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feat(bridge): session_id会话粘性透传(2026-09-11用户定夺:同会话固定账号省上游缓存钱,跨会话轮询)——llm_bridge四函数(llm_call/llm_call_msgs/llm_call_msgs_native/llm_infer)加session_id参数经payload._session_id透传(不进token缓存键);agent_loop_v2七处调用点接self.session_id(主循环native+降级重试+文本回退/技能选择/项目匹配/历史摘要/invoke_model);agent_loop v1四处任务循环用task:{task_id}做粘性键(任务内30轮同前缀);platform_model_tools.tool_invoke_model透传

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ymq 2026-09-11 15:30:30 +08:00
parent 58853de72d
commit 5c063ca588
4 changed files with 66 additions and 30 deletions
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11
pipeline_service/agent_loop.py
View File

@ -2034,7 +2034,8 @@ async def _exec_agent_tool(tool, params, workspace_dir, ctx=None):
from .platform_model_tools import exec_platform_model_tool from .platform_model_tools import exec_platform_model_tool
return await exec_platform_model_tool( return await exec_platform_model_tool(
tool, p, org_id=str((ctx or {}).get('org_id', '') or '0'), tool, p, org_id=str((ctx or {}).get('org_id', '') or '0'),
user_id=str((ctx or {}).get('user_id', '') or '')) user_id=str((ctx or {}).get('user_id', '') or ''),
project_id=str((ctx or {}).get('project_id', '') or ''))
if tool == 'read_file': if tool == 'read_file':
path = p.get('path', '') path = p.get('path', '')
if not path: return 'FAIL: 需要文件路径' if not path: return 'FAIL: 需要文件路径'
@ -2475,7 +2476,7 @@ async def role_agent_run(project_id, role, agent_id=None, model_name=None):
msgs.append({"role": "user", "content": _FORCE_PRODUCE_HINT}) msgs.append({"role": "user", "content": _FORCE_PRODUCE_HINT})
try: try:
resp = await asyncio.wait_for( resp = await asyncio.wait_for(
llm_call_msgs_native(msgs, tools=tools_schema, model=model_name, temperature=0.4, org_id=org_id), llm_call_msgs_native(msgs, tools=tools_schema, model=model_name, temperature=0.4, org_id=org_id, project_id=project_id, session_id='task:%s' % task_id),
timeout=_LLM_HARD_TIMEOUT) timeout=_LLM_HARD_TIMEOUT)
except Exception as e: except Exception as e:
err_msg = f"{type(e).__name__}: {str(e)[:400]}" err_msg = f"{type(e).__name__}: {str(e)[:400]}"
@ -3061,7 +3062,7 @@ async def pm_review_run(project_id, agent_id=None, model_name=None):
"随后必须立即输出 review_approve / review_reject / review_complete / review_rollback 之一，" "随后必须立即输出 review_approve / review_reject / review_complete / review_rollback 之一，"
"禁止再调用其它工具。"}) "禁止再调用其它工具。"})
try: try:
raw = await llm_call_msgs(msgs, model=model_name, temperature=0.3, org_id=org_id) raw = await llm_call_msgs(msgs, model=model_name, temperature=0.3, org_id=org_id, project_id=project_id, session_id='task:%s' % task_id)
except Exception as e: except Exception as e:
err_msg = f"{type(e).__name__}: {str(e)[:400]}" err_msg = f"{type(e).__name__}: {str(e)[:400]}"
from .task_capability import mark_failed from .task_capability import mark_failed
@ -3407,7 +3408,7 @@ async def qc_review_run(project_id, agent_id=None, model_name=None):
msgs.append({"role": "user", "content": msgs.append({"role": "user", "content":
"已检查足够信息。现在必须立即输出 review_approve 或 review_reject，禁止再调用其它工具。"}) "已检查足够信息。现在必须立即输出 review_approve 或 review_reject，禁止再调用其它工具。"})
try: try:
raw = await llm_call_msgs(msgs, model=model_name, temperature=0.2, org_id=org_id) raw = await llm_call_msgs(msgs, model=model_name, temperature=0.2, org_id=org_id, project_id=project_id, session_id='task:%s' % task_id)
except Exception as e: except Exception as e:
err_msg = f"{type(e).__name__}: {str(e)[:400]}" err_msg = f"{type(e).__name__}: {str(e)[:400]}"
from .task_capability import mark_failed from .task_capability import mark_failed
@ -3606,7 +3607,7 @@ async def retrospective_run(project_id, agent_id=None, model_name=None):
"已执行足够轮次。现在必须立即收尾：write_file 写复盘报告（若未写），然后输出 deliver 提交，禁止再调其它工具。"}) "已执行足够轮次。现在必须立即收尾：write_file 写复盘报告（若未写），然后输出 deliver 提交，禁止再调其它工具。"})
try: try:
raw = await asyncio.wait_for( raw = await asyncio.wait_for(
llm_call_msgs(msgs, model=model_name, temperature=0.3, org_id=org_id), llm_call_msgs(msgs, model=model_name, temperature=0.3, org_id=org_id, project_id=project_id, session_id='task:%s' % task_id),
timeout=_LLM_HARD_TIMEOUT) timeout=_LLM_HARD_TIMEOUT)
except Exception as e: except Exception as e:
err_msg = f"{type(e).__name__}: {str(e)[:400]}" err_msg = f"{type(e).__name__}: {str(e)[:400]}"

14
pipeline_service/agent_loop_v2.py
View File

@ -717,6 +717,7 @@ class AgentExecutor:
temperature=0, temperature=0,
org_id=self.org_id, org_id=self.org_id,
purpose='utility', purpose='utility',
session_id=self.session_id,
timeout=self._UTILITY_TIMEOUT, timeout=self._UTILITY_TIMEOUT,
) )
m = _re.search(r"\[[^\]]*\]", content or "") m = _re.search(r"\[[^\]]*\]", content or "")
@ -832,6 +833,7 @@ class AgentExecutor:
model=self.model_name, model=self.model_name,
temperature=self.config.temperature, temperature=self.config.temperature,
org_id=self.org_id, org_id=self.org_id,
session_id=self.session_id,
) )
except Exception as e: except Exception as e:
# 原生视觉降级（2026-09-10）：模型不支持多模态 content 数组时 # 原生视觉降级（2026-09-10）：模型不支持多模态 content 数组时
@ -842,7 +844,8 @@ class AgentExecutor:
try: try:
return await llm_call_msgs_native( return await llm_call_msgs_native(
self._msgs, tools=tools_schema, model=self.model_name, self._msgs, tools=tools_schema, model=self.model_name,
temperature=self.config.temperature, org_id=self.org_id) temperature=self.config.temperature, org_id=self.org_id,
session_id=self.session_id)
except Exception: except Exception:
pass pass
logger.error(f"native function calling failed, fallback to text: {e}") logger.error(f"native function calling failed, fallback to text: {e}")
@ -855,12 +858,14 @@ class AgentExecutor:
model=self.model_name, model=self.model_name,
temperature=self.config.temperature, temperature=self.config.temperature,
org_id=self.org_id, org_id=self.org_id,
session_id=self.session_id,
) )
except Exception as e: except Exception as e:
if self._degrade_images_if_needed(e): if self._degrade_images_if_needed(e):
content = await llm_call_msgs( content = await llm_call_msgs(
self._msgs, model=self.model_name, self._msgs, model=self.model_name,
temperature=self.config.temperature, org_id=self.org_id) temperature=self.config.temperature, org_id=self.org_id,
session_id=self.session_id)
else: else:
raise raise
return {"content": content or "", "tool_calls": []} return {"content": content or "", "tool_calls": []}
@ -1238,7 +1243,7 @@ class AgentExecutor:
pnames = [getattr(r, "name", "") for r in (all_recs or [])] pnames = [getattr(r, "name", "") for r in (all_recs or [])]
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\"。" 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\"。"
matched = await llm_call(classify_prompt, temperature=0.0, org_id=self.org_id, matched = await llm_call(classify_prompt, temperature=0.0, org_id=self.org_id,
purpose='utility') purpose='utility', session_id=self.session_id)
matched = matched.strip().strip('"').strip("'") matched = matched.strip().strip('"').strip("'")
recs2 = await sor.sqlExe( recs2 = await sor.sqlExe(
@ -1628,7 +1633,7 @@ class AgentExecutor:
from .platform_model_tools import tool_invoke_model from .platform_model_tools import tool_invoke_model
return await tool_invoke_model( return await tool_invoke_model(
p, self.org_id or "0", user_id=self.user_id or "", p, self.org_id or "0", user_id=self.user_id or "",
project_id=self.project_id or "") project_id=self.project_id or "", session_id=self.session_id or "")
async def _t_web_search(self, sor, p, pid): async def _t_web_search(self, sor, p, pid):
"""联网检索（薄壳委托 web_tools 唯一实现，SSRF 防护在其中）。""" """联网检索（薄壳委托 web_tools 唯一实现，SSRF 防护在其中）。"""
@ -2190,6 +2195,7 @@ class AgentExecutor:
model=self.model_name, model=self.model_name,
org_id=self.org_id, org_id=self.org_id,
purpose='utility', purpose='utility',
session_id=self.session_id,
timeout=self._UTILITY_TIMEOUT, timeout=self._UTILITY_TIMEOUT,
) )
return summary[:500] return summary[:500]

52
pipeline_service/llm_bridge.py
View File

@ -41,9 +41,14 @@ async def _self_base_url():
return "http://127.0.0.1:%d/pipeline-llm/api/v1" % port return "http://127.0.0.1:%d/pipeline-llm/api/v1" % port
async def _get_internal_token(org_id, user_id, model_name): async def _get_internal_token(org_id, user_id, model_name, project_id=''):
"""取/发内部短期 token。失败抛 ValueError（消息真实可行动）。""" """取/发内部短期 token。失败抛 ValueError（消息真实可行动）。
key = (org_id or '0', user_id or '', model_name or '')
project_id（2026-09-10 用户定夺）：随 token 落 pipeline_llm_tokens.project_id，
推理端点透传到 llm_usage.project_id 支撑按项目统计费用。缓存键必须含
project_id——否则同 org/user/model 的不同项目会串用同一 token，费用记错项目。
"""
key = (org_id or '0', user_id or '', model_name or '', project_id or '')
now = time.time() now = time.time()
ent = _token_cache.get(key) ent = _token_cache.get(key)
if ent and ent['calls'] < _TOKEN_MAX_LOCAL_CALLS \ if ent and ent['calls'] < _TOKEN_MAX_LOCAL_CALLS \
@ -52,7 +57,7 @@ async def _get_internal_token(org_id, user_id, model_name):
return ent['token'] return ent['token']
from .llm_proxy import create_llm_token from .llm_proxy import create_llm_token
ok, token = await create_llm_token( ok, token = await create_llm_token(
org_id or '0', project_id='', task_id='', model_name=model_name or '', org_id or '0', project_id=project_id or '', task_id='', model_name=model_name or '',
purpose='internal_bridge', ttl_hours=_TOKEN_TTL_HOURS, purpose='internal_bridge', ttl_hours=_TOKEN_TTL_HOURS,
max_calls=500, created_by=user_id or '') max_calls=500, created_by=user_id or '')
if not ok: if not ok:
@ -64,7 +69,8 @@ async def _get_internal_token(org_id, user_id, model_name):
return token return token
async def _http_chat(payload, org_id, user_id, model_name, timeout: int = 0): async def _http_chat(payload, org_id, user_id, model_name, timeout: int = 0,
project_id: str = ''):
"""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）。 """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）。
timeout：客户端等待秒数（0=缺省 330）。异步生成模型（视频等）端点侧最长 timeout：客户端等待秒数（0=缺省 330）。异步生成模型（视频等）端点侧最长
@ -72,7 +78,7 @@ async def _http_chat(payload, org_id, user_id, model_name, timeout: int = 0):
""" """
import aiohttp import aiohttp
token = await _get_internal_token(org_id, user_id, model_name) token = await _get_internal_token(org_id, user_id, model_name, project_id)
base = await _self_base_url() base = await _self_base_url()
# ⚠️ "Bearer " 前缀用拼接构造——字面量写在源码里会被脱敏工具替换成 *** # ⚠️ "Bearer " 前缀用拼接构造——字面量写在源码里会被脱敏工具替换成 ***
# （2026-09-04 实测：Authorization 头变成 "***plk-..." 致端点校验失败） # （2026-09-04 实测：Authorization 头变成 "***plk-..." 致端点校验失败）
@ -111,13 +117,16 @@ def _no_llm_error(model_name=None, org_id=None) -> ValueError:
async def llm_call(prompt: str, model: str = None, temperature: float = 0.7, async def llm_call(prompt: str, model: str = None, temperature: float = 0.7,
org_id: str = None, user_id: str = None, purpose: str = '', org_id: str = None, user_id: str = None, purpose: str = '',
timeout: int = 0) -> str: timeout: int = 0, project_id: str = '', session_id: str = '') -> str:
"""Call LLM and return text response. """Call LLM and return text response.
统一走模型治理推理 API（门禁链 + 双维度记账）。 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。 org_id 为空 = 系统级（'0'），与旧语义（不过滤机构）等价。
purpose='utility'：辅助任务（分类/选择/摘要），治理层按用途选模型链 purpose='utility'：辅助任务（分类/选择/摘要），治理层按用途选模型链
（机构策略配置辅助模型优先），经 payload 的 _purpose 键透传到端点。 （机构策略配置辅助模型优先），经 payload 的 _purpose 键透传到端点。
session_id（2026-09-11 用户定夺）：会话粘性键——同会话固定同一账号，
上游 KV/前缀缓存按账号隔离，固定账号命中缓存省钱；经 payload._session_id
透传到端点（不进 token 缓存键——token 与账号选择解耦）。
""" """
# 兼容旧优先级：harnessed_agent（若宿主加载了独立推理后端） # 兼容旧优先级：harnessed_agent（若宿主加载了独立推理后端）
try: try:
@ -141,7 +150,10 @@ async def llm_call(prompt: str, model: str = None, temperature: float = 0.7,
payload["_purpose"] = purpose payload["_purpose"] = purpose
if timeout: if timeout:
payload["_timeout"] = int(timeout) payload["_timeout"] = int(timeout)
data = await _http_chat(payload, org_id or '0', user_id or '', model or '') if session_id:
payload["_session_id"] = session_id
data = await _http_chat(payload, org_id or '0', user_id or '', model or '',
project_id=project_id or '')
try: try:
return data["choices"][0]["message"]["content"] return data["choices"][0]["message"]["content"]
except (KeyError, IndexError, TypeError) as e: except (KeyError, IndexError, TypeError) as e:
@ -156,18 +168,22 @@ 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, async def llm_call_msgs(messages: list, model: str = None, temperature: float = 0.7,
org_id: str = None, user_id: str = None, purpose: str = '', org_id: str = None, user_id: str = None, purpose: str = '',
timeout: int = 0) -> str: timeout: int = 0, project_id: str = '', session_id: str = '') -> str:
"""Call LLM with full message array (system/user/assistant). """Call LLM with full message array (system/user/assistant).
purpose='utility'：辅助任务（分类/选择/摘要），治理层按用途选模型链。 purpose='utility'：辅助任务（分类/选择/摘要），治理层按用途选模型链。
timeout：单次上游调用超时秒数（0=用端点默认；上限 900，超长文本提取用）。 timeout：单次上游调用超时秒数（0=用端点默认；上限 900，超长文本提取用）。
session_id（2026-09-11）：会话粘性账号键，经 payload._session_id 透传。
""" """
payload = {"model": model or '', "messages": messages, "temperature": temperature} payload = {"model": model or '', "messages": messages, "temperature": temperature}
if purpose: if purpose:
payload["_purpose"] = purpose payload["_purpose"] = purpose
if timeout: if timeout:
payload["_timeout"] = int(timeout) payload["_timeout"] = int(timeout)
data = await _http_chat(payload, org_id or '0', user_id or '', model or '') if session_id:
payload["_session_id"] = session_id
data = await _http_chat(payload, org_id or '0', user_id or '', model or '',
project_id=project_id or '')
try: try:
return data["choices"][0]["message"]["content"] return data["choices"][0]["message"]["content"]
except (KeyError, IndexError, TypeError) as e: except (KeyError, IndexError, TypeError) as e:
@ -177,13 +193,15 @@ async def llm_call_msgs(messages: list, model: str = None, temperature: float =
async def llm_call_msgs_native(messages: list, tools: list = None, model: str = None, async def llm_call_msgs_native(messages: list, tools: list = None, model: str = None,
temperature: float = 0.7, org_id: str = None, temperature: float = 0.7, org_id: str = None,
user_id: str = None, purpose: str = '') -> dict: user_id: str = None, purpose: str = '',
project_id: str = '', session_id: str = '') -> dict:
"""Native function calling. 传入 tools JSON schema，返回 message dict。 """Native function calling. 传入 tools JSON schema，返回 message dict。
Returns: Returns:
{"content": str, "tool_calls": [{"id","type","function":{"name","arguments"}}]} {"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。 当模型返回 tool_calls 时，content 通常为空字符串。
purpose='utility'：辅助任务（分类/选择/摘要），治理层按用途选模型链。 purpose='utility'：辅助任务（分类/选择/摘要），治理层按用途选模型链。
session_id（2026-09-11）：会话粘性账号键，经 payload._session_id 透传。
""" """
payload = {"model": model or '', "messages": messages, "temperature": temperature} payload = {"model": model or '', "messages": messages, "temperature": temperature}
if tools: if tools:
@ -191,7 +209,10 @@ async def llm_call_msgs_native(messages: list, tools: list = None, model: str =
payload["tool_choice"] = "auto" payload["tool_choice"] = "auto"
if purpose: if purpose:
payload["_purpose"] = purpose payload["_purpose"] = purpose
data = await _http_chat(payload, org_id or '0', user_id or '', model or '') if session_id:
payload["_session_id"] = session_id
data = await _http_chat(payload, org_id or '0', user_id or '', model or '',
project_id=project_id or '')
try: try:
msg = data["choices"][0]["message"] msg = data["choices"][0]["message"]
except (KeyError, IndexError, TypeError) as e: except (KeyError, IndexError, TypeError) as e:
@ -204,7 +225,8 @@ async def llm_call_msgs_native(messages: list, tools: list = None, model: str =
async def llm_infer(payload: dict, model: str = None, org_id: str = None, async def llm_infer(payload: dict, model: str = None, org_id: str = None,
user_id: str = None, timeout: int = 0) -> dict: user_id: str = None, timeout: int = 0,
project_id: str = '', session_id: str = '') -> dict:
"""通用推理（全能力，2026-09-07）：透传任意 payload 到统一推理端点， """通用推理（全能力，2026-09-07）：透传任意 payload 到统一推理端点，
返回上游响应 dict（OpenAI 兼容 choices；生成类另带 media/output/task_id）。 返回上游响应 dict（OpenAI 兼容 choices；生成类另带 media/output/task_id）。
@ -222,7 +244,9 @@ async def llm_infer(payload: dict, model: str = None, org_id: str = None,
body["model"] = model body["model"] = model
if timeout: if timeout:
body["_timeout"] = int(timeout) body["_timeout"] = int(timeout)
if session_id and not body.get('_session_id'):
body["_session_id"] = session_id
# 客户端等待须覆盖端点侧预算（_timeout 端点封顶 900）+ 余量，否则客户端先断连 # 客户端等待须覆盖端点侧预算（_timeout 端点封顶 900）+ 余量，否则客户端先断连
client_timeout = min(int(timeout or 0), 900) + 60 if timeout else 0 client_timeout = min(int(timeout or 0), 900) + 60 if timeout else 0
return await _http_chat(body, org_id or '0', user_id or '', model or '', return await _http_chat(body, org_id or '0', user_id or '', model or '',
timeout=client_timeout) timeout=client_timeout, project_id=project_id or '')

19
pipeline_service/platform_model_tools.py
View File

@ -214,7 +214,8 @@ def load_model_skill(model_name):
async def judge_completeness(model_name, capability, task, biz, async def judge_completeness(model_name, capability, task, biz,
description="", org_id="0", user_id=""): description="", org_id="0", user_id="",
project_id=""):
"""完备性判断主体（LLM 裁决，2026-09-08 用户定夺）。 """完备性判断主体（LLM 裁决，2026-09-08 用户定夺）。
① 模型有配套技能 → 按技能输入契约判断；② 无技能 → 按能力语义判断； ① 模型有配套技能 → 按技能输入契约判断；② 无技能 → 按能力语义判断；
@ -266,7 +267,7 @@ async def judge_completeness(model_name, capability, task, biz,
raw = await llm_call_msgs( raw = await llm_call_msgs(
[{"role": "user", "content": prompt}], [{"role": "user", "content": prompt}],
temperature=0, org_id=org_id or "0", user_id=user_id or "", temperature=0, org_id=org_id or "0", user_id=user_id or "",
purpose="utility", timeout=60) purpose="utility", timeout=60, project_id=project_id or "")
except Exception as e: except Exception as e:
logger.warning("judge_completeness LLM 失败（走代码表兜底）: %s", repr(e)[:150]) logger.warning("judge_completeness LLM 失败（走代码表兜底）: %s", repr(e)[:150])
return _check_completeness_code(cap, task, biz) return _check_completeness_code(cap, task, biz)
@ -338,7 +339,7 @@ async def _resolve_capability(model_name, org_id):
return "", "" return "", ""
async def tool_invoke_model(params, org_id, user_id=""): async def tool_invoke_model(params, org_id, user_id="", project_id="", session_id=""):
"""调用平台模型完成生成类任务（文生图/视频/语音等非对话能力）。 """调用平台模型完成生成类任务（文生图/视频/语音等非对话能力）。
流程：解析 task/model/capability/params → 未指定 model 时按 task 自动 流程：解析 task/model/capability/params → 未指定 model 时按 task 自动
@ -404,7 +405,8 @@ async def tool_invoke_model(params, org_id, user_id=""):
else: else:
gap = await judge_completeness(model, cap_gate, task, biz, gap = await judge_completeness(model, cap_gate, task, biz,
description=desc, org_id=org_id or "0", description=desc, org_id=org_id or "0",
user_id=user_id or "") user_id=user_id or "",
project_id=project_id or "")
cap = cap_gate cap = cap_gate
if gap: if gap:
logger.info("invoke_model 完备性门禁拦截（%s）: %s", cap or "?", gap[:80]) logger.info("invoke_model 完备性门禁拦截（%s）: %s", cap or "?", gap[:80])
@ -417,7 +419,8 @@ async def tool_invoke_model(params, org_id, user_id=""):
from .llm_bridge import llm_infer from .llm_bridge import llm_infer
data = await llm_infer( data = await llm_infer(
payload, model=model, org_id=org_id or "0", payload, model=model, org_id=org_id or "0",
user_id=user_id or "", timeout=600) user_id=user_id or "", timeout=600, project_id=project_id or "",
session_id=session_id or "")
except Exception as e: except Exception as e:
return "FAIL: 模型「" + model + "」调用失败：" + str(e)[:400] return "FAIL: 模型「" + model + "」调用失败：" + str(e)[:400]
@ -475,10 +478,12 @@ PLATFORM_MODEL_TOOLS_V1 = [
] ]
async def exec_platform_model_tool(tool, params, org_id, user_id=""): async def exec_platform_model_tool(tool, params, org_id, user_id="",
project_id=""):
"""v1/v2 统一分发入口（薄壳）。""" """v1/v2 统一分发入口（薄壳）。"""
if tool == "list_platform_models": if tool == "list_platform_models":
return await tool_list_platform_models(params, org_id) return await tool_list_platform_models(params, org_id)
if tool == "invoke_model": if tool == "invoke_model":
return await tool_invoke_model(params, org_id, user_id=user_id) return await tool_invoke_model(params, org_id, user_id=user_id,
project_id=project_id)
return "未实现: " + str(tool) return "未实现: " + str(tool)