# -*- coding: utf-8 -*- """ CodingWriter - 写入端协调器 把 judge / preproc / extractor / 两个 reconciler / store 串成一条端到端流程： classify_messages_scene(messages, llm) ├── chat → caller 把 messages strip_tool_messages 后走 chat 链 └── working / coding ├── recent history: 最近 K 个 user-turn（只作上下文） ├── new messages: 本次全部 turn（只做单条限长） ├── extractor.extract(...) ← 一次 LLM 产出双层草稿 ├── reconciler.reconcile(经验草稿) ← ADD/UPDATE/DELETE/SKIP ├── fact_reconciler.reconcile(事实草稿) ← INSERT/SUPERSEDE └── _crosslink(...) ← 同批全互链回填 """ import logging import time import uuid from typing import Any, Dict, List, Optional from ..agent.llm_provider import LLMProvider from ..core.embed_service import EmbedService from ..pipelines.base import ChatMessage from .extractor import CodingMemoryExtractor from .fact_reconciler import WorkingFactReconciler from .judge import classify_messages_is_coding from .preproc import has_any_tool_message, prepare_extractor_messages from .reconciler import CodingMemoryReconciler from .store import CodingMemoryStore from .types import FactReconcileOp, ReconcileOp, WorkingFact logger = logging.getLogger(__name__) class CodingWriter: """ Coding 写入端协调器。 生命周期：依赖外部传入的 store / extractor / reconciler；不负责 initialize/close。 """ def __init__( self, store: CodingMemoryStore, extractor: CodingMemoryExtractor, reconciler: CodingMemoryReconciler, llm_provider: LLMProvider, embed_service: EmbedService, fact_reconciler: Optional[WorkingFactReconciler] = None, cache: Any = None, ): self.store = store self.extractor = extractor self.reconciler = reconciler self.llm_provider = llm_provider self.embed_service = embed_service self.fact_reconciler = fact_reconciler or WorkingFactReconciler( store, embed_service, llm_provider ) # cache 仅用于把 request_id 串给各阶段（各阶段自己打点）；writer 不集中打点。 self.cache = cache async def write( self, messages: List[ChatMessage], *, user_id: str, agent_id: str = "default_agent", workspace_id: Optional[str] = None, branch: Optional[str] = None, session_id: Optional[str] = None, request_id: Optional[str] = None, source: str = "default", ) -> Dict[str, Any]: """ working/coding 段写入端到端。返回响应 dict（兼容 client.add 返回结构）： { "success": True, "scene": "productivity", "request_id": "...", "elapsed_ms": ..., "ops": [ {"action": "ADD", "draft_idx": 0, "target_experience_id": "...", "reason": ...}, ... ], "experience_ids": ["..."], # 所有 ADD/UPDATE 涉及的 experience_id "fact_ops": [ {"action": "INSERT", "draft_idx": 0, "fact_id": "...", ...}, ... ], "fact_ids": ["..."], # 这趟真正落库的 fact_id } """ request_id = request_id or str(uuid.uuid4()) t0 = time.perf_counter() if not messages: return self._empty_response(request_id, t0) tool_max_bytes = int( getattr(self.store.config.coding, "tool_result_max_bytes", 2048) or 2048 ) # 1. 历史与新输入分离。 # - history_messages：之前 write 的最近 5 个 user-turn，仅作上下文 # - current_messages：本次 write 的全部 turn，只限制单条消息体积，不裁 turn history_turns = int( getattr(self.store.config.extractor, "history_turns", 5) or 5 ) try: history_messages = await self.store.get_recent_trace_messages( user_id=user_id, agent_id=agent_id or "default_agent", max_user_turns=history_turns, source=source, ) except Exception as e: logger.warning(f"[coding-write] recent trace lookup failed, continue without history: {e}") history_messages = [] current_messages = prepare_extractor_messages( messages, tool_max_bytes=tool_max_bytes, ) # 2. 抽取双层 drafts（一次 LLM 调用） # # 刻意不给 extractor 看用户已有的 task。抽取只该忠实读这一段发生了什么， # 跟库里已有的什么关系是 reconcile 的事。把已有 task 摆到它面前，它会把 # 一个其实不同的任务往清单里某个措辞上靠，reconciler 随后判成 UPDATE 合并 # 掉，那个区别就没了——措辞对不上顶多多存一条，措辞被带偏是直接写错。 try: drafts, fact_drafts = await self.extractor.extract( messages, history_messages=history_messages, tool_result_max_bytes=tool_max_bytes, user_id=user_id, agent_id=agent_id, workspace_id=workspace_id, branch=branch, session_id=session_id, request_id=request_id, ) finally: # 历史服务于下一次 write；即使本次没跨过价值门槛，也不能丢掉上下文。 try: await self.store.append_trace( current_messages, request_id=request_id, user_id=user_id, agent_id=agent_id or "default_agent", session_id=session_id, workspace_id=workspace_id, branch=branch, source=source, ) except Exception as e: logger.warning(f"[coding-write] append trace failed: {e}") if not drafts and not fact_drafts: logger.info( f"[coding-write] no drafts produced (value bar); request_id={request_id}" ) return self._empty_response(request_id, t0) # extractor 只负责内容；调用来源由 SDK 入口注入，不能交给 LLM 生成。 for draft in drafts: draft.source = source # 3. 经验层 reconcile + 持久化 ops: List[ReconcileOp] = ( await self.reconciler.reconcile( drafts, request_id=request_id, user_id=user_id, agent_id=agent_id, session_id=session_id or "", ) if drafts else [] ) # 4. 事实层 reconcile + 持久化（独立链路，不受经验层结果影响） facts: List[WorkingFact] = [] fact_ops: List[FactReconcileOp] = [] if fact_drafts: facts, fact_ops = await self.fact_reconciler.reconcile( fact_drafts, user_id=user_id, agent_id=agent_id, session_id=session_id, workspace_id=workspace_id, branch=branch, source=source, ) experience_ids = [op.target_experience_id for op in ops if op.action in ("ADD", "UPDATE") and op.target_experience_id] fact_ids = [f.fact_id for f in facts] # 5. 同批全互链 await self._crosslink(experience_ids, facts, user_id=user_id) add_cnt = sum(1 for op in ops if op.action == "ADD") update_cnt = sum(1 for op in ops if op.action == "UPDATE") delete_cnt = sum(1 for op in ops if op.action == "DELETE") skip_cnt = sum(1 for op in ops if op.action == "SKIP") ops_summary = { "add": add_cnt, "update": update_cnt, "delete": delete_cnt, "skip": skip_cnt, "total": add_cnt + update_cnt + delete_cnt + skip_cnt, } insert_cnt = sum(1 for op in fact_ops if op.action == "INSERT") supersede_cnt = sum(1 for op in fact_ops if op.action == "SUPERSEDE") return { "success": True, "scene": "productivity", "request_id": request_id, "elapsed_ms": round((time.perf_counter() - t0) * 1000, 2), "ops": [op.to_dict() for op in ops], "ops_summary": ops_summary, "experience_ids": experience_ids, "fact_ops": [op.to_dict() for op in fact_ops], "fact_ops_summary": { "insert": insert_cnt, "supersede": supersede_cnt, "total": insert_cnt + supersede_cnt, }, "fact_ids": fact_ids, } # ------------------------------------------------------------ # 互链 # ------------------------------------------------------------ async def _crosslink( self, experience_ids: List[str], facts: List[WorkingFact], *, user_id: str, ) -> None: """把同一批落库的经验与事实两两连起来。 只连真入库的：经验取 ADD/UPDATE 的 id，事实取真正 INSERT 的；SKIP 和 DELETE 天然不在名单里。两边都取并集——UPDATE 的旧链要保留，事实那边可能 已经从 SUPERSEDE 过继来一批经验，不能覆盖掉。 """ if not experience_ids and not facts: return fact_ids = [f.fact_id for f in facts] # 正向：经验 ← 事实 if fact_ids: for eid in experience_ids: try: exp = await self.store.get_by_id(eid, user_id=user_id) if exp is None: continue merged = _union(exp.source_fact_ids, fact_ids) if merged != list(exp.source_fact_ids): await self.store.set_experience_source_facts(eid, merged) except Exception as e: logger.warning(f"[coding-write] crosslink experience {eid} failed: {e}") # 反向：事实 ← 经验 if experience_ids: for f in facts: try: merged = _union(f.derived_experience_ids, experience_ids) if merged != list(f.derived_experience_ids): await self.store.set_fact_derived_experiences(f.fact_id, merged) f.derived_experience_ids = merged except Exception as e: logger.warning( f"[coding-write] crosslink fact {f.fact_id} failed: {e}" ) @staticmethod def _empty_response(request_id: str, t0: float) -> Dict[str, Any]: return { "success": True, "scene": "productivity", "request_id": request_id, "elapsed_ms": round((time.perf_counter() - t0) * 1000, 2), "ops": [], "ops_summary": {"add": 0, "update": 0, "delete": 0, "skip": 0, "total": 0}, "experience_ids": [], "fact_ops": [], "fact_ops_summary": {"insert": 0, "supersede": 0, "total": 0}, "fact_ids": [], } def _union(base: List[str], extra: List[str]) -> List[str]: """保序并集。""" out = list(base) seen = set(out) for x in extra: if x not in seen: seen.add(x) out.append(x) return out # ================================================================ # Top-level helper（client.async_add 用） # ================================================================ async def is_coding_segment( messages: List[ChatMessage], llm_provider: LLMProvider, ) -> bool: """ 判定整段 messages 是否 coding 场景。无 tool 消息直接 False（O(1) 短路）。 """ if not has_any_tool_message(messages): return False return await classify_messages_is_coding(messages, llm_provider)