QihuAI Design
26075272d6
One-time alignment to close drift between the running container (qihu-aiflow / image qihuaiflow:with-deps) and this Git repository. Container hotfixes previously persisted only via `docker commit` are now captured in Git so Git becomes the single source of truth. Backend: - chat_service.py: accumulated fixes incl. EigenFlux README guidance - chat_context.py / chat_media.py: URL/web-repo context injection - api/url_reader.py: NEW SSRF-safe web/README reader (Issue #725) - skills_admin.py & Skills.tsx: 14-scene capability unification - pgvector_search.py, orchestrator.py, platform_knowledge.py, market.py, admin/models.py: drift sync Frontend: - ChatPage.tsx: collapsible AI think/act block (Issue #724) - MarketPage / EvolutionDashboard / TokenDashboard styling sync Migrations: - 018_normalize_marketapp_scene.sql: fixed capability allowlist (prevents capability wipe on restart) Alignment performed 2026-07-23 22:42:43. No deletions; secrets/venv/node_modules/ caches were not touched (tracked-source-only copy).
284 lines
13 KiB
Python
284 lines
13 KiB
Python
"""
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IM Bot 真实 LLM 调用。
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原实现位于 channels.py（通用频道管理模块），该模块已移除。
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为保持 IM Bot（微信机器人等）正常生成回复，将 _call_real_llm 迁移至此独立模块。
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行为（对齐 AI 聊天 — 2026-07-23 同步至最新）：
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- 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），
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不再对"制作图片"这类请求只回文本。
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- 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，
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多个适用时由模型自行判断选 1 个最优，通过 function calling 执行。
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- 2026-07-15：修复 trust_env=False + proxy 处理，避免容器坏代理导致调用失败；
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媒体生成前先经 _optimize_media_prompt 精炼提示词，提升 AGNES 生成质量。
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- 2026-07-23：对齐 Web Chat 最新能力：
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* 模型 failover（Auto 模式自动切换备选模型）
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* 平台知识库注入（get_platform_knowledge，技能清单实时同步）
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"""
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import asyncio
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import json
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import logging
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import re as _re
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from datetime import datetime, timezone, timedelta
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from typing import Optional
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from urllib.parse import urlparse as _urlparse
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import httpx
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from sqlalchemy.orm import Session
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from ..capability_registry import resolve_auto_model
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from ..models import AIModel, ModelProvider, TokenUsageLog, gen_uuid
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from app.core.observability import get_trace_id
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logger = logging.getLogger(__name__)
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# 香港出网代理(实测对 AGNES 延迟更低，2026-07-15 用户实测)，与 chat.py 同值。
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_HK_PROXY = "http://qyyfw:XHR521yhf@103.242.14.208:11495"
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def _proxy_for_url(url: str) -> str | None:
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"""按目标域名选择是否走代理（与 chat.py 同逻辑）。
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2026-07-15 调整：AGNES 改走香港代理(实测延迟更低)，不再直连。
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国内域名(尊重 NO_PROXY)直连，更稳定。
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"""
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import os
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host = (_urlparse(url).hostname or "").lower()
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if "agnes-ai.com" in host or "agnes-ai.space" in host:
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return _HK_PROXY
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if "qihuyun.com" in host:
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return None
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_no_proxy = [h.strip().lower() for h in os.environ.get("NO_PROXY", "").split(",") if h.strip()]
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for _np in _no_proxy:
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if host == _np or host.endswith("." + _np) or _np in host:
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return 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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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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IM 个人微信在独立线程中调用（无事件循环）→ asyncio.run；
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IM 多平台 Webhook 可能在 FastAPI 协程内调用（已有事件循环）→ 丢到线程池执行。
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timeout: 可选，秒数；超时抛出 asyncio.TimeoutError（由调用方捕获）。
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"""
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try:
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loop = asyncio.get_running_loop()
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except RuntimeError:
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loop = None
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if timeout is not None:
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async def _timed():
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return await asyncio.wait_for(coro, timeout=timeout)
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target = _timed()
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else:
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target = coro
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if loop is not None and loop.is_running():
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import concurrent.futures
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with concurrent.futures.ThreadPoolExecutor(1) as ex:
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return ex.submit(lambda: asyncio.run(target)).result(timeout=(timeout + 10) if timeout else None)
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return asyncio.run(target)
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def _log_media_usage(db: Session, user_id: str, ai_model, media_type: str, prompt: str):
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"""IM Bot 媒体生成记账：把图片/视频消耗写入 TokenUsageLog，供驾驶舱统计。
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此前 IM Bot 路径完全不写 TokenUsageLog，导致微信生成的图片/视频在驾驶舱里"查无此项"。
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媒体 API 不返回 token 用量，这里用与 chat.py _generate_media_task 一致的估算。
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"""
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if not user_id or not ai_model:
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return
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try:
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base = 1200 if media_type == "video" else 400
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est = base + (len(prompt or "") // 4)
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db.add(TokenUsageLog(
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id=gen_uuid(),
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user_id=user_id,
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model_id=ai_model.id,
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model_name=ai_model.name,
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trace_id=get_trace_id(),
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prompt_tokens=est,
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completion_tokens=0,
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total_tokens=est,
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created_at=datetime.now(timezone(timedelta(hours=8))).replace(tzinfo=None),
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))
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db.commit()
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except Exception as e:
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try:
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db.rollback()
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except Exception:
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pass
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logger.warning(f"[im_llm] 媒体 Token 记账失败(忽略): {e}")
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def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im", user_id: str = None) -> "str | dict":
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"""调用七虎AI 生成回复，供 IM Bot 复用。支持媒体意图路由、已安装技能自动发现、模型 failover。"""
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try:
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from app.api.chat import (
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_generate_media,
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detect_media_intent,
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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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)
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# ===== 1) 媒体意图路由（Issue 7）=====
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intent = detect_media_intent(prompt)
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if intent:
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ai_model = resolve_auto_model(intent, db)
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if ai_model:
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try:
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result = _run_async(_generate_media(db, ai_model, prompt, user_id), timeout=300)
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except Exception:
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# 任何未预期异常(如 AGNES 连接重置)统一兜底为友好提示，
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# 绝不直接把原始异常(如 "Server disconnected")甩给用户
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result = "⚠️ 媒体生成暂时失败（连接或服务波动），请稍后重试。"
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if result:
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# 媒体生成成功：返回结构化对象，交由 im_bot_user 在微信内联发送图片/视频
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s = result.strip()
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if s.startswith("<img") or s.startswith("<video"):
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m = _re.search(r'src="([^"]+)"', s)
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url = m.group(1) if m else ""
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media_type = "image" if s.startswith("<img") else "video"
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if not url:
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return "（生成完成，但未能提取媒体地址）"
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# IM Bot 路径此前完全不记账 → 补写媒体消耗，驾驶舱才看得到图片/视频数据
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_log_media_usage(db, user_id, ai_model, media_type, prompt)
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return {"__media__": True, "media_type": media_type, "url": url}
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# 媒体生成失败：_generate_media 已返回友好中文（如限频/队列繁忙），
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# 直接透传，绝不把原始提示词/标签/JSON 透传给用户
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if s.startswith("⚠️") or s.startswith("（媒体"):
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return s
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return "（媒体生成未能返回有效内容，请重试）"
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# ===== 2) 文本回复：模型 failover + 技能自动发现 + 平台知识库注入 =====
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from ..platform_knowledge import get_platform_knowledge
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# 构建候选模型列表（对齐 Web Chat failover）
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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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_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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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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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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# 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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try:
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pk = get_platform_knowledge(db=db)
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if pk:
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kb_context = f"【平台知识库——优先使用以下知识回答用户问题】\n{pk}"
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except Exception:
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pass
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# 尝试每个候选模型
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_retry_log = []
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for _try_model in _try_models:
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ai_model = _try_model
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provider = db.query(ModelProvider).filter(ModelProvider.id == ai_model.provider_id).first()
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api_key = ai_model.api_key or (provider.api_key if provider else None)
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api_url = ai_model.endpoint
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if not api_url and provider and provider.api_base_url:
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api_url = f"{provider.api_base_url.rstrip('/')}/v1/chat/completions"
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if not (api_key and api_url):
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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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system_prompt, function_definitions = _build_system_prompt(
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skills=skills,
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agents=[],
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kb_context=kb_context,
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user_content=prompt,
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mcp=[],
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auto_discovered=bool(skills),
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)
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt},
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]
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# 尝试调用
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try:
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max_rounds = 3
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ai_content = ""
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_http_ok = True
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for round_num in range(max_rounds):
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request_body = {"model": ai_model.name, "messages": messages, "max_tokens": 2048}
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if function_definitions and skills:
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request_body["tools"] = [
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{"type": "function", "function": fd} for fd in function_definitions
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]
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request_body["tool_choice"] = "auto"
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with httpx.Client(
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timeout=ai_model.timeout or 120,
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proxy=_proxy_for_url(api_url),
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trust_env=False,
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) as client:
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resp = client.post(
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api_url,
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headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
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json=request_body,
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)
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if resp.status_code != 200:
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_retry_log.append(f"{ai_model.name}: HTTP {resp.status_code}")
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_http_ok = False
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break
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data = resp.json()
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message = data.get("choices", [{}])[0].get("message", {})
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tool_calls = message.get("tool_calls", [])
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if tool_calls and skills:
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messages.append({
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"role": "assistant",
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"content": message.get("content") or "",
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"tool_calls": tool_calls,
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})
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for tc in tool_calls:
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func_name = tc.get("function", {}).get("name", "")
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try:
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func_args = json.loads(tc.get("function", {}).get("arguments", "{}"))
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except Exception:
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func_args = {"input": tc.get("function", {}).get("arguments", "")}
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tool_result = _execute_skill_tool(db, func_name, func_args, skills, prompt)
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messages.append({
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"role": "tool",
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"tool_call_id": tc.get("id", "call_1"),
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"content": tool_result,
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})
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continue
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ai_content = message.get("content", "")
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break
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# 检查本轮调用是否成功
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if _http_ok and ai_content:
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return ai_content or "（AI 回复为空）"
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# 本轮失败：记录日志，切换备选
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logger.warning(f"[im_llm] 模型 {ai_model.name} 失败，切换备选...")
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continue
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except Exception as exc:
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_retry_log.append(f"{ai_model.name}: {str(exc)[:60]}")
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logger.warning(f"[im_llm] 模型 {ai_model.name} 异常: {exc}")
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continue
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# 全部失败
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_summary = "; ".join(_retry_log[-3:])
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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。）"
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except Exception as e:
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return f"（AI 回复生成失败：{e}）"
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