger.error(f"Reranking failed: {exc}, falling back to vector scores")
|
||
|
||
# 统一返回结果
|
||
return retrieved_chunks[:final_top_k]
|
||
|
||
except Exception as e:
|
||
logger.error(f"Milvus query error: {e}, {traceback.format_exc()}")
|
||
return []
|
||
|
||
async def delete_file_chunks_only(self, db_id: str, file_id: str) -> None:
|
||
"""仅删除文件的chunks数据，保留元数据（用于更新操作）"""
|
||
collection = await self._get_milvus_collection(db_id)
|
||
|
||
if collection:
|
||
# 先查询文件是否存在，避免不必要的删除操作
|
||
try:
|
||
expr = f'file_id == "{file_id}"'
|
||
results = collection.query(expr=expr, output_fields=["id"], limit=1)
|
||
|
||
if not results:
|
||
logger.info(f"File {file_id} not found in Milvus, skipping delete operation")
|
||
else:
|
||
# 只有在文件确实存在时才执行删除
|
||
def _delete_from_milvus():
|
||
try:
|
||
collection.delete(expr)
|
||
logger.info(f"Deleted chunks for file {file_id} from Milvus")
|
||
except Exception as e:
|
||
logger.error(f"Error deleting file {file_id} from Milvus: {e}")
|
||
|
||
await asyncio.to_thread(_delete_from_milvus)
|
||
except Exception as e:
|
||
logger.error(f"Error checking file existence in Milvus: {e}")
|
||
# 注意：这里不删除 files_meta[file_id]，保留元数据用于后续操作
|
||
|
||
async def delete_file(self, db_id: str, file_id: str) -> None:
|
||
"""删除文件（包括元数据）"""
|
||
# 先删除 Milvus 中的 chunks 数据
|
||
await self.delete_file_chunks_only(db_id, file_id)
|
||
|
||
# 使用锁确保元数据操作的原子性
|
||
async with self._metadata_lock:
|
||
if file_id in self.files_meta:
|
||
del self.files_meta[file_id]
|
||
from yuxi.repositories.knowledge_file_repository import KnowledgeFileRepository
|
||
|
||
await KnowledgeFileRepository().delete(file_id)
|
||
|
||
async def get_file_basic_info(self, db_id: str, file_id: str) -> dict:
|
||
"""获取文件基本信息（仅元数据）"""
|
||
if file_id not in self.files_meta:
|
||
raise Exception(f"File not found: {file_id}")
|
||
|
||
return {"meta": self.files_meta[file_id]}
|
||
|
||
async def get_file_content(self, db_id: str, file_id: str) -> dict:
|
||
"""获取文件内容信息（chunks和lines）"""
|
||
if file_id not in self.files_meta:
|
||
raise Exception(f"File not found: {file_id}")
|
||
|
||
# 使用 Milvus 获取chunks
|
||
content_info = {"lines": []}
|
||
collection = await self._get_milvus_collection(db_id)
|
||
if collection:
|
||
try:
|
||
# 查询文档的所有chunks
|
||
expr = f'file_id == "{file_id}"'
|
||
results = collection.query(
|
||
expr=expr,
|
||
output_fields=["content", "chunk_id", "chunk_index"],
|
||
limit=10000, # 假设单个文件不会超过10000个chunks
|
||
)
|
||
|
||
# 构建chunks数据
|
||
doc_chunks = []
|
||
for result in results:
|
||
chunk_data = {
|
||
"id": result.get("chunk_id", ""),
|
||
"content": result.get("content", ""),
|
||
"chunk_order_index": result.get("chunk_index", 0),
|
||
}
|
||
doc_chunks.append(chunk_data)
|
||
|
||
# 按 chunk_order_index 排序
|
||
doc_chunks.sort(key=lambda x: x.get("chunk_order_index", 0))
|
||
content_info["lines"] = doc_chunks
|
||
|
||
except Exception as e:
|
||
logger.error(f"Failed to get file content from Milvus: {e}")
|
||
content_info["lines"] = []
|
||
|
||
# Try to read markdown content if available
|
||
file_meta = self.files_meta[file_id]
|
||
if file_meta.get("markdown_file"):
|
||
try:
|
||
content = await self._read_markdown_from_minio(file_meta["markdown_file"])
|
||
content_info["content"] = content
|
||
except Exception as e:
|
||
logger.error(f"Failed to read markdown file for {file_id}: {e}")
|
||
|
||
return content_info
|
||
|
||
async def get_file_info(self, db_id: str, file_id: str) -> dict:
|
||
"""获取文件完整信息（基本信息+内容信息）- 保持向后兼容"""
|
||
if file_id not in self.files_meta:
|
||
raise Exception(f"File not found: {file_id}")
|
||
|
||
# 合并基本信息和内容信息
|
||
basic_info = await self.get_file_basic_info(db_id, file_id)
|
||
content_info = await self.get_file_content(db_id, file_id)
|
||
|
||
return {**basic_info, **content_info}
|
||
|
||
def delete_database(self, db_id: str) -> dict:
|
||
"""删除数据库，同时清除Milvus中的集合"""
|
||
# Drop Milvus collection
|
||
try:
|
||
if utility.has_collection(db_id, using=self.connection_alias):
|
||
utility.drop_collection(db_id, using=self.connection_alias)
|
||
logger.info(f"Dropped Milvus collection for {db_id}")
|
||
else:
|
||
logger.info(f"Milvus collection {db_id} does not exist, skipping")
|
||
except Exception as e:
|
||
logger.error(f"Failed to drop Milvus collection {db_id}: {e}")
|
||
|
||
# Call base method to delete local files and metadata
|
||
return super().delete_database(db_id)
|
||
|
||
def get_query_params_config(self, db_id: str, **kwargs) -> dict:
|
||
"""获取 Milvus 知识库的查询参数配置"""
|
||
# 构建 Milvus 特定参数（不再从 reranker_config 读取）
|
||
options = [
|
||
{
|
||
"key": "search_mode",
|
||
"label": "检索模式",
|
||
"type": "select",
|
||
"default": "vector",
|
||
"options": [
|
||
{"value": "vector", "label": "向量检索", "description": "仅使用向量相似度检索"},
|
||
{"value": "keyword", "label": "BM25 全文检索", "description": "仅使用 Milvus BM25 检索"},
|
||
{"value": "hybrid", "label": "混合检索", "description": "Milvus 向量检索与 BM25 融合检索"},
|
||
],
|
||
"description": "选择检索模式",
|
||
},
|
||
{
|
||
"key": "final_top_k",
|
||
"label": "最终返回 Chunk 数",
|
||
"type": "number",
|
||
"default": 10,
|
||
"min": 1,
|
||
"max": 100,
|
||
"description": "重排序后返回给前端的文档数量",
|
||
},
|
||
{
|
||
"key": "similarity_threshold",
|
||
"label": "相似度阈值（0-1）",
|
||
"type": "number",
|
||
"default": 0.0,
|
||
"min": 0.0,
|
||
"max": 1.0,
|
||
"step": 0.1,
|
||
"description": "过滤相似度低于此值的结果",
|
||
},
|
||
{
|
||
"key": "bm25_top_k",
|
||
"label": "BM25 召回数量",
|
||
"type": "number",
|
||
"default": 50,
|
||
"min": 1,
|
||
"max": 200,
|
||
"description": "BM25 全文检索和混合检索中的 BM25 候选数量",
|
||
},
|
||
{
|
||
"key": "vector_weight",
|
||
"label": "向量检索权重",
|
||
"type": "number",
|
||
"default": 0.7,
|
||
"min": 0.0,
|
||
"max": 1.0,
|
||
"step": 0.1,
|
||
"description": "混合检索中向量召回结果的融合权重",
|
||
},
|
||
{
|
||
"key": "bm25_weight",
|
||
"label": "BM25 权重",
|
||
"type": "number",
|
||
"default": 0.3,
|
||
"min": 0.0,
|
||
"max": 1.0,
|
||
"step": 0.1,
|
||
"description": "混合检索中 BM25 召回结果的融合权重",
|
||
},
|
||
{
|
||
"key": "bm25_drop_ratio_search",
|
||
"label": "BM25 稀疏项丢弃比例",
|
||
"type": "number",
|
||
"default": 0.0,
|
||
"min": 0.0,
|
||
"max": 1.0,
|
||
"step": 0.1,
|
||
"description": "BM25 检索时丢弃低分稀疏项的比例，数值越大检索越快但可能降低召回",
|
||
},
|
||
{
|
||
"key": "include_distances",
|
||
"label": "显示相似度",
|
||
"type": "boolean",
|
||
"default": True,
|
||
"description": "在结果中显示相似度分数",
|
||
},
|
||
{
|
||
"key": "metric_type",
|
||
"label": "距离度量类型",
|
||
"type": "select",
|
||
"default": "COSINE",
|
||
"options": [
|
||
{"value": "COSINE", "label": "余弦相似度", "description": "适合文本语义相似度"},
|
||
{"value": "L2", "label": "欧几里得距离", "description": "适合数值型数据"},
|
||
{"value": "IP", "label": "内积", "description": "适合标准化向量"},
|
||
],
|
||
"description": "向量相似度计算方法",
|
||
},
|
||
{
|
||
"key": "use_reranker",
|
||
"label": "启用重排序",
|
||
"type": "boolean",
|
||
"default": False,
|
||
"description": "是否使用精排模型对检索结果进行重排序",
|
||
},
|
||
{
|
||
"key": "reranker_model",
|
||
"label": "重排序模型",
|
||
"type": "select",
|
||
"default": "",
|
||
"options": [
|
||
{"label": info.name, "value": model_id}
|
||
for model_id, info in kwargs.get("reranker_names", {}).items()
|
||
],
|
||
"description": "选择用于本次查询的重排序模型",
|
||
},
|
||
{
|
||
"key": "recall_top_k",
|
||
"label": "召回数量",
|
||
"type": "number",
|
||
"default": 50,
|
||
"min": 10,
|
||
"max": 200,
|
||
"description": "向量检索或混合检索保留的候选数量（启用重排序时有效）",
|
||
},
|
||
]
|
||
|
||
return {"type": "milvus", "options": options}
|
||
|
||
def __del__(self):
|
||
"""清理连接"""
|
||
try:
|
||
if hasattr(self, "connection_alias"):
|
||
connections.disconnect(self.connection_alias)
|
||
except Exception: # noqa: S110
|
||
pass
|