7023742880
- _no_llm_error: 错误写明模型名/原因/行动指引，禁止笼统No LLM API configured - _get_model_config: name/model_id 双匹配（与gateway解析语义一致），org过滤含系统级共享 - agent_loop_v2 run(): LLM调用异常捕获→yield error事件，前端显示真实错误不再卡死思考中
278 lines
11 KiB
Python
278 lines
11 KiB
Python
"""
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LLM bridge for pipeline handlers.
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Provides a simple async interface for handlers to call LLM APIs.
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Looks up model config from the llm database table first,
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falls back to environment variables.
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"""
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import json
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import logging
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import os
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logger = logging.getLogger("pipeline.llm_bridge")
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# Cache: model_name -> {api_base, api_key, model_id}
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_model_cache: dict = {}
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def _decrypt_key(encrypted: str) -> str:
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"""Decrypt api_key stored with password_encode. Falls back to plaintext."""
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if not encrypted:
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return ""
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try:
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from appPublic.rc4 import unpassword
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from appPublic.jsonConfig import getConfig
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config = getConfig()
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# 与 ahserver.globalEnv.get_password_key() 一致：password_key 为空时用默认 key
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key = config.password_key or 'QRIVSRHrthhwyjy176556332'
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# unpassword(code, key)：code=密文，key=密钥（之前参数顺序写反了）
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return unpassword(encrypted, key)
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except Exception:
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return encrypted # already plaintext or decrypt failed
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async def _get_model_config(model_name: str = None, org_id: str = None) -> dict:
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"""Look up model config from llm table. Returns dict with api_base, api_key, model_id.
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org_id 多租户隔离：非空时只取本机构(org_id=${org}$)的模型，不含系统级兜底；
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org_id 为空时不过滤（向后兼容）。
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"""
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global _model_cache
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cache_key = f"{model_name or ''}:{org_id or ''}"
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if model_name and cache_key in _model_cache:
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return _model_cache[cache_key]
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try:
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from sqlor.dbpools import DBPools
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db = DBPools()
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dbname = "pipeline"
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async with db.sqlorContext(dbname) as sor:
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if model_name:
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# name/model_id 双匹配：与 gateway._resolve_and_persist_model 语义一致——
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# 调用方传 llm.name 或 API 模型名（model_id）都能解析，避免语义漂移。
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sql = ("SELECT api_base, api_key, model_id FROM llm "
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"WHERE status='active' AND (name=${name}$ OR model_id=${name}$)")
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params = {"name": model_name}
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if org_id:
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# 与 gateway 一致：本机构模型 + 系统级共享（org_id 为空/0）
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sql += " AND (org_id=${org}$ OR org_id='' OR org_id='0')"
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params["org"] = org_id
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sql += " LIMIT 1"
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recs = await sor.sqlExe(sql, params)
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else:
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sql = "SELECT api_base, api_key, model_id, name FROM llm WHERE status='active'"
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params = {}
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if org_id:
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sql += " AND org_id=${org}$"
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params["org"] = org_id
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sql += " ORDER BY id LIMIT 1"
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recs = await sor.sqlExe(sql, params)
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if recs:
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r = recs[0]
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cfg = {
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"api_base": getattr(r, "api_base", "") or "",
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"api_key": _decrypt_key(getattr(r, "api_key", "") or ""),
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"model_id": getattr(r, "model_id", "") or "",
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}
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_model_cache[cache_key] = cfg
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return cfg
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except Exception as e:
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logger.warning("llm_bridge: DB lookup failed: %s", e)
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return {}
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def _no_llm_error(model_name=None, org_id=None) -> ValueError:
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"""模型不可用的错误必须真实可行动：写明模型名与原因，前端原样展示、运维据此处理。
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禁止笼统的 'No LLM API configured'——用户看到它只会卡住干等。
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"""
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if model_name:
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return ValueError(
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f"模型「{model_name}」不可用：llm 表中未找到可用配置"
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f"（name/model_id 不匹配、status 非 active"
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+ (f"，或不属于当前机构 org={org_id}" if org_id else "")
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+ "）。请在模型下拉里改选可用模型，或联系管理员在模型管理中补齐配置。"
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)
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return ValueError(
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"没有可用模型：当前会话未指定模型，且 llm 表无启用模型。"
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"请在模型下拉中选择模型，或联系管理员配置模型。"
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)
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# 瞬时错误重试：超时/连接错误/限流/服务端5xx 均重试；配置错误(无key)、鉴权/参数4xx 不重试
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_RETRYABLE_STATUS = (429, 500, 502, 503, 504)
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_LLM_MAX_ATTEMPTS = 3
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# 大上下文（角色 agent 多轮累积）生成偏慢，180s 曾触发超时（空 err=），放宽到 5 分钟；
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# connect 单独设 30s，连接建立失败能快速失败并重试，而不是干等 5 分钟。
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_LLM_TOTAL_TIMEOUT = 300
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_LLM_CONNECT_TIMEOUT = 30
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async def _post_chat_completion(url: str, headers: dict, payload: dict) -> dict:
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"""POST /chat/completions，带瞬时错误重试。返回解析后的 JSON dict。"""
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import aiohttp
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import asyncio
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last_exc = None
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for attempt in range(_LLM_MAX_ATTEMPTS):
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try:
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async with aiohttp.ClientSession() as session:
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async with session.post(
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url, headers=headers, json=payload,
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timeout=aiohttp.ClientTimeout(total=_LLM_TOTAL_TIMEOUT, connect=_LLM_CONNECT_TIMEOUT),
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) as resp:
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if resp.status != 200:
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text = await resp.text()
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err = ValueError("LLM API error %d: %s" % (resp.status, text[:300]))
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if resp.status in _RETRYABLE_STATUS and attempt < _LLM_MAX_ATTEMPTS - 1:
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last_exc = err
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await asyncio.sleep(2 * (attempt + 1))
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continue
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raise err
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data = await resp.json()
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# 网关偶发返回 200 但内容无 choices（错误 JSON），视为可重试的瞬时异常；
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# 不处理会导致上层 llm_call_msgs_native 的 data["choices"] 抛 KeyError。
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if not isinstance(data, dict) or "choices" not in data:
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err = ValueError("LLM 响应缺 choices: %s" % json.dumps(data, ensure_ascii=False)[:300])
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if attempt < _LLM_MAX_ATTEMPTS - 1:
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last_exc = err
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await asyncio.sleep(2 * (attempt + 1))
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continue
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raise err
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return data
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except (asyncio.TimeoutError, aiohttp.ClientError) as e:
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last_exc = e
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if attempt < _LLM_MAX_ATTEMPTS - 1:
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logger.warning("llm_bridge: 瞬时错误重试 %d/%d: %s", attempt + 1, _LLM_MAX_ATTEMPTS, e)
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await asyncio.sleep(2 * (attempt + 1))
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continue
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raise
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raise last_exc if last_exc else ValueError("LLM call failed")
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async def llm_call(prompt: str, model: str = None, temperature: float = 0.7, org_id: str = None) -> str:
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"""Call LLM and return text response.
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Backend priority:
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1. harnessed_agent.llm_chat (if loaded in ServerEnv)
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2. DB llm table (api_base + api_key)
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3. Environment variables (LLM_API_BASE, LLM_API_KEY, LLM_MODEL)
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org_id 多租户隔离：非空时 llm 表查询只取「本机构 + 系统级」模型。
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"""
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# Priority 1: harnessed_agent
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try:
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from ahserver.serverenv import ServerEnv
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env = ServerEnv()
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if hasattr(env, 'llm_chat'):
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result = await env.llm_chat(prompt, model=model, temperature=temperature)
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if isinstance(result, dict):
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return result.get("content", result.get("text", str(result)))
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return str(result)
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except Exception:
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pass
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# Priority 2: DB llm table
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cfg = await _get_model_config(model, org_id=org_id)
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if cfg.get("api_key") and cfg.get("api_base"):
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api_base = cfg["api_base"]
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api_key = cfg["api_key"]
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model_id = cfg.get("model_id") or model or "default"
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logger.info("llm_bridge: using DB model config for %s -> %s", model, api_base)
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else:
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# Priority 3: Environment variables
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api_base = os.environ.get("LLM_API_BASE", "https://api.openai.com/v1")
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api_key = os.environ.get("LLM_API_KEY", "")
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model_id = model or os.environ.get("LLM_MODEL", "gpt-4o-mini")
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if not api_key:
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raise _no_llm_error(model, org_id)
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import aiohttp
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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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payload = {
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"model": model_id,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": temperature,
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}
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url = api_base.rstrip("/") + "/chat/completions"
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data = await _post_chat_completion(url, headers, payload)
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return data["choices"][0]["message"]["content"]
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async def call_llm(tenant_id: str, prompt: str, model: str = None, temperature: float = 0.7) -> str:
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"""SDLC handler interface — delegates to llm_call."""
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return await llm_call(prompt, model=model, temperature=temperature)
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async def llm_call_msgs(messages: list, model: str = None, temperature: float = 0.7, org_id: str = None) -> str:
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"""Call LLM with full message array (system/user/assistant)."""
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import aiohttp
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cfg = await _get_model_config(model, org_id=org_id)
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if cfg.get("api_key") and cfg.get("api_base"):
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api_base = cfg["api_base"]
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api_key = cfg["api_key"]
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model_id = cfg.get("model_id") or model or "default"
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else:
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api_base = os.environ.get("LLM_API_BASE", "https://api.openai.com/v1")
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api_key = os.environ.get("LLM_API_KEY", "")
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model_id = model or os.environ.get("LLM_MODEL", "gpt-4o-mini")
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if not api_key:
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raise _no_llm_error(model, org_id)
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headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
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payload = {"model": model_id, "messages": messages, "temperature": temperature}
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url = api_base.rstrip("/") + "/chat/completions"
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data = await _post_chat_completion(url, headers, payload)
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return data["choices"][0]["message"]["content"]
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async def llm_call_msgs_native(messages: list, tools: list = None, model: str = None, temperature: float = 0.7, org_id: str = None) -> dict:
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"""Native function calling. 传入 tools JSON schema，返回 message dict。
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Returns:
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{"content": str, "tool_calls": [{"id","type","function":{"name","arguments"}}]}
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当模型返回 tool_calls 时，content 通常为空字符串。
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"""
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import aiohttp
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cfg = await _get_model_config(model, org_id=org_id)
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if cfg.get("api_key") and cfg.get("api_base"):
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api_base = cfg["api_base"]
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api_key = cfg["api_key"]
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model_id = cfg.get("model_id") or model or "default"
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else:
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api_base = os.environ.get("LLM_API_BASE", "https://api.openai.com/v1")
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api_key = os.environ.get("LLM_API_KEY", "")
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model_id = model or os.environ.get("LLM_MODEL", "gpt-4o-mini")
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if not api_key:
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raise _no_llm_error(model, org_id)
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headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
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payload = {"model": model_id, "messages": messages, "temperature": temperature}
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if tools:
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payload["tools"] = tools
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payload["tool_choice"] = "auto"
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url = api_base.rstrip("/") + "/chat/completions"
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data = await _post_chat_completion(url, headers, payload)
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msg = data["choices"][0]["message"]
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return {
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"content": msg.get("content") or "",
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"tool_calls": msg.get("tool_calls") or [],
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}
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