feat(avatar): honor square interaction authorization

Reviewed-on: #19

backend/app/services/avatar_service.py

@@ -1,16 +1,24 @@
 """数字分身管理服务层 — 同步连接数字分身应用的 SQLite 数据库"""
+import json
 import os
+from datetime import datetime, timedelta
 from typing import Optional, Tuple
 
-from sqlalchemy import create_engine, text
+from sqlalchemy import create_engine, select, text
 from sqlalchemy.orm import sessionmaker, Session
 
 from app.core.config import settings
+from app.core.logger import logger
+from app.models import UserPersonality, VirtualUser
 
 
 _engine = None
 _SessionLocal: Optional[sessionmaker] = None
 
+AVATAR_ACCOUNT_PREFIX = "__avatar__:"
+SQUARE_INTERACTION_PERMISSION = "interact"
+SQUARE_INTERACTION_ACTIONS = frozenset({"like", "collect", "comment", "reply"})
+
 
 def _get_engine_and_session():
     global _engine, _SessionLocal
@@ -66,6 +74,185 @@ def _get_global_token_balance(db: Session) -> int:
         return 0
 
 
+def _decode_config(value) -> dict:
+    if isinstance(value, dict):
+        return value
+    if isinstance(value, str):
+        try:
+            decoded = json.loads(value)
+            return decoded if isinstance(decoded, dict) else {}
+        except (json.JSONDecodeError, ValueError):
+            return {}
+    return {}
+
+
+def is_delegated_avatar_user(user: VirtualUser | None) -> bool:
+    return bool(user and (user.account or "").startswith(AVATAR_ACCOUNT_PREFIX))
+
+
+def delegated_avatar_id(user: VirtualUser | None) -> str:
+    if not is_delegated_avatar_user(user):
+        return ""
+    return (user.account or "")[len(AVATAR_ACCOUNT_PREFIX):]
+
+
+def _list_square_interaction_authorizations(db: Session) -> list[dict]:
+    """读取已明确授权分身参与广场互动的身份与会会令牌。"""
+    rows = db.execute(text("""
+        SELECT
+            a.id AS avatar_id,
+            a.name AS avatar_name,
+            a.display_name AS avatar_display_name,
+            a.description AS avatar_description,
+            a.photo_url AS avatar_photo_url,
+            a.config AS avatar_config,
+            u.huihui_user_id,
+            u.nickname AS owner_nickname,
+            u.avatar_url AS owner_avatar_url,
+            u.huihui_token
+        FROM avatars a
+        JOIN users u ON u.huihui_user_id = a.owner_id
+        WHERE a.status = 'active'
+    """)).fetchall()
+
+    authorized = []
+    for row in rows:
+        config = _decode_config(row.avatar_config)
+        permissions = config.get("authorizationPermissions", [])
+        if not isinstance(permissions, list) or SQUARE_INTERACTION_PERMISSION not in permissions:
+            continue
+        platform_uid = str(row.huihui_user_id or "").strip()
+        token = str(row.huihui_token or "").strip()
+        if not platform_uid or not token:
+            continue
+        authorized.append({
+            "avatar_id": str(row.avatar_id),
+            "avatar_name": row.avatar_display_name or row.avatar_name or row.owner_nickname or "数字分身",
+            "avatar_description": row.avatar_description or "",
+            "avatar_url": _resolve_photo_url(row.avatar_photo_url or row.owner_avatar_url or ""),
+            "config": config,
+            "platform_uid": platform_uid,
+            "token": token,
+        })
+    return authorized
+
+
+def get_square_interaction_permissions(avatar_id: str) -> frozenset[str]:
+    """实时复核授权；数据库不可用、令牌失效或撤权时一律拒绝执行。"""
+    avatar_db = get_session()
+    if avatar_db is None:
+        return frozenset()
+    try:
+        authorized_ids = {
+            item["avatar_id"] for item in _list_square_interaction_authorizations(avatar_db)
+        }
+        return SQUARE_INTERACTION_ACTIONS if avatar_id in authorized_ids else frozenset()
+    except Exception as exc:
+        logger.error(f"读取数字分身广场互动授权失败: {exc}")
+        return frozenset()
+    finally:
+        avatar_db.close()
+
+
+def _word_count_range(config: dict) -> tuple[int, int]:
+    ranges = {
+        "short": (10, 35),
+        "medium": (20, 60),
+        "long": (30, 80),
+    }
+    return ranges.get(str(config.get("responseLength") or "medium"), (20, 60))
+
+
+async def sync_square_interaction_users(db) -> set[str]:
+    """把已授权分身同步为调度器身份，并刷新其会会会话。"""
+    avatar_db = get_session()
+    if avatar_db is None:
+        logger.warning("数字分身数据库不可用，跳过广场互动授权同步")
+        return set()
+    try:
+        authorized = _list_square_interaction_authorizations(avatar_db)
+    except Exception as exc:
+        logger.error(f"同步数字分身广场互动授权失败: {exc}")
+        return set()
+    finally:
+        avatar_db.close()
+
+    from app.core.redis_client import delete_session, set_session
+
+    result = await db.execute(
+        select(VirtualUser).where(VirtualUser.account.like(f"{AVATAR_ACCOUNT_PREFIX}%"))
+    )
+    existing_users = {delegated_avatar_id(user): user for user in result.scalars().all()}
+    authorized_ids = {item["avatar_id"] for item in authorized}
+
+    for avatar_id, user in existing_users.items():
+        if avatar_id not in authorized_ids:
+            user.is_enabled = 0
+            user.status = 0
+            user.session_token = None
+            user.session_expires_at = None
+            await delete_session(user.id)
+
+    for item in authorized:
+        avatar_id = item["avatar_id"]
+        user = existing_users.get(avatar_id)
+        if user is None:
+            user = VirtualUser(
+                nickname=item["avatar_name"],
+                account=f"{AVATAR_ACCOUNT_PREFIX}{avatar_id}",
+                password_enc="",
+                status=2,
+                is_enabled=1,
+                platform_uid=item["platform_uid"],
+                remark="用户授权的数字分身广场互动身份",
+            )
+            db.add(user)
+            await db.flush()
+
+        expires_at = datetime.now() + timedelta(days=1)
+        user.nickname = item["avatar_name"]
+        user.real_name = item["avatar_name"]
+        user.avatar_url = item["avatar_url"]
+        user.platform_uid = item["platform_uid"]
+        user.session_token = item["token"]
+        user.session_expires_at = expires_at
+        user.last_login_at = datetime.now()
+        user.status = 2
+        user.is_enabled = 1
+
+        config = item["config"]
+        personality_result = await db.execute(
+            select(UserPersonality).where(UserPersonality.user_id == user.id)
+        )
+        personality = personality_result.scalar_one_or_none()
+        word_min, word_max = _word_count_range(config)
+        prompt_parts = [item["avatar_description"], str(config.get("systemPrompt") or "")]
+        style_prompt = "\n".join(part.strip() for part in prompt_parts if part and part.strip())
+        if personality is None:
+            personality = UserPersonality(user_id=user.id)
+            db.add(personality)
+        personality.language_style = str(config.get("replyStyle") or "professional")
+        personality.personality_desc = item["avatar_description"]
+        personality.comment_style_prompt = style_prompt
+        personality.word_count_min = word_min
+        personality.word_count_max = word_max
+
+        await set_session(user.id, {
+            "token": item["token"],
+            "session_id": f"avatar:{avatar_id}",
+            "platform_uid": item["platform_uid"],
+            "org_id": "",
+            "login_time": datetime.now().isoformat(),
+            "nickname": item["avatar_name"],
+            "real_name": item["avatar_name"],
+            "avatar": item["avatar_url"],
+            "delegated_avatar_id": avatar_id,
+        }, expire=86400)
+
+    await db.commit()
+    return authorized_ids
+
+
 class AvatarService:
 
     @staticmethod

backend/app/services/scheduler.py

@@ -23,6 +23,7 @@ class SchedulerService:
         from app.core.database import AsyncSessionLocal
         logger.info("⚡ 立即触发互动任务")
         async with AsyncSessionLocal() as session:
+            await self._sync_delegated_avatar_users(session)
             try:
                 max_concurrent = int(await self._get_config(session, "max_concurrent_users", "5"))
             except (TypeError, ValueError):
@@ -146,7 +147,9 @@ class SchedulerService:
     async def _check_sessions(self):
         """定时校验登录状态"""
         from app.services.news_service import news_service
+        from app.services.avatar_service import is_delegated_avatar_user
         async with AsyncSessionLocal() as db:
+            await self._sync_delegated_avatar_users(db)
             result = await db.execute(
                 select(VirtualUser).where(VirtualUser.status == 2, VirtualUser.is_enabled == 1)
             )
@@ -154,7 +157,7 @@ class SchedulerService:
             for user in users:
                 try:
                     valid = await news_service.check_session(db, user)
-                    if not valid:
+                    if not valid and not is_delegated_avatar_user(user):
                         logger.warning(f"用户 {user.account} 会话失效，尝试重登")
                         await news_service.login(db, user)
                 except Exception as e:
@@ -163,6 +166,7 @@ class SchedulerService:
     async def _run_interactions(self):
         """执行互动任务"""
         async with AsyncSessionLocal() as db:
+            await self._sync_delegated_avatar_users(db)
             # 检查调度器开关
             enabled = await self._get_config(db, "scheduler_enabled", "true")
             if enabled != "true":
@@ -184,8 +188,11 @@ class SchedulerService:
                 logger.debug(f"[调度] 当前北京时间 {now_time} 不在互动时段 {start_str}-{end_str}")
                 return
 
-            # 获取最小互动间隔（秒）
+            # 获取互动间隔范围（秒），与调度设置页面字段保持一致
-            min_interval = int(await self._get_config(db, "interact_min_interval", "300"))
+            min_interval = await self._get_int_config(db, "interact_interval_min", 300)
+            max_interval = await self._get_int_config(db, "interact_interval_max", min_interval)
+            min_interval = max(0, min_interval)
+            max_interval = max(min_interval, max_interval)
 
             # 获取最大并发
             max_concurrent = int(await self._get_config(db, "max_concurrent_users", "5"))
@@ -204,7 +211,7 @@ class SchedulerService:
                 await self._try_login_users(db)
                 return
 
-            # 检查互动间隔：过滤掉最近 min_interval 秒内已互动的用户
+            # 每个用户在其最小/最大间隔内取得稳定随机值，直到下次互动后再变化
             now_dt = datetime.now()
             eligible = []
             for u in all_users:
@@ -212,11 +219,17 @@ class SchedulerService:
                     eligible.append(u)
                 else:
                     elapsed = (now_dt - u.last_interact_at).total_seconds()
-                    if elapsed >= min_interval:
+                    interval = random.Random(
+                        f"{u.id}:{u.last_interact_at.isoformat()}"
+                    ).randint(min_interval, max_interval)
+                    if elapsed >= interval:
                         eligible.append(u)
 
             if not eligible:
-                logger.debug(f"[调度] 所有 {len(all_users)} 个用户在 {min_interval}s 内已互动，跳过本次")
+                logger.debug(
+                    f"[调度] 所有 {len(all_users)} 个用户尚未达到 "
+                    f"{min_interval}-{max_interval}s 随机互动间隔，跳过本次"
+                )
                 return
 
             # 按最后互动时间升序排序：最久没互动的用户优先
@@ -257,10 +270,12 @@ class SchedulerService:
     async def _try_login_users(self, db):
         """尝试登录未登录的用户"""
         from app.services.news_service import news_service
+        from app.services.avatar_service import AVATAR_ACCOUNT_PREFIX
         result = await db.execute(
             select(VirtualUser).where(
                 VirtualUser.status.in_([0, 3]),
-                VirtualUser.is_enabled == 1
+                VirtualUser.is_enabled == 1,
+                ~VirtualUser.account.like(f"{AVATAR_ACCOUNT_PREFIX}%"),
             ).limit(3)
         )
         users = result.scalars().all()
@@ -275,6 +290,11 @@ class SchedulerService:
         """执行单用户互动 - 基于真实接口"""
         from app.services.news_service import news_service
         from app.services.ai_service import ai_service
+        from app.services.avatar_service import (
+            delegated_avatar_id,
+            get_square_interaction_permissions,
+            is_delegated_avatar_user,
+        )
 
         async with AsyncSessionLocal() as db:
             try:
@@ -289,6 +309,23 @@ class SchedulerService:
                         "interactions": [],
                     }
 
+                allowed_actions = {"like", "collect", "comment", "reply", "forward"}
+                if is_delegated_avatar_user(user):
+                    allowed_actions = set(
+                        get_square_interaction_permissions(delegated_avatar_id(user))
+                    )
+                    if not allowed_actions:
+                        user.status = 0
+                        user.is_enabled = 0
+                        await db.commit()
+                        return {
+                            "user_id": user.id,
+                            "account": user.account,
+                            "status": "skipped",
+                            "reason": "avatar_interaction_not_authorized",
+                            "interactions": [],
+                        }
+
                 # 检查今日评论限额
                 can_comment = True
                 if user.today_comment_count >= user.daily_comment_limit:
@@ -398,14 +435,53 @@ class SchedulerService:
                 interactions_done = []
                 action_failures = []
 
-                # ① 先记录阅读（每次必做，模拟真实用户打开文章）
+                done_on_this = today_done.get(news_id, set())
+                wants = {
+                    "like": (
+                        "like" in allowed_actions
+                        and "like" not in done_on_this
+                        and random.random() < like_prob
+                    ),
+                    "collect": (
+                        "collect" in allowed_actions
+                        and "collect" not in done_on_this
+                        and random.random() < collect_prob
+                    ),
+                    "forward": (
+                        "forward" in allowed_actions
+                        and "forward" not in done_on_this
+                        and random.random() < forward_prob
+                    ),
+                    "reply": (
+                        "reply" in allowed_actions
+                        and can_comment
+                        and personality is not None
+                        and random.random() < reply_prob
+                    ),
+                    "comment": (
+                        "comment" in allowed_actions
+                        and can_comment
+                        and personality is not None
+                        and not already_commented_this
+                        and random.random() < comment_prob
+                    ),
+                }
+                if not any(wants.values()):
+                    return {
+                        "user_id": user.id,
+                        "account": user.account,
+                        "status": "skipped",
+                        "reason": "no_actions_triggered",
+                        "interactions": [],
+                        "article_id": news_id,
+                        "article_title": news_title,
+                    }
+
+                # 只有动作命中调度概率后才打开文章
                 await news_service.read_news(db, user, news_id)
 
-                # 今日已对此文章做过的互动类型
-                done_on_this = today_done.get(news_id, set())
-
                 # ② 点赞（每篇文章每用户每天只点赞一次）
-                if "like" not in done_on_this and random.random() < like_prob:
+                if wants["like"]:
                     success, err = await news_service.like_news(db, user, news_id, org_id=article_org_id, to_user_id=news_author, title=news_title)
                     await self._save_record(db, user, news_id, news_title, "like", None, 0, success, err)
                     if success:
@@ -415,16 +491,17 @@ class SchedulerService:
                         action_failures.append({"type": "like", "error": err})
 
                 # ③ 收藏（每篇文章每用户每天只收藏一次）
-                if "collect" not in done_on_this and random.random() < collect_prob:
+                if wants["collect"]:
                     success, err = await news_service.collect_news(db, user, news_id, org_id=article_org_id, to_user_id=news_author, title=news_title)
                     await self._save_record(db, user, news_id, news_title, "collect", None, 0, success, err)
                     if success:
                         interactions_done.append("collect")
+                        await self._incr_total(db, user_id)
                     else:
                         action_failures.append({"type": "collect", "error": err})
 
                 # ④ 转发（每篇文章每用户每天只转发一次）
-                if "forward" not in done_on_this and random.random() < forward_prob:
+                if wants["forward"]:
                     success, err = await news_service.forward_news(db, user, news_id)
                     await self._save_record(db, user, news_id, news_title, "forward", None, 0, success, err)
                     if success:
@@ -438,7 +515,7 @@ class SchedulerService:
                     style_prompt = personality.comment_style_prompt or ""
                     safe_word_max = min(personality.word_count_max, 80)
 
-                    if random.random() < reply_prob:
+                    if wants["reply"]:
                         reply_actions, reply_failures = await self._run_reply_interaction_chain(
                             db=db,
                             starter=user,
@@ -455,7 +532,7 @@ class SchedulerService:
                         action_failures.extend(reply_failures)
 
                     # 每篇文章每个用户每天只发一条顶层评论；回复不再要求先评论
-                    if not already_commented_this and random.random() < comment_prob:
+                    if wants["comment"]:
                         comment_text, tokens = await ai_service.generate_comment(
                             db, news_title, news_content,
                             style_prompt, personality.word_count_min, safe_word_max
@@ -679,6 +756,7 @@ class SchedulerService:
 
         async with AsyncSessionLocal() as db:
             try:
+                await self._sync_delegated_avatar_users(db)
                 now = datetime.now()
                 await db.execute(
                     update(PendingReplyTask)
@@ -706,6 +784,12 @@ class SchedulerService:
                 logger.error(f"待发送回复队列处理异常: {e}")
 
     async def _process_pending_reply_task(self, db, task: PendingReplyTask, news_service, ai_service):
+        from app.services.avatar_service import (
+            delegated_avatar_id,
+            get_square_interaction_permissions,
+            is_delegated_avatar_user,
+        )
+
         task.status = 1
         task.locked_at = datetime.now()
         task.attempts = (task.attempts or 0) + 1
@@ -716,6 +800,13 @@ class SchedulerService:
             task.status = 3
             task.last_error = "用户未登录或已禁用"
             return
+        if (
+            is_delegated_avatar_user(actor)
+            and "reply" not in get_square_interaction_permissions(delegated_avatar_id(actor))
+        ):
+            task.status = 3
+            task.last_error = "数字分身广场互动授权已撤销"
+            return
 
         reply_result = await self._post_contextual_reply(
             db=db,
@@ -858,6 +949,16 @@ class SchedulerService:
         except (TypeError, ValueError):
             return default
 
+    async def _sync_delegated_avatar_users(self, db):
+        from app.services.avatar_service import sync_square_interaction_users
+
+        try:
+            return await sync_square_interaction_users(db)
+        except Exception as exc:
+            await db.rollback()
+            logger.error(f"数字分身广场互动身份同步异常: {exc}")
+            return set()
+
     async def _incr_total(self, db, user_id: int):
         await db.execute(
             update(VirtualUser).where(VirtualUser.id == user_id).values(

backend/tests/test_avatar_square_authorization.py

@@ -0,0 +1,126 @@
+import json
+import os
+import sqlite3
+import tempfile
+import unittest
+from types import SimpleNamespace
+from unittest.mock import patch
+
+from app.services import avatar_service
+
+
+class AvatarSquareAuthorizationTests(unittest.TestCase):
+    def setUp(self):
+        fd, self.db_path = tempfile.mkstemp(suffix=".db")
+        os.close(fd)
+        connection = sqlite3.connect(self.db_path)
+        connection.executescript("""
+            CREATE TABLE users (
+                huihui_user_id TEXT,
+                nickname TEXT,
+                avatar_url TEXT,
+                huihui_token TEXT
+            );
+            CREATE TABLE avatars (
+                id TEXT,
+                owner_id TEXT,
+                name TEXT,
+                display_name TEXT,
+                description TEXT,
+                photo_url TEXT,
+                config TEXT,
+                status TEXT
+            );
+        """)
+        connection.execute(
+            "INSERT INTO users VALUES (?, ?, ?, ?)",
+            ("huihui-7", "主人", "/owner.jpg", "huihui-token"),
+        )
+        connection.commit()
+        connection.close()
+        avatar_service._engine = None
+        avatar_service._SessionLocal = None
+        self.path_patch = patch.object(avatar_service.settings, "AVATAR_DB_PATH", self.db_path)
+        self.path_patch.start()
+
+    def tearDown(self):
+        self.path_patch.stop()
+        if avatar_service._engine is not None:
+            avatar_service._engine.dispose()
+        avatar_service._engine = None
+        avatar_service._SessionLocal = None
+        os.unlink(self.db_path)
+
+    def _insert_avatar(self, permissions, *, status="active", token=None):
+        connection = sqlite3.connect(self.db_path)
+        connection.execute(
+            "INSERT INTO avatars VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
+            (
+                "avatar-7",
+                "huihui-7",
+                "avatar",
+                "小会",
+                "语气友好，表达简洁",
+                "/avatar.jpg",
+                json.dumps({
+                    "authorizationPermissions": permissions,
+                    "replyStyle": "warm",
+                    "responseLength": "short",
+                }),
+                status,
+            ),
+        )
+        if token is not None:
+            connection.execute(
+                "UPDATE users SET huihui_token = ? WHERE huihui_user_id = ?",
+                (token, "huihui-7"),
+            )
+        connection.commit()
+        connection.close()
+
+    def test_interact_permission_exposes_only_requested_square_actions(self):
+        self._insert_avatar(["chat", "interact"])
+
+        permissions = avatar_service.get_square_interaction_permissions("avatar-7")
+
+        self.assertEqual(
+            permissions,
+            frozenset({"like", "collect", "comment", "reply"}),
+        )
+        self.assertNotIn("forward", permissions)
+
+    def test_missing_permission_inactive_avatar_or_missing_token_denies_execution(self):
+        scenarios = [
+            (["chat"], "active", "huihui-token"),
+            (["interact"], "inactive", "huihui-token"),
+            (["interact"], "active", ""),
+        ]
+        for permissions, status, token in scenarios:
+            with self.subTest(permissions=permissions, status=status, token=token):
+                connection = sqlite3.connect(self.db_path)
+                connection.execute("DELETE FROM avatars")
+                connection.commit()
+                connection.close()
+                self._insert_avatar(permissions, status=status, token=token)
+                self.assertEqual(
+                    avatar_service.get_square_interaction_permissions("avatar-7"),
+                    frozenset(),
+                )
+
+    def test_delegated_avatar_identity_is_recognized_without_matching_normal_users(self):
+        delegated = SimpleNamespace(account="__avatar__:avatar-7")
+        normal = SimpleNamespace(account="13800000000")
+
+        self.assertTrue(avatar_service.is_delegated_avatar_user(delegated))
+        self.assertEqual(avatar_service.delegated_avatar_id(delegated), "avatar-7")
+        self.assertFalse(avatar_service.is_delegated_avatar_user(normal))
+        self.assertEqual(avatar_service.delegated_avatar_id(normal), "")
+
+    def test_response_length_maps_to_scheduler_comment_limits(self):
+        self.assertEqual(avatar_service._word_count_range({"responseLength": "short"}), (10, 35))
+        self.assertEqual(avatar_service._word_count_range({"responseLength": "long"}), (30, 80))
+        self.assertEqual(avatar_service._word_count_range({"responseLength": "unknown"}), (20, 60))
+
+
+if __name__ == "__main__":
+    unittest.main()

digital-avatar-app/backend/database.py

@@ -53,6 +53,9 @@ def init_db():
         ("knowledge_docs", "embedding_model", "VARCHAR DEFAULT ''"),
         ("knowledge_docs", "chunk_count", "INTEGER DEFAULT 0"),
         ("knowledge_docs", "vectorized_at", "TIMESTAMP"),
+        ("knowledge_docs", "error_message", "VARCHAR DEFAULT ''"),
+        ("knowledge_docs", "index_stage", "VARCHAR DEFAULT ''"),
+        ("knowledge_docs", "index_progress", "INTEGER DEFAULT 0"),
         ("avatars", "owner_id", "VARCHAR DEFAULT ''"),
         ("authorizations", "takeover_enabled", "BOOLEAN DEFAULT 0"),
         ("authorizations", "takeover_mode", "VARCHAR DEFAULT 'immediate'"),

digital-avatar-app/backend/embeddings.py

@@ -51,7 +51,7 @@ def _hash_embedding(texts, dim=EMBED_DIM):
     return vecs
 
 
-def embed(texts):
+def embed(texts, on_progress=None):
     """返回 list[list[float]]，与输入顺序一致。"""
     if not texts:
         return []
@@ -64,6 +64,7 @@ def embed(texts):
         except ValueError:
             batch_size = 10
         embeddings = []
+        total = len(texts)
         for start in range(0, len(texts), batch_size):
             batch = texts[start:start + batch_size]
             payload = json.dumps({"input": batch, "model": model}).encode("utf-8")
@@ -84,8 +85,13 @@ def embed(texts):
             if len(items) != len(batch):
                 raise ValueError("embedding response count does not match request")
             embeddings.extend(item["embedding"] for item in items)
+            if on_progress:
+                on_progress(len(embeddings), total)
         return embeddings
-    return _hash_embedding(texts)
+    vectors = _hash_embedding(texts)
+    if on_progress:
+        on_progress(len(vectors), len(texts))
+    return vectors
 
 
 def cosine(a, b):

digital-avatar-app/backend/main.py

@@ -20,6 +20,7 @@ import routers.chat
 import routers.takeover
 from responses import ok
 from services.chat_attachment_service import purge_expired_chat_attachments
+from services.knowledge_vectorizer import knowledge_vectorizer
 from services.token_billing import DEFAULT_TOKEN_GRANT, release_stale_reservations
 
 logger = logging.getLogger(__name__)
@@ -131,6 +132,7 @@ def on_startup():
 
     init_db()
     seed()
+    knowledge_vectorizer.start()
 
     # Release stale resources when startup is invoked again by a reload/test.
     stop_takeover_scheduler()

digital-avatar-app/backend/models.py

@@ -190,6 +190,9 @@ class KnowledgeDoc(Base):
     file_size = Column(Integer, default=0)
     file_url = Column(String, default="")
     status = Column(String, default="uploaded")  # uploaded | parsing | ready | failed
+    error_message = Column(String, default="")  # 建立索引失败原因
+    index_stage = Column(String, default="")  # queued | extracting | chunking | embedding | ready | failed
+    index_progress = Column(Integer, default=0)  # 0-100
     vectorized = Column(Boolean, default=False)  # 是否已向量化
     embedding_model = Column(String, default="")  # 向量模型标识
     chunk_count = Column(Integer, default=0)  # 切片数量
@@ -205,6 +208,9 @@ class KnowledgeDoc(Base):
             "fileSize": self.file_size,
             "fileUrl": self.file_url,
             "status": self.status,
+            "errorMessage": self.error_message or "",
+            "indexStage": self.index_stage or "",
+            "indexProgress": int(self.index_progress or 0),
             "vectorized": bool(self.vectorized),
             "embeddingModel": self.embedding_model,
             "chunkCount": self.chunk_count,

digital-avatar-app/backend/routers/chat.py

@@ -47,6 +47,25 @@ QA_SEMANTIC_THRESHOLD = 0.72
 QA_MATCH_MARGIN = 0.06
 KNOWLEDGE_MIN_SCORE = float(os.getenv("KNOWLEDGE_MIN_SCORE", "0.42"))
 
+_IMAGE_ACCESS_DENIAL_PATTERNS = (
+    re.compile(
+        r"(?:我|目前|暂时|这里|本身|系统)?\s*(?:无法|不能|没法|不支持)\s*"
+        r"(?:直接)?\s*(?:查看|看到|看见|识别|读取|访问|打开|分析|理解)"
+        r"(?:\s*(?:或|、|/)\s*(?:查看|看到|看见|识别|读取|访问|打开|分析|理解))*\s*"
+        r"(?:你(?:发|提供|上传)的|这张|该|当前)?\s*(?:图片|图像|照片|影像|文件)"
+    ),
+    re.compile(
+        r"(?:我|这里|目前|暂时)?\s*(?:看不到|看不见|未看到|没有看到|没收到|未收到)\s*"
+        r"(?:你(?:发|提供|上传)的|这张|该|当前)?\s*(?:图片|图像|照片|影像)"
+    ),
+    re.compile(
+        r"\b(?:i\s+)?(?:can(?:not|'t)|am\s+unable\s+to)\s+(?:directly\s+)?"
+        r"(?:view|see|access|read|analy[sz]e|recogni[sz]e)\s+"
+        r"(?:the\s+|this\s+|your\s+)?(?:image|photo|picture|scan)\b",
+        re.IGNORECASE,
+    ),
+)
+
 _WRITING_SYSTEM_PATTERNS = {
     "han": re.compile(r"[\u3400-\u4dbf\u4e00-\u9fff]"),
     "latin": re.compile(r"[A-Za-z\u00c0-\u024f]"),
@@ -178,6 +197,75 @@ def _image_retrieval_question(question: str, image_contexts: list[dict]) -> str:
     return "\n".join(part for part in parts if part).strip()
 
 
+def _answer_denies_available_image(answer: str) -> bool:
+    """Reject only whole-image access denials, not uncertainty about one field."""
+    value = re.sub(r"\s+", " ", answer or "").strip()
+    return any(pattern.search(value) for pattern in _IMAGE_ACCESS_DENIAL_PATTERNS)
+
+
+def _compact_context_text(value: Any, limit: int) -> str:
+    lines = [re.sub(r"\s+", " ", line).strip() for line in str(value or "").splitlines()]
+    text = "\n".join(line for line in lines if line).strip()
+    return text[:limit].rstrip()
+
+
+def _grounded_image_fallback(question: str, image_contexts: list[dict]) -> str:
+    """Build a safe answer from completed vision data when the chat model contradicts it."""
+    summaries: list[str] = []
+    facts: list[str] = []
+    excerpts: list[str] = []
+    warnings: list[str] = []
+    for context in image_contexts:
+        summary = _compact_context_text(context.get("summary"), 500)
+        if summary:
+            summaries.append(summary)
+        structured = context.get("structuredData") or {}
+        if isinstance(structured, dict):
+            for fact in structured.get("key_facts") or []:
+                value = _compact_context_text(fact, 300)
+                if value:
+                    facts.append(value)
+        extracted = _compact_context_text(context.get("extractedText"), 900)
+        if extracted:
+            excerpts.append(extracted)
+        warning = _compact_context_text(context.get("warning"), 300)
+        if warning:
+            warnings.append(warning)
+
+    summaries = list(dict.fromkeys(summaries))
+    facts = list(dict.fromkeys(facts))[:6]
+    excerpts = list(dict.fromkeys(excerpts))
+    warnings = list(dict.fromkeys(warnings))
+    writing_system = _dominant_writing_system(question)
+
+    if writing_system == "latin":
+        parts = []
+        if summaries:
+            parts.append("From the image, I can confirm: " + " ".join(summaries))
+        if facts:
+            parts.append("Key details:\n" + "\n".join(
+                f"{index}. {fact}" for index, fact in enumerate(facts, 1)
+            ))
+        elif excerpts:
+            parts.append("Visible text:\n" + excerpts[0])
+        if warnings:
+            parts.append("Please note: " + " ".join(warnings))
+        return "\n".join(parts).strip() or "The image is available, but there is not enough clear detail to confirm more."
+
+    parts = []
+    if summaries:
+        parts.append("从这张图中可以确认：" + "；".join(summaries).rstrip("。；") + "。")
+    if facts:
+        parts.append("其中比较明确的信息有：\n" + "\n".join(
+            f"{index}. {fact}" for index, fact in enumerate(facts, 1)
+        ))
+    elif excerpts:
+        parts.append("图中可见的主要文字是：\n" + excerpts[0])
+    if warnings:
+        parts.append("需要注意：" + "；".join(warnings).rstrip("。；") + "。")
+    return "\n".join(parts).strip() or "这张图已经看到了，但目前能确认的清晰信息比较有限。"
+
+
 def _run_billed_vision_call(
     db: Session,
     avatar: Avatar,
@@ -553,8 +641,10 @@ def _build_prompt(
     if image_contexts:
         image_material = json.dumps(image_contexts, ensure_ascii=False, default=str)
         system += (
-            "\n以下是当前会话图片经过视觉识别后得到的资料：\n"
+            "\n当前会话图片已经成功读取并完成内容识别，以下资料就是可直接使用的图片内容：\n"
             f"{image_material}"
+            "\n必须直接依据这些图片内容回答当前问题。禁止声称无法查看、看不到、未收到、无法识别、"
+            "无法读取或不能访问图片，也不要要求对方重新上传；只有资料明确标记读取失败时才可以请对方重发。"
             "\n图片资料可能包含 OCR 错字、模糊内容或用户尚未确认的信息，只能按可见内容谨慎表达。"
             "标准答题对中的事实优先级高于图片资料，知识库事实优先级高于模型推测；发生冲突时遵循更高优先级资料，"
             "并自然提醒对方核对原图。不得声称看到了图片中不存在的内容。"
@@ -815,6 +905,14 @@ def _resolve_reply(
         except Exception as exc:
             release_reservation(db, reservation, str(exc))
             raise
+    answer = str(answer or "").strip()
+    if image_contexts and _answer_denies_available_image(answer):
+        logger.warning(
+            "chat model contradicted ready image context avatar=%s source=%s",
+            avatar.id,
+            usage_source,
+        )
+        answer = _grounded_image_fallback(question, image_contexts)
     result = {
         "answer": answer,
         "source": "qa" if matched else (

digital-avatar-app/backend/routers/knowledge.py

@@ -1,8 +1,8 @@
 import os
 import json
-import logging
+import shutil
+import time
 import uuid
-from datetime import datetime, timezone
 
 from fastapi import APIRouter, UploadFile, File, Depends, Header, HTTPException
 from pydantic import BaseModel
@@ -12,16 +12,19 @@ from database import get_db
 from models import KnowledgeDoc, QAPair, KnowledgeChunk, Avatar, User
 from responses import ok, fail
 import embeddings
+from services.knowledge_vectorizer import knowledge_vectorizer
 
 router = APIRouter()
-logger = logging.getLogger(__name__)
-
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 UPLOAD_DIR = os.path.abspath(os.getenv("UPLOAD_DIR", os.path.join(BASE_DIR, "uploads")))
 os.makedirs(UPLOAD_DIR, exist_ok=True)
 
 ALLOWED_EXT = {".md", ".txt", ".pdf", ".doc", ".docx", ".xlsx"}
-MAX_UPLOAD_BYTES = 10 * 1024 * 1024
+MAX_UPLOAD_BYTES = 50 * 1024 * 1024
+UPLOAD_CHUNK_BYTES = 1024 * 1024
+MULTIPART_CHUNK_BYTES = 5 * 1024 * 1024
+MULTIPART_ROOT = ".multipart"
+MULTIPART_TTL_SECONDS = 24 * 60 * 60
 
 
 class QAIn(BaseModel):
@@ -34,6 +37,74 @@ class EnabledIn(BaseModel):
     enabled: bool = True
 
 
+class MultipartUploadIn(BaseModel):
+    filename: str
+    fileSize: int
+    totalChunks: int
+
+
+def _validate_document(filename: str, file_size: int):
+    ext = os.path.splitext(filename or "")[1].lower()
+    if ext not in ALLOWED_EXT:
+        return None, f"不支持的文件类型：{ext or '空'}，仅支持 md/txt/pdf/doc/docx/xlsx"
+    if file_size <= 0:
+        return None, "文件内容不能为空"
+    if file_size > MAX_UPLOAD_BYTES:
+        return None, "文件不能超过 50MB"
+    return ext, ""
+
+
+def _multipart_dir(avatar_id: str, upload_id: str) -> str:
+    safe_avatar_id = os.path.basename(avatar_id)
+    safe_upload_id = os.path.basename(upload_id)
+    if (
+        safe_avatar_id != avatar_id
+        or safe_upload_id != upload_id
+        or len(upload_id) != 32
+        or any(character not in "0123456789abcdef" for character in upload_id)
+    ):
+        raise HTTPException(status_code=400, detail="上传标识无效")
+    return os.path.join(UPLOAD_DIR, MULTIPART_ROOT, safe_avatar_id, safe_upload_id)
+
+
+def _purge_stale_multipart_uploads(avatar_id: str):
+    avatar_upload_root = os.path.join(UPLOAD_DIR, MULTIPART_ROOT, os.path.basename(avatar_id))
+    if not os.path.isdir(avatar_upload_root):
+        return
+    cutoff = time.time() - MULTIPART_TTL_SECONDS
+    for entry in os.scandir(avatar_upload_root):
+        if entry.is_dir(follow_symlinks=False) and entry.stat(follow_symlinks=False).st_mtime < cutoff:
+            shutil.rmtree(entry.path, ignore_errors=True)
+
+
+def _read_multipart_metadata(avatar_id: str, upload_id: str) -> tuple[str, dict]:
+    upload_dir = _multipart_dir(avatar_id, upload_id)
+    metadata_path = os.path.join(upload_dir, "metadata.json")
+    if not os.path.isfile(metadata_path):
+        raise HTTPException(status_code=404, detail="上传任务不存在或已过期")
+    with open(metadata_path, "r", encoding="utf-8") as stream:
+        return upload_dir, json.load(stream)
+
+
+def _create_knowledge_doc(db: Session, avatar_id: str, filename: str, ext: str, file_size: int, stored: str):
+    doc = KnowledgeDoc(
+        id=uuid.uuid4().hex,
+        avatar_id=avatar_id,
+        filename=filename,
+        file_type=ext.lstrip("."),
+        file_size=file_size,
+        file_url=f"/api/files/{avatar_id}/{stored}",
+        status="parsing",
+        index_stage="queued",
+        index_progress=0,
+    )
+    db.add(doc)
+    db.commit()
+    db.refresh(doc)
+    knowledge_vectorizer.enqueue(doc.id)
+    return doc
+
+
 def _doc_payload(doc: KnowledgeDoc) -> dict:
     payload = doc.to_dict()
     stored_name = os.path.basename(doc.file_url or "")
@@ -71,84 +142,178 @@ def list_docs(avatar_id: str, authorization: str = Header(None), db: Session = D
         .order_by(KnowledgeDoc.created_at.desc())
         .all()
     )
-    # Older synchronous uploads could be interrupted after persisting "parsing".
-    # New uploads are committed only after indexing finishes, so these rows are stale.
-    stale_docs = [doc for doc in docs if doc.status == "parsing"]
-    if stale_docs:
-        for doc in stale_docs:
-            doc.status = "failed"
-            doc.vectorized = False
-            doc.chunk_count = 0
-        db.commit()
     return ok([_doc_payload(d) for d in docs])
 
 
 @router.post("/avatar/{avatar_id}/knowledge/docs")
 async def upload_doc(avatar_id: str, file: UploadFile = File(...), authorization: str = Header(None), db: Session = Depends(get_db)):
     _require_owned_avatar(db, avatar_id, authorization)
-    ext = os.path.splitext(file.filename or "")[1].lower()
+    ext, validation_error = _validate_document(file.filename or "", 1)
-    if ext not in ALLOWED_EXT:
+    if validation_error:
-        return fail(f"不支持的文件类型：{ext or '空'}，仅支持 md/txt/pdf/doc/docx/xlsx", code=400)
+        return fail(validation_error, code=400)
     avatar_dir = os.path.join(UPLOAD_DIR, avatar_id)
     os.makedirs(avatar_dir, exist_ok=True)
     stored = f"{uuid.uuid4().hex}{ext}"
     path = os.path.join(avatar_dir, stored)
-    content = await file.read()
+    file_size = 0
-    if len(content) > MAX_UPLOAD_BYTES:
-        return fail("文件不能超过 10MB", code=400)
-    with open(path, "wb") as f:
-        f.write(content)
-    doc = KnowledgeDoc(
-        id=uuid.uuid4().hex,
-        avatar_id=avatar_id,
-        filename=file.filename,
-        file_type=ext.lstrip("."),
-        file_size=len(content),
-        file_url=f"/api/files/{avatar_id}/{stored}",
-        status="parsing",
-    )
-
-    # Complete extraction and embedding before the first database commit so a
-    # process restart cannot leave a permanent "parsing" row behind.
     try:
-        text = embeddings.extract_text(path, ext)
+        # Stream large files to disk so a 100MB upload does not occupy 100MB RAM.
-        chunks = embeddings.chunk_text(text)
+        with open(path, "wb") as f:
-        if not chunks:
+            while chunk := await file.read(UPLOAD_CHUNK_BYTES):
-            raise ValueError("文档没有可建立索引的文字内容")
+                file_size += len(chunk)
-        vectors = embeddings.embed(chunks)
+                if file_size > MAX_UPLOAD_BYTES:
-        if len(vectors) != len(chunks):
+                    raise ValueError("文件不能超过 50MB")
-            raise ValueError("向量服务返回数量与文档分段不一致")
+                f.write(chunk)
-        doc.vectorized = True
+    except ValueError as exc:
-        doc.embedding_model = embeddings.MODEL
+        if os.path.exists(path):
-        doc.chunk_count = len(chunks)
+            os.remove(path)
-        doc.vectorized_at = datetime.now(timezone.utc)
+        return fail(str(exc), code=400)
-        doc.status = "ready"
+    if file_size == 0:
-        db.add(doc)
+        if os.path.exists(path):
-        for i, (chunk, vector) in enumerate(zip(chunks, vectors)):
+            os.remove(path)
-            db.add(
+        return fail("文件内容不能为空", code=400)
-                KnowledgeChunk(
-                    doc_id=doc.id,
-                    avatar_id=avatar_id,
-                    content=chunk,
-                    vector=json.dumps(vector),
-                    chunk_index=i,
-                    embedding_model=embeddings.MODEL,
-                )
-            )
-        db.commit()
-        db.refresh(doc)
-    except Exception as exc:
-        db.rollback()
-        doc.status = "failed"
-        doc.vectorized = False
-        doc.embedding_model = ""
-        doc.chunk_count = 0
-        doc.vectorized_at = None
-        db.add(doc)
-        db.commit()
-        db.refresh(doc)
-        logger.exception("knowledge vectorization failed for %s: %s", doc.id, exc)
 
+    doc = _create_knowledge_doc(db, avatar_id, file.filename or stored, ext, file_size, stored)
+    return ok(_doc_payload(doc))
+
+
+@router.post("/avatar/{avatar_id}/knowledge/uploads")
+def create_multipart_upload(
+    avatar_id: str,
+    body: MultipartUploadIn,
+    authorization: str = Header(None),
+    db: Session = Depends(get_db),
+):
+    _require_owned_avatar(db, avatar_id, authorization)
+    ext, validation_error = _validate_document(body.filename, body.fileSize)
+    if validation_error:
+        return fail(validation_error, code=400)
+    expected_chunks = (body.fileSize + MULTIPART_CHUNK_BYTES - 1) // MULTIPART_CHUNK_BYTES
+    if body.totalChunks != expected_chunks:
+        return fail("文件分片数量不正确", code=400)
+
+    _purge_stale_multipart_uploads(avatar_id)
+    upload_id = uuid.uuid4().hex
+    upload_dir = _multipart_dir(avatar_id, upload_id)
+    os.makedirs(upload_dir, exist_ok=False)
+    metadata = {
+        "filename": body.filename,
+        "fileSize": body.fileSize,
+        "totalChunks": body.totalChunks,
+        "extension": ext,
+    }
+    with open(os.path.join(upload_dir, "metadata.json"), "w", encoding="utf-8") as stream:
+        json.dump(metadata, stream, ensure_ascii=False)
+    return ok({"uploadId": upload_id, "chunkSize": MULTIPART_CHUNK_BYTES})
+
+
+@router.post("/avatar/{avatar_id}/knowledge/uploads/{upload_id}/chunks/{chunk_index}")
+async def upload_multipart_chunk(
+    avatar_id: str,
+    upload_id: str,
+    chunk_index: int,
+    file: UploadFile = File(...),
+    authorization: str = Header(None),
+    db: Session = Depends(get_db),
+):
+    _require_owned_avatar(db, avatar_id, authorization)
+    upload_dir, metadata = _read_multipart_metadata(avatar_id, upload_id)
+    total_chunks = int(metadata["totalChunks"])
+    if chunk_index < 0 or chunk_index >= total_chunks:
+        return fail("文件分片序号不正确", code=400)
+
+    expected_size = min(
+        MULTIPART_CHUNK_BYTES,
+        int(metadata["fileSize"]) - chunk_index * MULTIPART_CHUNK_BYTES,
+    )
+    part_path = os.path.join(upload_dir, f"{chunk_index}.part")
+    temporary_path = f"{part_path}.uploading"
+    received = 0
+    try:
+        with open(temporary_path, "wb") as stream:
+            while chunk := await file.read(UPLOAD_CHUNK_BYTES):
+                received += len(chunk)
+                if received > expected_size:
+                    raise ValueError("文件分片大小不正确")
+                stream.write(chunk)
+        if received != expected_size:
+            raise ValueError("文件分片大小不正确")
+        os.replace(temporary_path, part_path)
+    except ValueError as exc:
+        if os.path.exists(temporary_path):
+            os.remove(temporary_path)
+        return fail(str(exc), code=400)
+    return ok({"chunkIndex": chunk_index, "uploadedBytes": received})
+
+
+@router.post("/avatar/{avatar_id}/knowledge/uploads/{upload_id}/complete")
+def complete_multipart_upload(
+    avatar_id: str,
+    upload_id: str,
+    authorization: str = Header(None),
+    db: Session = Depends(get_db),
+):
+    _require_owned_avatar(db, avatar_id, authorization)
+    upload_dir, metadata = _read_multipart_metadata(avatar_id, upload_id)
+    total_chunks = int(metadata["totalChunks"])
+    part_paths = [os.path.join(upload_dir, f"{index}.part") for index in range(total_chunks)]
+    if not all(os.path.isfile(path) for path in part_paths):
+        return fail("文件分片尚未上传完整", code=400)
+    if sum(os.path.getsize(path) for path in part_paths) != int(metadata["fileSize"]):
+        return fail("文件分片总大小不正确", code=400)
+
+    avatar_dir = os.path.join(UPLOAD_DIR, avatar_id)
+    os.makedirs(avatar_dir, exist_ok=True)
+    stored = f"{uuid.uuid4().hex}{metadata['extension']}"
+    final_path = os.path.join(avatar_dir, stored)
+    temporary_path = f"{final_path}.assembling"
+    try:
+        with open(temporary_path, "wb") as output:
+            for part_path in part_paths:
+                with open(part_path, "rb") as source:
+                    shutil.copyfileobj(source, output, UPLOAD_CHUNK_BYTES)
+        os.replace(temporary_path, final_path)
+        doc = _create_knowledge_doc(
+            db,
+            avatar_id,
+            metadata["filename"],
+            metadata["extension"],
+            int(metadata["fileSize"]),
+            stored,
+        )
+    except Exception:
+        if os.path.exists(temporary_path):
+            os.remove(temporary_path)
+        raise
+    shutil.rmtree(upload_dir, ignore_errors=True)
+    return ok(_doc_payload(doc))
+
+
+@router.post("/avatar/{avatar_id}/knowledge/docs/{doc_id}/retry")
+def retry_doc(avatar_id: str, doc_id: str, authorization: str = Header(None), db: Session = Depends(get_db)):
+    _require_owned_avatar(db, avatar_id, authorization)
+    doc = db.query(KnowledgeDoc).filter(
+        KnowledgeDoc.id == doc_id, KnowledgeDoc.avatar_id == avatar_id
+    ).first()
+    if not doc:
+        return fail("文档不存在", code=404)
+    if doc.vectorized and doc.status == "ready":
+        return ok(_doc_payload(doc))
+    stored_name = os.path.basename(doc.file_url or "")
+    if not stored_name or not os.path.isfile(os.path.join(UPLOAD_DIR, avatar_id, stored_name)):
+        return fail("原文件不可用，请重新上传", code=400)
+    db.query(KnowledgeChunk).filter(KnowledgeChunk.doc_id == doc.id).delete()
+    doc.status = "parsing"
+    doc.vectorized = False
+    doc.embedding_model = ""
+    doc.chunk_count = 0
+    doc.vectorized_at = None
+    doc.error_message = ""
+    doc.index_stage = "queued"
+    doc.index_progress = 0
+    db.commit()
+    db.refresh(doc)
+    knowledge_vectorizer.enqueue(doc.id)
     return ok(_doc_payload(doc))
 
 

digital-avatar-app/backend/services/knowledge_vectorizer.py

@@ -0,0 +1,143 @@
+"""Durable, serial knowledge-document indexing for the avatar knowledge base."""
+
+import json
+import logging
+import os
+import queue
+import threading
+from datetime import datetime, timezone
+
+from database import SessionLocal
+from models import KnowledgeChunk, KnowledgeDoc
+import embeddings
+
+logger = logging.getLogger(__name__)
+
+BACKEND_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+UPLOAD_DIR = os.path.abspath(
+    os.getenv("UPLOAD_DIR", os.path.join(BACKEND_DIR, "routers", "uploads"))
+)
+
+
+class KnowledgeVectorizer:
+    """Indexes one document at a time so slow providers cannot block uploads."""
+
+    def __init__(self):
+        self._queue: queue.Queue[str] = queue.Queue()
+        self._queued: set[str] = set()
+        self._lock = threading.Lock()
+        self._thread: threading.Thread | None = None
+
+    def start(self):
+        if self._thread and self._thread.is_alive():
+            return
+        self._thread = threading.Thread(
+            target=self._run, name="knowledge-vectorizer", daemon=True
+        )
+        self._thread.start()
+        db = SessionLocal()
+        try:
+            # A process restart must not abandon documents already accepted by upload.
+            for (doc_id,) in db.query(KnowledgeDoc.id).filter(KnowledgeDoc.status == "parsing"):
+                self.enqueue(doc_id)
+        finally:
+            db.close()
+
+    def enqueue(self, doc_id: str):
+        with self._lock:
+            if doc_id in self._queued:
+                return
+            self._queued.add(doc_id)
+        self._queue.put(doc_id)
+
+    def _run(self):
+        while True:
+            doc_id = self._queue.get()
+            try:
+                self.vectorize_document(doc_id)
+            except Exception:
+                logger.exception("Unexpected knowledge vectorizer failure for %s", doc_id)
+            finally:
+                with self._lock:
+                    self._queued.discard(doc_id)
+                self._queue.task_done()
+
+    def vectorize_document(self, doc_id: str):
+        db = SessionLocal()
+        try:
+            doc = db.get(KnowledgeDoc, doc_id)
+            if not doc or doc.status != "parsing":
+                return
+
+            stored_name = os.path.basename(doc.file_url or "")
+            path = os.path.join(UPLOAD_DIR, doc.avatar_id, stored_name)
+            if not stored_name or not os.path.isfile(path):
+                raise FileNotFoundError("原文件不可用，请重新上传")
+
+            self._set_progress(db, doc, "extracting", 8)
+            text = embeddings.extract_text(path, f".{doc.file_type}")
+            self._set_progress(db, doc, "chunking", 22)
+            chunks = embeddings.chunk_text(text)
+            if not chunks:
+                raise ValueError("文档没有可建立索引的文字内容")
+            self._set_progress(db, doc, "embedding", 30)
+
+            def embedding_progress(done: int, total: int):
+                percent = 30 + int((done / max(1, total)) * 65)
+                self._set_progress(db, doc, "embedding", min(percent, 95))
+
+            vectors = embeddings.embed(chunks, on_progress=embedding_progress)
+            if len(vectors) != len(chunks):
+                raise ValueError("向量服务返回数量与文档分段不一致")
+
+            # Commit the document and every chunk together. Chat only sees complete indexes.
+            db.query(KnowledgeChunk).filter(KnowledgeChunk.doc_id == doc.id).delete()
+            db.add_all(
+                [
+                    KnowledgeChunk(
+                        doc_id=doc.id,
+                        avatar_id=doc.avatar_id,
+                        content=chunk,
+                        vector=json.dumps(vector),
+                        chunk_index=index,
+                        embedding_model=embeddings.MODEL,
+                    )
+                    for index, (chunk, vector) in enumerate(zip(chunks, vectors))
+                ]
+            )
+            doc.vectorized = True
+            doc.embedding_model = embeddings.MODEL
+            doc.chunk_count = len(chunks)
+            doc.vectorized_at = datetime.now(timezone.utc)
+            doc.status = "ready"
+            doc.error_message = ""
+            doc.index_stage = "ready"
+            doc.index_progress = 100
+            db.commit()
+            logger.info("Knowledge document %s indexed with %s chunks", doc.id, len(chunks))
+        except Exception as exc:
+            db.rollback()
+            failed_doc = db.get(KnowledgeDoc, doc_id)
+            if failed_doc:
+                db.query(KnowledgeChunk).filter(KnowledgeChunk.doc_id == failed_doc.id).delete()
+                failed_doc.status = "failed"
+                failed_doc.vectorized = False
+                failed_doc.embedding_model = ""
+                failed_doc.chunk_count = 0
+                failed_doc.vectorized_at = None
+                failed_doc.error_message = str(exc)[:300] or "建立知识索引失败"
+                failed_doc.index_stage = "failed"
+                failed_doc.index_progress = 0
+                db.commit()
+            logger.exception("Knowledge vectorization failed for %s: %s", doc_id, exc)
+        finally:
+            db.close()
+
+    @staticmethod
+    def _set_progress(db, doc, stage: str, progress: int):
+        doc.index_stage = stage
+        doc.index_progress = progress
+        db.commit()
+
+
+knowledge_vectorizer = KnowledgeVectorizer()

digital-avatar-app/backend/services/takeover_service.py

@@ -50,8 +50,15 @@ BOXIM_IMAGE_MESSAGE_TYPE = 1
 BOXIM_IMAGE_PROMPT = "请看看这张图片。"
 BOXIM_IMAGE_UNAVAILABLE_REPLY = "这张图片我暂时没看清，麻烦重新发送一张清晰的原图。"
 IMAGE_CONTEXT_LOOKBACK_SECONDS = 1800
+IMAGE_REFERENCE_LOOKBACK_SECONDS = 172_800
 MAX_RECENT_IMAGE_CONTEXTS = 3
 
+_IMAGE_REFERENCE_PATTERN = re.compile(
+    r"(?:图片|图像|照片|截图|这张图|刚才.{0,8}图|病例|病历|检查单|检验单|化验单|报告|影像|"
+    r"\b(?:image|photo|picture|screenshot|scan|report)\b)",
+    re.IGNORECASE,
+)
+
 
 def _utcnow() -> datetime:
     return datetime.utcnow()
@@ -129,6 +136,10 @@ def _event_prompt(event: TakeoverMessage) -> str:
     return ""
 
 
+def _references_recent_image(value: str) -> bool:
+    return bool(_IMAGE_REFERENCE_PATTERN.search(value or ""))
+
+
 class TakeoverService:
     """Poll BOXIM, honor the owner grace period, then generate and send one reply."""
 
@@ -693,8 +704,14 @@ class TakeoverService:
         event: TakeoverMessage,
         current_source_ids: list[str],
     ) -> list[TakeoverMessage]:
-        """Recover a recent image that an older deployment recorded without a task."""
+        """Recover missed images, or reuse a referenced image from the last two days."""
-        threshold = event.send_time - timedelta(seconds=IMAGE_CONTEXT_LOOKBACK_SECONDS)
+        references_image = _references_recent_image(event.content)
+        lookback_seconds = (
+            IMAGE_REFERENCE_LOOKBACK_SECONDS
+            if references_image
+            else IMAGE_CONTEXT_LOOKBACK_SECONDS
+        )
+        threshold = event.send_time - timedelta(seconds=lookback_seconds)
         candidates = (
             db.query(TakeoverMessage)
             .filter(
@@ -714,7 +731,15 @@ class TakeoverService:
         if not candidates:
             return []
 
-        handled_ids = set(current_source_ids)
+        current_ids = set(current_source_ids)
+        if references_image:
+            return [
+                image
+                for image in reversed(candidates)
+                if image.boxim_message_id not in current_ids
+            ]
+
+        handled_ids = set(current_ids)
         task_sources = (
             db.query(TakeoverReplyTask.source_message_ids)
             .filter(

digital-avatar-app/backend/tests/test_chat_images.py

@@ -12,6 +12,7 @@ from main import app
 from models import ChatAttachment
 from routers.chat import (
     ChatIn,
+    _answer_denies_available_image,
     _attachment_contexts,
     _load_chat_attachments,
     _resolve_reply,
@@ -274,6 +275,47 @@ def test_image_context_keeps_standard_answer_authoritative():
     assert "标准答题对中的事实优先级高于图片资料" in system
 
 
+def test_ready_image_context_never_returns_whole_image_access_denial():
+    avatar = SimpleNamespace(
+        id="avatar-vision",
+        name="测试分身",
+        description="产品顾问",
+        config={},
+    )
+    model = Mock(return_value="抱歉，我无法查看或识别图片，请重新上传。")
+    result = _resolve_reply(
+        None,
+        avatar,
+        "请看看这张图片",
+        [],
+        qa_pairs=[],
+        search_fn=Mock(return_value=[]),
+        model_client=model,
+        image_contexts=[{
+            "id": "attachment",
+            "filename": "report.jpg",
+            "category": "medical_document",
+            "summary": "一份耳鼻喉科门诊记录",
+            "extractedText": "主诉：咽痛三天",
+            "structuredData": {"key_facts": ["主诉为咽痛三天"]},
+            "warning": "请核对原始资料",
+        }],
+    )
+
+    assert result["source"] == "vision"
+    assert "一份耳鼻喉科门诊记录" in result["answer"]
+    assert "主诉为咽痛三天" in result["answer"]
+    assert "无法查看" not in result["answer"]
+    system = model.call_args.kwargs["messages"][0]["content"]
+    assert "当前会话图片已经成功读取" in system
+    assert "禁止声称无法查看" in system
+
+
+def test_image_denial_detector_allows_uncertain_field_in_ready_image():
+    assert _answer_denies_available_image("我无法查看这张图片") is True
+    assert _answer_denies_available_image("图片中患者姓名无法辨认，主诉为咽痛三天。") is False
+
+
 def test_attachment_context_does_not_expose_internal_fields():
     row = SimpleNamespace(
         id="attachment",

digital-avatar-app/backend/tests/test_embeddings.py

@@ -49,6 +49,7 @@ class RemoteEmbeddingTests(unittest.TestCase):
         texts = [f"chunk-{index}" for index in range(14)]
         batch_sizes = []
         requested_urls = []
+        progress_updates = []
 
         def fake_urlopen(request, timeout):
             self.assertEqual(timeout, 30)
@@ -68,7 +69,10 @@ class RemoteEmbeddingTests(unittest.TestCase):
             "EMBEDDING_MODEL": "text-embedding-v4",
             "EMBEDDING_BATCH_SIZE": "10",
         }), patch("embeddings.urllib.request.urlopen", side_effect=fake_urlopen):
-            result = embeddings.embed(texts)
+            result = embeddings.embed(
+                texts,
+                on_progress=lambda completed, total: progress_updates.append((completed, total)),
+            )
 
         self.assertEqual(batch_sizes, [10, 4])
         self.assertEqual(requested_urls, [
@@ -76,6 +80,7 @@ class RemoteEmbeddingTests(unittest.TestCase):
             "https://embedding.example/v1/embeddings",
         ])
         self.assertEqual(result, [[float(index)] for index in range(14)])
+        self.assertEqual(progress_updates, [(10, 14), (14, 14)])
 
     def test_full_embedding_endpoint_is_not_modified(self):
         self.assertEqual(

digital-avatar-app/backend/tests/test_knowledge_storage.py

@@ -8,6 +8,7 @@ from database import SessionLocal
 from main import app
 from models import Avatar, KnowledgeChunk, KnowledgeDoc, QAPair
 from routers.knowledge import _doc_payload
+from services.knowledge_vectorizer import knowledge_vectorizer
 
 
 client = TestClient(app)
@@ -31,14 +32,14 @@ def test_doc_payload_reports_whether_the_persisted_file_exists(tmp_path: Path):
         assert _doc_payload(doc)["filePresent"] is True
 
 
-def test_upload_marks_vectorization_failure_instead_of_staying_processing(
+def test_upload_returns_before_background_vectorization(
     tmp_path: Path,
     authorization_context,
 ):
     context = authorization_context
     with (
         patch("routers.knowledge.UPLOAD_DIR", str(tmp_path)),
-        patch("routers.knowledge.embeddings.embed", side_effect=RuntimeError("provider unavailable")),
+        patch("routers.knowledge.knowledge_vectorizer.enqueue") as enqueue,
     ):
         response = client.post(
             f"/api/avatar/{context['avatar'].id}/knowledge/docs",
@@ -47,14 +48,15 @@ def test_upload_marks_vectorization_failure_instead_of_staying_processing(
         )
 
     payload = response.json()["data"]
-    assert payload["status"] == "failed"
+    assert payload["status"] == "parsing"
     assert payload["vectorized"] is False
     assert payload["chunkCount"] == 0
+    enqueue.assert_called_once_with(payload["id"])
 
     db = SessionLocal()
     try:
         stored = db.query(KnowledgeDoc).filter(KnowledgeDoc.id == payload["id"]).one()
-        assert stored.status == "failed"
+        assert stored.status == "parsing"
         assert db.query(KnowledgeChunk).filter(KnowledgeChunk.doc_id == stored.id).count() == 0
         db.delete(stored)
         db.commit()
@@ -62,14 +64,115 @@ def test_upload_marks_vectorization_failure_instead_of_staying_processing(
         db.close()
 
 
-def test_markdown_upload_commits_ready_document_and_chunks_together(
+def test_upload_rejects_oversize_file_before_queuing_indexing(
     tmp_path: Path,
     authorization_context,
 ):
     context = authorization_context
     with (
         patch("routers.knowledge.UPLOAD_DIR", str(tmp_path)),
-        patch("routers.knowledge.embeddings.embed", return_value=[[1.0, 0.0]]),
+        patch("routers.knowledge.MAX_UPLOAD_BYTES", 4),
+        patch("routers.knowledge.knowledge_vectorizer.enqueue") as enqueue,
+    ):
+        response = client.post(
+            f"/api/avatar/{context['avatar'].id}/knowledge/docs",
+            headers=context["owner_headers"],
+            files={"file": ("oversize.md", b"12345", "text/markdown")},
+        )
+
+    payload = response.json()
+    assert payload["code"] == 400
+    assert payload["message"] == "文件不能超过 50MB"
+    enqueue.assert_not_called()
+    assert not list((tmp_path / context["avatar"].id).glob("*"))
+
+
+def test_multipart_upload_reassembles_file_before_queuing_indexing(
+    tmp_path: Path,
+    authorization_context,
+):
+    context = authorization_context
+    avatar_id = context["avatar"].id
+    content = b"0123456789"
+    with (
+        patch("routers.knowledge.UPLOAD_DIR", str(tmp_path)),
+        patch("routers.knowledge.MULTIPART_CHUNK_BYTES", 4),
+        patch("routers.knowledge.knowledge_vectorizer.enqueue") as enqueue,
+    ):
+        created = client.post(
+            f"/api/avatar/{avatar_id}/knowledge/uploads",
+            headers=context["owner_headers"],
+            json={"filename": "large.pdf", "fileSize": len(content), "totalChunks": 3},
+        ).json()["data"]
+
+        for index, chunk in enumerate((content[:4], content[4:8], content[8:])):
+            response = client.post(
+                f"/api/avatar/{avatar_id}/knowledge/uploads/{created['uploadId']}/chunks/{index}",
+                headers=context["owner_headers"],
+                files={"file": (f"chunk-{index}", chunk, "application/octet-stream")},
+            )
+            assert response.json()["code"] == 200
+
+        completed = client.post(
+            f"/api/avatar/{avatar_id}/knowledge/uploads/{created['uploadId']}/complete",
+            headers=context["owner_headers"],
+        ).json()["data"]
+
+    assert completed["status"] == "parsing"
+    assert completed["fileSize"] == len(content)
+    enqueue.assert_called_once_with(completed["id"])
+    stored_path = tmp_path / avatar_id / Path(completed["fileUrl"]).name
+    assert stored_path.read_bytes() == content
+    assert not (tmp_path / ".multipart" / avatar_id / created["uploadId"]).exists()
+
+    db = SessionLocal()
+    try:
+        stored = db.query(KnowledgeDoc).filter(KnowledgeDoc.id == completed["id"]).one()
+        db.delete(stored)
+        db.commit()
+    finally:
+        db.close()
+
+
+def test_multipart_upload_rejects_incomplete_parts(
+    tmp_path: Path,
+    authorization_context,
+):
+    context = authorization_context
+    avatar_id = context["avatar"].id
+    with (
+        patch("routers.knowledge.UPLOAD_DIR", str(tmp_path)),
+        patch("routers.knowledge.MULTIPART_CHUNK_BYTES", 4),
+        patch("routers.knowledge.knowledge_vectorizer.enqueue") as enqueue,
+    ):
+        created = client.post(
+            f"/api/avatar/{avatar_id}/knowledge/uploads",
+            headers=context["owner_headers"],
+            json={"filename": "large.pdf", "fileSize": 6, "totalChunks": 2},
+        ).json()["data"]
+        client.post(
+            f"/api/avatar/{avatar_id}/knowledge/uploads/{created['uploadId']}/chunks/0",
+            headers=context["owner_headers"],
+            files={"file": ("chunk-0", b"0123", "application/octet-stream")},
+        )
+        response = client.post(
+            f"/api/avatar/{avatar_id}/knowledge/uploads/{created['uploadId']}/complete",
+            headers=context["owner_headers"],
+        )
+
+    assert response.json()["code"] == 400
+    assert response.json()["message"] == "文件分片尚未上传完整"
+    enqueue.assert_not_called()
+
+
+def test_background_vectorizer_commits_ready_document_and_chunks_together(
+    tmp_path: Path,
+    authorization_context,
+):
+    context = authorization_context
+    with (
+        patch("routers.knowledge.UPLOAD_DIR", str(tmp_path)),
+        patch("routers.knowledge.knowledge_vectorizer.enqueue"),
     ):
         response = client.post(
             f"/api/avatar/{context['avatar'].id}/knowledge/docs",
@@ -78,14 +181,21 @@ def test_markdown_upload_commits_ready_document_and_chunks_together(
         )
 
     payload = response.json()["data"]
-    assert payload["status"] == "ready"
+    assert payload["status"] == "parsing"
-    assert payload["vectorized"] is True
+    with (
-    assert payload["chunkCount"] == 1
+        patch("services.knowledge_vectorizer.UPLOAD_DIR", str(tmp_path)),
+        patch("services.knowledge_vectorizer.embeddings.embed", return_value=[[1.0, 0.0]]),
+    ):
+        knowledge_vectorizer.vectorize_document(payload["id"])
 
     db = SessionLocal()
     try:
         stored = db.query(KnowledgeDoc).filter(KnowledgeDoc.id == payload["id"]).one()
         assert stored.status == "ready"
+        assert stored.vectorized is True
+        assert stored.chunk_count == 1
+        assert stored.index_stage == "ready"
+        assert stored.index_progress == 100
         assert db.query(KnowledgeChunk).filter(KnowledgeChunk.doc_id == stored.id).count() == 1
         db.query(KnowledgeChunk).filter(KnowledgeChunk.doc_id == stored.id).delete()
         db.delete(stored)
@@ -94,6 +204,87 @@ def test_markdown_upload_commits_ready_document_and_chunks_together(
         db.close()
 
 
+def test_background_vectorizer_keeps_failure_reason_for_retry(
+    tmp_path: Path,
+    authorization_context,
+):
+    context = authorization_context
+    with (
+        patch("routers.knowledge.UPLOAD_DIR", str(tmp_path)),
+        patch("routers.knowledge.knowledge_vectorizer.enqueue"),
+    ):
+        response = client.post(
+            f"/api/avatar/{context['avatar'].id}/knowledge/docs",
+            headers=context["owner_headers"],
+            files={"file": ("knowledge.md", b"# Knowledge\n\nTest content", "text/markdown")},
+        )
+
+    payload = response.json()["data"]
+    with (
+        patch("services.knowledge_vectorizer.UPLOAD_DIR", str(tmp_path)),
+        patch("services.knowledge_vectorizer.embeddings.embed", side_effect=RuntimeError("provider unavailable")),
+    ):
+        knowledge_vectorizer.vectorize_document(payload["id"])
+
+    db = SessionLocal()
+    try:
+        stored = db.query(KnowledgeDoc).filter(KnowledgeDoc.id == payload["id"]).one()
+        assert stored.status == "failed"
+        assert stored.error_message == "provider unavailable"
+        assert db.query(KnowledgeChunk).filter(KnowledgeChunk.doc_id == stored.id).count() == 0
+        db.delete(stored)
+        db.commit()
+    finally:
+        db.close()
+
+
+def test_retry_queues_a_failed_document_again(
+    tmp_path: Path,
+    authorization_context,
+):
+    context = authorization_context
+    document_id = f"retry-doc-{context['suffix']}"
+    avatar_dir = tmp_path / context["avatar"].id
+    avatar_dir.mkdir()
+    (avatar_dir / "retry.md").write_text("retry content", encoding="utf-8")
+    db = SessionLocal()
+    try:
+        db.add(
+            KnowledgeDoc(
+                id=document_id,
+                avatar_id=context["avatar"].id,
+                filename="retry.md",
+                file_type="md",
+                file_url=f"/api/files/{context['avatar'].id}/retry.md",
+                status="failed",
+                error_message="provider unavailable",
+            )
+        )
+        db.commit()
+    finally:
+        db.close()
+
+    with (
+        patch("routers.knowledge.UPLOAD_DIR", str(tmp_path)),
+        patch("routers.knowledge.knowledge_vectorizer.enqueue") as enqueue,
+    ):
+        response = client.post(
+            f"/api/avatar/{context['avatar'].id}/knowledge/docs/{document_id}/retry",
+            headers=context["owner_headers"],
+        )
+
+    payload = response.json()["data"]
+    assert payload["status"] == "parsing"
+    assert payload["errorMessage"] == ""
+    enqueue.assert_called_once_with(document_id)
+    db = SessionLocal()
+    try:
+        db.query(KnowledgeDoc).filter(KnowledgeDoc.id == document_id).delete()
+        db.commit()
+    finally:
+        db.close()
+
+
 def test_each_avatar_has_an_independent_document_and_qa_scope(authorization_context):
     context = authorization_context
     first_avatar_id = context["avatar"].id

digital-avatar-app/backend/tests/test_takeover_service.py

@@ -363,6 +363,54 @@ async def test_followup_text_recovers_recent_image_recorded_without_task(service
         db.close()
 
 
+@pytest.mark.asyncio
+async def test_explicit_followup_reuses_handled_image_within_two_days(service_context):
+    session_factory, service, boxim, clock = service_context
+    await service.poll_and_process_messages()
+    image_message = {
+        "id": 116,
+        "localId": 116,
+        "sendId": 200,
+        "recvId": 100,
+        "sendTime": clock.millis(),
+        "type": 1,
+        "content": json.dumps({"originUrl": "https://cdn.example/handled-case.png"}),
+    }
+    boxim.messages.append(image_message)
+    await service.poll_messages()
+
+    db = session_factory()
+    try:
+        image_task = db.query(TakeoverReplyTask).filter_by(trigger_message_id="116").one()
+        image_task.status = "sent"
+        image_task.sent_at = clock.now()
+        db.commit()
+    finally:
+        db.close()
+
+    clock.advance(47 * 60 * 60)
+    boxim.messages.append(
+        {
+            "id": 117,
+            "localId": 117,
+            "sendId": 200,
+            "recvId": 100,
+            "sendTime": clock.millis(),
+            "type": 0,
+            "content": "重新看一下刚才那张病例图片",
+        }
+    )
+    await service.poll_messages()
+
+    db = session_factory()
+    try:
+        task = db.query(TakeoverReplyTask).filter_by(trigger_message_id="117").one()
+        assert task.source_message_ids == ["116", "117"]
+        assert task.prompt == "请看看这张图片。\n重新看一下刚才那张病例图片"
+    finally:
+        db.close()
+
+
 @pytest.mark.asyncio
 async def test_invalid_image_message_is_recorded_but_not_scheduled(service_context):
     session_factory, service, boxim, clock = service_context

digital-avatar-app/docs/H5_PRODUCTION_DEPLOYMENT.md

@@ -117,7 +117,7 @@ location /api/ {
     proxy_set_header X-Forwarded-Proto $scheme;
     proxy_buffering off;
     proxy_read_timeout 300s;
-    client_max_body_size 20m;
+    client_max_body_size 100m;
 }
 ```
 

digital-avatar-app/nginx.conf

@@ -23,6 +23,10 @@ http {
 
         root /usr/share/nginx/html;
         index index.html;
+        # Keep the application gateway aligned with the production edge gateway.
+        # Without this Nginx rejects ordinary PDF uploads with HTTP 413 before
+        # FastAPI can return its user-facing file-size validation message.
+        client_max_body_size 100m;
 
         # SPA 兜底（hash 路由下深链接也可正常加载）
         location / {

digital-avatar-app/src/api/index.ts

@@ -305,6 +305,9 @@ export interface KnowledgeDoc {
   vectorized?: boolean
   embeddingModel?: string
   chunkCount?: number
+  errorMessage?: string
+  indexStage?: string
+  indexProgress?: number
   createdAt: string
 }
 
@@ -330,12 +333,83 @@ export interface SearchResult {
 export const getKnowledgeDocs = (avatarId: string) =>
   request.get<KnowledgeDoc[]>(`/avatar/${avatarId}/knowledge/docs`)
 
-// 上传文档（支持 md/txt/pdf/doc/docx/xlsx）
+const KNOWLEDGE_UPLOAD_CHUNK_SIZE = 5 * 1024 * 1024
-export const uploadKnowledgeDoc = (avatarId: string, file: File) => {
+
+const uploadKnowledgeChunk = async (
+  avatarId: string,
+  uploadId: string,
+  chunkIndex: number,
+  chunk: Blob,
+  onProgress?: (loaded: number) => void
+) => {
+  const form = new FormData()
+  form.append('file', chunk, `chunk-${chunkIndex}`)
+  let reportedLoaded = 0
+  for (let attempt = 1; attempt <= 3; attempt += 1) {
+    try {
+      await request.post(
+        `/avatar/${avatarId}/knowledge/uploads/${uploadId}/chunks/${chunkIndex}`,
+        form,
+        {
+          headers: { 'Content-Type': 'multipart/form-data' },
+          timeout: 2 * 60 * 1000,
+          onUploadProgress: (event) => {
+            reportedLoaded = Math.max(reportedLoaded, Math.min(event.loaded, chunk.size))
+            onProgress?.(reportedLoaded)
+          }
+        }
+      )
+      return
+    } catch (error: any) {
+      const status = Number(error?.response?.status || 0)
+      const retryable = !status || status === 408 || status === 429 || status >= 500
+      if (!retryable || attempt === 3) throw error
+      await new Promise((resolve) => window.setTimeout(resolve, attempt * 800))
+    }
+  }
+}
+
+// 大文件拆成 5MB 分片，避免生产代理的请求体限制拦截整个文件。
+export const uploadKnowledgeDoc = async (
+  avatarId: string,
+  file: File,
+  onUploadProgress?: (loaded: number, total: number) => void
+) => {
+  if (file.size > KNOWLEDGE_UPLOAD_CHUNK_SIZE) {
+    const totalChunks = Math.ceil(file.size / KNOWLEDGE_UPLOAD_CHUNK_SIZE)
+    const upload: any = await request.post(`/avatar/${avatarId}/knowledge/uploads`, {
+      filename: file.name,
+      fileSize: file.size,
+      totalChunks
+    })
+    let uploadedBytes = 0
+    for (let index = 0; index < totalChunks; index += 1) {
+      const start = index * KNOWLEDGE_UPLOAD_CHUNK_SIZE
+      const chunk = file.slice(start, Math.min(start + KNOWLEDGE_UPLOAD_CHUNK_SIZE, file.size))
+      await uploadKnowledgeChunk(
+        avatarId,
+        upload.uploadId,
+        index,
+        chunk,
+        (chunkLoaded) => onUploadProgress?.(uploadedBytes + chunkLoaded, file.size)
+      )
+      uploadedBytes += chunk.size
+      onUploadProgress?.(uploadedBytes, file.size)
+    }
+    return request.post<KnowledgeDoc>(
+      `/avatar/${avatarId}/knowledge/uploads/${upload.uploadId}/complete`,
+      undefined,
+      { timeout: 2 * 60 * 1000 }
+    )
+  }
+
   const form = new FormData()
   form.append('file', file)
   return request.post<KnowledgeDoc>(`/avatar/${avatarId}/knowledge/docs`, form, {
-    headers: { 'Content-Type': 'multipart/form-data' }
+    headers: { 'Content-Type': 'multipart/form-data' },
+    // A slow mobile uplink must not be mistaken for a failed upload.
+    timeout: 10 * 60 * 1000,
+    onUploadProgress: (event) => onUploadProgress?.(event.loaded, event.total || file.size)
   })
 }
 
@@ -343,6 +417,9 @@ export const uploadKnowledgeDoc = (avatarId: string, file: File) => {
 export const deleteKnowledgeDoc = (avatarId: string, docId: string) =>
   request.delete(`/avatar/${avatarId}/knowledge/docs/${docId}`)
 
+export const retryKnowledgeDoc = (avatarId: string, docId: string) =>
+  request.post<KnowledgeDoc>(`/avatar/${avatarId}/knowledge/docs/${docId}/retry`)
+
 // 标准问答对列表
 export const getQAPairs = (avatarId: string) =>
   request.get<QAPair[]>(`/avatar/${avatarId}/knowledge/qa`)

digital-avatar-app/src/views/AuthorizationManage.vue

@@ -192,7 +192,7 @@ const permissionItems: Array<{
   {
     key: 'interact',
     title: '广场互动操作',
-    description: '点赞、收藏、评论、回复等操作',
+    description: '允许分身代表你点赞、收藏、评论和回复；时段、间隔与触发概率由广场调度设置统一控制',
     tone: 'pink',
   },
   {

digital-avatar-app/src/views/KnowledgeManage.vue

@@ -15,7 +15,7 @@
 
     <template v-else>
       <div class="tab-switcher" role="tablist" aria-label="知识库类型">
-        <button class="tab-btn" :class="{ active: activeTab === 'docs' }" role="tab" :aria-selected="activeTab === 'docs'" @click="activeTab = 'docs'">文档知识库 <b>{{ docs.length }}</b></button>
+        <button class="tab-btn" :class="{ active: activeTab === 'docs' }" role="tab" :aria-selected="activeTab === 'docs'" @click="activeTab = 'docs'">文档知识库 <b>{{ displayDocs.length }}</b></button>
         <button class="tab-btn" :class="{ active: activeTab === 'qa' }" role="tab" :aria-selected="activeTab === 'qa'" @click="activeTab = 'qa'">标准问答对 <b>{{ qaPairs.length }}</b></button>
       </div>
 
@@ -25,14 +25,14 @@
             <div class="upload-icon">📥</div>
             <p class="upload-title"><span class="upload-link">点击上传</span></p>
             <p class="upload-hint">支持 MD / TXT / PDF / DOC / DOCX / XLSX，上传后自动向量化</p>
-            <input ref="fileInput" type="file" accept=".md,.txt,.pdf,.doc,.docx,.xlsx" class="hidden-input" @change="onFileChange" />
+            <input ref="fileInput" type="file" multiple accept=".md,.txt,.pdf,.doc,.docx,.xlsx" class="hidden-input" @change="onFileChange" />
           </div>
-          <p v-if="uploading" class="uploading-text">上传并向量化中…</p>
+          <p v-if="uploading" class="uploading-text">{{ pendingUploads.length }} 个文件正在上传</p>
           <p v-if="uploadError" class="error-text">{{ uploadError }}</p>
         </div>
 
-        <div v-if="docs.length" class="mobile-card-list">
+        <div v-if="displayDocs.length" class="mobile-card-list">
-          <article v-for="doc in docs" :key="doc.id" class="knowledge-card">
+          <article v-for="doc in displayDocs" :key="doc.id" class="knowledge-card document-card">
             <div class="card-icon">{{ fileEmoji(doc.fileType) }}</div>
             <div class="card-content">
               <div class="card-title-row">
@@ -41,8 +41,19 @@
               </div>
               <p class="card-meta">{{ doc.fileType.toUpperCase() }} · {{ formatSize(doc.fileSize) }} · {{ formatDate(doc.createdAt) }}</p>
               <p class="card-detail">{{ documentState(doc).detail }}</p>
+              <div v-if="documentState(doc).progress !== undefined" class="progress-track" :aria-label="`${documentState(doc).label} ${documentState(doc).progress}%`">
+                <span class="progress-fill" :style="{ width: `${documentState(doc).progress}%` }"></span>
+              </div>
+            </div>
+            <div class="card-actions">
+              <button v-if="!doc.localUploading" class="card-delete" @click="removeDoc(doc.id)">{{ doc.localOnly ? '移除' : '删除' }}</button>
+            </div>
+            <div v-if="canRetryDoc(doc)" class="card-retry-area">
+              <span v-if="retryErrors[doc.id]" class="card-retry-error">{{ retryErrors[doc.id] }}</span>
+              <button class="card-retry" :disabled="retryingDocs[doc.id]" @click="retryDoc(doc)">
+                {{ retryingDocs[doc.id] ? '重新索引中…' : '重新索引' }}
+              </button>
             </div>
-            <button class="card-delete" @click="removeDoc(doc.id)">删除</button>
           </article>
         </div>
         <div v-else class="card-empty">📂 暂无文档，先上传一个知识文件</div>
@@ -78,7 +89,7 @@
 </template>
 
 <script setup lang="ts">
-import { ref, onMounted, computed } from 'vue'
+import { ref, onMounted, onUnmounted, computed } from 'vue'
 import { useRoute, useRouter } from 'vue-router'
 import { useAvatarStore } from '@/store/avatar'
 import { pickScopedAvatarId, unwrapListData } from '@/utils/avatar-page-data.js'
@@ -87,6 +98,7 @@ import {
   getKnowledgeDocs,
   uploadKnowledgeDoc,
   deleteKnowledgeDoc,
+  retryKnowledgeDoc,
   getQAPairs,
   deleteQAPair,
   searchKnowledge,
@@ -102,18 +114,30 @@ const avatarId = computed(() => pickScopedAvatarId(route.params.avatarId, store.
 const activeTab = ref<'docs' | 'qa'>('docs')
 
 const docs = ref<any[]>([])
+const pendingUploads = ref<any[]>([])
 const qaPairs = ref<any[]>([])
-const uploading = ref(false)
+const uploading = computed(() => pendingUploads.value.some((doc) => doc.localUploading))
 const uploadError = ref('')
 const dragOver = ref(false)
 const fileInput = ref<HTMLInputElement | null>(null)
+const retryingDocs = ref<Record<string, boolean>>({})
+const retryErrors = ref<Record<string, string>>({})
+let documentPollingTimer: ReturnType<typeof setInterval> | undefined
 
 const query = ref('')
 const searching = ref(false)
 const searched = ref(false)
 const searchResults = ref<any[]>([])
 
+const displayDocs = computed(() => [...pendingUploads.value, ...docs.value])
+
 const documentState = (doc: any) => {
+  if (doc.localUploading) {
+    return { tone: 'pending', label: '上传中', detail: `正在上传 ${doc.uploadProgress || 0}%`, progress: doc.uploadProgress || 0 }
+  }
+  if (doc.localOnly) {
+    return { tone: 'failed', label: '上传失败', detail: doc.errorMessage || '文件未上传成功，请移除后重试' }
+  }
   if (doc.filePresent === false) {
     return { tone: 'missing', label: '文件缺失', detail: '原文件不可用，请删除后重新上传' }
   }
@@ -121,9 +145,33 @@ const documentState = (doc: any) => {
     return { tone: 'ready', label: '已入库', detail: `已切分 ${doc.chunkCount || 0} 段，可用于对话` }
   }
   if (['uploaded', 'parsing'].includes(String(doc.status || '').toLowerCase())) {
-    return { tone: 'pending', label: '处理中', detail: '正在解析并建立知识索引' }
+    const stage = String(doc.indexStage || 'queued').toLowerCase()
+    const labels: Record<string, string> = {
+      queued: '等待处理', extracting: '解析文档', chunking: '切分文本', embedding: '向量化中'
+    }
+    const progress = Math.max(0, Math.min(99, Number(doc.indexProgress || 0)))
+    return { tone: 'pending', label: labels[stage] || '处理中', detail: `${labels[stage] || '正在建立知识索引'} ${progress}%`, progress }
   }
-  return { tone: 'failed', label: '处理失败', detail: '未能建立知识索引，请删除后重新上传' }
+  return { tone: 'failed', label: '处理失败', detail: doc.errorMessage || '未能建立知识索引，请重新索引或重新上传' }
+}
+
+const hasPendingDocuments = () => docs.value.some((doc) =>
+  ['uploaded', 'parsing'].includes(String(doc.status || '').toLowerCase())
+)
+
+const stopDocumentPolling = () => {
+  if (documentPollingTimer) {
+    clearInterval(documentPollingTimer)
+    documentPollingTimer = undefined
+  }
+}
+
+const startDocumentPolling = () => {
+  if (documentPollingTimer || !hasPendingDocuments()) return
+  documentPollingTimer = setInterval(async () => {
+    await loadDocs()
+    if (!hasPendingDocuments()) stopDocumentPolling()
+  }, 2000)
 }
 
 const loadDocs = async () => {
@@ -131,6 +179,7 @@ const loadDocs = async () => {
   try {
     const res: any = await getKnowledgeDocs(avatarId.value)
     docs.value = unwrapListData(res)
+    startDocumentPolling()
   } catch (e) {
     console.error(e)
   }
@@ -149,40 +198,92 @@ const loadQA = async () => {
 const triggerFile = () => fileInput.value?.click()
 
 const onFileChange = (e: Event) => {
-  const f = (e.target as HTMLInputElement).files?.[0]
+  const files = Array.from((e.target as HTMLInputElement).files || [])
-  if (f) doUpload(f)
+  if (files.length) uploadFiles(files)
   ;(e.target as HTMLInputElement).value = ''
 }
 
 const onDrop = (e: DragEvent) => {
   dragOver.value = false
-  const f = e.dataTransfer?.files?.[0]
+  const files = Array.from(e.dataTransfer?.files || [])
-  if (f) doUpload(f)
+  if (files.length) uploadFiles(files)
 }
 
-const doUpload = async (file: File) => {
+const uploadFiles = (files: File[]) => {
   uploadError.value = ''
-  const ext = '.' + (file.name.split('.').pop() || '').toLowerCase()
-  if (!['.md', '.txt', '.pdf', '.doc', '.docx', '.xlsx'].includes(ext)) {
-    uploadError.value = `不支持的类型：${ext}，仅支持 md/txt/pdf/doc/docx/xlsx`
-    return
-  }
   if (!avatarId.value) {
     uploadError.value = '请先创建数字分身'
     return
   }
-  uploading.value = true
+  for (const file of files) {
+    const ext = '.' + (file.name.split('.').pop() || '').toLowerCase()
+    if (!['.md', '.txt', '.pdf', '.doc', '.docx', '.xlsx'].includes(ext)) {
+      uploadError.value = `不支持的类型：${ext}，仅支持 md/txt/pdf/doc/docx/xlsx`
+      continue
+    }
+    void uploadOne(file, ext)
+  }
+}
+
+const uploadOne = async (file: File, ext: string) => {
+  if (!avatarId.value) return
+  const localId = `upload-${Date.now()}-${Math.random().toString(16).slice(2)}`
+  const card = {
+    id: localId,
+    filename: file.name,
+    fileType: ext.slice(1),
+    fileSize: file.size,
+    createdAt: new Date().toISOString(),
+    localUploading: true,
+    localOnly: true,
+    uploadProgress: 0,
+    errorMessage: ''
+  }
+  pendingUploads.value.unshift(card)
   try {
-    await uploadKnowledgeDoc(avatarId.value, file)
+    const created: any = await uploadKnowledgeDoc(avatarId.value, file, (loaded, total) => {
-    await loadDocs()
+      const current = pendingUploads.value.find((doc) => doc.id === localId)
+      if (current) current.uploadProgress = Math.min(99, Math.round((loaded / Math.max(1, total)) * 100))
+    })
+    pendingUploads.value = pendingUploads.value.filter((doc) => doc.id !== localId)
+    docs.value = [created, ...docs.value.filter((doc) => doc.id !== created.id)]
+    startDocumentPolling()
   } catch (e: any) {
-    uploadError.value = e?.message || '上传失败'
+    const current = pendingUploads.value.find((doc) => doc.id === localId)
+    if (current) {
+      current.localUploading = false
+      current.errorMessage = e?.message || '上传失败'
+    }
+  }
+}
+
+const canRetryDoc = (doc: any) =>
+  !doc.localOnly && doc.filePresent !== false && documentState(doc).tone === 'failed'
+
+const retryDoc = async (doc: any) => {
+  if (!avatarId.value || !canRetryDoc(doc) || retryingDocs.value[doc.id]) return
+  retryingDocs.value = { ...retryingDocs.value, [doc.id]: true }
+  retryErrors.value = { ...retryErrors.value, [doc.id]: '' }
+  try {
+    const updated: any = await retryKnowledgeDoc(avatarId.value, doc.id)
+    Object.assign(doc, updated)
+    startDocumentPolling()
+  } catch (e: any) {
+    retryErrors.value = {
+      ...retryErrors.value,
+      [doc.id]: e?.response?.data?.message || e?.response?.data?.detail || e?.message || '重新索引失败'
+    }
   } finally {
-    uploading.value = false
+    retryingDocs.value = { ...retryingDocs.value, [doc.id]: false }
   }
 }
 
 const removeDoc = async (id: string) => {
+  const local = pendingUploads.value.find((doc) => doc.id === id)
+  if (local?.localOnly) {
+    pendingUploads.value = pendingUploads.value.filter((doc) => doc.id !== id)
+    return
+  }
   if (!avatarId.value) return
   await deleteKnowledgeDoc(avatarId.value, id)
   await loadDocs()
@@ -257,6 +358,8 @@ onMounted(async () => {
   if (avatarId.value) store.currentAvatarId = avatarId.value
   await Promise.all([loadDocs(), loadQA()])
 })
+
+onUnmounted(stopDocumentPolling)
 </script>
 
 <style scoped>
@@ -292,6 +395,7 @@ onMounted(async () => {
 .panel-heading p { margin: -5px 0 0; color: #9398AE; font-size: 12px; }
 .mobile-card-list { display: grid; grid-template-columns: minmax(0, 1fr); width: 100%; min-width: 0; gap: 10px; }
 .knowledge-card { display: flex; align-items: center; width: 100%; min-width: 0; box-sizing: border-box; gap: 11px; padding: 14px; background: #fff; border: 1px solid #F1E1D3; border-radius: 16px; box-shadow: 0 5px 16px rgba(112, 62, 22, .04); }
+.document-card { display: grid; grid-template-columns: 42px minmax(0, 1fr) auto; align-items: center; }
 .card-icon { flex: 0 0 auto; width: 42px; height: 42px; display: grid; place-items: center; border-radius: 13px; background: #FFF3E6; font-size: 22px; }
 .card-content { min-width: 0; flex: 1; overflow: hidden; }
 .card-title-row { display: flex; align-items: center; gap: 8px; min-width: 0; }
@@ -300,7 +404,15 @@ onMounted(async () => {
 .status-pill.missing { color: #B91C1C; background: #FEF2F2; }
 .status-pill.failed { color: #B91C1C; background: #FEF2F2; }
 .card-meta, .card-detail { margin: 5px 0 0; color: #9398AE; font-size: 11px; line-height: 1.4; }.card-detail { color: #8B6B58; }
-.card-delete { flex: 0 0 auto; align-self: center; border: 0; color: #EF4444; background: #FEF2F2; border-radius: 8px; padding: 7px 9px; font-size: 12px; cursor: pointer; }
+.progress-track { width: 100%; height: 4px; margin-top: 8px; overflow: hidden; border-radius: 999px; background: #FDE7D1; }
+.progress-fill { display: block; height: 100%; border-radius: inherit; background: linear-gradient(90deg, #FB923C, #F97316); transition: width .25s ease; }
+.card-actions { flex: 0 0 auto; display: flex; align-items: center; }
+.card-delete, .card-retry { align-self: center; border: 0; border-radius: 8px; padding: 7px 9px; font-size: 12px; cursor: pointer; white-space: nowrap; }
+.card-delete { color: #EF4444; background: #FEF2F2; }
+.card-retry { color: #C15F18; background: #FFF3E6; }
+.card-retry:disabled { cursor: wait; opacity: .65; }
+.card-retry-area { grid-column: 1 / -1; display: flex; align-items: center; justify-content: flex-end; gap: 10px; min-width: 0; }
+.card-retry-error { min-width: 0; overflow: hidden; color: #DC2626; font-size: 11px; line-height: 1.35; text-overflow: ellipsis; white-space: nowrap; }
 .card-empty { padding: 42px 16px; border: 1px dashed #F1D9C3; border-radius: 16px; color: #9398AE; background: #fff; font-size: 14px; text-align: center; }
 .qa-card { align-items: stretch; text-align: left; }.qa-card.qa-disabled { opacity: .58; }
 .qa-card .card-content,
@@ -526,8 +638,11 @@ onMounted(async () => {
 @media (max-width: 520px) {
   .knowledge-panel { padding: 0 12px; }
   .knowledge-card { display: grid; grid-template-columns: 42px minmax(0, 1fr); align-items: start; gap: 10px; padding: 13px; }
+  .document-card { grid-template-columns: 42px minmax(0, 1fr) auto; }
   .card-content { grid-column: 2; }
-  .card-delete { grid-column: 2; justify-self: end; margin-top: -2px; }
+  .card-actions { grid-column: 3; grid-row: 1; }
+  .card-delete { justify-self: end; margin-top: -2px; }
+  .card-retry-area { grid-column: 1 / -1; }
   .qa-card { display: block; }
   .qa-card .card-content { width: 100%; grid-column: 1; }
   .card-title-row { align-items: flex-start; flex-wrap: wrap; gap: 5px 7px; }