""" Pilot Run Service - 试运行服务 用于执行记忆系统的试运行流程，不保存到 Neo4j。 """ import os import re import time from datetime import datetime from typing import Awaitable, Callable, Optional from app.core.logging_config import get_memory_logger, log_time from app.core.memory.models.message_models import ( ConversationContext, ConversationMessage, DialogData, ) from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import ( ExtractionOrchestrator, get_chunked_dialogs_from_preprocessed, ) from app.core.memory.utils.config.config_utils import ( get_pipeline_config, ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.schemas.memory_config_schema import MemoryConfig from sqlalchemy.orm import Session logger = get_memory_logger(__name__) async def run_pilot_extraction( memory_config: MemoryConfig, dialogue_text: str, db: Session, progress_callback: Optional[Callable[[str, str, Optional[dict]], Awaitable[None]]] = None, ) -> None: """ 执行试运行模式的知识提取流水线。 Args: memory_config: 从数据库加载的内存配置对象 dialogue_text: 输入的对话文本 progress_callback: 可选的进度回调函数 - 参数1 (stage): 当前处理阶段标识符 - 参数2 (message): 人类可读的进度消息 - 参数3 (data): 可选的附加数据字典 """ log_file = "logs/time.log" os.makedirs(os.path.dirname(log_file), exist_ok=True) timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") with open(log_file, "a", encoding="utf-8") as f: f.write(f"\n=== Pilot Run Started: {timestamp} ===\n") pipeline_start = time.time() neo4j_connector = None try: # 步骤 1: 初始化客户端 logger.info("Initializing clients...") step_start = time.time() client_factory = MemoryClientFactory(db) llm_client = client_factory.get_llm_client(str(memory_config.llm_model_id)) embedder_client = client_factory.get_embedder_client(str(memory_config.embedding_model_id)) neo4j_connector = Neo4jConnector() log_time("Client Initialization", time.time() - step_start, log_file) # 步骤 2: 解析对话文本 logger.info("Parsing dialogue text...") step_start = time.time() # 解析对话文本，支持 "用户:" 和 "AI:" 格式 pattern = r"(用户|AI)[：:]\s*([^\n]+(?:\n(?!(?:用户|AI)[：:])[^\n]*)*?)" matches = re.findall(pattern, dialogue_text, re.MULTILINE | re.DOTALL) messages = [ ConversationMessage(role=r, msg=c.strip()) for r, c in matches if c.strip() ] # 如果没有匹配到格式化的对话，将整个文本作为用户消息 if not messages: messages = [ConversationMessage(role="用户", msg=dialogue_text.strip())] context = ConversationContext(msgs=messages) dialog = DialogData( context=context, ref_id="pilot_dialog_1", end_user_id=str(memory_config.workspace_id), user_id=str(memory_config.tenant_id), apply_id=str(memory_config.config_id), metadata={"source": "pilot_run", "input_type": "frontend_text"}, ) if progress_callback: await progress_callback("text_preprocessing", "开始预处理文本...") chunked_dialogs = await get_chunked_dialogs_from_preprocessed( data=[dialog], chunker_strategy=memory_config.chunker_strategy, llm_client=llm_client, ) logger.info(f"Processed dialogue text: {len(messages)} messages") # 进度回调：输出每个分块的结果 if progress_callback: for dlg in chunked_dialogs: for i, chunk in enumerate(dlg.chunks): chunk_result = { "chunk_index": i + 1, "content": chunk.content[:200] + "..." if len(chunk.content) > 200 else chunk.content, "full_length": len(chunk.content), "dialog_id": dlg.id, "chunker_strategy": memory_config.chunker_strategy, } await progress_callback("text_preprocessing_result", f"分块 {i + 1} 处理完成", chunk_result) preprocessing_summary = { "total_chunks": sum(len(dlg.chunks) for dlg in chunked_dialogs), "total_dialogs": len(chunked_dialogs), "chunker_strategy": memory_config.chunker_strategy, } await progress_callback("text_preprocessing_complete", "预处理文本完成", preprocessing_summary) log_time("Data Loading & Chunking", time.time() - step_start, log_file) # 步骤 3: 初始化流水线编排器 logger.info("Initializing extraction orchestrator...") step_start = time.time() config = get_pipeline_config(memory_config) logger.info( f"Pipeline config loaded: enable_llm_dedup_blockwise={config.deduplication.enable_llm_dedup_blockwise}, " f"enable_llm_disambiguation={config.deduplication.enable_llm_disambiguation}" ) orchestrator = ExtractionOrchestrator( llm_client=llm_client, embedder_client=embedder_client, connector=neo4j_connector, config=config, progress_callback=progress_callback, embedding_id=str(memory_config.embedding_model_id), ) log_time("Orchestrator Initialization", time.time() - step_start, log_file) # 步骤 4: 执行知识提取流水线 logger.info("Running extraction pipeline...") step_start = time.time() if progress_callback: await progress_callback("knowledge_extraction", "正在知识抽取...") extraction_result = await orchestrator.run( dialog_data_list=chunked_dialogs, is_pilot_run=True, ) # 解包 extraction_result tuple (与 main.py 保持一致) ( dialogue_nodes, chunk_nodes, statement_nodes, entity_nodes, statement_chunk_edges, statement_entity_edges, entity_edges, ) = extraction_result log_time("Extraction Pipeline", time.time() - step_start, log_file) if progress_callback: await progress_callback("generating_results", "正在生成结果...") # 步骤 5: 生成记忆摘要（与 main.py 保持一致） try: logger.info("Generating memory summaries...") step_start = time.time() from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import ( memory_summary_generation, ) summaries = await memory_summary_generation( chunked_dialogs, llm_client=llm_client, embedder_client=embedder_client, ) log_time("Memory Summary Generation", time.time() - step_start, log_file) except Exception as e: logger.error(f"Memory summary step failed: {e}", exc_info=True) logger.info("Pilot run completed: Skipping Neo4j save") except Exception as e: logger.error(f"Pilot run failed: {e}", exc_info=True) raise finally: if neo4j_connector: try: await neo4j_connector.close() except Exception: pass total_time = time.time() - pipeline_start log_time("TOTAL PILOT RUN TIME", total_time, log_file) timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") with open(log_file, "a", encoding="utf-8") as f: f.write(f"=== Pilot Run Completed: {timestamp} ===\n\n") logger.info(f"Pilot run complete. Total time: {total_time:.2f}s")