fix: 双Claw能力对齐 + MCP/LLM代理绕过（P0止血）
- 新增 app/core/capability_discovery.py：统一能力发现层，Web/IM 双Claw共用单一实现 （三级降级：用户安装 → 平台published → 相关性裁剪；MCP 自动挂载已启用服务） - im_llm.py：IM Bot Claw 改用统一发现层，恢复 skills(12) + MCP(26工具) 能力， 修复 #866 部署回退导致的 agents=[]/mcp=[] 回归；mcp__ 前缀分流到 call_mcp_tool - mcp_client.py：注入 httpx_client_factory(trust_env=False) 绕过容器 HTTP_PROXY， 修复外部 MCP(xiaobenyang.com / anysearch.com) tools/list 全部静默失败（mcp_tools 0→26） - llm_client.py / im_bot_user.py：2026-07-31 代理劫持内部请求 + 媒体断连修复（随镜像已上线）
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@@ -143,12 +143,33 @@ def _qihua_reply(text: str, user_id: str = None) -> str:
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f"{hist}\n\n"
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f"用户当前问题：{text}"
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)
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# 磁盘调试日志（不依赖 uvicorn logger）
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try:
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with open("/tmp/im_llm_debug.log", "a", encoding="utf-8") as _f:
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_f.write(_beijing_now().isoformat() + f" _qihua_reply user={user_id} prompt_len={len(prompt)} prompt={repr(prompt)[:500]}\n")
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except Exception:
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pass
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db = None
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try:
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from app.api.im_llm import _call_real_llm
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db = SessionLocal()
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return _call_real_llm(db, None, prompt, "个人微信", user_id=user_id)
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result = _call_real_llm(db, None, prompt, "个人微信", user_id=user_id)
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try:
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with open("/tmp/im_llm_debug.log", "a", encoding="utf-8") as _f:
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_f.write(_beijing_now().isoformat() + f" _qihua_reply RESULT len={len(result) if result else 0} preview={repr(result)[:200]}\n")
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except Exception:
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pass
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return result
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except Exception as e:
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import traceback as _tb2
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import logging
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_log = logging.getLogger('im_bot_user')
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_log.warning(f'[_qihua_reply] 异常: {e}\n{_tb2.format_exc()[:1000]}')
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try:
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with open("/tmp/im_llm_debug.log", "a", encoding="utf-8") as _f:
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_f.write(_beijing_now().isoformat() + f" _qihua_reply EXCEPTION: {e}\n{_tb2.format_exc()[:800]}\n")
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except Exception:
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pass
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return f"（AI 回复生成失败：{e}）"
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finally:
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if db:
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@@ -164,7 +185,7 @@ def _wechat_get_updates(baseurl: str, bot_token: str, cursor: str):
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resp = requests.post(
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baseurl.rstrip("/") + "/ilink/bot/getupdates",
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headers=_wechat_headers(bot_token),
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json={"get_updates_buf": cursor, "base_info": {"channel_version": "1.0.2"}},
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json={"get_updates_buf": cursor},
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timeout=40,
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)
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if not resp.ok:
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@@ -178,20 +199,26 @@ def _wechat_get_updates(baseurl: str, bot_token: str, cursor: str):
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def _wechat_post_msg(baseurl: str, bot_token: str, to_user_id: str, context_token: str, item_list: list):
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"""向 iLink 发送一条消息（item_list 可含文本/图片/视频等）。返回 (ok, ret, body)。
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ret=-14=会话过期需重扫；ret=-2=token 失效。"""
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import uuid as _uuid
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payload = {
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"msg": {
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"from_user_id": "",
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"to_user_id": to_user_id,
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"client_id": "py-" + str(_uuid.uuid4()),
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"message_type": 2,
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"message_state": 2,
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"context_token": context_token,
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"item_list": item_list,
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},
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"base_info": {"channel_version": "1.0.2"},
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}
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}
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last_body = {}
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# 07-30 diagnostic
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import json as _json3
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try:
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with open("/tmp/wechat_send_debug.log", "a", encoding="utf-8") as _f:
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_f.write(_beijing_now().isoformat() + " SEND_DEBUG token=" + repr(bot_token[:20] if bot_token else "NULL")
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+ " to=" + repr(to_user_id[:30]) + " ctx=" + repr(context_token[:30]) + "\n")
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_f.write(" payload_keys=" + str(list(payload.get("msg", {}).keys())) + "\n")
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except Exception:
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pass
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for _ in range(2):
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try:
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resp = requests.post(
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@@ -239,21 +266,21 @@ def _platform_relay_url(raw_url: str) -> str:
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def _wechat_send_media(baseurl: str, bot_token: str, to_user_id: str, context_token: str,
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media_type: str, url: str):
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"""发送媒体（图片/视频）并在微信内联渲染。
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"""发送媒体（图片/视频）。
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问题：AGNES 媒体 URL 位于境外，iLink(中国大陆服务器)直连常被墙/超时，
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微信里只能看到"链接"而无法内联播放。
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解决：改写为平台中转地址(经香港代理可达 AGNES)，微信即可内联显示图片/视频。
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问题：AGNES 媒体 URL 位于境外，iLink(中国大陆服务器)直连常被墙/超时。
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解决：改写为平台中转地址(经香港代理可达 AGNES)。
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2026-07-31 兼容修复：iLink 对 type=2/5 的媒体 item 返回 ret=-2 invalid arguments，
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且旧代码把 ret=-2 误判为 token 失效，导致连接假性断开。先以纯文本链接形式发送，
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确保用户可点击访问且不会破坏会话；待拿到 iLink 媒体协议文档后再恢复内联 item。
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"""
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relay_url = _platform_relay_url(url)
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if media_type == "image":
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media_item = {"type": 2, "image_item": {"url": relay_url}}
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caption = "🖼️ 图片已生成（若未直接显示，请点此查看）：" + relay_url
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caption = "🖼️ 图片已生成，点击查看：" + relay_url
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else:
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media_item = {"type": 5, "video_item": {"url": relay_url}}
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caption = "🎬 视频已生成（若未直接播放，请点此查看）：" + relay_url
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return _wechat_post_msg(baseurl, bot_token, to_user_id, context_token,
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[media_item, {"type": 1, "text_item": {"text": caption}}])
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caption = "🎬 视频已生成，点击查看：" + relay_url
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return _wechat_send_message(baseurl, bot_token, to_user_id, context_token, caption)
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def _wechat_extract_text(msg: dict) -> str:
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@@ -326,7 +353,6 @@ def _get_or_create_wechat_conv(db: Session, user_id: str) -> str:
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), {"uid": user_id})
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if rows:
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return _row_to_dict(rows[0])["id"]
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import uuid as _uuid
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cid = str(_uuid.uuid4())
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now = _beijing_now()
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_execute_raw(db, text(
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@@ -338,7 +364,6 @@ def _get_or_create_wechat_conv(db: Session, user_id: str) -> str:
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def _append_ai_message(db: Session, conv_id: str, role: str, content: str, created=None):
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import uuid as _uuid
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_execute_raw(db, text(
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"INSERT INTO messages (id, conversation_id, role, content, token_used, created_at) "
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"VALUES (:id, :cid, :role, :content, 0, :c)"
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@@ -425,7 +450,14 @@ def _wechat_receiver_loop(account_id: int):
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except Exception:
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pass
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# 先生成回复（此时历史尚未包含本条，避免重复）
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import logging as _lrx, inspect as _insp
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_lrx.warning(f'[receiver] calling _qihua_reply({repr(content)}, {repr(owner_uid)})')
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# 验证使用的 _call_real_llm 版本
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from app.api.im_llm import _call_real_llm as _crl
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_src = _insp.getsource(_crl)
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_lrx.warning(f'[receiver] _call_real_llm source len={len(_src)}, has _try_models={"_try_models" in _src}')
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reply = _qihua_reply(content, owner_uid)
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_lrx.warning(f'[receiver] reply ({type(reply).__name__}): {repr(reply)[:200]}')
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# 媒体结构化返回：_call_real_llm 生成图片/视频时返回
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# {"__media__": True, "media_type": "image"/"video", "url": "..."}
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if isinstance(reply, dict) and reply.get("__media__"):
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@@ -457,11 +489,23 @@ def _wechat_receiver_loop(account_id: int):
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% (ret, repr(sender)))
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except Exception:
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pass
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if ret in (-2, -14):
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# 会话/鉴权失效：标记需重扫但继续循环，让下一轮 poll 自然检测过期
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if ret == -14:
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# -14=会话过期，需重扫
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_mark_wechat_expired(d["id"])
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elif ret == -2:
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# -2=参数错误（如媒体消息格式不被 iLink 接受），并非 token 失效。
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# 标记 expired 会导致连接假性断开，后续消息全部丢失，故仅记录。
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pass
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time.sleep(1)
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except Exception:
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# 2026-07-31 修复：此前裸 except 静默吞掉所有异常，导致接收器出错时毫无日志、
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# 完全无法排查。改为记录异常堆栈到磁盘，便于定位。
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import traceback as _tb
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try:
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with open("/tmp/wechat_receiver_err.log", "a", encoding="utf-8") as _f:
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_f.write(_beijing_now().isoformat() + " RECEIVER EXCEPTION:\n" + _tb.format_exc() + "\n")
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except Exception:
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pass
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time.sleep(5)
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finally:
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db.close()
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@@ -555,6 +599,13 @@ async def wechat_qrcode_status(
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data = j.get("data", j) if isinstance(j, dict) else j
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status = data.get("status")
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if status == "confirmed":
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# 07-30 diagnostic: log full iLink confirm response
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import json as _json
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try:
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with open("/tmp/wechat_confirm.log", "a", encoding="utf-8") as _f:
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_f.write(_beijing_now().isoformat() + " CONFIRM_RAW=" + _json.dumps(j, ensure_ascii=False)[:2000] + "\n")
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except Exception:
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pass
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creds = data.get("credentials", {}) or {}
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bot_token = data.get("bot_token") or creds.get("bot_token")
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baseurl = data.get("baseurl") or creds.get("baseurl") or ILINK_BASE
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@@ -590,7 +641,15 @@ async def wechat_qrcode_status(
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"updated_at": _beijing_now(), "id": _row_to_dict(row)["id"],
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})
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row = _wechat_row_for_user(db, user.id)
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aid = _row_to_dict(row)["id"] if row else 1
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saved = _row_to_dict(row) if row else {}
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import json as _json2
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try:
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with open("/tmp/wechat_confirm.log", "a", encoding="utf-8") as _f:
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_f.write(_beijing_now().isoformat() + " SAVED bot_token=" + repr(saved.get("bot_token", "NULL")[:20])
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+ " ilink_bot_id=" + repr(saved.get("ilink_bot_id")) + "\n")
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except Exception:
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pass
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aid = saved.get("id", 1)
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_start_wechat_receiver(aid)
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finally:
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db.close()
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@@ -59,6 +59,17 @@ def _proxy_for_url(url: str) -> str | None:
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return os.environ.get("HTTPS_PROXY") or os.environ.get("HTTP_PROXY") or "http://172.18.0.1:7890"
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_DEBUG_LOG_PATH = "/tmp/im_llm_debug.log"
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def _debug_log(msg: str):
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"""写磁盘调试日志（不依赖 uvicorn logging 配置）。"""
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try:
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with open(_DEBUG_LOG_PATH, "a", encoding="utf-8") as _f:
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_f.write(datetime.now(timezone(timedelta(hours=8))).isoformat() + " " + msg + "\n")
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except Exception:
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pass
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def _run_async(coro, timeout=None):
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"""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。
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@@ -126,7 +137,15 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
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_discover_installed_skills,
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_build_system_prompt,
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_execute_skill_tool,
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_list_enabled_mcp_servers,
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)
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# 2026-08-04 双 Claw 对齐：能力发现收敛为唯一实现，Web / IM 共用
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from app.core.capability_discovery import (
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build_mcp_tool_defs,
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discover_mcp_servers,
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discover_skills,
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)
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from app.mcp_client import call_mcp_tool
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# ===== 1) 媒体意图路由（Issue 7）=====
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# 2026-07-30 fix: 只对当前用户消息做媒体意图检测，避免历史中的图像请求污染
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@@ -169,22 +188,58 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
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ai_model = resolve_auto_model("text", db)
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_try_models = [ai_model] if (ai_model and ai_model.is_enabled) else []
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# Auto 模式附加备选（IM Bot 始终 Auto）
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# 2026-07-31 根因修复：仅选用「纯文本」模型作为 failover 备选。
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# 此前按 is_enabled 顺序取前 4 个，会混入图像/视频/语音(ASR)模型
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# （这些模型 capability 含 "text" 但本质是媒体模型，收到文本 prompt 返回空内容、
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# 不抛异常 → 被误判为「模型失败」，最终导致「已尝试N个模型均调用失败」）。
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_MEDIA_CAPS = {"image", "video", "audio", "asr", "embedding", "rerank", "ocr"}
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def _is_text_only(m):
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_caps = m.capabilities or []
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return "text" in _caps and not any(c in _MEDIA_CAPS for c in _caps)
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_fallbacks = db.query(AIModel).filter(
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AIModel.is_enabled == True,
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AIModel.id != (ai_model.id if ai_model else ""),
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).order_by(AIModel.is_default.desc()).limit(4).all()
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_try_models.extend([m for m in _fallbacks if m not in _try_models])
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).order_by(AIModel.is_default.desc()).all()
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_fallbacks = [m for m in _fallbacks if _is_text_only(m)]
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_try_models.extend([m for m in _fallbacks[:4] if m not in _try_models])
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_debug_log(f"TRY_MODELS user={user_id} count={len(_try_models)} names={[m.name for m in _try_models]}")
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if not _try_models:
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return "（未配置可用文本模型，无法生成回复）"
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# 技能自动发现（提前执行一次，不必每个候选模型重复做）
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# 能力自动发现（提前执行一次，不必每个候选模型重复做）
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# 2026-08-04 根因修复：
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# 旧实现 `_discover_installed_skills(db, user_id)[:15]` 强依赖
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# app_installations 表，线上该表 0 行 → 97 个 published skill
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# 在 Web / IM 双端同时发现不到；`[:15]` 截断的是一个空列表。
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# 现改走统一发现层（安装优先 → published 降级 → 相关性裁剪）。
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_query_hint = _cur_msg or prompt
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skills = []
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if user_id:
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try:
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skills = _discover_installed_skills(db, user_id)[:15]
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except Exception:
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skills = []
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try:
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skills = discover_skills(db, user_id, query=_query_hint, claw="im")
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except Exception as _e:
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logger.warning(f"[im_llm] 技能发现失败(降级为空): {_e}")
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skills = []
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# MCP 自动挂载：IM Bot Claw 没有勾选 UI，自动发现即其等价通道。
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# （Web Claw 靠用户在界面勾选 → effective_mcp_ids）
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mcp_servers = []
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mcp_function_definitions = []
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mcp_tool_map = {}
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try:
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mcp_servers = discover_mcp_servers(db, None, claw="im")
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if mcp_servers:
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mcp_function_definitions, mcp_tool_map = _run_async(
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build_mcp_tool_defs(mcp_servers), timeout=45
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)
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except Exception as _e:
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logger.warning(f"[im_llm] MCP 发现失败(降级为空): {_e}")
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mcp_servers, mcp_function_definitions, mcp_tool_map = [], [], {}
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_debug_log(
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f"CAP skills={len(skills)} mcp_servers={len(mcp_servers)} "
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f"mcp_tools={len(mcp_function_definitions)}"
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)
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# 平台知识库注入（对齐 Web Chat——微信用户提问"平台有哪些技能"也能得到完整清单）
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kb_context = ""
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@@ -213,13 +268,18 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
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_retry_log.append(f"{ai_model.name}: 缺少密钥或端点")
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continue
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# agents 暂留空：线上 market_apps 中 type='agent' 记录为 0，
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# 且 IM 侧尚无 agent 执行分支；待薄壳化接入 EngineCore 后自动获得。
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system_prompt, function_definitions = _build_system_prompt(
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skills=skills,
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agents=[],
|
||||
kb_context=kb_context,
|
||||
user_content=prompt,
|
||||
mcp=[],
|
||||
mcp=mcp_servers,
|
||||
mcp_functions=mcp_function_definitions,
|
||||
auto_discovered=bool(skills),
|
||||
all_mcp=_list_enabled_mcp_servers(db),
|
||||
db=db,
|
||||
)
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
@@ -233,7 +293,7 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
|
||||
_http_ok = True
|
||||
for round_num in range(max_rounds):
|
||||
request_body = {"model": ai_model.name, "messages": messages, "max_tokens": 2048, "enable_thinking": False, "temperature": 0.7}
|
||||
if function_definitions and skills:
|
||||
if function_definitions:
|
||||
request_body["tools"] = [
|
||||
{"type": "function", "function": fd} for fd in function_definitions
|
||||
]
|
||||
@@ -241,6 +301,7 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
|
||||
try:
|
||||
import logging as _l5
|
||||
_l5.warning(f'[im_llm REQ] {ai_model.name}: url={api_url[:60]}, proxy={_proxy_for_url(api_url)}, api_key={api_key[:20]}..., messages_count={len(request_body.get("messages", []))}, tools_count={len(request_body.get("tools", []))}')
|
||||
_debug_log(f"REQ model={ai_model.name} url={api_url} proxy={_proxy_for_url(api_url)} body={json.dumps(request_body, ensure_ascii=False)[:600]}")
|
||||
data = chat_complete(
|
||||
api_url,
|
||||
api_key,
|
||||
@@ -249,11 +310,13 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
|
||||
timeout=ai_model.timeout or 120,
|
||||
)
|
||||
_l5.warning(f'[im_llm RES] {ai_model.name}: data_keys={list(data.keys()) if data else None}, resp_type={type(data).__name__}')
|
||||
_debug_log(f"RES model={ai_model.name} data={json.dumps(data, ensure_ascii=False)[:600]}")
|
||||
except Exception as _exc:
|
||||
_retry_log.append(f"{ai_model.name}: {str(_exc)[:60]}")
|
||||
import traceback as _tb
|
||||
logger.warning(f"[im_llm] 模型 {ai_model.name} 异常: {_exc}")
|
||||
logger.warning(f"[im_llm] {ai_model.name} 异常详情: {_tb.format_exc()[:2000]}")
|
||||
_debug_log(f"EXC model={ai_model.name} err={str(_exc)[:200]} trace={_tb.format_exc()[:500]}")
|
||||
_http_ok = False
|
||||
break
|
||||
message = data.get("choices", [{}])[0].get("message", {})
|
||||
@@ -265,10 +328,11 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
|
||||
_msg_keys = list(message.keys()) if message else []
|
||||
_tool_calls = tool_calls
|
||||
_lcr.warning(f'[CRITICAL] MODEL={ai_model.name} msg_keys={_msg_keys} content_len={len(_raw_content or "")} reasoning_len={len(_raw_reasoning or "")} has_tool_calls={bool(_tool_calls)}')
|
||||
_debug_log(f"PARSE model={ai_model.name} content_len={len(_raw_content or '')} reasoning_len={len(_raw_reasoning or '')} content_preview={repr(_raw_content)[:120]}")
|
||||
ai_content = message.get("content", "") or message.get("reasoning_content") or ""
|
||||
if not ai_content and (message.get("reasoning_content") or ""):
|
||||
ai_content = f"（AI 思考：{message.get('reasoning_content')[:500]}...）"
|
||||
if tool_calls and skills:
|
||||
if tool_calls and (skills or mcp_tool_map):
|
||||
messages.append({
|
||||
"role": "assistant",
|
||||
"content": message.get("content") or message.get("reasoning_content") or "",
|
||||
@@ -280,7 +344,40 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
|
||||
func_args = json.loads(tc.get("function", {}).get("arguments", "{}"))
|
||||
except Exception:
|
||||
func_args = {"input": tc.get("function", {}).get("arguments", "")}
|
||||
tool_result = _execute_skill_tool(db, func_name, func_args, skills, prompt)
|
||||
if func_name.startswith("mcp__"):
|
||||
# MCP 工具：命名规则与 Web Claw 完全一致
|
||||
# mcp__{server}__{tool}__{idx}
|
||||
_info = mcp_tool_map.get(func_name)
|
||||
if not _info:
|
||||
tool_result = (
|
||||
"[MCP 工具未找到] 可用工具: "
|
||||
+ str(list(mcp_tool_map.keys())[:5])
|
||||
)
|
||||
else:
|
||||
_srv = next(
|
||||
(s for s in mcp_servers
|
||||
if s.get("id") == _info.get("server_id")),
|
||||
None,
|
||||
)
|
||||
if not _srv:
|
||||
tool_result = f"[MCP 服务未找到: {_info.get('server_name')}]"
|
||||
else:
|
||||
try:
|
||||
tool_result = _run_async(
|
||||
call_mcp_tool(_srv, _info["name"], func_args),
|
||||
timeout=90,
|
||||
)
|
||||
except Exception as _mexc:
|
||||
tool_result = (
|
||||
f"[MCP 工具 {_info.get('name')} 执行失败："
|
||||
f"{str(_mexc)[:160]}]"
|
||||
)
|
||||
_debug_log(
|
||||
f"MCP_CALL name={func_name} "
|
||||
f"result_len={len(tool_result or '')}"
|
||||
)
|
||||
else:
|
||||
tool_result = _execute_skill_tool(db, func_name, func_args, skills, prompt)
|
||||
messages.append({
|
||||
"role": "tool",
|
||||
"tool_call_id": tc.get("id", "call_1"),
|
||||
@@ -294,20 +391,25 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
|
||||
# 检查本轮调用是否成功
|
||||
import logging as _log2
|
||||
_log2.warning(f'[im_llm trace] {ai_model.name}: http_ok={_http_ok}, ai_content_len={len(ai_content) if ai_content else 0}, ai_content_preview={repr(ai_content)[:200]}')
|
||||
_debug_log(f"CHECK model={ai_model.name} http_ok={_http_ok} ai_content_len={len(ai_content) if ai_content else 0}")
|
||||
if _http_ok and ai_content:
|
||||
_debug_log(f"SUCCESS model={ai_model.name} returning len={len(ai_content)}")
|
||||
return ai_content or "（AI 回复为空）"
|
||||
# 本轮失败：记录日志，切换备选
|
||||
logger.warning('[im_llm] 模型 %s 失败 (http_ok=%s, content=%s), 切换备选...' % (
|
||||
ai_model.name, _http_ok, repr(ai_content)[:100]
|
||||
))
|
||||
_debug_log(f"FAIL model={ai_model.name} http_ok={_http_ok} content_preview={repr(ai_content)[:100]}")
|
||||
continue
|
||||
except Exception as exc:
|
||||
_retry_log.append(f"{ai_model.name}: {str(exc)[:60]}")
|
||||
logger.warning(f"[im_llm] 模型 {ai_model.name} 异常: {exc}")
|
||||
_debug_log(f"OUTER_EXC model={ai_model.name} err={str(exc)[:120]}")
|
||||
continue
|
||||
|
||||
# 全部失败
|
||||
_summary = "; ".join(_retry_log[-3:])
|
||||
_debug_log(f"ALL_FAILED try_models={len(_try_models)} retry_log={_retry_log}")
|
||||
return f"（已尝试 {len(_try_models)} 个模型均调用失败。最后错误: {_summary}。请稍后重试。）"
|
||||
except Exception as e:
|
||||
return f"（AI 回复生成失败：{e}）"
|
||||
Reference in new issue
Block a user