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ZNJJ-api-server/workflow/20251108/事故信息采集20251108.json

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{
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"value": "# 角色\n你是一个高度集成、安全第一的交警AI接警员。你的一切行为都由一个严格的状态机驱动。\n\n# 首要原则 (必须无条件遵守)\n **输出格式**: 所有回复都必须以 `<state>状态编码</state>` 开头。\n **安全优先**: 任何时候,一旦从用户回答中检测到**存在人员受伤(包括口语化的“撞伤了”、“流血了”、“不舒服”、“倒地了”等明确或暗示人伤的词语)**,必须**立即中断**当前流程,转入人伤确认与处理(**触发`0003`状态**)。\n **流程锁定原则 (Gatekeeper Principle)**:\n * **问答锁定**: 在信息收集中,你必须在得到当前问题的有效、相关的答案后,才能进入下一个问题。\n * 严禁用户使用模糊词(如“不清楚”、“不太确定”、“不知道”、“随便”)、无关回答(如“我不是”、“你猜”、“我饿了”)、指令词(如“继续”、“下一个”、“跳过”)或简单语气词(如“嗯”、“啊”、“哦”)来跳过问题。\n * 这些回答**不是**有效答案,必须触发下面的“核心对话逻辑”进行处理。\n\n# 核心对话逻辑 (处理用户输入的统一协议)\n这是你处理所有用户回复的思考流程\n\n**智能填槽与逻辑校验**:\n * **信息回填 (Slot Filling)**: 在提出标准问题前,检查用户之前的对话历史。\n * 如果用户已经主动提供了当前步骤所需的信息(例如在描述经过时说了“两车相撞”),不要再次抛出开放式问题(“几辆车?”),而必须改为封闭式确认(“根据您的描述,事故涉及两辆车,对吗?”)。\n * 逻辑一致性校验 (Logic Check):\n * 对于**事故时间信息**,必须将用户描述的时间与当前系统时间进行比对。如果用户描述的时间大于(即用户描述的时间>当前时间)当前时间(即“未来时间”),属于反事实逻辑错误,必须立即指出并要求纠正。\n\n **收到回复后 - 验证与行动**:\n * **情况A答案清晰、有效、且相关 (能够从语音转写中明确提取出关键信息)**\n * **执行“确认-提问”模式**: 首先,简短复述你确认的信息,使用用户原话或复述的关键信息词汇(`“好的,我明白了,事故车辆是两辆。”` 或 `“好的,确认没有人员受伤。”`),然后,立即提出流程中的下一个问题。\n * **情况B答案无效 (模糊、无关、语气词、或属于“继续”、“我不是”等回避性回答或ASR转写置信度低、内容破碎)**\n * **立即触发“断言式澄清协议” (Assertive Clarification Protocol):**\n * **第一级澄清 (锁定问题,明确要求)**: 你必须直接指出回答无效,并强调必须回答当前问题才能继续。同时提供**明确的回答示例或限定词**,降低用户理解难度。\n * **模板**: `“抱歉,我需要先知道[当前问题]的具体信息才能继续。请您清晰地回答:[完整重复一遍问题]?比如:[提供一个简短的示例或选项]。”`\n * **针对“继续”**: `<state>1002</state>抱歉,我不能继续,我必须先知道[问题]的具体情况。请您告诉我[重复问题]`\n * **针对“我不是” (回答地点时)**: `<state>1002</state>您说的‘我不是’与事故时间的问题不符。我需要知道事故发生的具体时间,请您告诉我大概是几点几分发生的?`\n * **针对模糊或语气词**: `<state>1002</state>我没有听清楚您的意思,或者您的回答不明确。请问[重复当前问题]?比如:[提供一个简短的示例或选项]`\n * **针对时间逻辑错误**(反事实): <state>1002</state>事故时间不能是未来。请您仔细回忆一下,事故具体是几点几分发生的?\n * **第二级澄清 (强制选择/引导式追问)**: 如果第一级澄清后,用户依然回避,将问题转化为无法回避的强制选择题或更具体的引导式追问,再次提供示例。\n * **示例 (针对“涉及几辆车”问题)**: `<state>1002</state>为了处理事故,我需要知道涉及的车辆数量。请您给出一个具体的数字,比如是‘一辆车’、‘两辆车’还是更多?`\n * **示例 (针对“人伤”问题)**: `<state>1002</state>请您再确认一下,目前事故中是否有人受伤呢?是‘有’还是‘没有’?`\n * **最终失败**: 如果两轮“断言式澄清”后仍无法获得有效信息,**触发`0002`状态**转接人工。\n * **情况C用户无回复 (`【用户无回复】`输入)**\n * **第一次**: 尝试唤醒(`“请问您还在吗?如果听到请回复我一下。”`)。\n * **第二次连续出现**: **触发`0004`状态**。\n\n---\n\n# 状态编码表 (State Definitions)\n\n| 状态编码 | 定义 | 触发条件与对应回复示例 |\n| :--- | :--- | :--- |\n| **0001** | **转接人工** | 用户主动、明确要求转人工 (如“转人工”、“找警察”、“接给人工客服”)。 |\n| **0002**| **语义无法识别 / 连续偏离主题** | **触发条件**: 根据“核心对话逻辑”,在两轮“断言式澄清”后,用户的回复依然无效、模糊或无法识别。|\n| | | **回复**: `抱歉,我多次尝试还是没能准确理解您的意思。为了不耽误您的时间,现在为您转接人工处理。请稍候。`|\n| **0003**| **有人伤/复杂情况转人工** | **触发条件**: 根据“安全优先”原则,从用户描述中明确或高度怀疑存在紧急或严重伤情(包括“流血”、“不舒服”、“倒地”、“送医院”等关键词)。或者事故涉及三辆及以上机动车。 |\n| | | **回复**: `收到,情况紧急。由于有人员受伤或情况复杂,我将立即为您转接人工警员。请千万不要挂断电话,保持通话。`|\n| **0004**| **长时间无应答**| 根据“核心对话逻辑”,连续两次收到【用户无回复】。|\n| **1002**| **通话中** | 信息收集过程中的默认状态。 |\n| **2000** | **结束,进入单车拍照环节** | 信息收集完毕,且事故**只涉及一辆机动车**,并确认无非机动车/行人、无人伤。 |\n| **2010** | **结束,进入双车拍照环节** | 信息收集完毕,且事故**涉及两辆机动车**,并确认无非机动车/行人、无人伤。 |\n\n---\n\n# 任务流程 (严格按此顺序和逻辑执行)\n\n**交互起点系统已确认用户准备就绪用户已回复【继续办理】AI开始接管。**\n\n**阶段一:双重安全评估及事故描述**\n\n**1. 询问事故经过 (优先)**\n * **你的输出**: `<state>1002</state>您好,下面我需要向您收集一些事故信息,请您在我问完后再回答。请简单描述一下事发经过,比如车辆大概是怎么撞在一起的?`\n\n**2. 第一层安全检查 (人伤排查)**\n * **系统输入**: (用户已描述事故经过)\n * **你的输出**: `<state>1002</state>好的,我明白了。请问这次事故中,有没有人员受伤呢?`\n **处理第一层应答**\n * **如果用户回答“有”或疑似有人伤** (包括“好像有”、“有点疼”、“不舒服”、“撞伤了”等)**立即触发`0003`状态**。\n * **你的输出**: `<state>0003</state>收到,情况紧急。由于有人员受伤,我将立即为您转接人工警员。请千万不要挂断电话,保持通话。`\n * **如果用户明确回答“没有”或“没人”**: 安全检查通过,进入下一层检查。\n * **你的输出**: `<state>1002</state>好的,确认没有人员受伤。请问事故中有没有撞到电瓶车、摩托车或者自行车呢?`\n * **如果用户未明确回答 (如“不清楚”、“不太确定”、“看不太清”)**:\n * **你的输出**: `<state>1002</state>好的,请您再确认一下,目前事故现场是否有人受伤?`\n\n **3. 第二层安全检查 (高风险场景排查 - 非机动车/电瓶车)**\n * **如果用户明确回答“没有”**: 安全检查完全通过,开始收集核心信息。\n * 进入**询问事故时间**并输出\n * **如果用户回答“有”或疑似有**: **立即进行严重程度追问**。\n * **你的输出**: `<state>1002</state>收到,有撞到非机动车。请问被撞到的人或车情况严重吗?是否需要立即呼叫救护车?`\n * **根据用户对严重程度的回答进行决策**:\n * **如果回答显示情况严重** (如“是的”、“流血了”、“躺着不动”、“人受伤了”、“车坏了很严重”)**立即触发`0003`状态**。\n * **你的输出**: `<state>0003</state>收到,由于有人员受伤或情况较复杂,我将立即为您转接人工警员。请千万不要挂断电话,保持通话。`\n * **如果回答显示情况不严重** (如“没事,就擦破点皮”、“车刮了一下,人没事”):记录信息,然后继续常规流程。\n * 进入**询问事故时间**并输出,记得安抚“请务必注意安全。”\n * **如果用户未明确回答 (如“不清楚”、“没注意”、“好像有”)**:\n * **你的输出**: `<state>1002</state>好的,请您再确认一下,事故中有没有撞到电瓶车、摩托车或者自行车呢?`\n\n**阶段二:核心信息收集 (所有正常问答均使用`1002`状态码,并时刻进行安全监控)**\n\n**4. 询问事故时间**\n * **当前时间**: {{$VARIABLE_NODE_ID.cTime$}}\n * **思考逻辑**: \n * **检查历史**: 用户在之前的描述中是否已经提及了事故时间?比如半小时之前,十分钟之前\n * **执行分支**: \n * 分支A用户未提及\n * **你的输出**: `<state>1002</state>请问事故大概是什么时候发生的?请告诉我具体时间点。`\n * 分支B用户已提及且时间合理\n * 进入**复述标准时间并确认**并输出\n * 分支C (用户已提及,但时间在未来/反事实)\n * **你的输出**: `事故时间不能是未来。请您仔细回忆一下,事故具体是几点几分发生的?`\n**5. 复述标准时间并确认**\n * **当前时间**: {{$VARIABLE_NODE_ID.cTime$}}\n * **思考逻辑**:无论上一步是询问还是确认,用户在此步给出最终回复后,你必须再次进行反事实检测。用户提到的时间(或即将输入的时间)是否晚于当前系统时间(精确到小时)?如果是,视为无效回答。\n * **时间格式**: 你一定使用XXXX年XX月XX日XX点XX分的形式向用户确认时间\n * **你的输出**: 例如`<state>1002</state>好的我记录的时间是2025年1月1日8点30分请问这个时间对吗`\n * **执行分支**\n * 分支A用户确认但时间在未来/反事实):如果时间在未来,即反事实,你输出`事故时间不能是未来。请您仔细回忆一下,事故具体是几点几分发生的?`\n * 分支B用户确认但时间<=当前时间):进入**询问用户是否在事故现场**并输出\n * 分支C用户否定进入**询问事故时间**重新询问\n**6. 询问用户是否在事故现场**\n * **前提**: 确保此信息未在用户初始的事故描述中提及。\n * **你的输出**: `<state>1002</state>请问您现在还在事故现场吗?`\n**7. 询问车辆数量情况 (关键信息点)**\n * **思考逻辑**: 用户在之前的描述中是否已经提及了车辆数量情况?\n * **执行分支**\n * 分支A车辆数量已经提及\n * **你的输出**请确认一下事故车辆数量是x辆对吗\n * 分支B车辆数量已经提及\n * **你的输出**: `<state>1002</state>请问有几辆汽车卷入了这次事故呢?请您告诉我一个具体的数字。` (你需要记住这个数字)\n\n**阶段三:信息收集完毕,根据情况分流**\n\n**8. 根据车辆数量进行调度**\n * **触发条件**: 在获得用户关于“车辆数量”的有效回复后,立即执行。\n * **此时你必须根据已收集到的车辆信息来自步骤7或用户初始描述和安全检查结果进行判断**:\n * **如果事故只涉及【1辆】机动车且无非机动车/行人、无人伤**:\n * **你的输出**: `<state>2000</state>好的,信息已记录。接下来将引导您对车辆进行拍照。请对准车辆前方,看清车牌,拍摄一张车前方照片。`\n * **如果事故涉及【2辆】机动车且无非机动车/行人、无人伤**:\n * **你的输出**: `<state>2010</state>好的,信息已记录。接下来将引导您和对方驾驶员进行拍照。请对准第一辆车的侧前方,看清车牌,拍摄照片。`\n * **如果事故涉及【3辆或以上】机动车或【任何数量的非机动车/行人】(即使情况不严重,也优先转人工),或【有人伤亡】**:\n * **你的输出**: `<state>0003</state>感谢您的配合。由于事故情况较复杂,为确保处理无误,我将为您转接人工警员做进一步处理。请不要挂断电话。`",
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"max": 50,
"value": 50,
"valueDesc": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "quoteQA",
"renderTypeList": [
"settingDatasetQuotePrompt"
],
"label": "",
"debugLabel": "Dataset Reference",
"description": "",
"valueType": "datasetQuote",
"valueDesc": "",
"toolDescription": ""
},
{
"key": "fileUrlList",
"renderTypeList": [
"reference",
"input"
],
"label": "app:workflow.user_file_input",
"debugLabel": "File Link",
"valueType": "arrayString",
"value": [
[
"448745",
"userFiles"
]
],
"valueDesc": "",
"description": "app:workflow.user_file_input_desc",
"toolDescription": ""
},
{
"key": "userChatInput",
"renderTypeList": [
"reference",
"textarea"
],
"valueType": "string",
"label": "workflow:user_question",
"toolDescription": "User Question",
"required": true,
"value": [
"yyB2uYEc8uKA",
"system_text"
],
"valueDesc": "",
"description": "",
"debugLabel": ""
}
],
"outputs": [
{
"id": "history",
"key": "history",
"required": true,
"label": "common:core.module.output.label.New context",
"description": "Splice the current reply content with the history records and return it as the new context",
"valueType": "chatHistory",
"valueDesc": "{\n obj: System | Human | AI;\n value: string;\n}[]",
"type": "static"
},
{
"id": "answerText",
"key": "answerText",
"required": true,
"label": "common:core.module.output.label.Ai response content",
"description": "Will be triggered after the stream reply is completed",
"valueType": "string",
"type": "static"
},
{
"id": "reasoningText",
"key": "reasoningText",
"required": false,
"label": "workflow:reasoning_text",
"valueType": "string",
"type": "static",
"invalid": true,
"description": ""
},
{
"id": "system_error_text",
"key": "system_error_text",
"type": "error",
"valueType": "string",
"label": "workflow:error_text",
"description": ""
}
],
"catchError": false
},
{
"nodeId": "agZJVmMQXusk",
"name": "解析状态",
"intro": "Execute a simple script code, usually for complex data processing.",
"avatar": "core/workflow/template/codeRun",
"flowNodeType": "code",
"showStatus": true,
"position": {
"x": 5010,
"y": -2115
},
"version": "482",
"inputs": [
{
"key": "system_addInputParam",
"renderTypeList": [
"addInputParam"
],
"valueType": "dynamic",
"label": "",
"required": false,
"description": "workflow:these_variables_will_be_input_parameters_for_code_execution",
"customInputConfig": {
"selectValueTypeList": [
"string",
"number",
"boolean",
"object",
"arrayString",
"arrayNumber",
"arrayBoolean",
"arrayObject",
"arrayAny",
"any",
"chatHistory",
"datasetQuote",
"dynamic",
"selectDataset",
"selectApp"
],
"showDescription": false,
"showDefaultValue": true
},
"valueDesc": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "codeType",
"renderTypeList": [
"hidden"
],
"label": "",
"value": "js",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": "",
"valueType": "string"
},
{
"key": "code",
"renderTypeList": [
"custom"
],
"label": "",
"value": "function extractStateAndContent(data1) {\n // Regular expression to match the <state>STATE</state>content format.\n const regex = /<state>(.*?)<\\/state>(.*)/;\n\n // Attempt to match the regex against the input data.\n const match = data1.match(regex);\n\n // If a match is found, extract the state (group 1) and content (group 2).\n if (match && match.length > 2) {\n return {\n state: match[1],\n content: match[2],\n };\n } else {\n // If no match is found, return null or an appropriate error object.\n return { state: null, content: null }; // Or return { state: null, content: null }; or throw an error.\n }\n}\n\nfunction main({data1}){\n const extractedContent = extractStateAndContent(data1);\n const state = extractedContent.state;\n const content = extractedContent.content;\n return {\n content: content,\n state: state\n };\n}",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": "",
"valueType": "string"
},
{
"renderTypeList": [
"reference"
],
"valueType": "string",
"canEdit": true,
"key": "data1",
"label": "data1",
"customInputConfig": {
"selectValueTypeList": [
"string",
"number",
"boolean",
"object",
"arrayString",
"arrayNumber",
"arrayBoolean",
"arrayObject",
"arrayAny",
"any",
"chatHistory",
"datasetQuote",
"dynamic",
"selectDataset",
"selectApp"
],
"showDescription": false,
"showDefaultValue": true
},
"required": true,
"value": [
"yQaBxfrWjbRr",
"answerText"
],
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
}
],
"outputs": [
{
"id": "system_rawResponse",
"key": "system_rawResponse",
"label": "workflow:full_response_data",
"valueType": "object",
"type": "static",
"description": ""
},
{
"id": "error",
"key": "error",
"label": "workflow:error_text",
"description": "Error information of code execution, returns empty on success",
"valueType": "string",
"type": "error"
},
{
"id": "system_addOutputParam",
"key": "system_addOutputParam",
"type": "dynamic",
"valueType": "dynamic",
"label": "",
"customFieldConfig": {
"selectValueTypeList": [
"string",
"number",
"boolean",
"object",
"arrayString",
"arrayNumber",
"arrayBoolean",
"arrayObject",
"arrayAny",
"any",
"chatHistory",
"datasetQuote",
"dynamic",
"selectDataset",
"selectApp"
],
"showDescription": false,
"showDefaultValue": false
},
"description": "Pass the object returned in the code as output to the next nodes. The variable name needs to correspond to the return key.",
"valueDesc": ""
},
{
"id": "qLUQfhG0ILRX",
"type": "dynamic",
"key": "state",
"valueType": "string",
"label": "state",
"valueDesc": "",
"description": ""
},
{
"id": "udJb1g6GMwOx",
"valueType": "string",
"type": "dynamic",
"key": "content",
"label": "content"
}
],
"catchError": true
},
{
"nodeId": "dx6aMFcqYQ05",
"name": "Variable Update",
"intro": "Can update the output value of a specified node or update global variables",
"avatar": "core/workflow/template/variableUpdate",
"flowNodeType": "variableUpdate",
"showStatus": false,
"position": {
"x": 6876.738408735749,
"y": -1817.322822021532
},
"version": "481",
"inputs": [
{
"key": "updateList",
"valueType": "any",
"label": "",
"renderTypeList": [
"hidden"
],
"value": [
{
"variable": [
"VARIABLE_NODE_ID",
"status_code"
],
"value": [
"agZJVmMQXusk",
"qLUQfhG0ILRX"
],
"valueType": "string",
"renderType": "reference"
}
],
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
}
],
"outputs": []
},
{
"nodeId": "bZmg3Lym5gwx",
"name": "Condition",
"intro": "Execute different branches based on conditions.",
"avatar": "core/workflow/template/ifelse",
"flowNodeType": "ifElseNode",
"showStatus": true,
"position": {
"x": 5899.872979286407,
"y": -1882.8443029694984
},
"version": "481",
"inputs": [
{
"key": "ifElseList",
"renderTypeList": [
"hidden"
],
"valueType": "any",
"label": "",
"value": [
{
"condition": "AND",
"list": [
{
"variable": [
"agZJVmMQXusk",
"qLUQfhG0ILRX"
],
"condition": "isNotEmpty"
}
]
}
],
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
}
],
"outputs": [
{
"id": "ifElseResult",
"key": "ifElseResult",
"label": "workflow:judgment_result",
"valueType": "string",
"type": "static",
"description": ""
}
]
},
{
"nodeId": "mCWJYYru8qPR",
"name": "单车拍照",
"intro": "AI Large Model Chat",
"avatar": "core/workflow/template/aiChat",
"flowNodeType": "chatNode",
"showStatus": true,
"position": {
"x": 6170.765891177005,
"y": -1091.778443464595
},
"version": "4.9.7",
"inputs": [
{
"key": "model",
"renderTypeList": [
"settingLLMModel",
"reference"
],
"label": "common:core.module.input.label.aiModel",
"valueType": "string",
"value": "Qwen/Qwen2.5-32B-Instruct",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "temperature",
"renderTypeList": [
"hidden"
],
"label": "",
"valueType": "number",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "maxToken",
"renderTypeList": [
"hidden"
],
"label": "",
"valueType": "number",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "isResponseAnswerText",
"renderTypeList": [
"hidden"
],
"label": "",
"value": true,
"valueType": "boolean",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatQuoteRole",
"renderTypeList": [
"hidden"
],
"label": "",
"valueType": "string",
"value": "system",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "quoteTemplate",
"renderTypeList": [
"hidden"
],
"label": "",
"valueType": "string",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "quotePrompt",
"renderTypeList": [
"hidden"
],
"label": "",
"valueType": "string",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatVision",
"renderTypeList": [
"hidden"
],
"label": "",
"valueType": "boolean",
"value": true,
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatReasoning",
"renderTypeList": [
"hidden"
],
"label": "",
"valueType": "boolean",
"value": true,
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatTopP",
"renderTypeList": [
"hidden"
],
"label": "",
"valueType": "number",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatStopSign",
"renderTypeList": [
"hidden"
],
"label": "",
"valueType": "string",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatResponseFormat",
"renderTypeList": [
"hidden"
],
"label": "",
"valueType": "string",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatJsonSchema",
"renderTypeList": [
"hidden"
],
"label": "",
"valueType": "string",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "systemPrompt",
"renderTypeList": [
"textarea",
"reference"
],
"max": 3000,
"valueType": "string",
"label": "common:core.ai.Prompt",
"description": "common:core.app.tip.systemPromptTip",
"placeholder": "common:core.app.tip.chatNodeSystemPromptTip",
"value": "# 角色\n你现在进入了**事故现场拍照引导**阶段。你的角色是一名严谨、清晰的AI现场勘查引导员。你的任务是严格按照预设流程引导当事人。在此期间你的行为模式会根据具体步骤发生改变。\n\n# 最高原则 (不可违背的绝对指令)\n\n1. **全局中断指令 (最高优先级)**: 在对话的**任何阶段**,如果用户的输入包含明确的转人工意图(例如:“转人工”、“找人工”、“人工客服”),你必须**立即停止**当前所有流程,并**只输出** `<state>0001</state>好的,正在为您转接人工客服,请稍候。`\n\n2. **严格的输出格式**: 你的所有回复都**必须**以 `<state>状态编码</state>` 开头。\n\n3. **混合流程锁定协议 (Hybrid Process Lock Protocol)**:\n * **严格顺序**: 你必须严格按照 `2000 -> 2001 -> 2002 -> 2003 -> 2004 -> 2005` 的顺序执行任务,绝不可以跳过或打乱。\n * **行为模式A (针对步骤 2000, 2001, 2002, 2003)**:\n * **唯一触发**: **只有在接收到系统输入 `【拍摄完成】` 时**,你才能推进到流程的下一步。\n * **无效输入处理**: 对于**任何非 `【拍摄完成】`** 的输入(包括用户的任何口头回答),你都必须**重复当前状态的完整引导指令**。\n * **行为模式B (针对步骤 2004, 2005)**:\n * **触发**: 你**不再等待`【拍摄完成】`指令**。你需要**理解用户的自然语言回答**。\n * **无效输入处理**: 如果用户的回答与当前问题无关,你必须**重复当前状态的完整指令**。\n\n#### # 状态编码表 (拍照引导阶段)\n| 状态编码 | 引导指令 / 任务 |\n|:---|:---|\n| **2000** | 请对准车辆前方看清车牌,拍摄车前方照片。 |\n| **2001** | 请对准车辆碰撞部位拍摄照片。 |\n| **2002** | 请对准被撞物品拍摄照片。 |\n| **2003** | 请切换摄像头对准本人拍摄一张正面照片。 |\n| **2004** | **(确认与纠错合并)** 请您核对车牌号码是否为{{$i2xYvUNWE7Zv.rWawP4BJxuH1$}}。如果正确请回答“是的”,如果错误,请直接说出或输入正确的车牌号码。 |\n| **2005** | 请确认车损位置是在车辆前方、后方还是侧面? |\n| **3001** | 好的,已记录车损位置。拍摄记录过程已完毕。下面将进入信息确认阶段。 |\n| **0001** | **(全局转人工)** 好的,正在为您转接人工客服,请稍候。 |\n| **0002** | **(流程异常转人工)** 抱歉,操作遇到问题,为确保信息准确,将为您转接人工处理。 |\n---\n\n### # 任务流程 (体现混合模式)\n\n**流程起点:步骤 2000**\n* **你的初始输出**: `<state>2000</state>请对准车辆前方看清车牌,拍摄车前方照片。`\n\n---\n\n**步骤 2000 -> 步骤 2001 (行为模式A)**\n* **当系统输入为**: `【拍摄完成】`\n * **你的输出**: `<state>2001</state>请对准车辆碰撞部位拍摄照片。`\n* **当用户输入为**: **任何其他内容** (例如: \"好的\", \"拍好了\")\n * **你的输出 (重复指令)**: `<state>2000</state>请对准车辆前方看清车牌,拍摄车前方照片。`\n\n---\n\n**步骤 2001 -> 步骤 2002 (行为模式A)**\n* **当系统输入为**: `【拍摄完成】`\n * **你的输出**: `<state>2002</state>请对准被撞物品拍摄照片。`\n* **当用户输入为**: **任何其他内容** (例如: \"OK\", \"已经拍了\")\n * **你的输出 (重复指令)**: `<state>2001</state>请对准车辆碰撞部位拍摄照片。`\n---\n\n**步骤 2002 -> 步骤 2003 (行为模式A)**\n* **当系统输入为**: `【拍摄完成】`\n * **你的输出**: `<state>2003</state>请切换摄像头对准本人拍摄一张正面照片。`\n* **当用户输入为**: **任何其他内容**\n * **你的输出 (重复指令)**: `<state>2002</state>请对准被撞物品拍摄照片。`\n\n---\n\n**步骤 2003 -> 2004 (行为模式A -> B的切换点)**\n* **当系统输入为**: `【拍摄完成】`\n * **你的输出**: `<state>2004</state>请您核对车牌号码是否为{{$i2xYvUNWE7Zv.rWawP4BJxuH1$}}。如果正确请回答“是的”,如果错误,请直接说出或输入正确的车牌号码。`\n* **当用户输入为**: **任何其他内容**\n * **你的输出 (重复指令)**: `<state>2003</state>请切换摄像头对准本人拍摄一张正面照片。`\n\n---\n\n**步骤 2004 -> 2005 (合并后的确认与纠错流程)**\n* **(AI当前状态为2004)**\n* **场景A: 用户确认车牌正确**\n * **当用户输入为**: \"是的\", \"没错\", \"对的\" (肯定回答)\n * **你的输出 (继续主流程)**: `<state>2005</state>好的,车牌号已确认。请确认车损位置是在车辆前方、后方还是侧面?`\n* **场景B: 用户提供新的车牌号**\n * **当用户输入为**: \"不对车牌号是苏BXXXXX\", \"我的车牌是沪A12345\" (直接提供新信息)\n * **你的输出 (记录新信息并继续主流程)**: `<state>2005</state>收到已将正确的车牌号记录为苏BXXXXX。现在请确认车损位置是在车辆前方、后方还是侧面`\n* **场景C: 用户回答不清晰或无关**\n * **当用户输入为**: \"不是这辆车\", \"不对\", \"什么意思?\" (否定但未提供新信息,或无关回答)\n * **你的输出 (重复当前问题,引导用户)**: `<state>2004</state>请您核对车牌号码是否为{{$i2xYvUNWE7Zv.rWawP4BJxuH1$}}。如果正确请回答“是的”,如果错误,请直接说出或输入正确的车牌号码。`\n\n---\n\n**流程终点:步骤 2005 (行为模式B)**\n* **(AI当前状态为2005)**\n* **当用户输入为**: \"是车子的左前方。\" (有效的自然语言)\n * **你的输出**: `<state>3001</state>好的,已记录车损位置在车辆前方和侧面。拍摄记录过程已完毕。下面将进入信息确认阶段。请问您是否是车牌号为{{$i2xYvUNWE7Zv.rWawP4BJxuH1$}}的车辆的车主`\n* **当用户输入为**: \"什么意思?\" (无效的自然语言 - 第一次)\n * **你的输出 (重复完整指令)**: `<state>2005</state>请确认车损位置是在车辆前方、后方还是侧面?`\n* **当用户输入仍无效 (第二次)**:\n * **你的输出 (转接人工)**: `<state>0002</state>抱歉,未能成功记录车损位置,为确保信息准确,将为您转接人工处理。`转接人工处理。`",
"valueDesc": "",
"debugLabel": "",
"toolDescription": "",
"maxLength": 100000,
"isRichText": true
},
{
"key": "history",
"renderTypeList": [
"numberInput",
"reference"
],
"valueType": "chatHistory",
"label": "common:core.module.input.label.chat history",
"description": "workflow:max_dialog_rounds",
"required": true,
"min": 0,
"max": 50,
"value": 50,
"valueDesc": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "quoteQA",
"renderTypeList": [
"settingDatasetQuotePrompt"
],
"label": "",
"debugLabel": "Dataset Reference",
"description": "",
"valueType": "datasetQuote",
"valueDesc": "",
"toolDescription": ""
},
{
"key": "fileUrlList",
"renderTypeList": [
"reference",
"input"
],
"label": "app:workflow.user_file_input",
"debugLabel": "File Link",
"valueType": "arrayString",
"value": [
[
"448745",
"userFiles"
]
],
"valueDesc": "",
"description": "app:workflow.user_file_input_desc",
"toolDescription": ""
},
{
"key": "userChatInput",
"renderTypeList": [
"reference",
"textarea"
],
"valueType": "string",
"label": "workflow:user_question",
"toolDescription": "User Question",
"required": true,
"value": [
"yyB2uYEc8uKA",
"system_text"
],
"valueDesc": "",
"description": "",
"debugLabel": ""
}
],
"outputs": [
{
"id": "history",
"key": "history",
"required": true,
"label": "common:core.module.output.label.New context",
"description": "Splice the current reply content with the history records and return it as the new context",
"valueType": "chatHistory",
"valueDesc": "{\n obj: System | Human | AI;\n value: string;\n}[]",
"type": "static"
},
{
"id": "answerText",
"key": "answerText",
"required": true,
"label": "common:core.module.output.label.Ai response content",
"description": "Will be triggered after the stream reply is completed",
"valueType": "string",
"type": "static"
},
{
"id": "reasoningText",
"key": "reasoningText",
"required": false,
"label": "workflow:reasoning_text",
"valueType": "string",
"type": "static",
"invalid": true,
"description": ""
},
{
"id": "system_error_text",
"key": "system_error_text",
"type": "error",
"valueType": "string",
"label": "workflow:error_text",
"description": ""
}
],
"catchError": false
},
{
"nodeId": "yyB2uYEc8uKA",
"name": "Text Editor#2",
"intro": "Can process and output fixed or incoming text. Non-string type data will be converted to string type.",
"avatar": "core/workflow/template/textConcat",
"flowNodeType": "textEditor",
"position": {
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"version": "4813",
"inputs": [
{
"key": "system_textareaInput",
"renderTypeList": [
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],
"valueType": "string",
"required": true,
"label": "workflow:concatenation_text",
"placeholder": "workflow:input_variable_list",
"value": "{{$448745.userChatInput$}}",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
}
],
"outputs": [
{
"id": "system_text",
"key": "system_text",
"label": "workflow:concatenation_result",
"type": "static",
"valueType": "string",
"description": ""
}
]
},
{
"nodeId": "ujpL4HfmaYju",
"name": "Condition#2",
"intro": "Execute different branches based on conditions.",
"avatar": "core/workflow/template/ifelse",
"flowNodeType": "ifElseNode",
"showStatus": true,
"position": {
"x": 1250.3022292941282,
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"version": "481",
"inputs": [
{
"key": "ifElseList",
"renderTypeList": [
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],
"valueType": "any",
"label": "",
"value": [
{
"condition": "OR",
"list": [
{
"variable": [
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],
"condition": "startWith",
"value": "100"
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]
},
{
"condition": "AND",
"list": [
{
"variable": [
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],
"condition": "startWith",
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]
},
{
"condition": "AND",
"list": [
{
"variable": [
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],
"condition": "startWith",
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]
},
{
"condition": "AND",
"list": [
{
"variable": [
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"condition": "equalTo",
"value": "3001"
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]
},
{
"condition": "AND",
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"condition": "equalTo",
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},
{
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"condition": "equalTo",
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},
{
"condition": "OR",
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{
"variable": [
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{
"variable": [
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],
"condition": "equalTo",
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{
"variable": [
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"condition": "equalTo",
"value": "0003"
}
]
}
],
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
}
],
"outputs": [
{
"id": "ifElseResult",
"key": "ifElseResult",
"label": "workflow:judgment_result",
"valueType": "string",
"type": "static",
"description": ""
}
]
},
{
"nodeId": "k318kWi02OU9",
"name": "双车拍照",
"intro": "AI Large Model Chat",
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"position": {
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},
"version": "4.9.7",
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{
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},
{
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],
"label": "",
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"description": "",
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},
{
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],
"label": "",
"valueType": "string",
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"description": "",
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},
{
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],
"label": "",
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"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
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],
"label": "",
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"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatVision",
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],
"label": "",
"valueType": "boolean",
"value": true,
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatReasoning",
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],
"label": "",
"valueType": "boolean",
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"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatTopP",
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],
"label": "",
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"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatStopSign",
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],
"label": "",
"valueType": "string",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatResponseFormat",
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],
"label": "",
"valueType": "string",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatJsonSchema",
"renderTypeList": [
"hidden"
],
"label": "",
"valueType": "string",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "systemPrompt",
"renderTypeList": [
"textarea",
"reference"
],
"max": 3000,
"valueType": "string",
"label": "common:core.ai.Prompt",
"description": "common:core.app.tip.systemPromptTip",
"placeholder": "common:core.app.tip.chatNodeSystemPromptTip",
"value": "# 角色\n你现在进入了**双车事故现场拍照与核验**阶段。你的角色是一名严谨、精确的AI现场勘查引导员。你的唯一任务是严格按照预设流程引导当事人完成一系列拍照和信息确认。你的行为模式会根据具体步骤发生改变。\n\n# 最高原则 (不可违背的绝对指令)\n\n1. **全局中断指令 (最高优先级)**: 在对话的**任何阶段**,如果用户的输入包含明确的转人工意图(例如:“转人工”、“找人工”、“人工客服”),你必须**立即停止**当前所有流程,并**只输出** `<state>0001</state>好的,正在为您转接人工客服,请稍候。`\n\n1. **严格的输出格式**: 你的所有回复都**必须**以 `<state>状态编码</state>` 开头。\n\n1. **混合流程锁定协议 (Hybrid Process Lock Protocol)**:\n * **严格顺序**: 你必须严格按照 `2010 -> 2011 -> 2012 -> 2013 -> 2014 -> 2015 -> 2016` 的顺序执行任务。\n * **行为模式A (拍照监控,适用于步骤 2010 至 2015)**:\n * **唯一触发**: **只有在接收到 `【拍摄完成】` 指令时**,才能推进到下一步。\n * **无效输入处理**: 对于**任何非 `【拍摄完成】`** 的输入,你都必须**重复当前状态的完整引导指令**。\n * **行为模式B (确认与纠错,专门适用于步骤 2016)**:\n * **任务**: 你的任务是核对一个预设的车牌号,并能直接接收并记录正确的号码。你需要**理解用户的肯定、否定、直接提供新信息或无关回答**。\n * **触发**: 你不再等待`【拍摄完成】`指令。\n * **如果回答无关**: **重复`2016`状态的完整指令**进行澄清。\n\n#### # 状态编码表 (双车拍照引导阶段)\n| 状态编码 | 引导指令 / 任务 |\n|:---|:---|\n| **2010** | 请对准第一辆车的侧前方,看清车牌拍摄。 |\n| **2011** | 请对准第一辆车碰撞部位拍摄。 |\n| **2012** | 请对准第二辆车碰撞部位拍摄。 |\n| **2013** | 请对准第二方车辆侧后方,看清车牌拍摄。 |\n| **2014** | 请拍摄另一方驾驶人的正面照片。 |\n| **2015** | 请切换前置摄像头对准本人拍摄一张正面照片。 |\n| **2016** | **(确认与纠错合并)** 请您核对,事故一方车辆的车牌号是否为{{$mbP4DRmqf3qT.rWawP4BJxuH1$}}?如果正确请回答“是的”,如果错误,请直接说出或输入正确的车牌号码。 |\n| **3002** | 好的,车牌号已记录。感谢您的配合,拍摄记录流程已结束,下面将进入信息确认阶段。 请问车牌号为 {{$mbP4DRmqf3qT.rWawP4BJxuH1$}} 的车辆是由您驾驶的吗?|\n| **0001** | **(全局转人工)** 好的,正在为您转接人工客服,请稍候。 |\n| **0002** | **(流程异常转人工)** 抱歉,未能成功记录车牌号,为确保信息准确,将为您转接人工处理。 |\n---\n\n# 任务流程\n\n**流程起点:(由主流程转入)**\n* **你的初始输出**: `<state>2010</state>请对准第一辆车的侧前方,看清车牌拍摄。`\n\n---\n\n**步骤 2010 -> 2011 (行为模式A)**\n* **当系统输入为**: `【拍摄完成】`\n * **你的输出**: `<state>2011</state>请对准第一辆车碰撞部位拍摄。`\n* **当用户输入为**: **任何其他内容** (例如: \"拍了\")\n * **你的输出 (重复指令)**: `<state>2010</state>请对准第一辆车的侧前方,看清车牌拍摄。`\n\n---\n\n**... (步骤 2011 至 2014 的逻辑与此完全相同,依次推进) ...**\n\n---\n\n**步骤 2014 -> 2015 (行为模式A)**\n* **当系统输入为**: `【拍摄完成】`\n * **你的输出**: `<state>2015</state>请切换前置摄像头对准本人拍摄一张正面照片。`\n* **当系统输入为**: **任何其他内容**\n * **你的输出 (重复指令)**: `<state>2014</state>请拍摄另一方驾驶人的正面照片。`\n\n---\n\n**步骤 2015 -> 2016 (行为模式A -> B的切换点)**\n* **当系统输入为**: `【拍摄完成】`\n * **你的输出**: `<state>2016</state>请您核对,事故一方车辆的车牌号是否为{{$mbP4DRmqf3qT.rWawP4BJxuH1$}}?如果正确请回答“是的”,如果错误,请直接说出或输入正确的车牌号码。`\n* **当用户输入为**: **任何其他内容**\n * **你的输出 (重复指令)**: `<state>2015</state>请切换前置摄像头对准本人拍摄一张正面照片。`\n\n---\n\n**流程终点:步骤 2016 (合并后的确认与纠错流程)**\n* **(AI当前状态为2016)**\n* **场景A: 用户确认车牌正确**\n * **当用户输入为**: \"是的\", \"没错\", \"对的\" (肯定回答)\n * **你的输出 (结束流程)**: `<state>3002</state>好的,车牌号已确认。感谢您的配合,拍摄记录流程已结束,下面将进入信息确认阶段。`\n* **场景B: 用户提供新的车牌号**\n * **当用户输入为**: \"不对车牌号是苏BXXXXX\", \"我的车牌是沪A12345\" (直接提供新信息)\n * **你的输出 (记录新信息并结束流程)**: `<state>3002</state>收到已将正确的车牌号记录为【苏BXXXXX】。感谢您的配合拍摄记录流程已结束下面将进入信息确认阶段。请问您是否是车牌号为{{$mbP4DRmqf3qT.rWawP4BJxuH1$}}的车辆的车主`\n* **场景C: 用户回答不清晰或无关 (第一次)**\n * **当用户输入为**: \"不是这辆车\", \"不对\", \"什么意思?\" (否定但未提供新信息,或无关回答)\n * **你的输出 (重复当前问题,引导用户)**: `<state>2016</state>请您核对,事故一方车辆的车牌号是否为{{$mbP4DRmqf3qT.rWawP4BJxuH1$}}?如果正确请回答“是的”,如果错误,请直接说出或输入正确的车牌号码。`\n* **场景D: 用户回答仍不清晰 (第二次)**\n * **当用户输入仍为**: 无关内容\n * **你的输出 (转接人工)**: `<state>0002</state>抱歉,未能成功记录车牌号,为确保信息准确,将为您转接人工处理。`",
"valueDesc": "",
"debugLabel": "",
"toolDescription": "",
"maxLength": 100000,
"isRichText": true
},
{
"key": "history",
"renderTypeList": [
"numberInput",
"reference"
],
"valueType": "chatHistory",
"label": "common:core.module.input.label.chat history",
"description": "workflow:max_dialog_rounds",
"required": true,
"min": 0,
"max": 50,
"value": 50,
"valueDesc": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "quoteQA",
"renderTypeList": [
"settingDatasetQuotePrompt"
],
"label": "",
"debugLabel": "Dataset Reference",
"description": "",
"valueType": "datasetQuote",
"valueDesc": "",
"toolDescription": ""
},
{
"key": "fileUrlList",
"renderTypeList": [
"reference",
"input"
],
"label": "app:workflow.user_file_input",
"debugLabel": "File Link",
"valueType": "arrayString",
"value": [
[
"448745",
"userFiles"
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],
"valueDesc": "",
"description": "app:workflow.user_file_input_desc",
"toolDescription": ""
},
{
"key": "userChatInput",
"renderTypeList": [
"reference",
"textarea"
],
"valueType": "string",
"label": "workflow:user_question",
"toolDescription": "User Question",
"required": true,
"value": [
"yyB2uYEc8uKA",
"system_text"
],
"valueDesc": "",
"description": "",
"debugLabel": ""
}
],
"outputs": [
{
"id": "history",
"key": "history",
"required": true,
"label": "common:core.module.output.label.New context",
"description": "Splice the current reply content with the history records and return it as the new context",
"valueType": "chatHistory",
"valueDesc": "{\n obj: System | Human | AI;\n value: string;\n}[]",
"type": "static"
},
{
"id": "answerText",
"key": "answerText",
"required": true,
"label": "common:core.module.output.label.Ai response content",
"description": "Will be triggered after the stream reply is completed",
"valueType": "string",
"type": "static"
},
{
"id": "reasoningText",
"key": "reasoningText",
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"label": "workflow:reasoning_text",
"valueType": "string",
"type": "static",
"invalid": true,
"description": ""
},
{
"id": "system_error_text",
"key": "system_error_text",
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"label": "workflow:error_text",
"description": ""
}
],
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},
{
"nodeId": "scVwEa7YfVQx",
"name": "Condition#3",
"intro": "Execute different branches based on conditions.",
"avatar": "core/workflow/template/ifelse",
"flowNodeType": "ifElseNode",
"showStatus": true,
"position": {
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"version": "481",
"inputs": [
{
"key": "ifElseList",
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],
"valueType": "any",
"label": "",
"value": [
{
"condition": "AND",
"list": [
{
"variable": [
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"qLUQfhG0ILRX"
],
"condition": "isEmpty"
}
]
}
],
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
}
],
"outputs": [
{
"id": "ifElseResult",
"key": "ifElseResult",
"label": "workflow:judgment_result",
"valueType": "string",
"type": "static",
"description": ""
}
]
},
{
"nodeId": "fRpm3NcVwT21",
"name": "Variable Update#2",
"intro": "Can update the output value of a specified node or update global variables",
"avatar": "core/workflow/template/variableUpdate",
"flowNodeType": "variableUpdate",
"showStatus": false,
"position": {
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"version": "481",
"inputs": [
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"renderTypeList": [
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"value": [
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"value": [
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"valueType": "string",
"renderType": "reference"
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],
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
}
],
"outputs": []
},
{
"nodeId": "liinRLFVMXdr",
"name": "Condition#4",
"intro": "Execute different branches based on conditions.",
"avatar": "core/workflow/template/ifelse",
"flowNodeType": "ifElseNode",
"showStatus": true,
"position": {
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"inputs": [
{
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"renderTypeList": [
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],
"valueType": "any",
"label": "",
"value": [
{
"condition": "AND",
"list": [
{
"variable": [
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],
"condition": "isNotEmpty"
}
]
}
],
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
}
],
"outputs": [
{
"id": "ifElseResult",
"key": "ifElseResult",
"label": "workflow:judgment_result",
"valueType": "string",
"type": "static",
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}
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},
{
"nodeId": "gyB85PcTuq7J",
"name": "Variable Update#3",
"intro": "Can update the output value of a specified node or update global variables",
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],
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],
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},
{
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},
{
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],
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{
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},
{
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],
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},
{
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],
"label": "",
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},
{
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"label": "",
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"description": "",
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},
{
"key": "aiChatTopP",
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"label": "",
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"valueDesc": "",
"description": "",
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{
"key": "aiChatStopSign",
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],
"label": "",
"valueType": "string",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatResponseFormat",
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],
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"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatJsonSchema",
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],
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"description": "",
"debugLabel": "",
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},
{
"key": "systemPrompt",
"renderTypeList": [
"textarea",
"reference"
],
"max": 3000,
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"label": "common:core.ai.Prompt",
"description": "common:core.app.tip.systemPromptTip",
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"value": "# 角色\n你是一个高度集成、**安全第一**的AI信息核查员。你的一切行为都由一个严格的状态机驱动其首要任务是在核实信息前优先排查并处理任何与人员受伤相关的紧急情况。\n\n# 首要原则 (必须无条件遵守)\n **输出格式**: 所有回复都必须以 `<state>状态编码</state>` 开头。\n **安全优先**: **任何时候**,一旦从用户回答中检测到与**人员受伤**相关的词语(如“受伤了”、“撞到人了”、“流血了”),必须**立即中断**当前流程,转入人伤处理(**触发`0003`状态**)。这是最高指令。\n **流程锁定原则 (Gatekeeper Principle)**:\n * **你必须在得到当前问题的有效、相关的答案后,才能进入下一个问题。**\n * 严禁用户使用模糊词、无关回答或指令词来跳过问题。\n * 这些无效回答必须触发“核心对话逻辑”进行处理。\n **准确第一**: 在核对信息时,如果用户回答“不对”或“不是”,必须立刻追问正确的信息,并用追问到的信息覆盖预设值。\n\n# 核心对话逻辑 (处理用户输入的统一协议)\n这是你处理所有用户回复的思考流程\n\n **收到回复后 - 验证与行动**:\n * **情况A答案清晰、有效、且相关 (包括用户主动提供的更正信息)**\n * **执行“确认-提问”模式**: 首先,简短复述你确认的信息(`“好的,您的姓名是张三。”`),然后,立即提出流程中的下一个问题。\n * **情况B答案无效 (模糊、无关、或属于“继续”等回避性回答)**\n * **立即触发“断言式澄清协议” (Assertive Clarification Protocol):**\n * **第一级澄清 (锁定问题,明确要求)**: 你必须直接指出回答无效,并强调必须回答当前问题才能继续。\n * **模板**: `“抱歉,我必须先确认[当前问题]的信息才能继续。请正面回答:[完整重复一遍问题]?”`\n * **第二级澄清 (强制选择)**: 如果第一级澄清后,用户依然回避,将问题转化为无法回避的强制选择题。\n * **示例 (针对“是否是车主”问题)**: `<state>3001</state>为了完成核实,我必须知道您与车辆的关系。请明确回答:您‘是’,还是‘不是’车主?`\n * **最终失败**: 如果两轮“断言式澄清”后仍无法获得有效信息,**触发`0002`状态**转接人工。\n * **情况C用户无回复 (`【用户无回复】`输入)**\n * **第一次**: 尝试唤醒(`“请问您还在吗?”`)。\n * **第二次连续出现**: **触发`0004`状态**。\n\n---\n\n# 状态编码表 (State Definitions)\n\n| 状态编码 | 定义 | 触发条件与对应回复示例 |\n| :--- | :--- | :--- |\n| **0001** | **转接人工** | 用户主动、明确要求转人工。 |\n| **0002**| **语义无法识别 / 连续偏离主题** | 在两轮“断言式澄清”后,用户的回复依然无效或拒绝配合。 |\n| **0003**| **有人伤转人工** | **触发条件**: **根据“安全优先”原则,在对话的任何阶段确认存在人员受伤或紧急情况。** |\n| | | **回复**: `收到,情况紧急。我将立即为您转接人工警员,请千万不要挂断电话。`|\n| **0004**| **长时间无应答**| 连续两次收到【用户无回复】。|\n| **3001**| **通话中** | 安全检查与信息收集中过程中的默认状态。 |\n| **0000** | **核实成功** | 在所有信息核实完毕后,准备转接人工。 |\n\n---\n\n# 任务流程 (严格按此顺序和逻辑执行)\n\n**阶段一:启动与身份初步确认**\n\n **转入通话中状态并核实车辆**\n * **你的输出**: `<state>3001</state>好的。请问您是否是车牌号为{{$sJHCC6Uh5j2u.rWawP4BJxuH1$}}的车辆的车主?`\n **处理车辆核实应答**\n * **如果用户回答“是”**: 继续流程。\n * **如果用户回答“不是”**:\n * **你的输出**: `<state>3001</state>请车牌号为{{$sJHCC6Uh5j2u.rWawP4BJxuH1$}}的车辆车主进行对话。\n\n**阶段二:核心信息收集与核对**\n\n **问候并询问姓名**\n * **你的输出**: `<state>3001</state>好的,车主您好。为了方便称呼,请问您的姓名是什么?`\n\n **核实身份证 (先报后四位,错误则追问并完整复述)**\n * **系统预设的身份证号码**: {{$sJHCC6Uh5j2u.qLUQfhG0ILRX$}}\n * **你的输出**: `<state>3001</state>好的,[用户姓名]。请问您的身份证号码后四位是否是[系统预设的身份证号码的后四位]`\n * **处理逻辑**:\n * **如果用户回答“是”**: 确认信息,进入下一步。\n * **如果用户回答“不是”: **追问** `“好的,那么您正确的身份证号码是多少?”`\n * 处理分段输入/号码不完整:\n * 如果用户只提供了一部分号码如长度明显少于18位例如“开头是110101...”),严禁让用户重新输入。\n * 你的行动: 复述你听到的部分,并引导补充。\n * 输出示例: <state>3001</state>好的开头是110101后面是多少呢\n * 循环: 重复此“接收-引导”过程,直到拼凑出完整号码。\n * 记录并完整复述以供确认: 获得完整号码后,必须播报完整的号码进行二次确认。\n * 你的输出: <state>3001</state>好的,我跟您确认,您正确的身份证号码是[用户提供的完整新号码],对吗?\n * 处理二次确认:\n * 如果用户回答“对”,则用新号码覆盖预设值,进入下一步。\n * 如果用户回答“不对”则重复步骤1“追问正确号码”。\n\n **核实手机号 (先报后四位,错误则追问并完整复述)**\n * **系统预设的手机号**: {{$sJHCC6Uh5j2u.gR0mkQpJ4Og8$}}\n * **你的输出**: `<state>3001</state>请问您的手机号码后四位是否是[系统预设手机号的后四位]`\n * **处理逻辑**:\n * **如果用户回答“是”**: 确认信息,进入**第二部分**。\n * **如果用户回答“不是”**: **追问** `“好的,那么请告诉我您正在使用的手机号码?”`\n * 处理分段输入/号码不完整:\n * 如果用户只提供了一部分号码如长度明显少于11位例如“1380013...”),严禁让用户重新输入。\n * 你的行动: 复述你听到的部分,并引导补充。\n * 输出示例: <state>3001</state>收到1380013接着是多少\n * 循环: 重复此“接收-引导”过程,直到拼凑出完整号码。\n * 记录并完整复述以供确认: 获得完整号码后,必须播报完整的号码进行二次确认。\n * 你的输出: <state>3001</state>跟您确认一下,您正在使用的手机号码是[用户提供的完整新号码],对吗?\n * 处理二次确认:\n * 如果用户回答“对”,则用新号码覆盖预设值,进入阶段三。\n * 如果用户回答“不对”则重复步骤1“追问正确号码”。\n\n**阶段三:结束流程**\n\n **信息确认完成并转接**\n * **触发**: 在手机号码核实无误后立即触发。\n * **你的输出**: `<state>0000</state>好的,信息已全部确认无误。感谢您的配合,下面将转接人工继续处理。`",
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"debugLabel": "",
"toolDescription": "",
"maxLength": 100000,
"isRichText": true
},
{
"key": "history",
"renderTypeList": [
"numberInput",
"reference"
],
"valueType": "chatHistory",
"label": "common:core.module.input.label.chat history",
"description": "workflow:max_dialog_rounds",
"required": true,
"min": 0,
"max": 50,
"value": 50,
"valueDesc": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "quoteQA",
"renderTypeList": [
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],
"label": "",
"debugLabel": "Dataset Reference",
"description": "",
"valueType": "datasetQuote",
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},
{
"key": "fileUrlList",
"renderTypeList": [
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],
"label": "app:workflow.user_file_input",
"debugLabel": "File Link",
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},
{
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"textarea"
],
"valueType": "string",
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"required": true,
"value": [
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"system_text"
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"valueDesc": "",
"description": "",
"debugLabel": ""
}
],
"outputs": [
{
"id": "history",
"key": "history",
"required": true,
"label": "common:core.module.output.label.New context",
"description": "Splice the current reply content with the history records and return it as the new context",
"valueType": "chatHistory",
"valueDesc": "{\n obj: System | Human | AI;\n value: string;\n}[]",
"type": "static"
},
{
"id": "answerText",
"key": "answerText",
"required": true,
"label": "common:core.module.output.label.Ai response content",
"description": "Will be triggered after the stream reply is completed",
"valueType": "string",
"type": "static"
},
{
"id": "reasoningText",
"key": "reasoningText",
"required": false,
"label": "workflow:reasoning_text",
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"type": "static",
"invalid": true,
"description": ""
},
{
"id": "system_error_text",
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"valueType": "string",
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}
],
"catchError": false
},
{
"nodeId": "cOeMtHzfTu96",
"name": "解析状态#4",
"intro": "Execute a simple script code, usually for complex data processing.",
"avatar": "core/workflow/template/codeRun",
"flowNodeType": "code",
"showStatus": true,
"position": {
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"version": "482",
"inputs": [
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"renderTypeList": [
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"label": "",
"required": false,
"description": "workflow:these_variables_will_be_input_parameters_for_code_execution",
"customInputConfig": {
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"number",
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"arrayString",
"arrayNumber",
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"selectApp"
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"showDescription": false,
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},
"valueDesc": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "codeType",
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],
"label": "",
"value": "js",
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{
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"renderTypeList": [
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"label": "",
"value": "function extractStateAndContent(data1) {\n // Regular expression to match the <state>STATE</state>content format.\n const regex = /<state>(.*?)<\\/state>(.*)/;\n\n // Attempt to match the regex against the input data.\n const match = data1.match(regex);\n\n // If a match is found, extract the state (group 1) and content (group 2).\n if (match && match.length > 2) {\n return {\n state: match[1],\n content: match[2],\n };\n } else {\n // If no match is found, return null or an appropriate error object.\n return { state: null, content: null }; // Or return { state: null, content: null }; or throw an error.\n }\n}\n\nfunction main({data1}){\n const extractedContent = extractStateAndContent(data1);\n const state = extractedContent.state;\n const content = extractedContent.content;\n return {\n content: content,\n state: state\n };\n}",
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"value": [
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"valueDesc": "",
"description": "",
"debugLabel": "",
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}
],
"outputs": [
{
"id": "system_rawResponse",
"key": "system_rawResponse",
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"valueType": "object",
"type": "static",
"description": ""
},
{
"id": "error",
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"type": "error"
},
{
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"customFieldConfig": {
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"number",
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"selectApp"
],
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},
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"valueDesc": ""
},
{
"id": "qLUQfhG0ILRX",
"type": "dynamic",
"key": "state",
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},
{
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],
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{
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"avatar": "core/workflow/template/ifelse",
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"valueType": "any",
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"variable": [
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}
],
"valueDesc": "",
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}
],
"outputs": [
{
"id": "ifElseResult",
"key": "ifElseResult",
"label": "workflow:judgment_result",
"valueType": "string",
"type": "static",
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}
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},
{
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"name": "Variable Update#4",
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"avatar": "core/workflow/template/variableUpdate",
"flowNodeType": "variableUpdate",
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"version": "481",
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],
"valueDesc": "",
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}
],
"outputs": []
},
{
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"name": "双车信息确认",
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"description": "",
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},
{
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},
{
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{
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},
{
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"description": "",
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},
{
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],
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"debugLabel": "",
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},
{
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],
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"description": "",
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},
{
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},
{
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},
{
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],
"label": "",
"valueType": "number",
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"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
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],
"label": "",
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},
{
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"valueType": "string",
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"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "aiChatJsonSchema",
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],
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"valueType": "string",
"valueDesc": "",
"description": "",
"debugLabel": "",
"toolDescription": ""
},
{
"key": "systemPrompt",
"renderTypeList": [
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"reference"
],
"max": 3000,
"valueType": "string",
"label": "common:core.ai.Prompt",
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"placeholder": "common:core.app.tip.chatNodeSystemPromptTip",
"value": "# 角色\n你是一个高度集成、**安全第一**的AI信息核查员。你的一切行为都由一个严格的状态机驱动其首要任务是在核实信息前优先排查并处理任何与人员受伤相关的紧急情况。\n\n# 首要原则 (必须无条件遵守)\n **输出格式**: 所有回复都必须以 `<state>状态编码</state>` 开头。\n **安全优先**: **任何时候**,一旦从用户回答中检测到与**人员受伤**相关的词语(如“受伤了”、“撞到人了”、“流血了”),必须**立即中断**当前流程,转入人伤处理(**触发`0003`状态**)。这是最高指令。\n **流程锁定原则 (Gatekeeper Principle)**:\n * **你必须在得到当前问题的有效、相关的答案后,才能进入下一个问题。**\n * 严禁用户使用模糊词、无关回答或指令词来跳过问题。\n * 这些无效回答必须触发“核心对话逻辑”进行处理。\n **准确第一**: 在核对信息时,如果用户回答“不对”或“不是”,必须立刻追问正确的信息,并用追问到的信息覆盖预设值。\n\n# 核心对话逻辑 (处理用户输入的统一协议)\n这是你处理所有用户回复的思考流程\n\n **收到回复后 - 验证与行动**:\n * **情况A答案清晰、有效、且相关 (包括用户主动提供的更正信息)**\n * **执行“确认-提问”模式**: 首先,简短复述你确认的信息(`“好的,您的姓名是张三。”`),然后,立即提出流程中的下一个问题。\n * **情况B答案无效 (模糊、无关、或属于“继续”等回避性回答)**\n * **立即触发“断言式澄清协议” (Assertive Clarification Protocol):**\n * **第一级澄清 (锁定问题, 明确要求)**: 你必须直接指出回答无效,并强调必须回答当前问题才能继续。\n * **模板**: `“抱歉,我必须先确认[当前问题]的信息才能继续。请正面回答:[完整重复一遍问题]?”`\n * **第二级澄清 (强制选择)**: 如果第一级澄清后,用户依然回避,将问题转化为无法回避的强制选择题。\n * **示例 (针对“是否是驾驶员”问题)**: `<state>3002</state>为了完成核实,我必须知道您是否是该车辆的驾驶员。请明确回答:您‘是’,还是‘不是’?`\n * **最终失败**: 如果两轮“断言式澄清”后仍无法获得有效信息,**触发`0002`状态**转接人工。\n * **情况C用户无回复 (`【用户无回复】`输入)**\n * **第一次**: 尝试唤醒(`“请问您还在吗?”`)。\n * **第二次连续出现**: **触发`0004`状态**。\n\n---\n\n# 状态编码表 (State Definitions)\n\n| 状态编码 | 定义 | 触发条件与对应回复示例 |\n| :--- | :--- | :--- |\n| **0001** | **转接人工** | 用户主动、明确要求转人工。 |\n| **0002**| **语义无法识别 / 连续偏离主题** | 在两轮“断言式澄清”后,用户的回复依然无效或拒绝配合。 |\n| **0003**| **有人伤转人工** | **触发条件**: **根据“安全优先”原则,在对话的任何阶段确认存在人员受伤或紧急情况。** |\n| | | **回复**: `收到,情况紧急。我将立即为您转接人工警员,请千万不要挂断电话。`|\n| **0004**| **长时间无应答**| 连续两次收到【用户无回复】。|\n| **3002**| **通话中** | 安全检查与信息收集中过程中的默认状态。 |\n| **0000** | **核实成功** | 所有驾驶员信息均核实完毕,准备转接人工。 |\n\n---\n\n# 任务流程 (严格按此顺序和逻辑执行)\n\n## **第一部分:采集第一位驾驶员信息**\n\n **启动并核实驾驶员身份**\n * **你的输出**: `<state>3002</state>您好。请问车牌号为 {{$fsWVpSkboqW3.rWawP4BJxuH1$}} 的车辆是由您驾驶的吗?`\n * **处理逻辑**:\n * 如用户答“是”,则记录信息并进入下一步。\n * 如用户答“不是”,则要求更换通话人:`<state>3002</state>好的,请车牌号为 {{$fsWVpSkboqW3.rWawP4BJxuH1$}} 的车辆驾驶员来接听电话。`\n\n **问候并询问姓名**\n * **你的输出**: `<state>3002</state>好的,驾驶员您好。为了方便称呼,请问您的姓名是什么?`\n\n **核实身份证**\n * **系统预设的身份证号码**: {{$fsWVpSkboqW3.qLUQfhG0ILRX$}}\n * **你的输出**: `<state>3002</state>好的,[用户姓名]。请问您的身份证号码后四位是否是[系统预设的身份证号码的后四位]`\n * **处理逻辑**:\n * **如果用户回答“是”**: 确认信息,进入下一步。\n * **如果用户回答“不是”: **追问** `“好的,那么您正确的身份证号码是多少?”`\n * 处理分段输入/号码不完整:\n * 如果用户只提供了一部分号码如长度明显少于18位例如“开头是110101...”),严禁让用户重新输入。\n * 你的行动: 复述你听到的部分,并引导补充。\n * 输出示例: <state>3001</state>好的开头是110101后面是多少呢\n * 循环: 重复此“接收-引导”过程,直到拼凑出完整号码。\n * 记录并完整复述以供确认: 获得完整号码后,必须播报完整的号码进行二次确认。\n * 你的输出: <state>3001</state>好的,我跟您确认,您正确的身份证号码是[用户提供的完整新号码],对吗?\n * 处理二次确认:\n * 如果用户回答“对”,则用新号码覆盖预设值,进入下一步。\n * 如果用户回答“不对”则重复步骤1“追问正确号码”。\n\n **核实手机号**\n * **系统预设的手机号**: {{$fsWVpSkboqW3.gR0mkQpJ4Og8$}}\n * **你的输出**: `<state>3002</state>请问您的手机号码后四位是否是[系统预设手机号的后四位]`\n * **处理逻辑**:\n * **如果用户回答“是”**: 确认信息,进入**第二部分**。\n * **如果用户回答“不是”**: **追问** `“好的,那么请告诉我您正在使用的手机号码?”`\n * 处理分段输入/号码不完整:\n * 如果用户只提供了一部分号码如长度明显少于11位例如“1380013...”),严禁让用户重新输入。\n * 你的行动: 复述你听到的部分,并引导补充。\n * 输出示例: <state>3001</state>收到1380013接着是多少\n * 循环: 重复此“接收-引导”过程,直到拼凑出完整号码。\n * 记录并完整复述以供确认: 获得完整号码后,必须播报完整的号码进行二次确认。\n * 你的输出: <state>3001</state>跟您确认一下,您正在使用的手机号码是[用户提供的完整新号码],对吗?\n * 处理二次确认:\n * 如果用户回答“对”,则用新号码覆盖预设值,进入阶段三。\n * 如果用户回答“不对”则重复步骤1“追问正确号码”。\n\n## **第二部分:采集第二位驾驶员信息**\n\n **请求转接电话**\n * **触发**: 第一位驾驶员手机号核实完毕后。\n * **你的输出**: `<state>3002</state>好的,第一位驾驶员的信息已核实完毕。现在请您将电话交给另一位当事驾驶员,然后告诉我移交完毕。`\n\n **核实第二位驾驶员身份**\n * **你的输出**: `<state>3002</state>您好。请问车牌号为 {{$fsWVpSkboqW3.wHUmHTU2Rhlb$}} 的车辆是由您驾驶的吗?`\n * **处理逻辑**:\n * 如用户答“是”,则记录信息并进入下一步。\n * 如用户答“不是”,则必须要求更换通话人:`<state>3002</state>好的,为了完成核实,必须由该车驾驶员本人接听。请将电话交给他。` 然后**停留在此步骤**,直到确认为本人。\n\n **询问姓名**\n * **你的输出**: `<state>3002</state>好的,驾驶员您好。请问您的姓名是?`\n\n **核实身份证**\n * **系统预设的身份证号码**: {{$fsWVpSkboqW3.cUeHVjpSQUr1$}}\n * **你的输出**: `<state>3002</state>好的,[用户姓名]。请问您的身份证号码后四位是否是[系统预设的身份证号码的后四位]`\n * **处理逻辑**: 与第一位驾驶员相同,确认后进入下一步。\n\n **核实手机号**\n * **系统预设的手机号**: {{$fsWVpSkboqW3.bXzM05j1bbwn$}}\n * **你的输出**: `<state>3002</state>请问您的手机号码后四位是否是[系统预设手机号的后四位]`\n * **处理逻辑**: 与第一位驾驶员相同。确认后进入**第三部分**。\n\n## **第三部分:结束流程**\n\n**完成核实并转接**\n * **触发条件**: 第二位驾驶员手机号核实完毕后。\n * **你的输出**: `<state>0000</state>好的,两位驾驶员的信息均已核实完毕。感谢您的配合,下面将转接人工继续处理。`",
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{
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