From ac6b2352801d2e0cd5e0c9c67bfdf119c2ca9882 Mon Sep 17 00:00:00 2001 From: francis <1485059943@qq.com> Date: Tue, 16 Sep 2025 15:54:41 +0800 Subject: [PATCH] feat: plan ask (#5650) * feat: plan ask * fix * fix: unit test * fix: build * refactor: plan llm call --- .../template/system/interactive/type.ts | 4 +- packages/service/core/ai/llm/agentCall.ts | 21 +- .../workflow/dispatch/ai/agent/constants.ts | 36 +- .../core/workflow/dispatch/ai/agent/index.ts | 624 +++++++++--------- .../dispatch/ai/agent/sub/ask/constants.ts | 61 -- .../ai/agent/sub/plan/ask/constants.ts | 91 +++ .../dispatch/ai/agent/sub/plan/constants.ts | 3 +- .../dispatch/ai/agent/sub/plan/index.ts | 101 ++- .../dispatch/ai/agent/sub/plan/prompt.ts | 117 ++-- .../core/workflow/dispatch/ai/agent/utils.ts | 4 + .../core/workflow/dispatch/tools/readFiles.ts | 2 +- .../PluginRunBox/components/RenderInput.tsx | 6 +- .../PluginRunBox/components/RenderOutput.tsx | 3 +- .../NodeFormInput/InputFormEditModal.tsx | 6 +- projects/app/test/web/core/app/utils.test.ts | 91 ++- 15 files changed, 682 insertions(+), 488 deletions(-) delete mode 100644 packages/service/core/workflow/dispatch/ai/agent/sub/ask/constants.ts create mode 100644 packages/service/core/workflow/dispatch/ai/agent/sub/plan/ask/constants.ts diff --git a/packages/global/core/workflow/template/system/interactive/type.ts b/packages/global/core/workflow/template/system/interactive/type.ts index 64317a6b0..eef3b649b 100644 --- a/packages/global/core/workflow/template/system/interactive/type.ts +++ b/packages/global/core/workflow/template/system/interactive/type.ts @@ -1,6 +1,6 @@ import type { NodeOutputItemType } from '../../../../chat/type'; -import type { FlowNodeInputTypeEnum } from 'core/workflow/node/constant'; -import type { WorkflowIOValueTypeEnum } from 'core/workflow/constants'; +import type { FlowNodeInputTypeEnum } from '../../../../../core/workflow/node/constant'; +import type { WorkflowIOValueTypeEnum } from '../../../../../core/workflow/constants'; import type { ChatCompletionMessageParam } from '../../../../ai/type'; import type { RuntimeEdgeItemType } from '../../../type/edge'; diff --git a/packages/service/core/ai/llm/agentCall.ts b/packages/service/core/ai/llm/agentCall.ts index 58baac3ae..52c9f5632 100644 --- a/packages/service/core/ai/llm/agentCall.ts +++ b/packages/service/core/ai/llm/agentCall.ts @@ -6,7 +6,10 @@ import type { import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants'; import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt'; import type { AIChatItemType, AIChatItemValueItemType } from '@fastgpt/global/core/chat/type'; -import type { WorkflowInteractiveResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type'; +import type { + InteractiveNodeResponseType, + WorkflowInteractiveResponseType +} from '@fastgpt/global/core/workflow/template/system/interactive/type'; import type { CreateLLMResponseProps, ResponseEvents } from './request'; import { createLLMResponse } from './request'; import type { LLMModelItemType } from '@fastgpt/global/core/ai/model.d'; @@ -39,13 +42,14 @@ type RunAgentCallProps = { response: string; usages: ChatNodeUsageType[]; isEnd: boolean; + interactive?: InteractiveNodeResponseType; }>; } & ResponseEvents; type RunAgentResponse = { completeMessages: ChatCompletionMessageParam[]; assistantResponses: AIChatItemValueItemType[]; - interactiveResponse?: WorkflowInteractiveResponseType; + interactiveResponse?: InteractiveNodeResponseType; // Usage inputTokens: number; @@ -71,7 +75,7 @@ export const runAgentCall = async ({ let runTimes = 0; const assistantResponses: AIChatItemValueItemType[] = []; - let interactiveResponse: WorkflowInteractiveResponseType | undefined; + let interactiveResponse: InteractiveNodeResponseType | undefined; let requestMessages = messages; @@ -127,7 +131,7 @@ export const runAgentCall = async ({ let isEndSign = false; for await (const tool of toolCalls) { // TODO: 加入交互节点处理 - const { response, usages, isEnd } = await handleToolResponse({ + const { response, usages, isEnd, interactive } = await handleToolResponse({ call: tool, messages: requestMessages.slice(0, requestMessagesLength) // 取原来 request 的上下文 }); @@ -142,8 +146,12 @@ export const runAgentCall = async ({ content: response }); subAppUsages.push(...usages); - } + if (interactive) { + interactiveResponse = interactive; + isEndSign = true; + } + } // TODO: 移动到工作流里 assistantResponses concat const currentAssistantResponses = GPTMessages2Chats({ messages: requestMessages.slice(requestMessagesLength), @@ -168,6 +176,7 @@ export const runAgentCall = async ({ outputTokens, completeMessages: requestMessages, assistantResponses, - subAppUsages + subAppUsages, + interactiveResponse }; }; diff --git a/packages/service/core/workflow/dispatch/ai/agent/constants.ts b/packages/service/core/workflow/dispatch/ai/agent/constants.ts index 8998d6b81..0453bb001 100644 --- a/packages/service/core/workflow/dispatch/ai/agent/constants.ts +++ b/packages/service/core/workflow/dispatch/ai/agent/constants.ts @@ -1,18 +1,30 @@ import { SubAppIds } from './sub/constants'; export const getMasterAgentDefaultPrompt = () => { - return `## 角色 -你是一个智能的任务协调者和监督者，负责分析用户需求并合理调度各种工具和子应用来完成复杂任务。 + return `## 角色定位 +你是一个高级任务调度器（Task Orchestrator），负责分析用户需求、制定执行策略、协调多个专业Agent协同工作，确保任务高质量完成。 -## 工作内容 -1. 判断用户问题是否复杂，如果任务复杂，则先使用 "${SubAppIds.plan}" 制定计划，然后根据计划逐步完成任务 -2. 按需调用工具来辅助完成任务 -3. 整合各个工具的执行结果，形成完整的回答 +## 核心职责 +### 任务分析与规划 +- **复杂度评估**：判断任务是否需要分解（涉及多个领域、多个步骤、需要不同专业能力） +- 简单任务：直接调用相关工具或Agent完成 +- 复杂任务：先调用 "${SubAppIds.plan}" 制定详细计划，再按计划执行 -## 注意事项 -- 优先使用最相关和最可靠的工具 -- 如果某个工具执行失败，要有备选方案 -- 保持回答的逻辑性和连贯性 -- 对于不确定的信息要明确标注 -- 始终以用户需求为中心，提供有价值的帮助`; +### 粒度控制 +- **原子任务原则**：每个子任务应该是单一、明确、可独立完成的 +- **专业匹配原则**：每个子任务分配给最适合的专业Agent +- **避免任务过载**：不要将多个复杂任务打包给单个Agent + +### 结果验证 +- 检查每个子任务是否达到预期目标 +- 验证输出格式和内容完整性 +- 确保各部分结果的一致性和连贯性 + +## 关键原则 +1. **分而治之**：复杂任务必须分解，不能一次性丢给单个Agent +2. **专业对口**：根据任务特性选择最合适的Agent +3. **循序渐进**：按照逻辑顺序执行，确保前后连贯 +4. **结果导向**：关注最终用户需求的满足度 +5. **灵活调整**：根据执行情况动态调整策略 +`; }; diff --git a/packages/service/core/workflow/dispatch/ai/agent/index.ts b/packages/service/core/workflow/dispatch/ai/agent/index.ts index 29705786b..453304a1b 100644 --- a/packages/service/core/workflow/dispatch/ai/agent/index.ts +++ b/packages/service/core/workflow/dispatch/ai/agent/index.ts @@ -4,6 +4,7 @@ import { WorkflowIOValueTypeEnum } from '@fastgpt/global/core/workflow/constants'; import { + ConfirmPlanAgentText, DispatchNodeResponseKeyEnum, SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants'; @@ -56,6 +57,8 @@ import { addFilePrompt2Input, getFileInputPrompt } from './sub/file/utils'; import type { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type'; import { dispatchFileRead } from './sub/file'; import { dispatchApp, dispatchPlugin } from './sub/app'; +import type { InteractiveNodeResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type'; +import { getNanoid } from '@fastgpt/global/common/string/tools'; export type DispatchAgentModuleProps = ModuleDispatchProps<{ [NodeInputKeyEnum.history]?: ChatItemType[]; @@ -183,7 +186,7 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise // Get master request messages const systemMessages = chats2GPTMessages({ - messages: getSystemPrompt_ChatItemType(systemPrompt || getMasterAgentDefaultPrompt()), + messages: getSystemPrompt_ChatItemType(getMasterAgentDefaultPrompt()), reserveId: false }); const historyMessages: ChatCompletionMessageParam[] = (() => { @@ -193,6 +196,10 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise return chats2GPTMessages({ messages: chatHistories, reserveId: false }); })(); + + if (lastInteractive?.type !== 'userSelect' && lastInteractive?.type !== 'userInput') { + userChatInput = query[0].text?.content ?? userChatInput; + } const userMessages = chats2GPTMessages({ messages: [ { @@ -205,38 +212,56 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise ], reserveId: false }); + const requestMessages = [...systemMessages, ...historyMessages, ...userMessages]; + let planMessages: ChatCompletionMessageParam[] = []; // TODO: 执行 plan function(只有lastInteractive userselect/userInput 时候，才不需要进入 plan) - if (lastInteractive?.type !== 'userSelect' && lastInteractive?.type !== 'userInput') { - // const planResponse = xxxx - // requestMessages.push(一组 toolcall) + if ( + lastInteractive?.type !== 'userSelect' && + lastInteractive?.type !== 'userInput' && + userChatInput !== ConfirmPlanAgentText + ) { + const { completeMessages, toolMessages, usages, interactiveResponse } = + await dispatchPlanAgent({ + messages: requestMessages, + subApps: subAppList, + model, + temperature, + top_p: aiChatTopP, + stream, + onReasoning: ({ text }: { text: string }) => { + workflowStreamResponse?.({ + event: SseResponseEventEnum.answer, + data: textAdaptGptResponse({ + reasoning_content: text + }) + }); + }, + onStreaming: ({ text }: { text: string }) => { + workflowStreamResponse?.({ + event: SseResponseEventEnum.answer, + data: textAdaptGptResponse({ + text + }) + }); + } + }); - workflowStreamResponse?.({ - event: SseResponseEventEnum.answer, - data: textAdaptGptResponse({ - text: '这是 plan' - }) - }); + if (toolMessages) requestMessages.push(...toolMessages); return { [DispatchNodeResponseKeyEnum.memories]: { - [planMessagesKey]: [ - { - role: 'user', - content: '测试' - }, - { - role: 'assistant', - content: '测试' - } - ] + [masterMessagesKey]: filterMemoryMessages(requestMessages), + [planMessagesKey]: filterMemoryMessages(completeMessages) }, + [DispatchNodeResponseKeyEnum.interactive]: interactiveResponse + // Mock：返回 plan check - [DispatchNodeResponseKeyEnum.interactive]: { - type: 'agentPlanCheck', - params: {} - } + // [DispatchNodeResponseKeyEnum.interactive]: { + // type: 'agentPlanCheck', + // params: {} + // } // Mock: 返回 plan user select // [DispatchNodeResponseKeyEnum.interactive]: { @@ -254,7 +279,7 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise // } // ] // } - // }, + // } // Mock: 返回 plan user input // [DispatchNodeResponseKeyEnum.interactive]: { // type: 'agentPlanAskUserForm', @@ -283,296 +308,307 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise } const dispatchFlowResponse: ChatHistoryItemResType[] = []; - // console.log(JSON.stringify(requestMessages, null, 2)); - const { completeMessages, assistantResponses, inputTokens, outputTokens, subAppUsages } = - await runAgentCall({ - maxRunAgentTimes: 100, - interactiveEntryToolParams: lastInteractive?.toolParams, - body: { - messages: requestMessages, - model: agentModel, - temperature, - stream, - top_p: aiChatTopP, - subApps: subAppList - }, + const { + completeMessages, + assistantResponses, + inputTokens, + outputTokens, + subAppUsages, + interactiveResponse + } = await runAgentCall({ + maxRunAgentTimes: 100, + interactiveEntryToolParams: lastInteractive?.toolParams, + body: { + messages: requestMessages, + model: agentModel, + temperature, + stream, + top_p: aiChatTopP, + subApps: subAppList + }, - userKey: externalProvider.openaiAccount, - isAborted: res ? () => res.closed : undefined, - getToolInfo: getSubAppInfo, + userKey: externalProvider.openaiAccount, + isAborted: res ? () => res.closed : undefined, + getToolInfo: getSubAppInfo, - onReasoning({ text }) { - workflowStreamResponse?.({ + onReasoning({ text }) { + workflowStreamResponse?.({ + event: SseResponseEventEnum.answer, + data: textAdaptGptResponse({ + reasoning_content: text + }) + }); + }, + onStreaming({ text }) { + workflowStreamResponse?.({ + event: SseResponseEventEnum.answer, + data: textAdaptGptResponse({ + text + }) + }); + }, + onToolCall({ call }) { + const subApp = getSubAppInfo(call.function.name); + workflowStreamResponse?.({ + id: call.id, + event: SseResponseEventEnum.toolCall, + data: { + tool: { + id: `${nodeId}/${call.function.name}`, + toolName: subApp?.name || call.function.name, + toolAvatar: subApp?.avatar || '', + functionName: call.function.name, + params: call.function.arguments ?? '' + } + } + }); + }, + onToolParam({ call, params }) { + workflowStreamResponse?.({ + id: call.id, + event: SseResponseEventEnum.toolParams, + data: { + tool: { + params + } + } + }); + }, + + handleToolResponse: async ({ call, messages }) => { + const toolId = call.function.name; + const childWorkflowStreamResponse = getWorkflowChildResponseWrite({ + subAppId: `${nodeId}/${toolId}`, + id: call.id, + fn: workflowStreamResponse + }); + const onReasoning = ({ text }: { text: string }) => { + childWorkflowStreamResponse?.({ event: SseResponseEventEnum.answer, data: textAdaptGptResponse({ reasoning_content: text }) }); - }, - onStreaming({ text }) { - workflowStreamResponse?.({ + }; + const onStreaming = ({ text }: { text: string }) => { + childWorkflowStreamResponse?.({ event: SseResponseEventEnum.answer, data: textAdaptGptResponse({ text }) }); - }, - onToolCall({ call }) { - const subApp = getSubAppInfo(call.function.name); - workflowStreamResponse?.({ - id: call.id, - event: SseResponseEventEnum.toolCall, - data: { - tool: { - id: `${nodeId}/${call.function.name}`, - toolName: subApp?.name || call.function.name, - toolAvatar: subApp?.avatar || '', - functionName: call.function.name, - params: call.function.arguments ?? '' - } - } - }); - }, - onToolParam({ call, params }) { - workflowStreamResponse?.({ - id: call.id, - event: SseResponseEventEnum.toolParams, - data: { - tool: { - params - } - } - }); - }, + }; - handleToolResponse: async ({ call, messages }) => { - const toolId = call.function.name; - const childWorkflowStreamResponse = getWorkflowChildResponseWrite({ - subAppId: `${nodeId}/${toolId}`, - id: call.id, - fn: workflowStreamResponse - }); - const onReasoning = ({ text }: { text: string }) => { - childWorkflowStreamResponse?.({ - event: SseResponseEventEnum.answer, - data: textAdaptGptResponse({ - reasoning_content: text - }) - }); - }; - const onStreaming = ({ text }: { text: string }) => { - childWorkflowStreamResponse?.({ - event: SseResponseEventEnum.answer, - data: textAdaptGptResponse({ - text - }) - }); - }; - - const { - response, - usages = [], - isEnd - } = await (async () => { - try { - if (toolId === SubAppIds.stop) { - return { - response: '', - usages: [], - isEnd: true - }; - } else if (toolId === SubAppIds.plan) { - const { response, usages } = await dispatchPlanAgent({ - messages, - tools: subAppList, - model, - temperature, - top_p: aiChatTopP, - stream, - onReasoning, - onStreaming - }); - - return { - response, - usages, - isEnd: false - }; - } else if (toolId === SubAppIds.model) { - const { systemPrompt, task } = parseToolArgs<{ - systemPrompt: string; - task: string; - }>(call.function.arguments); - - const { response, usages } = await dispatchModelAgent({ - model, - temperature, - top_p: aiChatTopP, - stream, - systemPrompt, - task, - onReasoning, - onStreaming - }); - return { - response, - usages, - isEnd: false - }; - } else if (toolId === SubAppIds.fileRead) { - const { file_indexes } = parseToolArgs<{ - file_indexes: string[]; - }>(call.function.arguments); - if (!Array.isArray(file_indexes)) { - return { - response: 'file_indexes is not array', - usages: [], - isEnd: false - }; - } - - const files = file_indexes.map((index) => ({ - index, - url: filesMap[index] - })); - const result = await dispatchFileRead({ - files, - teamId: runningUserInfo.teamId, - tmbId: runningUserInfo.tmbId, - customPdfParse: chatConfig?.fileSelectConfig?.customPdfParse - }); - return { - response: result.response, - usages: result.usages, - isEnd: false - }; - } - // User Sub App - else { - const node = subAppsMap.get(toolId); - if (!node) { - return { - response: 'Can not find the tool', - usages: [], - isEnd: false - }; - } - - const toolCallParams = parseToolArgs(call.function.arguments); - // Get params - const requestParams = (() => { - const params: Record = toolCallParams; - - node.inputs.forEach((input) => { - if (input.key in toolCallParams) { - return; - } - // Skip some special key - if ( - [ - NodeInputKeyEnum.childrenNodeIdList, - NodeInputKeyEnum.systemInputConfig - ].includes(input.key as NodeInputKeyEnum) - ) { - params[input.key] = input.value; - return; - } - - // replace {{$xx.xx$}} and {{xx}} variables - let value = replaceEditorVariable({ - text: input.value, - nodes: runtimeNodes, - variables - }); - - // replace reference variables - value = getReferenceVariableValue({ - value, - nodes: runtimeNodes, - variables - }); - - params[input.key] = valueTypeFormat(value, input.valueType); - }); - - return params; - })(); - - if (node.flowNodeType === FlowNodeTypeEnum.tool) { - const { response, usages } = await dispatchTool({ - node, - params: requestParams, - runningUserInfo, - runningAppInfo, - variables, - workflowStreamResponse: childWorkflowStreamResponse - }); - return { - response, - usages, - isEnd: false - }; - } else if ( - node.flowNodeType === FlowNodeTypeEnum.appModule || - node.flowNodeType === FlowNodeTypeEnum.pluginModule - ) { - const fn = - node.flowNodeType === FlowNodeTypeEnum.appModule ? dispatchApp : dispatchPlugin; - - const { response, usages } = await fn({ - ...props, - node, - workflowStreamResponse: childWorkflowStreamResponse, - callParams: { - appId: node.pluginId, - version: node.version, - ...requestParams - } - }); - - return { - response, - usages, - isEnd: false - }; - } else { - return { - response: 'Can not find the tool', - usages: [], - isEnd: false - }; - } - } - } catch (error) { + const { + response, + usages = [], + isEnd, + interactive + } = await (async () => { + try { + if (toolId === SubAppIds.stop) { return { - response: getErrText(error), + response: '', usages: [], + isEnd: true + }; + } else if (toolId === SubAppIds.plan) { + const { completeMessages, response, usages, interactiveResponse } = + await dispatchPlanAgent({ + messages, + subApps: subAppList, + model, + temperature, + top_p: aiChatTopP, + stream, + onReasoning, + onStreaming + }); + + planMessages = completeMessages; + + return { + response, + usages, + isEnd: false, + interactive: interactiveResponse + }; + } else if (toolId === SubAppIds.model) { + const { systemPrompt, task } = parseToolArgs<{ + systemPrompt: string; + task: string; + }>(call.function.arguments); + + const { response, usages } = await dispatchModelAgent({ + model, + temperature, + top_p: aiChatTopP, + stream, + systemPrompt, + task, + onReasoning, + onStreaming + }); + return { + response, + usages, + isEnd: false + }; + } else if (toolId === SubAppIds.fileRead) { + const { file_indexes } = parseToolArgs<{ + file_indexes: string[]; + }>(call.function.arguments); + if (!Array.isArray(file_indexes)) { + return { + response: 'file_indexes is not array', + usages: [], + isEnd: false + }; + } + + const files = file_indexes.map((index) => ({ + index, + url: filesMap[index] + })); + const result = await dispatchFileRead({ + files, + teamId: runningUserInfo.teamId, + tmbId: runningUserInfo.tmbId, + customPdfParse: chatConfig?.fileSelectConfig?.customPdfParse + }); + return { + response: result.response, + usages: result.usages, isEnd: false }; } - })(); + // User Sub App + else { + const node = subAppsMap.get(toolId); + if (!node) { + return { + response: 'Can not find the tool', + usages: [], + isEnd: false + }; + } - // Push stream response - workflowStreamResponse?.({ - id: call.id, - event: SseResponseEventEnum.toolResponse, - data: { - tool: { - id: call.id, - response + const toolCallParams = parseToolArgs(call.function.arguments); + // Get params + const requestParams = (() => { + const params: Record = toolCallParams; + + node.inputs.forEach((input) => { + if (input.key in toolCallParams) { + return; + } + // Skip some special key + if ( + [ + NodeInputKeyEnum.childrenNodeIdList, + NodeInputKeyEnum.systemInputConfig + ].includes(input.key as NodeInputKeyEnum) + ) { + params[input.key] = input.value; + return; + } + + // replace {{$xx.xx$}} and {{xx}} variables + let value = replaceEditorVariable({ + text: input.value, + nodes: runtimeNodes, + variables + }); + + // replace reference variables + value = getReferenceVariableValue({ + value, + nodes: runtimeNodes, + variables + }); + + params[input.key] = valueTypeFormat(value, input.valueType); + }); + + return params; + })(); + + if (node.flowNodeType === FlowNodeTypeEnum.tool) { + const { response, usages } = await dispatchTool({ + node, + params: requestParams, + runningUserInfo, + runningAppInfo, + variables, + workflowStreamResponse: childWorkflowStreamResponse + }); + return { + response, + usages, + isEnd: false + }; + } else if ( + node.flowNodeType === FlowNodeTypeEnum.appModule || + node.flowNodeType === FlowNodeTypeEnum.pluginModule + ) { + const fn = + node.flowNodeType === FlowNodeTypeEnum.appModule ? dispatchApp : dispatchPlugin; + + const { response, usages } = await fn({ + ...props, + node, + workflowStreamResponse: childWorkflowStreamResponse, + callParams: { + appId: node.pluginId, + version: node.version, + ...requestParams + } + }); + + return { + response, + usages, + isEnd: false + }; + } else { + return { + response: 'Can not find the tool', + usages: [], + isEnd: false + }; } } - }); + } catch (error) { + return { + response: getErrText(error), + usages: [], + isEnd: false + }; + } + })(); - // TODO: 推送账单 + // Push stream response + workflowStreamResponse?.({ + id: call.id, + event: SseResponseEventEnum.toolResponse, + data: { + tool: { + id: call.id, + response + } + } + }); - return { - response, - usages, - isEnd - }; - } - }); + // TODO: 推送账单 + + return { + response, + usages, + isEnd, + interactive + }; + } + }); // Usage count const { totalPoints: modelTotalPoints, modelName } = formatModelChars2Points({ @@ -597,7 +633,7 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise // TODO: 需要对 memoryMessages 单独建表存储 [DispatchNodeResponseKeyEnum.memories]: { [masterMessagesKey]: filterMemoryMessages(completeMessages), - [planMessagesKey]: [] // TODO: plan messages 需要记录 + [planMessagesKey]: [filterMemoryMessages(planMessages)] }, [DispatchNodeResponseKeyEnum.assistantResponses]: previewAssistantResponses, [DispatchNodeResponseKeyEnum.nodeResponse]: { @@ -628,7 +664,7 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise // Tool usage ...subAppUsages ], - [DispatchNodeResponseKeyEnum.interactive]: undefined + [DispatchNodeResponseKeyEnum.interactive]: interactiveResponse }; } catch (error) { return getNodeErrResponse({ error }); diff --git a/packages/service/core/workflow/dispatch/ai/agent/sub/ask/constants.ts b/packages/service/core/workflow/dispatch/ai/agent/sub/ask/constants.ts deleted file mode 100644 index 82a365d96..000000000 --- a/packages/service/core/workflow/dispatch/ai/agent/sub/ask/constants.ts +++ /dev/null @@ -1,61 +0,0 @@ -import type { ChatCompletionTool } from '@fastgpt/global/core/ai/type'; -import { SubAppIds } from '../constants'; - -export const AskAgentTool: ChatCompletionTool = { - type: 'function', - function: { - name: SubAppIds.ask, - description: '调用此工具，向用户发起交互式提问', - parameters: { - type: 'object', - properties: { - mode: { - type: 'string', - enum: ['userSelect', 'formInput', 'userInput'], - description: '交互模式' - }, - prompt: { - type: 'string', - description: '向用户展示的提示信息' - }, - options: { - type: 'array', - description: '当 mode=userSelect 时可供选择的选项', - items: { - type: 'string' - } - }, - form: { - type: 'array', - description: '当 mode=formInput 时需要填写的表单字段列表', - items: { - type: 'object', - properties: { - field: { - type: 'string', - description: '字段名，如 name, age, 同时会展示给用户一样的label' - }, - type: { - type: 'string', - enum: ['textInput', 'numberInput', 'singleSelect', 'multiSelect'], - description: '字段输入类型' - }, - required: { type: 'boolean', description: '该字段是否必填', default: false }, - options: { - type: 'array', - description: '当 type 为 singleSelect 或 multiSelect 时的可选项', - items: { type: 'string' } - } - }, - required: ['field', 'type'] - } - }, - userInput: { - type: 'string', - description: '当 mode=userInput 时用户自由输入的内容' - } - }, - required: ['mode', 'prompt'] - } - } -}; diff --git a/packages/service/core/workflow/dispatch/ai/agent/sub/plan/ask/constants.ts b/packages/service/core/workflow/dispatch/ai/agent/sub/plan/ask/constants.ts new file mode 100644 index 000000000..03c892133 --- /dev/null +++ b/packages/service/core/workflow/dispatch/ai/agent/sub/plan/ask/constants.ts @@ -0,0 +1,91 @@ +import type { ChatCompletionTool } from '@fastgpt/global/core/ai/type'; +import { SubAppIds } from '../../constants'; + +export type AskAgentToolParamsType = Partial<{ + mode: 'select' | 'formInput' | 'input'; + prompt: string; + options: string[]; + form: { + field: string; + type: 'textInput' | 'numberInput' | 'singleSelect' | 'multiSelect'; + required: boolean; + options: string[]; + }[]; +}>; + +export const AskAgentTool: ChatCompletionTool = { + type: 'function', + function: { + name: SubAppIds.ask, + description: ` +调用此工具时，LLM 可以向用户发起一次交互式问题。 +参数结构非常简洁，仅包含 mode、prompt、options 三个字段，但通过不同的组合方式可以覆盖两种主要交互场景： +1. mode = "select" + - 用于枚举型选择（如调查问卷、多项选择、分支逻辑）。 + - prompt: 展示在问题顶部的主要提问文案。 + - options: 字符串数组，表示可供选择的选项。前端渲染时可以将每个选项显示为卡片、列表项或按钮。 + - 场景示例： + * 让用户在几个备选方案中选择最贴近的情况。 + * 希望结果结构化，便于后续逻辑分支。 +2. mode = "input" + - 用于自由文本输入，适合用户提供个性化或开放式回答。 + - prompt: 展示的问题提示，引导用户填写。 + - options: 此模式下通常留空或忽略。 + - 场景示例： + * 需要用户补充说明原因、填写备注、输入 URL/编号等。 + * 当 "select" 的选项无法覆盖用户真实答案时，可以再调用一次 "input" 追问。 + +使用建议： +- 优先使用 "select" 以获得结构化结果，减少歧义。 +- 当问题答案无法预先枚举，或需要用户自由表达时，使用 "input"。 +- 如果需要“Something else”选项，可以把它放进 options 里作为一个普通选项，然后再根据用户选择调用一次 "input" 让用户详细描述。 + `, + parameters: { + type: 'object', + properties: { + mode: { + type: 'string', + enum: ['select', 'input'], + description: '交互模式' + }, + prompt: { + type: 'string', + description: '向用户展示的提示信息' + }, + options: { + type: 'array', + description: '当 mode=select 时可供选择的选项', + items: { + type: 'string' + } + } + // form: { + // type: 'array', + // description: '当 mode=formInput 时需要填写的表单字段列表', + // items: { + // type: 'object', + // properties: { + // field: { + // type: 'string', + // description: '字段名，如 name, age, 同时会展示给用户一样的label' + // }, + // type: { + // type: 'string', + // enum: ['textInput', 'numberInput', 'singleSelect', 'multiSelect'], + // description: '字段输入类型' + // }, + // required: { type: 'boolean', description: '该字段是否必填', default: false }, + // options: { + // type: 'array', + // description: '当 type 为 singleSelect 或 multiSelect 时的可选项', + // items: { type: 'string' } + // } + // }, + // required: ['field', 'type'] + // } + // }, + }, + required: ['mode', 'prompt'] + } + } +}; diff --git a/packages/service/core/workflow/dispatch/ai/agent/sub/plan/constants.ts b/packages/service/core/workflow/dispatch/ai/agent/sub/plan/constants.ts index d5f23093d..1ba0af251 100644 --- a/packages/service/core/workflow/dispatch/ai/agent/sub/plan/constants.ts +++ b/packages/service/core/workflow/dispatch/ai/agent/sub/plan/constants.ts @@ -5,6 +5,7 @@ export const PlanAgentTool: ChatCompletionTool = { type: 'function', function: { name: SubAppIds.plan, - description: '分析和拆解用户问题，制定分步计划。' + description: '分析和拆解用户问题，制定分步计划。', + parameters: {} } }; diff --git a/packages/service/core/workflow/dispatch/ai/agent/sub/plan/index.ts b/packages/service/core/workflow/dispatch/ai/agent/sub/plan/index.ts index 850059233..7a0cce293 100644 --- a/packages/service/core/workflow/dispatch/ai/agent/sub/plan/index.ts +++ b/packages/service/core/workflow/dispatch/ai/agent/sub/plan/index.ts @@ -8,10 +8,16 @@ import { getLLMModel } from '../../../../../../ai/model'; import { formatModelChars2Points } from '../../../../../../../support/wallet/usage/utils'; import type { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type'; import { SubAppIds } from '../constants'; +import type { InteractiveNodeResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type'; +import { runAgentCall } from '../../../../../../../core/ai/llm/agentCall'; +import { parseToolArgs } from '../../../utils'; +import { AskAgentTool, type AskAgentToolParamsType } from './ask/constants'; +import { getNanoid } from '@fastgpt/global/common/string/tools'; +import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants'; type PlanAgentConfig = { model: string; - customSystemPrompt?: string; + systemPrompt?: string; temperature?: number; top_p?: number; stream?: boolean; @@ -19,7 +25,7 @@ type PlanAgentConfig = { type DispatchPlanAgentProps = PlanAgentConfig & { messages: ChatCompletionMessageParam[]; - tools: ChatCompletionTool[]; + subApps: ChatCompletionTool[]; onReasoning: ResponseEvents['onReasoning']; onStreaming: ResponseEvents['onStreaming']; }; @@ -27,14 +33,17 @@ type DispatchPlanAgentProps = PlanAgentConfig & { type DispatchPlanAgentResponse = { response: string; usages: ChatNodeUsageType[]; + completeMessages: ChatCompletionMessageParam[]; + toolMessages?: ChatCompletionMessageParam[]; + interactiveResponse?: InteractiveNodeResponseType; }; export const dispatchPlanAgent = async ({ messages, - tools, + subApps, model, - customSystemPrompt, + systemPrompt, temperature, top_p, stream, @@ -46,14 +55,21 @@ export const dispatchPlanAgent = async ({ const requestMessages: ChatCompletionMessageParam[] = [ { role: 'system', - content: getPlanAgentPrompt(customSystemPrompt) + content: getPlanAgentPrompt(systemPrompt) }, - ...messages.filter((item) => item.role !== 'system'), - { role: 'user', content: 'Start plan' } + ...messages.filter((item) => item.role !== 'system') ]; - const filterPlanTools = tools.filter((item) => item.function.name !== SubAppIds.plan); + const filterPlanTools = subApps.filter((item) => item.function.name !== SubAppIds.plan); + filterPlanTools.push(AskAgentTool); - const { answerText, usage } = await createLLMResponse({ + const { + reasoningText, + answerText, + toolCalls = [], + usage, + getEmptyResponseTip, + completeMessages + } = await createLLMResponse({ body: { model: modelData.model, temperature, @@ -70,12 +86,74 @@ export const dispatchPlanAgent = async ({ onStreaming }); + if (!answerText && !reasoningText && !toolCalls.length) { + return Promise.reject(getEmptyResponseTip()); + } + + // TODO: 需要考虑多个 Interactive 并发的情况 + let interactiveResponse: InteractiveNodeResponseType = { + type: 'agentPlanCheck', + params: {} + }; + + for await (const call of toolCalls) { + const toolId = call.function.name; + + if (toolId === SubAppIds.ask) { + const params = parseToolArgs(call.function.arguments); + + if (params.mode === 'select') { + interactiveResponse = { + type: 'agentPlanAskUserSelect', + params: { + description: params?.prompt ?? '选择选项', + userSelectOptions: params?.options?.map((v, i) => { + return { key: `option${i}`, value: v }; + }) + } + } as InteractiveNodeResponseType; + } + if (params.mode === 'input') { + interactiveResponse = { + type: 'agentPlanAskQuery', + params: { + content: params?.prompt ?? '输入详细信息' + } + }; + } + } + } + const { totalPoints, modelName } = formatModelChars2Points({ model: modelData.model, inputTokens: usage.inputTokens, outputTokens: usage.outputTokens }); + const toolMessages: ChatCompletionMessageParam[] = []; + if (answerText) { + const toolId = getNanoid(6); + const toolCall: ChatCompletionMessageParam = { + role: ChatCompletionRequestMessageRoleEnum.Assistant, + tool_calls: [ + { + id: toolId, + type: 'function', + function: { + name: SubAppIds.plan, + arguments: '' + } + } + ] + }; + const toolCallResponse: ChatCompletionMessageParam = { + role: ChatCompletionRequestMessageRoleEnum.Tool, + tool_call_id: toolId, + content: answerText + }; + toolMessages.push(toolCall, toolCallResponse); + } + return { response: answerText, usages: [ @@ -86,6 +164,9 @@ export const dispatchPlanAgent = async ({ inputTokens: usage.inputTokens, outputTokens: usage.outputTokens } - ] + ], + completeMessages, + toolMessages, + interactiveResponse }; }; diff --git a/packages/service/core/workflow/dispatch/ai/agent/sub/plan/prompt.ts b/packages/service/core/workflow/dispatch/ai/agent/sub/plan/prompt.ts index b9f279425..1c3a73d27 100644 --- a/packages/service/core/workflow/dispatch/ai/agent/sub/plan/prompt.ts +++ b/packages/service/core/workflow/dispatch/ai/agent/sub/plan/prompt.ts @@ -11,37 +11,53 @@ ${ : '' } - -根据用户提供的主题或目标，生成一份详细、可执行的项目计划文档，包含合理的阶段划分与具体待办事项。 - - - -- 用户输入：一个需要制定的主题、目标或任务描述。 -- 输入格式：自然语言描述，可能包含背景、目标、约束、优先级、本地化偏好。 - - 1. 解析用户输入，提取核心目标、关键要素、约束与本地化偏好。 -2. 评估任务复杂度（简单：2-3 步；复杂：4-7 步），据此确定阶段数量。 -3. 各阶段生成 3-5 条可执行 Todo，动词开头，MECE 且无重叠。 +2. 评估任务复杂度, 据此确定阶段数量。 +3. 禁止调用除"ask_agent"以外的任何工具. 4. 语言风格本地化（根据用户输入语言进行术语与语序调整）。 -5. 产出完整计划，严格使用占位符 [主题] 与标记体系；确保编号连续、标签闭合、结构清晰。 +5. 严格按照 JSON Schema 生成完整计划，不得输出多余内容。 +6. 仅在缺少关键信息时使用"ask_agent"工具询问用户（如：未指定目的地、预算、时间等必要细节） +7. 如果信息充足或用户已回答询问，必须直接输出JSON格式的完整计划，不再调用工具 -- 必须严格遵循以下注释标记格式： - * 标记主标题 - * 标记整体描述 - * 和 包裹步骤块 - * 标记步骤标题 - * 标记步骤描述 - * 和 包裹待办列表 - * 标记单个待办事项 - * 添加重要注释或备注 -- 步骤数量随复杂度自动调整；每步 3-5 条 Todo。 -- 编号（N、X）必须连续、准确。 -- 描述语言简洁、专业、可操作；各阶段逻辑递进、MECE。 -- 进行本地化调整（术语、量词、表达习惯）。 +- 必须严格输出 JSON，不能包含代码块标记（如 \`\`\`）、注释或额外说明文字。 +- 输出结构必须符合以下 JSON Schema： +\`\`\`json +{ + "type": "object", + "properties": { + "task": { + "type": "string", + "description": "任务主题, 准确覆盖本次所有执行步骤的核心内容和维度" + }, + "steps": { + "type": "array", + "description": "阶段步骤列表", + "items": { + "type": "object", + "properties": { + "id": { + "type": "string", + "description": "唯一标识" + }, + "title": { + "type": "string", + "description": "阶段标题" + }, + "description": { + "type": "string", + "description": "阶段描述, 并在末尾@对应任务将要移交使用的工具/子智能体" + }, + }, + "required": ["id", "title", "description"] + } + } + }, + "required": ["title", "description", "steps"] +} +\`\`\` @@ -52,42 +68,21 @@ ${ - # [主题] 深度调研计划 - - 全面了解 [主题] 的 [核心维度描述] - - - ## Step 1: [阶段名称] - [阶段目标描述] - ### Todo List - - - [ ] [具体任务描述] - - [ ] [具体任务描述] - - [ ] [具体任务描述] - - [可选备注] - - - - ## Step 2: [阶段名称] - [阶段目标描述] - ### Todo List - - - [ ] [具体任务描述] - - [ ] [具体任务描述] - - [ ] [具体任务描述] - - [可选备注] - - - - + { + "task": "[主题] 深度调研计划", + "steps": [ + { + "id": "[id]", + "title": "[阶段名称]", + "description": "[阶段描述] @sub_agent" + }, + { + "id": "[id]", + "title": "[阶段名称]", + "description": "[阶段描述] @sub_agent" + } + ] + } `; }; diff --git a/packages/service/core/workflow/dispatch/ai/agent/utils.ts b/packages/service/core/workflow/dispatch/ai/agent/utils.ts index 8178de20d..0dc4a71a9 100644 --- a/packages/service/core/workflow/dispatch/ai/agent/utils.ts +++ b/packages/service/core/workflow/dispatch/ai/agent/utils.ts @@ -1,3 +1,7 @@ +import type { + ChatCompletionAssistantMessageParam, + ChatCompletionMessageParam +} from '@fastgpt/global/core/ai/type'; import type { ToolNodeItemType } from './type'; const namespaceMap = new Map([ diff --git a/packages/service/core/workflow/dispatch/tools/readFiles.ts b/packages/service/core/workflow/dispatch/tools/readFiles.ts index 4726b7b04..e66068284 100644 --- a/packages/service/core/workflow/dispatch/tools/readFiles.ts +++ b/packages/service/core/workflow/dispatch/tools/readFiles.ts @@ -101,7 +101,7 @@ export const getHistoryFileLinks = (histories: ChatItemType[]) => { return histories .filter((item) => { if (item.obj === ChatRoleEnum.Human) { - return item.value.filter((value) => value.type === 'file'); + return item.value.filter((value) => value.file); } return false; }) diff --git a/projects/app/src/components/core/chat/ChatContainer/PluginRunBox/components/RenderInput.tsx b/projects/app/src/components/core/chat/ChatContainer/PluginRunBox/components/RenderInput.tsx index 2b2c4ac50..0a09672e3 100644 --- a/projects/app/src/components/core/chat/ChatContainer/PluginRunBox/components/RenderInput.tsx +++ b/projects/app/src/components/core/chat/ChatContainer/PluginRunBox/components/RenderInput.tsx @@ -83,7 +83,7 @@ const RenderInput = () => { if (histories.length === 0) return pluginInputs; try { const historyValue = histories[0]?.value as UserChatItemValueItemType[]; - const inputValueString = historyValue.find((item) => item.type === 'text')?.text?.content; + const inputValueString = historyValue.find((item) => item.text?.content)?.text?.content; if (!inputValueString) return pluginInputs; return JSON.parse(inputValueString) as FlowNodeInputItemType[]; @@ -135,7 +135,7 @@ const RenderInput = () => { if (!historyValue) return undefined; try { - const inputValueString = historyValue.find((item) => item.type === 'text')?.text?.content; + const inputValueString = historyValue.find((item) => item.text?.content)?.text?.content; return ( inputValueString && JSON.parse(inputValueString).reduce( @@ -160,7 +160,7 @@ const RenderInput = () => { // Parse history file const historyFileList = (() => { const historyValue = histories[0]?.value as UserChatItemValueItemType[]; - return historyValue?.filter((item) => item.type === 'file').map((item) => item.file); + return historyValue?.filter((item) => item.file).map((item) => item.file); })(); reset({ diff --git a/projects/app/src/components/core/chat/ChatContainer/PluginRunBox/components/RenderOutput.tsx b/projects/app/src/components/core/chat/ChatContainer/PluginRunBox/components/RenderOutput.tsx index d5bfe5157..efe26f696 100644 --- a/projects/app/src/components/core/chat/ChatContainer/PluginRunBox/components/RenderOutput.tsx +++ b/projects/app/src/components/core/chat/ChatContainer/PluginRunBox/components/RenderOutput.tsx @@ -8,6 +8,7 @@ import AIResponseBox from '../../../components/AIResponseBox'; import { useTranslation } from 'next-i18next'; import ComplianceTip from '@/components/common/ComplianceTip/index'; import { ChatRecordContext } from '@/web/core/chat/context/chatRecordContext'; +import type { AIChatItemValueItemType } from '@fastgpt/global/core/chat/type'; const RenderOutput = () => { const { t } = useTranslation(); @@ -38,7 +39,7 @@ const RenderOutput = () => { diff --git a/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/nodes/NodeFormInput/InputFormEditModal.tsx b/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/nodes/NodeFormInput/InputFormEditModal.tsx index a05ce2b65..d445c5e71 100644 --- a/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/nodes/NodeFormInput/InputFormEditModal.tsx +++ b/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/nodes/NodeFormInput/InputFormEditModal.tsx @@ -73,9 +73,9 @@ const InputFormEditModal = ({ } ]; - const defaultValueType = inputTypeList - .flat() - .find((item) => item.value === inputType)?.defaultValueType; + const defaultValueType = + inputTypeList.flat().find((item) => item.value === inputType)?.defaultValueType ?? + WorkflowIOValueTypeEnum.string; const onSubmitSuccess = useCallback( (data: UserInputFormItemType, action: 'confirm' | 'continue') => { diff --git a/projects/app/test/web/core/app/utils.test.ts b/projects/app/test/web/core/app/utils.test.ts index b2614022b..06e65be07 100644 --- a/projects/app/test/web/core/app/utils.test.ts +++ b/projects/app/test/web/core/app/utils.test.ts @@ -1,29 +1,28 @@ import { describe, expect, it } from 'vitest'; -import { - form2AppWorkflow, - filterSensitiveFormData, - getAppQGuideCustomURL -} from '@/web/core/app/utils'; +import { filterSensitiveFormData, getAppQGuideCustomURL } from '@/web/core/app/utils'; +import { form2AppWorkflow } from '@/pageComponents/app/detail/Edit/SimpleApp/utils'; import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant'; import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants'; import { getDefaultAppForm } from '@fastgpt/global/core/app/utils'; +import type { AppFormEditFormType } from '@fastgpt/global/core/app/type'; describe('form2AppWorkflow', () => { const mockT = (str: string) => str; it('should generate simple chat workflow when no datasets or tools selected', () => { - const form = { + const form: AppFormEditFormType = { aiSettings: { - model: 'gpt-3.5', - temperature: 0.7, - maxToken: 2000, - systemPrompt: 'You are a helpful assistant', + [NodeInputKeyEnum.aiModel]: 'gpt-3.5', + [NodeInputKeyEnum.aiChatTemperature]: 0.7, + [NodeInputKeyEnum.aiChatMaxToken]: 2000, + [NodeInputKeyEnum.aiSystemPrompt]: 'You are a helpful assistant', maxHistories: 5, - aiChatReasoning: true, - aiChatTopP: 0.8, - aiChatStopSign: '', - aiChatResponseFormat: '', - aiChatJsonSchema: '' + [NodeInputKeyEnum.aiChatIsResponseText]: true, + [NodeInputKeyEnum.aiChatReasoning]: true, + [NodeInputKeyEnum.aiChatTopP]: 0.8, + [NodeInputKeyEnum.aiChatStopSign]: '', + [NodeInputKeyEnum.aiChatResponseFormat]: '', + [NodeInputKeyEnum.aiChatJsonSchema]: '' }, dataset: { datasets: [], @@ -49,21 +48,29 @@ describe('form2AppWorkflow', () => { }); it('should generate dataset workflow when datasets are selected', () => { - const form = { + const form: AppFormEditFormType = { aiSettings: { - model: 'gpt-3.5', - temperature: 0.7, - maxToken: 2000, - systemPrompt: 'You are a helpful assistant', + [NodeInputKeyEnum.aiModel]: 'gpt-3.5', + [NodeInputKeyEnum.aiChatTemperature]: 0.7, + [NodeInputKeyEnum.aiChatMaxToken]: 2000, + [NodeInputKeyEnum.aiSystemPrompt]: 'You are a helpful assistant', maxHistories: 5, - aiChatReasoning: true, - aiChatTopP: 0.8, - aiChatStopSign: '', - aiChatResponseFormat: '', - aiChatJsonSchema: '' + [NodeInputKeyEnum.aiChatIsResponseText]: true, + [NodeInputKeyEnum.aiChatReasoning]: true, + [NodeInputKeyEnum.aiChatTopP]: 0.8, + [NodeInputKeyEnum.aiChatStopSign]: '', + [NodeInputKeyEnum.aiChatResponseFormat]: '', + [NodeInputKeyEnum.aiChatJsonSchema]: '' }, dataset: { - datasets: ['dataset1'], + datasets: [ + { + datasetId: 'dataset1', + avatar: '', + name: 'Test Dataset', + vectorModel: { model: 'text-embedding-ada-002' } as any + } + ], similarity: 0.8, limit: 1500, searchMode: 'embedding', @@ -88,14 +95,32 @@ describe('form2AppWorkflow', () => { describe('filterSensitiveFormData', () => { it('should filter sensitive data from app form', () => { - const appForm = { + const appForm: AppFormEditFormType = { aiSettings: { - model: 'gpt-4', - temperature: 0.8 + [NodeInputKeyEnum.aiModel]: 'gpt-4', + [NodeInputKeyEnum.aiChatTemperature]: 0.8, + maxHistories: 5, + [NodeInputKeyEnum.aiChatIsResponseText]: true }, dataset: { - datasets: ['sensitive-dataset'], - similarity: 0.9 + datasets: [ + { + datasetId: 'sensitive-dataset', + avatar: '', + name: 'Sensitive Dataset', + vectorModel: { model: 'text-embedding-ada-002' } as any + } + ], + searchMode: 'embedding' as any, + similarity: 0.9, + limit: 1500, + embeddingWeight: 0.7, + usingReRank: false, + rerankModel: '', + rerankWeight: 0.5, + datasetSearchUsingExtensionQuery: false, + datasetSearchExtensionModel: '', + datasetSearchExtensionBg: '' }, selectedTools: [], chatConfig: {} @@ -125,7 +150,7 @@ describe('getAppQGuideCustomURL', () => { ] } ] - }; + } as any; const result = getAppQGuideCustomURL(appDetail); expect(result).toBe('https://example.com'); @@ -139,7 +164,7 @@ describe('getAppQGuideCustomURL', () => { inputs: [] } ] - }; + } as any; const result = getAppQGuideCustomURL(appDetail); expect(result).toBe('');