363 lines
12 KiB
Python
363 lines
12 KiB
Python
"""
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图谱构建服务
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接口2：使用Zep API构建Standalone Graph
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"""
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import os
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import uuid
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import time
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import threading
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from typing import Dict, Any, List, Optional, Callable
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from dataclasses import dataclass
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from ..config import Config
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from ..adapters.graph.factory import create_graph_provider
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from ..models.task import TaskManager, TaskStatus
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from ..utils.logger import get_logger
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from .text_processor import TextProcessor
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from ..utils.locale import t, get_locale, set_locale
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logger = get_logger('mirofish.graph_builder')
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@dataclass
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class GraphInfo:
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"""图谱信息"""
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graph_id: str
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node_count: int
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edge_count: int
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entity_types: List[str]
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def to_dict(self) -> Dict[str, Any]:
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return {
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"graph_id": self.graph_id,
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"node_count": self.node_count,
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"edge_count": self.edge_count,
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"entity_types": self.entity_types,
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}
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class GraphBuilderService:
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"""
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图谱构建服务
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负责调用Zep API构建知识图谱
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"""
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def __init__(self, api_key: Optional[str] = None):
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self.api_key = api_key
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self.provider = create_graph_provider()
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self.task_manager = TaskManager()
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def build_graph_async(
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self,
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text: str,
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ontology: Dict[str, Any],
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graph_name: str = "MiroFish Graph",
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chunk_size: int = 500,
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chunk_overlap: int = 50,
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batch_size: int = 3
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) -> str:
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"""
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异步构建图谱
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Args:
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text: 输入文本
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ontology: 本体定义（来自接口1的输出）
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graph_name: 图谱名称
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chunk_size: 文本块大小
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chunk_overlap: 块重叠大小
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batch_size: 每批发送的块数量
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Returns:
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任务ID
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"""
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# 创建任务
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task_id = self.task_manager.create_task(
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task_type="graph_build",
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metadata={
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"graph_name": graph_name,
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"chunk_size": chunk_size,
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"text_length": len(text),
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}
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)
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# Capture locale before spawning background thread
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current_locale = get_locale()
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# 在后台线程中执行构建
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thread = threading.Thread(
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target=self._build_graph_worker,
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args=(task_id, text, ontology, graph_name, chunk_size, chunk_overlap, batch_size, current_locale)
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)
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thread.daemon = True
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thread.start()
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return task_id
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def _build_graph_worker(
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self,
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task_id: str,
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text: str,
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ontology: Dict[str, Any],
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graph_name: str,
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chunk_size: int,
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chunk_overlap: int,
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batch_size: int,
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locale: str = 'zh'
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):
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"""图谱构建工作线程"""
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set_locale(locale)
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try:
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self.task_manager.update_task(
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task_id,
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status=TaskStatus.PROCESSING,
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progress=5,
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message=t('progress.startBuildingGraph')
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)
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# 1. 创建图谱
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graph_id = self.create_graph(graph_name)
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self.task_manager.update_task(
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task_id,
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progress=10,
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message=t('progress.graphCreated', graphId=graph_id)
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)
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# 2. 设置本体
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self.set_ontology(graph_id, ontology)
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self.task_manager.update_task(
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task_id,
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progress=15,
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message=t('progress.ontologySet')
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)
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# 3. 文本分块
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chunks = TextProcessor.split_text(text, chunk_size, chunk_overlap)
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total_chunks = len(chunks)
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self.task_manager.update_task(
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task_id,
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progress=20,
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message=t('progress.textSplit', count=total_chunks)
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)
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# 4. 分批发送数据
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episode_uuids = self.add_text_batches(
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graph_id, chunks, batch_size,
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lambda msg, prog: self.task_manager.update_task(
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task_id,
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progress=20 + int(prog * 0.4), # 20-60%
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message=msg
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)
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)
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# 5. 等待Zep处理完成
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self.task_manager.update_task(
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task_id,
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progress=60,
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message=t('progress.waitingZepProcess')
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)
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self._wait_for_episodes(
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episode_uuids,
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lambda msg, prog: self.task_manager.update_task(
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task_id,
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progress=60 + int(prog * 0.3), # 60-90%
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message=msg
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)
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)
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# 6. 获取图谱信息
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self.task_manager.update_task(
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task_id,
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progress=90,
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message=t('progress.fetchingGraphInfo')
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)
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graph_info = self._get_graph_info(graph_id)
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# 完成
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self.task_manager.complete_task(task_id, {
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"graph_id": graph_id,
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"graph_info": graph_info.to_dict(),
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"chunks_processed": total_chunks,
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})
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except Exception as e:
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import traceback
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error_msg = f"{str(e)}\n{traceback.format_exc()}"
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self.task_manager.fail_task(task_id, error_msg)
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def create_graph(self, name: str) -> str:
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"""创建Zep图谱（公开方法）"""
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graph_id = f"mirofish_{uuid.uuid4().hex[:16]}"
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self.provider.add_episode(graph_id, f"Graph created: {name}", {"type": "graph_created", "name": name})
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return graph_id
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def set_ontology(self, graph_id: str, ontology: Dict[str, Any]):
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"""设置图谱本体（公开方法）"""
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self.provider.add_episode(
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graph_id,
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os.linesep.join([f"Ontology: {ontology.get('entity_types', [])}", f"Edges: {ontology.get('edge_types', [])}"]),
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{"type": "ontology"},
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)
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def add_text_batches(
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self,
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graph_id: str,
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chunks: List[str],
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batch_size: int = 3,
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progress_callback: Optional[Callable] = None
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) -> List[str]:
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"""分批添加文本到图谱，返回所有 episode 的 uuid 列表"""
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episode_uuids = []
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total_chunks = len(chunks)
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for i in range(0, total_chunks, batch_size):
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batch_chunks = chunks[i:i + batch_size]
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batch_num = i // batch_size + 1
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total_batches = (total_chunks + batch_size - 1) // batch_size
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if progress_callback:
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progress = (i + len(batch_chunks)) / total_chunks
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progress_callback(
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t('progress.sendingBatch', current=batch_num, total=total_batches, chunks=len(batch_chunks)),
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progress
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)
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# 发送到统一图谱 provider。agent 模式下这只是 episode 存储；
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# 三元组抽取由 CLI/MCP runner 生成 extract_triples request。
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try:
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for chunk in batch_chunks:
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result = self.provider.add_episode(graph_id, chunk, {"type": "seed_chunk", "batch": batch_num})
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episode_uuids.append(str(result.get("episode_index", uuid.uuid4().hex)))
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# 避免请求过快
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time.sleep(1)
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except Exception as e:
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if progress_callback:
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progress_callback(t('progress.batchFailed', batch=batch_num, error=str(e)), 0)
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raise
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return episode_uuids
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def _wait_for_episodes(
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self,
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episode_uuids: List[str],
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progress_callback: Optional[Callable] = None,
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timeout: int = 600
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):
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"""等待所有 episode 处理完成（通过查询每个 episode 的 processed 状态）"""
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if not episode_uuids:
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if progress_callback:
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progress_callback(t('progress.noEpisodesWait'), 1.0)
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return
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total_episodes = len(episode_uuids)
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if progress_callback:
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progress_callback(t('progress.waitingEpisodes', count=total_episodes), 1.0)
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def _get_graph_info(self, graph_id: str) -> GraphInfo:
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"""获取图谱信息"""
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nodes = self.provider.list_entities(graph_id)
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edges = self.provider.search(graph_id, "", limit=10000)
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# 统计实体类型
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entity_types = set()
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for node in nodes:
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if node.get("labels"):
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for label in node["labels"]:
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if label not in ["Entity", "Node"]:
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entity_types.add(label)
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return GraphInfo(
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graph_id=graph_id,
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node_count=len(nodes),
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edge_count=len(edges),
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entity_types=list(entity_types)
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)
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def get_graph_data(self, graph_id: str) -> Dict[str, Any]:
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"""
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获取完整图谱数据（包含详细信息）
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Args:
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graph_id: 图谱ID
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Returns:
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包含nodes和edges的字典，包括时间信息、属性等详细数据
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"""
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nodes = self.provider.list_entities(graph_id)
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edges = self.provider.search(graph_id, "", limit=10000)
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# 创建节点映射用于获取节点名称
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node_map = {}
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for node in nodes:
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node_map[node.get("uuid", "")] = node.get("name", "") or ""
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nodes_data = []
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for node in nodes:
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# 获取创建时间
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created_at = node.get("created_at")
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if created_at:
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created_at = str(created_at)
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nodes_data.append({
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"uuid": node.get("uuid", ""),
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"name": node.get("name", ""),
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"labels": node.get("labels", []) or [],
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"summary": node.get("summary", "") or "",
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"attributes": node.get("attributes", {}) or {},
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"created_at": created_at,
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})
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edges_data = []
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for edge in edges:
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# 获取时间信息
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created_at = edge.get("created_at")
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valid_at = edge.get("valid_at")
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invalid_at = edge.get("invalid_at")
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expired_at = edge.get("expired_at")
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# 获取 episodes
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episodes = edge.get("episodes") or edge.get("episode_ids")
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if episodes and not isinstance(episodes, list):
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episodes = [str(episodes)]
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elif episodes:
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episodes = [str(e) for e in episodes]
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# 获取 fact_type
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fact_type = edge.get("fact_type") or edge.get("predicate") or edge.get("name", "")
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edges_data.append({
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"uuid": edge.get("uuid", ""),
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"name": edge.get("predicate") or edge.get("name", ""),
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"fact": edge.get("fact", ""),
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"fact_type": fact_type,
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"source_node_uuid": edge.get("source_node_uuid", ""),
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"target_node_uuid": edge.get("target_node_uuid", ""),
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"source_node_name": node_map.get(edge.get("source_node_uuid", ""), ""),
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"target_node_name": node_map.get(edge.get("target_node_uuid", ""), ""),
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"attributes": edge.get("attributes", {}) or edge.get("metadata", {}),
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"created_at": str(created_at) if created_at else None,
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"valid_at": str(valid_at) if valid_at else None,
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"invalid_at": str(invalid_at) if invalid_at else None,
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"expired_at": str(expired_at) if expired_at else None,
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"episodes": episodes or [],
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})
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return {
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"graph_id": graph_id,
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"nodes": nodes_data,
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"edges": edges_data,
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"node_count": len(nodes_data),
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"edge_count": len(edges_data),
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}
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def delete_graph(self, graph_id: str):
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"""删除图谱"""
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self.provider.clear_run_graph(graph_id)
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