7ed0ed9b80
llmage 网关偶发返回 200 但内容无 choices(错误JSON)，llm_call_msgs_native 的 data[choices] 直接 KeyError，导致 role_agent 任务失败并报人工介入。_post_chat_completion 对无 choices 的 200 响应视为可重试异常，重试后仍失败则 raise 带网关原始内容的 ValueError。
256 lines
10 KiB
Python
256 lines
10 KiB
Python
"""
|
||
LLM bridge for pipeline handlers.
|
||
|
||
Provides a simple async interface for handlers to call LLM APIs.
|
||
Looks up model config from the llm database table first,
|
||
falls back to environment variables.
|
||
"""
|
||
|
||
import json
|
||
import logging
|
||
import os
|
||
|
||
logger = logging.getLogger("pipeline.llm_bridge")
|
||
|
||
# Cache: model_name -> {api_base, api_key, model_id}
|
||
_model_cache: dict = {}
|
||
|
||
|
||
def _decrypt_key(encrypted: str) -> str:
|
||
"""Decrypt api_key stored with password_encode. Falls back to plaintext."""
|
||
if not encrypted:
|
||
return ""
|
||
try:
|
||
from appPublic.rc4 import unpassword
|
||
from appPublic.jsonConfig import getConfig
|
||
config = getConfig()
|
||
# 与 ahserver.globalEnv.get_password_key() 一致：password_key 为空时用默认 key
|
||
key = config.password_key or 'QRIVSRHrthhwyjy176556332'
|
||
# unpassword(code, key)：code=密文，key=密钥（之前参数顺序写反了）
|
||
return unpassword(encrypted, key)
|
||
except Exception:
|
||
return encrypted # already plaintext or decrypt failed
|
||
|
||
|
||
async def _get_model_config(model_name: str = None, org_id: str = None) -> dict:
|
||
"""Look up model config from llm table. Returns dict with api_base, api_key, model_id.
|
||
|
||
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；
|
||
org_id 为空时不过滤（向后兼容）。
|
||
"""
|
||
global _model_cache
|
||
|
||
cache_key = f"{model_name or ''}:{org_id or ''}"
|
||
if model_name and cache_key in _model_cache:
|
||
return _model_cache[cache_key]
|
||
|
||
try:
|
||
from sqlor.dbpools import DBPools
|
||
db = DBPools()
|
||
dbname = "pipeline"
|
||
async with db.sqlorContext(dbname) as sor:
|
||
if model_name:
|
||
sql = "SELECT api_base, api_key, model_id FROM llm WHERE name=${name}$ AND status='active'"
|
||
params = {"name": model_name}
|
||
if org_id:
|
||
sql += " AND org_id=${org}$"
|
||
params["org"] = org_id
|
||
sql += " LIMIT 1"
|
||
recs = await sor.sqlExe(sql, params)
|
||
else:
|
||
sql = "SELECT api_base, api_key, model_id, name FROM llm WHERE status='active'"
|
||
params = {}
|
||
if org_id:
|
||
sql += " AND org_id=${org}$"
|
||
params["org"] = org_id
|
||
sql += " ORDER BY id LIMIT 1"
|
||
recs = await sor.sqlExe(sql, params)
|
||
if recs:
|
||
r = recs[0]
|
||
cfg = {
|
||
"api_base": getattr(r, "api_base", "") or "",
|
||
"api_key": _decrypt_key(getattr(r, "api_key", "") or ""),
|
||
"model_id": getattr(r, "model_id", "") or "",
|
||
}
|
||
_model_cache[cache_key] = cfg
|
||
return cfg
|
||
except Exception as e:
|
||
logger.warning("llm_bridge: DB lookup failed: %s", e)
|
||
|
||
return {}
|
||
|
||
|
||
# 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
|
||
_RETRYABLE_STATUS = (429, 500, 502, 503, 504)
|
||
_LLM_MAX_ATTEMPTS = 3
|
||
# 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；
|
||
# 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。
|
||
_LLM_TOTAL_TIMEOUT = 300
|
||
_LLM_CONNECT_TIMEOUT = 30
|
||
|
||
|
||
async def _post_chat_completion(url: str, headers: dict, payload: dict) -> dict:
|
||
"""POST /chat/completions，带瞬时错误重试。返回解析后的 JSON dict。"""
|
||
import aiohttp
|
||
import asyncio
|
||
|
||
last_exc = None
|
||
for attempt in range(_LLM_MAX_ATTEMPTS):
|
||
try:
|
||
async with aiohttp.ClientSession() as session:
|
||
async with session.post(
|
||
url, headers=headers, json=payload,
|
||
timeout=aiohttp.ClientTimeout(total=_LLM_TOTAL_TIMEOUT, connect=_LLM_CONNECT_TIMEOUT),
|
||
) as resp:
|
||
if resp.status != 200:
|
||
text = await resp.text()
|
||
err = ValueError("LLM API error %d: %s" % (resp.status, text[:300]))
|
||
if resp.status in _RETRYABLE_STATUS and attempt < _LLM_MAX_ATTEMPTS - 1:
|
||
last_exc = err
|
||
await asyncio.sleep(2 * (attempt + 1))
|
||
continue
|
||
raise err
|
||
data = await resp.json()
|
||
# 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；
|
||
# 不处理会导致上层 llm_call_msgs_native 的 data["choices"] 抛 KeyError。
|
||
if not isinstance(data, dict) or "choices" not in data:
|
||
err = ValueError("LLM 响应缺 choices: %s" % json.dumps(data, ensure_ascii=False)[:300])
|
||
if attempt < _LLM_MAX_ATTEMPTS - 1:
|
||
last_exc = err
|
||
await asyncio.sleep(2 * (attempt + 1))
|
||
continue
|
||
raise err
|
||
return data
|
||
except (asyncio.TimeoutError, aiohttp.ClientError) as e:
|
||
last_exc = e
|
||
if attempt < _LLM_MAX_ATTEMPTS - 1:
|
||
logger.warning("llm_bridge: 瞬时错误重试 %d/%d: %s", attempt + 1, _LLM_MAX_ATTEMPTS, e)
|
||
await asyncio.sleep(2 * (attempt + 1))
|
||
continue
|
||
raise
|
||
|
||
raise last_exc if last_exc else ValueError("LLM call failed")
|
||
|
||
|
||
async def llm_call(prompt: str, model: str = None, temperature: float = 0.7, org_id: str = None) -> str:
|
||
"""Call LLM and return text response.
|
||
|
||
Backend priority:
|
||
1. harnessed_agent.llm_chat (if loaded in ServerEnv)
|
||
2. DB llm table (api_base + api_key)
|
||
3. Environment variables (LLM_API_BASE, LLM_API_KEY, LLM_MODEL)
|
||
|
||
org_id 多租户隔离：非空时 llm 表查询只取「本机构 + 系统级」模型。
|
||
"""
|
||
# Priority 1: harnessed_agent
|
||
try:
|
||
from ahserver.serverenv import ServerEnv
|
||
env = ServerEnv()
|
||
if hasattr(env, 'llm_chat'):
|
||
result = await env.llm_chat(prompt, model=model, temperature=temperature)
|
||
if isinstance(result, dict):
|
||
return result.get("content", result.get("text", str(result)))
|
||
return str(result)
|
||
except Exception:
|
||
pass
|
||
|
||
# Priority 2: DB llm table
|
||
cfg = await _get_model_config(model, org_id=org_id)
|
||
if cfg.get("api_key") and cfg.get("api_base"):
|
||
api_base = cfg["api_base"]
|
||
api_key = cfg["api_key"]
|
||
model_id = cfg.get("model_id") or model or "default"
|
||
logger.info("llm_bridge: using DB model config for %s -> %s", model, api_base)
|
||
else:
|
||
# Priority 3: Environment variables
|
||
api_base = os.environ.get("LLM_API_BASE", "https://api.openai.com/v1")
|
||
api_key = os.environ.get("LLM_API_KEY", "")
|
||
model_id = model or os.environ.get("LLM_MODEL", "gpt-4o-mini")
|
||
|
||
if not api_key:
|
||
raise ValueError("No LLM API configured. Please add a model in the llm table or set LLM_API_KEY env var.")
|
||
|
||
import aiohttp
|
||
|
||
headers = {
|
||
"Authorization": f"Bearer {api_key}",
|
||
"Content-Type": "application/json",
|
||
}
|
||
payload = {
|
||
"model": model_id,
|
||
"messages": [{"role": "user", "content": prompt}],
|
||
"temperature": temperature,
|
||
}
|
||
|
||
url = api_base.rstrip("/") + "/chat/completions"
|
||
data = await _post_chat_completion(url, headers, payload)
|
||
return data["choices"][0]["message"]["content"]
|
||
|
||
|
||
async def call_llm(tenant_id: str, prompt: str, model: str = None, temperature: float = 0.7) -> str:
|
||
"""SDLC handler interface — delegates to llm_call."""
|
||
return await llm_call(prompt, model=model, temperature=temperature)
|
||
|
||
|
||
async def llm_call_msgs(messages: list, model: str = None, temperature: float = 0.7, org_id: str = None) -> str:
|
||
"""Call LLM with full message array (system/user/assistant)."""
|
||
import aiohttp
|
||
|
||
cfg = await _get_model_config(model, org_id=org_id)
|
||
if cfg.get("api_key") and cfg.get("api_base"):
|
||
api_base = cfg["api_base"]
|
||
api_key = cfg["api_key"]
|
||
model_id = cfg.get("model_id") or model or "default"
|
||
else:
|
||
api_base = os.environ.get("LLM_API_BASE", "https://api.openai.com/v1")
|
||
api_key = os.environ.get("LLM_API_KEY", "")
|
||
model_id = model or os.environ.get("LLM_MODEL", "gpt-4o-mini")
|
||
|
||
if not api_key:
|
||
raise ValueError("No LLM API configured")
|
||
|
||
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
|
||
payload = {"model": model_id, "messages": messages, "temperature": temperature}
|
||
|
||
url = api_base.rstrip("/") + "/chat/completions"
|
||
data = await _post_chat_completion(url, headers, payload)
|
||
return data["choices"][0]["message"]["content"]
|
||
|
||
|
||
async def llm_call_msgs_native(messages: list, tools: list = None, model: str = None, temperature: float = 0.7, org_id: str = None) -> dict:
|
||
"""Native function calling. 传入 tools JSON schema，返回 message dict。
|
||
|
||
Returns:
|
||
{"content": str, "tool_calls": [{"id","type","function":{"name","arguments"}}]}
|
||
当模型返回 tool_calls 时，content 通常为空字符串。
|
||
"""
|
||
import aiohttp
|
||
|
||
cfg = await _get_model_config(model, org_id=org_id)
|
||
if cfg.get("api_key") and cfg.get("api_base"):
|
||
api_base = cfg["api_base"]
|
||
api_key = cfg["api_key"]
|
||
model_id = cfg.get("model_id") or model or "default"
|
||
else:
|
||
api_base = os.environ.get("LLM_API_BASE", "https://api.openai.com/v1")
|
||
api_key = os.environ.get("LLM_API_KEY", "")
|
||
model_id = model or os.environ.get("LLM_MODEL", "gpt-4o-mini")
|
||
|
||
if not api_key:
|
||
raise ValueError("No LLM API configured")
|
||
|
||
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
|
||
payload = {"model": model_id, "messages": messages, "temperature": temperature}
|
||
if tools:
|
||
payload["tools"] = tools
|
||
payload["tool_choice"] = "auto"
|
||
|
||
url = api_base.rstrip("/") + "/chat/completions"
|
||
data = await _post_chat_completion(url, headers, payload)
|
||
msg = data["choices"][0]["message"]
|
||
return {
|
||
"content": msg.get("content") or "",
|
||
"tool_calls": msg.get("tool_calls") or [],
|
||
}
|
||
|