fix: 修复AI聊天/IM多项问题(媒体直连+技能自动发现+agent/mcp上下文)
- Issue4/7: 图像/视频生成对AGNES国内域名强制直连(trust_env=False)，修复Server disconnected(代理172.18.0.1:7890返回502) - Issue8: 修复_parse_skill_content缺失def导致的NameError；新增_discover_installed_skills默认技能自动发现 - Issue2/3: system prompt注入agent资料与mcp描述/endpoint - Issue7: IM Bot制作图片走Auto路由媒体模型
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@@ -3,7 +3,15 @@ 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）：
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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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"""
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import asyncio
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import json
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from typing import Optional
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import httpx
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@@ -13,9 +21,54 @@ from ..capability_registry import resolve_auto_model
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from ..models import AIModel, ModelProvider
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def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im") -> str:
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"""调用七虎AI 默认文本模型生成回复，供 IM Bot 复用。"""
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def _run_async(coro):
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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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"""
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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 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(coro)).result()
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return asyncio.run(coro)
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def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im", user_id: str = None) -> str:
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"""调用七虎AI 生成回复，供 IM Bot 复用。支持媒体意图路由与已安装技能自动发现。"""
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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))
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except Exception as e:
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result = f"（媒体生成失败：{e}）"
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if result:
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# IM 不渲染 HTML，把 <img>/<video> 还原为纯文本链接返回
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if result.startswith("<img") or result.startswith("<video"):
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import re as _re
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m = _re.search(r'src="([^"]+)"', result)
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url = m.group(1) if m else ""
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label = "图片" if result.startswith("<img") else "视频"
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return f"已为你生成{label}：{url}" if url else result
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return result
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# ===== 2) 文本回复 + 技能自动发现（Issue 8）=====
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ai_model = resolve_auto_model("text", db)
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if not ai_model:
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return "（未配置默认文本模型，无法生成回复）"
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@@ -26,20 +79,69 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
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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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return "（模型未配置 API Key 或地址，无法生成回复）"
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# 自动发现本账号已安装且平台已部署的技能
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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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system_prompt, function_definitions = _build_system_prompt(
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skills=skills,
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agents=[],
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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": "你是七虎AI 助手，负责在即时通讯中回复用户消息。"},
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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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request_body = {"model": ai_model.name, "messages": messages, "max_tokens": 2048}
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with httpx.Client(timeout=ai_model.timeout or 120) 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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return f"（AI 模型调用失败 HTTP {resp.status_code}）"
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data = resp.json()
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return data.get("choices", [{}])[0].get("message", {}).get("content", "")
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max_rounds = 3
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ai_content = ""
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for _ 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(timeout=ai_model.timeout or 120) 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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return f"（AI 模型调用失败 HTTP {resp.status_code}）"
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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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return ai_content or "（AI 回复为空）"
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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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