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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@@ -107,6 +107,8 @@ from app.api.chat_media import (_generate_media_task, detect_ambiguous_creative_
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from app.api.chat_skill import (_discover_installed_skills, _execute_skill_tool, _resolve_market_agents, _resolve_market_skills)
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from app.api.chat_utils import (SendMessageRequest, _proxy_for_url, cst_now, logger)
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# 统一模型层(07-24 红线)：web chat 与 IM bot 共用标准 OpenAI SDK 客户端
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from app.core.llm_client import achat_complete
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@@ -2370,168 +2372,21 @@ async def _send_message_core(
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async with httpx.AsyncClient(
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timeout=ai_model.timeout or 120,
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proxy=_proxy_for_url(api_url),
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trust_env=False,
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) as client:
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async with span("llm.completion", model=ai_model.id if ai_model else None):
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resp = await client.post(
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async with span("llm.completion", model=ai_model.id if ai_model else None):
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try:
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data = await achat_complete(
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api_url,
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headers={
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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},
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json=request_body
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api_key,
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request_body,
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proxy=_proxy_for_url(api_url),
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timeout=ai_model.timeout or 120,
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)
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if resp.status_code != 200:
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except Exception as _exc:
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if round_num == 0:
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ai_content = f"AI模型调用失败 (HTTP {resp.status_code}), 请检查模型配置。"
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ai_content = f"AI模型调用失败 ({type(_exc).__name__}: {str(_exc)[:120]}), 请检查模型配置。"
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logger.warning(f"[chat] 模型 {ai_model.name} 调用异常: {_exc}")
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break
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data = resp.json()
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choice = data.get("choices", [{}])[0]
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message = choice.get("message", {})
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@@ -24,6 +24,7 @@ from typing import Optional
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from urllib.parse import urlparse as _urlparse
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import httpx
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from 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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@@ -226,21 +227,19 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
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{"type": "function", "function": fd} for fd in function_definitions
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]
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request_body["tool_choice"] = "auto"
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with httpx.Client(
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timeout=ai_model.timeout or 120,
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proxy=_proxy_for_url(api_url),
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trust_env=False,
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) as client:
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resp = client.post(
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try:
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data = chat_complete(
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api_url,
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headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
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json=request_body,
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api_key,
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request_body,
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proxy=_proxy_for_url(api_url),
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timeout=ai_model.timeout or 120,
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)
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if resp.status_code != 200:
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_retry_log.append(f"{ai_model.name}: HTTP {resp.status_code}")
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_http_ok = False
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break
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data = resp.json()
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except Exception as _exc:
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_retry_log.append(f"{ai_model.name}: {str(_exc)[:60]}")
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logger.warning(f"[im_llm] 模型 {ai_model.name} 异常: {_exc}")
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_http_ok = False
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break
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message = data.get("choices", [{}])[0].get("message", {})
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tool_calls = message.get("tool_calls", [])
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if tool_calls and skills:
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@@ -0,0 +1,181 @@
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"""Shared, standards-based LLM client for web chat + IM bot.
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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
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(`openai.AsyncOpenAI`)——它是 OpenAI 兼容端点的事实标准客户端。平台本身已在讲
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OpenAI 协议(`/v1/chat/completions`)，因此零协议改造成本，并天然获得：
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* 原生 SSE 流式(`stream=True`)；
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* 结构化的 tool_calls / usage 解析；
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* 统一的代理(trust_env=False + 显式 proxy)与超时处理。
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本模块取代 chat_service.py 与 im_llm.py 中两套手写 httpx 循环，使 web chat 与
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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」
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红线在模型层的落地。
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API:
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achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=120, stream=False)
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- stream=False -> dict，形状对齐上游 `data` 响应：
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{"choices":[{"message":{"content","tool_calls","reasoning_content"}}], "usage":{...}}
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- stream=True -> async generator，逐块产出 ("content"|"reasoning", text) 增量
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"""
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from __future__ import annotations
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import httpx
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from openai import AsyncOpenAI, OpenAI
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def _base_url(api_url: str) -> str:
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"""从完整 /v1/chat/completions 或 /v1/embeddings 地址推导 OpenAI SDK 所需的 base_url。"""
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u = (api_url or "").rstrip()
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for suf in ("/v1/chat/completions", "/chat/completions", "/v1/embeddings", "/embeddings"):
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if u.endswith(suf):
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return u[: -len(suf)]
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return u
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def build_openai_client(api_url, api_key, *, proxy=None, timeout=120) -> AsyncOpenAI:
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base = _base_url(api_url)
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# 始终 trust_env=False：避免容器 HTTP_PROXY 对国内端点(siliconflow 等)造成 502；
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# 代理经 proxy 显式传入（AGNES 等），未传则直连。
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http_client = httpx.AsyncClient(proxy=proxy, trust_env=False)
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return AsyncOpenAI(
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base_url=base,
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api_key=api_key or "EMPTY",
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timeout=timeout,
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http_client=http_client,
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)
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def _create_kwargs(request_body: dict) -> dict:
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kw = {
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"model": request_body.get("model"),
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"messages": request_body.get("messages", []),
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"stream": False,
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}
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if request_body.get("temperature") is not None:
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kw["temperature"] = request_body["temperature"]
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if request_body.get("top_p") is not None:
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kw["top_p"] = request_body["top_p"]
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if request_body.get("max_tokens") is not None:
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kw["max_tokens"] = request_body["max_tokens"]
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if request_body.get("stop") is not None:
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kw["stop"] = request_body["stop"]
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tools = request_body.get("tools")
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if tools:
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kw["tools"] = tools
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kw["tool_choice"] = request_body.get("tool_choice", "auto")
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return kw
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def _normalize_tool_calls(tcs):
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if not tcs:
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return None
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out = []
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for tc in tcs:
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out.append(
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{
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"id": getattr(tc, "id", None),
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"type": "function",
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"function": {
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"name": getattr(tc.function, "name", None),
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"arguments": getattr(tc.function, "arguments", "") or "",
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},
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}
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)
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return out
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async def achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=120, stream=False):
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"""统一的 OpenAI 兼容聊天补全。详见模块 docstring。"""
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if stream:
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async def _gen():
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client = build_openai_client(api_url, api_key, proxy=proxy, timeout=timeout)
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try:
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gen = await client.chat.completions.create(
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**_create_kwargs(request_body), stream=True
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)
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async for chunk in gen:
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if not chunk.choices:
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continue
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delta = chunk.choices[0].delta
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rc = getattr(delta, "reasoning_content", None)
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if rc:
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yield ("reasoning", rc)
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ct = getattr(delta, "content", None)
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if ct:
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yield ("content", ct)
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finally:
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await client.close()
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return _gen()
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client = build_openai_client(api_url, api_key, proxy=proxy, timeout=timeout)
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async with client:
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resp = await client.chat.completions.create(**_create_kwargs(request_body))
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msg = resp.choices[0].message
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return {
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"choices": [
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{
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"message": {
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"content": msg.content or "",
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"tool_calls": _normalize_tool_calls(getattr(msg, "tool_calls", None)),
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"reasoning_content": getattr(msg, "reasoning_content", None),
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}
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}
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],
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"usage": (resp.usage.model_dump() if resp.usage else {}),
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}
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def build_openai_client_sync(api_url, api_key, *, proxy=None, timeout=120) -> OpenAI:
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"""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）。"""
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base = _base_url(api_url)
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http_client = httpx.Client(proxy=proxy, trust_env=False)
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return OpenAI(base_url=base, api_key=api_key or "EMPTY", timeout=timeout, http_client=http_client)
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def chat_complete(api_url, api_key, request_body, *, proxy=None, timeout=120):
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"""同步版（与 achat_complete 行为/返回形状完全一致），供非 async 上下文使用。"""
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client = build_openai_client_sync(api_url, api_key, proxy=proxy, timeout=timeout)
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with client:
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resp = client.chat.completions.create(**_create_kwargs(request_body))
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msg = resp.choices[0].message
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return {
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"choices": [
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{
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"message": {
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"content": msg.content or "",
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"tool_calls": _normalize_tool_calls(getattr(msg, "tool_calls", None)),
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"reasoning_content": getattr(msg, "reasoning_content", None),
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}
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}
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],
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"usage": (resp.usage.model_dump() if resp.usage else {}),
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}
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# ---------------------------------------------------------------------------
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# 嵌入（Embeddings）：OpenAI 兼容 /v1/embeddings，收敛散落的裸 httpx 嵌入调用
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# ---------------------------------------------------------------------------
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def _embed_kwargs(input, model):
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return {"model": model, "input": input if isinstance(input, list) else [input]}
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async def aembed(api_url, api_key, input, model, *, proxy=None, timeout=60) -> list:
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"""统一的 OpenAI 兼容文本嵌入（异步）。返回 list[list[float]]。
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供 pgvector_search.get_embedding 等 embeddings 路径复用，把散落的裸 httpx
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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）。
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"""
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client = build_openai_client(api_url, api_key, proxy=proxy, timeout=timeout)
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async with client:
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resp = await client.embeddings.create(**_embed_kwargs(input, model))
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return [d.embedding for d in resp.data]
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def embed(api_url, api_key, input, model, *, proxy=None, timeout=60) -> list:
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"""同步版嵌入（与非 async 上下文复用同一标准模型层）。"""
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client = build_openai_client_sync(api_url, api_key, proxy=proxy, timeout=timeout)
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with client:
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resp = client.embeddings.create(**_embed_kwargs(input, model))
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return [d.embedding for d in resp.data]
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@@ -1,6 +1,6 @@
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"""
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Qihu-AIFlow 统一可观测基座 (Phase 2.1)
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=====================================
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Qihu-AIFlow 统一可观测基座 (Phase 2.1 + Phase 1.I OTel 增强)
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=============================================================
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提供贯穿一次请求的全局链路 ID（trace_id）与结构化日志能力，
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为后续重构（上下文引擎、媒体/LLM 成本归因）建立回归基线。
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@@ -10,13 +10,17 @@ Qihu-AIFlow 统一可观测基座 (Phase 2.1)
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TraceIdFilter 自动注入，调用方无需改动。
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- 不引入第三方依赖（structlog 未安装），自带可选的 JSON formatter。
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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
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span（经 app.core.otel 桥接，OTLP 导出到 Grafana）。OTel 失败时自动降级为 no-op，
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绝不改变既有日志行为与请求链路。
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使用方式：
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from app.core.observability import set_trace_id, get_trace_id, span
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# 在中间件里为每个请求注入：
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set_trace_id(request_id)
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# 在关键调用处打计时 span（自动带 trace_id）：
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# 在关键调用处打计时 span（自动带 trace_id + OTel span）：
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async with span("chat.send_message", conv_id=conv_id):
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return await _send_message_core(...)
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"""
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@@ -26,6 +30,8 @@ import uuid
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import json
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from contextvars import ContextVar
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from app.core.otel import current_span_cm, record_span_metrics
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# ---------------------------------------------------------------------------
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# trace_id：贯穿一次请求（含其内所有子调用/协程）的全局链路 ID
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# ---------------------------------------------------------------------------
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@@ -103,10 +109,10 @@ def get_logger(name: str | None = None) -> logging.Logger:
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# ---------------------------------------------------------------------------
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# span：结构化计时上下文管理器（耗时 + trace_id + 自定义字段）
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# span：结构化计时上下文管理器（耗时 + trace_id + 自定义字段 + OTel 嵌套）
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# ---------------------------------------------------------------------------
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class _Span:
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"""同步/异步通用的计时 span。退出时记录 elapsed_ms 与状态。"""
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"""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。"""
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def __init__(self, name: str, **fields):
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self.name = name
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@@ -114,6 +120,7 @@ class _Span:
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self._logger = logging.getLogger("aiflow.span")
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self._start = None
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self.elapsed_ms = 0.0
|
||||
self._otel_cm = None # 嵌套的 OTel span 上下文管理器
|
||||
|
||||
def _enter(self):
|
||||
self._start = time.perf_counter()
|
||||
@@ -121,6 +128,10 @@ class _Span:
|
||||
f"SPAN_START {self.name}",
|
||||
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
|
||||
|
||||
def _exit(self, exc_type, exc, tb):
|
||||
@@ -137,6 +148,24 @@ class _Span:
|
||||
**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
|
||||
|
||||
# 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：
|
||||
- initialize / notifications/initialized / tools/list / tools/call 标准流程
|
||||
- streamable_http：POST JSON-RPC，响应可能是 SSE（event: message / data: {...}）或纯 JSON
|
||||
- SSE 解析兼容
|
||||
- 直连外部端点（trust_env=False，不绕容器代理）
|
||||
|
||||
供 chat.py 在 AI 对话中把 MCP 工具暴露为 function calling，并代理执行 tools/call。
|
||||
设计基准（标准对照表见 STANDARDS_AUDIT_2026-07-24.md §E）：
|
||||
- 采用 Anthropic 官方 `mcp` SDK 处理 JSON-RPC 帧 + 协议版本协商，
|
||||
不再手写解析、不再写死 MCP_PROTOCOL_VERSION 常量（旧实现写死 "2024-11-05"，
|
||||
上游协议一升级即碎）。
|
||||
- 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 / 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 logging
|
||||
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__)
|
||||
|
||||
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
|
||||
_TOOLS_CACHE: dict = {}
|
||||
_TOOLS_CACHE_TTL = 120 # 秒
|
||||
|
||||
|
||||
def _parse_sse(text: str):
|
||||
"""从响应文本解析 JSON-RPC 结果。兼容 SSE（data: 行）与纯 JSON。"""
|
||||
if not text:
|
||||
return None
|
||||
text = text.strip()
|
||||
# SSE 格式：多行 "event: message" / "data: {...}"
|
||||
data_lines = []
|
||||
for line in text.splitlines():
|
||||
ls = line.strip()
|
||||
if ls.startswith("data:"):
|
||||
data_lines.append(ls[5:].strip())
|
||||
if data_lines:
|
||||
last = data_lines[-1]
|
||||
try:
|
||||
return json.loads(last)
|
||||
except Exception:
|
||||
pass
|
||||
# 纯 JSON
|
||||
try:
|
||||
return json.loads(text)
|
||||
except Exception:
|
||||
return None
|
||||
def _transport_cm(server: dict, timeout: float):
|
||||
"""按 server['transport_type'] 选择官方 SDK 传输层。
|
||||
|
||||
返回异步上下文管理器，进入后产出 (read_stream, write_stream, ...)。
|
||||
"""
|
||||
url = (server.get("url") or "").strip()
|
||||
ttype = (server.get("transport_type") or "streamable_http").lower()
|
||||
headers = server.get("headers") or {}
|
||||
if ttype == "sse":
|
||||
return sse_client(url, headers=headers, timeout=timeout)
|
||||
# 默认 streamable_http（官方 SDK 标准传输）
|
||||
return streamablehttp_client(url, headers=headers, timeout=timeout)
|
||||
|
||||
|
||||
async def _handshake_and_request(
|
||||
url: str,
|
||||
headers: dict,
|
||||
method: str,
|
||||
params: dict,
|
||||
timeout: float = 30.0,
|
||||
async def _session_request(
|
||||
server: 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）。
|
||||
返回解析后的 JSON-RPC 响应 dict，或 None。
|
||||
"""
|
||||
base_headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Accept": "application/json, text/event-stream",
|
||||
}
|
||||
base_headers.update(headers or {})
|
||||
url = (server.get("url") or "").strip()
|
||||
if not url:
|
||||
return None
|
||||
try:
|
||||
async with _transport_cm(server, timeout) as streams:
|
||||
read, write, *_ = streams
|
||||
async with ClientSession(read, write) as session:
|
||||
# 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:
|
||||
# 1) initialize
|
||||
r = await client.post(
|
||||
url,
|
||||
headers=base_headers,
|
||||
json={
|
||||
"jsonrpc": "2.0",
|
||||
"id": 1,
|
||||
"method": "initialize",
|
||||
"params": {
|
||||
"protocolVersion": MCP_PROTOCOL_VERSION,
|
||||
"capabilities": {},
|
||||
"clientInfo": {"name": "aiflow", "version": "1.0"},
|
||||
},
|
||||
},
|
||||
)
|
||||
if r.status_code >= 400:
|
||||
logger.warning(f"[MCP] initialize 失败 {url} -> HTTP {r.status_code}")
|
||||
return None
|
||||
sid = r.headers.get("mcp-session-id") or r.headers.get("Mcp-Session-Id")
|
||||
req_headers = dict(base_headers)
|
||||
if sid:
|
||||
req_headers["Mcp-Session-Id"] = sid
|
||||
|
||||
# 2) initialized 通知（不需要响应）
|
||||
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)
|
||||
if method == "tools/list":
|
||||
result = await session.list_tools()
|
||||
tools = [t.model_dump() for t in (result.tools or [])]
|
||||
return {"result": {"tools": tools}}
|
||||
elif method == "tools/call":
|
||||
res = await session.call_tool(
|
||||
params.get("name"), params.get("arguments") or {}
|
||||
)
|
||||
content = []
|
||||
for blk in (res.content or []):
|
||||
btype = getattr(blk, "type", "text")
|
||||
if btype == "text":
|
||||
content.append({"type": "text", "text": getattr(blk, "text", "")})
|
||||
else:
|
||||
content.append({"type": btype, "text": str(blk)})
|
||||
return {
|
||||
"result": {
|
||||
"content": content,
|
||||
"isError": bool(getattr(res, "isError", False)),
|
||||
}
|
||||
}
|
||||
except Exception as e:
|
||||
logger.warning(f"[MCP] {method} 异常 {url}: {e}")
|
||||
return None
|
||||
return None
|
||||
|
||||
|
||||
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:
|
||||
return cached[1]
|
||||
try:
|
||||
data = await _handshake_and_request(
|
||||
url, server.get("headers") or {}, "tools/list", {}, timeout
|
||||
)
|
||||
data = await _session_request(server, "tools/list", {}, timeout)
|
||||
tools = []
|
||||
if data and "result" in data:
|
||||
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]"
|
||||
logger.info(f"[MCP] 执行工具调用 tool={tool_name} server={server.get('name')} url={url}")
|
||||
try:
|
||||
data = await _handshake_and_request(
|
||||
url,
|
||||
server.get("headers") or {},
|
||||
data = await _session_request(
|
||||
server,
|
||||
"tools/call",
|
||||
{"name": tool_name, "arguments": arguments or {}},
|
||||
timeout,
|
||||
@@ -169,7 +140,7 @@ async def call_mcp_tool(
|
||||
return f"[MCP 工具 {tool_name} 调用失败：无响应]"
|
||||
if "error" in data:
|
||||
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", {})
|
||||
content = result.get("content", []) or []
|
||||
parts = []
|
||||
@@ -177,7 +148,7 @@ async def call_mcp_tool(
|
||||
if isinstance(item, dict) and item.get("type") == "text":
|
||||
parts.append(item.get("text", ""))
|
||||
else:
|
||||
parts.append(json.dumps(item, ensure_ascii=False))
|
||||
parts.append(__import__("json").dumps(item, ensure_ascii=False))
|
||||
text = "\n".join(parts).strip()
|
||||
if result.get("isError"):
|
||||
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:
|
||||
return cached
|
||||
try:
|
||||
import httpx
|
||||
async with httpx.AsyncClient(timeout=30, trust_env=False) as client:
|
||||
resp = await client.post(
|
||||
f"{EMBEDDING_API_URL}/v1/embeddings",
|
||||
headers={
|
||||
"Authorization": f"Bearer {EMBEDDING_API_KEY}",
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
json={
|
||||
"model": EMBEDDING_MODEL,
|
||||
"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
|
||||
# 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
|
||||
from app.core.llm_client import aembed
|
||||
embedding = await aembed(
|
||||
EMBEDDING_API_URL, EMBEDDING_API_KEY, text[:8000], EMBEDDING_MODEL, timeout=30
|
||||
)
|
||||
if embedding:
|
||||
_embed_cache_put(text, EMBEDDING_MODEL, embedding)
|
||||
return embedding
|
||||
logger.error("Embedding API returned empty")
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(f"Embedding generation failed: {e}")
|
||||
return None
|
||||
|
||||
Reference in new issue
Block a user