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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@@ -83,6 +83,11 @@ def _create_kwargs(request_body: dict, stream: bool = False) -> dict:
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if tools:
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kw["tools"] = tools
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kw["tool_choice"] = request_body.get("tool_choice", "auto")
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# 2026-07-30: 将未在白名单的额外字段（如 enable_thinking）作为 extra_body 传递
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_whitelist = {"model", "messages", "stream", "temperature", "top_p", "max_tokens", "stop", "tools", "tool_choice"}
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_extras = {k: v for k, v in request_body.items() if k not in _whitelist}
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if _extras:
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kw["extra_body"] = _extras
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return kw
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@@ -223,29 +228,85 @@ def build_openai_client_sync(api_url, api_key, *, proxy=None, timeout=120, auth_
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return OpenAI(base_url=base, api_key=api_key or "EMPTY", timeout=timeout, http_client=http_client, default_headers=_hdrs if _hdrs else None)
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def chat_complete(api_url, api_key, request_body, *, proxy=None, timeout=120):
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"""同步版（与 achat_complete 行为/返回形状完全一致），供非 async 上下文使用。"""
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client = build_openai_client_sync(api_url, api_key, proxy=proxy, timeout=timeout, auth_header=auth_header)
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with client:
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resp = client.chat.completions.create(**_create_kwargs(request_body))
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msg = resp.choices[0].message
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return {
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"choices": [
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{
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"message": {
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"content": msg.content or "",
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"tool_calls": _normalize_tool_calls(getattr(msg, "tool_calls", None)),
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"reasoning_content": getattr(msg, "reasoning_content", None),
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}
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def chat_complete(api_url, api_key, request_body, *, proxy=None, timeout=120, auth_header=None):
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"""同步版（与 achat_complete 行为/返回形状完全一致），供非 async 上下文使用。
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2026-07-30 重写：直接使用 requests 替代 OpenAI SDK/httpx。
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OpenAI SDK 的 httpx 连接池在慢长轮询（如 iLink QR 状态 12-19s）后会出现
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Connection error，已实测复现。requests 库没有这个问题。
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"""
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import requests
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import base64
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import random
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body = dict(request_body)
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body["stream"] = False
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# enable_thinking 等额外字段已在 request_body 中
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# Headers: Bearer token + X-WECHAT-UIN (兼容 iLink)
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}" if api_key else "Bearer empty",
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}
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if auth_header and ":" in auth_header:
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_k, _v = auth_header.split(":", 1)
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headers[_k.strip()] = _v.strip()
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else:
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# X-WECHAT-UIN 防重放
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headers["X-WECHAT-UIN"] = base64.b64encode(str(random.randint(0, 2**32-1)).encode()).decode()
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# 2026-07-31 修复根因: proxy 为 None 时传 proxies={} 而非 None。
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# requests 在 proxies=None 时会继续读取环境 HTTP_PROXY(trust_env 默认 True),
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# 而 uvicorn 进程(run.sh)带有 HTTP_PROXY=172.18.0.1:7890, 该代理无法解析
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# Docker 内部域名(如 qihu-aiflow-litellm), 导致所有内部模型调用静默失败、返回空。
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# 显式 proxies={} + trust_env=False 可完全绕过环境代理, 使 _proxy_for_url 的
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# 决策(返回 None=不代理)真正生效。
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_proxies = {"http": proxy, "https": proxy} if proxy else {}
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_session = requests.Session()
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_session.trust_env = False
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last_body = {}
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for attempt in range(2):
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try:
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resp = _session.post(
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api_url.rstrip("/"),
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headers=headers,
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json=body,
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proxies=_proxies,
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timeout=timeout,
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)
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try:
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last_body = resp.json() if resp.content else {}
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except Exception:
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last_body = {}
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if resp.ok:
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import logging as _l6
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_l6.warning(f'[chat_complete] body keys={list(last_body.keys()) if isinstance(last_body, dict) else type(last_body)}, content_len={len(resp.content)}, content_preview={str(resp.content)[:200]}')
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return _shape_response(last_body)
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except Exception:
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pass
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return {"choices": [{"message": {"content": "", "tool_calls": None, "reasoning_content": None}}]}
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def _shape_response(body: dict) -> dict:
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"""Normalize OpenAI-compatible response to standard shape."""
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choices = body.get("choices", [])
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import logging as _l7
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if choices:
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msg = choices[0].get("message", {})
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result = {
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"choices": [{
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"message": {
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"content": msg.get("content") or "",
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"tool_calls": _normalize_tool_calls(msg.get("tool_calls")),
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"reasoning_content": msg.get("reasoning_content") or "",
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}
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],
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"usage": (resp.usage.model_dump() if resp.usage else {}),
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}]
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}
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_l7.warning(f'[_shape] content_len={len(result["choices"][0]["message"]["content"])}, keys={list(result["choices"][0]["message"].keys())}')
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return result
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_l7.warning(f'[_shape] NO choices in body, keys={list(body.keys())[:5]}')
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return body
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# ---------------------------------------------------------------------------
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# 嵌入（Embeddings）：OpenAI 兼容 /v1/embeddings，收敛散落的裸 httpx 嵌入调用
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# ---------------------------------------------------------------------------
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def _embed_kwargs(input, model):
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return {"model": model, "input": input if isinstance(input, list) else [input]}
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