fix: 解决调用ir_execution_service stream接口时,接口日志中缺少llm调用日志的问题-agent-runtime-AtomGit

Mapplications/ir_execution_service/ir_execution_service/ir_execution_service_app.py+2-0

	@@ -44,6 +44,7 @@ from .runtime_support.core_log_bridge import install_core_log_bridge
44	from .runtime_support.interface_logger import (	44	from .runtime_support.interface_logger import (
45	init_interface_logger_from_env,	45	init_interface_logger_from_env,
46	install_core_interface_sink,	46	install_core_interface_sink,
		47	+ install_runner_llm_stream_interface_callbacks,
47	install_runner_tool_interface_callbacks,	48	install_runner_tool_interface_callbacks,
48	log_server,	49	log_server,
49	set_request_context,	50	set_request_context,
	@@ -58,6 +59,7 @@ install_runner_tool_alarm_callbacks()
58	init_interface_logger_from_env()	59	init_interface_logger_from_env()
59	install_core_interface_sink()	60	install_core_interface_sink()
60	install_runner_tool_interface_callbacks()	61	install_runner_tool_interface_callbacks()
		62	+install_runner_llm_stream_interface_callbacks()
61		63
62	# Workflow 默认超时较短，复杂 DSL 续跑时容易误判超时。	64	# Workflow 默认超时较短，复杂 DSL 续跑时容易误判超时。
63	os.environ.setdefault("WORKFLOW_EXECUTE_TIMEOUT", "300")	65	os.environ.setdefault("WORKFLOW_EXECUTE_TIMEOUT", "300")

Mapplications/ir_execution_service/ir_execution_service/runtime_support/interface_logger.py+90-0

	@@ -26,6 +26,43 @@ _SERVER_SOURCE_IP: contextvars.ContextVar[str] = contextvars.ContextVar("interfa
26	_TOOL_INTERFACE_T0: dict[str, float] = {}	26	_TOOL_INTERFACE_T0: dict[str, float] = {}
27	_RUNNER_TOOL_INTERFACE_REGISTERED = False	27	_RUNNER_TOOL_INTERFACE_REGISTERED = False
28		28
		29	+_RUNNER_LLM_STREAM_INTERFACE_REGISTERED = False
		30	+
		31	+
		32	+def _llm_stream_output_looks_terminal(*, result: Any) -> bool:
		33	+ """判断是否为单次流式 HTTP 的收尾 payload（避免对 per_item 的每个 chunk 打 interface）。"""
		34	+ if isinstance(result, list):
		35	+ return True
		36	+ if result is None:
		37	+ return False
		38	+ if getattr(result, "usage_metadata", None) is not None:
		39	+ return True
		40	+ fr = getattr(result, "finish_reason", None)
		41	+ if fr is None:
		42	+ return False
		43	+ s = str(fr).strip().lower()
		44	+ return bool(s) and s not in ("null", "none")
		45	+
		46	+
		47	+def _model_name_from_configs(*, model_config: Any, model_client_config: Any) -> str:
		48	+ if model_config is not None:
		49	+ mn = getattr(model_config, "model_name", None)
		50	+ if mn:
		51	+ return str(mn)
		52	+ m = getattr(model_config, "model", None)
		53	+ if m:
		54	+ return str(m)
		55	+ return ""
		56	+
		57	+
		58	+def _model_provider_str(model_client_config: Any) -> str:
		59	+ if model_client_config is None:
		60	+ return ""
		61	+ cp = getattr(model_client_config, "client_provider", None)
		62	+ if cp is None:
		63	+ return ""
		64	+ return str(getattr(cp, "value", cp))
		65	+
29		66
30	def set_request_context(*, request_id: str, source_ip: str = "") -> None:	67	def set_request_context(*, request_id: str, source_ip: str = "") -> None:
31	_REQUEST_ID.set(str(request_id or "").strip())	68	_REQUEST_ID.set(str(request_id or "").strip())
	@@ -357,3 +394,56 @@ def install_runner_tool_interface_callbacks() -> None:
357	_RUNNER_TOOL_INTERFACE_REGISTERED = True	394	_RUNNER_TOOL_INTERFACE_REGISTERED = True
358	_LOG.info("Interface runner tool callbacks installed.")	395	_LOG.info("Interface runner tool callbacks installed.")
359		396
		397	+
		398	+def install_runner_llm_stream_interface_callbacks() -> None:
		399	+ """流式 LLM 成功结束时写一条 interface（补充 core 内 stream 无「API response received」日志的空档）。
		400	+
		401	+ Model.stream 对 LLM_STREAM_OUTPUT 使用 emit_after 默认 per_item，每个 chunk 触发一次；
		402	+ 仅在收尾 chunk（finish_reason / usage_metadata）或 once 模式下的 list 结果时记录，避免刷屏。
		403	+ 耗时固定为 0（不在此维护起止时钟）。流式失败由 core_log_bridge 写 interface。
		404	+ """
		405	+ global _RUNNER_LLM_STREAM_INTERFACE_REGISTERED
		406	+ if _RUNNER_LLM_STREAM_INTERFACE_REGISTERED:
		407	+ return
		408	+
		409	+ from openjiuwen.core.runner import Runner
		410	+ from openjiuwen.core.runner.callback.events import LLMCallEvents
		411	+
		412	+ fw = Runner.callback_framework
		413	+
		414	+ async def _on_llm_stream_output(
		415	+ *,
		416	+ result: Any = None,
		417	+ model_config: Any = None,
		418	+ model_client_config: Any = None,
		419	+ **_: Any,
		420	+ ) -> None:
		421	+ if not _llm_stream_output_looks_terminal(result=result):
		422	+ return
		423	+ mn = _model_name_from_configs(
		424	+ model_config=model_config, model_client_config=model_client_config
		425	+ )
		426	+ prov = _model_provider_str(model_client_config)
		427	+ add_info: dict[str, Any] = {
		428	+ "source": "runner_callback",
		429	+ "event_type": "llm_stream_end",
		430	+ "is_stream": True,
		431	+ "model_name": mn,
		432	+ "model_provider": prov,
		433	+ }
		434	+ if isinstance(result, list):
		435	+ add_info["chunk_count"] = len(result)
		436	+ log_client(
		437	+ interface_name="llm.call",
		438	+ cost_ms=0.0,
		439	+ ok=True,
		440	+ return_code=0,
		441	+ return_info="stream completed",
		442	+ dest_ip="",
		443	+ add_info=add_info,
		444	+ )
		445	+
		446	+ fw.register_sync(LLMCallEvents.LLM_STREAM_OUTPUT, _on_llm_stream_output, priority=-1000)
		447	+ _RUNNER_LLM_STREAM_INTERFACE_REGISTERED = True
		448	+ _LOG.info("Interface runner LLM stream callbacks installed.")
		449	+

Aapplications/ir_execution_service/ir_execution_service/test/llm_test.json+166-0

@@ -0,0 +1,166 @@
	1	+{
	2	+ "id": "f6753e87-8e2f-491c-acb1-9f33c3fc6746",
	3	+ "version": "draft",
	4	+ "name": "llm_json_2_lr",
	5	+ "description": "测试大模型输出为json：包含支持的Array类型：[String]/[Integer]/[Number]/[Boolean]/[Object]/[Array] ",
	6	+ "start_id": [
	7	+ "start_Ag0l2"
	8	+ ],
	9	+ "end_id": [
	10	+ "end_Ag0l2"
	11	+ ],
	12	+ "components": [
	13	+ {
	14	+ "id": "start_Ag0l2",
	15	+ "version": "",
	16	+ "name": "开始",
	17	+ "description": "",
	18	+ "type": 1,
	19	+ "type_version": "1.0.0",
	20	+ "inputs": {
	21	+ "query": "${query}"
	22	+ },
	23	+ "outputs": {},
	24	+ "branches": [],
	25	+ "configs": {}
	26	+ },
	27	+ {
	28	+ "id": "end_Ag0l2",
	29	+ "version": "",
	30	+ "name": "结束",
	31	+ "description": "",
	32	+ "type": 2,
	33	+ "type_version": "1.0.0",
	34	+ "inputs": {
	35	+ "result": "${llm_JtqFn}"
	36	+ },
	37	+ "outputs": {},
	38	+ "branches": [],
	39	+ "configs": {
	40	+ "response_template": "",
	41	+ "stream_output": true
	42	+ }
	43	+ },
	44	+ {
	45	+ "id": "llm_JtqFn",
	46	+ "version": "",
	47	+ "name": "大模型1",
	48	+ "description": "",
	49	+ "type": 3,
	50	+ "type_version": "1.0.0",
	51	+ "inputs": {
	52	+ "input": "${start_Ag0l2.query}"
	53	+ },
	54	+ "outputs": {
	55	+ "string_array": "${string_array}",
	56	+ "integer_array": "${integer_array}",
	57	+ "boolean_array": "${boolean_array}",
	58	+ "number_array": "${number_array}"
	59	+ },
	60	+ "branches": [],
	61	+ "configs": {
	62	+ "model": {
	63	+ "model_id": "106",
	64	+ "model_client_config": {
	65	+ "client_provider": "openai",
	66	+ "api_key": null,
	67	+ "api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
	68	+ "timeout": 90,
	69	+ "model_name": "qwen-plus",
	70	+ "parameters": {
	71	+ "temperature": 0.2,
	72	+ "max_tokens": 4000,
	73	+ "top_p": 0.2
	74	+ }
	75	+ },
	76	+ "request_config": {
	77	+ "model_name": "qwen-plus",
	78	+ "temperature": 0.2,
	79	+ "top_p": 0.2,
	80	+ "stream": true
	81	+ }
	82	+ },
	83	+ "template_content": [
	84	+ {
	85	+ "role": "system",
	86	+ "content": "请根据描述，输出一个json对象：\n对象必须包含以下 5 个字段，每个字段是一个数组：\n1. string_array：字符串数组，至少包含3个水果名，如 [\"苹果\", \"香蕉\"]\n2. integer_array：整数数组，至少包含3个正整数\n3. boolean_array：布尔数组，包含3个值，只能是 true 或 false\n4. number_array：浮点数数组，至少包含3个浮点数"
	87	+ },
	88	+ {
	89	+ "role": "user",
	90	+ "content": "{{input}}"
	91	+ }
	92	+ ],
	93	+ "response_format_type": "json",
	94	+ "output_config": {
	95	+ "string_array": {
	96	+ "type": "array",
	97	+ "description": "",
	98	+ "items": {
	99	+ "type": "string"
	100	+ },
	101	+ "required": true
	102	+ },
	103	+ "integer_array": {
	104	+ "type": "array",
	105	+ "description": "",
	106	+ "items": {
	107	+ "type": "integer"
	108	+ },
	109	+ "required": true
	110	+ },
	111	+ "boolean_array": {
	112	+ "type": "array",
	113	+ "description": "",
	114	+ "items": {
	115	+ "type": "boolean"
	116	+ },
	117	+ "required": true
	118	+ },
	119	+ "number_array": {
	120	+ "type": "array",
	121	+ "description": "",
	122	+ "items": {
	123	+ "type": "number"
	124	+ },
	125	+ "required": true
	126	+ }
	127	+ },
	128	+ "enable_history": false
	129	+ }
	130	+ }
	131	+ ],
	132	+ "connections": [
	133	+ {
	134	+ "source": "start_Ag0l2",
	135	+ "branch_id": "",
	136	+ "target": "llm_JtqFn"
	137	+ },
	138	+ {
	139	+ "source": "llm_JtqFn",
	140	+ "branch_id": "",
	141	+ "target": "end_Ag0l2"
	142	+ }
	143	+ ],
	144	+ "inputs": {
	145	+ "type": "object",
	146	+ "properties": {
	147	+ "query": {
	148	+ "type": "string",
	149	+ "description": ""
	150	+ }
	151	+ },
	152	+ "required": [
	153	+ "query"
	154	+ ]
	155	+ },
	156	+ "outputs": {
	157	+ "result": {
	158	+ "type": "ref",
	159	+ "description": ""
	160	+ }
	161	+ },
	162	+ "configs": {},
	163	+ "dependencies": {
	164	+ "workflows": []
	165	+ }
	166	+}

Aapplications/ir_execution_service/ir_execution_service/test/test_llm2.json+134-0

@@ -0,0 +1,134 @@
	1	+{
	2	+ "id": "44b9a508-d73c-414d-8401-1413804f72a6",
	3	+ "version": "draft",
	4	+ "name": "test_llm_end_002",
	5	+ "description": "start-llm-end",
	6	+ "start_id": [
	7	+ "start_X66EV"
	8	+ ],
	9	+ "end_id": [
	10	+ "end_X66EV"
	11	+ ],
	12	+ "components": [
	13	+ {
	14	+ "id": "start_X66EV",
	15	+ "version": "",
	16	+ "name": "开始",
	17	+ "description": "",
	18	+ "type": 1,
	19	+ "type_version": "1.0.0",
	20	+ "inputs": {
	21	+ "query": "${query}"
	22	+ },
	23	+ "outputs": {},
	24	+ "branches": [],
	25	+ "configs": {}
	26	+ },
	27	+ {
	28	+ "id": "end_X66EV",
	29	+ "version": "",
	30	+ "name": "结束",
	31	+ "description": "",
	32	+ "type": 2,
	33	+ "type_version": "1.0.0",
	34	+ "inputs": {
	35	+ "result": "${llm_mqZ1U.output}"
	36	+ },
	37	+ "outputs": {},
	38	+ "branches": [],
	39	+ "configs": {
	40	+ "response_template": "end组件的输出{{result}}",
	41	+ "stream_output": true
	42	+ }
	43	+ },
	44	+ {
	45	+ "id": "llm_mqZ1U",
	46	+ "version": "",
	47	+ "name": "大模型",
	48	+ "description": "",
	49	+ "type": 3,
	50	+ "type_version": "1.0.0",
	51	+ "inputs": {
	52	+ "input": "${start_X66EV.query}"
	53	+ },
	54	+ "outputs": {
	55	+ "output": "${output}"
	56	+ },
	57	+ "branches": [],
	58	+ "configs": {
	59	+ "model": {
	60	+ "model_id": "132",
	61	+ "model_client_config": {
	62	+ "client_provider": "openai",
	63	+ "api_key": null,
	64	+ "api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1/",
	65	+ "timeout": 300,
	66	+ "model_name": "qwen-plus",
	67	+ "parameters": {
	68	+ "temperature": 0.7,
	69	+ "max_tokens": 4000,
	70	+ "top_p": 0.9
	71	+ }
	72	+ },
	73	+ "request_config": {
	74	+ "model_name": "qwen-plus",
	75	+ "temperature": 0.7,
	76	+ "top_p": 0.9,
	77	+ "stream": true
	78	+ }
	79	+ },
	80	+ "template_content": [
	81	+ {
	82	+ "role": "system",
	83	+ "content": ""
	84	+ },
	85	+ {
	86	+ "role": "user",
	87	+ "content": "{{input}}"
	88	+ }
	89	+ ],
	90	+ "response_format_type": "text",
	91	+ "output_config": {
	92	+ "output": {
	93	+ "type": "string",
	94	+ "description": "",
	95	+ "required": true
	96	+ }
	97	+ },
	98	+ "enable_history": false
	99	+ }
	100	+ }
	101	+ ],
	102	+ "connections": [
	103	+ {
	104	+ "source": "start_X66EV",
	105	+ "branch_id": "",
	106	+ "target": "llm_mqZ1U"
	107	+ },
	108	+ {
	109	+ "source": "llm_mqZ1U",
	110	+ "branch_id": "",
	111	+ "target": "end_X66EV"
	112	+ }
	113	+ ],
	114	+ "inputs": {
	115	+ "type": "object",
	116	+ "properties": {
	117	+ "query": {
	118	+ "type": "string",
	119	+ "description": ""
	120	+ }
	121	+ },
	122	+ "required": []
	123	+ },
	124	+ "outputs": {
	125	+ "result": {
	126	+ "type": "ref",
	127	+ "description": ""
	128	+ }
	129	+ },
	130	+ "configs": {},
	131	+ "dependencies": {
	132	+ "workflows": []
	133	+ }
	134	+}