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chore: normalize line endings to LF via .gitattributes

Browse Source 
Root-cause fix for mixed CRLF/LF in the repo (no .gitattributes previously).
Adds '* text=auto eol=lf' and explicit binary rules so all future checkouts
are LF. Renormalizes all tracked text files (no logical content change).
Backup tags: image qihuaiflow:backup-lineend-20260725-121632, git backup-lineend-pre-20260725-121632.
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QihuAI Design committed 2026-07-25 12:16:39 +08:00
1 parent 2a7bb875f9
commit 771c8456b8
39 files changed
+34810 -34777
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backend/python/app/api/admin/agents_admin.py
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backend/python/app/api/admin/market_admin.py
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backend/python/app/api/admin/mcp_market_admin.py
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backend/python/app/api/admin/settings.py
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backend/python/app/api/admin/skills_admin.py
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backend/python/app/api/chat_context.py
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backend/python/app/api/chat_knowledge.py
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# Auto-extracted from chat.py during Phase 1 refactor (Strangler Fig).
# Source of truth: scripts/phase1_extract.py. Do not hand-merge back.
from fastapi import APIRouter, Depends, HTTPException, Query, Response
from sqlalchemy.orm import Session
from sqlalchemy import and_, desc, asc, func
from pydantic import BaseModel, Field
from typing import Optional
import uuid
from datetime import datetime, timezone, timedelta
import re
import json
import asyncio
import math
import httpx
import os
import logging
from ..database import get_db, SessionLocal
from ..models import (
User, Conversation, Message, AIModel, ModelProvider, TokenUsageLog,
KnowledgeBase, KnowledgeDocument, KnowledgeDocumentChunk, EvolutionSkill,
EvolutionTrajectory, MarketApp, AppPurchase, AppInstallation,
MCPServer,
gen_uuid
)
from ..auth import get_current_user
from ..platform_knowledge import get_platform_knowledge
from ..mcp_client import list_mcp_tools, call_mcp_tool
from ..middleware.log_service import LogService
from ..capability_registry import resolve_auto_model
from app.api.chat_utils import (cst_now)
def _build_knowledge_graph(db: Session, kb: KnowledgeBase) -> dict:
"""从知识库文档构建知识图谱节点和边，返回 graph_data。"""
documents = db.query(KnowledgeDocument).filter(
and_(
KnowledgeDocument.knowledge_base_id == kb.id,
KnowledgeDocument.status == "completed",
)
).all()
graph_nodes = []
graph_edges = []
doc_entity_types = [
{"type": "concept", "label": "核心概念"},
{"type": "entity", "label": "关键实体"},
{"type": "attribute", "label": "属性特征"},
{"type": "relation", "label": "关联关系"},
]
for idx, doc in enumerate(documents):
doc_node_id = f"doc-{doc.id[:8]}"
graph_nodes.append({
"id": doc_node_id, "label": doc.filename, "type": "document", "size": 18, "group": "document",
})
filename_base = doc.filename.rsplit(".", 1)[0]
words = filename_base.replace("_", " ").replace("-", " ").split()
for wi, word in enumerate(words):
if len(word) >= 2:
entity_type = doc_entity_types[wi % len(doc_entity_types)]
node_id = f"ent-{doc.id[:8]}-{wi}"
graph_nodes.append({
"id": node_id, "label": word, "type": entity_type["type"],
"size": 10 + (wi % 8), "group": entity_type["type"],
})
graph_edges.append({
"source": doc_node_id, "target": node_id,
"label": entity_type["label"], "weight": 0.5 + (0.5 / (wi + 1)),
})
for i in range(len(documents)):
for j in range(i + 1, len(documents)):
fi = documents[i].filename.lower().rsplit(".", 1)[0]
fj = documents[j].filename.lower().rsplit(".", 1)[0]
words_i = set(fi.replace("_", " ").replace("-", " ").split())
words_j = set(fj.replace("_", " ").replace("-", " ").split())
common = words_i & words_j
if common and len(common) > 0:
graph_edges.append({
"source": f"doc-{documents[i].id[:8]}",
"target": f"doc-{documents[j].id[:8]}",
"label": f"共有关键词: {', '.join(list(common)[:3])}",
"weight": min(0.9, 0.3 * len(common)),
})
graph_data = {
"nodes": graph_nodes,
"edges": graph_edges,
"stats": {
"total_nodes": len(graph_nodes), "total_edges": len(graph_edges),
"document_count": len(documents),
},
}
kb.knowledge_graph_count = len(graph_nodes)
kb.updated_at = cst_now()
return graph_data
# Auto-extracted from chat.py during Phase 1 refactor (Strangler Fig).
# Source of truth: scripts/phase1_extract.py. Do not hand-merge back.
from fastapi import APIRouter, Depends, HTTPException, Query, Response
from sqlalchemy.orm import Session
from sqlalchemy import and_, desc, asc, func
from pydantic import BaseModel, Field
from typing import Optional
import uuid
from datetime import datetime, timezone, timedelta
import re
import json
import asyncio
import math
import httpx
import os
import logging
from ..database import get_db, SessionLocal
from ..models import (
User, Conversation, Message, AIModel, ModelProvider, TokenUsageLog,
KnowledgeBase, KnowledgeDocument, KnowledgeDocumentChunk, EvolutionSkill,
EvolutionTrajectory, MarketApp, AppPurchase, AppInstallation,
MCPServer,
gen_uuid
)
from ..auth import get_current_user
from ..platform_knowledge import get_platform_knowledge
from ..mcp_client import list_mcp_tools, call_mcp_tool
from ..middleware.log_service import LogService
from ..capability_registry import resolve_auto_model
from app.api.chat_utils import (cst_now)
def _build_knowledge_graph(db: Session, kb: KnowledgeBase) -> dict:
"""从知识库文档构建知识图谱节点和边，返回 graph_data。"""
documents = db.query(KnowledgeDocument).filter(
and_(
KnowledgeDocument.knowledge_base_id == kb.id,
KnowledgeDocument.status == "completed",
)
).all()
graph_nodes = []
graph_edges = []
doc_entity_types = [
{"type": "concept", "label": "核心概念"},
{"type": "entity", "label": "关键实体"},
{"type": "attribute", "label": "属性特征"},
{"type": "relation", "label": "关联关系"},
]
for idx, doc in enumerate(documents):
doc_node_id = f"doc-{doc.id[:8]}"
graph_nodes.append({
"id": doc_node_id, "label": doc.filename, "type": "document", "size": 18, "group": "document",
})
filename_base = doc.filename.rsplit(".", 1)[0]
words = filename_base.replace("_", " ").replace("-", " ").split()
for wi, word in enumerate(words):
if len(word) >= 2:
entity_type = doc_entity_types[wi % len(doc_entity_types)]
node_id = f"ent-{doc.id[:8]}-{wi}"
graph_nodes.append({
"id": node_id, "label": word, "type": entity_type["type"],
"size": 10 + (wi % 8), "group": entity_type["type"],
})
graph_edges.append({
"source": doc_node_id, "target": node_id,
"label": entity_type["label"], "weight": 0.5 + (0.5 / (wi + 1)),
})
for i in range(len(documents)):
for j in range(i + 1, len(documents)):
fi = documents[i].filename.lower().rsplit(".", 1)[0]
fj = documents[j].filename.lower().rsplit(".", 1)[0]
words_i = set(fi.replace("_", " ").replace("-", " ").split())
words_j = set(fj.replace("_", " ").replace("-", " ").split())
common = words_i & words_j
if common and len(common) > 0:
graph_edges.append({
"source": f"doc-{documents[i].id[:8]}",
"target": f"doc-{documents[j].id[:8]}",
"label": f"共有关键词: {', '.join(list(common)[:3])}",
"weight": min(0.9, 0.3 * len(common)),
})
graph_data = {
"nodes": graph_nodes,
"edges": graph_edges,
"stats": {
"total_nodes": len(graph_nodes), "total_edges": len(graph_edges),
"document_count": len(documents),
},
}
kb.knowledge_graph_count = len(graph_nodes)
kb.updated_at = cst_now()
return graph_data
backend/python/app/api/chat_mcp.py
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# Auto-extracted from chat.py during Phase 1 refactor (Strangler Fig).
# Source of truth: scripts/phase1_extract.py. Do not hand-merge back.
from fastapi import APIRouter, Depends, HTTPException, Query, Response
from sqlalchemy.orm import Session
from sqlalchemy import and_, desc, asc, func
from pydantic import BaseModel, Field
from typing import Optional
import uuid
from datetime import datetime, timezone, timedelta
import re
import json
import asyncio
import math
import httpx
import os
import logging
from ..database import get_db, SessionLocal
from ..models import (
User, Conversation, Message, AIModel, ModelProvider, TokenUsageLog,
KnowledgeBase, KnowledgeDocument, KnowledgeDocumentChunk, EvolutionSkill,
EvolutionTrajectory, MarketApp, AppPurchase, AppInstallation,
MCPServer,
gen_uuid
)
from ..auth import get_current_user
from ..platform_knowledge import get_platform_knowledge
from ..mcp_client import list_mcp_tools, call_mcp_tool
from ..middleware.log_service import LogService
from ..capability_registry import resolve_auto_model
def _resolve_market_mcp(db: Session, mcp_ids: list, user_id: str) -> list:
"""
加载用户选择的 MCP 应用配置（MarketApp.type == 'mcp'）。
返回: [{id, name, transport, endpoint, description}]
"""
if not mcp_ids:
return []
mcps = db.query(MarketApp).filter(
MarketApp.id.in_(mcp_ids),
MarketApp.type == "mcp"
).all()
results = []
for m in mcps:
config = {}
try:
if m.skill_content:
config = json.loads(m.skill_content)
except Exception:
config = {}
results.append({
"id": m.id,
"name": m.name,
"transport": m.mcp_transport or config.get("transport", "stdio"),
"endpoint": m.agent_endpoint or config.get("endpoint", "") or config.get("url", ""),
"description": m.description or config.get("description", ""),
})
return results
def _resolve_active_mcp_servers(db: Session, mcp_ids: list) -> list:
"""
解析用户选中的 MCP 服务连接信息（来自 mcp_servers 表，import 与后台添加通用）。
返回: [{id, name, description, transport, endpoint, url, headers}]
"""
if not mcp_ids:
return []
servers = db.query(MCPServer).filter(
MCPServer.id.in_(mcp_ids),
MCPServer.is_enabled == True,
).all()
out = []
for s in servers:
try:
headers = json.loads(s.headers) if isinstance(s.headers, str) else (s.headers or {})
except Exception:
headers = {}
out.append({
"id": s.id,
"name": s.name,
"description": s.description or "",
"transport": s.transport_type or "streamable_http",
"endpoint": s.url or "",
"url": s.url or "",
"headers": headers,
})
return out
def _list_enabled_mcp_servers(db: Session) -> list:
"""返回平台所有已启用(is_enabled=True)的 MCP 服务清单，供 AI 准确回答"平台有哪些 MCP"。"""
servers = db.query(MCPServer).filter(MCPServer.is_enabled == True).all()
out = []
for s in servers:
try:
headers = json.loads(s.headers) if isinstance(s.headers, str) else (s.headers or {})
except Exception:
headers = {}
out.append({
"id": s.id,
"name": s.name,
"description": s.description or "",
"transport": s.transport_type or "streamable_http",
"endpoint": s.url or "",
"url": s.url or "",
"headers": headers,
})
return out
# Auto-extracted from chat.py during Phase 1 refactor (Strangler Fig).
# Source of truth: scripts/phase1_extract.py. Do not hand-merge back.
from fastapi import APIRouter, Depends, HTTPException, Query, Response
from sqlalchemy.orm import Session
from sqlalchemy import and_, desc, asc, func
from pydantic import BaseModel, Field
from typing import Optional
import uuid
from datetime import datetime, timezone, timedelta
import re
import json
import asyncio
import math
import httpx
import os
import logging
from ..database import get_db, SessionLocal
from ..models import (
User, Conversation, Message, AIModel, ModelProvider, TokenUsageLog,
KnowledgeBase, KnowledgeDocument, KnowledgeDocumentChunk, EvolutionSkill,
EvolutionTrajectory, MarketApp, AppPurchase, AppInstallation,
MCPServer,
gen_uuid
)
from ..auth import get_current_user
from ..platform_knowledge import get_platform_knowledge
from ..mcp_client import list_mcp_tools, call_mcp_tool
from ..middleware.log_service import LogService
from ..capability_registry import resolve_auto_model
def _resolve_market_mcp(db: Session, mcp_ids: list, user_id: str) -> list:
"""
加载用户选择的 MCP 应用配置（MarketApp.type == 'mcp'）。
返回: [{id, name, transport, endpoint, description}]
"""
if not mcp_ids:
return []
mcps = db.query(MarketApp).filter(
MarketApp.id.in_(mcp_ids),
MarketApp.type == "mcp"
).all()
results = []
for m in mcps:
config = {}
try:
if m.skill_content:
config = json.loads(m.skill_content)
except Exception:
config = {}
results.append({
"id": m.id,
"name": m.name,
"transport": m.mcp_transport or config.get("transport", "stdio"),
"endpoint": m.agent_endpoint or config.get("endpoint", "") or config.get("url", ""),
"description": m.description or config.get("description", ""),
})
return results
def _resolve_active_mcp_servers(db: Session, mcp_ids: list) -> list:
"""
解析用户选中的 MCP 服务连接信息（来自 mcp_servers 表，import 与后台添加通用）。
返回: [{id, name, description, transport, endpoint, url, headers}]
"""
if not mcp_ids:
return []
servers = db.query(MCPServer).filter(
MCPServer.id.in_(mcp_ids),
MCPServer.is_enabled == True,
).all()
out = []
for s in servers:
try:
headers = json.loads(s.headers) if isinstance(s.headers, str) else (s.headers or {})
except Exception:
headers = {}
out.append({
"id": s.id,
"name": s.name,
"description": s.description or "",
"transport": s.transport_type or "streamable_http",
"endpoint": s.url or "",
"url": s.url or "",
"headers": headers,
})
return out
def _list_enabled_mcp_servers(db: Session) -> list:
"""返回平台所有已启用(is_enabled=True)的 MCP 服务清单，供 AI 准确回答"平台有哪些 MCP"。"""
servers = db.query(MCPServer).filter(MCPServer.is_enabled == True).all()
out = []
for s in servers:
try:
headers = json.loads(s.headers) if isinstance(s.headers, str) else (s.headers or {})
except Exception:
headers = {}
out.append({
"id": s.id,
"name": s.name,
"description": s.description or "",
"transport": s.transport_type or "streamable_http",
"endpoint": s.url or "",
"url": s.url or "",
"headers": headers,
})
return out
backend/python/app/api/chat_service.py
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backend/python/app/api/chat_skill.py
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backend/python/app/api/im_llm.py
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"""
IM Bot 真实 LLM 调用。
原实现位于 channels.py（通用频道管理模块），该模块已移除。
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。
行为（对齐 AI 聊天 — 2026-07-23 同步至最新）：
- 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。
- 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。
- 2026-07-23：对齐 Web Chat 最新能力：
* 模型 failover（Auto 模式自动切换备选模型）
* 平台知识库注入（get_platform_knowledge，技能清单实时同步）
"""
import asyncio
import json
import logging
import re as _re
from datetime import datetime, timezone, timedelta
from typing import Optional
from urllib.parse import urlparse as _urlparse
import httpx
from app.core.llm_client import chat_complete # 统一模型层(07-24): web/IM 共用标准 OpenAI SDK 客户端
from sqlalchemy.orm import Session
from ..capability_registry import resolve_auto_model
from ..models import AIModel, ModelProvider, TokenUsageLog, gen_uuid
from app.core.observability import get_trace_id
logger = logging.getLogger(__name__)
# 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。
_HK_PROXY = "http://qyyfw:XHR521yhf@103.242.14.208:11495"
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）。
2026-07-15 调整：AGNES 改走香港代理(实测延迟更低)，不再直连。
国内域名(尊重 NO_PROXY)直连，更稳定。
"""
import os
host = (_urlparse(url).hostname or "").lower()
if "agnes-ai.com" in host or "agnes-ai.space" in host:
return _HK_PROXY
if "qihuyun.com" in host:
return None
_no_proxy = [h.strip().lower() for h in os.environ.get("NO_PROXY", "").split(",") if h.strip()]
for _np in _no_proxy:
if host == _np or host.endswith("." + _np) or _np 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, timeout=None):
"""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。
IM 个人微信在独立线程中调用（无事件循环）→ asyncio.run；
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。
timeout: 可选，秒数；超时抛出 asyncio.TimeoutError（由调用方捕获）。
"""
try:
loop = asyncio.get_running_loop()
except RuntimeError:
loop = None
if timeout is not None:
async def _timed():
return await asyncio.wait_for(coro, timeout=timeout)
target = _timed()
else:
target = coro
if loop is not None and loop.is_running():
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor(1) as ex:
return ex.submit(lambda: asyncio.run(target)).result(timeout=(timeout + 10) if timeout else None)
return asyncio.run(target)
def _log_media_usage(db: Session, user_id: str, ai_model, media_type: str, prompt: str):
"""IM Bot 媒体生成记账：把图片/视频消耗写入 TokenUsageLog，供驾驶舱统计。
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"。
媒体 API 不返回 token 用量，这里用与 chat.py _generate_media_task 一致的估算。
"""
if not user_id or not ai_model:
return
try:
base = 1200 if media_type == "video" else 400
est = base + (len(prompt or "") // 4)
db.add(TokenUsageLog(
id=gen_uuid(),
user_id=user_id,
model_id=ai_model.id,
model_name=ai_model.name,
trace_id=get_trace_id(),
prompt_tokens=est,
completion_tokens=0,
total_tokens=est,
created_at=datetime.now(timezone(timedelta(hours=8))).replace(tzinfo=None),
))
db.commit()
except Exception as e:
try:
db.rollback()
except Exception:
pass
logger.warning(f"[im_llm] 媒体 Token 记账失败(忽略): {e}")
def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im", user_id: str = None) -> "str | dict":
"""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。"""
try:
from app.api.chat import (
_generate_media,
detect_media_intent,
_discover_installed_skills,
_build_system_prompt,
_execute_skill_tool,
)
# ===== 1) 媒体意图路由（Issue 7）=====
intent = detect_media_intent(prompt)
if intent:
ai_model = resolve_auto_model(intent, db)
if ai_model:
try:
result = _run_async(_generate_media(db, ai_model, prompt, user_id), timeout=300)
except Exception:
# 任何未预期异常(如 AGNES 连接重置)统一兜底为友好提示，
# 绝不直接把原始异常(如 "Server disconnected")甩给用户
result = "⚠️ 媒体生成暂时失败（连接或服务波动），请稍后重试。"
if result:
# 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
s = result.strip()
if s.startswith("<img") or s.startswith("<video"):
m = _re.search(r'src="([^"]+)"', s)
url = m.group(1) if m else ""
media_type = "image" if s.startswith("<img") else "video"
if not url:
return "（生成完成，但未能提取媒体地址）"
# 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
_log_media_usage(db, user_id, ai_model, media_type, prompt)
return {"__media__": True, "media_type": media_type, "url": url}
# 媒体生成失败：_generate_media 已返回友好中文（如限频/队列繁忙），
# 直接透传，绝不把原始提示词/标签/JSON 透传给用户
if s.startswith("⚠️") or s.startswith("（媒体"):
return s
return "（媒体生成未能返回有效内容，请重试）"
# ===== 2) 文本回复：模型 failover + 技能自动发现 + 平台知识库注入 =====
from ..platform_knowledge import get_platform_knowledge
# 构建候选模型列表（对齐 Web Chat failover）
ai_model = resolve_auto_model("text", db)
_try_models = [ai_model] if (ai_model and ai_model.is_enabled) else []
# Auto 模式附加备选（IM Bot 始终 Auto）
_fallbacks = db.query(AIModel).filter(
AIModel.is_enabled == True,
AIModel.id != (ai_model.id if ai_model else ""),
).order_by(AIModel.is_default.desc()).limit(4).all()
_try_models.extend([m for m in _fallbacks if m not in _try_models])
if not _try_models:
return "（未配置可用文本模型，无法生成回复）"
# 技能自动发现（提前执行一次，不必每个候选模型重复做）
skills = []
if user_id:
try:
skills = _discover_installed_skills(db, user_id)[:15]
except Exception:
skills = []
# 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）
kb_context = ""
try:
pk = get_platform_knowledge(db=db)
if pk:
kb_context = f"【平台知识库——优先使用以下知识回答用户问题】\n{pk}"
except Exception:
pass
# 尝试每个候选模型
_retry_log = []
for _try_model in _try_models:
ai_model = _try_model
provider = db.query(ModelProvider).filter(ModelProvider.id == ai_model.provider_id).first()
api_key = ai_model.api_key or (provider.api_key if provider else None)
api_url = ai_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):
_retry_log.append(f"{ai_model.name}: 缺少密钥或端点")
continue
system_prompt, function_definitions = _build_system_prompt(
skills=skills,
agents=[],
kb_context=kb_context,
user_content=prompt,
mcp=[],
auto_discovered=bool(skills),
)
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt},
]
# 尝试调用
try:
max_rounds = 3
ai_content = ""
_http_ok = True
for round_num in range(max_rounds):
request_body = {"model": ai_model.name, "messages": messages, "max_tokens": 2048}
if function_definitions and skills:
request_body["tools"] = [
{"type": "function", "function": fd} for fd in function_definitions
]
request_body["tool_choice"] = "auto"
try:
data = chat_complete(
api_url,
api_key,
request_body,
proxy=_proxy_for_url(api_url),
timeout=ai_model.timeout or 120,
)
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}")
_http_ok = False
break
message = data.get("choices", [{}])[0].get("message", {})
tool_calls = message.get("tool_calls", [])
if tool_calls and skills:
messages.append({
"role": "assistant",
"content": message.get("content") or "",
"tool_calls": tool_calls,
})
for tc in tool_calls:
func_name = tc.get("function", {}).get("name", "")
try:
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)
messages.append({
"role": "tool",
"tool_call_id": tc.get("id", "call_1"),
"content": tool_result,
})
continue
ai_content = message.get("content", "")
break
# 检查本轮调用是否成功
if _http_ok and 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(f"[im_llm] 模型 {ai_model.name} 失败，切换备选...")
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}")
continue
# 全部失败
_summary = "; ".join(_retry_log[-3:])
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}）"
"""
IM Bot 真实 LLM 调用。
原实现位于 channels.py（通用频道管理模块），该模块已移除。
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。
行为（对齐 AI 聊天 — 2026-07-23 同步至最新）：
- 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。
- 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。
- 2026-07-23：对齐 Web Chat 最新能力：
* 模型 failover（Auto 模式自动切换备选模型）
* 平台知识库注入（get_platform_knowledge，技能清单实时同步）
"""
import asyncio
import json
import logging
import re as _re
from datetime import datetime, timezone, timedelta
from typing import Optional
from urllib.parse import urlparse as _urlparse
import httpx
from app.core.llm_client import chat_complete # 统一模型层(07-24): web/IM 共用标准 OpenAI SDK 客户端
from sqlalchemy.orm import Session
from ..capability_registry import resolve_auto_model
from ..models import AIModel, ModelProvider, TokenUsageLog, gen_uuid
from app.core.observability import get_trace_id
logger = logging.getLogger(__name__)
# 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。
_HK_PROXY = "http://qyyfw:XHR521yhf@103.242.14.208:11495"
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）。
2026-07-15 调整：AGNES 改走香港代理(实测延迟更低)，不再直连。
国内域名(尊重 NO_PROXY)直连，更稳定。
"""
import os
host = (_urlparse(url).hostname or "").lower()
if "agnes-ai.com" in host or "agnes-ai.space" in host:
return _HK_PROXY
if "qihuyun.com" in host:
return None
_no_proxy = [h.strip().lower() for h in os.environ.get("NO_PROXY", "").split(",") if h.strip()]
for _np in _no_proxy:
if host == _np or host.endswith("." + _np) or _np 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, timeout=None):
"""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。
IM 个人微信在独立线程中调用（无事件循环）→ asyncio.run；
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。
timeout: 可选，秒数；超时抛出 asyncio.TimeoutError（由调用方捕获）。
"""
try:
loop = asyncio.get_running_loop()
except RuntimeError:
loop = None
if timeout is not None:
async def _timed():
return await asyncio.wait_for(coro, timeout=timeout)
target = _timed()
else:
target = coro
if loop is not None and loop.is_running():
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor(1) as ex:
return ex.submit(lambda: asyncio.run(target)).result(timeout=(timeout + 10) if timeout else None)
return asyncio.run(target)
def _log_media_usage(db: Session, user_id: str, ai_model, media_type: str, prompt: str):
"""IM Bot 媒体生成记账：把图片/视频消耗写入 TokenUsageLog，供驾驶舱统计。
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"。
媒体 API 不返回 token 用量，这里用与 chat.py _generate_media_task 一致的估算。
"""
if not user_id or not ai_model:
return
try:
base = 1200 if media_type == "video" else 400
est = base + (len(prompt or "") // 4)
db.add(TokenUsageLog(
id=gen_uuid(),
user_id=user_id,
model_id=ai_model.id,
model_name=ai_model.name,
trace_id=get_trace_id(),
prompt_tokens=est,
completion_tokens=0,
total_tokens=est,
created_at=datetime.now(timezone(timedelta(hours=8))).replace(tzinfo=None),
))
db.commit()
except Exception as e:
try:
db.rollback()
except Exception:
pass
logger.warning(f"[im_llm] 媒体 Token 记账失败(忽略): {e}")
def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im", user_id: str = None) -> "str | dict":
"""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。"""
try:
from app.api.chat import (
_generate_media,
detect_media_intent,
_discover_installed_skills,
_build_system_prompt,
_execute_skill_tool,
)
# ===== 1) 媒体意图路由（Issue 7）=====
intent = detect_media_intent(prompt)
if intent:
ai_model = resolve_auto_model(intent, db)
if ai_model:
try:
result = _run_async(_generate_media(db, ai_model, prompt, user_id), timeout=300)
except Exception:
# 任何未预期异常(如 AGNES 连接重置)统一兜底为友好提示，
# 绝不直接把原始异常(如 "Server disconnected")甩给用户
result = "⚠️ 媒体生成暂时失败（连接或服务波动），请稍后重试。"
if result:
# 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
s = result.strip()
if s.startswith("<img") or s.startswith("<video"):
m = _re.search(r'src="([^"]+)"', s)
url = m.group(1) if m else ""
media_type = "image" if s.startswith("<img") else "video"
if not url:
return "（生成完成，但未能提取媒体地址）"
# 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
_log_media_usage(db, user_id, ai_model, media_type, prompt)
return {"__media__": True, "media_type": media_type, "url": url}
# 媒体生成失败：_generate_media 已返回友好中文（如限频/队列繁忙），
# 直接透传，绝不把原始提示词/标签/JSON 透传给用户
if s.startswith("⚠️") or s.startswith("（媒体"):
return s
return "（媒体生成未能返回有效内容，请重试）"
# ===== 2) 文本回复：模型 failover + 技能自动发现 + 平台知识库注入 =====
from ..platform_knowledge import get_platform_knowledge
# 构建候选模型列表（对齐 Web Chat failover）
ai_model = resolve_auto_model("text", db)
_try_models = [ai_model] if (ai_model and ai_model.is_enabled) else []
# Auto 模式附加备选（IM Bot 始终 Auto）
_fallbacks = db.query(AIModel).filter(
AIModel.is_enabled == True,
AIModel.id != (ai_model.id if ai_model else ""),
).order_by(AIModel.is_default.desc()).limit(4).all()
_try_models.extend([m for m in _fallbacks if m not in _try_models])
if not _try_models:
return "（未配置可用文本模型，无法生成回复）"
# 技能自动发现（提前执行一次，不必每个候选模型重复做）
skills = []
if user_id:
try:
skills = _discover_installed_skills(db, user_id)[:15]
except Exception:
skills = []
# 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）
kb_context = ""
try:
pk = get_platform_knowledge(db=db)
if pk:
kb_context = f"【平台知识库——优先使用以下知识回答用户问题】\n{pk}"
except Exception:
pass
# 尝试每个候选模型
_retry_log = []
for _try_model in _try_models:
ai_model = _try_model
provider = db.query(ModelProvider).filter(ModelProvider.id == ai_model.provider_id).first()
api_key = ai_model.api_key or (provider.api_key if provider else None)
api_url = ai_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):
_retry_log.append(f"{ai_model.name}: 缺少密钥或端点")
continue
system_prompt, function_definitions = _build_system_prompt(
skills=skills,
agents=[],
kb_context=kb_context,
user_content=prompt,
mcp=[],
auto_discovered=bool(skills),
)
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt},
]
# 尝试调用
try:
max_rounds = 3
ai_content = ""
_http_ok = True
for round_num in range(max_rounds):
request_body = {"model": ai_model.name, "messages": messages, "max_tokens": 2048}
if function_definitions and skills:
request_body["tools"] = [
{"type": "function", "function": fd} for fd in function_definitions
]
request_body["tool_choice"] = "auto"
try:
data = chat_complete(
api_url,
api_key,
request_body,
proxy=_proxy_for_url(api_url),
timeout=ai_model.timeout or 120,
)
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}")
_http_ok = False
break
message = data.get("choices", [{}])[0].get("message", {})
tool_calls = message.get("tool_calls", [])
if tool_calls and skills:
messages.append({
"role": "assistant",
"content": message.get("content") or "",
"tool_calls": tool_calls,
})
for tc in tool_calls:
func_name = tc.get("function", {}).get("name", "")
try:
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)
messages.append({
"role": "tool",
"tool_call_id": tc.get("id", "call_1"),
"content": tool_result,
})
continue
ai_content = message.get("content", "")
break
# 检查本轮调用是否成功
if _http_ok and 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(f"[im_llm] 模型 {ai_model.name} 失败，切换备选...")
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}")
continue
# 全部失败
_summary = "; ".join(_retry_log[-3:])
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}）"
backend/python/app/api/market.py
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