feat(avatar): add private vision chat support

Reviewed-on: #9

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(
+                columns = (
-                    "SELECT COUNT(*) FROM information_schema.COLUMNS "
+                    (
-                    "WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_model_configs' "
+                        "usage_scope",
-                    "AND COLUMN_NAME = 'usage_scope'"
-                ))
-                if result.scalar_one() == 0:
-                    await conn.execute(text(
                         "ALTER TABLE ai_model_configs ADD COLUMN usage_scope "
-                        "VARCHAR(16) NOT NULL DEFAULT 'general' AFTER provider"
+                        "VARCHAR(16) NOT NULL DEFAULT 'general' AFTER provider",
-                    ))
+                    ),
-                    logger.info("AI模型配置表已增加 usage_scope 字段")
+                    (
+                        "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/embeddings.py

@@ -18,6 +18,14 @@ EMBED_DIM = 256
 MODEL = os.getenv("EMBEDDING_MODEL", "mock-hash-embed-v1")
 
 
+def _embedding_endpoint(api_url):
+    """Accept either an OpenAI-compatible base URL or its full endpoint."""
+    api_url = (api_url or "").strip().rstrip("/")
+    if not api_url or api_url.endswith("/embeddings"):
+        return api_url
+    return f"{api_url}/embeddings"
+
+
 def _tokenize(text):
     text = (text or "").lower()
     # 英文/数字按词，CJK 逐字（中文无空格，需拆到字级才能命中子词）
@@ -47,7 +55,7 @@ def embed(texts):
     """返回 list[list[float]]，与输入顺序一致。"""
     if not texts:
         return []
-    api_url = os.getenv("EMBEDDING_API_URL")
+    api_url = _embedding_endpoint(os.getenv("EMBEDDING_API_URL"))
     if api_url:
         api_key = os.getenv("EMBEDDING_API_KEY", "")
         model = os.getenv("EMBEDDING_MODEL", "text-embedding-3-small")

digital-avatar-app/backend/main.py

@@ -19,11 +19,13 @@ 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.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")
 
@@ -132,6 +134,15 @@ def on_startup():
 
     # 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:
@@ -196,6 +207,51 @@ 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

@@ -257,6 +257,44 @@ 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
+    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
@@ -49,14 +62,35 @@ _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 +111,228 @@ 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 _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]
+    attachment = ChatAttachment(
+        avatar_id=avatar.id,
+        uploader_kind=uploader_kind,
+        filename=filename,
+        mime_type=(file.content_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 HTTPException(status_code=400, detail=str(exc)) from exc
+    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 HTTPException(status_code=502, detail=str(exc)) from exc
+    finally:
+        content = b""
+
+
 def _normalize_question(value: str) -> str:
     value = (value or "").strip().lower()
     value = re.sub(r"\s+", "", value)
@@ -233,6 +489,7 @@ 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 +498,7 @@ 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 +523,24 @@ 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以下是当前会话图片经过视觉识别后得到的资料：\n"
+            f"{image_material}"
+            "\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 +553,11 @@ 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 +720,17 @@ 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 +741,19 @@ 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)
+        retrieval_question = _image_retrieval_question(question, image_contexts)
-        messages = _build_prompt(avatar, history, question, hits)
+        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,
@@ -498,7 +790,9 @@ def _resolve_reply(
             raise
     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 +808,17 @@ 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 +830,21 @@ def _stream_reply(
                 question,
                 references,
                 standard_answer=matched.answer,
+                image_contexts=image_contexts,
             )
         else:
-            references = _search_knowledge(db, avatar.id, question)
+            retrieval_question = _image_retrieval_question(question, image_contexts)
-            source = "knowledge" if references else "qwen"
+            references = _search_knowledge(db, avatar.id, retrieval_question)
-            messages = _build_prompt(avatar, history, question, references)
+            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 +948,62 @@ 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 +1018,17 @@ 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 +1038,17 @@ 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 +1056,18 @@ 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/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/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_chat_images.py

@@ -0,0 +1,273 @@
+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,
+    _attachment_contexts,
+    _load_chat_attachments,
+    _resolve_reply,
+)
+from services.chat_attachment_service import purge_expired_chat_attachments
+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_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_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

@@ -48,9 +48,11 @@ class RemoteEmbeddingTests(unittest.TestCase):
     def test_large_input_is_split_into_provider_safe_batches(self):
         texts = [f"chunk-{index}" for index in range(14)]
         batch_sizes = []
+        requested_urls = []
 
         def fake_urlopen(request, timeout):
             self.assertEqual(timeout, 30)
+            requested_urls.append(request.full_url)
             payload = json.loads(request.data.decode("utf-8"))
             batch_sizes.append(len(payload["input"]))
             return FakeResponse({
@@ -61,7 +63,7 @@ class RemoteEmbeddingTests(unittest.TestCase):
             })
 
         with patch.dict(os.environ, {
-            "EMBEDDING_API_URL": "https://embedding.example/v1/embeddings",
+            "EMBEDDING_API_URL": "https://embedding.example/v1",
             "EMBEDDING_API_KEY": "test-key",
             "EMBEDDING_MODEL": "text-embedding-v4",
             "EMBEDDING_BATCH_SIZE": "10",
@@ -69,8 +71,18 @@ class RemoteEmbeddingTests(unittest.TestCase):
             result = embeddings.embed(texts)
 
         self.assertEqual(batch_sizes, [10, 4])
+        self.assertEqual(requested_urls, [
+            "https://embedding.example/v1/embeddings",
+            "https://embedding.example/v1/embeddings",
+        ])
         self.assertEqual(result, [[float(index)] for index in range(14)])
 
+    def test_full_embedding_endpoint_is_not_modified(self):
+        self.assertEqual(
+            embeddings._embedding_endpoint("https://embedding.example/v1/embeddings/"),
+            "https://embedding.example/v1/embeddings",
+        )
+
 
 if __name__ == "__main__":
     unittest.main()

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()
@@ -47,6 +48,10 @@ def test_scheduler_uses_boxim_and_restart_safe_service(
     assert config["BOXIM_API_BASE_URL"] == "https://im.example/api"
     mock_takeover_class.assert_called_once_with(main.SessionLocal, boxim)
 
+    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
     assert poll_call.args[0] is takeover.poll_messages
@@ -62,6 +67,7 @@ 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 +79,7 @@ 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 +87,14 @@ 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_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

@@ -47,10 +47,26 @@ HUIHUI_PAYMENT_TIMEOUT_SECONDS=30
 DATABASE_URL=sqlite:////data/avatar.db
 UPLOAD_DIR=/data/uploads
 CHAT_MODEL_CONFIG_URL=http://<huihuisquare-api>/api/ai-models/runtime/digital-avatar
+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。
 
+`EMBEDDING_API_URL` 同时支持 OpenAI 兼容基础地址（如上面的 `/v1`）和完整的 `/v1/embeddings` 地址，后端会统一请求 `/embeddings`。发布后必须在后端容器内执行一次最小向量探针，确认返回向量数量和维度，而不能只检查 `/api/health`。
+
 积分充值使用会会支付体系的 `payment-v3/payment/pay`，渠道值为 `WECHAT` / `ALIPAY`，端内支付场景为 `APP`，微信内 H5 使用 `JSAPI`。`HUIHUI_PAYMENT_CALLBACK_SECRET` 只用于为每笔订单生成 HMAC 回调签名，不会发送到前端或直接出现在回调地址中。支付回调确认状态成功且金额与套餐价格完全一致后才增加积分，重复回调不会重复到账。
 
 ## 3. 构建与发布
@@ -74,6 +90,7 @@ docker compose build --pull avatar-backend avatar-frontend
 docker compose up -d avatar-backend avatar-frontend
 docker compose ps
 curl -fsS http://127.0.0.1:8099/api/health
+docker compose exec avatar-backend python -c 'import embeddings; v=embeddings.embed(["部署向量探针"]); print(len(v), len(v[0]))'
 ```
 
 生产编排应把示例中的测试端口改为内网暴露，由统一 HTTPS 网关接入。后端暂时使用 SQLite，必须保持单实例写入；若扩展为多后端实例，应先迁移到 PostgreSQL，并把延迟接管任务改为共享队列。
@@ -102,7 +119,9 @@ location /api/ {
 }
 ```
 
-`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
 
@@ -116,6 +135,9 @@ 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/src/api/index.ts

@@ -374,15 +374,35 @@ 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 +421,41 @@ 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 +478,10 @@ 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>
+      <div v-if="pendingImages.length" class="pending-images">
-      <button class="send-btn" type="submit" :disabled="sending || !inputText.trim()">发送</button>
+        <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 removePendingImage = (localId: string) => {
-  const question = value.trim()
+  const target = pendingImages.value.find((image) => image.localId === localId)
-  if (!question || sending.value) return
+  if (target) {
-  lastQuestion.value = question
+    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
+  if (!lastRequest.value || sending.value) return
-  const last = messages.value[messages.value.length - 1]
+  while (messages.value[messages.value.length - 1]?.role === 'assistant') messages.value.pop()
-  if (last?.role === 'user') messages.value.pop()
+  if (messages.value[messages.value.length - 1]?.role === 'user') messages.value.pop()
-  sendMessage(lastQuestion.value)
+  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>

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
 }