refactor(llm): agent调用收敛到模型治理统一推理API——llm_bridge改HTTP自调用薄客户端(短期token+本进程端口),删旧llm表直查;llm_proxy委托chat_inference;gateway/agent_loop/bug_flow模型解析与缺省模型改走治理链/统一解析,清除写死模型名
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ymq
parent
0aa632dd28
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f3f82dd448
@ -107,10 +107,10 @@ async def _resolve_llm_context(sor, project_id, role, model_name=None):
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3. 用户当前模型（项目创建者 created_by → pipeline_agent_settings.default_llm_id → llm.name，缺省）
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4. RoleSpec.model_name（角色专属模型）
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5. 产线 default_model（pipelines.default_model）
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6. 全局默认 "deepseek-v4-pro"
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6. 留空 → 治理链按机构策略选缺省模型（主→备链），不再写死模型名
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org_id：从 sd_projects.org_id 读，传给 llm_bridge 做多租户隔离
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（llm 表查询只取「本机构 + 系统级 org_id='0'」的模型）。
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（模型治理查询只取「本机构 + 系统级 org_id='0'」的模型）。
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"""
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org_id = ""
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pipeline_id = ""
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@ -159,22 +159,24 @@ async def _resolve_llm_context(sor, project_id, role, model_name=None):
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except Exception:
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pass
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if not model_name:
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# 6. 全局默认
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model_name = "deepseek-v4-pro"
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# 6. 全局缺省：留空交给治理链按机构策略选缺省模型（主模型→备链）
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model_name = ""
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return model_name, org_id
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async def _check_org_llm(sor, org_id):
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"""检测机构是否配置了 LLM 模型（严格本机构隔离，不含系统级兜底）。
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"""检测机构是否配置了 LLM 模型（查模型治理新表 llm_model；系统级兜底可见）。
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返回 (missing: bool, available_names: list)。org_id 为空或 '0'（系统级）时不过滤，
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视为已配置（系统级模型对超管可用）。机构没配 llm 时角色/pm/qc 应冒泡问题暂停。
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视为已配置（系统级模型对超管可用）。机构没配模型时角色/pm/qc 应冒泡问题暂停。
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"""
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if not org_id or org_id == '0':
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return False, []
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try:
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recs = await sor.sqlExe(
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"SELECT name FROM llm WHERE org_id=${org}$ AND status='active'", {"org": org_id})
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"SELECT name FROM llm_model "
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"WHERE (org_id=${org}$ OR org_id='' OR org_id='0') AND status='active'",
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{"org": org_id})
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names = [getattr(r, "name", "") or "" for r in (recs or [])]
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return (len(names) == 0), names
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except Exception:
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@ -471,6 +471,7 @@ class AgentExecutor:
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model=self.model_name,
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temperature=0,
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org_id=self.org_id,
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purpose='utility',
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)
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m = _re.search(r"\[[^\]]*\]", content or "")
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if m:
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@ -885,7 +886,8 @@ class AgentExecutor:
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{"s": self.space})
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pnames = [getattr(r, "name", "") for r in (all_recs or [])]
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classify_prompt = f"用户输入: {name}\n项目列表: {', '.join(pnames)}\n\n判断用户想要哪个项目。只回复项目名或\"不存在\"。"
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matched = await llm_call(classify_prompt, temperature=0.0, org_id=self.org_id)
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matched = await llm_call(classify_prompt, temperature=0.0, org_id=self.org_id,
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purpose='utility')
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matched = matched.strip().strip('"').strip("'")
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recs2 = await sor.sqlExe(
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@ -1438,6 +1440,7 @@ class AgentExecutor:
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f"请用3-5句话总结以下对话的关键信息：\n\n{text[:4000]}",
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temperature=0.1,
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org_id=self.org_id,
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purpose='utility',
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)
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return summary[:500]
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except Exception:
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@ -207,7 +207,7 @@ async def _advance_bug_states(sor):
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async def _resolve_model_org(sor, project_id):
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"""解析 bug 确认用的模型 + org_id（简版：项目缺省模型 → deepseek-v4-pro）。"""
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"""解析 bug 确认用的模型 + org_id（项目缺省模型 → 产线缺省 → 空=机构策略缺省模型）。"""
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org_id = ""
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model = ""
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try:
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@ -226,7 +226,8 @@ async def _resolve_model_org(sor, project_id):
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except Exception:
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pass
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await sor.sqlExe("COMMIT", {})
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return (model or "deepseek-v4-pro"), (org_id or "0")
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# 模型名为空 = 交给治理链按机构策略选缺省模型（不再写死模型名）
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return model, (org_id or "0")
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def _parse_bug_decisions(raw):
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@ -101,8 +101,9 @@ class Gateway:
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ctx["default_llm_id"] = getattr(recs[0], "default_llm_id", "") or ""
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# 单独查 llm.name（避免 JOIN 触发两表字段 collation 不一致）
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if ctx["default_llm_id"]:
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# 2026-09-04 切换到模型治理新表（旧 llm 表停用）
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llm_recs = await sor.sqlExe(
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"SELECT name FROM llm WHERE id=${id}$ AND status='active'",
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"SELECT name FROM llm_model WHERE id=${id}$ AND status='active'",
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{"id": ctx["default_llm_id"]})
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if llm_recs:
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ctx["default_llm_name"] = getattr(llm_recs[0], "name", "") or ""
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@ -206,7 +207,7 @@ class Gateway:
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model_id: str) -> str:
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"""校验前端选中的模型并持久化到项目。返回 llm.name（空 = 无效/未持久化，调用方走回退链）。
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model_id 兼容两种取值：llm.id（主键）或 llm.model_id（API 模型名，如 deepseek-v4-pro）——
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model_id 兼容两种取值：llm_model.id（主键）或 llm_model.vendor_model_id（API 模型名）——
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前端 UiCode 的 valueField 历史上两种都用过，这里统一解析，避免语义漂移。
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多租户隔离：非系统级用户只能选本机构 + 系统级共享模型。
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"""
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@ -224,9 +225,10 @@ class Gateway:
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org_id = getattr(urecs[0], "orgid", "") or ""
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except Exception:
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pass
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# 2026-09-04 切换到模型治理新表（旧 llm 表停用）
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recs = await sor.sqlExe(
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"SELECT id, name, org_id FROM llm "
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"WHERE status='active' AND (id=${m}$ OR model_id=${m}$) LIMIT 1",
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"SELECT id, name, org_id FROM llm_model "
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"WHERE status='active' AND (id=${m}$ OR vendor_model_id=${m}$) LIMIT 1",
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{"m": model_id})
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await sor.sqlExe("COMMIT", {})
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if not recs:
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@ -50,6 +50,7 @@ async def intent_classify(intents: list, message: str, context: dict = None) ->
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prompt=f"{prompt}\n\n用户输入：{message}",
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model=None,
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temperature=0.2,
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purpose='utility',
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)
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raw = raw.strip()
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if raw.startswith("```"):
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@ -1,292 +1,137 @@
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"""
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LLM bridge for pipeline handlers.
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LLM bridge for pipeline handlers — 统一收敛到模型治理模块（pipeline-llm）推理 API。
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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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2026-09-04 改造：产线平台所有模型调用切换到 /pipeline-llm/api/v1（OpenAI 兼容，
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分类照 llmage）。本模块不再直查旧 `llm` 表——签名保持不变，内部改为：
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签发内部短期 token（机构隔离，真 key 不出进程）
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→ HTTP 自调用本进程 /pipeline-llm/api/v1/chat/completions
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→ 门禁链（限流/限额/主备容错/端点轮转/预授权）+ 双维度记账由端点侧执行
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自调用走 127.0.0.1:<本进程端口>（web/worker 进程都起 HTTP，worker 端口 9090+N）。
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每次自调用带机构/用户上下文，用量流水可归属到真实用户。
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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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import time
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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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# 内部自调用 token 缓存：(org_id, user_id, model_name) -> {token, calls, expires_at}
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# 每次 LLM 调用都签发新 token 会让 tokens 表膨胀，故按上下文缓存复用；
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# 接近调用上限或临近过期时换新（上限/过期由签发侧强制，本地计数只是提前量）。
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_token_cache: dict = {}
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_TOKEN_MAX_LOCAL_CALLS = 400 # token max_calls=500，留 100 余量防竞态
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_TOKEN_REFRESH_BEFORE_EXPIRY = 600 # 距过期不足 10 分钟即换新
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_TOKEN_TTL_HOURS = 8
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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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async def _self_base_url():
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"""本进程推理 API 基址（自调用，不出本机）。"""
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port = 9090
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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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from ahserver.serverenv import ServerEnv
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p = getattr(ServerEnv(), 'port', None)
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if p:
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port = int(p)
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except (TypeError, ValueError):
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port = 9090
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except Exception:
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return encrypted # already plaintext or decrypt failed
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port = 9090
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return "http://127.0.0.1:%d/pipeline-llm/api/v1" % port
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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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async def _get_internal_token(org_id, user_id, model_name):
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"""取/发内部短期 token。失败抛 ValueError（消息真实可行动）。"""
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key = (org_id or '0', user_id or '', model_name or '')
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now = time.time()
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ent = _token_cache.get(key)
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if ent and ent['calls'] < _TOKEN_MAX_LOCAL_CALLS \
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and ent['expires_at'] - now > _TOKEN_REFRESH_BEFORE_EXPIRY:
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ent['calls'] += 1
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return ent['token']
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from .llm_proxy import create_llm_token
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ok, token = await create_llm_token(
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org_id or '0', project_id='', task_id='', model_name=model_name or '',
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purpose='internal_bridge', ttl_hours=_TOKEN_TTL_HOURS,
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max_calls=500, created_by=user_id or '')
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if not ok:
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raise ValueError('内部 LLM token 签发失败：%s（模型治理模块未就绪或机构标识缺失）' % token)
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_token_cache[key] = {
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'token': token, 'calls': 1,
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'expires_at': now + _TOKEN_TTL_HOURS * 3600,
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}
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return token
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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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async def _http_chat(payload, org_id, user_id, model_name):
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"""POST 本进程推理端点。返回上游响应 dict；失败抛 ValueError（消息真实可行动）。"""
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import aiohttp
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token = await _get_internal_token(org_id, user_id, model_name)
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base = await _self_base_url()
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headers = {"Authorization": "***" + token, "Content-Type": "application/json"}
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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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async with aiohttp.ClientSession() as session:
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async with session.post(
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base + "/chat/completions", headers=headers, json=payload,
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timeout=aiohttp.ClientTimeout(total=330, connect=30),
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) as resp:
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text = await resp.text()
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status = resp.status
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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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raise ValueError(
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'LLM 推理端点不可达（%s/chat/completions）：%s。'
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'请检查本进程服务是否正常' % (base, e))
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try:
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data = json.loads(text)
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except Exception:
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raise ValueError('LLM 推理端点返回非 JSON（HTTP %s）：%s' % (status, text[:200]))
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if isinstance(data, dict) and data.get('error'):
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err = data['error']
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msg = err.get('message', '') if isinstance(err, dict) else str(err)
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raise ValueError(msg or 'LLM 推理失败（无详情）')
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return data
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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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"""兜底错误（正常路径错误消息来自端点侧，这里只防解析异常）。"""
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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:
|
||||
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")
|
||||
|
||||
|
||||
# ────────────────────────── pipeline_llm 治理钩子 ──────────────────────────
|
||||
# 机构配置了治理（llm_org_policy / llm_model 有记录）→ 调用走门禁链（限流/限额/
|
||||
# 主备容错/端点选择/预授权），调用后按实际用量结算（双维度记账）。
|
||||
# 未配置治理 → 返回 __LEGACY__ 走下方旧 llm 表逻辑（向后兼容，现有调用零改动）。
|
||||
# 治理真实失败（限流/余额不足/候选耗尽）→ 抛 ValueError，消息真实可行动，禁止静默回退
|
||||
# （否则治理被绕过，限额形同虚设）。
|
||||
|
||||
async def _resolve_cfg_with_govern(model, org_id, user_id, est_text):
|
||||
"""返回 (cfg, gctx)。gctx 非空 = 本次调用受治理（调用后须结算）。"""
|
||||
gctx = None
|
||||
try:
|
||||
from pipeline_llm.gateway import govern_resolve
|
||||
except ImportError:
|
||||
govern_resolve = None
|
||||
if govern_resolve is not None and org_id:
|
||||
try:
|
||||
est = max((len(est_text) if est_text else 0) // 2, 200)
|
||||
ok, res = await govern_resolve(
|
||||
org_id=org_id, user_id=user_id or '',
|
||||
model_name=model or '', est_tokens=est)
|
||||
if ok and isinstance(res, dict):
|
||||
gctx = res
|
||||
elif res != '__LEGACY__':
|
||||
raise ValueError(res)
|
||||
except ValueError:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.warning("llm_bridge: 治理前置异常（回退旧表）: %s", e)
|
||||
if gctx:
|
||||
cfg = {"api_base": gctx["api_base"], "api_key": gctx["api_key"],
|
||||
"model_id": gctx["model_id"]}
|
||||
return cfg, gctx
|
||||
cfg = await _get_model_config(model, org_id=org_id)
|
||||
return cfg, None
|
||||
|
||||
|
||||
async def _settle_govern(gctx, data, est_text):
|
||||
"""调用成功后结算：优先上游真实 usage，缺失则按文本长度估算（note=est）。"""
|
||||
if not gctx:
|
||||
return
|
||||
try:
|
||||
from pipeline_llm.gateway import govern_settle
|
||||
usage = (data or {}).get('usage') or {}
|
||||
rt = int(usage.get('prompt_tokens') or 0)
|
||||
ct = int(usage.get('completion_tokens') or 0)
|
||||
note = ''
|
||||
if not usage:
|
||||
rt = rt or max((len(est_text) if est_text else 0) // 2, 100)
|
||||
ct = ct or 200
|
||||
note = 'est'
|
||||
await govern_settle(gctx, True, rt, ct, note)
|
||||
except Exception as e:
|
||||
logger.warning("llm_bridge: 治理结算失败（不阻断调用）: %s", e)
|
||||
|
||||
|
||||
async def _settle_govern_failed(gctx, note):
|
||||
"""调用失败时结算：释放预授权，写 failed 流水。"""
|
||||
if not gctx:
|
||||
return
|
||||
try:
|
||||
from pipeline_llm.gateway import govern_settle
|
||||
await govern_settle(gctx, False, 0, 0, str(note)[:200])
|
||||
except Exception as e:
|
||||
logger.warning("llm_bridge: 治理失败结算异常: %s", e)
|
||||
|
||||
|
||||
def _msgs_text(messages):
|
||||
try:
|
||||
return ' '.join(str(m.get('content', '')) for m in (messages or []) if isinstance(m, dict))
|
||||
except Exception:
|
||||
return ''
|
||||
"模型「%s」调用失败：机构 %s 未完成模型治理接入（无容错策略或模型未注册）。"
|
||||
"请在模型治理→组织容错策略配置主/备模型。" % (model_name or '(缺省)', org_id or '(未指定)'))
|
||||
|
||||
|
||||
async def llm_call(prompt: str, model: str = None, temperature: float = 0.7,
|
||||
org_id: str = None, user_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。
|
||||
统一走模型治理推理 API（门禁链 + 双维度记账）。
|
||||
org_id 为空 = 系统级（'0'），与旧语义（不过滤机构）等价。
|
||||
"""
|
||||
# Priority 1: harnessed_agent
|
||||
# 兼容旧优先级：harnessed_agent（若宿主加载了独立推理后端）
|
||||
try:
|
||||
from ahserver.serverenv import ServerEnv
|
||||
env = ServerEnv()
|
||||
if hasattr(env, 'llm_chat'):
|
||||
result = await env.llm_chat(prompt, model=model, temperature=temperature)
|
||||
fn = getattr(env, 'llm_chat', None)
|
||||
if callable(fn):
|
||||
result = await fn(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（治理启用时前置走门禁链）
|
||||
cfg, gctx = await _resolve_cfg_with_govern(model, org_id, user_id, prompt)
|
||||
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 %s model config for %s -> %s",
|
||||
"governed" if gctx else "DB", 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 _no_llm_error(model, org_id)
|
||||
|
||||
import aiohttp
|
||||
|
||||
headers = {
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
payload = {
|
||||
"model": model_id,
|
||||
"model": model or '',
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"temperature": temperature,
|
||||
}
|
||||
|
||||
url = api_base.rstrip("/") + "/chat/completions"
|
||||
data = await _http_chat(payload, org_id or '0', user_id or '', model or '')
|
||||
try:
|
||||
data = await _post_chat_completion(url, headers, payload)
|
||||
except Exception as e:
|
||||
await _settle_govern_failed(gctx, e)
|
||||
raise
|
||||
await _settle_govern(gctx, data, prompt)
|
||||
return data["choices"][0]["message"]["content"]
|
||||
return data["choices"][0]["message"]["content"]
|
||||
except (KeyError, IndexError, TypeError) as e:
|
||||
raise ValueError('LLM 响应缺 choices: %s' % (
|
||||
json.dumps(data, ensure_ascii=False, default=str)[:300])) from e
|
||||
|
||||
|
||||
async def call_llm(tenant_id: str, prompt: str, model: str = None, temperature: float = 0.7) -> str:
|
||||
@ -297,32 +142,13 @@ async def call_llm(tenant_id: str, prompt: str, model: str = None, temperature:
|
||||
async def llm_call_msgs(messages: list, model: str = None, temperature: float = 0.7,
|
||||
org_id: str = None, user_id: str = None) -> str:
|
||||
"""Call LLM with full message array (system/user/assistant)."""
|
||||
import aiohttp
|
||||
|
||||
cfg, gctx = await _resolve_cfg_with_govern(model, org_id, user_id, _msgs_text(messages))
|
||||
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 _no_llm_error(model, org_id)
|
||||
|
||||
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
|
||||
payload = {"model": model_id, "messages": messages, "temperature": temperature}
|
||||
|
||||
url = api_base.rstrip("/") + "/chat/completions"
|
||||
payload = {"model": model or '', "messages": messages, "temperature": temperature}
|
||||
data = await _http_chat(payload, org_id or '0', user_id or '', model or '')
|
||||
try:
|
||||
data = await _post_chat_completion(url, headers, payload)
|
||||
except Exception as e:
|
||||
await _settle_govern_failed(gctx, e)
|
||||
raise
|
||||
await _settle_govern(gctx, data, _msgs_text(messages))
|
||||
return data["choices"][0]["message"]["content"]
|
||||
return data["choices"][0]["message"]["content"]
|
||||
except (KeyError, IndexError, TypeError) as e:
|
||||
raise ValueError('LLM 响应缺 choices: %s' % (
|
||||
json.dumps(data, ensure_ascii=False, default=str)[:300])) from e
|
||||
|
||||
|
||||
async def llm_call_msgs_native(messages: list, tools: list = None, model: str = None,
|
||||
@ -334,37 +160,17 @@ async def llm_call_msgs_native(messages: list, tools: list = None, model: str =
|
||||
{"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, gctx = await _resolve_cfg_with_govern(model, org_id, user_id, _msgs_text(messages))
|
||||
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 _no_llm_error(model, org_id)
|
||||
|
||||
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
|
||||
payload = {"model": model_id, "messages": messages, "temperature": temperature}
|
||||
payload = {"model": model or '', "messages": messages, "temperature": temperature}
|
||||
if tools:
|
||||
payload["tools"] = tools
|
||||
payload["tool_choice"] = "auto"
|
||||
|
||||
url = api_base.rstrip("/") + "/chat/completions"
|
||||
data = await _http_chat(payload, org_id or '0', user_id or '', model or '')
|
||||
try:
|
||||
data = await _post_chat_completion(url, headers, payload)
|
||||
except Exception as e:
|
||||
await _settle_govern_failed(gctx, e)
|
||||
raise
|
||||
await _settle_govern(gctx, data, _msgs_text(messages))
|
||||
msg = data["choices"][0]["message"]
|
||||
msg = data["choices"][0]["message"]
|
||||
except (KeyError, IndexError, TypeError) as e:
|
||||
raise ValueError('LLM 响应缺 choices: %s' % (
|
||||
json.dumps(data, ensure_ascii=False, default=str)[:300])) from e
|
||||
return {
|
||||
"content": msg.get("content") or "",
|
||||
"tool_calls": msg.get("tool_calls") or [],
|
||||
}
|
||||
|
||||
|
||||
@ -159,7 +159,7 @@ async def verify_llm_token(token):
|
||||
async with db.sqlorContext(dbname) as sor:
|
||||
recs = await sor.sqlExe(
|
||||
"SELECT id, org_id, project_id, task_id, model_name, status, expires_at, "
|
||||
"max_calls, call_count FROM pipeline_llm_tokens WHERE token=${t}$",
|
||||
"max_calls, call_count, created_by FROM pipeline_llm_tokens WHERE token=${t}$",
|
||||
{"t": token})
|
||||
await sor.sqlExe("COMMIT", {})
|
||||
if not recs:
|
||||
@ -187,6 +187,7 @@ async def verify_llm_token(token):
|
||||
"project_id": getattr(r, 'project_id', '') or '',
|
||||
"task_id": getattr(r, 'task_id', '') or '',
|
||||
"model_name": getattr(r, 'model_name', '') or '',
|
||||
"created_by": '',
|
||||
}
|
||||
|
||||
|
||||
@ -251,13 +252,14 @@ async def _record_usage(token_id, usage):
|
||||
|
||||
|
||||
async def proxy_chat_completion(token, payload):
|
||||
"""OpenAI 兼容代理转发。
|
||||
"""OpenAI 兼容代理转发（2026-09-04 起委托模型治理统一推理引擎）。
|
||||
|
||||
token 运行环境持有的短期 token（当 api_key 用）
|
||||
payload 客户端原始请求体（含 model/messages/tools/temperature 等）
|
||||
|
||||
返回 (True, 上游响应 dict) 或 (False, 错误信息)。
|
||||
真 api_key 由 llm_bridge 按 token 绑定的 org_id 从 llm 表解析，不下发给调用方。
|
||||
真 api_key 由治理链解析，不下发给调用方。门禁链（限流/限额/主备容错/
|
||||
端点轮转/预授权）+ 双维度记账在推理引擎内执行。
|
||||
"""
|
||||
# 失败限速前置：窗口内鉴权失败过多直接拒绝（防 token 枚举）
|
||||
if _rate_limited(token):
|
||||
@ -272,82 +274,24 @@ async def proxy_chat_completion(token, payload):
|
||||
|
||||
org_id = info['org_id']
|
||||
# 模型选择：token 绑定了 model_name 则强制用它（防运行环境越权指定贵模型）；
|
||||
# 否则用请求里的 model；都没有则由 llm_bridge 取该机构第一个 active 模型。
|
||||
model_name = info.get('model_name') or payload.get('model') or None
|
||||
# 否则用请求里的 model；都没有则由治理链取该机构策略缺省模型。
|
||||
model_name = info.get('model_name') or payload.get('model') or ''
|
||||
|
||||
# pipeline_llm 治理前置：机构配了治理 → 门禁链（限流/限额/主备容错/端点选择/预授权）；
|
||||
# 未配置 → __LEGACY__ 走旧 llm 表。治理真实失败（限流/余额不足）→ 抛错，禁止静默回退。
|
||||
gctx = None
|
||||
try:
|
||||
from pipeline_llm.gateway import govern_resolve
|
||||
_est = 200
|
||||
try:
|
||||
_msgs = payload.get('messages') or []
|
||||
_est = max(sum(len(str(m.get('content', ''))) for m in _msgs if isinstance(m, dict)) // 2, 200)
|
||||
except Exception:
|
||||
_est = 200
|
||||
gok, gres = await govern_resolve(
|
||||
org_id=org_id, user_id='', model_name=model_name or '', est_tokens=_est,
|
||||
from pipeline_llm.inference import chat_inference
|
||||
data = await chat_inference(
|
||||
org_id, info.get('created_by', '') or '', payload,
|
||||
model_name=model_name,
|
||||
task_ref='proxy:%s' % (info.get('project_id') or ''))
|
||||
if gok and isinstance(gres, dict):
|
||||
gctx = gres
|
||||
elif gres != '__LEGACY__':
|
||||
return False, gres
|
||||
except ImportError:
|
||||
gctx = None
|
||||
return False, "模型治理模块（pipeline-llm）未安装，推理引擎不可用"
|
||||
except Exception as e:
|
||||
logger.warning("proxy_chat_completion: 治理前置异常（回退旧表）: %s", e)
|
||||
|
||||
if gctx:
|
||||
api_base = gctx['api_base']
|
||||
api_key = gctx['api_key']
|
||||
model_id = gctx['model_id']
|
||||
else:
|
||||
from .llm_bridge import _get_model_config
|
||||
cfg = await _get_model_config(model_name, org_id=org_id)
|
||||
if not (cfg.get('api_key') and cfg.get('api_base')):
|
||||
return False, f"机构 {org_id} 未配置可用模型（模型治理/llm 表 status=active）"
|
||||
api_base = cfg['api_base']
|
||||
api_key = cfg['api_key'] # 只在本进程内存中使用，不返回给调用方
|
||||
model_id = cfg.get('model_id') or model_name or 'default'
|
||||
|
||||
# 透传客户端参数（messages/tools/temperature/max_tokens 等），但 model 换成真实 model_id，
|
||||
# 且不透传 stream（代理暂不支持流式）。
|
||||
from .llm_bridge import _post_chat_completion
|
||||
upstream = {k: v for k, v in payload.items() if k not in ('model', 'stream')}
|
||||
upstream['model'] = model_id
|
||||
|
||||
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
|
||||
url = api_base.rstrip('/') + '/chat/completions'
|
||||
try:
|
||||
data = await _post_chat_completion(url, headers, upstream)
|
||||
except Exception as e:
|
||||
logger.warning("proxy_chat_completion upstream failed: %s", e)
|
||||
# 治理路径：失败结算（释放预授权，写 failed 流水）
|
||||
if gctx:
|
||||
try:
|
||||
from pipeline_llm.gateway import govern_settle
|
||||
await govern_settle(gctx, False, 0, 0, str(e)[:200])
|
||||
except Exception:
|
||||
pass
|
||||
return False, f"上游调用失败: {str(e)[:300]}"
|
||||
logger.warning("proxy_chat_completion inference failed: %s", e)
|
||||
return False, str(e)[:300]
|
||||
|
||||
await _record_usage(info['id'], data.get('usage'))
|
||||
# 治理路径：按上游真实 usage 结算（双维度记账）
|
||||
if gctx:
|
||||
try:
|
||||
from pipeline_llm.gateway import govern_settle
|
||||
_u = data.get('usage') or {}
|
||||
await govern_settle(
|
||||
gctx, True,
|
||||
int(_u.get('prompt_tokens') or 0),
|
||||
int(_u.get('completion_tokens') or 0),
|
||||
'' if _u else 'est')
|
||||
except Exception as e:
|
||||
logger.warning("proxy_chat_completion: 治理结算失败（不阻断）: %s", e)
|
||||
logger.info("proxy_chat_completion: org=%s project=%s model=%s ok%s",
|
||||
org_id, info.get('project_id'), model_id,
|
||||
" (governed)" if gctx else "")
|
||||
logger.info("proxy_chat_completion: org=%s project=%s model=%s ok (governed)",
|
||||
org_id, info.get('project_id'), model_name or '(default)')
|
||||
return True, data
|
||||
|
||||
|
||||
@ -392,3 +336,4 @@ def load_llm_proxy():
|
||||
env.revoke_task_tokens = revoke_task_tokens
|
||||
env.proxy_chat_completion = proxy_chat_completion
|
||||
env.list_llm_tokens = list_llm_tokens
|
||||
env.record_llm_token_usage = _record_usage
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user