fix(avatar): upload knowledge files in chunks

Reviewed-on: #17

backend/app/api/endpoints/ai_models.py

@@ -37,6 +37,8 @@ async def create_model(req: AIModelCreateRequest, db=Depends(get_db)):
         api_base_url=req.api_base_url,
         api_key_enc=encrypt(req.api_key) if req.api_key else None,
         model_version=req.model_version,
+        vision_model_version=req.vision_model_version,
+        ocr_model_version=req.ocr_model_version,
         temperature=req.temperature,
         max_tokens=req.max_tokens,
         timeout_seconds=req.timeout_seconds,
@@ -100,6 +102,8 @@ async def get_digital_avatar_runtime_model(
         "api_base_url": model.api_base_url or "https://api.openai.com/v1",
         "api_key": decrypt(model.api_key_enc) if model.api_key_enc else "",
         "model": model.model_version or model.model_name,
+        "vision_model": model.vision_model_version or "qwen3.6-flash",
+        "ocr_model": model.ocr_model_version or "qwen-vl-ocr",
         "temperature": model.temperature,
         "max_tokens": model.max_tokens,
         "timeout_seconds": model.timeout_seconds,
@@ -129,6 +133,8 @@ def _format_model(m: AIModelConfig) -> dict:
         "usage_scope": m.usage_scope,
         "api_base_url": m.api_base_url, "has_api_key": bool(m.api_key_enc),
         "model_version": m.model_version, "temperature": m.temperature,
+        "vision_model_version": m.vision_model_version,
+        "ocr_model_version": m.ocr_model_version,
         "max_tokens": m.max_tokens, "timeout_seconds": m.timeout_seconds,
         "is_default": m.is_default, "is_enabled": m.is_enabled,
         "created_at": m.created_at.isoformat(),

backend/app/core/database.py

@@ -66,21 +66,36 @@ async def init_db():
             PendingReplyTask, TokenStat, AIModelConfig, SystemConfig, LoginLog
         )
         async with engine.begin() as conn:
-            await conn.execute(text("SELECT GET_LOCK('ai_model_usage_scope_migration', 30)"))
+            await conn.execute(text("SELECT GET_LOCK('ai_model_config_migration', 30)"))
             try:
-                result = await conn.execute(text(
-                    "SELECT COUNT(*) FROM information_schema.COLUMNS "
-                    "WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_model_configs' "
-                    "AND COLUMN_NAME = 'usage_scope'"
-                ))
-                if result.scalar_one() == 0:
-                    await conn.execute(text(
+                columns = (
+                    (
+                        "usage_scope",
                         "ALTER TABLE ai_model_configs ADD COLUMN usage_scope "
-                        "VARCHAR(16) NOT NULL DEFAULT 'general' AFTER provider"
-                    ))
-                    logger.info("AI模型配置表已增加 usage_scope 字段")
+                        "VARCHAR(16) NOT NULL DEFAULT 'general' AFTER provider",
+                    ),
+                    (
+                        "vision_model_version",
+                        "ALTER TABLE ai_model_configs ADD COLUMN vision_model_version "
+                        "VARCHAR(64) NULL AFTER model_version",
+                    ),
+                    (
+                        "ocr_model_version",
+                        "ALTER TABLE ai_model_configs ADD COLUMN ocr_model_version "
+                        "VARCHAR(64) NULL AFTER vision_model_version",
+                    ),
+                )
+                for column_name, ddl in columns:
+                    result = await conn.execute(text(
+                        "SELECT COUNT(*) FROM information_schema.COLUMNS "
+                        "WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_model_configs' "
+                        "AND COLUMN_NAME = :column_name"
+                    ), {"column_name": column_name})
+                    if result.scalar_one() == 0:
+                        await conn.execute(text(ddl))
+                        logger.info("AI模型配置表已增加 %s 字段", column_name)
             finally:
-                await conn.execute(text("SELECT RELEASE_LOCK('ai_model_usage_scope_migration')"))
+                await conn.execute(text("SELECT RELEASE_LOCK('ai_model_config_migration')"))
         logger.info("✅ 数据库模型注册成功")
         logger.info("✅ 数据库初始化完成")
 

backend/app/models/__init__.py

@@ -126,6 +126,8 @@ class AIModelConfig(Base):
     api_base_url: Mapped[str | None] = mapped_column(String(256))
     api_key_enc: Mapped[str | None] = mapped_column(String(512))
     model_version: Mapped[str | None] = mapped_column(String(64))
+    vision_model_version: Mapped[str | None] = mapped_column(String(64))
+    ocr_model_version: Mapped[str | None] = mapped_column(String(64))
     temperature: Mapped[float] = mapped_column(Float, default=0.7)
     max_tokens: Mapped[int] = mapped_column(Integer, default=1000)
     timeout_seconds: Mapped[int] = mapped_column(Integer, default=30)

backend/app/schemas/__init__.py

@@ -158,6 +158,8 @@ class AIModelCreateRequest(BaseModel):
     api_base_url: Optional[str] = None
     api_key: Optional[str] = None
     model_version: Optional[str] = None
+    vision_model_version: Optional[str] = Field(None, max_length=64)
+    ocr_model_version: Optional[str] = Field(None, max_length=64)
     temperature: float = Field(default=0.7, ge=0.0, le=2.0)
     max_tokens: int = Field(default=1000, ge=1, le=32000)
     timeout_seconds: int = Field(default=30, ge=5, le=300)
@@ -171,6 +173,8 @@ class AIModelUpdateRequest(BaseModel):
     api_base_url: Optional[str] = None
     api_key: Optional[str] = None
     model_version: Optional[str] = None
+    vision_model_version: Optional[str] = Field(None, max_length=64)
+    ocr_model_version: Optional[str] = Field(None, max_length=64)
     temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
     max_tokens: Optional[int] = Field(None, ge=1, le=32000)
     timeout_seconds: Optional[int] = Field(None, ge=5, le=300)
@@ -186,6 +190,8 @@ class AIModelResponse(BaseModel):
     api_base_url: Optional[str]
     has_api_key: bool
     model_version: Optional[str]
+    vision_model_version: Optional[str]
+    ocr_model_version: Optional[str]
     temperature: float
     max_tokens: int
     timeout_seconds: int

digital-avatar-app/backend/database.py

@@ -1,16 +1,30 @@
 import os
 
-from sqlalchemy import create_engine
+from sqlalchemy import create_engine, event
 from sqlalchemy.orm import sessionmaker, declarative_base, Session
 
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 DB_FILE = os.path.join(BASE_DIR, "avatar.db")
 DATABASE_URL = os.getenv("DATABASE_URL", f"sqlite:///{DB_FILE}")
 
+IS_SQLITE = DATABASE_URL.startswith("sqlite:")
 engine = create_engine(
     DATABASE_URL,
-    connect_args={"check_same_thread": False} if DATABASE_URL.startswith("sqlite:") else {},
+    connect_args={"check_same_thread": False, "timeout": 30} if IS_SQLITE else {},
 )
+
+
+if IS_SQLITE:
+    @event.listens_for(engine, "connect")
+    def _configure_sqlite_connection(dbapi_connection, _connection_record):
+        cursor = dbapi_connection.cursor()
+        try:
+            cursor.execute("PRAGMA synchronous=NORMAL")
+            cursor.execute("PRAGMA busy_timeout=30000")
+        finally:
+            cursor.close()
+
+
 SessionLocal = sessionmaker(bind=engine, autoflush=False, expire_on_commit=False)
 Base = declarative_base()
 
@@ -26,6 +40,10 @@ def get_db():
 def init_db():
     import models
 
+    if IS_SQLITE:
+        with engine.connect() as conn:
+            conn.exec_driver_sql("PRAGMA journal_mode=WAL")
+            conn.commit()
     Base.metadata.create_all(bind=engine)
 
     # 轻量迁移：为已存在的表补充新列（SQLite 不支持自动 ALTER，逐列尝试）
@@ -35,6 +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'"),
@@ -45,6 +66,7 @@ def init_db():
         ("token_account", "total_consumed", "BIGINT DEFAULT 0"),
         ("token_account", "created_at", "TIMESTAMP"),
         ("token_account", "updated_at", "TIMESTAMP"),
+        ("takeover_messages", "attachment_id", "VARCHAR DEFAULT NULL"),
     )
     _normalize_optional_unique_values()
     _normalize_takeover_delays()

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

@@ -19,11 +19,14 @@ import routers.huihui_auth
 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__)
 
 takeover_scheduler = None
+maintenance_scheduler = None
 
 app = FastAPI(title="会会数字分身 API", version="1.0.0")
 
@@ -129,9 +132,19 @@ def on_startup():
 
     init_db()
     seed()
+    knowledge_vectorizer.start()
 
     # Release stale resources when startup is invoked again by a reload/test.
     stop_takeover_scheduler()
+    stop_maintenance_scheduler()
+    try:
+        start_maintenance_scheduler()
+    except Exception as exc:
+        stop_maintenance_scheduler()
+        logger.warning(
+            "Failed to initialize chat attachment cleanup, app will continue: %s",
+            exc,
+        )
 
     # --- Takeover scheduler ---
     try:
@@ -152,7 +165,14 @@ def on_startup():
         boxim_client = BoxIMClient(boxim_config)
 
         from services.takeover_service import TakeoverService
-        takeover_service = TakeoverService(SessionLocal, boxim_client)
+        takeover_service = TakeoverService(
+            SessionLocal,
+            boxim_client,
+            poll_concurrency=int(os.getenv("BOXIM_POLL_CONCURRENCY", "8")),
+            max_message_age_seconds=int(
+                os.getenv("BOXIM_MAX_MESSAGE_AGE_SECONDS", "600")
+            ),
+        )
 
         poll_interval = max(0.5, float(os.getenv("BOXIM_POLL_INTERVAL_SECONDS", "1")))
         takeover_scheduler = AsyncIOScheduler()
@@ -196,6 +216,51 @@ def stop_takeover_scheduler():
         finally:
             takeover_scheduler = None
 
+
+def purge_expired_chat_attachments_job():
+    db = SessionLocal()
+    try:
+        count = purge_expired_chat_attachments(db)
+        if count:
+            logger.info("Purged %s expired chat image attachment(s)", count)
+    except Exception as exc:
+        db.rollback()
+        logger.warning("Failed to purge expired chat image attachments: %s", exc)
+    finally:
+        db.close()
+
+
+def start_maintenance_scheduler():
+    global maintenance_scheduler
+
+    purge_expired_chat_attachments_job()
+    interval_minutes = max(
+        5, min(1440, int(os.getenv("CHAT_ATTACHMENT_CLEANUP_MINUTES", "60")))
+    )
+    maintenance_scheduler = AsyncIOScheduler()
+    maintenance_scheduler.add_job(
+        purge_expired_chat_attachments_job,
+        trigger=IntervalTrigger(minutes=interval_minutes),
+        id="chat_attachment_cleanup",
+        max_instances=1,
+        coalesce=True,
+    )
+    maintenance_scheduler.start()
+
+
+def stop_maintenance_scheduler():
+    global maintenance_scheduler
+
+    if maintenance_scheduler is not None:
+        try:
+            if maintenance_scheduler.running:
+                maintenance_scheduler.shutdown(wait=False)
+        except Exception as exc:
+            logger.warning("Failed to stop maintenance scheduler cleanly: %s", exc)
+        finally:
+            maintenance_scheduler = None
+
 @app.on_event("shutdown")
 def on_shutdown():
     stop_takeover_scheduler()
+    stop_maintenance_scheduler()

digital-avatar-app/backend/models.py

@@ -120,13 +120,14 @@ class TakeoverMessage(Base):
     direction = Column(String, nullable=False)  # incoming | outgoing
     message_type = Column(Integer, default=0)
     content = Column(Text, default="")
+    attachment_id = Column(String, nullable=True)
     is_avatar = Column(Boolean, default=False)
     send_time = Column(DateTime, nullable=False)
     created_at = Column(DateTime, server_default=func.now())
 
 
 class TakeoverReplyTask(Base):
-    """Restart-safe three-second BOXIM reply task."""
+    """Restart-safe delayed BOXIM reply task."""
 
     __tablename__ = "takeover_reply_tasks"
     __table_args__ = (
@@ -189,6 +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
@@ -204,6 +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,
@@ -257,6 +264,44 @@ class KnowledgeChunk(Base):
         }
 
 
+class ChatAttachment(Base):
+    """Private, avatar-scoped result of one chat image analysis."""
+
+    __tablename__ = "chat_attachments"
+    id = Column(String, primary_key=True, default=lambda: uuid.uuid4().hex)
+    avatar_id = Column(String, nullable=False, default="", index=True)
+    uploader_kind = Column(String, default="owner")  # owner | public | boxim
+    filename = Column(String, default="")
+    mime_type = Column(String, default="")
+    file_size = Column(Integer, default=0)
+    status = Column(String, default="processing")  # processing | ready | failed
+    category = Column(String, default="general_image")
+    summary = Column(Text, default="")
+    extracted_text = Column(Text, default="")
+    structured_data = Column(JSON, default=dict)
+    warning = Column(Text, default="")
+    vision_model = Column(String, default="")
+    ocr_model = Column(String, default="")
+    used_at = Column(DateTime)
+    expires_at = Column(DateTime, nullable=False)
+    created_at = Column(DateTime, server_default=func.now())
+
+    def to_dict(self):
+        return {
+            "id": self.id,
+            "avatarId": self.avatar_id,
+            "filename": self.filename,
+            "mimeType": self.mime_type,
+            "fileSize": self.file_size,
+            "status": self.status,
+            "category": self.category,
+            "summary": self.summary,
+            "warning": self.warning,
+            "expiresAt": _iso(self.expires_at),
+            "createdAt": _iso(self.created_at),
+        }
+
+
 class TokenAccount(Base):
     __tablename__ = "token_account"
     id = Column(Integer, primary_key=True)

digital-avatar-app/backend/requirements.txt

@@ -8,3 +8,4 @@ pypdf
 python-docx
 openpyxl
 apscheduler>=3.10
+Pillow>=10.4

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

@@ -1,21 +1,32 @@
 import difflib
 import json
+import logging
 import os
 import re
 import secrets
 import string
+from datetime import datetime, timedelta
 from typing import Any, Callable
 
 import httpx
-from fastapi import APIRouter, Body, Depends, Header, HTTPException
+from fastapi import APIRouter, Body, Depends, File, Header, HTTPException, UploadFile
 from fastapi.responses import StreamingResponse
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 from sqlalchemy.orm import Session
 
 import embeddings
 from database import get_db
-from models import Avatar, KnowledgeChunk, KnowledgeDoc, QAPair, User
+from models import Avatar, ChatAttachment, KnowledgeChunk, KnowledgeDoc, QAPair, User
 from responses import ok, fail
+from services.vision_service import (
+    GENERAL_VISION_PROMPT,
+    MEDICAL_OCR_PROMPT,
+    ImageValidationError,
+    build_attachment_warning,
+    call_vision_model,
+    parse_vision_analysis,
+    prepare_image,
+)
 from services.token_billing import (
     InsufficientTokensError,
     estimate_fallback_usage,
@@ -24,8 +35,10 @@ from services.token_billing import (
     settle_reservation,
 )
 from services.chat_model_config import ChatModelConfig, get_chat_model_config
+from services.chat_attachment_service import purge_expired_chat_attachments
 
 router = APIRouter(tags=["数字分身聊天"])
+logger = logging.getLogger(__name__)
 
 MAX_MESSAGE_LENGTH = 4000
 MAX_HISTORY_MESSAGES = 10
@@ -34,6 +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]"),
@@ -49,14 +81,35 @@ _KOREAN_HANGUL = re.compile(r"[\uac00-\ud7af\u1100-\u11ff]")
 
 
 class ChatMessage(BaseModel):
+    model_config = ConfigDict(populate_by_name=True)
+
     role: str = Field(pattern="^(user|assistant)$")
     content: str = Field(min_length=1, max_length=MAX_MESSAGE_LENGTH)
+    attachment_ids: list[str] = Field(
+        default_factory=list,
+        alias="attachmentIds",
+        max_length=3,
+    )
 
 
 class ChatIn(BaseModel):
-    message: str = Field(min_length=1, max_length=MAX_MESSAGE_LENGTH)
+    model_config = ConfigDict(populate_by_name=True)
+
+    message: str = Field(default="", max_length=MAX_MESSAGE_LENGTH)
+    attachment_ids: list[str] = Field(
+        default_factory=list,
+        alias="attachmentIds",
+        max_length=3,
+    )
     history: list[ChatMessage] = Field(default_factory=list, max_length=MAX_HISTORY_MESSAGES)
 
+    @model_validator(mode="after")
+    def require_message_or_image(self):
+        self.message = self.message.strip()
+        if not self.message and not self.attachment_ids:
+            raise ValueError("请输入消息或选择图片")
+        return self
+
 
 def _resolve_user(authorization: str | None, db: Session):
     if not authorization:
@@ -77,6 +130,324 @@ def _require_owned_avatar(db: Session, avatar_id: str, authorization: str | None
     return avatar
 
 
+def _attachment_expiry() -> datetime:
+    retention_hours = max(
+        1, min(168, int(os.getenv("CHAT_ATTACHMENT_RETENTION_HOURS", "24")))
+    )
+    return datetime.utcnow() + timedelta(hours=retention_hours)
+
+
+def _chat_attachment_ids(body: ChatIn) -> list[str]:
+    values = list(body.attachment_ids)
+    for message in body.history[-MAX_HISTORY_MESSAGES:]:
+        values.extend(message.attachment_ids)
+    unique = list(dict.fromkeys(str(value).strip() for value in values if str(value).strip()))
+    if len(unique) > 3:
+        raise HTTPException(status_code=400, detail="一次会话最多引用 3 张图片")
+    return unique
+
+
+def _load_chat_attachments(db: Session, avatar_id: str, body: ChatIn) -> list[ChatAttachment]:
+    attachment_ids = _chat_attachment_ids(body)
+    if not attachment_ids:
+        return []
+    purge_expired_chat_attachments(db)
+    rows = db.query(ChatAttachment).filter(
+        ChatAttachment.avatar_id == avatar_id,
+        ChatAttachment.id.in_(attachment_ids),
+    ).all()
+    by_id = {row.id: row for row in rows}
+    if len(by_id) != len(attachment_ids):
+        raise HTTPException(status_code=400, detail="图片资料不存在、已过期或不属于当前分身")
+    ordered = [by_id[attachment_id] for attachment_id in attachment_ids]
+    if any(row.status != "ready" for row in ordered):
+        raise HTTPException(status_code=409, detail="图片尚未识别完成，请稍后重试")
+    now = datetime.utcnow()
+    for row in ordered:
+        row.used_at = now
+    db.commit()
+    return ordered
+
+
+def _attachment_contexts(rows: list[ChatAttachment]) -> list[dict]:
+    contexts = []
+    remaining_text = 12000
+    for row in rows:
+        extracted = (row.extracted_text or "")[:remaining_text]
+        remaining_text = max(0, remaining_text - len(extracted))
+        contexts.append({
+            "id": row.id,
+            "filename": row.filename,
+            "category": row.category,
+            "summary": row.summary,
+            "extractedText": extracted,
+            "structuredData": row.structured_data or {},
+            "warning": row.warning,
+        })
+    return contexts
+
+
+def _image_retrieval_question(question: str, image_contexts: list[dict]) -> str:
+    parts = [question.strip()]
+    for context in image_contexts:
+        parts.extend([
+            str(context.get("summary") or "")[:600],
+            str(context.get("extractedText") or "")[:1200],
+        ])
+    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,
+    prepared,
+    *,
+    model: str,
+    prompt: str,
+    source: str,
+    json_output: bool,
+    model_config: ChatModelConfig,
+) -> dict:
+    estimate_messages = [{
+        "role": "user",
+        "content": f"[一张待识别图片]\n{prompt}",
+    }]
+    reservation = reserve_avatar_tokens(
+        db,
+        avatar,
+        source,
+        model,
+        estimate_messages,
+        model_config.vision_max_tokens,
+        minimum_reserve_tokens=max(
+            1000, int(os.getenv("VISION_TOKEN_RESERVE", "12000"))
+        ),
+    )
+    try:
+        result = call_vision_model(
+            prepared,
+            model_config,
+            model=model,
+            prompt=prompt,
+            json_output=json_output,
+        )
+        settle_reservation(
+            db,
+            reservation,
+            result.get("usage"),
+            fallback_total=estimate_fallback_usage(
+                estimate_messages, result.get("content") or ""
+            ),
+        )
+        return result
+    except Exception as exc:
+        release_reservation(db, reservation, str(exc))
+        raise
+
+
+async def _analyze_uploaded_image(
+    db: Session,
+    avatar: Avatar,
+    file: UploadFile,
+    *,
+    uploader_kind: str,
+) -> ChatAttachment:
+    max_bytes = max(1024, int(os.getenv("CHAT_IMAGE_MAX_BYTES", str(8 * 1024 * 1024))))
+    content = await file.read(max_bytes + 1)
+    filename = os.path.basename(file.filename or "图片")[:255]
+    try:
+        return _analyze_image_bytes(
+            db,
+            avatar,
+            content,
+            filename=filename,
+            mime_type=file.content_type or "",
+            uploader_kind=uploader_kind,
+        )
+    except ImageValidationError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+    except InsufficientTokensError:
+        raise
+    except RuntimeError as exc:
+        raise HTTPException(status_code=502, detail=str(exc)) from exc
+    finally:
+        content = b""
+
+
+def _analyze_image_bytes(
+    db: Session,
+    avatar: Avatar,
+    content: bytes,
+    *,
+    filename: str,
+    mime_type: str,
+    uploader_kind: str,
+) -> ChatAttachment:
+    """Analyze image bytes from either HTTP upload or BOXIM without persisting raw data."""
+    attachment = ChatAttachment(
+        avatar_id=avatar.id,
+        uploader_kind=uploader_kind,
+        filename=filename,
+        mime_type=(mime_type or "")[:100],
+        file_size=len(content),
+        status="processing",
+        expires_at=_attachment_expiry(),
+    )
+    db.add(attachment)
+    db.commit()
+    db.refresh(attachment)
+
+    try:
+        prepared = prepare_image(content)
+        model_config = get_chat_model_config()
+        vision_result = _run_billed_vision_call(
+            db,
+            avatar,
+            prepared,
+            model=model_config.vision_model,
+            prompt=GENERAL_VISION_PROMPT,
+            source="vision_image",
+            json_output=True,
+            model_config=model_config,
+        )
+        analysis = parse_vision_analysis(vision_result["content"])
+        extracted_text = analysis.get("visible_text") or ""
+        ocr_model = ""
+        ocr_failed = False
+        if analysis["category"] == "medical_document" and model_config.ocr_model:
+            try:
+                ocr_result = _run_billed_vision_call(
+                    db,
+                    avatar,
+                    prepared,
+                    model=model_config.ocr_model,
+                    prompt=MEDICAL_OCR_PROMPT,
+                    source="vision_medical_ocr",
+                    json_output=False,
+                    model_config=model_config,
+                )
+                extracted_text = ocr_result["content"]
+                ocr_model = model_config.ocr_model
+            except (RuntimeError, InsufficientTokensError):
+                ocr_failed = True
+                logger.warning(
+                    "medical OCR degraded for attachment %s avatar %s",
+                    attachment.id,
+                    avatar.id,
+                )
+
+        attachment.mime_type = prepared.mime_type
+        attachment.status = "ready"
+        attachment.category = analysis["category"]
+        attachment.summary = analysis.get("summary") or "图片内容已识别"
+        attachment.extracted_text = extracted_text
+        attachment.structured_data = analysis
+        attachment.warning = build_attachment_warning(analysis, ocr_failed=ocr_failed)
+        attachment.vision_model = model_config.vision_model
+        attachment.ocr_model = ocr_model
+        db.commit()
+        db.refresh(attachment)
+        logger.info(
+            "chat image ready attachment=%s avatar=%s category=%s model=%s ocr=%s",
+            attachment.id,
+            avatar.id,
+            attachment.category,
+            attachment.vision_model,
+            bool(attachment.ocr_model),
+        )
+        return attachment
+    except ImageValidationError as exc:
+        attachment.status = "failed"
+        attachment.warning = str(exc)
+        db.commit()
+        raise
+    except InsufficientTokensError:
+        attachment.status = "failed"
+        attachment.warning = "积分余额不足"
+        db.commit()
+        raise
+    except RuntimeError as exc:
+        attachment.status = "failed"
+        attachment.warning = str(exc)
+        db.commit()
+        logger.warning(
+            "chat image failed attachment=%s avatar=%s error=%s",
+            attachment.id,
+            avatar.id,
+            type(exc).__name__,
+        )
+        raise
+
+
 def _normalize_question(value: str) -> str:
     value = (value or "").strip().lower()
     value = re.sub(r"\s+", "", value)
@@ -233,6 +604,7 @@ def _build_prompt(
     knowledge_hits: list[dict],
     *,
     standard_answer: str = "",
+    image_contexts: list[dict] | None = None,
 ) -> list[dict]:
     config = _config(avatar)
     description = (getattr(avatar, "description", "") or "").strip()
@@ -241,6 +613,7 @@ def _build_prompt(
         for hit in knowledge_hits
         if hit.get("snippet")
     )
+    image_contexts = image_contexts or []
     profile_items = [
         (label, config[key])
         for label, key in (
@@ -265,6 +638,26 @@ def _build_prompt(
         )
     if config["systemPrompt"]:
         system += f"\n额外系统提示词：{config['systemPrompt']}"
+    if image_contexts:
+        image_material = json.dumps(image_contexts, ensure_ascii=False, default=str)
+        system += (
+            "\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。"
+        )
+        if any(
+            context.get("category") in {"medical_document", "medical_image"}
+            for context in image_contexts
+        ):
+            system += (
+                "\n本次包含医疗资料。可以整理病例原文、解释指标含义和提示需要关注的异常，但不能仅凭图片作出"
+                "确定诊断、疾病分期、处方、停药或治疗决定。医学影像只能客观描述，并提醒结合正规报告和医生意见。"
+                "回答结尾用与用户相同的语言简短说明图片识别结果仅供辅助，不能替代医生诊断。"
+            )
     if standard_answer:
         system += (
             f"\n以下是本次问题命中的已确认标准答案：\n{standard_answer.strip()}"
@@ -277,6 +670,11 @@ def _build_prompt(
             "\n涉及事实、专业判断、地址、流程、数据或建议时，只能依据本人资料、标准问答形成的上下文"
             "和以上可靠资料作答，不要补充资料之外的通用知识或自行推测。"
         )
+    elif image_contexts:
+        system += (
+            "\n本次没有命中标准答题对或文件知识库，但已提供图片识别资料。只能围绕图片中的可确认内容、"
+            "本人资料和当前对话作答；不要补充图片之外的事实、专业判断或具体建议。"
+        )
     else:
         system += (
             "\n本次问题没有检索到可靠资料。除自然寒暄和基于本人资料的回答外，不要凭通用知识给出事实、"
@@ -439,14 +837,17 @@ def _resolve_reply(
     search_fn: Callable[..., list[dict]] | None = None,
     model_client: Callable[..., str] | None = None,
     usage_source: str = "chat",
+    image_contexts: list[dict] | None = None,
 ) -> dict:
+    image_contexts = image_contexts or []
+    question = question.strip() or "请根据这张图片说明可确认的内容。"
     if qa_pairs is None:
         qa_pairs = db.query(QAPair).filter(QAPair.avatar_id == avatar.id).all()
     matched = _match_standard_qa(question, qa_pairs)
     adapt_qa_language = bool(
         matched and _qa_requires_language_adaptation(question, matched.answer)
     )
-    if matched and not adapt_qa_language:
+    if matched and not adapt_qa_language and not image_contexts:
         return {"answer": matched.answer, "source": "qa", "references": []}
 
     if matched:
@@ -457,11 +858,19 @@ def _resolve_reply(
             question,
             hits,
             standard_answer=matched.answer,
+            image_contexts=image_contexts,
         )
     else:
         search_fn = search_fn or (lambda query, avatar_id: _search_knowledge(db, avatar_id, query))
-        hits = search_fn(question, avatar.id)
-        messages = _build_prompt(avatar, history, question, hits)
+        retrieval_question = _image_retrieval_question(question, image_contexts)
+        hits = search_fn(retrieval_question, avatar.id)
+        messages = _build_prompt(
+            avatar,
+            history,
+            question,
+            hits,
+            image_contexts=image_contexts,
+        )
     config = _config(avatar)
     temperature = 0.0 if matched else min(
         0.45 if hits else 0.25,
@@ -496,9 +905,19 @@ 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 ("knowledge" if hits else "qwen"),
+        "source": "qa" if matched else (
+            "knowledge" if hits else ("vision" if image_contexts else "qwen")
+        ),
         "references": hits,
     }
     if token_usage:
@@ -514,14 +933,17 @@ def _stream_reply(
     *,
     public: bool = False,
     usage_source: str = "chat_stream",
+    image_contexts: list[dict] | None = None,
 ):
+    image_contexts = image_contexts or []
+    question = question.strip() or "请根据这张图片说明可确认的内容。"
     qa_pairs = db.query(QAPair).filter(QAPair.avatar_id == avatar.id).all()
     matched = _match_standard_qa(question, qa_pairs)
     adapt_qa_language = bool(
         matched and _qa_requires_language_adaptation(question, matched.answer)
     )
     messages, reservation = [], None
-    if matched and not adapt_qa_language:
+    if matched and not adapt_qa_language and not image_contexts:
         source, references, chunks = "qa", [], _iter_text_chunks(matched.answer)
     else:
         if matched:
@@ -533,11 +955,21 @@ def _stream_reply(
                 question,
                 references,
                 standard_answer=matched.answer,
+                image_contexts=image_contexts,
             )
         else:
-            references = _search_knowledge(db, avatar.id, question)
-            source = "knowledge" if references else "qwen"
-            messages = _build_prompt(avatar, history, question, references)
+            retrieval_question = _image_retrieval_question(question, image_contexts)
+            references = _search_knowledge(db, avatar.id, retrieval_question)
+            source = "knowledge" if references else (
+                "vision" if image_contexts else "qwen"
+            )
+            messages = _build_prompt(
+                avatar,
+                history,
+                question,
+                references,
+                image_contexts=image_contexts,
+            )
         config = _config(avatar)
         temperature = 0.0 if matched else min(
             0.45 if references else 0.25,
@@ -641,11 +1073,62 @@ def get_shared_avatar(share_token: str, db: Session = Depends(get_db)):
     return ok(_public_avatar_payload(_require_shared_avatar(db, share_token)))
 
 
+@router.post("/avatar/{avatar_id}/chat/images")
+async def upload_chat_image(
+    avatar_id: str,
+    file: UploadFile = File(...),
+    authorization: str = Header(None),
+    db: Session = Depends(get_db),
+):
+    avatar = _require_owned_avatar(db, avatar_id, authorization)
+    purge_expired_chat_attachments(db)
+    try:
+        attachment = await _analyze_uploaded_image(
+            db,
+            avatar,
+            file,
+            uploader_kind="owner",
+        )
+        return ok(attachment.to_dict())
+    except InsufficientTokensError as exc:
+        raise HTTPException(status_code=402, detail=str(exc)) from exc
+
+
+@router.post("/public/avatar/{share_token}/chat/images")
+async def upload_public_chat_image(
+    share_token: str,
+    file: UploadFile = File(...),
+    db: Session = Depends(get_db),
+):
+    avatar = _require_shared_avatar(db, share_token)
+    purge_expired_chat_attachments(db)
+    try:
+        attachment = await _analyze_uploaded_image(
+            db,
+            avatar,
+            file,
+            uploader_kind="public",
+        )
+        return ok(attachment.to_dict())
+    except InsufficientTokensError as exc:
+        raise HTTPException(status_code=402, detail=str(exc)) from exc
+
+
 @router.post("/public/avatar/{share_token}/chat")
 def public_chat(share_token: str, body: ChatIn = Body(...), db: Session = Depends(get_db)):
     avatar = _require_shared_avatar(db, share_token)
+    image_contexts = _attachment_contexts(
+        _load_chat_attachments(db, avatar.id, body)
+    )
     try:
-        result = _resolve_reply(db, avatar, body.message, body.history, usage_source="public_chat")
+        result = _resolve_reply(
+            db,
+            avatar,
+            body.message,
+            body.history,
+            usage_source="public_chat",
+            image_contexts=image_contexts,
+        )
         # 公开访客无需获知知识文件名、检索分数或内部答复来源。
         result["references"] = []
         result["source"] = "public"
@@ -660,8 +1143,17 @@ def public_chat(share_token: str, body: ChatIn = Body(...), db: Session = Depend
 @router.post("/avatar/{avatar_id}/chat")
 def chat(avatar_id: str, body: ChatIn = Body(...), authorization: str = Header(None), db: Session = Depends(get_db)):
     avatar = _require_owned_avatar(db, avatar_id, authorization)
+    image_contexts = _attachment_contexts(
+        _load_chat_attachments(db, avatar.id, body)
+    )
     try:
-        return ok(_resolve_reply(db, avatar, body.message, body.history))
+        return ok(_resolve_reply(
+            db,
+            avatar,
+            body.message,
+            body.history,
+            image_contexts=image_contexts,
+        ))
     except InsufficientTokensError as exc:
         return fail(str(exc), code=402)
     except RuntimeError as exc:
@@ -671,7 +1163,17 @@ def chat(avatar_id: str, body: ChatIn = Body(...), authorization: str = Header(N
 @router.post("/avatar/{avatar_id}/chat/stream")
 def chat_stream(avatar_id: str, body: ChatIn = Body(...), authorization: str = Header(None), db: Session = Depends(get_db)):
     try:
-        return _stream_reply(db, _require_owned_avatar(db, avatar_id, authorization), body.message, body.history)
+        avatar = _require_owned_avatar(db, avatar_id, authorization)
+        image_contexts = _attachment_contexts(
+            _load_chat_attachments(db, avatar.id, body)
+        )
+        return _stream_reply(
+            db,
+            avatar,
+            body.message,
+            body.history,
+            image_contexts=image_contexts,
+        )
     except InsufficientTokensError as exc:
         raise HTTPException(status_code=402, detail=str(exc)) from exc
 
@@ -679,13 +1181,18 @@ def chat_stream(avatar_id: str, body: ChatIn = Body(...), authorization: str = H
 @router.post("/public/avatar/{share_token}/chat/stream")
 def public_chat_stream(share_token: str, body: ChatIn = Body(...), db: Session = Depends(get_db)):
     try:
+        avatar = _require_shared_avatar(db, share_token)
+        image_contexts = _attachment_contexts(
+            _load_chat_attachments(db, avatar.id, body)
+        )
         return _stream_reply(
             db,
-            _require_shared_avatar(db, share_token),
+            avatar,
             body.message,
             body.history,
             public=True,
             usage_source="public_chat_stream",
+            image_contexts=image_contexts,
         )
     except InsufficientTokensError as exc:
         raise HTTPException(status_code=402, detail=str(exc)) from exc

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()
-    if ext not in ALLOWED_EXT:
-        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)
+    ext, validation_error = _validate_document(file.filename or "", 1)
+    if validation_error:
+        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()
-    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.
+    file_size = 0
     try:
-        text = embeddings.extract_text(path, ext)
-        chunks = embeddings.chunk_text(text)
-        if not chunks:
-            raise ValueError("文档没有可建立索引的文字内容")
-        vectors = embeddings.embed(chunks)
-        if len(vectors) != len(chunks):
-            raise ValueError("向量服务返回数量与文档分段不一致")
-        doc.vectorized = True
-        doc.embedding_model = embeddings.MODEL
-        doc.chunk_count = len(chunks)
-        doc.vectorized_at = datetime.now(timezone.utc)
-        doc.status = "ready"
-        db.add(doc)
-        for i, (chunk, vector) in enumerate(zip(chunks, vectors)):
-            db.add(
-                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)
+        # Stream large files to disk so a 100MB upload does not occupy 100MB RAM.
+        with open(path, "wb") as f:
+            while chunk := await file.read(UPLOAD_CHUNK_BYTES):
+                file_size += len(chunk)
+                if file_size > MAX_UPLOAD_BYTES:
+                    raise ValueError("文件不能超过 50MB")
+                f.write(chunk)
+    except ValueError as exc:
+        if os.path.exists(path):
+            os.remove(path)
+        return fail(str(exc), code=400)
+    if file_size == 0:
+        if os.path.exists(path):
+            os.remove(path)
+        return fail("文件内容不能为空", code=400)
 
+    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/boxim_image_service.py

@@ -0,0 +1,151 @@
+"""Parse and safely download image payloads from BOXIM private messages."""
+
+import ipaddress
+import json
+import os
+import socket
+from dataclasses import dataclass
+from pathlib import PurePosixPath
+from urllib.parse import unquote, urljoin, urlsplit
+
+import httpx
+
+
+MAX_REDIRECTS = 3
+
+
+class BoxIMImageError(RuntimeError):
+    pass
+
+
+@dataclass(frozen=True)
+class DownloadedBoxIMImage:
+    content: bytes
+    filename: str
+    mime_type: str
+    source_url: str
+
+
+def parse_boxim_image_url(content: str, *, base_url: str = "") -> str:
+    try:
+        payload = json.loads(content or "")
+    except (TypeError, ValueError) as exc:
+        raise BoxIMImageError("BOXIM 图片消息格式无效") from exc
+    if not isinstance(payload, dict):
+        raise BoxIMImageError("BOXIM 图片消息格式无效")
+
+    value = payload.get("originUrl") or payload.get("thumbUrl") or payload.get("url")
+    if not isinstance(value, str) or not value.strip():
+        raise BoxIMImageError("BOXIM 图片消息缺少图片地址")
+    value = value.strip()
+    if value.startswith("/"):
+        if not base_url:
+            raise BoxIMImageError("BOXIM 图片地址不完整")
+        value = urljoin(f"{base_url.rstrip('/')}/", value)
+    return value
+
+
+def _configured_hosts(name: str) -> set[str]:
+    return {
+        value.strip().lower().rstrip(".")
+        for value in os.getenv(name, "").split(",")
+        if value.strip()
+    }
+
+
+def _host_matches(host: str, configured: set[str]) -> bool:
+    return any(host == value or host.endswith(f".{value}") for value in configured)
+
+
+def _resolved_addresses(host: str, port: int) -> set[ipaddress.IPv4Address | ipaddress.IPv6Address]:
+    try:
+        return {
+            ipaddress.ip_address(item[4][0])
+            for item in socket.getaddrinfo(host, port, type=socket.SOCK_STREAM)
+        }
+    except (OSError, ValueError) as exc:
+        raise BoxIMImageError("BOXIM 图片地址无法解析") from exc
+
+
+def _is_safe_remote_url(url: str) -> None:
+    parsed = urlsplit(url)
+    scheme = parsed.scheme.lower()
+    allow_http = os.getenv("BOXIM_IMAGE_ALLOW_HTTP", "").lower() in {"1", "true", "yes"}
+    if scheme not in ({"https", "http"} if allow_http else {"https"}):
+        raise BoxIMImageError("BOXIM 图片地址必须使用 HTTPS")
+    if parsed.username or parsed.password or not parsed.hostname:
+        raise BoxIMImageError("BOXIM 图片地址无效")
+
+    host = parsed.hostname.lower().rstrip(".")
+    allowed_hosts = _configured_hosts("BOXIM_IMAGE_ALLOWED_HOSTS")
+    if allowed_hosts and not _host_matches(host, allowed_hosts):
+        raise BoxIMImageError("BOXIM 图片地址不在允许的域名范围内")
+
+    private_hosts = _configured_hosts("BOXIM_IMAGE_PRIVATE_HOSTS")
+    try:
+        addresses = {ipaddress.ip_address(host)}
+    except ValueError:
+        addresses = _resolved_addresses(host, parsed.port or (443 if scheme == "https" else 80))
+    if not addresses:
+        raise BoxIMImageError("BOXIM 图片地址无法解析")
+    if _host_matches(host, private_hosts):
+        return
+    if any(not address.is_global for address in addresses):
+        raise BoxIMImageError("BOXIM 图片地址指向受限网络")
+
+
+def _filename_from_url(url: str) -> str:
+    value = unquote(PurePosixPath(urlsplit(url).path).name).strip()
+    value = value.replace("\x00", "")
+    return (value or "boxim-image")[:255]
+
+
+def download_boxim_image(
+    content: str,
+    *,
+    base_url: str = "",
+    transport: httpx.BaseTransport | None = None,
+) -> DownloadedBoxIMImage:
+    """Download one BOXIM image without redirects or oversized responses escaping checks."""
+    url = parse_boxim_image_url(content, base_url=base_url)
+    max_bytes = max(1024, int(os.getenv("CHAT_IMAGE_MAX_BYTES", str(8 * 1024 * 1024))))
+    timeout = max(1.0, min(float(os.getenv("BOXIM_IMAGE_TIMEOUT_SECONDS", "15")), 60.0))
+
+    with httpx.Client(
+        timeout=timeout,
+        follow_redirects=False,
+        trust_env=False,
+        transport=transport,
+    ) as client:
+        for _ in range(MAX_REDIRECTS + 1):
+            _is_safe_remote_url(url)
+            try:
+                with client.stream("GET", url, headers={"Accept": "image/*"}) as response:
+                    if response.status_code in {301, 302, 303, 307, 308}:
+                        location = response.headers.get("location", "").strip()
+                        if not location:
+                            raise BoxIMImageError("BOXIM 图片跳转地址无效")
+                        url = urljoin(url, location)
+                        continue
+                    response.raise_for_status()
+                    raw_length = response.headers.get("content-length", "")
+                    if raw_length.isdigit() and int(raw_length) > max_bytes:
+                        raise BoxIMImageError("BOXIM 图片超过大小限制")
+                    chunks = bytearray()
+                    for chunk in response.iter_bytes():
+                        chunks.extend(chunk)
+                        if len(chunks) > max_bytes:
+                            raise BoxIMImageError("BOXIM 图片超过大小限制")
+                    if not chunks:
+                        raise BoxIMImageError("BOXIM 图片内容为空")
+                    return DownloadedBoxIMImage(
+                        content=bytes(chunks),
+                        filename=_filename_from_url(url),
+                        mime_type=response.headers.get("content-type", "").split(";", 1)[0][:100],
+                        source_url=url,
+                    )
+            except BoxIMImageError:
+                raise
+            except (httpx.HTTPError, OSError) as exc:
+                raise BoxIMImageError("BOXIM 图片下载失败") from exc
+    raise BoxIMImageError("BOXIM 图片跳转次数过多")

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

@@ -0,0 +1,20 @@
+from datetime import datetime
+
+from sqlalchemy.orm import Session
+
+from models import ChatAttachment
+
+
+def purge_expired_chat_attachments(
+    db: Session,
+    *,
+    now: datetime | None = None,
+) -> int:
+    """Remove expired derived image data; raw image bytes are never persisted."""
+    count = db.query(ChatAttachment).filter(
+        ChatAttachment.expires_at < (now or datetime.utcnow())
+    ).delete(synchronize_session=False)
+    if count:
+        db.commit()
+        db.expire_all()
+    return count

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

@@ -16,6 +16,10 @@ class ChatModelConfig:
     model: str
     max_tokens: int
     timeout_seconds: float
+    vision_model: str
+    ocr_model: str
+    vision_max_tokens: int
+    vision_timeout_seconds: float
     source: str
 
 
@@ -33,6 +37,10 @@ def _environment_config() -> ChatModelConfig:
         model=os.getenv("CHAT_MODEL", "qwen-plus"),
         max_tokens=max(128, int(os.getenv("CHAT_MAX_OUTPUT_TOKENS", "1024"))),
         timeout_seconds=max(5.0, float(os.getenv("CHAT_TIMEOUT_SECONDS", "30"))),
+        vision_model=os.getenv("VISION_MODEL", "qwen3.6-flash"),
+        ocr_model=os.getenv("VISION_OCR_MODEL", "qwen-vl-ocr"),
+        vision_max_tokens=max(256, int(os.getenv("VISION_MAX_OUTPUT_TOKENS", "2048"))),
+        vision_timeout_seconds=max(10.0, float(os.getenv("VISION_TIMEOUT_SECONDS", "90"))),
         source="environment",
     )
 
@@ -60,6 +68,20 @@ def _fetch_runtime_config() -> ChatModelConfig | None:
         model=model,
         max_tokens=max(128, int(payload.get("max_tokens") or 1024)),
         timeout_seconds=max(5.0, float(payload.get("timeout_seconds") or 30)),
+        vision_model=str(
+            payload.get("vision_model")
+            or os.getenv("VISION_MODEL", "qwen3.6-flash")
+        ),
+        ocr_model=str(
+            payload.get("ocr_model")
+            or os.getenv("VISION_OCR_MODEL", "qwen-vl-ocr")
+        ),
+        vision_max_tokens=max(
+            256, int(os.getenv("VISION_MAX_OUTPUT_TOKENS", "2048"))
+        ),
+        vision_timeout_seconds=max(
+            10.0, float(os.getenv("VISION_TIMEOUT_SECONDS", "90"))
+        ),
         source="admin",
     )
 

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

@@ -3,6 +3,7 @@
 import asyncio
 import hashlib
 import logging
+import os
 import re
 import secrets
 import time
@@ -13,19 +14,27 @@ from sqlalchemy.orm import Session
 
 from models import (
     Avatar,
+    ChatAttachment,
     TakeoverCursor,
     TakeoverMessage,
     TakeoverReplyTask,
     User,
 )
 from services.boxim_client import BoxIMClient, BoxIMError
+from services.boxim_image_service import (
+    BoxIMImageError,
+    download_boxim_image,
+    parse_boxim_image_url,
+)
+from services.vision_service import ImageValidationError
 
 logger = logging.getLogger(__name__)
 
 ACTIVE_TASK_STATUSES = ("pending", "generating", "ready", "sending")
 GENERATABLE_TASK_STATUSES = ("pending",)
 MAX_PROMPT_LENGTH = 4000
-MAX_STALE_SECONDS = 120
+DEFAULT_MAX_MESSAGE_AGE_SECONDS = 600
+MAX_SEND_OVERDUE_SECONDS = 120
 STUCK_LOCK_SECONDS = 90
 TAKEOVER_PERMISSION = "takeover"
 TAKEOVER_DELAY_KEY = "takeoverReplyDelaySeconds"
@@ -36,6 +45,19 @@ HUMAN_PAUSE_SECONDS = 600
 RATE_LIMIT_WINDOW_SECONDS = 300
 RATE_LIMIT_MAX_REPLIES = 5
 AVATAR_LOCAL_ID_PREFIX = "880"
+BOXIM_TEXT_MESSAGE_TYPE = 0
+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:
@@ -106,6 +128,18 @@ def _configured_reply_delay(avatar: Avatar, fallback: int | None = None) -> int:
     return delay
 
 
+def _event_prompt(event: TakeoverMessage) -> str:
+    if event.message_type == BOXIM_TEXT_MESSAGE_TYPE:
+        return event.content.strip()
+    if event.message_type == BOXIM_IMAGE_MESSAGE_TYPE:
+        return BOXIM_IMAGE_PROMPT
+    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."""
 
@@ -115,15 +149,20 @@ class TakeoverService:
         boxim_client: BoxIMClient,
         *,
         reply_delay_seconds: int | None = None,
+        poll_concurrency: int = 8,
+        max_message_age_seconds: int = DEFAULT_MAX_MESSAGE_AGE_SECONDS,
         now: Callable[[], datetime] = _utcnow,
     ):
         self.session_factory = session_factory
         self.boxim = boxim_client
         self.reply_delay_seconds = reply_delay_seconds
+        self.poll_concurrency = max(1, min(int(poll_concurrency), 64))
+        self.max_message_age_seconds = max(60, int(max_message_age_seconds))
         self.now = now
         self._sessions: dict[str, dict] = {}
         self._poll_lock = asyncio.Lock()
         self._process_lock = asyncio.Lock()
+        self._persist_lock = asyncio.Lock()
 
     async def poll_and_process_messages(self):
         """Run one complete cycle for callers that do not use the split scheduler."""
@@ -138,8 +177,48 @@ class TakeoverService:
             self._recover_stuck_tasks()
             avatar_ids = self._enabled_avatar_ids()
             self._cancel_disabled_tasks(set(avatar_ids))
-            for avatar_id in avatar_ids:
-                await self._sync_avatar(avatar_id)
+            self._ensure_takeover_cursors(avatar_ids)
+            semaphore = asyncio.Semaphore(self.poll_concurrency)
+
+            async def sync(avatar_id: str):
+                async with semaphore:
+                    return await self._sync_avatar(avatar_id)
+
+            results = await asyncio.gather(
+                *(sync(avatar_id) for avatar_id in avatar_ids),
+                return_exceptions=True,
+            )
+            for avatar_id, result in zip(avatar_ids, results):
+                if isinstance(result, Exception):
+                    logger.warning("BOXIM poll crashed for avatar %s: %s", avatar_id, result)
+
+    def _ensure_takeover_cursors(self, avatar_ids: list[str]):
+        """Create durable cursors before concurrent network polling starts."""
+        if not avatar_ids:
+            return
+        db = self.session_factory()
+        try:
+            existing = {
+                row[0]
+                for row in db.query(TakeoverCursor.avatar_id)
+                .filter(TakeoverCursor.avatar_id.in_(avatar_ids))
+                .all()
+            }
+            avatars = (
+                db.query(Avatar.id, Avatar.owner_id)
+                .filter(
+                    Avatar.id.in_(
+                        [avatar_id for avatar_id in avatar_ids if avatar_id not in existing]
+                    )
+                )
+                .all()
+            )
+            for avatar_id, owner_id in avatars:
+                db.add(TakeoverCursor(avatar_id=avatar_id, owner_id=owner_id))
+            if avatars:
+                db.commit()
+        finally:
+            db.close()
 
     async def process_reply_tasks(self):
         """Generate and send replies independently from BOXIM's long poll."""
@@ -290,7 +369,10 @@ class TakeoverService:
             if not cursor:
                 cursor = TakeoverCursor(avatar_id=avatar.id, owner_id=avatar.owner_id)
                 db.add(cursor)
-                db.flush()
+                db.commit()
+            else:
+                # Release SQLite's read transaction before the long network poll.
+                db.commit()
             if not user or not user.huihui_token:
                 self._record_connection_failure(
                     db,
@@ -341,13 +423,6 @@ class TakeoverService:
             max_message_id = _numeric_id(cursor.last_message_id)
             read_receipts: dict[str, int] = {}
             for message in messages:
-                self._record_message(
-                    db,
-                    avatar,
-                    cursor.boxim_owner_id,
-                    message,
-                    schedule_reply=not priming,
-                )
                 message_id = _numeric_id(message.get("id"))
                 max_message_id = max(max_message_id, message_id)
                 send_id = str(message.get("sendId") or "")
@@ -362,11 +437,22 @@ class TakeoverService:
                     session["access_token"], peer_id, message_id
                 )
 
-            cursor.last_message_id = str(max_message_id)
-            cursor.initialized = True
-            cursor.last_polled_at = self.now()
-            cursor.last_error = ""
-            db.commit()
+            # Keep SQLite write transactions short. The read-receipt request above
+            # can block on the network and must not hold the database write lock.
+            async with self._persist_lock:
+                for message in messages:
+                    self._record_message(
+                        db,
+                        avatar,
+                        cursor.boxim_owner_id,
+                        message,
+                        schedule_reply=not priming,
+                    )
+                cursor.last_message_id = str(max_message_id)
+                cursor.initialized = True
+                cursor.last_polled_at = self.now()
+                cursor.last_error = ""
+                db.commit()
             return True
         except Exception:
             db.rollback()
@@ -450,18 +536,58 @@ class TakeoverService:
             if not is_avatar:
                 self._cancel_conversation(db, avatar.owner_id, peer_id, "owner_replied")
             return
-        if not schedule_reply or event.message_type != 0 or not event.content.strip():
+        if not schedule_reply or event.message_type not in {
+            BOXIM_TEXT_MESSAGE_TYPE,
+            BOXIM_IMAGE_MESSAGE_TYPE,
+        }:
             return
-        if (now - send_time).total_seconds() > MAX_STALE_SECONDS:
+        if event.message_type == BOXIM_TEXT_MESSAGE_TYPE and not event.content.strip():
+            return
+        if event.message_type == BOXIM_IMAGE_MESSAGE_TYPE:
+            try:
+                parse_boxim_image_url(
+                    event.content,
+                    base_url=getattr(self.boxim, "im_base_url", ""),
+                )
+            except BoxIMImageError as exc:
+                logger.warning(
+                    "Ignored invalid BOXIM image message %s for avatar %s: %s",
+                    message_id,
+                    avatar.id,
+                    exc,
+                )
+                return
+        if (now - send_time).total_seconds() > self.max_message_age_seconds:
+            logger.info(
+                "Ignored stale BOXIM message %s for avatar %s (age=%ss)",
+                message_id,
+                avatar.id,
+                int((now - send_time).total_seconds()),
+            )
             return
         if is_avatar:
             self._cancel_conversation(db, avatar.owner_id, peer_id, "peer_avatar_message")
+            logger.info(
+                "Skipped BOXIM reply for avatar %s message %s: peer_avatar_message",
+                avatar.id,
+                message_id,
+            )
             return
         if self._human_pause_active(db, avatar.owner_id, peer_id, now):
             self._cancel_conversation(db, avatar.owner_id, peer_id, "owner_active")
+            logger.info(
+                "Skipped BOXIM reply for avatar %s message %s: owner_active",
+                avatar.id,
+                message_id,
+            )
             return
         if self._conversation_rate_limited(db, avatar.owner_id, peer_id, now):
             self._cancel_conversation(db, avatar.owner_id, peer_id, "rate_limited")
+            logger.info(
+                "Skipped BOXIM reply for avatar %s message %s: rate_limited",
+                avatar.id,
+                message_id,
+            )
             return
         self._schedule_reply(db, avatar, event)
 
@@ -537,11 +663,22 @@ class TakeoverService:
                 task.status = "cancelled"
                 task.cancel_reason = "newer_incoming_message"
                 task.locked_at = None
-        prompt_parts.append(event.content.strip())
+        if event.message_type == BOXIM_TEXT_MESSAGE_TYPE:
+            for image_event in self._recent_unhandled_images(
+                db,
+                avatar,
+                event,
+                source_ids,
+            ):
+                prompt_parts.append(_event_prompt(image_event))
+                source_ids.append(image_event.boxim_message_id)
+        prompt_parts.append(_event_prompt(event))
         source_ids.append(event.boxim_message_id)
         prompt = "\n".join(part for part in prompt_parts if part).strip()[-MAX_PROMPT_LENGTH:]
-        due_at = event.send_time + timedelta(
-            seconds=_configured_reply_delay(avatar, self.reply_delay_seconds)
+        due_at = max(
+            event.send_time
+            + timedelta(seconds=_configured_reply_delay(avatar, self.reply_delay_seconds)),
+            self.now(),
         )
         task_id = secrets.token_hex(16)
         local_id = _avatar_local_id(avatar.owner_id, event.boxim_message_id)
@@ -560,6 +697,68 @@ class TakeoverService:
             )
         )
 
+    @staticmethod
+    def _recent_unhandled_images(
+        db: Session,
+        avatar: Avatar,
+        event: TakeoverMessage,
+        current_source_ids: list[str],
+    ) -> list[TakeoverMessage]:
+        """Recover missed images, or reuse a referenced image from the last two days."""
+        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(
+                TakeoverMessage.avatar_id == avatar.id,
+                TakeoverMessage.owner_id == avatar.owner_id,
+                TakeoverMessage.peer_id == event.peer_id,
+                TakeoverMessage.direction == "incoming",
+                TakeoverMessage.message_type == BOXIM_IMAGE_MESSAGE_TYPE,
+                TakeoverMessage.is_avatar.is_(False),
+                TakeoverMessage.send_time >= threshold,
+                TakeoverMessage.send_time <= event.send_time,
+            )
+            .order_by(TakeoverMessage.send_time.desc())
+            .limit(MAX_RECENT_IMAGE_CONTEXTS)
+            .all()
+        )
+        if not candidates:
+            return []
+
+        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(
+                TakeoverReplyTask.avatar_id == avatar.id,
+                TakeoverReplyTask.owner_id == avatar.owner_id,
+                TakeoverReplyTask.peer_id == event.peer_id,
+                TakeoverReplyTask.created_at >= threshold,
+            )
+            .all()
+        )
+        for (source_message_ids,) in task_sources:
+            handled_ids.update(source_message_ids or [])
+
+        return [
+            image
+            for image in reversed(candidates)
+            if image.boxim_message_id not in handled_ids
+        ]
+
     async def _prepare_replies(self) -> int:
         db = self.session_factory()
         try:
@@ -594,6 +793,50 @@ class TakeoverService:
         results = await asyncio.gather(*(generate(task_id) for task_id in task_ids))
         return sum(bool(result) for result in results)
 
+    def _takeover_image_attachment(
+        self,
+        db: Session,
+        avatar: Avatar,
+        event: TakeoverMessage,
+    ) -> ChatAttachment:
+        now = self.now()
+        if event.attachment_id:
+            cached = db.get(ChatAttachment, event.attachment_id)
+            if cached and cached.status == "ready" and cached.expires_at > now:
+                cached.used_at = now
+                db.commit()
+                return cached
+
+        downloaded = download_boxim_image(
+            event.content,
+            base_url=getattr(
+                self.boxim,
+                "im_base_url",
+                os.getenv("BOXIM_API_BASE_URL", "https://im.99hui.com/api"),
+            ),
+        )
+        from routers.chat import _analyze_image_bytes
+
+        attachment = _analyze_image_bytes(
+            db,
+            avatar,
+            downloaded.content,
+            filename=downloaded.filename,
+            mime_type=downloaded.mime_type,
+            uploader_kind="boxim",
+        )
+        event.attachment_id = attachment.id
+        attachment.used_at = now
+        db.commit()
+        logger.info(
+            "BOXIM image analyzed message=%s attachment=%s avatar=%s category=%s",
+            event.boxim_message_id,
+            attachment.id,
+            avatar.id,
+            attachment.category,
+        )
+        return attachment
+
     def _generate_reply(self, task_id: str) -> bool:
         db = self.session_factory()
         try:
@@ -612,6 +855,21 @@ class TakeoverService:
             db.commit()
 
             excluded_ids = set(task.source_message_ids or [])
+            source_events = {
+                event.boxim_message_id: event
+                for event in (
+                    db.query(TakeoverMessage)
+                    .filter(
+                        TakeoverMessage.owner_id == task.owner_id,
+                        TakeoverMessage.peer_id == task.peer_id,
+                        TakeoverMessage.avatar_id == task.avatar_id,
+                        TakeoverMessage.boxim_message_id.in_(excluded_ids),
+                    )
+                    .all()
+                    if excluded_ids
+                    else []
+                )
+            }
             events = (
                 db.query(TakeoverMessage)
                 .filter(
@@ -623,9 +881,31 @@ class TakeoverService:
                 .limit(30)
                 .all()
             )
+            image_attachments = []
+            image_failed = False
+            for message_id in (task.source_message_ids or [])[-3:]:
+                event = source_events.get(message_id)
+                if not event or event.message_type != BOXIM_IMAGE_MESSAGE_TYPE:
+                    continue
+                try:
+                    image_attachments.append(
+                        self._takeover_image_attachment(db, avatar, event)
+                    )
+                except (BoxIMImageError, ImageValidationError) as exc:
+                    image_failed = True
+                    logger.warning(
+                        "BOXIM image unavailable message=%s avatar=%s: %s",
+                        event.boxim_message_id,
+                        avatar.id,
+                        exc,
+                    )
             history = []
             for event in reversed(events):
-                if event.boxim_message_id in excluded_ids or not event.content.strip():
+                if (
+                    event.boxim_message_id in excluded_ids
+                    or event.message_type != BOXIM_TEXT_MESSAGE_TYPE
+                    or not event.content.strip()
+                ):
                     continue
                 if event.direction == "incoming" and event.is_avatar:
                     continue
@@ -637,10 +917,21 @@ class TakeoverService:
                 )
             history = history[-10:]
 
-            from routers.chat import _resolve_reply
+            from routers.chat import _attachment_contexts, _resolve_reply
 
-            result = _resolve_reply(db, avatar, task.prompt, history, usage_source="takeover")
-            answer = _plain_text_reply(result.get("answer", ""))
+            image_contexts = _attachment_contexts(image_attachments)
+            if image_failed and not image_contexts:
+                answer = BOXIM_IMAGE_UNAVAILABLE_REPLY
+            else:
+                result = _resolve_reply(
+                    db,
+                    avatar,
+                    task.prompt,
+                    history,
+                    usage_source="takeover",
+                    image_contexts=image_contexts,
+                )
+                answer = _plain_text_reply(result.get("answer", ""))
             db.refresh(task)
             if task.status != "generating":
                 return False
@@ -701,7 +992,7 @@ class TakeoverService:
                 task.cancel_reason = "takeover_disabled"
                 db.commit()
                 return False
-            if (self.now() - task.scheduled_at).total_seconds() > MAX_STALE_SECONDS:
+            if (self.now() - task.scheduled_at).total_seconds() > MAX_SEND_OVERDUE_SECONDS:
                 task.status = "cancelled"
                 task.cancel_reason = "stale_reply"
                 db.commit()

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

@@ -79,12 +79,17 @@ def reserve_avatar_tokens(
     model: str,
     messages: list[dict],
     max_output_tokens: int,
+    *,
+    minimum_reserve_tokens: int = 0,
 ) -> TokenReservation:
     user = avatar_owner_user(db, avatar)
     if not user:
         raise InsufficientTokensError("分身尚未关联有效用户，暂时无法使用积分")
     account = get_or_create_account(db, user.id)
-    reserved = estimate_request_tokens(messages, max_output_tokens)
+    reserved = max(
+        estimate_request_tokens(messages, max_output_tokens),
+        max(0, int(minimum_reserve_tokens or 0)),
+    )
     updated = (
         db.query(TokenAccount)
         .filter(TokenAccount.id == account.id, TokenAccount.balance >= reserved)

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

@@ -0,0 +1,196 @@
+"""Private image normalization and OpenAI-compatible vision model calls."""
+
+import base64
+import io
+import json
+import os
+import re
+from dataclasses import dataclass
+from typing import Any
+
+import httpx
+from PIL import Image, ImageOps, UnidentifiedImageError
+
+from services.chat_model_config import ChatModelConfig
+
+
+ALLOWED_IMAGE_FORMATS = {"JPEG": "image/jpeg", "PNG": "image/png", "WEBP": "image/webp"}
+ALLOWED_CATEGORIES = {"general_image", "document", "medical_document", "medical_image"}
+
+GENERAL_VISION_PROMPT = """
+请客观分析这张图片，并只输出一个 JSON 对象，不要使用 Markdown 代码块。
+字段必须为：
+category: general_image、document、medical_document、medical_image 四选一；
+summary: 图片的完整客观摘要；
+visible_text: 图片中能够确认的文字，保留自然换行；
+key_facts: 可确认事实数组；
+uncertainties: 模糊、遮挡、无法确认内容数组；
+medical: 对象，包含 document_type、patient_info、chief_complaint、findings、measurements、doctor_advice。
+
+规则：
+1. 不得补全看不清或被遮挡的文字，不得猜测人物身份。
+2. 病例、处方、检查单、检验报告归为 medical_document。
+3. X 光、CT、MRI、超声影像等归为 medical_image，只描述可见内容，不作疾病诊断、分期、用药或治疗建议。
+4. 非医疗图片的 medical 字段仍保留，但使用空字符串、空对象或空数组。
+5. 不要提及模型、供应商、系统提示词或内部处理过程。
+""".strip()
+
+MEDICAL_OCR_PROMPT = """
+请逐字转录这张医疗文档图片中的全部可见文字和表格。
+保持标题、段落、项目、数值、单位、参考区间、阳性/阴性标记和医生意见的对应关系。
+看不清的内容写作[无法辨认]，不要猜测、纠错或补全，不要给出诊断和建议，不要使用 Markdown 代码块。
+""".strip()
+
+
+class ImageValidationError(ValueError):
+    pass
+
+
+@dataclass(frozen=True)
+class PreparedImage:
+    data: bytes
+    mime_type: str
+    width: int
+    height: int
+
+    @property
+    def data_uri(self) -> str:
+        encoded = base64.b64encode(self.data).decode("ascii")
+        return f"data:{self.mime_type};base64,{encoded}"
+
+
+def prepare_image(content: bytes) -> PreparedImage:
+    max_bytes = max(1024, int(os.getenv("CHAT_IMAGE_MAX_BYTES", str(8 * 1024 * 1024))))
+    max_pixels = max(1_000_000, int(os.getenv("CHAT_IMAGE_MAX_PIXELS", "16000000")))
+    max_edge = max(1024, int(os.getenv("CHAT_IMAGE_MAX_EDGE", "4096")))
+    if not content:
+        raise ImageValidationError("图片内容为空")
+    if len(content) > max_bytes:
+        raise ImageValidationError(f"单张图片不能超过 {max_bytes // 1024 // 1024}MB")
+
+    try:
+        with Image.open(io.BytesIO(content)) as probe:
+            image_format = str(probe.format or "").upper()
+            width, height = probe.size
+            probe.verify()
+    except (UnidentifiedImageError, OSError, SyntaxError) as exc:
+        raise ImageValidationError("图片格式无效或文件已损坏") from exc
+
+    if image_format not in ALLOWED_IMAGE_FORMATS:
+        raise ImageValidationError("仅支持 JPG、PNG、WebP 图片")
+    if width <= 0 or height <= 0 or width * height > max_pixels:
+        raise ImageValidationError("图片像素过大，请压缩后重新上传")
+
+    try:
+        with Image.open(io.BytesIO(content)) as original:
+            image = ImageOps.exif_transpose(original)
+            image.load()
+            if max(image.size) > max_edge:
+                image.thumbnail((max_edge, max_edge), Image.Resampling.LANCZOS)
+            if image.mode in {"RGBA", "LA"}:
+                canvas = Image.new("RGB", image.size, "white")
+                alpha = image.getchannel("A")
+                canvas.paste(image.convert("RGB"), mask=alpha)
+                image = canvas
+            elif image.mode != "RGB":
+                image = image.convert("RGB")
+            output = io.BytesIO()
+            image.save(output, format="JPEG", quality=92, optimize=True)
+            normalized = output.getvalue()
+            normalized_width, normalized_height = image.size
+    except (OSError, ValueError) as exc:
+        raise ImageValidationError("图片解码失败，请重新选择图片") from exc
+
+    return PreparedImage(
+        data=normalized,
+        mime_type="image/jpeg",
+        width=normalized_width,
+        height=normalized_height,
+    )
+
+
+def call_vision_model(
+    prepared: PreparedImage,
+    model_config: ChatModelConfig,
+    *,
+    model: str,
+    prompt: str,
+    json_output: bool,
+) -> dict:
+    if not model_config.api_key:
+        raise RuntimeError("视觉模型服务未配置")
+    payload: dict[str, Any] = {
+        "model": model,
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image_url", "image_url": {"url": prepared.data_uri}},
+                    {"type": "text", "text": prompt},
+                ],
+            }
+        ],
+        "temperature": 0,
+        "max_tokens": model_config.vision_max_tokens,
+    }
+    if json_output:
+        payload["response_format"] = {"type": "json_object"}
+    try:
+        response = httpx.post(
+            f"{model_config.api_base_url}/chat/completions",
+            headers={"Authorization": f"Bearer {model_config.api_key}"},
+            json=payload,
+            timeout=model_config.vision_timeout_seconds,
+        )
+        response.raise_for_status()
+        data = response.json()
+        content = data.get("choices", [{}])[0].get("message", {}).get("content", "")
+    except (httpx.HTTPError, ValueError, KeyError, IndexError) as exc:
+        raise RuntimeError("图片识别服务暂时不可用") from exc
+    if not isinstance(content, str) or not content.strip():
+        raise RuntimeError("图片识别服务没有返回有效结果")
+    return {"content": content.strip(), "usage": data.get("usage") or {}}
+
+
+def parse_vision_analysis(content: str) -> dict:
+    value = (content or "").strip()
+    fenced = re.match(r"^```(?:json)?\s*(.*?)\s*```$", value, re.DOTALL | re.IGNORECASE)
+    if fenced:
+        value = fenced.group(1).strip()
+    try:
+        payload = json.loads(value)
+    except (TypeError, ValueError) as exc:
+        raise RuntimeError("图片识别结果格式无效") from exc
+    if not isinstance(payload, dict):
+        raise RuntimeError("图片识别结果格式无效")
+
+    category = str(payload.get("category") or "general_image").strip().lower()
+    if category not in ALLOWED_CATEGORIES:
+        category = "general_image"
+    medical = payload.get("medical") if isinstance(payload.get("medical"), dict) else {}
+    return {
+        "category": category,
+        "summary": str(payload.get("summary") or "").strip(),
+        "visible_text": str(payload.get("visible_text") or "").strip(),
+        "key_facts": _string_list(payload.get("key_facts")),
+        "uncertainties": _string_list(payload.get("uncertainties")),
+        "medical": medical,
+    }
+
+
+def build_attachment_warning(analysis: dict, *, ocr_failed: bool = False) -> str:
+    warnings = list(analysis.get("uncertainties") or [])
+    category = analysis.get("category")
+    if ocr_failed:
+        warnings.append("精确文字识别暂时不可用，请人工核对图片原文")
+    if category == "medical_document":
+        warnings.append("病例识别结果仅供辅助，不能替代医生诊断，请核对原始文档")
+    elif category == "medical_image":
+        warnings.append("医学影像仅作客观描述，不能替代影像报告和医生诊断")
+    return "；".join(dict.fromkeys(item for item in warnings if item))
+
+
+def _string_list(value: Any) -> list[str]:
+    if not isinstance(value, list):
+        return []
+    return [str(item).strip() for item in value if str(item).strip()]

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

@@ -5,6 +5,7 @@ from database import init_db, SessionLocal
 from models import (
     Authorization,
     Avatar,
+    ChatAttachment,
     TakeoverCursor,
     TakeoverMessage,
     TakeoverReplyTask,
@@ -95,6 +96,9 @@ def authorization_context():
     finally:
         db.rollback()
         avatar_ids = [avatar.id, other_avatar.id]
+        db.query(ChatAttachment).filter(
+            ChatAttachment.avatar_id.in_(avatar_ids)
+        ).delete(synchronize_session=False)
         db.query(TakeoverReplyTask).filter(
             TakeoverReplyTask.avatar_id.in_(avatar_ids)
         ).delete(synchronize_session=False)

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

@@ -0,0 +1,71 @@
+import ipaddress
+import json
+
+import httpx
+import pytest
+
+from services.boxim_image_service import (
+    BoxIMImageError,
+    download_boxim_image,
+    parse_boxim_image_url,
+)
+
+
+def test_parse_boxim_image_prefers_origin_and_supports_relative_url():
+    content = json.dumps({"originUrl": "/files/original.png", "thumbUrl": "/thumb.png"})
+    assert parse_boxim_image_url(content, base_url="https://im.example/api") == (
+        "https://im.example/files/original.png"
+    )
+
+
+def test_download_boxim_image_streams_public_https(monkeypatch):
+    monkeypatch.setattr(
+        "services.boxim_image_service._resolved_addresses",
+        lambda _host, _port: {ipaddress.ip_address("8.8.8.8")},
+    )
+    transport = httpx.MockTransport(
+        lambda request: httpx.Response(
+            200,
+            headers={"content-type": "image/png"},
+            content=b"png-bytes",
+            request=request,
+        )
+    )
+
+    image = download_boxim_image(
+        json.dumps({"originUrl": "https://cdn.example/case%20photo.png"}),
+        transport=transport,
+    )
+
+    assert image.content == b"png-bytes"
+    assert image.filename == "case photo.png"
+    assert image.mime_type == "image/png"
+
+
+def test_download_boxim_image_rejects_private_network_url():
+    with pytest.raises(BoxIMImageError, match="受限网络"):
+        download_boxim_image(
+            json.dumps({"originUrl": "https://127.0.0.1/private.png"}),
+            transport=httpx.MockTransport(lambda request: httpx.Response(200, request=request)),
+        )
+
+
+def test_download_boxim_image_stops_oversized_stream(monkeypatch):
+    monkeypatch.setenv("CHAT_IMAGE_MAX_BYTES", "1024")
+    monkeypatch.setattr(
+        "services.boxim_image_service._resolved_addresses",
+        lambda _host, _port: {ipaddress.ip_address("8.8.8.8")},
+    )
+    transport = httpx.MockTransport(
+        lambda request: httpx.Response(
+            200,
+            headers={"content-length": "2048"},
+            request=request,
+        )
+    )
+
+    with pytest.raises(BoxIMImageError, match="超过大小限制"):
+        download_boxim_image(
+            json.dumps({"originUrl": "https://cdn.example/large.png"}),
+            transport=transport,
+        )

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

@@ -0,0 +1,332 @@
+import json
+from datetime import datetime, timedelta
+from types import SimpleNamespace
+from unittest.mock import Mock, patch
+
+import pytest
+from fastapi import HTTPException
+from fastapi.testclient import TestClient
+
+from database import SessionLocal
+from main import app
+from models import ChatAttachment
+from routers.chat import (
+    ChatIn,
+    _answer_denies_available_image,
+    _attachment_contexts,
+    _load_chat_attachments,
+    _resolve_reply,
+)
+from services.chat_attachment_service import purge_expired_chat_attachments
+from services.token_billing import InsufficientTokensError
+from services.vision_service import PreparedImage
+
+
+client = TestClient(app)
+
+
+GENERAL_RESULT = {
+    "content": json.dumps({
+        "category": "general_image",
+        "summary": "一张包含产品路线图的截图",
+        "visible_text": "产品路线图",
+        "key_facts": ["包含三个阶段"],
+        "uncertainties": [],
+        "medical": {},
+    }, ensure_ascii=False),
+    "usage": {"total_tokens": 120},
+}
+
+
+def test_owner_can_upload_and_cache_image_analysis(authorization_context):
+    context = authorization_context
+    prepared = PreparedImage(b"jpeg", "image/jpeg", 100, 80)
+    with (
+        patch("routers.chat.prepare_image", return_value=prepared),
+        patch("routers.chat._run_billed_vision_call", return_value=GENERAL_RESULT),
+    ):
+        response = client.post(
+            f"/api/avatar/{context['avatar'].id}/chat/images",
+            headers=context["owner_headers"],
+            files={"file": ("roadmap.png", b"image-bytes", "image/png")},
+        )
+
+    assert response.status_code == 200
+    payload = response.json()["data"]
+    assert payload["status"] == "ready"
+    assert payload["category"] == "general_image"
+    assert payload["summary"] == "一张包含产品路线图的截图"
+    db = SessionLocal()
+    try:
+        stored = db.query(ChatAttachment).filter(ChatAttachment.id == payload["id"]).one()
+        assert stored.avatar_id == context["avatar"].id
+        assert stored.extracted_text == "产品路线图"
+        assert stored.structured_data["key_facts"] == ["包含三个阶段"]
+    finally:
+        db.close()
+
+
+def test_non_owner_cannot_upload_chat_image(authorization_context):
+    context = authorization_context
+    response = client.post(
+        f"/api/avatar/{context['avatar'].id}/chat/images",
+        headers=context["other_headers"],
+        files={"file": ("private.png", b"image-bytes", "image/png")},
+    )
+
+    assert response.status_code == 403
+
+
+def test_image_upload_preserves_insufficient_points_response(authorization_context):
+    context = authorization_context
+    with patch(
+        "routers.chat._analyze_image_bytes",
+        side_effect=InsufficientTokensError("积分余额不足"),
+    ):
+        response = client.post(
+            f"/api/avatar/{context['avatar'].id}/chat/images",
+            headers=context["owner_headers"],
+            files={"file": ("private.png", b"image-bytes", "image/png")},
+        )
+
+    assert response.status_code == 402
+    assert response.json()["detail"] == "积分余额不足"
+
+
+def test_public_share_can_upload_without_exposing_analysis_details(authorization_context):
+    context = authorization_context
+    db = SessionLocal()
+    try:
+        avatar = db.get(type(context["avatar"]), context["avatar"].id)
+        avatar.share_token = f"share-{context['suffix']}"
+        db.commit()
+        share_token = avatar.share_token
+    finally:
+        db.close()
+
+    with (
+        patch(
+            "routers.chat.prepare_image",
+            return_value=PreparedImage(b"jpeg", "image/jpeg", 100, 80),
+        ),
+        patch("routers.chat._run_billed_vision_call", return_value=GENERAL_RESULT),
+    ):
+        response = client.post(
+            f"/api/public/avatar/{share_token}/chat/images",
+            files={"file": ("visitor.png", b"image-bytes", "image/png")},
+        )
+
+    assert response.status_code == 200
+    payload = response.json()["data"]
+    assert payload["status"] == "ready"
+    assert "structuredData" not in payload
+    assert "extractedText" not in payload
+    assert "visionModel" not in payload
+    assert "ocrModel" not in payload
+
+    db = SessionLocal()
+    try:
+        stored = db.get(ChatAttachment, payload["id"])
+        assert stored.uploader_kind == "public"
+        assert stored.avatar_id == context["avatar"].id
+    finally:
+        db.close()
+
+
+def test_medical_document_uses_ocr_result(authorization_context):
+    context = authorization_context
+    general = {
+        "content": json.dumps({
+            "category": "medical_document",
+            "summary": "血常规报告",
+            "visible_text": "初步文字",
+            "key_facts": [],
+            "uncertainties": [],
+            "medical": {"document_type": "检验报告"},
+        }, ensure_ascii=False),
+        "usage": {},
+    }
+    ocr = {"content": "白细胞 11.2 x10^9/L", "usage": {}}
+    with (
+        patch("routers.chat.prepare_image", return_value=PreparedImage(b"jpeg", "image/jpeg", 100, 80)),
+        patch("routers.chat._run_billed_vision_call", side_effect=[general, ocr]) as model,
+    ):
+        response = client.post(
+            f"/api/avatar/{context['avatar'].id}/chat/images",
+            headers=context["owner_headers"],
+            files={"file": ("report.jpg", b"image-bytes", "image/jpeg")},
+        )
+
+    assert response.status_code == 200
+    attachment_id = response.json()["data"]["id"]
+    assert model.call_count == 2
+    assert model.call_args_list[1].kwargs["source"] == "vision_medical_ocr"
+    db = SessionLocal()
+    try:
+        stored = db.query(ChatAttachment).filter(ChatAttachment.id == attachment_id).one()
+        assert stored.extracted_text == "白细胞 11.2 x10^9/L"
+        assert stored.ocr_model == "qwen-vl-ocr"
+        assert "不能替代医生诊断" in stored.warning
+    finally:
+        db.close()
+
+
+def test_attachment_cannot_cross_avatar_boundary(authorization_context):
+    context = authorization_context
+    db = SessionLocal()
+    try:
+        attachment = ChatAttachment(
+            avatar_id=context["avatar"].id,
+            filename="private.jpg",
+            status="ready",
+            expires_at=datetime.utcnow() + timedelta(hours=1),
+        )
+        db.add(attachment)
+        db.commit()
+        body = ChatIn(message="看看图片", attachmentIds=[attachment.id])
+        with pytest.raises(HTTPException, match="不属于当前分身") as caught:
+            _load_chat_attachments(db, context["other_avatar"].id, body)
+        assert caught.value.status_code == 400
+    finally:
+        db.close()
+
+
+def test_expired_attachment_is_removed(authorization_context):
+    context = authorization_context
+    db = SessionLocal()
+    try:
+        attachment = ChatAttachment(
+            avatar_id=context["avatar"].id,
+            filename="expired.jpg",
+            status="ready",
+            expires_at=datetime.utcnow() - timedelta(seconds=1),
+        )
+        db.add(attachment)
+        db.commit()
+        attachment_id = attachment.id
+        body = ChatIn(message="看看图片", attachmentIds=[attachment_id])
+        with pytest.raises(HTTPException):
+            _load_chat_attachments(db, context["avatar"].id, body)
+        assert db.query(ChatAttachment).filter(ChatAttachment.id == attachment_id).first() is None
+    finally:
+        db.close()
+
+
+def test_cleanup_keeps_unexpired_attachment(authorization_context):
+    context = authorization_context
+    now = datetime.utcnow()
+    db = SessionLocal()
+    try:
+        expired = ChatAttachment(
+            avatar_id=context["avatar"].id,
+            filename="expired.jpg",
+            status="ready",
+            expires_at=now - timedelta(seconds=1),
+        )
+        active = ChatAttachment(
+            avatar_id=context["avatar"].id,
+            filename="active.jpg",
+            status="ready",
+            expires_at=now + timedelta(hours=1),
+        )
+        db.add_all([expired, active])
+        db.commit()
+        expired_id, active_id = expired.id, active.id
+
+        assert purge_expired_chat_attachments(db, now=now) == 1
+        assert db.get(ChatAttachment, expired_id) is None
+        assert db.get(ChatAttachment, active_id) is not None
+    finally:
+        db.close()
+
+
+def test_image_context_keeps_standard_answer_authoritative():
+    avatar = SimpleNamespace(
+        id="avatar-vision",
+        name="测试分身",
+        description="产品顾问",
+        config={},
+    )
+    model = Mock(return_value="标准退款期限是七天；图片显示的是商品包装。")
+    result = _resolve_reply(
+        None,
+        avatar,
+        "退款期限是多少？",
+        [],
+        qa_pairs=[SimpleNamespace(question="退款期限是多少？", answer="七天", enabled=True)],
+        search_fn=Mock(return_value=[]),
+        model_client=model,
+        image_contexts=[{
+            "id": "attachment",
+            "filename": "product.jpg",
+            "category": "general_image",
+            "summary": "商品包装",
+            "extractedText": "",
+            "structuredData": {},
+            "warning": "",
+        }],
+    )
+
+    assert result["source"] == "qa"
+    system = model.call_args.kwargs["messages"][0]["content"]
+    assert "已确认标准答案" in system
+    assert "七天" in system
+    assert "商品包装" in system
+    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",
+        filename="case.jpg",
+        category="medical_document",
+        summary="门诊病例",
+        extracted_text="主诉：咳嗽",
+        structured_data={"medical": {"chief_complaint": "咳嗽"}},
+        warning="请核对原文",
+    )
+    context = _attachment_contexts([row])[0]
+    assert context["filename"] == "case.jpg"
+    assert "avatar_id" not in context
+    assert "vision_model" not in context

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

@@ -26,6 +26,8 @@ def test_admin_runtime_config_takes_priority(monkeypatch):
             "api_base_url": "https://model.test/v1/",
             "api_key": "runtime-key",
             "model": "avatar-model",
+            "vision_model": "avatar-vision-model",
+            "ocr_model": "avatar-ocr-model",
             "max_tokens": 2048,
             "timeout_seconds": 42,
         }
@@ -37,6 +39,8 @@ def test_admin_runtime_config_takes_priority(monkeypatch):
     assert config.source == "admin"
     assert config.api_base_url == "https://model.test/v1"
     assert config.model == "avatar-model"
+    assert config.vision_model == "avatar-vision-model"
+    assert config.ocr_model == "avatar-ocr-model"
     assert config.max_tokens == 2048
     request.assert_called_once_with(
         "http://config.test/runtime",
@@ -51,6 +55,8 @@ def test_runtime_failure_falls_back_to_environment(monkeypatch):
     monkeypatch.setenv("CHAT_API_URL", "https://fallback.test/v1/")
     monkeypatch.setenv("CHAT_API_KEY", "fallback-key")
     monkeypatch.setenv("CHAT_MODEL", "fallback-model")
+    monkeypatch.setenv("VISION_MODEL", "fallback-vision")
+    monkeypatch.setenv("VISION_OCR_MODEL", "fallback-ocr")
     monkeypatch.setenv("CHAT_MAX_OUTPUT_TOKENS", "1536")
 
     request = httpx.Request("GET", "http://config.test/runtime")
@@ -64,6 +70,8 @@ def test_runtime_failure_falls_back_to_environment(monkeypatch):
     assert config.api_base_url == "https://fallback.test/v1"
     assert config.api_key == "fallback-key"
     assert config.model == "fallback-model"
+    assert config.vision_model == "fallback-vision"
+    assert config.ocr_model == "fallback-ocr"
     assert config.max_tokens == 1536
 
 

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["vectorized"] is True
-    assert payload["chunkCount"] == 1
+    assert payload["status"] == "parsing"
+    with (
+        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_scheduler.py

@@ -20,8 +20,9 @@ def test_scheduler_uses_boxim_and_restart_safe_service(
 ):
     import main
 
+    maintenance_scheduler = MagicMock()
     scheduler = MagicMock()
-    mock_scheduler_class.return_value = scheduler
+    mock_scheduler_class.side_effect = [maintenance_scheduler, scheduler]
     boxim = MagicMock()
     mock_boxim_class.return_value = boxim
     takeover = MagicMock()
@@ -45,7 +46,16 @@ def test_scheduler_uses_boxim_and_restart_safe_service(
     config = mock_boxim_class.call_args.args[0]
     assert config["HUIHUI_PLATFORM_BASE_URL"] == "https://open.example/api"
     assert config["BOXIM_API_BASE_URL"] == "https://im.example/api"
-    mock_takeover_class.assert_called_once_with(main.SessionLocal, boxim)
+    mock_takeover_class.assert_called_once_with(
+        main.SessionLocal,
+        boxim,
+        poll_concurrency=8,
+        max_message_age_seconds=600,
+    )
+
+    maintenance_scheduler.add_job.assert_called_once()
+    assert maintenance_scheduler.add_job.call_args.kwargs["id"] == "chat_attachment_cleanup"
+    maintenance_scheduler.start.assert_called_once_with()
 
     assert scheduler.add_job.call_count == 2
     poll_call, process_call = scheduler.add_job.call_args_list
@@ -62,6 +72,7 @@ def test_scheduler_uses_boxim_and_restart_safe_service(
     scheduler.start.assert_called_once_with()
 
     main.takeover_scheduler = None
+    main.maintenance_scheduler = None
 
 
 @patch("main.AsyncIOScheduler")
@@ -73,6 +84,7 @@ def test_scheduler_failure_does_not_stop_the_api(mock_scheduler_class):
         main.on_startup()
 
     assert main.takeover_scheduler is None
+    assert main.maintenance_scheduler is None
 
 
 def test_shutdown_stops_only_the_scheduler():
@@ -80,9 +92,14 @@ def test_shutdown_stops_only_the_scheduler():
 
     scheduler = MagicMock()
     scheduler.running = True
+    maintenance_scheduler = MagicMock()
+    maintenance_scheduler.running = True
     main.takeover_scheduler = scheduler
+    main.maintenance_scheduler = maintenance_scheduler
 
     main.on_shutdown()
 
     scheduler.shutdown.assert_called_once_with(wait=False)
+    maintenance_scheduler.shutdown.assert_called_once_with(wait=False)
     assert main.takeover_scheduler is None
+    assert main.maintenance_scheduler is None

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

@@ -1,5 +1,7 @@
 """End-to-end service tests for BOXIM takeover timing and human priority."""
 
+import asyncio
+import json
 from datetime import datetime, timedelta, timezone
 from threading import Barrier
 from unittest.mock import AsyncMock, patch
@@ -9,8 +11,9 @@ from sqlalchemy import create_engine
 from sqlalchemy.orm import sessionmaker
 
 from database import Base
-from models import Avatar, TakeoverCursor, TakeoverMessage, TakeoverReplyTask, User
+from models import Avatar, ChatAttachment, TakeoverCursor, TakeoverMessage, TakeoverReplyTask, User
 from services.boxim_client import BoxIMError
+from services.boxim_image_service import DownloadedBoxIMImage
 from services.takeover_service import (
     AVATAR_LOCAL_ID_PREFIX,
     TakeoverService,
@@ -62,6 +65,49 @@ class FakeBoxIM:
         return {"id": 900 + len(self.sent), "localId": int(local_id)}
 
 
+class ConcurrentPollingBoxIM(FakeBoxIM):
+    def __init__(self):
+        super().__init__()
+        self.active_polls = 0
+        self.peak_active_polls = 0
+
+    async def exchange_access_token(self, huihui_token):
+        return {"accessToken": huihui_token, "accessTokenExpiresIn": 3600}
+
+    async def get_self(self, access_token):
+        return {"id": 100 if access_token == "prod-huihui-token" else 101}
+
+    async def fetch_private_messages(self, access_token, min_id="0"):
+        self.active_polls += 1
+        self.peak_active_polls = max(self.peak_active_polls, self.active_polls)
+        await asyncio.sleep(0.05)
+        self.active_polls -= 1
+        return []
+
+
+class ConcurrentMessagePollingBoxIM(ConcurrentPollingBoxIM):
+    async def fetch_private_messages(self, access_token, min_id="0"):
+        await super().fetch_private_messages(access_token, min_id)
+        owner_id = 100 if access_token == "prod-huihui-token" else 101
+        return [
+            {
+                "id": owner_id,
+                "localId": owner_id,
+                "sendId": owner_id + 100,
+                "recvId": owner_id,
+                "sendTime": 1_700_000_000_000,
+                "type": 0,
+                "content": "并发写入测试",
+            }
+        ]
+
+    async def mark_private_messages_read(self, access_token, friend_id, message_id):
+        await asyncio.sleep(0.05)
+        self.read_receipts.append(
+            {"friendId": str(friend_id), "messageId": str(message_id)}
+        )
+
+
 @pytest.fixture
 def service_context(tmp_path):
     engine = create_engine(
@@ -150,6 +196,247 @@ async def test_incoming_message_is_prepared_then_sent_at_three_seconds(service_c
         db.close()
 
 
+@pytest.mark.asyncio
+async def test_incoming_image_is_analyzed_and_used_in_takeover_reply(service_context):
+    session_factory, service, boxim, clock = service_context
+    await service.poll_and_process_messages()
+    boxim.messages.append(
+        {
+            "id": 111,
+            "localId": 111,
+            "sendId": 200,
+            "recvId": 100,
+            "sendTime": clock.millis(),
+            "type": 1,
+            "content": json.dumps(
+                {
+                    "originUrl": "https://cdn.example/case.png",
+                    "thumbUrl": "https://cdn.example/case-thumb.png",
+                }
+            ),
+        }
+    )
+
+    await service.poll_and_process_messages()
+    db = session_factory()
+    try:
+        scheduled = db.query(TakeoverReplyTask).filter_by(trigger_message_id="111").one()
+        assert scheduled.status == "pending"
+        assert scheduled.prompt == "请看看这张图片。"
+    finally:
+        db.close()
+
+    clock.advance(3)
+
+    def analyze(db, avatar, content, **kwargs):
+        assert content == b"image-content"
+        attachment = ChatAttachment(
+            avatar_id=avatar.id,
+            uploader_kind=kwargs["uploader_kind"],
+            filename=kwargs["filename"],
+            mime_type="image/jpeg",
+            file_size=len(content),
+            status="ready",
+            category="medical_document",
+            summary="一张门诊病例",
+            extracted_text="主诉：咳嗽三天",
+            structured_data={"medical": {"chief_complaint": "咳嗽三天"}},
+            warning="请核对原始资料",
+            expires_at=clock.now() + timedelta(hours=24),
+        )
+        db.add(attachment)
+        db.commit()
+        db.refresh(attachment)
+        return attachment
+
+    downloaded = DownloadedBoxIMImage(
+        content=b"image-content",
+        filename="case.png",
+        mime_type="image/png",
+        source_url="https://cdn.example/case.png",
+    )
+    with (
+        patch("services.takeover_service.download_boxim_image", return_value=downloaded),
+        patch("routers.chat._analyze_image_bytes", side_effect=analyze) as analyzer,
+        patch("routers.chat._resolve_reply", return_value={"answer": "这份资料里写的是咳嗽三天。"}) as resolver,
+    ):
+        await service.poll_and_process_messages()
+
+    analyzer.assert_called_once()
+    assert resolver.call_args.args[2] == "请看看这张图片。"
+    image_contexts = resolver.call_args.kwargs["image_contexts"]
+    assert image_contexts[0]["summary"] == "一张门诊病例"
+    assert image_contexts[0]["extractedText"] == "主诉：咳嗽三天"
+    assert [item["content"] for item in boxim.sent] == ["这份资料里写的是咳嗽三天。"]
+
+    db = session_factory()
+    try:
+        event = db.query(TakeoverMessage).filter_by(boxim_message_id="111").one()
+        task = db.query(TakeoverReplyTask).filter_by(trigger_message_id="111").one()
+        assert event.attachment_id
+        assert db.get(ChatAttachment, event.attachment_id).uploader_kind == "boxim"
+        assert task.status == "sent"
+        with patch(
+            "services.takeover_service.download_boxim_image",
+            side_effect=AssertionError("cached image must not be downloaded again"),
+        ):
+            cached = service._takeover_image_attachment(db, db.get(Avatar, "avatar-1"), event)
+        assert cached.id == event.attachment_id
+    finally:
+        db.close()
+
+
+@pytest.mark.asyncio
+async def test_followup_text_recovers_recent_image_recorded_without_task(service_context):
+    session_factory, service, boxim, clock = service_context
+    await service.poll_and_process_messages()
+    image_message = {
+        "id": 113,
+        "localId": 113,
+        "sendId": 200,
+        "recvId": 100,
+        "sendTime": clock.millis(),
+        "type": 1,
+        "content": json.dumps(
+            {
+                "originUrl": "https://cdn.example/case.png",
+                "thumbUrl": "https://cdn.example/case-thumb.png",
+            }
+        ),
+    }
+
+    db = session_factory()
+    try:
+        avatar = db.get(Avatar, "avatar-1")
+        service._record_message(db, avatar, "100", image_message, schedule_reply=False)
+        cursor = db.query(TakeoverCursor).one()
+        cursor.last_message_id = "113"
+        db.commit()
+    finally:
+        db.close()
+
+    clock.advance(60)
+    boxim.messages.extend(
+        [
+            image_message,
+            {
+                "id": 114,
+                "localId": 114,
+                "sendId": 200,
+                "recvId": 100,
+                "sendTime": clock.millis(),
+                "type": 0,
+                "content": "请帮我看看这张图",
+            },
+        ]
+    )
+    await service.poll_messages()
+
+    db = session_factory()
+    try:
+        task = db.query(TakeoverReplyTask).filter_by(trigger_message_id="114").one()
+        assert task.source_message_ids == ["113", "114"]
+        assert task.prompt == "请看看这张图片。\n请帮我看看这张图"
+    finally:
+        db.close()
+
+    clock.advance(1)
+    boxim.messages.append(
+        {
+            "id": 115,
+            "localId": 115,
+            "sendId": 200,
+            "recvId": 100,
+            "sendTime": clock.millis(),
+            "type": 0,
+            "content": "图里写了什么",
+        }
+    )
+    await service.poll_messages()
+
+    db = session_factory()
+    try:
+        latest = db.query(TakeoverReplyTask).filter_by(trigger_message_id="115").one()
+        assert latest.source_message_ids == ["113", "114", "115"]
+        assert latest.source_message_ids.count("113") == 1
+    finally:
+        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
+    await service.poll_and_process_messages()
+    boxim.messages.append(
+        {
+            "id": 112,
+            "localId": 112,
+            "sendId": 200,
+            "recvId": 100,
+            "sendTime": clock.millis(),
+            "type": 1,
+            "content": json.dumps({"width": 100, "height": 100}),
+        }
+    )
+
+    await service.poll_and_process_messages()
+
+    db = session_factory()
+    try:
+        assert db.query(TakeoverMessage).filter_by(boxim_message_id="112").one()
+        assert db.query(TakeoverReplyTask).count() == 0
+    finally:
+        db.close()
+
+
 @pytest.mark.asyncio
 async def test_default_reply_delay_is_three_minutes(service_context):
     session_factory, service, boxim, clock = service_context
@@ -183,6 +470,87 @@ async def test_default_reply_delay_is_three_minutes(service_context):
     assert [item["content"] for item in boxim.sent] == ["好的"]
 
 
+@pytest.mark.asyncio
+async def test_multiple_avatar_owners_are_polled_concurrently(service_context):
+    session_factory, _service, _boxim, clock = service_context
+    db = session_factory()
+    try:
+        db.add_all(
+            [
+                User(
+                    id="owner-local-2",
+                    huihui_user_id="owner-huihui-2",
+                    huihui_token="prod-huihui-token-2",
+                    app_token="app-token-2",
+                ),
+                Avatar(
+                    id="avatar-2",
+                    owner_id="owner-huihui-2",
+                    name="分身二",
+                    status="active",
+                    config={"authorizationPermissions": ["chat", "takeover"]},
+                ),
+            ]
+        )
+        db.commit()
+    finally:
+        db.close()
+
+    boxim = ConcurrentMessagePollingBoxIM()
+    service = TakeoverService(
+        session_factory,
+        boxim,
+        poll_concurrency=2,
+        now=clock.now,
+    )
+
+    await service.poll_messages()
+
+    assert boxim.peak_active_polls == 2
+    db = session_factory()
+    try:
+        assert db.query(TakeoverCursor).filter(TakeoverCursor.initialized.is_(True)).count() == 2
+        assert db.query(TakeoverMessage).count() == 2
+        assert len(boxim.read_receipts) == 2
+    finally:
+        db.close()
+
+
+@pytest.mark.asyncio
+async def test_delayed_poll_still_schedules_recent_message(service_context):
+    session_factory, service, boxim, clock = service_context
+    await service.poll_messages()
+    delayed_send_time = int(
+        (clock.value - timedelta(seconds=150)).replace(tzinfo=timezone.utc).timestamp()
+        * 1000
+    )
+    boxim.messages.append(
+        {
+            "id": 13,
+            "localId": 13,
+            "sendId": 200,
+            "recvId": 100,
+            "sendTime": delayed_send_time,
+            "type": 0,
+            "content": "排队后仍需回复",
+        }
+    )
+
+    await service.poll_messages()
+
+    db = session_factory()
+    try:
+        task = db.query(TakeoverReplyTask).one()
+        assert task.status == "pending"
+        assert task.scheduled_at == clock.now()
+    finally:
+        db.close()
+
+    with patch("routers.chat._resolve_reply", return_value={"answer": "已经收到"}):
+        await service.process_reply_tasks()
+    assert [item["content"] for item in boxim.sent] == ["已经收到"]
+
+
 @pytest.mark.asyncio
 async def test_avatar_origin_message_never_schedules_a_reply(service_context):
     session_factory, service, boxim, clock = service_context

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

@@ -0,0 +1,98 @@
+import io
+import json
+from unittest.mock import Mock, patch
+
+import pytest
+from PIL import Image
+
+from services.chat_model_config import ChatModelConfig
+from services.vision_service import (
+    ImageValidationError,
+    build_attachment_warning,
+    call_vision_model,
+    parse_vision_analysis,
+    prepare_image,
+)
+
+
+def _image_bytes(fmt="PNG", size=(120, 80)):
+    output = io.BytesIO()
+    Image.new("RGB", size, "#f97316").save(output, format=fmt)
+    return output.getvalue()
+
+
+def _config():
+    return ChatModelConfig(
+        api_base_url="https://model.test/v1",
+        api_key="secret-key",
+        model="chat-model",
+        max_tokens=1024,
+        timeout_seconds=30,
+        vision_model="vision-model",
+        ocr_model="ocr-model",
+        vision_max_tokens=2048,
+        vision_timeout_seconds=90,
+        source="test",
+    )
+
+
+def test_prepare_image_validates_and_reencodes_without_metadata():
+    prepared = prepare_image(_image_bytes())
+
+    assert prepared.mime_type == "image/jpeg"
+    assert prepared.width == 120
+    assert prepared.height == 80
+    with Image.open(io.BytesIO(prepared.data)) as image:
+        assert image.format == "JPEG"
+        assert not image.getexif()
+
+
+def test_prepare_image_rejects_non_image_content():
+    with pytest.raises(ImageValidationError, match="格式无效"):
+        prepare_image(b"not-an-image")
+
+
+def test_vision_request_uses_openai_compatible_image_content():
+    response = Mock()
+    response.raise_for_status.return_value = None
+    response.json.return_value = {
+        "choices": [{"message": {"content": '{"category":"general_image"}'}}],
+        "usage": {"total_tokens": 88},
+    }
+    prepared = prepare_image(_image_bytes())
+
+    with patch("services.vision_service.httpx.post", return_value=response) as request:
+        result = call_vision_model(
+            prepared,
+            _config(),
+            model="vision-model",
+            prompt="describe",
+            json_output=True,
+        )
+
+    payload = request.call_args.kwargs["json"]
+    content = payload["messages"][0]["content"]
+    assert payload["model"] == "vision-model"
+    assert payload["response_format"] == {"type": "json_object"}
+    assert content[0]["type"] == "image_url"
+    assert content[0]["image_url"]["url"].startswith("data:image/jpeg;base64,")
+    assert content[1] == {"type": "text", "text": "describe"}
+    assert result["usage"]["total_tokens"] == 88
+
+
+def test_parse_medical_analysis_and_build_warning():
+    analysis = parse_vision_analysis(json.dumps({
+        "category": "medical_document",
+        "summary": "血常规报告",
+        "visible_text": "白细胞 11.2",
+        "key_facts": ["白细胞偏高"],
+        "uncertainties": ["日期模糊"],
+        "medical": {"document_type": "检验报告"},
+    }, ensure_ascii=False))
+
+    assert analysis["category"] == "medical_document"
+    assert analysis["medical"]["document_type"] == "检验报告"
+    warning = build_attachment_warning(analysis, ocr_failed=True)
+    assert "日期模糊" in warning
+    assert "人工核对" in warning
+    assert "不能替代医生诊断" in warning

digital-avatar-app/docs/H5_PRODUCTION_DEPLOYMENT.md

@@ -39,6 +39,8 @@ HUIHUI_ACCESS_ID=<production-access-id>
 HUIHUI_ACCESS_SECRET=<production-access-secret>
 HUIHUI_CLIENT_CODE=<production-client-code>
 BOXIM_TIMEOUT_SECONDS=20
+BOXIM_POLL_CONCURRENCY=8
+BOXIM_MAX_MESSAGE_AGE_SECONDS=600
 HUIHUI_PAYMENT_BASE_URL=https://open.99hui.com/api/payment-v3
 HUIHUI_PAYMENT_CALLBACK_BASE_URL=https://digital.99hui.com
 HUIHUI_PAYMENT_CALLBACK_SECRET=<至少32位随机密钥>
@@ -51,6 +53,16 @@ EMBEDDING_API_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
 EMBEDDING_API_KEY=<production-embedding-api-key>
 EMBEDDING_MODEL=text-embedding-v3
 EMBEDDING_BATCH_SIZE=10
+
+VISION_MODEL=qwen3.6-flash
+VISION_OCR_MODEL=qwen-vl-ocr
+VISION_MAX_OUTPUT_TOKENS=2048
+VISION_TIMEOUT_SECONDS=90
+VISION_TOKEN_RESERVE=12000
+CHAT_IMAGE_MAX_BYTES=8388608
+CHAT_IMAGE_MAX_PIXELS=16000000
+CHAT_ATTACHMENT_RETENTION_HOURS=24
+CHAT_ATTACHMENT_CLEANUP_MINUTES=60
 ```
 
 如生产 AI 配置中心不可用，还应提供当前项目支持的 `OPENAI_API_KEY`、`OPENAI_BASE_URL`、`CHAT_MODEL` 等兜底配置。`/data` 必须挂载持久卷，数据库与知识库文件不可存放在容器临时层。
@@ -105,11 +117,13 @@ 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;
 }
 ```
 
-`proxy_buffering off` 用于数字分身 SSE 流式吐字，`client_max_body_size` 用于知识库文件上传。网关和应用日志必须关闭完整 URL 查询参数记录，任何异常日志都不得输出 token、Authorization 或平台密钥。建议同时设置严格的 `Referrer-Policy: no-referrer`。
+`proxy_buffering off` 用于数字分身 SSE 流式吐字，`client_max_body_size` 同时用于知识库文件和聊天图片上传。应用只保存图片识别结果，不保存原图；识别结果 24 小时失效，后台默认每小时清理一次。公开分享图片识别会消耗分身所有者积分，生产网关应针对 `/api/public/avatar/*/chat/images` 设置每 IP 和每分享令牌的上传频率限制，防止恶意消耗。
+
+网关和应用日志必须关闭完整 URL 查询参数记录，任何异常日志都不得输出 token、Authorization、图片 Base64、病例正文或平台密钥。建议同时设置严格的 `Referrer-Policy: no-referrer`。
 
 ## 5. 发布验收
 
@@ -123,6 +137,9 @@ location /api/ {
 8. 重建容器后数据库、头像、知识库文档仍存在，`/api/health` 返回成功。
 9. `https://digital.99hui.com/api/health` 可访问，证书域名和有效期正确，HTTP 自动跳转 HTTPS。
 10. 微信和支付宝各创建一笔最小套餐订单，未付款时积分不变；支付成功后回调到账一次，重复回调积分不重复增加。
+11. 私聊和公开分享各上传 JPG、PNG、WebP 图片并完成追问；上传非图片、超过 8MB 或跨分身附件时必须拒绝。
+12. 病例图片可以提取可见文字并标记待核对内容，医学影像不作确定诊断；视觉与 OCR 调用分别扣减积分。
+13. 检查服务器上传目录不残留聊天原图，数据库过期图片识别记录在清理周期后删除，日志不出现 Base64 或病例正文。
 
 ## 6. 回滚
 

digital-avatar-app/docs/IMAGE_MEDICAL_UNDERSTANDING_DESIGN.md

@@ -0,0 +1,184 @@
+# 数字分身图片与病例理解详细设计
+
+## 1. 目标与边界
+
+本功能让数字分身在私聊和公开分享聊天中接收图片，并围绕图片内容继续使用现有的“标准答题对 -> 分身独立知识库 -> Qwen 兼容模型”链路回答。
+
+第一期支持 JPEG、PNG、WebP，覆盖以下场景：
+
+1. 普通照片、截图、图表和界面图片的内容理解。
+2. 病例、处方、检查单、检验报告等图片文档的文字和表格提取。
+3. X 光、CT、MRI 等医学影像的客观可见内容描述。
+
+第一期不把通用视觉模型的输出当作医学诊断，不自动把图片或病例写入知识库，不保存原图供长期访问，也不支持 DICOM 原始影像。
+
+## 2. 核心原则
+
+- **资料优先级不变**：标准答题对最高，分身独立知识库其次，图片识别结果属于待核对的会话资料，最后才由模型组织表达。
+- **病例最小留存**：应用不把原图写入业务存储，上传内容在内存中归一化并调用视觉服务；数据库只保存结构化结果和必要元数据。
+- **严格隔离**：每条图片记录必须绑定 `avatar_id`，私聊校验分身所有者，公开聊天校验分享令牌对应的分身。
+- **不确定性显式化**：OCR 看不清、表格列错位、医学影像无法确认时必须指出待核对项，不允许补齐缺失内容。
+- **可计量**：视觉理解和病例 OCR 分别计入分身所有者的积分消耗，失败时释放预留积分。
+- **可降级**：OCR 失败但通用视觉结果有效时仍可回答；视觉主调用失败则不进入聊天发送。
+
+## 3. 总体流程
+
+```text
+用户选择图片
+  -> 前端本地预览
+  -> 私聊/公开图片上传接口
+  -> 文件大小、MIME、真实格式、像素数校验
+  -> 自动旋转、缩放、去 EXIF、统一 JPEG
+  -> 通用视觉模型分类并输出结构化 JSON
+  -> 若为病例/检查单，再调用 OCR 模型精确转录
+  -> 保存结构化结果，不持久化原图
+  -> 返回 attachmentId
+  -> 用户发送文字 + attachmentIds
+  -> 标准答题对匹配
+  -> 用文字 + 图片提取结果检索独立知识库
+  -> 把标准答案、知识片段、图片资料注入系统上下文
+  -> Qwen SSE 流式回答
+```
+
+## 4. 模型编排
+
+### 4.1 通用视觉模型
+
+默认 `qwen3.6-flash`，可在后台数字分身专用模型配置中修改。输入为归一化后的 Base64 Data URL，要求返回 JSON：
+
+```json
+{
+  "category": "general_image|document|medical_document|medical_image",
+  "summary": "客观、完整的图片描述",
+  "visible_text": "图片中可确认的文字",
+  "key_facts": ["事实1", "事实2"],
+  "uncertainties": ["无法确认的内容"],
+  "medical": {
+    "document_type": "",
+    "patient_info": {},
+    "chief_complaint": "",
+    "findings": [],
+    "measurements": [],
+    "doctor_advice": ""
+  }
+}
+```
+
+模型提示词禁止诊断、补全被遮挡文字、猜测患者身份和输出模型信息。
+
+### 4.2 病例 OCR
+
+当 `category=medical_document` 时追加调用 `qwen-vl-ocr`，按原布局转录文字和表格。OCR 文本优先替换通用视觉输出中的 `visible_text`，但保留通用视觉模型提供的分类、摘要和不确定项。
+
+### 4.3 医学影像
+
+当 `category=medical_image` 时只保存客观描述，不输出疾病结论、分期、用药或治疗方案。聊天提示词必须要求结合正规影像报告和医生意见，并显示“图片识别结果仅供辅助，不能替代医生诊断”。
+
+## 5. 数据模型
+
+新增 `chat_attachments`：
+
+| 字段 | 说明 |
+|---|---|
+| `id` | 不可猜测的附件 ID |
+| `avatar_id` | 所属数字分身，强制隔离 |
+| `filename` | 原文件名，去除路径 |
+| `mime_type` / `file_size` | 上传元数据 |
+| `status` | `processing / ready / failed` |
+| `category` | 图片分类 |
+| `summary` | 通用视觉摘要 |
+| `extracted_text` | 可确认文字/OCR 结果 |
+| `structured_data` | 结构化 JSON |
+| `warning` | 不确定项和医学提示 |
+| `vision_model` / `ocr_model` | 实际调用模型 |
+| `created_at` / `used_at` | 创建和最近使用时间 |
+
+不保存公开原图 URL。应用层不落盘原图；框架上传缓冲在请求结束时关闭，处理结果在 24 小时后自动清理。
+
+## 6. API 设计
+
+### 6.1 上传并解析
+
+- `POST /api/avatar/{avatar_id}/chat/images`
+- `POST /api/public/avatar/{share_token}/chat/images`
+- `multipart/form-data: file`
+
+成功返回：
+
+```json
+{
+  "id": "attachment-id",
+  "filename": "病例.jpg",
+  "status": "ready",
+  "category": "medical_document",
+  "summary": "门诊检查单",
+  "warning": "部分手写内容需要人工核对"
+}
+```
+
+### 6.2 聊天
+
+原聊天接口增加：
+
+```json
+{
+  "message": "请帮我看看异常指标",
+  "attachmentIds": ["attachment-id"],
+  "history": [
+    {"role": "user", "content": "上一条问题", "attachmentIds": ["attachment-id"]}
+  ]
+}
+```
+
+当前消息最多 3 张图，历史最多引用最近 3 个不同附件。后端只读取与当前 `avatar_id` 相同且状态为 `ready` 的记录。
+
+## 7. 安全与隐私
+
+- 单图最大 8MB，解码后最大 1600 万像素，最长边归一化到 4096 像素以内。
+- 使用 Pillow 验证真实图片格式并防止解压炸弹；重新编码时清除 EXIF、GPS 和其他元数据。
+- 图片不会写入 FastAPI `StaticFiles` 或知识库目录，模型请求和日志不得输出 Base64 内容。
+- 日志只记录附件 ID、分身 ID、状态、耗时和模型，不记录图片 Base64、OCR 全文、病例内容或 API Key。
+- 公开分享上传仍消耗分身所有者积分；余额不足时拒绝视觉调用。
+- 生产环境需要补充用户授权、数据处理协议、存储地域和模型供应商留存策略确认。
+
+## 8. 前端交互
+
+- 输入框左侧增加图片按钮，支持相册选择和移动端拍照。
+- 选择后显示本地缩略图和“正在识别图片”，识别完成前禁止发送。
+- 用户可删除待发送图片；发送后图片保留在当前会话气泡中，但刷新页面后不恢复原图。
+- 病例和医学影像在输入区及回答下方显示辅助提示，不使用恐吓式红色告警。
+- 上传或识别失败时保留文字输入，明确提示重新选择图片，不产生空白消息。
+
+## 9. 配置
+
+数字分身专用模型配置新增：
+
+- `vision_model_version`，默认 `qwen3.6-flash`
+- `ocr_model_version`，默认 `qwen-vl-ocr`
+
+环境变量兜底：
+
+```dotenv
+VISION_MODEL=qwen3.6-flash
+VISION_OCR_MODEL=qwen-vl-ocr
+VISION_MAX_OUTPUT_TOKENS=2048
+VISION_TIMEOUT_SECONDS=90
+VISION_TOKEN_RESERVE=12000
+CHAT_IMAGE_MAX_BYTES=8388608
+CHAT_IMAGE_MAX_PIXELS=16000000
+CHAT_ATTACHMENT_RETENTION_HOURS=24
+CHAT_ATTACHMENT_CLEANUP_MINUTES=60
+```
+
+视觉调用复用数字分身专用配置的 `api_base_url` 和 `api_key`，不额外复制密钥。
+
+## 10. 验收标准
+
+1. 普通照片、截图和图表能够返回与图片一致的描述并支持追问。
+2. 病例图片可以提取标题、患者字段、检查结果、异常指标和医生意见，模糊内容明确标记待核对。
+3. 上传后服务器业务目录不残留原图，响应和日志不包含 Base64 或完整病例正文。
+4. A 分身无法引用 B 分身附件；公开分享令牌无法访问其他分身附件。
+5. 有图片时标准答题对仍作为最高优先级事实，知识库命中次之。
+6. 视觉与 OCR 积分分别结算，失败调用释放预留积分。
+7. SSE 打字效果、Markdown、用户头像、公开分享和纯文本聊天均无回归。
+8. CT、MRI、X 光回答不作确定诊断，并显示人工复核提示。

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）
-export const uploadKnowledgeDoc = (avatarId: string, file: File) => {
+const KNOWLEDGE_UPLOAD_CHUNK_SIZE = 5 * 1024 * 1024
+
+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`)
@@ -374,15 +451,35 @@ export const searchKnowledge = (avatarId: string, q: string, topK = 5) =>
 export interface ChatMessage {
   role: 'user' | 'assistant'
   content: string
+  attachmentIds?: string[]
 }
 
 export interface ChatResponse {
   answer: string
-  source: 'qa' | 'knowledge' | 'qwen'
+  source: 'qa' | 'knowledge' | 'vision' | 'qwen'
   references?: Array<{ docId?: string; filename?: string; fileType?: string; snippet?: string; score?: number }>
 }
 
-export const sendAvatarChat = (avatarId: string, payload: { message: string; history?: ChatMessage[] }) =>
+export interface ChatAttachment {
+  id: string
+  avatarId: string
+  filename: string
+  mimeType: string
+  fileSize: number
+  status: 'processing' | 'ready' | 'failed'
+  category: 'general_image' | 'document' | 'medical_document' | 'medical_image'
+  summary: string
+  warning: string
+  expiresAt: string
+}
+
+export interface ChatPayload {
+  message: string
+  attachmentIds?: string[]
+  history?: ChatMessage[]
+}
+
+export const sendAvatarChat = (avatarId: string, payload: ChatPayload) =>
   request.post<ChatResponse>(`/avatar/${avatarId}/chat`, payload)
 
 export interface PublicAvatar {
@@ -401,19 +498,41 @@ export const createAvatarShareLink = (avatarId: string) =>
 export const getPublicAvatar = (shareToken: string) =>
   request.get<PublicAvatar>(`/public/avatar/${shareToken}`)
 
-export const sendPublicAvatarChat = (shareToken: string, payload: { message: string; history?: ChatMessage[] }) =>
+export const sendPublicAvatarChat = (shareToken: string, payload: ChatPayload) =>
   request.post<ChatResponse>(`/public/avatar/${shareToken}/chat`, payload)
 
+const imageForm = (file: File) => {
+  const form = new FormData()
+  form.append('file', file)
+  return form
+}
+
+export const uploadAvatarChatImage = (avatarId: string, file: File) =>
+  request.post<ChatAttachment>(`/avatar/${avatarId}/chat/images`, imageForm(file), {
+    headers: { 'Content-Type': 'multipart/form-data' },
+    timeout: 120000
+  })
+
+export const uploadPublicAvatarChatImage = (shareToken: string, file: File) =>
+  request.post<ChatAttachment>(`/public/avatar/${shareToken}/chat/images`, imageForm(file), {
+    headers: { 'Content-Type': 'multipart/form-data' },
+    timeout: 120000
+  })
+
 type ChatStreamHandlers = {
   onMeta: (meta: Pick<ChatResponse, 'source' | 'references'>) => void
   onDelta: (content: string) => void
 }
 
-const streamChat = async (path: string, payload: { message: string; history?: ChatMessage[] }, handlers: ChatStreamHandlers) => {
+const streamChat = async (path: string, payload: ChatPayload, handlers: ChatStreamHandlers) => {
   const headers: Record<string, string> = { 'Content-Type': 'application/json', Accept: 'text/event-stream' }
   if (_authToken) headers.Authorization = `Bearer ${_authToken}`
   const response = await fetch(`${resolveBaseURL()}${path}`, { method: 'POST', headers, body: JSON.stringify(payload) })
-  if (!response.ok || !response.body) throw new Error(`对话请求失败（${response.status}）`)
+  if (!response.ok) {
+    const errorBody = await response.json().catch(() => null)
+    throw new Error(errorBody?.detail || errorBody?.message || `对话请求失败（${response.status}）`)
+  }
+  if (!response.body) throw new Error('对话响应为空，请稍后重试')
 
   const reader = response.body.getReader()
   const decoder = new TextDecoder()
@@ -436,10 +555,10 @@ const streamChat = async (path: string, payload: { message: string; history?: Ch
   }
 }
 
-export const streamAvatarChat = (avatarId: string, payload: { message: string; history?: ChatMessage[] }, handlers: ChatStreamHandlers) =>
+export const streamAvatarChat = (avatarId: string, payload: ChatPayload, handlers: ChatStreamHandlers) =>
   streamChat(`/avatar/${avatarId}/chat/stream`, payload, handlers)
 
-export const streamPublicAvatarChat = (shareToken: string, payload: { message: string; history?: ChatMessage[] }, handlers: ChatStreamHandlers) =>
+export const streamPublicAvatarChat = (shareToken: string, payload: ChatPayload, handlers: ChatStreamHandlers) =>
   streamChat(`/public/avatar/${shareToken}/chat/stream`, payload, handlers)
 
 // ==================== 会会用户资料 API ====================

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

@@ -1,5 +1,5 @@
 <template>
-  <div class="chat-page">
+  <div class="chat-page" :class="{ 'has-pending-images': pendingImages.length }">
     <header class="chat-header">
       <button v-if="!isPublic" class="back-btn" @click="router.back()">‹</button>
       <div class="avatar-heading">
@@ -31,6 +31,12 @@
           <span v-else>{{ avatar?.emoji || '🤖' }}</span>
         </div>
         <div class="message-column">
+          <div v-if="message.attachments?.length" class="message-images">
+            <figure v-for="attachment in message.attachments" :key="attachment.id" class="message-image-card">
+              <img :src="attachment.previewUrl" :alt="attachment.filename" />
+              <figcaption v-if="attachment.warning">{{ attachment.warning }}</figcaption>
+            </figure>
+          </div>
           <div class="message-bubble" :class="{ streaming: sending && message.role === 'assistant' && index === messages.length - 1 }">
             <template v-if="message.role === 'assistant'">
               <span
@@ -70,24 +76,66 @@
     </main>
 
     <form class="composer" @submit.prevent="sendMessage(inputText)">
-      <textarea v-model="inputText" rows="1" :disabled="sending" placeholder="输入你想聊的内容…" @keydown.enter.exact.prevent="sendMessage(inputText)"></textarea>
-      <button class="send-btn" type="submit" :disabled="sending || !inputText.trim()">发送</button>
+      <div v-if="pendingImages.length" class="pending-images">
+        <div v-for="image in pendingImages" :key="image.localId" class="pending-image" :class="image.status">
+          <img :src="image.previewUrl" :alt="image.filename" />
+          <div class="pending-image-copy">
+            <strong>{{ image.status === 'uploading' ? '正在识别图片…' : image.summary || image.filename }}</strong>
+            <span>{{ image.status === 'uploading' ? '正在提取图片中的可见内容' : categoryLabel(image.category) }}</span>
+          </div>
+          <button type="button" aria-label="移除图片" :disabled="sending" @click="removePendingImage(image.localId)">×</button>
+        </div>
+        <p v-if="hasPendingMedicalImage" class="medical-note">病例与医学影像识别仅供辅助，请以原始资料和医生意见为准。</p>
+      </div>
+      <div class="composer-row">
+        <button class="image-btn" type="button" :disabled="sending || uploadingImage || pendingImages.length >= 3" aria-label="选择图片" @click="imageInput?.click()">
+          <svg viewBox="0 0 24 24" aria-hidden="true"><path d="M4 5.5A2.5 2.5 0 0 1 6.5 3h11A2.5 2.5 0 0 1 20 5.5v13a2.5 2.5 0 0 1-2.5 2.5h-11A2.5 2.5 0 0 1 4 18.5v-13Zm2 12.7 3.8-4.2 2.7 2.8 1.7-1.8 3.8 3.2V5.5a.5.5 0 0 0-.5-.5h-11a.5.5 0 0 0-.5.5v12.7Zm8.3-7.8a1.7 1.7 0 1 0 0-3.4 1.7 1.7 0 0 0 0 3.4Z"/></svg>
+        </button>
+        <input ref="imageInput" class="image-input" type="file" accept="image/jpeg,image/png,image/webp" multiple @change="selectImages" />
+        <textarea v-model="inputText" rows="1" :disabled="sending" placeholder="输入问题，或选择一张图片…" @keydown.enter.exact.prevent="sendMessage(inputText)"></textarea>
+        <button class="send-btn" type="submit" :disabled="sending || uploadingImage || (!inputText.trim() && !readyPendingImages.length)">发送</button>
+      </div>
     </form>
   </div>
 </template>
 
 <script setup lang="ts">
-import { computed, nextTick, onMounted, reactive, ref } from 'vue'
+import { computed, nextTick, onBeforeUnmount, onMounted, reactive, ref } from 'vue'
 import { useRoute, useRouter } from 'vue-router'
-import { getAvatarDetail, getPublicAvatar, streamAvatarChat, streamPublicAvatarChat, type ChatMessage } from '@/api'
+import {
+  getAvatarDetail,
+  getPublicAvatar,
+  streamAvatarChat,
+  streamPublicAvatarChat,
+  uploadAvatarChatImage,
+  uploadPublicAvatarChatImage,
+  type ChatAttachment,
+  type ChatMessage
+} from '@/api'
 import { useAvatarStore } from '@/store/avatar'
 import { useUserStore } from '@/store/user'
 import { renderChatMarkdownCharacters } from '@/utils/chat-markdown.js'
 
 type DisplayMessage = ChatMessage & {
-  source?: 'qa' | 'knowledge' | 'qwen' | 'public'
+  source?: 'qa' | 'knowledge' | 'vision' | 'qwen' | 'public'
   references?: Array<{ filename?: string }>
   characters?: string[]
+  attachments?: MessageAttachment[]
+}
+
+type MessageAttachment = {
+  id: string
+  filename: string
+  previewUrl: string
+  category?: ChatAttachment['category']
+  summary?: string
+  warning?: string
+}
+
+type PendingImage = MessageAttachment & {
+  localId: string
+  attachmentId?: string
+  status: 'uploading' | 'ready'
 }
 
 const route = useRoute()
@@ -103,8 +151,11 @@ const inputText = ref('')
 const sending = ref(false)
 const thinking = ref(false)
 const errorMessage = ref('')
-const lastQuestion = ref('')
+const lastRequest = ref<{ question: string; attachments: MessageAttachment[] } | null>(null)
 const messageList = ref<HTMLElement | null>(null)
+const imageInput = ref<HTMLInputElement | null>(null)
+const pendingImages = ref<PendingImage[]>([])
+const previewUrls = new Set<string>()
 let scrollFrame: number | null = null
 
 const userAvatarUrl = computed(() => userStore.user?.avatarUrl || store.userProfile?.avatarUrl || '')
@@ -115,14 +166,25 @@ const avatarStatus = computed(() => {
   if (status === 'training') return { tone: 'training', label: '知识训练中' }
   return { tone: 'active', label: '在线，随时可以和我聊聊' }
 })
+const readyPendingImages = computed(() => pendingImages.value.filter((image) => image.status === 'ready' && image.attachmentId))
+const uploadingImage = computed(() => pendingImages.value.some((image) => image.status === 'uploading'))
+const hasPendingMedicalImage = computed(() => readyPendingImages.value.some((image) => ['medical_document', 'medical_image'].includes(image.category || '')))
 
 const sourceLabels: Record<NonNullable<DisplayMessage['source']>, string> = {
   qa: '标准问答对',
   knowledge: '参考文件知识库',
+  vision: '图片理解',
   qwen: '智能回答',
   public: ''
 }
 const sourceLabel = (source?: DisplayMessage['source']) => source ? sourceLabels[source] : ''
+const categoryLabels: Record<ChatAttachment['category'], string> = {
+  general_image: '图片内容已识别',
+  document: '文档图片已识别',
+  medical_document: '病例文字已提取，请核对原文',
+  medical_image: '医学影像已作客观描述'
+}
+const categoryLabel = (category?: ChatAttachment['category']) => category ? categoryLabels[category] : '图片内容已识别'
 
 const scrollToBottom = async () => {
   await nextTick()
@@ -214,20 +276,90 @@ const loadAvatar = async () => {
   document.title = avatar.value?.displayName || avatar.value?.name || '会会数字分身'
 }
 
-const sendMessage = async (value: string) => {
-  const question = value.trim()
-  if (!question || sending.value) return
-  lastQuestion.value = question
+const removePendingImage = (localId: string) => {
+  const target = pendingImages.value.find((image) => image.localId === localId)
+  if (target) {
+    URL.revokeObjectURL(target.previewUrl)
+    previewUrls.delete(target.previewUrl)
+  }
+  pendingImages.value = pendingImages.value.filter((image) => image.localId !== localId)
+}
+
+const selectImages = async (event: Event) => {
+  const input = event.target as HTMLInputElement
+  const slots = Math.max(0, 3 - pendingImages.value.length)
+  const files = Array.from(input.files || []).slice(0, slots)
+  input.value = ''
+  for (const file of files) {
+    if (!['image/jpeg', 'image/png', 'image/webp'].includes(file.type)) {
+      errorMessage.value = '仅支持 JPG、PNG、WebP 图片'
+      continue
+    }
+    if (file.size > 8 * 1024 * 1024) {
+      errorMessage.value = '单张图片不能超过 8MB'
+      continue
+    }
+    const previewUrl = URL.createObjectURL(file)
+    previewUrls.add(previewUrl)
+    const localId = `local-${Date.now()}-${Math.random().toString(16).slice(2)}`
+    pendingImages.value.push({
+      id: localId,
+      localId,
+      filename: file.name,
+      previewUrl,
+      status: 'uploading'
+    })
+    errorMessage.value = ''
+    try {
+      const result = isPublic
+        ? await uploadPublicAvatarChatImage(shareToken, file)
+        : await uploadAvatarChatImage(avatarId.value, file)
+      const pending = pendingImages.value.find((image) => image.localId === localId)
+      if (!pending) continue
+      Object.assign(pending, {
+        id: result.id,
+        attachmentId: result.id,
+        status: 'ready',
+        category: result.category,
+        summary: result.summary,
+        warning: result.warning
+      })
+    } catch (error: any) {
+      removePendingImage(localId)
+      errorMessage.value = error?.response?.data?.detail || error?.message || '图片识别失败，请重新选择图片'
+    }
+  }
+}
+
+const sendMessage = async (value: string, retryAttachments?: MessageAttachment[]) => {
+  const selectedAttachments = retryAttachments || readyPendingImages.value.map((image) => ({
+    id: image.attachmentId || image.id,
+    filename: image.filename,
+    previewUrl: image.previewUrl,
+    category: image.category,
+    summary: image.summary,
+    warning: image.warning
+  }))
+  const question = value.trim() || (selectedAttachments.length ? '请帮我看看这张图片。' : '')
+  if (!question || sending.value || (!retryAttachments && uploadingImage.value)) return
+  const history = messages.value.slice(-10).map(({ role, content, attachments }) => ({
+    role,
+    content,
+    attachmentIds: attachments?.map((attachment) => attachment.id) || []
+  }))
+  lastRequest.value = { question, attachments: selectedAttachments }
   inputText.value = ''
   errorMessage.value = ''
-  messages.value.push({ role: 'user', content: question })
+  if (!retryAttachments) pendingImages.value = []
+  messages.value.push({ role: 'user', content: question, attachments: selectedAttachments })
   sending.value = true
   thinking.value = true
   await scrollToBottom()
   try {
     const payload = {
       message: question,
-      history: messages.value.slice(-10).map(({ role, content }) => ({ role, content }))
+      attachmentIds: selectedAttachments.map((attachment) => attachment.id),
+      history
     }
     const streamed = createStreamReply()
     const handlers = {
@@ -256,13 +388,17 @@ const sendMessage = async (value: string) => {
 }
 
 const retryLast = () => {
-  if (!lastQuestion.value || sending.value) return
-  const last = messages.value[messages.value.length - 1]
-  if (last?.role === 'user') messages.value.pop()
-  sendMessage(lastQuestion.value)
+  if (!lastRequest.value || sending.value) return
+  while (messages.value[messages.value.length - 1]?.role === 'assistant') messages.value.pop()
+  if (messages.value[messages.value.length - 1]?.role === 'user') messages.value.pop()
+  void sendMessage(lastRequest.value.question, lastRequest.value.attachments)
 }
 
 onMounted(loadAvatar)
+onBeforeUnmount(() => {
+  previewUrls.forEach((url) => URL.revokeObjectURL(url))
+  previewUrls.clear()
+})
 </script>
 
 <style scoped>
@@ -275,6 +411,7 @@ onMounted(loadAvatar)
 .avatar-heading h1 { margin: 0; font-size: 17px; }
 .online-state { display: flex; align-items: center; gap: 4px; margin-top: 3px; font-size: 11px; opacity: .9; }.online-state i { width: 7px; height: 7px; border-radius: 50%; background: #86EFAC; box-shadow: 0 0 0 2px rgba(255,255,255,.22); }.online-state.training i { background: #FDE68A; }.online-state.inactive i { background: #FDA4AF; }
 .message-list { min-height: 0; flex: 1 1 auto; width: min(760px, 100%); box-sizing: border-box; margin: 0 auto; padding: 24px 18px 120px; overflow-y: auto; overscroll-behavior: contain; }
+.chat-page.has-pending-images .message-list { padding-bottom: min(330px, 42vh); }
 .welcome-card { padding: 28px 20px; text-align: center; background: rgba(255,255,255,.72); border: 1px solid #FFE1C2; border-radius: 22px; box-shadow: 0 10px 28px rgba(181, 99, 35, .08); }
 .welcome-avatar { width: 64px; height: 64px; display: grid; place-items: center; margin: 0 auto 14px; overflow: hidden; border: 3px solid #fff; border-radius: 50%; background: #FFE4C7; box-shadow: 0 7px 16px rgba(181, 99, 35, .18); font-size: 32px; }.welcome-avatar img { width: 100%; height: 100%; object-fit: cover; }
 .welcome-card h2 { margin: 0 0 8px; font-size: 20px; }.welcome-description { max-width: 340px; margin: 0 auto; color: #8B6B58; font-size: 14px; line-height: 1.65; }
@@ -282,6 +419,10 @@ onMounted(loadAvatar)
 .message-row.user { justify-content: flex-end; }
 .message-avatar { flex: 0 0 auto; width: 42px; height: 42px; display: grid; place-items: center; overflow: hidden; border: 2px solid rgba(255,255,255,.9); border-radius: 14px; background: #FFE4C7; box-shadow: 0 3px 10px rgba(96, 52, 21, .12); font-size: 16px; }.message-avatar img { width: 100%; height: 100%; object-fit: cover; }.user-message-face { color: #fff; background: #D97706; }
 .message-column { max-width: min(78%, 560px); }
+.message-images { display: grid; grid-template-columns: repeat(2, minmax(0, 150px)); gap: 8px; margin-bottom: 8px; }
+.message-image-card { margin: 0; overflow: hidden; border: 1px solid #F4D4B8; border-radius: 14px; background: #fff; box-shadow: 0 4px 14px rgba(96, 52, 21, .08); }
+.message-image-card img { display: block; width: 100%; max-height: 210px; object-fit: cover; }
+.message-image-card figcaption { padding: 7px 9px; color: #8A5A3B; background: #FFF6ED; font-size: 10px; line-height: 1.45; }
 .message-bubble { padding: 12px 14px; white-space: pre-wrap; line-height: 1.6; font-size: 15px; border-radius: 4px 16px 16px 16px; background: white; box-shadow: 0 3px 12px rgba(96, 52, 21, .07); }
 .message-bubble.streaming::after { content: ''; display: inline-block; width: 2px; height: 1.05em; margin-left: 3px; vertical-align: -0.16em; background: currentColor; animation: type-cursor .75s step-end infinite; }
 .typing-character { display: inline-block; animation: character-in .24s cubic-bezier(.2,.72,.25,1) both; }.typing-character.newline { display: block; height: 0; }
@@ -298,7 +439,27 @@ onMounted(loadAvatar)
 @keyframes type-cursor { 50% { opacity: 0; } }
 @keyframes character-in { from { opacity: 0; transform: translateY(3px); } to { opacity: 1; transform: translateY(0); } }
 .chat-error { margin: 4px auto; color: #B42318; font-size: 13px; }.chat-error button { border: 0; background: none; color: #C15F18; cursor: pointer; text-decoration: underline; }
-.composer { position: fixed; left: 0; right: 0; bottom: 0; display: flex; gap: 10px; padding: 12px max(18px, calc((100vw - 760px) / 2 + 18px)); background: rgba(255,255,255,.92); border-top: 1px solid #F4DCC7; backdrop-filter: blur(12px); }
+.composer { position: fixed; left: 0; right: 0; bottom: 0; display: flex; flex-direction: column; gap: 9px; padding: 10px max(18px, calc((100vw - 760px) / 2 + 18px)) 12px; background: rgba(255,255,255,.94); border-top: 1px solid #F4DCC7; backdrop-filter: blur(14px); }
+.composer-row { display: flex; align-items: flex-end; gap: 9px; }
 .composer textarea { flex: 1; resize: none; min-height: 22px; max-height: 100px; padding: 11px 13px; border: 1px solid #EED8C5; border-radius: 13px; font: inherit; color: #3B2417; outline: none; }.composer textarea:focus { border-color: #F97316; }
+.image-input { display: none; }
+.image-btn { flex: 0 0 auto; width: 44px; height: 44px; display: grid; place-items: center; border: 1px solid #EED8C5; border-radius: 13px; color: #C65A11; background: #FFF8F1; cursor: pointer; }
+.image-btn svg { width: 22px; height: 22px; fill: currentColor; }
+.image-btn:disabled { opacity: .4; cursor: not-allowed; }
+.pending-images { display: grid; gap: 7px; }
+.pending-image { display: grid; grid-template-columns: 48px minmax(0, 1fr) 30px; align-items: center; gap: 9px; min-height: 48px; padding: 6px 8px; border: 1px solid #F1D4BB; border-radius: 14px; background: #FFF9F3; }
+.pending-image img { width: 48px; height: 48px; object-fit: cover; border-radius: 10px; }
+.pending-image-copy { min-width: 0; display: flex; flex-direction: column; gap: 2px; }
+.pending-image-copy strong { overflow: hidden; color: #4B2B19; font-size: 12px; text-overflow: ellipsis; white-space: nowrap; }
+.pending-image-copy span { color: #9A7159; font-size: 10px; }
+.pending-image.uploading strong::after { content: ''; display: inline-block; width: 7px; height: 7px; margin-left: 7px; border: 2px solid #F6B889; border-top-color: #F97316; border-radius: 50%; animation: image-spin .7s linear infinite; }
+.pending-image > button { width: 28px; height: 28px; border: 0; border-radius: 9px; color: #9A7159; background: #F8E8D9; font-size: 19px; cursor: pointer; }
+.medical-note { margin: 0; padding: 0 2px; color: #9A5A2E; font-size: 10px; line-height: 1.45; }
 .send-btn { align-self: flex-end; padding: 11px 18px; border: 0; border-radius: 12px; color: white; background: #F97316; cursor: pointer; }.send-btn:disabled { opacity: .45; cursor: not-allowed; }
+@keyframes image-spin { to { transform: rotate(360deg); } }
+@media (max-width: 520px) {
+  .message-images { grid-template-columns: minmax(0, 220px); }
+  .message-column { max-width: 80%; }
+  .send-btn { padding-inline: 14px; }
+}
 </style>

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">
-          <article v-for="doc in docs" :key="doc.id" class="knowledge-card">
+        <div v-if="displayDocs.length" class="mobile-card-list">
+          <article v-for="doc in displayDocs" :key="doc.id" class="knowledge-card">
             <div class="card-icon">{{ fileEmoji(doc.fileType) }}</div>
             <div class="card-content">
               <div class="card-title-row">
@@ -41,8 +41,14 @@
               </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="documentState(doc).tone === 'failed'" class="card-retry" @click="retryDoc(doc.id)">重新索引</button>
+              <button v-if="!doc.localUploading" class="card-delete" @click="removeDoc(doc.id)">{{ doc.localOnly ? '移除' : '删除' }}</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 +84,7 @@
 </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 +93,7 @@ import {
   getKnowledgeDocs,
   uploadKnowledgeDoc,
   deleteKnowledgeDoc,
+  retryKnowledgeDoc,
   getQAPairs,
   deleteQAPair,
   searchKnowledge,
@@ -102,18 +109,28 @@ 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)
+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 +138,33 @@ 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 +172,7 @@ const loadDocs = async () => {
   try {
     const res: any = await getKnowledgeDocs(avatarId.value)
     docs.value = unwrapListData(res)
+    startDocumentPolling()
   } catch (e) {
     console.error(e)
   }
@@ -149,40 +191,82 @@ const loadQA = async () => {
 const triggerFile = () => fileInput.value?.click()
 
 const onFileChange = (e: Event) => {
-  const f = (e.target as HTMLInputElement).files?.[0]
-  if (f) doUpload(f)
+  const files = Array.from((e.target as HTMLInputElement).files || [])
+  if (files.length) uploadFiles(files)
   ;(e.target as HTMLInputElement).value = ''
 }
 
 const onDrop = (e: DragEvent) => {
   dragOver.value = false
-  const f = e.dataTransfer?.files?.[0]
-  if (f) doUpload(f)
+  const files = Array.from(e.dataTransfer?.files || [])
+  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) => {
+      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) {
+    const current = pendingUploads.value.find((doc) => doc.id === localId)
+    if (current) {
+      current.localUploading = false
+      current.errorMessage = e?.message || '上传失败'
+    }
+  }
+}
+
+const retryDoc = async (id: string) => {
+  if (!avatarId.value) return
+  uploadError.value = ''
+  try {
+    await retryKnowledgeDoc(avatarId.value, id)
     await loadDocs()
   } catch (e: any) {
-    uploadError.value = e?.message || '上传失败'
-  } finally {
-    uploading.value = false
+    uploadError.value = e?.message || '重新索引失败'
   }
 }
 
 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 +341,8 @@ onMounted(async () => {
   if (avatarId.value) store.currentAvatarId = avatarId.value
   await Promise.all([loadDocs(), loadQA()])
 })
+
+onUnmounted(stopDocumentPolling)
 </script>
 
 <style scoped>
@@ -300,7 +386,12 @@ 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; flex-direction: column; align-items: stretch; gap: 6px; }
+.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-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,

frontend/src/views/AIModels.vue

@@ -24,6 +24,8 @@
         </div>
         <div class="model-meta">
           <span>版本: {{ m.model_version || '--' }}</span>
+          <span v-if="m.usage_scope === 'digital_avatar'">视觉: {{ m.vision_model_version || 'qwen3.6-flash' }}</span>
+          <span v-if="m.usage_scope === 'digital_avatar'">病例OCR: {{ m.ocr_model_version || 'qwen-vl-ocr' }}</span>
           <span>温度: {{ m.temperature }}</span>
           <span>Max Tokens: {{ m.max_tokens }}</span>
           <span>超时: {{ m.timeout_seconds }}s</span>
@@ -68,6 +70,15 @@
         <el-form-item label="模型版本">
           <el-input v-model="form.model_version" placeholder="如: gpt-4-turbo, glm-4" />
         </el-form-item>
+        <template v-if="form.usage_scope === 'digital_avatar'">
+          <el-form-item label="视觉模型">
+            <el-input v-model="form.vision_model_version" placeholder="如: qwen3.6-flash" />
+          </el-form-item>
+          <el-form-item label="病例OCR模型">
+            <el-input v-model="form.ocr_model_version" placeholder="如: qwen-vl-ocr" />
+            <div class="scope-tip">识别为病例、处方、检查单后自动调用，普通图片不会重复调用。</div>
+          </el-form-item>
+        </template>
         <el-row :gutter="16">
           <el-col :span="12">
             <el-form-item label="温度">
@@ -142,7 +153,7 @@ const testing = ref(false)
 
 const providerLabels = { openai: 'OpenAI', zhipu: '智谱GLM', wenxin: '文心一言', qianwen: '通义千问', local: '本地模型' }
 const scopeLabels = { general: '通用业务', digital_avatar: '数字分身专用' }
-const form = reactive({ model_name: '', provider: 'openai', usage_scope: 'general', api_base_url: '', api_key: '', model_version: '', temperature: 0.7, max_tokens: 1000, timeout_seconds: 30, is_default: 0 })
+const form = reactive({ model_name: '', provider: 'openai', usage_scope: 'general', api_base_url: '', api_key: '', model_version: '', vision_model_version: 'qwen3.6-flash', ocr_model_version: 'qwen-vl-ocr', temperature: 0.7, max_tokens: 1000, timeout_seconds: 30, is_default: 0 })
 const rules = { model_name: [{ required: true, message: '请输入模型名称' }], provider: [{ required: true }], usage_scope: [{ required: true }] }
 
 async function load() {
@@ -166,13 +177,13 @@ function onProviderChange(provider) {
 
 function openCreate() {
   editModel.value = null
-  Object.assign(form, { model_name: '', provider: 'openai', usage_scope: 'general', api_base_url: PROVIDER_DEFAULTS.openai.api_base_url, api_key: '', model_version: PROVIDER_DEFAULTS.openai.model_version, temperature: 0.7, max_tokens: 1000, timeout_seconds: 30, is_default: 0 })
+  Object.assign(form, { model_name: '', provider: 'openai', usage_scope: 'general', api_base_url: PROVIDER_DEFAULTS.openai.api_base_url, api_key: '', model_version: PROVIDER_DEFAULTS.openai.model_version, vision_model_version: 'qwen3.6-flash', ocr_model_version: 'qwen-vl-ocr', temperature: 0.7, max_tokens: 1000, timeout_seconds: 30, is_default: 0 })
   dialogVisible.value = true
 }
 
 function openEdit(m) {
   editModel.value = m
-  Object.assign(form, { model_name: m.model_name, provider: m.provider, usage_scope: m.usage_scope || 'general', api_base_url: m.api_base_url || '', api_key: '', model_version: m.model_version || '', temperature: m.temperature, max_tokens: m.max_tokens, timeout_seconds: m.timeout_seconds, is_default: m.is_default })
+  Object.assign(form, { model_name: m.model_name, provider: m.provider, usage_scope: m.usage_scope || 'general', api_base_url: m.api_base_url || '', api_key: '', model_version: m.model_version || '', vision_model_version: m.vision_model_version || 'qwen3.6-flash', ocr_model_version: m.ocr_model_version || 'qwen-vl-ocr', temperature: m.temperature, max_tokens: m.max_tokens, timeout_seconds: m.timeout_seconds, is_default: m.is_default })
   dialogVisible.value = true
 }