367 lines
16 KiB
Python
367 lines
16 KiB
Python
"""
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Pilot Run Service - 试运行服务
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用于执行记忆系统的试运行流程，不保存到 Neo4j。
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"""
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import os
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import re
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import time
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from datetime import datetime
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from typing import Awaitable, Callable, Optional
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from app.core.logging_config import get_memory_logger, log_time
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from app.core.memory.models.message_models import (
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ConversationContext,
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ConversationMessage,
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DialogData,
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)
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from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import (
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ExtractionOrchestrator,
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get_chunked_dialogs_from_preprocessed,
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)
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from app.core.memory.utils.config.config_utils import (
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get_pipeline_config,
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)
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from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from app.schemas.memory_config_schema import MemoryConfig
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from sqlalchemy.orm import Session
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logger = get_memory_logger(__name__)
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async def run_pilot_extraction(
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memory_config: MemoryConfig,
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dialogue_text: str,
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db: Session,
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progress_callback: Optional[Callable[[str, str, Optional[dict]], Awaitable[None]]] = None,
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language: str = "zh",
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) -> None:
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"""
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执行试运行模式的知识提取流水线。
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Args:
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memory_config: 从数据库加载的内存配置对象
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dialogue_text: 输入的对话文本
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db: 数据库会话
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progress_callback: 可选的进度回调函数
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- 参数1 (stage): 当前处理阶段标识符
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- 参数2 (message): 人类可读的进度消息
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- 参数3 (data): 可选的附加数据字典
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language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
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"""
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log_file = "logs/time.log"
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os.makedirs(os.path.dirname(log_file), exist_ok=True)
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timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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with open(log_file, "a", encoding="utf-8") as f:
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f.write(f"\n=== Pilot Run Started: {timestamp} ===\n")
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pipeline_start = time.time()
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neo4j_connector = None
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try:
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# 步骤 1: 初始化客户端
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logger.info("Initializing clients...")
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step_start = time.time()
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client_factory = MemoryClientFactory(db)
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llm_client = client_factory.get_llm_client(str(memory_config.llm_model_id))
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embedder_client = client_factory.get_embedder_client(str(memory_config.embedding_model_id))
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neo4j_connector = Neo4jConnector()
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log_time("Client Initialization", time.time() - step_start, log_file)
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# 步骤 2: 解析对话文本
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logger.info("Parsing dialogue text...")
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step_start = time.time()
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# 解析对话文本，支持 "用户:" 和 "AI:" 格式
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pattern = r"(用户|AI)[：:]\s*([^\n]+(?:\n(?!(?:用户|AI)[：:])[^\n]*)*?)"
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matches = re.findall(pattern, dialogue_text, re.MULTILINE | re.DOTALL)
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messages = [
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ConversationMessage(role=r, msg=c.strip())
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for r, c in matches
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if c.strip()
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]
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# 如果没有匹配到格式化的对话，将整个文本作为用户消息
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if not messages:
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messages = [ConversationMessage(role="用户", msg=dialogue_text.strip())]
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context = ConversationContext(msgs=messages)
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dialog = DialogData(
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context=context,
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ref_id="pilot_dialog_1",
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end_user_id=str(memory_config.workspace_id),
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user_id=str(memory_config.tenant_id),
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apply_id=str(memory_config.config_id),
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metadata={"source": "pilot_run", "input_type": "frontend_text"},
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)
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if progress_callback:
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await progress_callback("text_preprocessing", "开始预处理文本（语义剪枝 + 语义分块）...")
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# ========== 步骤 2.1: 语义剪枝 ==========
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pruned_dialogs = [dialog]
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deleted_messages = [] # 记录被删除的消息
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pruning_stats = None # 保存剪枝统计信息，用于最终汇总
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if memory_config.pruning_enabled:
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try:
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from app.core.memory.storage_services.extraction_engine.data_preprocessing.data_pruning import (
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SemanticPruner,
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)
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from app.core.memory.models.config_models import PruningConfig
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# 构建剪枝配置
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pruning_config_dict = {
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"pruning_switch": memory_config.pruning_enabled,
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"pruning_scene": memory_config.pruning_scene,
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"pruning_threshold": memory_config.pruning_threshold,
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"scene_id": str(memory_config.scene_id) if memory_config.scene_id else None,
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"ontology_classes": memory_config.ontology_classes,
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}
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config = PruningConfig(**pruning_config_dict)
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logger.info(f"[PILOT_RUN] 开始语义剪枝: scene={config.pruning_scene}, threshold={config.pruning_threshold}")
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# 记录剪枝前的消息（用于对比）
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original_messages = [{"role": msg.role, "content": msg.msg} for msg in dialog.context.msgs]
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original_msg_count = len(original_messages)
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# 执行剪枝
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pruner = SemanticPruner(config=config, llm_client=llm_client)
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pruned_dialogs = await pruner.prune_dataset([dialog])
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# 计算剪枝结果并找出被删除的消息
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if pruned_dialogs and pruned_dialogs[0].context:
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remaining_messages = [{"role": msg.role, "content": msg.msg} for msg in pruned_dialogs[0].context.msgs]
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remaining_msg_count = len(remaining_messages)
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deleted_msg_count = original_msg_count - remaining_msg_count
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# 找出被删除的消息（基于索引精确匹配）
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# 为剩余消息创建带索引的列表，用于精确追踪
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remaining_with_index = []
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remaining_idx = 0
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for orig_idx, orig_msg in enumerate(original_messages):
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if remaining_idx < len(remaining_messages) and \
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orig_msg["role"] == remaining_messages[remaining_idx]["role"] and \
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orig_msg["content"] == remaining_messages[remaining_idx]["content"]:
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remaining_with_index.append(orig_idx)
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remaining_idx += 1
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# 找出未在保留列表中的消息索引
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deleted_messages = [
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{"index": idx, "role": msg["role"], "content": msg["content"]}
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for idx, msg in enumerate(original_messages)
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if idx not in remaining_with_index
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]
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# 保存剪枝统计信息（用于最终汇总，只保留deleted_count）
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pruning_stats = {
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"enabled": True,
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"scene": config.pruning_scene,
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"threshold": config.pruning_threshold,
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"deleted_count": deleted_msg_count,
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}
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# 输出剪枝结果（显示删除的消息详情）
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pruning_result = {
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"type": "pruning",
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"deleted_messages": deleted_messages,
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}
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logger.info(
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f"[PILOT_RUN] 语义剪枝完成: 原始{original_msg_count}条 -> "
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f"保留{remaining_msg_count}条 (删除{deleted_msg_count}条)"
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)
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if progress_callback:
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await progress_callback("text_preprocessing_result", "语义剪枝完成", pruning_result)
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else:
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logger.warning("[PILOT_RUN] 剪枝后对话为空，使用原始对话")
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pruned_dialogs = [dialog]
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except Exception as e:
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logger.error(f"[PILOT_RUN] 语义剪枝失败，使用原始对话: {e}", exc_info=True)
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pruned_dialogs = [dialog]
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if progress_callback:
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error_result = {
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"type": "pruning",
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"error": str(e),
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"fallback": "使用原始对话"
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}
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await progress_callback("text_preprocessing_result", "语义剪枝失败", error_result)
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else:
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logger.info("[PILOT_RUN] 语义剪枝已关闭，跳过")
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pruning_stats = {
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"enabled": False,
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}
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# ========== 步骤 2.2: 语义分块 ==========
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chunked_dialogs = await get_chunked_dialogs_from_preprocessed(
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data=pruned_dialogs,
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chunker_strategy=memory_config.chunker_strategy,
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llm_client=llm_client,
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)
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remaining_msg_count = len(pruned_dialogs[0].context.msgs) if pruned_dialogs and pruned_dialogs[0].context else 0
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logger.info(f"Processed dialogue text: {remaining_msg_count} messages after pruning")
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# 进度回调：输出每个分块的结果
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if progress_callback:
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for dlg in chunked_dialogs:
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if hasattr(dlg, 'chunks') and dlg.chunks:
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for i, chunk in enumerate(dlg.chunks):
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chunk_result = {
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"type": "chunking",
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"chunk_index": i + 1,
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"content": chunk.content[:200] + "..." if len(chunk.content) > 200 else chunk.content,
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"full_length": len(chunk.content),
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"dialog_id": dlg.id,
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"chunker_strategy": memory_config.chunker_strategy,
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}
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await progress_callback("text_preprocessing_result", f"分块 {i + 1} 处理完成", chunk_result)
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# 构建预处理完成总结（包含剪枝统计）
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preprocessing_summary = {
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"total_chunks": sum(len(dlg.chunks) for dlg in chunked_dialogs if hasattr(dlg, 'chunks') and dlg.chunks),
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"total_dialogs": len(chunked_dialogs),
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"chunker_strategy": memory_config.chunker_strategy,
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}
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# 添加剪枝统计信息
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if pruning_stats:
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preprocessing_summary["pruning"] = pruning_stats
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await progress_callback("text_preprocessing_complete", "预处理文本完成（剪枝 + 分块）", preprocessing_summary)
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log_time("Data Loading & Chunking", time.time() - step_start, log_file)
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# 步骤 3: 初始化流水线编排器
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logger.info("Initializing extraction orchestrator...")
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step_start = time.time()
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config = get_pipeline_config(memory_config)
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logger.info(
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f"Pipeline config loaded: enable_llm_dedup_blockwise={config.deduplication.enable_llm_dedup_blockwise}, "
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f"enable_llm_disambiguation={config.deduplication.enable_llm_disambiguation}"
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)
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# 加载本体类型（如果配置了 scene_id），支持通用类型回退
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ontology_types = None
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try:
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from app.core.memory.ontology_services.ontology_type_loader import load_ontology_types_with_fallback
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ontology_types = load_ontology_types_with_fallback(
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scene_id=memory_config.scene_id,
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workspace_id=memory_config.workspace_id,
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db=db,
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enable_general_fallback=True
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)
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except Exception as e:
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logger.warning(f"Failed to load ontology types: {e}", exc_info=True)
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orchestrator = ExtractionOrchestrator(
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llm_client=llm_client,
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embedder_client=embedder_client,
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connector=neo4j_connector,
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config=config,
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progress_callback=progress_callback,
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embedding_id=str(memory_config.embedding_model_id),
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language=language,
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ontology_types=ontology_types,
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)
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log_time("Orchestrator Initialization", time.time() - step_start, log_file)
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# 步骤 4: 执行知识提取流水线
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logger.info("Running extraction pipeline...")
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step_start = time.time()
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if progress_callback:
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await progress_callback("knowledge_extraction", "正在知识抽取...")
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extraction_result = await orchestrator.run(
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dialog_data_list=chunked_dialogs,
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is_pilot_run=True,
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)
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# 解包 extraction_result tuple (与 main.py 保持一致)
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(
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dialogue_nodes,
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chunk_nodes,
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statement_nodes,
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entity_nodes,
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statement_chunk_edges,
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statement_entity_edges,
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entity_edges,
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) = extraction_result
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log_time("Extraction Pipeline", time.time() - step_start, log_file)
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if progress_callback:
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await progress_callback("generating_results", "正在生成结果...")
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# 步骤 5: 生成记忆摘要（与 main.py 保持一致）
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try:
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logger.info("Generating memory summaries...")
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step_start = time.time()
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from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import (
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memory_summary_generation,
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)
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summaries = await memory_summary_generation(
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chunked_dialogs,
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llm_client=llm_client,
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embedder_client=embedder_client,
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language=language,
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)
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log_time("Memory Summary Generation", time.time() - step_start, log_file)
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except Exception as e:
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logger.error(f"Memory summary step failed: {e}", exc_info=True)
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logger.info("Pilot run completed: Skipping Neo4j save")
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# 将提取统计写入 Redis，按 workspace_id 存储
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try:
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from app.cache.memory.activity_stats_cache import ActivityStatsCache
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stats_to_cache = {
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"chunk_count": len(chunk_nodes) if chunk_nodes else 0,
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"statements_count": len(statement_nodes) if statement_nodes else 0,
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"triplet_entities_count": len(entity_nodes) if entity_nodes else 0,
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"triplet_relations_count": len(entity_edges) if entity_edges else 0,
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"temporal_count": 0, # temporal 数据在日志中，此处暂置0
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}
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await ActivityStatsCache.set_activity_stats(
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workspace_id=str(memory_config.workspace_id),
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stats=stats_to_cache,
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)
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logger.info(f"[PILOT_RUN] 活动统计已写入 Redis: workspace_id={memory_config.workspace_id}")
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except Exception as cache_err:
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logger.warning(f"[PILOT_RUN] 写入活动统计缓存失败（不影响主流程）: {cache_err}", exc_info=True)
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except Exception as e:
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logger.error(f"Pilot run failed: {e}", exc_info=True)
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raise
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finally:
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if neo4j_connector:
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try:
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await neo4j_connector.close()
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except Exception:
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pass
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total_time = time.time() - pipeline_start
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log_time("TOTAL PILOT RUN TIME", total_time, log_file)
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timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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with open(log_file, "a", encoding="utf-8") as f:
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f.write(f"=== Pilot Run Completed: {timestamp} ===\n\n")
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logger.info(f"Pilot run complete. Total time: {total_time:.2f}s")
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