fix: 全面修复6项问题——Auto模型路由/媒体提示词优化/微信Bot/Token驾驶舱/技能市场清空

1. resolve_auto_model: image/video/audio无显式默认时自动发现启用模型(原返回None→文本模型回退→AI说无法生成)
2. 新增_optimize_media_prompt: 调用文本LLM精炼用户原始输入为AGNES高质量提示词(去除对话噪音+英文优化)
3. detect_media_intent: 扩展关键词覆盖多轮对话模式(做图片/生成个图/画一下/制作视频等)
4. chat.py+im_llm.py: 所有httpx调用补trust_env=False+_proxy_for_url, 规避容器坏代理
5. token_usage.py: 模型名回退链(model表→日志自带的model_name→auto标签), 不再显示unknown
6. skills_admin.py: 新增POST /batch-delete批量删除API(ids空则删全部)
7. 技能市场数据已清空, 用户可重新导入技能

backend/python/app/api/admin/skills_admin.py

@@ -16,7 +16,8 @@ from sqlalchemy import desc, func, or_, and_
 from ...database import get_db
 from ...models import (
     User, EvolutionSkill, SkillVersion, SkillPublishRecord,
-    EvolutionEvent, TeamSharedSkill
+    EvolutionEvent, TeamSharedSkill, MarketApp, AppInstallation,
+    AppPurchase, AppReview
 )
 from ...auth import get_current_admin
 from ...middleware.log_service import LogService
@@ -429,7 +430,55 @@ async def update_skill(
 
 
 # ============================================================
-# 删除技能
+# 批量删除技能（用于一次性清空技能市场）
+# ============================================================
+
+
+class BatchDeleteSkillsRequest(BaseModel):
+    ids: list[str] = []  # 要删除的技能 ID 列表；传空列表则删除所有
+
+
+@router.post("/batch-delete")
+async def batch_delete_skills(
+    req: BatchDeleteSkillsRequest,
+    admin_user=Depends(get_current_admin),
+    db: Session = Depends(get_db),
+):
+    """批量删除技能。ids 为空则删除全部技能。"""
+    if req.ids:
+        skills = db.query(EvolutionSkill).filter(EvolutionSkill.id.in_(req.ids)).all()
+    else:
+        skills = db.query(EvolutionSkill).all()
+
+    deleted_count = 0
+    for skill in skills:
+        skill_name = skill.skill_name
+        # 删除版本历史
+        db.query(SkillVersion).filter(SkillVersion.skill_name == skill_name).delete()
+        # 删除发布记录
+        db.query(SkillPublishRecord).filter(SkillPublishRecord.skill_id == skill.id).delete()
+        # 删除团队共享
+        db.query(TeamSharedSkill).filter(TeamSharedSkill.skill_id == skill.id).delete()
+        # 同步清理市场应用表中的技能
+        market_app = db.query(MarketApp).filter(
+            MarketApp.type == "skill",
+            MarketApp.name == skill_name,
+        ).first()
+        if market_app:
+            # 清理关联的安装和购买记录
+            db.query(AppInstallation).filter(AppInstallation.app_id == market_app.id).delete()
+            db.query(AppPurchase).filter(AppPurchase.app_id == market_app.id).delete()
+            db.query(AppReview).filter(AppReview.app_id == market_app.id).delete()
+            db.delete(market_app)
+        db.delete(skill)
+        deleted_count += 1
+
+    db.commit()
+    return {"code": 0, "data": {"deleted": deleted_count}, "message": f"已删除 {deleted_count} 个技能"}
+
+
+# ============================================================
+# 删除技能（单个）
 # ============================================================
 
 @router.delete("/{skill_id}")

backend/python/app/api/chat.py

@@ -48,14 +48,21 @@ def detect_media_intent(text: str) -> str | None:
     if t.endswith(("？", "?")) or any(q in t for q in ("吗", "呢", "怎么", "如何", "能否", "是否可以", "可以吗", "会不会", "why", "how")):
         return None
     video_kw = ("视频", "短片", "影片", "vide", "动画视频", "一段视频", "做个视频", "生成视频",
-                "视频生成", "来段视频", "拍个视频", "做段视频", "视频剪辑")
+                "视频生成", "来段视频", "拍个视频", "做段视频", "视频剪辑", "做视频",
+                "生成一个视频", "制作一个视频", "帮我做视频", "制作视频",
+                "生成短片", "做短片", "制作短片", "一段短片")
     image_kw = ("图片", "图像", "插画", "海报", "照片", "头像", "壁纸", "漫画", "表情包", "logo", "图标",
                 "imag", "画一张", "画张", "画一只", "画只", "画个", "画幅",
                 "来张", "来一张", "来幅", "发张图", "发个图",
                 "生成图片", "生成一张", "生成一幅", "生成图", "图像生成",
                 "做一张", "做张图", "做张", "出张图", "出一张图",
                 "帮我画", "给我画", "请画", "绘制", "作画", "ai绘画", "ai画图",
-                "画个图", "画张图", "画一幅图", "一张图", "一幅画", "生成一幅")
+                "画个图", "画张图", "画一幅图", "一张图", "一幅画", "生成一幅",
+                "画画", "画一下", "画出来", "生成个图", "生成一张图", "生成个图片",
+                "做图片", "做个图", "制图", "整张图", "整张图出来", "搞张图",
+                "帮我生成图片", "帮我生成图像", "给我生成图片", "给我生成图像",
+                "生成一张图片", "生成一个图片", "制作一张图", "制作一张图片",
+                "来一张图片", "画一张图片", "画一幅", "画一副")
     audio_kw = ("语音", "配音", "朗读", "播报", "念一段", "有声", "tts", "音频", "生成语音",
                 "读一下", "念一下", "语音合成")
     if any(k in t for k in video_kw):
@@ -152,6 +159,86 @@ def _proxy_for_url(url: str) -> str | None:
     return _outbound_proxy()
 
 
+async def _optimize_media_prompt(raw_prompt: str, media_type: str, db=None) -> str:
+    """使用平台文本模型对用户原始输入做「媒体提示词增强优化」。
+
+    AGNES 图像/视频模型对 prompt 质量高度敏感：
+    - 用户在多轮对话中说"帮我生成一张刚才讨论的产品图"包含大量会话噪音
+    - AGNES 需要的是**详细的、描述性的、英文优先或中英混合**的视觉提示词
+    - 缺乏构图/风格/光影/质量等指导词时，生成结果与预期差异巨大
+
+    本函数把用户原文交给文本 LLM 精炼为符合 AGNES 预期的优质 prompt。
+    失败时回退到原始输入(不做额外引导，保证必达)。
+    """
+    if not raw_prompt or len(raw_prompt.strip()) < 2:
+        return raw_prompt
+    # 如果原始输入已经很长(>200 字)且英文为主，可能本身就是一个不错的 prompt，直接返回
+    stripped = raw_prompt.strip()
+    ascii_chars = sum(1 for c in stripped if ord(c) < 128)
+    if len(stripped) > 200 and ascii_chars / max(len(stripped), 1) > 0.6:
+        return stripped
+    # 用文本 LLM 做翻译增强
+    try:
+        from .capability_registry import resolve_auto_model as _resolve_auto
+        from ..models import ModelProvider as MP
+        from ..database import SessionLocal as SL
+        import httpx as _httpx
+
+        # 独立数据库会话，避免与主流程竞争
+        _db = db
+        _should_close = False
+        if _db is None:
+            _db = SL()
+            _should_close = True
+        try:
+            text_model = _resolve_auto("text", _db)
+            if not text_model:
+                return stripped
+            provider = _db.query(MP).filter(MP.id == text_model.provider_id).first()
+            api_key = text_model.api_key or (provider.api_key if provider else None)
+            api_url = text_model.endpoint
+            if not api_url and provider and provider.api_base_url:
+                api_url = f"{provider.api_base_url.rstrip('/')}/v1/chat/completions"
+            if not (api_key and api_url):
+                return stripped
+
+            prompt_zh = (
+                "你是一个专业的AI图像/视频提示词工程师。请将以下用户需求提炼成"
+                f"一段高质量的{'图像' if media_type == 'image' else '视频'}生成"
+                "提示词。要求：\n"
+                "1. 用英文书写（AGNES模型对英文响应最佳），核心关键词可保留中文；\n"
+                "2. 添加构图/风格/光影/色彩/质量描述（如 highly detailed, 8K, "
+                "professional lighting, masterpiece, cinematic等）；\n"
+                "3. 去掉与视觉无关的对话噪音；\n"
+                "4. 只输出最终prompt文本，不要任何解释、不要引号包裹、不要\"Prompt:\"前缀。\n\n"
+                f"用户需求：{stripped}"
+            )
+            messages = [
+                {"role": "system", "content": "You are a professional prompt engineer. Output ONLY the final prompt text, no explanations."},
+                {"role": "user", "content": prompt_zh},
+            ]
+            body = {"model": text_model.name, "messages": messages, "max_tokens": 512, "temperature": 0.7}
+            async with _httpx.AsyncClient(
+                timeout=30.0, proxy=_proxy_for_url(api_url), trust_env=False
+            ) as client:
+                resp = await client.post(
+                    api_url,
+                    headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
+                    json=body,
+                )
+                if resp.status_code == 200:
+                    data = resp.json()
+                    opt = data.get("choices", [{}])[0].get("message", {}).get("content", "").strip()
+                    if opt and len(opt) > 5:
+                        return opt
+        finally:
+            if _should_close:
+                _db.close()
+    except Exception:
+        pass
+    return stripped
+
+
 async def _generate_media(db, ai_model, prompt: str, user_id: str = None) -> str:
     """图像/视频生成模型统一入口：按 capabilities 调用 AGNES 等媒体生成 API。
     返回可直接在 AI 聊天中渲染的富文本(含 <img>/<video> 标签)。"""
@@ -171,12 +258,11 @@ async def _generate_media(db, ai_model, prompt: str, user_id: str = None) -> str
             api_url = f"{provider.api_base_url.rstrip('/')}/v1/images/generations" if provider else ""
         if not api_url:
             return "⚠️ 该图像模型未配置 Endpoint。"
-        # 注意：extra_body 是 OpenAI SDK 专属参数，不能直接作为原始 JSON 字段下发，
+        # 先优化 prompt（用户原始输入含多轮对话噪音 → 高质量图像生成提示词）
-        # AGNES 收到未知顶层字段会直接断开连接（表现为 "Server disconnected without
+        optimized_prompt = await _optimize_media_prompt(prompt, "image", db)
-        # sending a response"）。保持与 OpenAI 兼容的极简结构即可。
         payload = {
             "model": ai_model.name,
-            "prompt": prompt,
+            "prompt": optimized_prompt,
             "size": "1024x768",
         }
         try:
@@ -206,10 +292,12 @@ async def _generate_media(db, ai_model, prompt: str, user_id: str = None) -> str
             api_url = f"{provider.api_base_url.rstrip('/')}/v1/videos" if provider else ""
         if not api_url:
             return "⚠️ 该视频模型未配置 Endpoint。"
+        # 优化 prompt（用户输入含多轮对话噪音 → 高质量视频提示词）
+        optimized_prompt = await _optimize_media_prompt(prompt, "video", db)
         base = api_url.split("/v1")[0] if "/v1" in api_url else api_url
         payload = {
             "model": ai_model.name,
-            "prompt": prompt,
+            "prompt": optimized_prompt,
             "height": 768,
             "width": 1152,
             "num_frames": 121,
@@ -1762,7 +1850,11 @@ async def send_message(
                             ]
                             request_body["tool_choice"] = "auto"
 
-                        async with httpx.AsyncClient(timeout=ai_model.timeout or 120) as client:
+                        async with httpx.AsyncClient(
+                            timeout=ai_model.timeout or 120,
+                            proxy=_proxy_for_url(api_url),
+                            trust_env=False,
+                        ) as client:
                             resp = await client.post(
                                 api_url,
                                 headers={

backend/python/app/api/im_llm.py

@@ -6,13 +6,17 @@ IM Bot 真实 LLM 调用。
 
 行为（对齐 AI 聊天）：
 - Issue 7：图像/视频/语音意图优先走平台媒体模型（与 AI 聊天同一套 Auto 路由），
-  不再对“制作图片”这类请求只回文本。
+  不再对"制作图片"这类请求只回文本。
 - Issue 8：默认 Auto 模型；每轮先自动发现本账号已安装且平台已部署的技能，
   多个适用时由模型自行判断选 1 个最优，通过 function calling 执行。
+- 2026-07-15：修复 trust_env=False + proxy 处理，避免容器坏代理导致调用失败；
+  媒体生成前先经 _optimize_media_prompt 精炼提示词，提升 AGNES 生成质量。
 """
 import asyncio
 import json
+import re as _re
 from typing import Optional
+from urllib.parse import urlparse as _urlparse
 
 import httpx
 from sqlalchemy.orm import Session
@@ -21,6 +25,20 @@ from ..capability_registry import resolve_auto_model
 from ..models import AIModel, ModelProvider
 
 
+def _proxy_for_url(url: str) -> str | None:
+    """按目标域名选择是否走代理（与 chat.py 同逻辑）。
+
+    对 AGNES 等国内域名返回 None（直连），平台自有域名直连，其余走智能路由代理。
+    """
+    import os
+    host = (_urlparse(url).hostname or "").lower()
+    if "agnes-ai.com" in host or "agnes-ai.space" in host:
+        return None
+    if "qihuyun.com" in host:
+        return None
+    return os.environ.get("HTTPS_PROXY") or os.environ.get("HTTP_PROXY") or "http://172.18.0.1:7890"
+
+
 def _run_async(coro):
     """在同步上下文中运行协程，兼容「无事件循环」与「已有事件循环」两种场景。
 
@@ -61,7 +79,6 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
                 if result:
                     # IM 不渲染 HTML，把 <img>/<video> 还原为纯文本链接返回
                     if result.startswith("<img") or result.startswith("<video"):
-                        import re as _re
                         m = _re.search(r'src="([^"]+)"', result)
                         url = m.group(1) if m else ""
                         label = "图片" if result.startswith("<img") else "视频"
@@ -110,7 +127,11 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
                     {"type": "function", "function": fd} for fd in function_definitions
                 ]
                 request_body["tool_choice"] = "auto"
-            with httpx.Client(timeout=ai_model.timeout or 120) as client:
+            with httpx.Client(
+                timeout=ai_model.timeout or 120,
+                proxy=_proxy_for_url(api_url),
+                trust_env=False,
+            ) as client:
                 resp = client.post(
                     api_url,
                     headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},

backend/python/app/api/token_usage.py

@@ -84,9 +84,20 @@ def get_rankings(
     models_list = []
     for r in model_rankings:
         model = db.query(AIModel).filter(AIModel.id == r.model_id).first()
+        if model:
+            model_name = model.name
+        else:
+            # 模型已被删除，从最近一条日志中取 model_name 作为回退
+            recent = db.query(TokenUsageLog.model_name).filter(
+                TokenUsageLog.model_id == r.model_id,
+                TokenUsageLog.model_name.isnot(None),
+            ).first()
+            model_name = (recent.model_name if recent else None) or (
+                "Auto 智能匹配" if r.model_id == "auto" else "未知模型"
+            )
         models_list.append({
             "model_id": r.model_id,
-            "model_name": model.name if model else "unknown",
+            "model_name": model_name,
             "total_tokens": int(r.total),
             "usage_count": int(r.count),
         })
@@ -162,10 +173,18 @@ def get_token_logs(
     result = []
     for log_item in logs:
         model = db.query(AIModel).filter(AIModel.id == log_item.model_id).first()
+        if model:
+            model_name = model.name
+        elif log_item.model_name:
+            model_name = log_item.model_name  # 日志自带的名称
+        elif log_item.model_id == "auto":
+            model_name = "🤖 Auto 智能匹配"
+        else:
+            model_name = "未知模型 (ID: {})".format(log_item.model_id[:8] if log_item.model_id else "?")
         result.append({
             "id": log_item.id,
             "model_id": log_item.model_id,
-            "model_name": model.name if model else "unknown",
+            "model_name": model_name,
             "prompt_tokens": log_item.prompt_tokens or 0,
             "completion_tokens": log_item.completion_tokens or 0,
             "total_tokens": log_item.total_tokens or 0,

backend/python/app/capability_registry.py

@@ -77,7 +77,8 @@ def resolve_auto_model(capability: str, db: Session) -> Optional[AIModel]:
     根据能力解析平台默认模型：
     1. 优先使用后台"按能力默认模型"中配置的值
     2. 文本类能力若未单独配置，回退到全局默认模型(is_default)
-    3. 其余能力(embedding/rerank/asr 等)若未配置则返回 None
+    3. 媒体类能力(image/video/audio)若未配置，自动发现第一个启用且具备该能力的模型
+    4. 其余能力(embedding/rerank/asr 等)若未配置则返回 None
     """
     data = get_capability_defaults(db)
     mid = data.get(capability)
@@ -94,4 +95,16 @@ def resolve_auto_model(capability: str, db: Session) -> Optional[AIModel]:
         ).first()
         if m:
             return m
+    # 媒体类能力(image/video/audio)：无显式配置时自动发现任一启用的模型
+    # 避免用户未在后台配置"能力默认模型"时 Auto 回退到文本模型导致"无法生成图片/视频"
+    if capability in ("image", "video", "audio"):
+        from sqlalchemy import text as _text
+        m = db.query(AIModel).filter(
+            AIModel.is_enabled == True,
+            AIModel.capabilities.op("JSON_CONTAINS")(
+                AIModel.capabilities, f'"{capability}"'
+            ),
+        ).first()
+        if m:
+            return m
     return None