chore(align): sync Git with running container as authoritative source
Standards alignment (STANDARDS_AUDIT_2026-07-24): Phase 1 标准化核心 + Phase 2 质量层 landed in the running container and captured in Git so the three sources (container := image := Git) agree. Backend (Phase 1 标准化核心): - mcp_client.py: replaced hand-rolled JSON-RPC with official `mcp` SDK (ClientSession, streamable_http/sse transports, protocol auto-negotiation) - core/otel.py + core/observability.py: standard OpenTelemetry SDK bridge (OTLP gRPC), wired to Grafana Alloy (qihu-aiflow-alloy:4317) which forwards metrics to Prometheus; emits aiflow_span_duration_ms / aiflow_span_count - deployments: Grafana Alloy container + Prometheus remote-write receiver Backend (Phase 2 质量层): - core/rag_pipeline.py: standardized RAG (recursive overlap chunking + rerank hook) reusing pgvector_search - core/llm_client.py: SDK-ified LLM + embeddings (aembed/embed) collapsing hand-rolled httpx calls; trust_env=False to bypass container proxy Migrations: - 018_normalize_marketapp_scene.sql: fixed capability allowlist (prevents capability wipe on restart) Alignment performed 2026-07-24 12:18:51. No deletions; secrets/venv/node_modules/ caches were not touched (tracked-source-only copy).
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@@ -1,283 +1,282 @@
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"""
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"""
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IM Bot 真实 LLM 调用。
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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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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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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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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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- 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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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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- 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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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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- 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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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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- 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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* 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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* 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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"""
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import asyncio
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import asyncio
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import json
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import json
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import logging
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import logging
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import re as _re
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import re as _re
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from datetime import datetime, timezone, timedelta
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from datetime import datetime, timezone, timedelta
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from typing import Optional
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from typing import Optional
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from urllib.parse import urlparse as _urlparse
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from urllib.parse import urlparse as _urlparse
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import httpx
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import httpx
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from sqlalchemy.orm import Session
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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
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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 ..capability_registry import resolve_auto_model
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from app.core.observability import get_trace_id
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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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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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# 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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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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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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"""
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国内域名(尊重 NO_PROXY)直连，更稳定。
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import os
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"""
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host = (_urlparse(url).hostname or "").lower()
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import os
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if "agnes-ai.com" in host or "agnes-ai.space" in host:
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host = (_urlparse(url).hostname or "").lower()
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return _HK_PROXY
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if "agnes-ai.com" in host or "agnes-ai.space" in host:
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if "qihuyun.com" in host:
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return _HK_PROXY
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return None
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if "qihuyun.com" in host:
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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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return None
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for _np in _no_proxy:
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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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if host == _np or host.endswith("." + _np) or _np in host:
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for _np in _no_proxy:
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return None
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if host == _np or host.endswith("." + _np) or _np in host:
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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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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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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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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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timeout: 可选，秒数；超时抛出 asyncio.TimeoutError（由调用方捕获）。
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try:
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"""
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loop = asyncio.get_running_loop()
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try:
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except RuntimeError:
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loop = asyncio.get_running_loop()
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loop = None
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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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if timeout is not None:
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return await asyncio.wait_for(coro, timeout=timeout)
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async def _timed():
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target = _timed()
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return await asyncio.wait_for(coro, timeout=timeout)
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else:
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target = _timed()
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target = coro
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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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if loop is not None and loop.is_running():
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with concurrent.futures.ThreadPoolExecutor(1) as ex:
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import concurrent.futures
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return ex.submit(lambda: asyncio.run(target)).result(timeout=(timeout + 10) if timeout else None)
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with concurrent.futures.ThreadPoolExecutor(1) as ex:
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return asyncio.run(target)
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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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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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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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if not user_id or not ai_model:
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"""
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return
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if not user_id or not ai_model:
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try:
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return
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base = 1200 if media_type == "video" else 400
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try:
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est = base + (len(prompt or "") // 4)
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base = 1200 if media_type == "video" else 400
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db.add(TokenUsageLog(
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est = base + (len(prompt or "") // 4)
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id=gen_uuid(),
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db.add(TokenUsageLog(
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user_id=user_id,
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id=gen_uuid(),
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model_id=ai_model.id,
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user_id=user_id,
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model_name=ai_model.name,
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model_id=ai_model.id,
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trace_id=get_trace_id(),
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model_name=ai_model.name,
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prompt_tokens=est,
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trace_id=get_trace_id(),
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completion_tokens=0,
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prompt_tokens=est,
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total_tokens=est,
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completion_tokens=0,
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created_at=datetime.now(timezone(timedelta(hours=8))).replace(tzinfo=None),
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total_tokens=est,
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))
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created_at=datetime.now(timezone(timedelta(hours=8))).replace(tzinfo=None),
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db.commit()
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))
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except Exception as e:
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db.commit()
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try:
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except Exception as e:
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db.rollback()
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try:
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except Exception:
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db.rollback()
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pass
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except Exception:
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logger.warning(f"[im_llm] 媒体 Token 记账失败(忽略): {e}")
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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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|
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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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def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im", user_id: str = None) -> "str | dict":
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try:
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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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from app.api.chat import (
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try:
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_generate_media,
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from app.api.chat import (
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detect_media_intent,
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_generate_media,
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_discover_installed_skills,
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detect_media_intent,
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_build_system_prompt,
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_discover_installed_skills,
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_execute_skill_tool,
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_build_system_prompt,
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)
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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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# ===== 1) 媒体意图路由（Issue 7）=====
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if intent:
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intent = detect_media_intent(prompt)
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ai_model = resolve_auto_model(intent, db)
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if intent:
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if ai_model:
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ai_model = resolve_auto_model(intent, db)
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try:
|
if ai_model:
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result = _run_async(_generate_media(db, ai_model, prompt, user_id), timeout=300)
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try:
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except Exception:
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result = _run_async(_generate_media(db, ai_model, prompt, user_id), timeout=300)
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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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except Exception:
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# 绝不直接把原始异常(如 "Server disconnected")甩给用户
|
# 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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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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# 绝不直接把原始异常(如 "Server disconnected")甩给用户
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if result:
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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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# 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
|
if result:
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s = result.strip()
|
# 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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if s.startswith("<img") or s.startswith("<video"):
|
s = result.strip()
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m = _re.search(r'src="([^"]+)"', s)
|
if s.startswith("<img") or s.startswith("<video"):
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url = m.group(1) if m else ""
|
m = _re.search(r'src="([^"]+)"', s)
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media_type = "image" if s.startswith("<img") else "video"
|
url = m.group(1) if m else ""
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if not url:
|
media_type = "image" if s.startswith("<img") else "video"
|
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return "（生成完成，但未能提取媒体地址）"
|
if not url:
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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
|
return "（生成完成，但未能提取媒体地址）"
|
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_log_media_usage(db, user_id, ai_model, media_type, prompt)
|
# 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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return {"__media__": True, "media_type": media_type, "url": url}
|
_log_media_usage(db, user_id, ai_model, media_type, prompt)
|
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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），
|
return {"__media__": True, "media_type": media_type, "url": url}
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# 直接透传，绝不把原始提示词/标签/JSON 透传给用户
|
# 媒体生成失败：_generate_media 已返回友好中文（如限频/队列繁忙），
|
||||||
if s.startswith("⚠️") or s.startswith("（媒体"):
|
# 直接透传，绝不把原始提示词/标签/JSON 透传给用户
|
||||||
return s
|
if s.startswith("⚠️") or s.startswith("（媒体"):
|
||||||
return "（媒体生成未能返回有效内容，请重试）"
|
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 =====
|
|
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from ..platform_knowledge import get_platform_knowledge
|
# ===== 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
|
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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)
|
# 构建候选模型列表（对齐 Web Chat failover）
|
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_try_models = [ai_model] if (ai_model and ai_model.is_enabled) else []
|
ai_model = resolve_auto_model("text", db)
|
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# Auto 模式附加备选（IM Bot 始终 Auto）
|
_try_models = [ai_model] if (ai_model and ai_model.is_enabled) else []
|
||||||
_fallbacks = db.query(AIModel).filter(
|
# Auto 模式附加备选（IM Bot 始终 Auto）
|
||||||
AIModel.is_enabled == True,
|
_fallbacks = db.query(AIModel).filter(
|
||||||
AIModel.id != (ai_model.id if ai_model else ""),
|
AIModel.is_enabled == True,
|
||||||
).order_by(AIModel.is_default.desc()).limit(4).all()
|
AIModel.id != (ai_model.id if ai_model else ""),
|
||||||
_try_models.extend([m for m in _fallbacks if m not in _try_models])
|
).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）"
|
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 = []
|
# 技能自动发现（提前执行一次，不必每个候选模型重复做）
|
||||||
if user_id:
|
skills = []
|
||||||
try:
|
if user_id:
|
||||||
skills = _discover_installed_skills(db, user_id)[:15]
|
try:
|
||||||
except Exception:
|
skills = _discover_installed_skills(db, user_id)[:15]
|
||||||
skills = []
|
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 = ""
|
# 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）
|
||||||
try:
|
kb_context = ""
|
||||||
pk = get_platform_knowledge(db=db)
|
try:
|
||||||
if pk:
|
pk = get_platform_knowledge(db=db)
|
||||||
kb_context = f"【平台知识库——优先使用以下知识回答用户问题】\n{pk}"
|
if pk:
|
||||||
except Exception:
|
kb_context = f"【平台知识库——优先使用以下知识回答用户问题】\n{pk}"
|
||||||
pass
|
except Exception:
|
||||||
|
pass
|
||||||
# 尝试每个候选模型
|
|
||||||
_retry_log = []
|
# 尝试每个候选模型
|
||||||
for _try_model in _try_models:
|
_retry_log = []
|
||||||
ai_model = _try_model
|
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)
|
provider = db.query(ModelProvider).filter(ModelProvider.id == ai_model.provider_id).first()
|
||||||
api_url = ai_model.endpoint
|
api_key = ai_model.api_key or (provider.api_key if provider else None)
|
||||||
if not api_url and provider and provider.api_base_url:
|
api_url = ai_model.endpoint
|
||||||
api_url = f"{provider.api_base_url.rstrip('/')}/v1/chat/completions"
|
if not api_url and provider and provider.api_base_url:
|
||||||
if not (api_key and api_url):
|
api_url = f"{provider.api_base_url.rstrip('/')}/v1/chat/completions"
|
||||||
_retry_log.append(f"{ai_model.name}: 缺少密钥或端点")
|
if not (api_key and api_url):
|
||||||
continue
|
_retry_log.append(f"{ai_model.name}: 缺少密钥或端点")
|
||||||
|
continue
|
||||||
system_prompt, function_definitions = _build_system_prompt(
|
|
||||||
skills=skills,
|
system_prompt, function_definitions = _build_system_prompt(
|
||||||
agents=[],
|
skills=skills,
|
||||||
kb_context=kb_context,
|
agents=[],
|
||||||
user_content=prompt,
|
kb_context=kb_context,
|
||||||
mcp=[],
|
user_content=prompt,
|
||||||
auto_discovered=bool(skills),
|
mcp=[],
|
||||||
)
|
auto_discovered=bool(skills),
|
||||||
messages = [
|
)
|
||||||
{"role": "system", "content": system_prompt},
|
messages = [
|
||||||
{"role": "user", "content": prompt},
|
{"role": "system", "content": system_prompt},
|
||||||
]
|
{"role": "user", "content": prompt},
|
||||||
|
]
|
||||||
# 尝试调用
|
|
||||||
try:
|
# 尝试调用
|
||||||
max_rounds = 3
|
try:
|
||||||
ai_content = ""
|
max_rounds = 3
|
||||||
_http_ok = True
|
ai_content = ""
|
||||||
for round_num in range(max_rounds):
|
_http_ok = True
|
||||||
request_body = {"model": ai_model.name, "messages": messages, "max_tokens": 2048}
|
for round_num in range(max_rounds):
|
||||||
if function_definitions and skills:
|
request_body = {"model": ai_model.name, "messages": messages, "max_tokens": 2048}
|
||||||
request_body["tools"] = [
|
if function_definitions and skills:
|
||||||
{"type": "function", "function": fd} for fd in function_definitions
|
request_body["tools"] = [
|
||||||
]
|
{"type": "function", "function": fd} for fd in function_definitions
|
||||||
request_body["tool_choice"] = "auto"
|
]
|
||||||
with httpx.Client(
|
request_body["tool_choice"] = "auto"
|
||||||
timeout=ai_model.timeout or 120,
|
try:
|
||||||
proxy=_proxy_for_url(api_url),
|
data = chat_complete(
|
||||||
trust_env=False,
|
api_url,
|
||||||
) as client:
|
api_key,
|
||||||
resp = client.post(
|
request_body,
|
||||||
api_url,
|
proxy=_proxy_for_url(api_url),
|
||||||
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
|
timeout=ai_model.timeout or 120,
|
||||||
json=request_body,
|
)
|
||||||
)
|
except Exception as _exc:
|
||||||
if resp.status_code != 200:
|
_retry_log.append(f"{ai_model.name}: {str(_exc)[:60]}")
|
||||||
_retry_log.append(f"{ai_model.name}: HTTP {resp.status_code}")
|
logger.warning(f"[im_llm] 模型 {ai_model.name} 异常: {_exc}")
|
||||||
_http_ok = False
|
_http_ok = False
|
||||||
break
|
break
|
||||||
data = resp.json()
|
message = data.get("choices", [{}])[0].get("message", {})
|
||||||
message = data.get("choices", [{}])[0].get("message", {})
|
tool_calls = message.get("tool_calls", [])
|
||||||
tool_calls = message.get("tool_calls", [])
|
if tool_calls and skills:
|
||||||
if tool_calls and skills:
|
messages.append({
|
||||||
messages.append({
|
"role": "assistant",
|
||||||
"role": "assistant",
|
"content": message.get("content") or "",
|
||||||
"content": message.get("content") or "",
|
"tool_calls": tool_calls,
|
||||||
"tool_calls": tool_calls,
|
})
|
||||||
})
|
for tc in tool_calls:
|
||||||
for tc in tool_calls:
|
func_name = tc.get("function", {}).get("name", "")
|
||||||
func_name = tc.get("function", {}).get("name", "")
|
try:
|
||||||
try:
|
func_args = json.loads(tc.get("function", {}).get("arguments", "{}"))
|
||||||
func_args = json.loads(tc.get("function", {}).get("arguments", "{}"))
|
except Exception:
|
||||||
except Exception:
|
func_args = {"input": tc.get("function", {}).get("arguments", "")}
|
||||||
func_args = {"input": tc.get("function", {}).get("arguments", "")}
|
tool_result = _execute_skill_tool(db, func_name, func_args, skills, prompt)
|
||||||
tool_result = _execute_skill_tool(db, func_name, func_args, skills, prompt)
|
messages.append({
|
||||||
messages.append({
|
"role": "tool",
|
||||||
"role": "tool",
|
"tool_call_id": tc.get("id", "call_1"),
|
||||||
"tool_call_id": tc.get("id", "call_1"),
|
"content": tool_result,
|
||||||
"content": tool_result,
|
})
|
||||||
})
|
continue
|
||||||
continue
|
ai_content = message.get("content", "")
|
||||||
ai_content = message.get("content", "")
|
break
|
||||||
break
|
|
||||||
|
# 检查本轮调用是否成功
|
||||||
# 检查本轮调用是否成功
|
if _http_ok and ai_content:
|
||||||
if _http_ok and ai_content:
|
return ai_content or "（AI 回复为空）"
|
||||||
return ai_content or "（AI 回复为空）"
|
# 本轮失败：记录日志，切换备选
|
||||||
# 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...")
|
||||||
logger.warning(f"[im_llm] 模型 {ai_model.name} 失败，切换备选...")
|
continue
|
||||||
continue
|
except Exception as exc:
|
||||||
except Exception as exc:
|
_retry_log.append(f"{ai_model.name}: {str(exc)[:60]}")
|
||||||
_retry_log.append(f"{ai_model.name}: {str(exc)[:60]}")
|
logger.warning(f"[im_llm] 模型 {ai_model.name} 异常: {exc}")
|
||||||
logger.warning(f"[im_llm] 模型 {ai_model.name} 异常: {exc}")
|
continue
|
||||||
continue
|
|
||||||
|
# 全部失败
|
||||||
# 全部失败
|
_summary = "; ".join(_retry_log[-3:])
|
||||||
_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。）"
|
||||||
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:
|
||||||
except Exception as e:
|
return f"（AI 回复生成失败：{e}）"
|
||||||
return f"（AI 回复生成失败：{e}）"
|
|
||||||
@@ -0,0 +1,181 @@
|
|||||||
|
"""Shared, standards-based LLM client for web chat + IM bot.
|
||||||
|
|
||||||
|
平台红线(07-24)：模型层以成熟/开源标准为设计基准。本模块使用官方 OpenAI SDK
|
||||||
|
(`openai.AsyncOpenAI`)——它是 OpenAI 兼容端点的事实标准客户端。平台本身已在讲
|
||||||
|
OpenAI 协议(`/v1/chat/completions`)，因此零协议改造成本，并天然获得：
|
||||||
|
* 原生 SSE 流式(`stream=True`)；
|
||||||
|
* 结构化的 tool_calls / usage 解析；
|
||||||
|
* 统一的代理(trust_env=False + 显式 proxy)与超时处理。
|
||||||
|
|
||||||
|
本模块取代 chat_service.py 与 im_llm.py 中两套手写 httpx 循环，使 web chat 与
|
||||||
|
IM bot 共用**同一模型层**、同一套 failover/代理/超时语义——这是「各端能力统一对齐」
|
||||||
|
红线在模型层的落地。
|
||||||
|
|
||||||
|
API:
|
||||||
|
achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=120, stream=False)
|
||||||
|
- stream=False -> dict，形状对齐上游 `data` 响应：
|
||||||
|
{"choices":[{"message":{"content","tool_calls","reasoning_content"}}], "usage":{...}}
|
||||||
|
- stream=True -> async generator，逐块产出 ("content"|"reasoning", text) 增量
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import httpx
|
||||||
|
from openai import AsyncOpenAI, OpenAI
|
||||||
|
|
||||||
|
|
||||||
|
def _base_url(api_url: str) -> str:
|
||||||
|
"""从完整 /v1/chat/completions 或 /v1/embeddings 地址推导 OpenAI SDK 所需的 base_url。"""
|
||||||
|
u = (api_url or "").rstrip()
|
||||||
|
for suf in ("/v1/chat/completions", "/chat/completions", "/v1/embeddings", "/embeddings"):
|
||||||
|
if u.endswith(suf):
|
||||||
|
return u[: -len(suf)]
|
||||||
|
return u
|
||||||
|
|
||||||
|
|
||||||
|
def build_openai_client(api_url, api_key, *, proxy=None, timeout=120) -> AsyncOpenAI:
|
||||||
|
base = _base_url(api_url)
|
||||||
|
# 始终 trust_env=False：避免容器 HTTP_PROXY 对国内端点(siliconflow 等)造成 502；
|
||||||
|
# 代理经 proxy 显式传入（AGNES 等），未传则直连。
|
||||||
|
http_client = httpx.AsyncClient(proxy=proxy, trust_env=False)
|
||||||
|
return AsyncOpenAI(
|
||||||
|
base_url=base,
|
||||||
|
api_key=api_key or "EMPTY",
|
||||||
|
timeout=timeout,
|
||||||
|
http_client=http_client,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def _create_kwargs(request_body: dict) -> dict:
|
||||||
|
kw = {
|
||||||
|
"model": request_body.get("model"),
|
||||||
|
"messages": request_body.get("messages", []),
|
||||||
|
"stream": False,
|
||||||
|
}
|
||||||
|
if request_body.get("temperature") is not None:
|
||||||
|
kw["temperature"] = request_body["temperature"]
|
||||||
|
if request_body.get("top_p") is not None:
|
||||||
|
kw["top_p"] = request_body["top_p"]
|
||||||
|
if request_body.get("max_tokens") is not None:
|
||||||
|
kw["max_tokens"] = request_body["max_tokens"]
|
||||||
|
if request_body.get("stop") is not None:
|
||||||
|
kw["stop"] = request_body["stop"]
|
||||||
|
tools = request_body.get("tools")
|
||||||
|
if tools:
|
||||||
|
kw["tools"] = tools
|
||||||
|
kw["tool_choice"] = request_body.get("tool_choice", "auto")
|
||||||
|
return kw
|
||||||
|
|
||||||
|
|
||||||
|
def _normalize_tool_calls(tcs):
|
||||||
|
if not tcs:
|
||||||
|
return None
|
||||||
|
out = []
|
||||||
|
for tc in tcs:
|
||||||
|
out.append(
|
||||||
|
{
|
||||||
|
"id": getattr(tc, "id", None),
|
||||||
|
"type": "function",
|
||||||
|
"function": {
|
||||||
|
"name": getattr(tc.function, "name", None),
|
||||||
|
"arguments": getattr(tc.function, "arguments", "") or "",
|
||||||
|
},
|
||||||
|
}
|
||||||
|
)
|
||||||
|
return out
|
||||||
|
|
||||||
|
|
||||||
|
async def achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=120, stream=False):
|
||||||
|
"""统一的 OpenAI 兼容聊天补全。详见模块 docstring。"""
|
||||||
|
if stream:
|
||||||
|
|
||||||
|
async def _gen():
|
||||||
|
client = build_openai_client(api_url, api_key, proxy=proxy, timeout=timeout)
|
||||||
|
try:
|
||||||
|
gen = await client.chat.completions.create(
|
||||||
|
**_create_kwargs(request_body), stream=True
|
||||||
|
)
|
||||||
|
async for chunk in gen:
|
||||||
|
if not chunk.choices:
|
||||||
|
continue
|
||||||
|
delta = chunk.choices[0].delta
|
||||||
|
rc = getattr(delta, "reasoning_content", None)
|
||||||
|
if rc:
|
||||||
|
yield ("reasoning", rc)
|
||||||
|
ct = getattr(delta, "content", None)
|
||||||
|
if ct:
|
||||||
|
yield ("content", ct)
|
||||||
|
finally:
|
||||||
|
await client.close()
|
||||||
|
|
||||||
|
return _gen()
|
||||||
|
|
||||||
|
client = build_openai_client(api_url, api_key, proxy=proxy, timeout=timeout)
|
||||||
|
async with client:
|
||||||
|
resp = await client.chat.completions.create(**_create_kwargs(request_body))
|
||||||
|
msg = resp.choices[0].message
|
||||||
|
return {
|
||||||
|
"choices": [
|
||||||
|
{
|
||||||
|
"message": {
|
||||||
|
"content": msg.content or "",
|
||||||
|
"tool_calls": _normalize_tool_calls(getattr(msg, "tool_calls", None)),
|
||||||
|
"reasoning_content": getattr(msg, "reasoning_content", None),
|
||||||
|
}
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"usage": (resp.usage.model_dump() if resp.usage else {}),
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def build_openai_client_sync(api_url, api_key, *, proxy=None, timeout=120) -> OpenAI:
|
||||||
|
"""同步客户端工厂（供 IM bot 等同步上下文复用同一标准模型层）。"""
|
||||||
|
base = _base_url(api_url)
|
||||||
|
http_client = httpx.Client(proxy=proxy, trust_env=False)
|
||||||
|
return OpenAI(base_url=base, api_key=api_key or "EMPTY", timeout=timeout, http_client=http_client)
|
||||||
|
|
||||||
|
|
||||||
|
def chat_complete(api_url, api_key, request_body, *, proxy=None, timeout=120):
|
||||||
|
"""同步版（与 achat_complete 行为/返回形状完全一致），供非 async 上下文使用。"""
|
||||||
|
client = build_openai_client_sync(api_url, api_key, proxy=proxy, timeout=timeout)
|
||||||
|
with client:
|
||||||
|
resp = client.chat.completions.create(**_create_kwargs(request_body))
|
||||||
|
msg = resp.choices[0].message
|
||||||
|
return {
|
||||||
|
"choices": [
|
||||||
|
{
|
||||||
|
"message": {
|
||||||
|
"content": msg.content or "",
|
||||||
|
"tool_calls": _normalize_tool_calls(getattr(msg, "tool_calls", None)),
|
||||||
|
"reasoning_content": getattr(msg, "reasoning_content", None),
|
||||||
|
}
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"usage": (resp.usage.model_dump() if resp.usage else {}),
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# 嵌入（Embeddings）：OpenAI 兼容 /v1/embeddings，收敛散落的裸 httpx 嵌入调用
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
def _embed_kwargs(input, model):
|
||||||
|
return {"model": model, "input": input if isinstance(input, list) else [input]}
|
||||||
|
|
||||||
|
|
||||||
|
async def aembed(api_url, api_key, input, model, *, proxy=None, timeout=60) -> list:
|
||||||
|
"""统一的 OpenAI 兼容文本嵌入（异步）。返回 list[list[float]]。
|
||||||
|
|
||||||
|
供 pgvector_search.get_embedding 等 embeddings 路径复用，把散落的裸 httpx
|
||||||
|
嵌入调用收敛到同一标准模型层（与聊天共用代理/超时/failover 语义）。
|
||||||
|
"""
|
||||||
|
client = build_openai_client(api_url, api_key, proxy=proxy, timeout=timeout)
|
||||||
|
async with client:
|
||||||
|
resp = await client.embeddings.create(**_embed_kwargs(input, model))
|
||||||
|
return [d.embedding for d in resp.data]
|
||||||
|
|
||||||
|
|
||||||
|
def embed(api_url, api_key, input, model, *, proxy=None, timeout=60) -> list:
|
||||||
|
"""同步版嵌入（与非 async 上下文复用同一标准模型层）。"""
|
||||||
|
client = build_openai_client_sync(api_url, api_key, proxy=proxy, timeout=timeout)
|
||||||
|
with client:
|
||||||
|
resp = client.embeddings.create(**_embed_kwargs(input, model))
|
||||||
|
return [d.embedding for d in resp.data]
|
||||||
@@ -1,6 +1,6 @@
|
|||||||
"""
|
"""
|
||||||
Qihu-AIFlow 统一可观测基座 (Phase 2.1)
|
Qihu-AIFlow 统一可观测基座 (Phase 2.1 + Phase 1.I OTel 增强)
|
||||||
=====================================
|
=============================================================
|
||||||
提供贯穿一次请求的全局链路 ID（trace_id）与结构化日志能力，
|
提供贯穿一次请求的全局链路 ID（trace_id）与结构化日志能力，
|
||||||
为后续重构（上下文引擎、媒体/LLM 成本归因）建立回归基线。
|
为后续重构（上下文引擎、媒体/LLM 成本归因）建立回归基线。
|
||||||
|
|
||||||
@@ -10,13 +10,17 @@ Qihu-AIFlow 统一可观测基座 (Phase 2.1)
|
|||||||
TraceIdFilter 自动注入，调用方无需改动。
|
TraceIdFilter 自动注入，调用方无需改动。
|
||||||
- 不引入第三方依赖（structlog 未安装），自带可选的 JSON formatter。
|
- 不引入第三方依赖（structlog 未安装），自带可选的 JSON formatter。
|
||||||
|
|
||||||
|
Phase 1.I 增强：span() 在保持原有日志计时的同时，额外嵌套一个标准 OpenTelemetry
|
||||||
|
span（经 app.core.otel 桥接，OTLP 导出到 Grafana）。OTel 失败时自动降级为 no-op，
|
||||||
|
绝不改变既有日志行为与请求链路。
|
||||||
|
|
||||||
使用方式：
|
使用方式：
|
||||||
from app.core.observability import set_trace_id, get_trace_id, span
|
from app.core.observability import set_trace_id, get_trace_id, span
|
||||||
|
|
||||||
# 在中间件里为每个请求注入：
|
# 在中间件里为每个请求注入：
|
||||||
set_trace_id(request_id)
|
set_trace_id(request_id)
|
||||||
|
|
||||||
# 在关键调用处打计时 span（自动带 trace_id）：
|
# 在关键调用处打计时 span（自动带 trace_id + OTel span）：
|
||||||
async with span("chat.send_message", conv_id=conv_id):
|
async with span("chat.send_message", conv_id=conv_id):
|
||||||
return await _send_message_core(...)
|
return await _send_message_core(...)
|
||||||
"""
|
"""
|
||||||
@@ -26,6 +30,8 @@ import uuid
|
|||||||
import json
|
import json
|
||||||
from contextvars import ContextVar
|
from contextvars import ContextVar
|
||||||
|
|
||||||
|
from app.core.otel import current_span_cm, record_span_metrics
|
||||||
|
|
||||||
# ---------------------------------------------------------------------------
|
# ---------------------------------------------------------------------------
|
||||||
# trace_id：贯穿一次请求（含其内所有子调用/协程）的全局链路 ID
|
# trace_id：贯穿一次请求（含其内所有子调用/协程）的全局链路 ID
|
||||||
# ---------------------------------------------------------------------------
|
# ---------------------------------------------------------------------------
|
||||||
@@ -103,10 +109,10 @@ def get_logger(name: str | None = None) -> logging.Logger:
|
|||||||
|
|
||||||
|
|
||||||
# ---------------------------------------------------------------------------
|
# ---------------------------------------------------------------------------
|
||||||
# span：结构化计时上下文管理器（耗时 + trace_id + 自定义字段）
|
# span：结构化计时上下文管理器（耗时 + trace_id + 自定义字段 + OTel 嵌套）
|
||||||
# ---------------------------------------------------------------------------
|
# ---------------------------------------------------------------------------
|
||||||
class _Span:
|
class _Span:
|
||||||
"""同步/异步通用的计时 span。退出时记录 elapsed_ms 与状态。"""
|
"""同步/异步通用的计时 span。退出时记录 elapsed_ms 与状态，并结束嵌套的 OTel span。"""
|
||||||
|
|
||||||
def __init__(self, name: str, **fields):
|
def __init__(self, name: str, **fields):
|
||||||
self.name = name
|
self.name = name
|
||||||
@@ -114,6 +120,7 @@ class _Span:
|
|||||||
self._logger = logging.getLogger("aiflow.span")
|
self._logger = logging.getLogger("aiflow.span")
|
||||||
self._start = None
|
self._start = None
|
||||||
self.elapsed_ms = 0.0
|
self.elapsed_ms = 0.0
|
||||||
|
self._otel_cm = None # 嵌套的 OTel span 上下文管理器
|
||||||
|
|
||||||
def _enter(self):
|
def _enter(self):
|
||||||
self._start = time.perf_counter()
|
self._start = time.perf_counter()
|
||||||
@@ -121,6 +128,10 @@ class _Span:
|
|||||||
f"SPAN_START {self.name}",
|
f"SPAN_START {self.name}",
|
||||||
extra={"span": self.name, "event": "start", "trace_id": get_trace_id(), **self.fields},
|
extra={"span": self.name, "event": "start", "trace_id": get_trace_id(), **self.fields},
|
||||||
)
|
)
|
||||||
|
# OTel 嵌套 span（失败安全：current_span_cm 返回 None 时 no-op）
|
||||||
|
self._otel_cm = current_span_cm(self.name, self.fields)
|
||||||
|
if self._otel_cm is not None:
|
||||||
|
self._otel_cm.__enter__()
|
||||||
return self
|
return self
|
||||||
|
|
||||||
def _exit(self, exc_type, exc, tb):
|
def _exit(self, exc_type, exc, tb):
|
||||||
@@ -137,6 +148,24 @@ class _Span:
|
|||||||
**self.fields,
|
**self.fields,
|
||||||
},
|
},
|
||||||
)
|
)
|
||||||
|
# 结束嵌套的 OTel span（失败安全）
|
||||||
|
if self._otel_cm is not None:
|
||||||
|
try:
|
||||||
|
from opentelemetry import trace as _ot
|
||||||
|
if exc_type is not None:
|
||||||
|
self._otel_cm.set_status(
|
||||||
|
_ot.trace.Status(_ot.trace.StatusCode.ERROR, str(exc_type.__name__))
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
self._otel_cm.set_status(_ot.trace.Status(_ot.trace.StatusCode.OK))
|
||||||
|
self._otel_cm.__exit__(exc_type, exc, tb)
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
# 记录 span 指标（耗时直方图 + 计数），供 Grafana 出图（失败安全）
|
||||||
|
try:
|
||||||
|
record_span_metrics(self.name, self.elapsed_ms, status)
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
return False # 不吞掉异常
|
return False # 不吞掉异常
|
||||||
|
|
||||||
# 同步协议
|
# 同步协议
|
||||||
|
|||||||
@@ -0,0 +1,131 @@
|
|||||||
|
"""
|
||||||
|
OpenTelemetry 桥接 (Phase 1.I 标准化)
|
||||||
|
=====================================
|
||||||
|
设计基准（标准对照表见 STANDARDS_AUDIT_2026-07-24.md §I）：
|
||||||
|
- 标准：OpenTelemetry SDK + OTLP gRPC exporter —— 云原生可观测事实标准。
|
||||||
|
- traces：span 经 observability.span() 嵌套导出（见 observability.py）。
|
||||||
|
- metrics：MeterProvider 经 OTLP 导出 span 耗时直方图 + 调用计数，供 Grafana 出图。
|
||||||
|
- 失败安全（生产红线）：若 OTLP 端点不可达 / 初始化异常，自动降级为 no-op，
|
||||||
|
绝不阻断业务请求、不产生未捕获异常。
|
||||||
|
- 门控：OTEL_ENABLED=1 默认开启；置 0 则零开销、不走 OTel。
|
||||||
|
- 发射目标：OTEL_EXPORTER_OTLP_ENDPOINT，默认 http://qihu-aiflow-alloy:4317
|
||||||
|
（经 Grafana Alloy 桥接 OTLP→Prometheus，再由 Prometheus 喂给 Grafana 出图）。
|
||||||
|
|
||||||
|
对外 API：
|
||||||
|
init_otel() 首次调用时构建 TracerProvider + MeterProvider（幂等）
|
||||||
|
get_tracer() 返回 tracer（未启用/失败则 None）
|
||||||
|
get_meter() 返回 meter（未启用/失败则 None）
|
||||||
|
current_span_cm(name) 返回一个 OTel span 的上下文管理器（供 observability 嵌套）
|
||||||
|
record_span_metrics(name, elapsed_ms, status) 记录 span 指标（耗时直方图 + 计数）
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import logging
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
_ENABLED = os.getenv("OTEL_ENABLED", "1") not in ("0", "false", "False", "")
|
||||||
|
_ENDPOINT = os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://qihu-aiflow-alloy:4317").rstrip("/")
|
||||||
|
if not _ENDPOINT.startswith("http://") and not _ENDPOINT.startswith("https://"):
|
||||||
|
_ENDPOINT = "http://" + _ENDPOINT
|
||||||
|
|
||||||
|
_tracer = None
|
||||||
|
_meter = None
|
||||||
|
_initialized = False
|
||||||
|
_duration_hist = None
|
||||||
|
_span_counter = None
|
||||||
|
|
||||||
|
|
||||||
|
def init_otel() -> None:
|
||||||
|
"""构建 TracerProvider + MeterProvider（幂等）。失败则降级为 no-op。"""
|
||||||
|
global _tracer, _meter, _initialized
|
||||||
|
if _initialized:
|
||||||
|
return
|
||||||
|
_initialized = True
|
||||||
|
if not _ENABLED:
|
||||||
|
logger.info("[otel] disabled (OTEL_ENABLED=0)")
|
||||||
|
return
|
||||||
|
try:
|
||||||
|
from opentelemetry import trace, metrics
|
||||||
|
from opentelemetry.sdk.resources import Resource
|
||||||
|
from opentelemetry.semconv.resource import ResourceAttributes
|
||||||
|
from opentelemetry.sdk.trace import TracerProvider
|
||||||
|
from opentelemetry.sdk.trace.export import BatchSpanProcessor
|
||||||
|
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
|
||||||
|
OTLPSpanExporter,
|
||||||
|
)
|
||||||
|
from opentelemetry.sdk.metrics import MeterProvider
|
||||||
|
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
|
||||||
|
from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import (
|
||||||
|
OTLPMetricExporter,
|
||||||
|
)
|
||||||
|
|
||||||
|
resource = Resource.create({ResourceAttributes.SERVICE_NAME: "qihu-aiflow"})
|
||||||
|
|
||||||
|
# --- traces ---
|
||||||
|
trace_provider = TracerProvider(resource=resource)
|
||||||
|
trace_provider.add_span_processor(
|
||||||
|
BatchSpanProcessor(OTLPSpanExporter(endpoint=_ENDPOINT, insecure=True))
|
||||||
|
)
|
||||||
|
trace.set_tracer_provider(trace_provider)
|
||||||
|
_tracer = trace.get_tracer("qihu-aiflow")
|
||||||
|
|
||||||
|
# --- metrics ---
|
||||||
|
metric_reader = PeriodicExportingMetricReader(
|
||||||
|
OTLPMetricExporter(endpoint=_ENDPOINT, insecure=True)
|
||||||
|
)
|
||||||
|
meter_provider = MeterProvider(resource=resource, metric_readers=[metric_reader])
|
||||||
|
metrics.set_meter_provider(meter_provider)
|
||||||
|
_meter = metrics.get_meter("qihu-aiflow")
|
||||||
|
|
||||||
|
logger.info(f"[otel] tracer+meter ready -> {_ENDPOINT}")
|
||||||
|
except Exception as e: # pragma: no cover - 降级保护
|
||||||
|
logger.warning(f"[otel] init failed, degraded to no-op: {e}")
|
||||||
|
_tracer = None
|
||||||
|
_meter = None
|
||||||
|
|
||||||
|
|
||||||
|
def get_tracer():
|
||||||
|
"""返回 tracer；首次调用触发 init。未启用/失败返回 None。"""
|
||||||
|
if _tracer is None and _ENABLED:
|
||||||
|
init_otel()
|
||||||
|
return _tracer
|
||||||
|
|
||||||
|
|
||||||
|
def get_meter():
|
||||||
|
"""返回 meter；首次调用触发 init。未启用/失败返回 None。"""
|
||||||
|
if _meter is None and _ENABLED:
|
||||||
|
init_otel()
|
||||||
|
return _meter
|
||||||
|
|
||||||
|
|
||||||
|
def current_span_cm(name: str, attributes: dict | None = None):
|
||||||
|
"""返回一个 OTel span 的上下文管理器（同步/异步通用）。失败时返回 None。"""
|
||||||
|
tr = get_tracer()
|
||||||
|
if tr is None:
|
||||||
|
return None
|
||||||
|
try:
|
||||||
|
return tr.start_as_current_span(name, attributes=attributes or {})
|
||||||
|
except Exception as e: # pragma: no cover
|
||||||
|
logger.warning(f"[otel] start_span failed: {e}")
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
def record_span_metrics(name: str, elapsed_ms: float, status: str) -> None:
|
||||||
|
"""记录 span 指标：耗时直方图 + 调用计数（供 Grafana 出图）。失败安全。"""
|
||||||
|
m = get_meter()
|
||||||
|
if m is None:
|
||||||
|
return
|
||||||
|
global _duration_hist, _span_counter
|
||||||
|
try:
|
||||||
|
if _duration_hist is None:
|
||||||
|
_duration_hist = m.create_histogram(
|
||||||
|
"aiflow_span_duration_ms", "Span duration in milliseconds"
|
||||||
|
)
|
||||||
|
_span_counter = m.create_counter("aiflow_span_count", "Span invocation count")
|
||||||
|
attrs = {"span": name, "status": status}
|
||||||
|
_duration_hist.record(elapsed_ms, attrs)
|
||||||
|
_span_counter.add(1, attrs)
|
||||||
|
except Exception: # pragma: no cover
|
||||||
|
pass
|
||||||
|
|
||||||
@@ -0,0 +1,97 @@
|
|||||||
|
"""标准 RAG 管线 (Phase 2.C 标准化)
|
||||||
|
====================================
|
||||||
|
设计基准（标准对照表见 STANDARDS_AUDIT_2026-07-24.md §C）：
|
||||||
|
- 对齐 LlamaIndex / LangChain 的管线形态：load -> chunk(overlap) -> embed -> store -> retrieve -> rerank。
|
||||||
|
- 复用现有 pgvector 后端（document_embeddings 表 + ivfflat）与共享 embeddings 客户端
|
||||||
|
（app.core.llm_client.aembed）；嵌入维度(1024)、表结构、混合检索(RRF)全部沿用，不重复造轮子。
|
||||||
|
- 不引入 LlamaIndex 全量重依赖：平台运行时为最小 fastapi/httpx 容器，引入 LlamaIndex 栈有体积/冲突风险；
|
||||||
|
故以「接口对齐标准」的方式实现标准管线（递归重叠分块 + 召回 + rerank 钩子）。后续若评估可接受，
|
||||||
|
可平滑替换为 llama_index.core 的 VectorStoreIndex + PGVectorStore（对外接口一致）。
|
||||||
|
|
||||||
|
对外 API：
|
||||||
|
retrieve(query, kb_id, top_k=5, rerank=True) -> [dict] # 标准检索（向量召回 + 可选 rerank）
|
||||||
|
index_document(text, doc_id, kb_id, ...) -> int # 标准索引（递归重叠分块后逐块入库）
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
|
||||||
|
from app.pgvector_search import vector_search, index_document as _pg_index
|
||||||
|
|
||||||
|
logger = logging.getLogger("aiflow.rag")
|
||||||
|
|
||||||
|
# 标准递归分块分隔符（与 LlamaIndex SentenceSplitter / LangChain RecursiveCharacterTextSplitter 同序）
|
||||||
|
_SEPARATORS = ["\n\n", "\n", "。", "！", "？", "；", ".", "!", "?", ";", " ", ""]
|
||||||
|
|
||||||
|
|
||||||
|
def _recursive_split(text: str, chunk_size: int = 500, chunk_overlap: int = 80):
|
||||||
|
"""递归字符分块（带重叠），对齐标准 Splitter 算法。
|
||||||
|
|
||||||
|
逐层用分隔符切分；某段仍超长时递归细切；最终兜底硬切（带重叠步长）。
|
||||||
|
"""
|
||||||
|
text = text or ""
|
||||||
|
if len(text) <= chunk_size:
|
||||||
|
return [text] if text.strip() else []
|
||||||
|
for sep in _SEPARATORS:
|
||||||
|
if not sep:
|
||||||
|
# 兜底：硬切（带重叠步长）
|
||||||
|
step = max(1, chunk_size - chunk_overlap)
|
||||||
|
return [text[i : i + chunk_size] for i in range(0, len(text), step)]
|
||||||
|
parts = text.split(sep)
|
||||||
|
if len(parts) <= 1:
|
||||||
|
continue # 该分隔符在文本中未出现，尝试更细的分隔符
|
||||||
|
chunks, cur = [], ""
|
||||||
|
for p in parts:
|
||||||
|
cand = (cur + sep + p) if cur else p
|
||||||
|
if len(cand) <= chunk_size:
|
||||||
|
cur = cand
|
||||||
|
else:
|
||||||
|
if cur:
|
||||||
|
chunks.append(cur)
|
||||||
|
cur = ""
|
||||||
|
if len(p) > chunk_size:
|
||||||
|
chunks.extend(_recursive_split(p, chunk_size, chunk_overlap))
|
||||||
|
else:
|
||||||
|
cur = p
|
||||||
|
if cur:
|
||||||
|
chunks.append(cur)
|
||||||
|
if chunks:
|
||||||
|
return [c for c in chunks if c.strip()]
|
||||||
|
return [text]
|
||||||
|
|
||||||
|
|
||||||
|
async def index_document(
|
||||||
|
text: str,
|
||||||
|
doc_id: str,
|
||||||
|
kb_id: str,
|
||||||
|
chunk_size: int = 500,
|
||||||
|
chunk_overlap: int = 80,
|
||||||
|
metadata: dict | None = None,
|
||||||
|
) -> int:
|
||||||
|
"""标准索引：递归重叠分块后逐块存入 pgvector。返回成功分块数。"""
|
||||||
|
chunks = _recursive_split(text, chunk_size, chunk_overlap)
|
||||||
|
ok = 0
|
||||||
|
for i, ch in enumerate(chunks):
|
||||||
|
cid = f"{doc_id}_{i}"
|
||||||
|
if await _pg_index(doc_id, kb_id, ch, chunk_id=cid, metadata=metadata):
|
||||||
|
ok += 1
|
||||||
|
logger.info(f"[rag] indexed {doc_id}: {ok}/{len(chunks)} chunks")
|
||||||
|
return ok
|
||||||
|
|
||||||
|
|
||||||
|
async def retrieve(
|
||||||
|
query: str,
|
||||||
|
kb_id: str | None = None,
|
||||||
|
top_k: int = 5,
|
||||||
|
rerank: bool = True,
|
||||||
|
) -> list:
|
||||||
|
"""标准检索：向量召回 + 可选 rerank。
|
||||||
|
|
||||||
|
返回 [{doc_id, chunk_id, content, score, metadata}, ...]。
|
||||||
|
rerank 钩子默认按向量余弦分排序；接入标准 cross-encoder
|
||||||
|
（sentence-transformers CrossEncoder）即替换此处，对外接口不变。
|
||||||
|
"""
|
||||||
|
results = await vector_search(query, kb_id, top_k=top_k * 2 if rerank else top_k)
|
||||||
|
if not results:
|
||||||
|
return results
|
||||||
|
if rerank and len(results) > 1:
|
||||||
|
results = sorted(results, key=lambda r: r.get("score", 0), reverse=True)
|
||||||
|
return results[:top_k]
|
||||||
@@ -1,122 +1,96 @@
|
|||||||
"""
|
"""
|
||||||
MCP (Model Context Protocol) 客户端 —— 异步实现 streamable_http 传输。
|
MCP (Model Context Protocol) 客户端 —— 基于官方 `mcp` Python SDK (Phase 1.E 标准化)
|
||||||
|
|
||||||
支持：
|
设计基准（标准对照表见 STANDARDS_AUDIT_2026-07-24.md §E）：
|
||||||
- initialize / notifications/initialized / tools/list / tools/call 标准流程
|
- 采用 Anthropic 官方 `mcp` SDK 处理 JSON-RPC 帧 + 协议版本协商，
|
||||||
- streamable_http：POST JSON-RPC，响应可能是 SSE（event: message / data: {...}）或纯 JSON
|
不再手写解析、不再写死 MCP_PROTOCOL_VERSION 常量（旧实现写死 "2024-11-05"，
|
||||||
- SSE 解析兼容
|
上游协议一升级即碎）。
|
||||||
- 直连外部端点（trust_env=False，不绕容器代理）
|
- 支持 streamable_http（默认）与 sse 两种传输，按 server['transport_type'] 选择。
|
||||||
|
- 公共 API 保持向后兼容：list_mcp_tools(server, timeout) / call_mcp_tool(server, tool_name, arguments, timeout)
|
||||||
供 chat.py 在 AI 对话中把 MCP 工具暴露为 function calling，并代理执行 tools/call。
|
供 chat.py / chat_service.py / chat_agents.py / chat_skill.py / chat_mcp.py /
|
||||||
|
chat_context.py / chat_knowledge.py / chat_media.py / chat_utils.py / core/aca.py
|
||||||
|
共 10+ 处调用，签名与返回结构（tools 数组 / 文本结果字符串）不变。
|
||||||
|
- 工具列表缓存（按 url）与错误处理保持原行为，避免重复握手、保证降级安全。
|
||||||
"""
|
"""
|
||||||
|
|
||||||
import json
|
|
||||||
import logging
|
|
||||||
import asyncio
|
import asyncio
|
||||||
|
import logging
|
||||||
from typing import Optional
|
from typing import Optional
|
||||||
|
|
||||||
import httpx
|
from mcp import ClientSession
|
||||||
|
from mcp.client.sse import sse_client
|
||||||
|
from mcp.client.streamable_http import streamablehttp_client
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
MCP_PROTOCOL_VERSION = "2024-11-05"
|
|
||||||
|
|
||||||
# 工具列表缓存（url -> (timestamp, tools)），避免同一请求内 / 短时间重复握手
|
# 工具列表缓存（url -> (timestamp, tools)），避免同一请求内 / 短时间重复握手
|
||||||
_TOOLS_CACHE: dict = {}
|
_TOOLS_CACHE: dict = {}
|
||||||
_TOOLS_CACHE_TTL = 120 # 秒
|
_TOOLS_CACHE_TTL = 120 # 秒
|
||||||
|
|
||||||
|
|
||||||
def _parse_sse(text: str):
|
def _transport_cm(server: dict, timeout: float):
|
||||||
"""从响应文本解析 JSON-RPC 结果。兼容 SSE（data: 行）与纯 JSON。"""
|
"""按 server['transport_type'] 选择官方 SDK 传输层。
|
||||||
if not text:
|
|
||||||
return None
|
返回异步上下文管理器，进入后产出 (read_stream, write_stream, ...)。
|
||||||
text = text.strip()
|
"""
|
||||||
# SSE 格式：多行 "event: message" / "data: {...}"
|
url = (server.get("url") or "").strip()
|
||||||
data_lines = []
|
ttype = (server.get("transport_type") or "streamable_http").lower()
|
||||||
for line in text.splitlines():
|
headers = server.get("headers") or {}
|
||||||
ls = line.strip()
|
if ttype == "sse":
|
||||||
if ls.startswith("data:"):
|
return sse_client(url, headers=headers, timeout=timeout)
|
||||||
data_lines.append(ls[5:].strip())
|
# 默认 streamable_http（官方 SDK 标准传输）
|
||||||
if data_lines:
|
return streamablehttp_client(url, headers=headers, timeout=timeout)
|
||||||
last = data_lines[-1]
|
|
||||||
try:
|
|
||||||
return json.loads(last)
|
|
||||||
except Exception:
|
|
||||||
pass
|
|
||||||
# 纯 JSON
|
|
||||||
try:
|
|
||||||
return json.loads(text)
|
|
||||||
except Exception:
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
async def _handshake_and_request(
|
async def _session_request(
|
||||||
url: str,
|
server: dict, method: str, params: dict, timeout: float = 30.0
|
||||||
headers: dict,
|
|
||||||
method: str,
|
|
||||||
params: dict,
|
|
||||||
timeout: float = 30.0,
|
|
||||||
):
|
):
|
||||||
|
"""用官方 ClientSession 完成一次标准 MCP 调用（initialize -> 目标请求）。
|
||||||
|
|
||||||
|
返回解析后的 dict：{"result": {...}} 或 None（失败）。
|
||||||
|
协议版本由 SDK 自动协商，不再硬编码。
|
||||||
"""
|
"""
|
||||||
完成一次完整 MCP 会话（initialize -> initialized -> 目标请求）。
|
url = (server.get("url") or "").strip()
|
||||||
返回解析后的 JSON-RPC 响应 dict，或 None。
|
if not url:
|
||||||
"""
|
return None
|
||||||
base_headers = {
|
try:
|
||||||
"Content-Type": "application/json",
|
async with _transport_cm(server, timeout) as streams:
|
||||||
"Accept": "application/json, text/event-stream",
|
read, write, *_ = streams
|
||||||
}
|
async with ClientSession(read, write) as session:
|
||||||
base_headers.update(headers or {})
|
# 标准握手：initialize 会发送 initialize 请求并自动补 notifications/initialized
|
||||||
|
await session.initialize()
|
||||||
|
# 防御性补发 initialized 通知（部分服务端要求显式接收）
|
||||||
|
try:
|
||||||
|
if hasattr(session, "send_notification"):
|
||||||
|
await session.send_notification("notifications/initialized", {})
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
|
||||||
async with httpx.AsyncClient(timeout=timeout, trust_env=False) as client:
|
if method == "tools/list":
|
||||||
# 1) initialize
|
result = await session.list_tools()
|
||||||
r = await client.post(
|
tools = [t.model_dump() for t in (result.tools or [])]
|
||||||
url,
|
return {"result": {"tools": tools}}
|
||||||
headers=base_headers,
|
elif method == "tools/call":
|
||||||
json={
|
res = await session.call_tool(
|
||||||
"jsonrpc": "2.0",
|
params.get("name"), params.get("arguments") or {}
|
||||||
"id": 1,
|
)
|
||||||
"method": "initialize",
|
content = []
|
||||||
"params": {
|
for blk in (res.content or []):
|
||||||
"protocolVersion": MCP_PROTOCOL_VERSION,
|
btype = getattr(blk, "type", "text")
|
||||||
"capabilities": {},
|
if btype == "text":
|
||||||
"clientInfo": {"name": "aiflow", "version": "1.0"},
|
content.append({"type": "text", "text": getattr(blk, "text", "")})
|
||||||
},
|
else:
|
||||||
},
|
content.append({"type": btype, "text": str(blk)})
|
||||||
)
|
return {
|
||||||
if r.status_code >= 400:
|
"result": {
|
||||||
logger.warning(f"[MCP] initialize 失败 {url} -> HTTP {r.status_code}")
|
"content": content,
|
||||||
return None
|
"isError": bool(getattr(res, "isError", False)),
|
||||||
sid = r.headers.get("mcp-session-id") or r.headers.get("Mcp-Session-Id")
|
}
|
||||||
req_headers = dict(base_headers)
|
}
|
||||||
if sid:
|
except Exception as e:
|
||||||
req_headers["Mcp-Session-Id"] = sid
|
logger.warning(f"[MCP] {method} 异常 {url}: {e}")
|
||||||
|
return None
|
||||||
# 2) initialized 通知（不需要响应）
|
return None
|
||||||
try:
|
|
||||||
await client.post(
|
|
||||||
url,
|
|
||||||
headers=req_headers,
|
|
||||||
json={"jsonrpc": "2.0", "method": "notifications/initialized"},
|
|
||||||
)
|
|
||||||
except Exception:
|
|
||||||
pass
|
|
||||||
|
|
||||||
# 3) 目标请求
|
|
||||||
rid = 2 if method != "tools/call" else 3
|
|
||||||
r2 = await client.post(
|
|
||||||
url,
|
|
||||||
headers=req_headers,
|
|
||||||
json={
|
|
||||||
"jsonrpc": "2.0",
|
|
||||||
"id": rid,
|
|
||||||
"method": method,
|
|
||||||
"params": params,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
if r2.status_code >= 400:
|
|
||||||
logger.warning(f"[MCP] {method} 失败 {url} -> HTTP {r2.status_code}")
|
|
||||||
return None
|
|
||||||
return _parse_sse(r2.text)
|
|
||||||
|
|
||||||
|
|
||||||
async def list_mcp_tools(server: dict, timeout: float = 30.0) -> list:
|
async def list_mcp_tools(server: dict, timeout: float = 30.0) -> list:
|
||||||
@@ -134,9 +108,7 @@ async def list_mcp_tools(server: dict, timeout: float = 30.0) -> list:
|
|||||||
if cached and (now - cached[0]) < _TOOLS_CACHE_TTL:
|
if cached and (now - cached[0]) < _TOOLS_CACHE_TTL:
|
||||||
return cached[1]
|
return cached[1]
|
||||||
try:
|
try:
|
||||||
data = await _handshake_and_request(
|
data = await _session_request(server, "tools/list", {}, timeout)
|
||||||
url, server.get("headers") or {}, "tools/list", {}, timeout
|
|
||||||
)
|
|
||||||
tools = []
|
tools = []
|
||||||
if data and "result" in data:
|
if data and "result" in data:
|
||||||
tools = data["result"].get("tools", []) or []
|
tools = data["result"].get("tools", []) or []
|
||||||
@@ -158,9 +130,8 @@ async def call_mcp_tool(
|
|||||||
return f"[MCP 工具 {tool_name} 执行失败：服务端地址为空]"
|
return f"[MCP 工具 {tool_name} 执行失败：服务端地址为空]"
|
||||||
logger.info(f"[MCP] 执行工具调用 tool={tool_name} server={server.get('name')} url={url}")
|
logger.info(f"[MCP] 执行工具调用 tool={tool_name} server={server.get('name')} url={url}")
|
||||||
try:
|
try:
|
||||||
data = await _handshake_and_request(
|
data = await _session_request(
|
||||||
url,
|
server,
|
||||||
server.get("headers") or {},
|
|
||||||
"tools/call",
|
"tools/call",
|
||||||
{"name": tool_name, "arguments": arguments or {}},
|
{"name": tool_name, "arguments": arguments or {}},
|
||||||
timeout,
|
timeout,
|
||||||
@@ -169,7 +140,7 @@ async def call_mcp_tool(
|
|||||||
return f"[MCP 工具 {tool_name} 调用失败：无响应]"
|
return f"[MCP 工具 {tool_name} 调用失败：无响应]"
|
||||||
if "error" in data:
|
if "error" in data:
|
||||||
err = data["error"]
|
err = data["error"]
|
||||||
return f"[MCP 工具 {tool_name} 返回错误] {json.dumps(err, ensure_ascii=False)}"
|
return f"[MCP 工具 {tool_name} 返回错误] {__import__('json').dumps(err, ensure_ascii=False)}"
|
||||||
result = data.get("result", {})
|
result = data.get("result", {})
|
||||||
content = result.get("content", []) or []
|
content = result.get("content", []) or []
|
||||||
parts = []
|
parts = []
|
||||||
@@ -177,7 +148,7 @@ async def call_mcp_tool(
|
|||||||
if isinstance(item, dict) and item.get("type") == "text":
|
if isinstance(item, dict) and item.get("type") == "text":
|
||||||
parts.append(item.get("text", ""))
|
parts.append(item.get("text", ""))
|
||||||
else:
|
else:
|
||||||
parts.append(json.dumps(item, ensure_ascii=False))
|
parts.append(__import__("json").dumps(item, ensure_ascii=False))
|
||||||
text = "\n".join(parts).strip()
|
text = "\n".join(parts).strip()
|
||||||
if result.get("isError"):
|
if result.get("isError"):
|
||||||
return f"[MCP 工具 {tool_name} 执行报错]\n{text}"
|
return f"[MCP 工具 {tool_name} 执行报错]\n{text}"
|
||||||
|
|||||||
@@ -135,27 +135,16 @@ async def get_embedding(text: str) -> Optional[List[float]]:
|
|||||||
if cached:
|
if cached:
|
||||||
return cached
|
return cached
|
||||||
try:
|
try:
|
||||||
import httpx
|
# 收敛到共享标准模型层（app.core.llm_client.aembed），与聊天共用代理/超时/failover 语义
|
||||||
async with httpx.AsyncClient(timeout=30, trust_env=False) as client:
|
from app.core.llm_client import aembed
|
||||||
resp = await client.post(
|
embedding = await aembed(
|
||||||
f"{EMBEDDING_API_URL}/v1/embeddings",
|
EMBEDDING_API_URL, EMBEDDING_API_KEY, text[:8000], EMBEDDING_MODEL, timeout=30
|
||||||
headers={
|
)
|
||||||
"Authorization": f"Bearer {EMBEDDING_API_KEY}",
|
if embedding:
|
||||||
"Content-Type": "application/json",
|
_embed_cache_put(text, EMBEDDING_MODEL, embedding)
|
||||||
},
|
return embedding
|
||||||
json={
|
logger.error("Embedding API returned empty")
|
||||||
"model": EMBEDDING_MODEL,
|
return None
|
||||||
"input": text[:8000], # 截断防止超长
|
|
||||||
},
|
|
||||||
)
|
|
||||||
if resp.status_code == 200:
|
|
||||||
data = resp.json()
|
|
||||||
embedding = data["data"][0]["embedding"]
|
|
||||||
_embed_cache_put(text, EMBEDDING_MODEL, embedding)
|
|
||||||
return embedding
|
|
||||||
else:
|
|
||||||
logger.error(f"Embedding API error: {resp.status_code} {resp.text[:200]}")
|
|
||||||
return None
|
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error(f"Embedding generation failed: {e}")
|
logger.error(f"Embedding generation failed: {e}")
|
||||||
return None
|
return None
|
||||||
|
|||||||
Reference in new issue
Block a user