feat:客户agent以及记忆模块优化

.gitignore

@@ -76,8 +76,4 @@ data/output/
 DK2_客服Agent模块完整开发计划（v1.1）.md
 DK4_客服Agent需求文档（修复完善版v1.4）.md
 客服Agent模块 · TODO List（最终修复版v1.2）.md
-三层记忆模块需求规格说明书.md
-客服Agent记忆模块TODO清单.md
-客服Agent记忆模块开发计划.md
-开发计划_用户画像置信度实时更新.md
-客服Agent记忆模块接入说明.md
+客服Agent长期记忆与置信度最终开发文档.md

agent/client_agent/session.py

@@ -7,6 +7,7 @@ import uuid
 from inspect import isawaitable
 
 from agent.customer_agent.session import SessionOwnershipError
+from service.client_agent.archive_schedule import SessionArchiveSchedule
 
 
 async def _config(config_getter, key: str, default):
@@ -23,16 +24,41 @@ class ClientSessionService:
         self.redis = redis
         self.config_getter = config_getter
         self.clock = clock
+        self.archive_schedule = SessionArchiveSchedule(redis)
+
+    async def _archive_grace(self) -> int:
+        """读取 Redis 消息归档保护缓冲时间，默认 60 秒。"""
+        return await _config(
+            self.config_getter, "agent.customer.session.archive_grace", 60
+        )
 
     async def create_session(self, customer_id: int) -> str:
         """Create a session whose Redis owner value is the current customer."""
         session_id = uuid.uuid4().hex
+        now = self.clock()
         ttl = await _config(self.config_getter, "agent.customer.session.ttl", 1800)
-        await self.redis.set(f"session:{session_id}", str(customer_id), ex=ttl)
+        max_lifetime = await _config(
+            self.config_getter, "agent.customer.session.max_lifetime", 86400
+        )
+        grace = await self._archive_grace()
+        effective_ttl = min(ttl, max_lifetime)
+        await self.redis.set(
+            f"session:{session_id}", str(customer_id), ex=effective_ttl + grace
+        )
         await self.redis.rpush(f"session:{session_id}:messages", "")
-        await self.redis.expire(f"session:{session_id}:messages", ttl)
+        await self.redis.expire(
+            f"session:{session_id}:messages", effective_ttl + grace
+        )
+        await self.archive_schedule.register(
+            session_id=session_id,
+            created_at=now,
+            last_activity_at=now,
+            archive_due_at=now + ttl,
+            absolute_expire_at=now + max_lifetime,
+        )
         return session_id
 
+
     async def verify_session_ownership(self, session_id: str, *, customer_id: int) -> None:
         """Reject missing sessions and sessions owned by another customer."""
         owner = await self.redis.get(f"session:{session_id}")
@@ -43,6 +69,31 @@ class ClientSessionService:
         if str(owner) != str(customer_id):
             raise SessionOwnershipError(403, "无权访问该会话")
 
+    async def touch_session(self, session_id: str, *, customer_id: int) -> None:
+        """刷新所有权 TTL 和自动归档截止时间，但不突破最长生命周期。"""
+        await self.verify_session_ownership(session_id, customer_id=customer_id)
+        now = self.clock()
+        meta = await self.redis.hgetall(
+            self.archive_schedule.meta_key(session_id)
+        )
+        absolute_expire_at = float(meta.get("absolute_expire_at", now))
+        remaining = int(absolute_expire_at - now)
+        if remaining <= 0:
+            raise SessionOwnershipError(404, "会话不存在或已过期")
+        ttl = await _config(self.config_getter, "agent.customer.session.ttl", 1800)
+        effective_ttl = min(ttl, remaining)
+        grace = await self._archive_grace()
+        await self.redis.expire(f"session:{session_id}", effective_ttl + grace)
+        await self.archive_schedule.touch(
+            session_id=session_id,
+            last_activity_at=now,
+            archive_due_at=now + effective_ttl,
+        )
+
+    async def forget_session(self, session_id: str) -> None:
+        """移除会话的自动归档调度记录。"""
+        await self.archive_schedule.remove(session_id)
+
     async def consume_chat_quota(self, session_id: str, *, customer_id: int) -> int | None:
         """Apply the existing per-session rate limit with a customer-scoped key."""
         window = await _config(

agent/customer_agent/session.py

@@ -59,8 +59,8 @@ class AnonymousSessionService:
         await self.redis.zremrangebyscore(key, 0, (now - window) * 1000)
         count = await self.redis.zcard(key)
         if count >= maximum:
-            entries = self.redis.sorted_sets.get(key, [])
-            oldest = min((score for score, _ in entries), default=now * 1000)
+            entries = await self.redis.zrange(key, 0, 0, withscores=True)
+            oldest = entries[0][1] if entries else now * 1000
             return max(1, int((oldest / 1000 + window) - now))
         await self.redis.zadd(key, {uuid.uuid4().hex: now * 1000})
         await self.redis.expire(key, window)

api/chat/client_agent.py

@@ -65,6 +65,7 @@ async def chat(
                 headers={"Retry-After": str(retry_after)},
                 content=response.model_dump(),
             )
+        await runtime.session_service.touch_session(session_id, customer_id=user.id)
         result = await runtime.agent.handle(
             session_id,
             query,
@@ -114,6 +115,7 @@ async def end_session(
     )
     if warnings:
         return success({"session_id": session_id, "archived": False, "warnings": warnings})
+    await runtime.session_service.forget_session(session_id)
     if hasattr(runtime.redis, "delete"):
         await runtime.redis.delete(
             f"session:{session_id}",

main.py

@@ -1,5 +1,6 @@
 """应用入口：四库异步生命周期 + 中间件 + 全局异常 + 路由装配。"""
 from contextlib import asynccontextmanager
+import asyncio
 
 import uvicorn
 from fastapi import FastAPI
@@ -12,6 +13,7 @@ from service.customer_agent.bootstrap import (
     build_default_runtime,
 )
 from service.client_agent.bootstrap import build_default_runtime as build_client_runtime
+from service.client_agent.idle_archive_worker import IdleArchiveWorker
 from utils.exceptions import register_exception_handlers
 from utils.logger import setup_logging
 from utils.request_id import RequestIdMiddleware
@@ -23,8 +25,18 @@ async def lifespan(app: FastAPI):
     await ensure_collections()
     app.state.customer_agent_runtime = build_default_runtime()
     app.state.client_agent_runtime = build_client_runtime()
+    app.state.client_agent_archive_worker = IdleArchiveWorker(
+        redis=app.state.client_agent_runtime.redis,
+        memory_service=app.state.client_agent_runtime.memory_service,
+    )
+    await app.state.client_agent_archive_worker.recover_due_index()
+    app.state.client_agent_archive_task = asyncio.create_task(
+        app.state.client_agent_archive_worker.run()
+    )
     app.state.knowledge_upload_service = build_default_knowledge_upload_service()
     yield
+    await app.state.client_agent_archive_worker.stop()
+    await app.state.client_agent_archive_task
     await database.dispose()
 
 
@@ -38,3 +50,6 @@ app.include_router(api_router)
 @app.get("/")
 async def root():
     return {"message": "智能公募基金系统 API", "docs": "/docs"}
+
+if __name__ == '__main__':
+    uvicorn.run(app, host="127.0.0.1", port=8000)

model/memory_unit.py

@@ -35,7 +35,6 @@ class MemoryUnit(Base):
     confidence_reason: Mapped[str | None] = mapped_column(String(255))
     confidence_update_time: Mapped[datetime | None] = mapped_column(DateTime)
     evidence_count: Mapped[int] = mapped_column(Integer, server_default="0")
-    conflict_count: Mapped[int] = mapped_column(Integer, server_default="0")
     recall_count: Mapped[int] = mapped_column(Integer, server_default="0")
     memory_version: Mapped[int] = mapped_column(Integer, server_default="1")
     update_time: Mapped[datetime | None] = mapped_column(DateTime)
@@ -53,4 +52,3 @@ class MemoryUnit(Base):
     last_sync_error: Mapped[str | None] = mapped_column(String(500))
     next_retry_at: Mapped[datetime | None] = mapped_column(DateTime)
     last_synced_at: Mapped[datetime | None] = mapped_column(DateTime)
-

rag/intent.py

@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 import logging
+import re
 from enum import StrEnum
 
 
@@ -12,32 +13,51 @@ class Intent(StrEnum):
     GUIDE_PURCHASE = "guide_purchase"
     WANT_ADVISOR = "want_advisor"
     NL2SQL_REQUEST = "nl2sql_request"
-    COMPLAIN = "complain"
     KNOWLEDGE_QA = "knowledge_qa"
+    COMPANY_INFO = "company_info"
+    CHITCHAT = "chitchat"
+    OFF_TOPIC = "off_topic"
     NO_MATCH = "no_match"
 
 
 INTENT_VALUES = frozenset(item.value for item in Intent)
 
+# 按长度降序匹配，避免短标签误命中长标签的一部分
+_INTENT_PATTERN = re.compile(
+    "|".join(re.escape(value) for value in sorted(INTENT_VALUES, key=len, reverse=True))
+)
+
+INTENT_SYSTEM_PROMPT = (
+    "你是华夏科技（一家基金代销金融机构）智能客服的意图分类器。"
+    "请根据用户输入，从以下选项中选择最匹配的意图，并仅输出对应的英文标签（不输出任何其他内容）：\n"
+    "- guide_purchase: 用户询问如何购买基金、开户、注册等引导类问题\n"
+    "- want_advisor: 用户希望获得个性化基金推荐或投资顾问服务\n"
+    "- knowledge_qa: 用户询问基金相关的知识性问题，如净值、费率、风险、申赎规则等\n"
+    "- company_info: 用户询问华夏科技公司本身的信息，如公司全称、成立时间、牌照、总部地址、"
+    "客服电话、服务时间、官网、投诉渠道等\n"
+    "- nl2sql_request: 用户要求查询具体数据或账户信息\n"
+    "- chitchat: 普通寒暄，如问候、致谢、告别、询问你是谁/你能做什么、在吗等一两句话的闲聊\n"
+    "- off_topic: 用户要求你实质性地处理与金融、基金、公司业务无关的事情，"
+    "如写代码、讲笑话、写作文、问天气、聊政治、情感咨询、做数学题等\n"
+    "- no_match: 无法归入以上任何一类\n"
+    "注意：寒暄性质的一两句话归为 chitchat；一旦用户提出金融之外的实质性请求，归为 off_topic。\n"
+    "仅输出一个小写英文标签，不要输出解释、标点或换行。"
+)
+
+
+def parse_intent(raw: str | None) -> Intent:
+    """从模型原始输出中提取第一个合法标签，提取不到则返回 NO_MATCH。"""
+    if not raw:
+        return Intent.NO_MATCH
+    match = _INTENT_PATTERN.search(raw.lower())
+    return Intent(match.group(0)) if match else Intent.NO_MATCH
+
 
 async def intent_recognize(query: str, *, llm_client) -> Intent:
     if not query or not query.strip():
         return Intent.NO_MATCH
     messages = [
-        {
-            "role": "system",
-            "content": (
-                "你是一个意图分类器。请根据用户输入，从以下选项中选择最匹配的意图，"
-                "并仅输出对应的英文标签（不输出任何其他内容）：\n"
-                "- guide_purchase: 用户询问如何购买基金、开户、注册等引导类问题\n"
-                "- want_advisor: 用户希望获得个性化基金推荐或投资顾问服务\n"
-                "- knowledge_qa: 用户询问基金相关的知识性问题，如净值、费率、风险等\n"
-                "- nl2sql_request: 用户要求查询具体数据或账户信息\n"
-                "- complain: 用户表达不满或投诉\n"
-                "- no_match: 以上都不匹配\n"
-                "仅输出一个小写英文标签，不要输出解释、标点或换行。"
-            ),
-        },
+        {"role": "system", "content": INTENT_SYSTEM_PROMPT},
         {"role": "user", "content": query},
     ]
     try:
@@ -45,5 +65,4 @@ async def intent_recognize(query: str, *, llm_client) -> Intent:
     except Exception:
         logger.exception("intent recognition failed")
         return Intent.NO_MATCH
-    value = raw.strip().strip('`').lower()
-    return Intent(value) if value in INTENT_VALUES else Intent.NO_MATCH
+    return parse_intent(raw)

rag/retrieve.py

@@ -90,9 +90,12 @@ def _format_candidates(candidates) -> list[dict]:
             distance = hit.get("distance")
             raw_score = hit.get("score")
             if distance is not None:
-                score = 1.0 - distance
+                # All project Milvus collections use COSINE. For COSINE,
+                # Milvus returns a similarity score: larger means more relevant.
+                # Do not invert it as if it were an L2 distance.
+                score = float(distance)
             else:
-                score = raw_score
+                score = float(raw_score) if raw_score is not None else None
             if score is None or score < threshold:
                 continue
             sources.append(
@@ -160,4 +163,4 @@ async def retrieve_with_status(
     except Exception:
         logger.exception("Milvus retrieval failed")
         return {"status": "milvus_unavailable", "sources": []}
-    return {"status": "ok", "sources": _format_candidates(candidates)}
+    return {"status": "ok", "sources": _format_candidates(candidates)}

repositories/conversation_archive.py

@@ -31,6 +31,12 @@ class ConversationArchiveRepo(BaseRepository):
         if not rows:
             return 0
 
+        session_ids = {row.get("session_id") for row in rows}
+        if len(session_ids) != 1 or None in session_ids:
+            raise ValueError("archive_batch 只能接收同一会话的完整消息")
+        if any(not row.get("message_id") for row in rows):
+            raise ValueError("archive_batch 的 message_id 不能为空")
+
         session_id = rows[0]["session_id"]
         message_ids = [row["message_id"] for row in rows]
         existing = await self.db.scalars(
@@ -65,4 +71,3 @@ class ConversationArchiveRepo(BaseRepository):
 
 
 __all__ = ["ConversationArchiveRepo"]
-

repositories/memory_unit.py

@@ -28,15 +28,14 @@ class MemoryUnitRepo(BaseRepository):
         return obj
 
     async def find_exact(
-        self, customer_id: int, memory_type: str, tag: str, content: str
+        self, customer_id: int, memory_type: str, tag: str
     ) -> MemoryUnit | None:
-        """按客户、类型、标签和内容精确查找去重对象。"""
+        """按客户、类型和标签查找当前有效记忆。"""
         return await self.db.scalar(
             select(MemoryUnit).where(
                 MemoryUnit.customer_id == customer_id,
                 MemoryUnit.memory_type == memory_type,
                 MemoryUnit.tag == tag,
-                MemoryUnit.content == content,
                 MemoryUnit.status.not_in(INACTIVE_STATUSES),
             )
         )
@@ -70,16 +69,52 @@ class MemoryUnitRepo(BaseRepository):
         return list((await self.db.scalars(statement)).all())
 
     async def merge_evidence(
-        self, memory: MemoryUnit, *, evidence_count: int = 1, conflict_count: int = 0
+        self,
+        memory: MemoryUnit,
+        *,
+        content: str | None = None,
+        source: str | None = None,
+        evidence_count: int = 1,
+        evidence_ref: list[dict] | None = None,
     ) -> MemoryUnit:
-        """合并证据和冲突计数，不改变客户隔离范围。"""
+        """更新最新内容并合并证据，不改变客户隔离范围。"""
+        if content:
+            memory.content = content
+        if source:
+            memory.source = source
         memory.evidence_count = (memory.evidence_count or 0) + evidence_count
-        memory.conflict_count = (memory.conflict_count or 0) + conflict_count
+        if evidence_ref:
+            memory.evidence_ref = [*(memory.evidence_ref or []), *evidence_ref]
+        memory.last_verified_at = datetime.now()
         memory.update_time = datetime.now()
         await self.db.commit()
         await self.db.refresh(memory)
         return memory
 
+    async def list_for_confidence_refresh(
+        self, customer_id: int, limit: int = 500
+    ) -> list[MemoryUnit]:
+        """返回需要重新计算时间衰减置信度的有效记忆。"""
+        statement = (
+            select(MemoryUnit)
+            .where(
+                MemoryUnit.customer_id == customer_id,
+                MemoryUnit.status.in_(ACTIVE_STATUSES),
+            )
+            .order_by(MemoryUnit.id)
+            .limit(limit)
+        )
+        return list((await self.db.scalars(statement)).all())
+
+    async def update_confidence(self, memory_id: int, **values) -> None:
+        """只更新置信度字段，不改变记忆内容和证据。"""
+        memory = await self.db.get(MemoryUnit, memory_id)
+        if memory is None:
+            return
+        for key, value in values.items():
+            setattr(memory, key, value)
+        await self.db.commit()
+
     async def update_sync_status(self, memory_id: int, **values) -> None:
         """更新向量/图谱索引 ID 和同步状态。"""
         memory = await self.db.get(MemoryUnit, memory_id)

service/client_agent/archive_lock.py

@@ -0,0 +1,53 @@
+"""Redis 会话归档分布式锁。"""
+
+from __future__ import annotations
+
+import uuid
+
+
+class SessionArchiveLock:
+    """保证同一会话同一时刻只被一个归档任务处理。"""
+
+    KEY = "session:archive:lock:{session_id}"
+    DEFAULT_TTL = 120
+    RELEASE_SCRIPT = """
+    if redis.call('get', KEYS[1]) == ARGV[1] then
+        return redis.call('del', KEYS[1])
+    end
+    return 0
+    """
+
+    def __init__(self, redis, *, default_ttl: int = DEFAULT_TTL):
+        self.redis = redis
+        self.default_ttl = default_ttl
+
+    @classmethod
+    def key(cls, session_id: str) -> str:
+        """生成会话级锁 Key。"""
+        return cls.KEY.format(session_id=session_id)
+
+    async def acquire(self, session_id: str, *, ttl: int | None = None) -> str | None:
+        """尝试获取锁，成功返回随机 token，失败返回 None。"""
+        lock_ttl = self.default_ttl if ttl is None else ttl
+        if lock_ttl <= 0:
+            raise ValueError("锁 TTL 必须为正整数")
+        token = uuid.uuid4().hex
+        acquired = await self.redis.set(
+            self.key(session_id), token, nx=True, ex=lock_ttl
+        )
+        return token if acquired else None
+
+    async def release(self, session_id: str, token: str) -> bool:
+        """仅当 token 属于当前持有者时释放锁。"""
+        if not token:
+            return False
+        result = await self.redis.eval(
+            self.RELEASE_SCRIPT,
+            1,
+            self.key(session_id),
+            token,
+        )
+        return bool(result)
+
+
+__all__ = ["SessionArchiveLock"]

service/client_agent/archive_schedule.py

@@ -0,0 +1,132 @@
+"""Redis client_agent 会话自动归档调度数据结构。"""
+
+from __future__ import annotations
+
+
+class SessionArchiveSchedule:
+    """管理空闲会话的归档截止时间，不负责实际归档。"""
+
+    DUE_KEY = "session:archive:due"
+    META_KEY = "session:{session_id}:meta"
+
+    def __init__(self, redis):
+        self.redis = redis
+
+    @classmethod
+    def meta_key(cls, session_id: str) -> str:
+        """生成与短期记忆共用的会话元数据 Key。"""
+        return cls.META_KEY.format(session_id=session_id)
+
+    async def register(
+        self,
+        *,
+        session_id: str,
+        created_at: float,
+        last_activity_at: float,
+        archive_due_at: float,
+        absolute_expire_at: float,
+    ) -> None:
+        """登记新会话的归档时间和生命周期信息。"""
+        await self.redis.hset(
+            self.meta_key(session_id),
+            mapping={
+                "created_at": str(created_at),
+                "last_activity_at": str(last_activity_at),
+                "archive_due_at": str(archive_due_at),
+                "absolute_expire_at": str(absolute_expire_at),
+                "archive_status": "pending",
+                "archive_retry_count": "0",
+            },
+        )
+        await self.redis.expire(
+            self.meta_key(session_id),
+            max(1, int(absolute_expire_at - created_at)),
+        )
+        await self.redis.zadd(self.DUE_KEY, {session_id: archive_due_at})
+
+    async def touch(
+        self,
+        *,
+        session_id: str,
+        last_activity_at: float,
+        archive_due_at: float,
+    ) -> None:
+        """更新会话最近活动时间和下一次归档截止时间。"""
+        await self.redis.hset(
+            self.meta_key(session_id),
+            mapping={
+                "last_activity_at": str(last_activity_at),
+                "archive_due_at": str(archive_due_at),
+                "archive_status": "pending",
+            },
+        )
+        await self.redis.zadd(self.DUE_KEY, {session_id: archive_due_at})
+
+    async def list_due(self, *, now: float, limit: int = 100) -> list[str]:
+        """返回归档截止时间已到的会话。"""
+        if limit <= 0:
+            return []
+        values = await self.redis.zrangebyscore(
+            self.DUE_KEY, 0, now, start=0, num=limit
+        )
+        return [value.decode() if isinstance(value, bytes) else str(value) for value in values]
+
+    async def get_meta(self, session_id: str) -> dict:
+        """读取会话归档元数据。"""
+        return await self.redis.hgetall(self.meta_key(session_id))
+
+    async def reschedule(self, session_id: str, *, due_at: float) -> None:
+        """更新下一次扫描时间，保留当前调度记录。"""
+        await self.redis.zadd(self.DUE_KEY, {session_id: due_at})
+
+    async def rebuild_due_index(self, *, limit: int = 10000) -> int:
+        """从会话元数据重建丢失的到期索引。"""
+        rebuilt = 0
+        async for raw_key in self.redis.scan_iter(match="session:*:meta", count=100):
+            key = raw_key.decode() if isinstance(raw_key, bytes) else raw_key
+            prefix = "session:"
+            suffix = ":meta"
+            if not key.startswith(prefix) or not key.endswith(suffix):
+                continue
+            session_id = key[len(prefix) : -len(suffix)]
+            meta = await self.redis.hgetall(key)
+            due_at = meta.get("archive_due_at")
+            if due_at is None or meta.get("archive_status") == "success":
+                continue
+            await self.redis.zadd(self.DUE_KEY, {session_id: float(due_at)})
+            rebuilt += 1
+            if rebuilt >= limit:
+                break
+        return rebuilt
+
+    async def mark_processing(self, session_id: str) -> None:
+        """标记会话进入归档处理状态。"""
+        await self.redis.hset(
+            self.meta_key(session_id), mapping={"archive_status": "processing"}
+        )
+
+    async def mark_retry(
+        self,
+        session_id: str,
+        *,
+        retry_count: int,
+        error: str,
+        next_retry_at: float,
+    ) -> None:
+        """记录归档失败信息和下一次重试时间。"""
+        await self.redis.hset(
+            self.meta_key(session_id),
+            mapping={
+                "archive_status": "retry",
+                "archive_retry_count": str(retry_count),
+                "last_archive_error": error[:500],
+                "next_retry_at": str(next_retry_at),
+            },
+        )
+
+    async def remove(self, session_id: str) -> None:
+        """移除归档调度记录，不删除会话消息或短期记忆元数据。"""
+        await self.redis.zrem(self.DUE_KEY, session_id)
+
+
+__all__ = ["SessionArchiveSchedule"]

service/client_agent/idle_archive_worker.py

@@ -0,0 +1,144 @@
+"""client_agent 空闲会话自动归档后台任务。"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import time
+
+from .archive_lock import SessionArchiveLock
+from .archive_schedule import SessionArchiveSchedule
+
+
+logger = logging.getLogger("client_agent.idle_archive_worker")
+
+
+class IdleArchiveWorker:
+    """扫描到期会话并调用统一 MemoryService 完成归档。"""
+
+    RETRY_BASE_DELAY = 30
+    RETRY_MAX_DELAY = 15 * 60
+
+    def __init__(
+        self,
+        *,
+        redis,
+        memory_service,
+        interval: int = 10,
+        batch_size: int = 100,
+        lock_ttl: int = 120,
+        clock=time.time,
+    ):
+        self.redis = redis
+        self.memory_service = memory_service
+        self.interval = interval
+        self.batch_size = batch_size
+        self.clock = clock
+        self.schedule = SessionArchiveSchedule(redis)
+        self.lock = SessionArchiveLock(redis, default_ttl=lock_ttl)
+        self._stop_event = asyncio.Event()
+
+    async def archive_due_sessions(self) -> int:
+        """处理一批到期会话，返回成功归档数量。"""
+        session_ids = await self.schedule.list_due(
+            now=self.clock(), limit=self.batch_size
+        )
+        archived_count = 0
+        for session_id in session_ids:
+            token = await self.lock.acquire(session_id)
+            if token is None:
+                continue
+            try:
+                if await self._archive_one(session_id):
+                    archived_count += 1
+            finally:
+                await self.lock.release(session_id, token)
+        return archived_count
+
+    async def recover_due_index(self) -> int:
+        """应用启动时从会话元数据恢复归档调度索引。"""
+        try:
+            return await self.schedule.rebuild_due_index()
+        except Exception:
+            logger.exception("failed to rebuild idle archive due index")
+            return 0
+
+    async def _archive_one(self, session_id: str) -> bool:
+        """归档单个会话；失败时保留消息并推迟下一次扫描。"""
+        owner = await self.redis.get(f"session:{session_id}")
+        if owner is None:
+            await self.schedule.remove(session_id)
+            return False
+        owner = owner.decode() if isinstance(owner, bytes) else owner
+        meta = await self.schedule.get_meta(session_id)
+        retry_count = int(meta.get("archive_retry_count", 0) or 0)
+        await self.schedule.mark_processing(session_id)
+        try:
+            warnings = await self.memory_service.close_session(
+                customer_id=int(owner),
+                session_id=session_id,
+            )
+            if warnings:
+                await self._schedule_retry(
+                    session_id,
+                    retry_count=retry_count + 1,
+                    error=";".join(warnings),
+                )
+                return False
+            await self.redis.delete(f"session:{session_id}")
+            await self.schedule.remove(session_id)
+            return True
+        except Exception as exc:
+            logger.exception("idle session archive failed: session_id=%s", session_id)
+            await self._schedule_retry(
+                session_id,
+                retry_count=retry_count + 1,
+                error=f"{type(exc).__name__}: {exc}",
+            )
+            return False
+
+    async def _schedule_retry(
+        self, session_id: str, *, retry_count: int, error: str
+    ) -> None:
+        """按指数退避登记下一次归档尝试。"""
+        delay = min(
+            self.RETRY_BASE_DELAY * (2 ** max(0, retry_count - 1)),
+            self.RETRY_MAX_DELAY,
+        )
+        next_retry_at = self.clock() + delay
+        await self.schedule.mark_retry(
+            session_id,
+            retry_count=retry_count,
+            error=error,
+            next_retry_at=next_retry_at,
+        )
+        await self.schedule.reschedule(session_id, due_at=next_retry_at)
+
+    async def retry_now(self, session_id: str) -> None:
+        """提供手工补偿入口，立即把指定会话放回扫描队列。"""
+        await self.redis.hset(
+            self.schedule.meta_key(session_id),
+            mapping={"archive_status": "pending"},
+        )
+        await self.schedule.reschedule(session_id, due_at=self.clock())
+
+    async def run(self) -> None:
+        """启动后台循环，异常不会击穿主应用。"""
+        while not self._stop_event.is_set():
+            try:
+                await self.archive_due_sessions()
+            except asyncio.CancelledError:
+                raise
+            except Exception:
+                logger.exception("idle archive scan failed")
+            try:
+                await asyncio.wait_for(self._stop_event.wait(), timeout=self.interval)
+            except asyncio.TimeoutError:
+                continue
+
+    async def stop(self) -> None:
+        """请求后台循环停止。"""
+        self._stop_event.set()
+
+
+__all__ = ["IdleArchiveWorker"]

service/client_agent/memory_extractor.py

@@ -16,9 +16,12 @@ class DialogueMemoryExtractor:
 你是客服记忆提取器，只提取用户明确表达或稳定陈述的客户信息。
 只返回 JSON 数组，不要输出 Markdown 或解释文字。
 每项必须包含：tag、content、memory_type、source。
+对于用户反复询问的产品或投资主题，也可以提取兴趣信号，并额外返回 signal_type=interest_query。
+兴趣信号必须使用 CUSTOMER_PREFERENCE 和 dialogue_inferred，tag 使用稳定、可归一化的英文主题名。
+兴趣主题只允许以下五类：conservative_interest、steady_interest、balanced_interest、enterprising_interest、aggressive_interest。
 memory_type 只能是 PROFILE_FACT、PROFILE_CANDIDATE、CUSTOMER_PREFERENCE、INVESTMENT_GOAL、SERVICE_FACT。
 source 只能是 dialogue_confirmed、dialogue_stated、dialogue_inferred。
-客服知识问题、产品政策、寒暄、客服回复内容不要提取。
+单次客服知识问题不要作为长期记忆保存；如果问题反映出客户对某个产品或投资主题的关注，使用 signal_type=interest_query 表示兴趣信号。
 不确定的信息使用 dialogue_inferred，无法形成客户画像的信息不要提取。
 """.strip()
 
@@ -37,8 +40,56 @@ source 只能是 dialogue_confirmed、dialogue_stated、dialogue_inferred。
                 ),
             }
         )
-        response = await self.llm_client.chat(prompt, temperature=0, max_tokens=800)
-        return self._parse(response)
+        interest_signal = self._interest_fallback(query)
+        try:
+            response = await self.llm_client.chat(prompt, temperature=0, max_tokens=800)
+            result = self._parse(response)
+        except Exception:
+            if interest_signal:
+                return [interest_signal]
+            raise
+        if interest_signal and not any(
+            item.get("signal_type") == "interest_query" for item in result
+        ):
+            result.append(interest_signal)
+        return result
+
+    @staticmethod
+    def _interest_fallback(query: str) -> dict[str, str] | None:
+        """为产品兴趣问题提供确定性兜底，避免依赖 LLM 输出可选字段。"""
+        text = query.strip().lower()
+        if not text or not any(
+            marker in text
+            for marker in ("基金", "理财", "投资", "产品", "fund", "investment")
+        ):
+            return None
+        if not any(
+            marker in text
+            for marker in ("哪些", "什么", "怎么选", "推荐", "适合", "比较", "了解", "有哪些", "what", "which", "how")
+        ):
+            return None
+
+        risk_topics = (
+            (("保守", "conservative"), "conservative_interest", "用户关注保守型投资产品"),
+            (("稳健", "steady", "moderate"), "steady_interest", "用户关注稳健型投资产品"),
+            (("平衡", "balanced"), "balanced_interest", "用户关注平衡型投资产品"),
+            (("进取", "enterprising"), "enterprising_interest", "用户关注进取型投资产品"),
+            (("激进", "aggressive", "高风险", "high risk"), "aggressive_interest", "用户关注激进型投资产品"),
+        )
+        for markers, topic_tag, topic_content in risk_topics:
+            if any(marker in text for marker in markers):
+                tag = topic_tag
+                content = topic_content
+                break
+        else:
+            return None
+        return {
+            "tag": tag,
+            "content": content,
+            "memory_type": "CUSTOMER_PREFERENCE",
+            "source": "dialogue_inferred",
+            "signal_type": "interest_query",
+        }
 
     @staticmethod
     def _parse(response: str) -> list[dict[str, str]]:
@@ -60,14 +111,15 @@ source 只能是 dialogue_confirmed、dialogue_stated、dialogue_inferred。
                 continue
             if item["memory_type"] not in valid_types or item["source"] not in valid_sources:
                 continue
-            result.append(
-                {
+            candidate = {
                     "tag": str(item["tag"])[:64],
                     "content": str(item["content"])[:512],
                     "memory_type": item["memory_type"],
                     "source": item["source"],
                 }
-            )
+            if item.get("signal_type") == "interest_query":
+                candidate["signal_type"] = "interest_query"
+            result.append(candidate)
         return result
 
 

service/client_agent/runtime.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import contextvars
+import logging
 import uuid
 from types import SimpleNamespace
 
@@ -22,6 +23,8 @@ _active_messages = contextvars.ContextVar("client_agent_messages", default=None)
 _active_warnings = contextvars.ContextVar("client_agent_memory_warnings", default=None)
 _active_memory_context = contextvars.ContextVar("client_agent_memory_context", default=None)
 
+logger = logging.getLogger(__name__)
+
 
 class MemoryConversationContext:
     """将现有客服 Agent 的上下文接口桥接到 MemoryService 短期记忆。"""
@@ -90,7 +93,14 @@ class MemoryAwareClientAgent:
         try:
             result = await self.agent.handle(session_id, query, trace_id=trace_id)
             if self.extractor is not None:
-                await self._save_candidates(customer_id, session_id, query, memory_context, warnings)
+                await self._save_candidates(
+                    customer_id,
+                    session_id,
+                    query,
+                    memory_context,
+                    warnings,
+                    trace_id=trace_id,
+                )
             result["memory_warnings"] = list(warnings)
             return result
         finally:
@@ -99,17 +109,42 @@ class MemoryAwareClientAgent:
             _active_warnings.reset(warnings_token)
             _active_memory_context.reset(context_token)
 
-    async def _save_candidates(self, customer_id, session_id, query, context, warnings):
-        """提取并保存候选客户记忆，任何失败都只写入 warning。"""
+    async def _save_candidates(
+        self,
+        customer_id,
+        session_id,
+        query,
+        context,
+        warnings,
+        *,
+        trace_id: str | None = None,
+    ):
+        """提取并保存候选客户记忆，单个阶段或候选失败不影响客服回答。"""
         try:
             candidates = await self.extractor.extract(
                 query,
                 context={
                     "profile": context.customer_profile,
-                    "memories": [item.model_dump(mode="json") for item in context.long_term_memories],
+                    "memories": [self._memory_to_dict(item) for item in context.long_term_memories],
                 },
             )
-            for candidate in candidates:
+        except Exception as exc:
+            logger.exception(
+                "client memory extraction failed: trace_id=%s customer_id=%s session_id=%s",
+                trace_id,
+                customer_id,
+                session_id,
+            )
+            warnings.append(f"memory_extraction_failed:{type(exc).__name__}")
+            return
+
+        for index, candidate in enumerate(candidates):
+            try:
+                evidence_count = 1
+                if candidate.get("signal_type") == "interest_query":
+                    # 兴趣主题首次出现即保存为候选，后续由长期记忆按精确内容合并证据。
+                    candidate["memory_type"] = "CUSTOMER_PREFERENCE"
+                    candidate["source"] = "dialogue_inferred"
                 memory = MemoryUnitDTO(
                     customer_id=customer_id,
                     session_id=session_id,
@@ -118,14 +153,31 @@ class MemoryAwareClientAgent:
                     content=candidate["content"],
                     source=candidate["source"],
                     evidence_ref=[{"session_id": session_id, "query": query}],
+                    evidence_count=evidence_count,
                 )
                 _, save_warnings = await self.memory_service.save_memory(
                     customer_id=customer_id,
                     memory=memory,
                 )
                 warnings.extend(save_warnings)
-        except Exception as exc:
-            warnings.append(f"memory_candidate_extract_failed:{type(exc).__name__}")
+            except Exception as exc:
+                logger.exception(
+                    "client memory candidate save failed: trace_id=%s customer_id=%s session_id=%s index=%s",
+                    trace_id,
+                    customer_id,
+                    session_id,
+                    index,
+                )
+                warnings.append(f"memory_save_failed:{type(exc).__name__}")
+
+    @staticmethod
+    def _memory_to_dict(item) -> dict:
+        """将召回记忆兼容转换为可序列化字典。"""
+        if hasattr(item, "model_dump"):
+            return item.model_dump(mode="json")
+        if isinstance(item, dict):
+            return dict(item)
+        raise TypeError(f"unsupported memory context item: {type(item).__name__}")
 
 
 def build_client_runtime(

service/customer_agent/chat.py

@@ -54,19 +54,28 @@ class AnonymousCustomerAgent:
 
         intent = await _maybe_await(self.intent_recognize(query))
         sources = []
-        if intent is Intent.GUIDE_PURCHASE:
+        if intent == Intent.GUIDE_PURCHASE:
             answer = await _config(
                 self.config_getter,
                 "agent.customer.template.guide_purchase",
                 "请前往开户页面办理。",
             )
-        elif intent is Intent.WANT_ADVISOR:
+        elif intent == Intent.WANT_ADVISOR:
             answer = await _config(
                 self.config_getter,
                 "agent.customer.template.guide_advisor",
                 "如需基金推荐，请联系投资顾问。",
             )
-        elif intent is Intent.KNOWLEDGE_QA:
+        elif intent == Intent.OFF_TOPIC:
+            answer = await _config(
+                self.config_getter,
+                "agent.customer.template.off_topic",
+                "我是华夏科技的智能客服，只能解答基金与公司业务相关的问题，"
+                "您可以问我基金知识、开户流程或公司信息～",
+            )
+        elif intent == Intent.CHITCHAT:
+            answer = await self._chitchat(session_id)
+        elif intent in (Intent.KNOWLEDGE_QA, Intent.COMPANY_INFO):
             try:
                 sources = await _maybe_await(self.rag_retrieve(query, None))
             except Exception:
@@ -112,6 +121,31 @@ class AnonymousCustomerAgent:
             "trace_id": trace_id,
         }
 
+    async def _chitchat(self, session_id: str) -> str:
+        """带对话历史调用 LLM 做受限闲聊，失败时退回固定话术。"""
+        messages = await self.context.get(session_id)
+        prompt = [
+            {
+                "role": "system",
+                "content": (
+                    "你是华夏科技（基金代销金融机构）的智能客服助手。"
+                    "用户正在与你寒暄，请用一两句话简短、友好地回应，"
+                    "并自然地引导用户咨询基金知识、开户流程或公司信息。"
+                    "不得推荐任何基金产品，不得谈论具体收益，不得回答金融之外的实质性问题。"
+                ),
+            },
+            *messages,
+        ]
+        try:
+            return await _maybe_await(self.generate_answer(prompt))
+        except Exception:
+            return await _config(
+                self.config_getter,
+                "agent.customer.template.chitchat_fallback",
+                "您好，我是华夏科技的智能客服，很高兴为您服务！"
+                "您可以问我基金知识、开户流程或公司信息～",
+            )
+
     @staticmethod
     def _contains_sensitive_input(query: str) -> bool:
         return bool(

service/memory/facade.py

@@ -13,6 +13,7 @@ from .customer_relation import CustomerRelationMemory
 from .customer_product import CustomerProductMemory
 from .context_builder import build_customer_memory_context
 from .long_term import LongTermMemoryService
+from .interest_topic import InterestTopicTracker
 from .profile import CustomerProfileMemory
 from .schemas import CustomerMemoryContext, MemoryUnitDTO, ShortTermMessage
 from .short_term import ShortTermMemory
@@ -34,6 +35,7 @@ class MemoryService:
         long_term: LongTermMemoryService | None = None,
         archiver: ConversationArchiver | None = None,
         rank_tool: FinalConfidenceRankTool | None = None,
+        interest_tracker: InterestTopicTracker | None = None,
     ):
         self.session_factory = session_factory or get_session_factory()
         self.short_term = short_term or ShortTermMemory()
@@ -44,6 +46,7 @@ class MemoryService:
         self.long_term = long_term or LongTermMemoryService()
         self.archiver = archiver or ConversationArchiver(short_term=self.short_term)
         self.rank_tool = rank_tool or FinalConfidenceRankTool()
+        self.interest_tracker = interest_tracker or InterestTopicTracker(self.short_term.redis)
 
     @asynccontextmanager
     async def _db(self):
@@ -67,6 +70,14 @@ class MemoryService:
         warnings.extend(self.short_term.last_warnings)
 
         async with self._db() as db:
+            try:
+                refresh_confidence = getattr(self.long_term, "refresh_confidence", None)
+                if refresh_confidence is not None:
+                    await refresh_confidence(
+                        db, customer_id, limit=max(limit, 10)
+                    )
+            except Exception as exc:
+                warnings.append(f"confidence_refresh_failed:{type(exc).__name__}")
             profile, profile_warnings = await self.profile.get(db, customer_id)
             warnings.extend(profile_warnings)
             try:
@@ -140,6 +151,10 @@ class MemoryService:
         async with self._db() as db:
             return await self.long_term.save(db, memory)
 
+    async def record_interest_signal(self, *, customer_id: int, tag: str) -> tuple[int, bool]:
+        """记录一次重复兴趣主题信号，达到阈值后允许写入长期记忆。"""
+        return await self.interest_tracker.record(customer_id, tag)
+
     async def close_session(
         self,
         *,

service/memory/interest_topic.py

@@ -0,0 +1,38 @@
+"""客服对话中的重复兴趣主题计数。"""
+
+from __future__ import annotations
+
+import hashlib
+from datetime import datetime
+
+
+class InterestTopicTracker:
+    """使用 Redis 统计客户当天对同一主题的重复关注次数。"""
+
+    KEY_PREFIX = "customer:memory:interest"
+    WINDOW_SECONDS = 24 * 60 * 60
+    DEFAULT_THRESHOLD = 3
+
+    def __init__(self, redis, *, threshold: int = DEFAULT_THRESHOLD):
+        self.redis = redis
+        self.threshold = threshold
+
+    @classmethod
+    def key(cls, customer_id: int, tag: str, now: datetime | None = None) -> str:
+        """生成按客户、自然日和主题隔离的 Redis 计数 Key。"""
+        day = (now or datetime.now()).strftime("%Y%m%d")
+        digest = hashlib.sha256(tag.strip().lower().encode("utf-8")).hexdigest()[:16]
+        return f"{cls.KEY_PREFIX}:{customer_id}:{day}:{digest}"
+
+    async def record(self, customer_id: int, tag: str) -> tuple[int, bool]:
+        """记录一次主题关注，返回累计次数和是否达到长期记忆阈值。"""
+        if not tag or not tag.strip():
+            raise ValueError("兴趣主题不能为空")
+        key = self.key(customer_id, tag)
+        count = await self.redis.incr(key)
+        if count == 1:
+            await self.redis.expire(key, self.WINDOW_SECONDS)
+        return int(count), int(count) >= self.threshold
+
+
+__all__ = ["InterestTopicTracker"]

service/memory/long_term.py

@@ -39,14 +39,26 @@ class LongTermMemoryService:
             memory.customer_id,
             memory.memory_type.value,
             memory.tag,
-            memory.content,
         )
         warnings: list[str] = []
         if existing is not None:
-            existing = await repo.merge_evidence(existing)
+            existing = await repo.merge_evidence(
+                existing,
+                content=memory.content,
+                source=memory.source.value,
+                evidence_count=max(memory.evidence_count or 1, 1),
+                evidence_ref=memory.evidence_ref,
+            )
             entity = existing
         else:
-            values = memory.model_dump(mode="json", exclude={"id", "milvus_id", "graph_node_id"})
+            # final_score 是召回重排阶段的临时分数，不属于 MySQL 主体字段。
+            values = memory.model_dump(
+                mode="json",
+                exclude={"id", "milvus_id", "graph_node_id", "final_score"},
+            )
+            now = datetime.now()
+            values["last_verified_at"] = values.get("last_verified_at") or now
+            values["update_time"] = values.get("update_time") or now
             values["memory_type"] = memory.memory_type.value
             values["source"] = memory.source.value
             confidence_result, confidence_warning = self._calculate_confidence(memory)
@@ -112,7 +124,6 @@ class LongTermMemoryService:
                 tag=memory.tag,
                 source=source,
                 evidence_count=memory.evidence_count or 0,
-                conflict_count=memory.conflict_count or 0,
                 age_days=age_days,
                 memory_type=memory_type,
             )
@@ -143,6 +154,19 @@ class LongTermMemoryService:
         )
         return [self._to_dto(entity) for entity in entities], []
 
+    async def refresh_confidence(
+        self, db, customer_id: int, *, limit: int = 500
+    ) -> int:
+        """按当前时间刷新有效记忆的置信度并持久化结果。"""
+        repo = self.repository_factory(db)
+        entities = await repo.list_for_confidence_refresh(customer_id, limit=limit)
+        refreshed = 0
+        for entity in entities:
+            confidence_result, _ = self._calculate_confidence(entity)
+            await repo.update_confidence(entity.id, **confidence_result)
+            refreshed += 1
+        return refreshed
+
     async def retry_pending(self, db, *, limit: int = 100) -> dict[str, int]:
         """重试 MySQL 中缺少外部索引或同步失败的记忆。"""
         entities = await self.repository_factory(db).list_pending_sync(limit)
@@ -178,7 +202,6 @@ class LongTermMemoryService:
             "confidence_reason": getattr(entity, "confidence_reason", None),
             "confidence_update_time": getattr(entity, "confidence_update_time", None),
             "evidence_count": entity.evidence_count or 0,
-            "conflict_count": entity.conflict_count or 0,
             "recall_count": entity.recall_count or 0,
             "status": entity.status,
             "valid_from": entity.valid_from,

service/memory/schemas.py

@@ -80,7 +80,6 @@ class MemoryUnitDTO(BaseModel):
     confidence_update_time: datetime | None = None
     final_score: float | None = Field(default=None, ge=0.0, le=1.0)
     evidence_count: int = Field(default=0, ge=0)
-    conflict_count: int = Field(default=0, ge=0)
     recall_count: int = Field(default=0, ge=0)
     status: MemoryStatus = MemoryStatus.CANDIDATE
     valid_from: datetime | None = None

service/memory/short_term.py

@@ -184,7 +184,10 @@ class ShortTermMemory:
         idle_ttl = await _config(
             self.config_getter, "agent.customer.session.ttl", 1800
         )
-        ttl = min(idle_ttl, remaining)
+        archive_grace = await _config(
+            self.config_getter, "agent.customer.session.archive_grace", 60
+        )
+        ttl = min(idle_ttl + archive_grace, remaining)
         await self.redis.expire(self.message_key(session_id), ttl)
         await self.redis.expire(self.meta_key(session_id), remaining)
 

sql/schema.sql

@@ -444,7 +444,6 @@ CREATE TABLE IF NOT EXISTS memory_unit (
     confidence_reason  VARCHAR(255) NULL COMMENT '本次置信度评估原因',
     confidence_update_time DATETIME NULL COMMENT '置信度最近计算时间',
     evidence_count   INT NOT NULL DEFAULT 0 COMMENT '证据数',
-    conflict_count   INT NOT NULL DEFAULT 0 COMMENT '冲突数',
     recall_count     INT NOT NULL DEFAULT 0 COMMENT '召回数',
     memory_version   INT NOT NULL DEFAULT 1 COMMENT '记忆版本号',
     update_time      DATETIME NULL,

tool/confidence.py

@@ -6,7 +6,7 @@ from typing import Any
 
 
 class BaseConfidenceCalcTool:
-    """根据来源、证据、冲突和时间计算单条记忆的长期置信度。"""
+    """根据来源、证据和时间计算单条客服记忆的长期置信度。"""
 
     SOURCE_INITIAL = {
         "dialogue_confirmed": 0.75,
@@ -20,23 +20,21 @@ class BaseConfidenceCalcTool:
         "SERVICE_FACT": 0.75,
     }
     DEFAULT_THRESHOLD = 0.80
-    VERSION = "confidence-v1"
+    VERSION = "confidence-v2"
 
     def calc(
         self,
         tag: str,
         source: str,
         evidence_count: int,
-        conflict_count: int,
         age_days: int,
     ) -> float:
         """计算基础置信度分数，返回范围为 0 到 1 的浮点数。"""
-        self._validate(tag, source, evidence_count, conflict_count, age_days)
+        self._validate(tag, source, evidence_count, age_days)
         base = self.SOURCE_INITIAL[source]
         gain = min(evidence_count * 0.05, 0.30)
-        penalty = min(conflict_count * 0.10, 0.50)
         decay = max(0.80, 1 - age_days / 365 * 0.20)
-        return max(0.0, min(1.0, (base + gain - penalty) * decay))
+        return max(0.0, min(1.0, (base + gain) * decay))
 
     def evaluate(
         self,
@@ -44,13 +42,12 @@ class BaseConfidenceCalcTool:
         tag: str,
         source: str,
         evidence_count: int,
-        conflict_count: int,
         age_days: int,
         memory_type: str | None = None,
         threshold: float | None = None,
     ) -> dict[str, Any]:
         """返回可供记忆模块保存的完整置信度评估结果。"""
-        score = self.calc(tag, source, evidence_count, conflict_count, age_days)
+        score = self.calc(tag, source, evidence_count, age_days)
         if threshold is None:
             threshold = self.MEMORY_THRESHOLDS.get(
                 memory_type or "", self.DEFAULT_THRESHOLD
@@ -64,11 +61,10 @@ class BaseConfidenceCalcTool:
             "confidence": score,
             "status": status,
             "evidence_count": evidence_count,
-            "conflict_count": conflict_count,
             "age_days": age_days,
             "threshold": threshold,
             "confidence_reason": self._reason(
-                source, evidence_count, conflict_count, age_days
+                source, evidence_count, age_days
             ),
             "confidence_version": self.VERSION,
         }
@@ -87,7 +83,6 @@ class BaseConfidenceCalcTool:
         tag: str,
         source: str,
         evidence_count: int,
-        conflict_count: int,
         age_days: int,
     ) -> None:
         """校验工具输入，避免非法计数污染记忆分数。"""
@@ -97,18 +92,17 @@ class BaseConfidenceCalcTool:
             raise ValueError(f"不支持的客服对话来源: {source}")
         for name, value in (
             ("evidence_count", evidence_count),
-            ("conflict_count", conflict_count),
             ("age_days", age_days),
         ):
             if not isinstance(value, int) or isinstance(value, bool) or value < 0:
                 raise ValueError(f"{name} 必须是非负整数")
 
     @staticmethod
-    def _reason(source: str, evidence_count: int, conflict_count: int, age_days: int) -> str:
+    def _reason(source: str, evidence_count: int, age_days: int) -> str:
         """生成便于审计和排查的评分原因。"""
         return (
-            f"来源={source}; 支持证据={evidence_count}; 冲突证据={conflict_count}; "
-            f"存在天数={age_days}; 采用证据增益、冲突惩罚和时间衰减"
+            f"来源={source}; 支持证据={evidence_count}; "
+            f"存在天数={age_days}; 采用证据增益和时间衰减"
         )
 
 

tool/confidence_rank.py

@@ -14,8 +14,7 @@ class FinalConfidenceRankTool:
         "semantic": 0.30,
         "timeliness": 0.20,
         "accuracy": 0.20,
-        "base": 0.25,
-        "conflict": 0.05,
+        "base": 0.30,
     }
     INVALID_STATUSES = frozenset({"expired", "rejected", "archived"})
 
@@ -39,13 +38,11 @@ class FinalConfidenceRankTool:
             timeliness = self._calc_timeliness(unit.get("age_days", 0))
             accuracy = self._bounded(unit.get("historical_accuracy", 0.5), 0.5)
             base = self._bounded(unit.get("confidence", 0.5), 0.5)
-            conflict_penalty = min(self._non_negative_int(unit.get("conflict_count", 0)), 5) * 0.1
             final_score = (
                 self.WEIGHTS["semantic"] * semantic
                 + self.WEIGHTS["timeliness"] * timeliness
                 + self.WEIGHTS["accuracy"] * accuracy
                 + self.WEIGHTS["base"] * base
-                - self.WEIGHTS["conflict"] * conflict_penalty
             )
             unit["final_score"] = max(0.0, min(1.0, final_score))
             ranked.append(unit)
@@ -78,14 +75,6 @@ class FinalConfidenceRankTool:
             return default
         return max(0.0, min(1.0, value))
 
-    @staticmethod
-    def _non_negative_int(value: Any) -> int:
-        """将冲突次数转换为非负整数。"""
-        try:
-            return max(0, int(value))
-        except (TypeError, ValueError):
-            return 0
-
     @staticmethod
     def _calc_timeliness(age_days: Any) -> float:
         """按每年 20% 计算平滑时效分，最低保留 0.8。"""

tool/llm.py

@@ -204,7 +204,7 @@ class LLMClient:
         payload = {
             "model": backend.embed_model,
             "input": texts,
-            "dimensions": self.cfg.embed_dimensions,
+            # "dimensions": self.cfg.embed_dimensions,
         }
         async with backend.client(self.cfg.timeout) as client:
             r = await client.post(url, headers=backend.headers, json=payload)