409 lines
16 KiB
Python
409 lines
16 KiB
Python
import os
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import traceback
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from lightrag import LightRAG, QueryParam
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from lightrag.kg.shared_storage import initialize_pipeline_status
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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from lightrag.utils import EmbeddingFunc, setup_logger
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from neo4j import GraphDatabase
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from pymilvus import connections, utility
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from src.knowledge.base import KnowledgeBase
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from src.knowledge.indexing import process_file_to_markdown, process_url_to_markdown
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from src.knowledge.utils.kb_utils import get_embedding_config, prepare_item_metadata
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from src.utils import hashstr, logger
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from src.utils.datetime_utils import shanghai_now
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LIGHTRAG_LLM_PROVIDER = os.getenv("LIGHTRAG_LLM_PROVIDER", "siliconflow")
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LIGHTRAG_LLM_NAME = os.getenv("LIGHTRAG_LLM_NAME", "zai-org/GLM-4.5-Air")
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class LightRagKB(KnowledgeBase):
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"""基于 LightRAG 的知识库实现"""
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def __init__(self, work_dir: str, **kwargs):
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"""
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初始化 LightRAG 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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# 存储 LightRAG 实例映射 {db_id: LightRAG}
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self.instances: dict[str, LightRAG] = {}
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# 设置 LightRAG 日志
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log_dir = os.path.join(work_dir, "logs", "lightrag")
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os.makedirs(log_dir, exist_ok=True)
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setup_logger(
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"lightrag",
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log_file_path=os.path.join(log_dir, f"lightrag_{shanghai_now().strftime('%Y-%m-%d')}.log"),
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)
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logger.info("LightRagKB 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 "lightrag"
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def delete_database(self, db_id: str) -> dict:
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"""删除数据库，同时清除Milvus和Neo4j中的数据"""
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# Drop Milvus collection
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try:
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milvus_uri = os.getenv("MILVUS_URI", "http://localhost:19530")
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milvus_token = os.getenv("MILVUS_TOKEN", "")
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connection_alias = f"lightrag_{hashstr(db_id, 6)}"
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connections.connect(alias=connection_alias, uri=milvus_uri, token=milvus_token)
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# 删除 LightRAG 创建的三个集合
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collection_names = [f"{db_id}_chunks", f"{db_id}_relationships", f"{db_id}_entities"]
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for collection_name in collection_names:
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if utility.has_collection(collection_name, using=connection_alias):
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utility.drop_collection(collection_name, using=connection_alias)
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logger.info(f"Dropped Milvus collection {collection_name}")
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else:
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logger.info(f"Milvus collection {collection_name} does not exist, skipping")
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connections.disconnect(connection_alias)
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except Exception as e:
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logger.error(f"Failed to drop Milvus collection {db_id}: {e}")
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# Delete Neo4j data
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neo4j_uri = os.getenv("NEO4J_URI", "bolt://localhost:7687")
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neo4j_username = os.getenv("NEO4J_USERNAME", "neo4j")
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neo4j_password = os.getenv("NEO4J_PASSWORD", "0123456789")
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try:
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driver = GraphDatabase.driver(neo4j_uri, auth=(neo4j_username, neo4j_password))
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with driver.session() as session:
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# 删除带有特定 db_id 标签的节点和关系
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session.run(
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"""
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MATCH (n:`"""
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+ db_id
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+ """`)
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DETACH DELETE n
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"""
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)
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logger.info(f"Deleted Neo4j nodes and relationships for workspace {db_id}")
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except Exception as e:
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logger.error(f"Failed to delete Neo4j data for {db_id}: {e}")
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finally:
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if "driver" in locals():
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driver.close()
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# Delete local files and metadata
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return super().delete_database(db_id)
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async def _create_kb_instance(self, db_id: str, kb_config: dict) -> LightRAG:
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"""创建 LightRAG 实例"""
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logger.info(f"Creating LightRAG instance 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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llm_info = self.databases_meta[db_id].get("llm_info", {})
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embed_info = self.databases_meta[db_id].get("embed_info", {})
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# 读取在创建数据库时透传的附加参数（包括语言）
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metadata = self.databases_meta[db_id].get("metadata", {}) or {}
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addon_params = {}
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if isinstance(metadata.get("addon_params"), dict):
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addon_params.update(metadata.get("addon_params", {}))
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# 兼容直接放在 metadata 下的 language
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if isinstance(metadata.get("language"), str) and metadata.get("language"):
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addon_params.setdefault("language", metadata.get("language"))
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# 默认语言从环境变量读取，默认 English
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addon_params.setdefault("language", os.getenv("SUMMARY_LANGUAGE", "English"))
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# 创建工作目录
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working_dir = os.path.join(self.work_dir, db_id)
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os.makedirs(working_dir, exist_ok=True)
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# 创建 LightRAG 实例
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rag = LightRAG(
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working_dir=working_dir,
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workspace=db_id,
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llm_model_func=self._get_llm_func(llm_info),
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embedding_func=self._get_embedding_func(embed_info),
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vector_storage="MilvusVectorDBStorage",
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kv_storage="JsonKVStorage",
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graph_storage="Neo4JStorage",
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doc_status_storage="JsonDocStatusStorage",
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log_file_path=os.path.join(working_dir, "lightrag.log"),
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addon_params=addon_params,
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)
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return rag
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async def _initialize_kb_instance(self, instance: LightRAG) -> None:
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"""初始化 LightRAG 实例"""
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logger.info(f"Initializing LightRAG instance for {instance.working_dir}")
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await instance.initialize_storages()
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await initialize_pipeline_status()
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async def _get_lightrag_instance(self, db_id: str) -> LightRAG | None:
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"""获取或创建 LightRAG 实例"""
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if db_id in self.instances:
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return self.instances[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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# 创建实例
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rag = await self._create_kb_instance(db_id, {})
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# 异步初始化存储
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await self._initialize_kb_instance(rag)
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self.instances[db_id] = rag
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return rag
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except Exception as e:
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logger.error(f"Failed to create LightRAG instance 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 _get_llm_func(self, llm_info: dict):
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"""获取 LLM 函数"""
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from src.models import select_model
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# 如果用户选择了LLM，使用用户选择的；否则使用环境变量默认值
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if llm_info and llm_info.get("model_spec"):
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model_spec = llm_info["model_spec"]
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logger.info(f"Using user-selected LLM spec: {model_spec}")
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elif llm_info and llm_info.get("provider") and llm_info.get("model_name"):
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model_spec = f"{llm_info['provider']}/{llm_info['model_name']}"
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logger.info(f"Using user-selected LLM: {model_spec}")
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else:
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provider = LIGHTRAG_LLM_PROVIDER
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model_name = LIGHTRAG_LLM_NAME
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model_spec = f"{provider}/{model_name}"
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logger.info(f"Using default LLM from environment: {provider}/{model_name}")
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model = select_model(model_spec=model_spec)
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async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs):
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return await openai_complete_if_cache(
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model=model.model_name,
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prompt=prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key=model.api_key,
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base_url=model.base_url,
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**kwargs,
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)
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return llm_model_func
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def _get_embedding_func(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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return EmbeddingFunc(
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embedding_dim=config_dict["dimension"],
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max_token_size=4096,
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func=lambda texts: openai_embed(
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texts=texts,
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model=config_dict["model"],
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api_key=config_dict["api_key"],
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base_url=config_dict["base_url"].replace("/embeddings", ""),
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),
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)
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async def add_content(self, db_id: str, items: list[str], params: dict | None = 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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rag = await self._get_lightrag_instance(db_id)
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if not rag:
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raise ValueError(f"Failed to get LightRAG instance for {db_id}")
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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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item_path = metadata["path"]
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# 添加文件记录
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file_record = metadata.copy()
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self.files_meta[file_id] = file_record
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self._save_metadata()
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self._add_to_processing_queue(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 process_file_to_markdown(item, params=params)
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markdown_content_lines = markdown_content[:100].replace("\n", " ")
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logger.info(f"Markdown content: {markdown_content_lines}...")
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else: # URL
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markdown_content = await process_url_to_markdown(item, params=params)
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# 使用 LightRAG 插入内容
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await rag.ainsert(input=markdown_content, ids=file_id, file_paths=item_path)
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logger.info(f"Inserted {content_type} {item} into LightRAG. 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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error_msg = str(e)
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logger.error(f"处理{content_type} {item} 失败: {error_msg}, {traceback.format_exc()}")
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self.files_meta[file_id]["status"] = "failed"
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self.files_meta[file_id]["error"] = error_msg
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self._save_metadata()
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file_record["status"] = "failed"
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file_record["error"] = error_msg
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finally:
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self._remove_from_processing_queue(file_id)
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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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rag = await self._get_lightrag_instance(db_id)
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if not rag:
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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
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params_dict = {
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"mode": "mix",
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"only_need_context": True,
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"top_k": 10,
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} | kwargs
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param = QueryParam(**params_dict)
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# 执行查询
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response = await rag.aquery(query_text, param)
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logger.debug(f"Query response: {response}")
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return response
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except Exception as e:
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logger.error(f"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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rag = await self._get_lightrag_instance(db_id)
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if rag:
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try:
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# 使用 LightRAG 删除文档
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await rag.adelete_by_doc_id(file_id)
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except Exception as e:
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logger.error(f"Error deleting file {file_id} from LightRAG: {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_basic_info(self, db_id: str, file_id: str) -> dict:
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"""获取文件基本信息（仅元数据）"""
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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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return {"meta": self.files_meta[file_id]}
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async def get_file_content(self, db_id: str, file_id: str) -> dict:
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"""获取文件内容信息（chunks和lines）"""
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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 LightRAG 获取 chunks
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content_info = {"lines": []}
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rag = await self._get_lightrag_instance(db_id)
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if rag:
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try:
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# 获取文档的所有 chunks
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# LightRAG v1.4+ 使用 JsonKVStorage，通过 _data 属性访问所有数据
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if hasattr(rag.text_chunks, "_data"):
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all_chunks = dict(rag.text_chunks._data)
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else:
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logger.warning("text_chunks does not have _data attribute, cannot get file content")
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return content_info
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# 筛选属于该文档的 chunks
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doc_chunks = []
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for chunk_id, chunk_data in all_chunks.items():
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if isinstance(chunk_data, dict) and chunk_data.get("full_doc_id") == file_id:
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chunk_data["id"] = chunk_id
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chunk_data["content_vector"] = []
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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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content_info["lines"] = doc_chunks
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return content_info
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except Exception as e:
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logger.error(f"Failed to get file content from LightRAG: {e}")
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content_info["lines"] = []
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return content_info
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return content_info
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async def get_file_info(self, db_id: str, file_id: str) -> dict:
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"""获取文件完整信息（基本信息+内容信息）- 保持向后兼容"""
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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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# 合并基本信息和内容信息
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basic_info = await self.get_file_basic_info(db_id, file_id)
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content_info = await self.get_file_content(db_id, file_id)
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return {**basic_info, **content_info}
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async def export_data(self, db_id: str, format: str = "csv", **kwargs) -> str:
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"""
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使用 LightRAG 原生功能导出知识库数据。
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[注意] 此功能当前已禁用。
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"""
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# TODO: 修复 LightRAG 库与 Milvus 后端不兼容的问题
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# 当前调用 aexport_data 会导致 "'MilvusVectorDBStorage' object has no attribute 'client_storage'" 错误。
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# 在 lightrag 库修复此问题前，暂时禁用此功能。
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raise NotImplementedError("由于 LightRAG 库与 Milvus 后端不兼容，原生导出功能暂不可用。等待上游库修复。")
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# --- 以下为待修复后启用的代码 ---
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# logger.info(f"Exporting data for db_id {db_id} in format {format} with options {kwargs}")
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# rag = await self._get_lightrag_instance(db_id)
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# if not rag:
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# raise ValueError(f"Failed to get LightRAG instance for {db_id}")
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# export_dir = os.path.join(self.work_dir, db_id, "exports")
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# os.makedirs(export_dir, exist_ok=True)
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# timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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# output_filename = f"export_{db_id}_{timestamp}.{format}"
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# output_filepath = os.path.join(export_dir, output_filename)
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# include_vectors = kwargs.get('include_vectors', False)
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# # 直接调用 lightrag 的异步导出功能
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# # 之前的测试表明 aexport_data 确实存在，并且 to_thread 会导致 loop 问题
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# await rag.aexport_data(
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# output_path=output_filepath,
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# file_format=format,
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# include_vector_data=include_vectors
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# )
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# logger.info(f"Successfully created export file: {output_filepath}")
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# return output_filepath
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