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https://github.com/labring/FastGPT.git
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feat: plan response in ui
This commit is contained in:
archer
parent
2d44dce370
commit
00f7afe304
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@ -15,6 +15,7 @@ import type { FlowNodeInputItemType } from '../workflow/type/io';
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import type { FlowNodeTemplateType } from '../workflow/type/node.d';
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import { ChatCompletionMessageParam } from '../ai/type';
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import type { RequireOnlyOne } from '../../common/type/utils';
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import type { AgentPlanType } from '../../../service/core/workflow/dispatch/ai/agent/sub/plan/type';
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/* --------- chat ---------- */
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export type ChatSchemaType = {
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@ -86,17 +87,10 @@ export type AIChatItemValueItemType = {
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};
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tool: ToolModuleResponseItemType;
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interactive: WorkflowInteractiveResponseType;
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text?: {
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content: string;
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};
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reasoning?: {
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content: string;
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};
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interactive?: WorkflowInteractiveResponseType;
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agentPlan: AgentPlanType;
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// Abandon
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tools?: ToolModuleResponseItemType[];
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tools: ToolModuleResponseItemType[];
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}>;
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export type AIChatItemType = {
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obj: ChatRoleEnum.AI;
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@ -15,7 +15,8 @@ export enum SseResponseEventEnum {
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flowResponses = 'flowResponses', // sse response request
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updateVariables = 'updateVariables',
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interactive = 'interactive' // user select
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interactive = 'interactive', // user select
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agentPlan = 'agentPlan' // agent plan
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}
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export enum DispatchNodeResponseKeyEnum {
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@ -19,13 +19,13 @@ type InteractiveNodeType = {
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nodeOutputs?: NodeOutputItemType[];
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};
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type ChildrenInteractive = InteractiveNodeType & {
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export type ChildrenInteractive = InteractiveNodeType & {
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type: 'childrenInteractive';
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params: {
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childrenResponse: WorkflowInteractiveResponseType;
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};
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};
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type ToolCallChildrenInteractive = InteractiveNodeType & {
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export type ToolCallChildrenInteractive = InteractiveNodeType & {
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type: 'toolChildrenInteractive';
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params: {
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childrenResponse: WorkflowInteractiveResponseType;
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@ -37,7 +37,7 @@ type ToolCallChildrenInteractive = InteractiveNodeType & {
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};
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// Loop bode
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type LoopInteractive = InteractiveNodeType & {
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export type LoopInteractive = InteractiveNodeType & {
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type: 'loopInteractive';
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params: {
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loopResult: any[];
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@ -1,213 +0,0 @@
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import type {
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ChatCompletionMessageParam,
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ChatCompletionTool,
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ChatCompletionMessageToolCall
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} from '@fastgpt/global/core/ai/type';
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import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
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import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
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import type { AIChatItemType, AIChatItemValueItemType } from '@fastgpt/global/core/chat/type';
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import type {
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InteractiveNodeResponseType,
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WorkflowInteractiveResponseType
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} from '@fastgpt/global/core/workflow/template/system/interactive/type';
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import type { CreateLLMResponseProps, ResponseEvents } from './request';
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import { createLLMResponse } from './request';
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import type { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
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import type { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
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import { countGptMessagesTokens, countPromptTokens } from '../../../common/string/tiktoken/index';
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import { addLog } from '../../../common/system/log';
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import type { AgentPlanStepType } from '../../workflow/dispatch/ai/agent/sub/plan/type';
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import { calculateCompressionThresholds } from './compress/constants';
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import { compressRequestMessages, compressToolcallResponse } from './compress';
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type RunAgentCallProps = {
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maxRunAgentTimes: number;
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interactiveEntryToolParams?: WorkflowInteractiveResponseType['toolParams'];
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currentStep: AgentPlanStepType;
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body: {
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messages: ChatCompletionMessageParam[];
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model: LLMModelItemType;
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temperature?: number;
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top_p?: number;
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stream?: boolean;
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subApps: ChatCompletionTool[];
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};
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userKey?: CreateLLMResponseProps['userKey'];
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isAborted?: CreateLLMResponseProps['isAborted'];
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getToolInfo: (id: string) => {
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name: string;
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avatar: string;
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};
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handleToolResponse: (e: {
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call: ChatCompletionMessageToolCall;
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messages: ChatCompletionMessageParam[];
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}) => Promise<{
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response: string;
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usages: ChatNodeUsageType[];
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interactive?: InteractiveNodeResponseType;
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}>;
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} & ResponseEvents;
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type RunAgentResponse = {
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completeMessages: ChatCompletionMessageParam[];
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assistantResponses: AIChatItemValueItemType[];
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interactiveResponse?: InteractiveNodeResponseType;
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// Usage
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inputTokens: number;
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outputTokens: number;
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subAppUsages: ChatNodeUsageType[];
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};
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export const runAgentCall = async ({
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maxRunAgentTimes,
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interactiveEntryToolParams,
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currentStep,
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body: { model, messages, stream, temperature, top_p, subApps },
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userKey,
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isAborted,
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handleToolResponse,
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getToolInfo,
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onReasoning,
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onStreaming,
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onToolCall,
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onToolParam
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}: RunAgentCallProps): Promise<RunAgentResponse> => {
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let runTimes = 0;
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const assistantResponses: AIChatItemValueItemType[] = [];
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let interactiveResponse: InteractiveNodeResponseType | undefined;
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let requestMessages = messages;
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let inputTokens: number = 0;
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let outputTokens: number = 0;
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const subAppUsages: ChatNodeUsageType[] = [];
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// TODO: interactive rewrite messages
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while (runTimes < maxRunAgentTimes) {
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// TODO: 费用检测
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runTimes++;
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// 对请求的 requestMessages 进行压缩
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const taskDescription = currentStep.description || currentStep.title;
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if (taskDescription) {
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const result = await compressRequestMessages(requestMessages, model, taskDescription);
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requestMessages = result.messages;
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inputTokens += result.usage?.inputTokens || 0;
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outputTokens += result.usage?.outputTokens || 0;
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}
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// Request LLM
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let {
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reasoningText: reasoningContent,
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answerText: answer,
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toolCalls = [],
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usage,
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getEmptyResponseTip,
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completeMessages
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} = await createLLMResponse({
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body: {
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model,
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messages: requestMessages,
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tool_choice: 'auto',
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toolCallMode: model.toolChoice ? 'toolChoice' : 'prompt',
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tools: subApps,
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parallel_tool_calls: true,
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stream,
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temperature,
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top_p
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},
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userKey,
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isAborted,
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onReasoning,
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onStreaming,
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onToolCall,
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onToolParam
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});
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if (!answer && !reasoningContent && !toolCalls.length) {
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return Promise.reject(getEmptyResponseTip());
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}
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const requestMessagesLength = requestMessages.length;
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requestMessages = completeMessages.slice();
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for await (const tool of toolCalls) {
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// TODO: 加入交互节点处理
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// Call tool and compress tool response
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const { response, usages, interactive } = await handleToolResponse({
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call: tool,
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messages: requestMessages.slice(0, requestMessagesLength)
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}).then(async (res) => {
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const thresholds = calculateCompressionThresholds(model.maxContext);
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const toolTokenCount = await countPromptTokens(res.response);
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const response = await (async () => {
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if (toolTokenCount > thresholds.singleTool.threshold && currentStep) {
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const taskDescription = currentStep.description || currentStep.title;
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return await compressToolcallResponse(
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res.response,
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model,
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tool.function.name,
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taskDescription,
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thresholds.singleTool.target
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);
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} else {
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return res.response;
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}
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})();
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return {
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...res,
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response
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};
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});
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requestMessages.push({
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tool_call_id: tool.id,
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role: ChatCompletionRequestMessageRoleEnum.Tool,
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content: response
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});
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subAppUsages.push(...usages);
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if (interactive) {
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interactiveResponse = interactive;
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}
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}
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// TODO: 移动到工作流里 assistantResponses concat
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const currentAssistantResponses = GPTMessages2Chats({
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messages: requestMessages.slice(requestMessagesLength),
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getToolInfo
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})[0] as AIChatItemType;
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if (currentAssistantResponses) {
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assistantResponses.push(...currentAssistantResponses.value);
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}
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// Usage concat
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inputTokens += usage.inputTokens;
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outputTokens += usage.outputTokens;
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if (toolCalls.length === 0) {
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break;
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}
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}
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return {
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inputTokens,
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outputTokens,
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completeMessages: requestMessages,
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assistantResponses,
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subAppUsages,
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interactiveResponse
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};
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};
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@ -30,7 +30,7 @@ export type ResponseEvents = {
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onStreaming?: (e: { text: string }) => void;
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onReasoning?: (e: { text: string }) => void;
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onToolCall?: (e: { call: ChatCompletionMessageToolCall }) => void;
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onToolParam?: (e: { call: ChatCompletionMessageToolCall; params: string }) => void;
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onToolParam?: (e: { tool: ChatCompletionMessageToolCall; params: string }) => void;
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};
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export type CreateLLMResponseProps<T extends CompletionsBodyType = CompletionsBodyType> = {
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@ -254,7 +254,7 @@ export const createStreamResponse = async ({
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if (currentTool && arg) {
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currentTool.function.arguments += arg;
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onToolParam?.({ call: currentTool, params: arg });
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onToolParam?.({ tool: currentTool, params: arg });
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}
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}
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});
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@ -0,0 +1,196 @@
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import type { AgentPlanStepType } from './sub/plan/type';
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import { getLLMModel } from '../../../../ai/model';
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import { countPromptTokens } from '../../../../../common/string/tiktoken/index';
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import { createLLMResponse } from '../../../../ai/llm/request';
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import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
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import { addLog } from '../../../../../common/system/log';
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import { calculateCompressionThresholds } from '../../../../ai/llm/compress/constants';
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export const getMasterAgentSystemPrompt = async ({
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steps,
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step,
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userInput,
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background = '',
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model
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}: {
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steps: AgentPlanStepType[];
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step: AgentPlanStepType;
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userInput: string;
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background?: string;
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model: string;
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}) => {
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/**
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* 压缩步骤提示词（Depends on）
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* 当 stepPrompt 的 token 长度超过模型最大长度的 15% 时，调用 LLM 压缩到 12%
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*/
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const compressStepPrompt = async (
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stepPrompt: string,
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model: string,
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currentDescription: string
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): Promise<string> => {
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if (!stepPrompt) return stepPrompt;
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const modelData = getLLMModel(model);
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if (!modelData) return stepPrompt;
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const tokenCount = await countPromptTokens(stepPrompt);
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const thresholds = calculateCompressionThresholds(modelData.maxContext);
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const maxTokenThreshold = thresholds.dependsOn.threshold;
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if (tokenCount <= maxTokenThreshold) {
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return stepPrompt;
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}
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const targetTokens = thresholds.dependsOn.target;
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const compressionSystemPrompt = `<role>
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你是工作流步骤历史压缩专家，擅长从多个已执行步骤的结果中提取关键信息。
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你的任务是对工作流的执行历史进行智能压缩，在保留关键信息的同时，大幅降低 token 消耗。
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</role>
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<task_context>
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输入内容是按照"步骤ID → 步骤标题 → 执行结果"格式组织的多个步骤记录。
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你需要根据当前任务目标，对这些历史记录进行分级压缩。
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</task_context>
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<compression_workflow>
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**第一阶段：快速扫描与相关性评估**
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在开始压缩前，请先在内心完成以下思考（不需要输出）：
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1. 浏览所有步骤，识别每个步骤与当前任务目标的相关性
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2. 为每个步骤标注相关性等级：
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- [高]：直接支撑当前任务，包含关键数据或结论
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- [中]：间接相关，提供背景信息或辅助判断
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- [低]：弱相关或无关，可大幅精简或省略
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3. 确定压缩策略：基于相关性等级，决定每个步骤的保留程度
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**第二阶段：执行分级压缩**
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根据第一阶段的评估，按以下策略压缩：
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1. **高相关步骤**（保留度 80-100%）
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- 完整保留：步骤ID、标题、核心执行结果
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- 保留所有：具体数据、关键结论、链接引用、重要发现
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- 仅精简：去除啰嗦的过程描述和冗余表达
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2. **中等相关步骤**（保留度 40-60%）
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- 保留：步骤ID、标题、核心要点
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- 提炼：将执行结果浓缩为 2-3 句话
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- 去除：详细过程、重复信息、次要细节
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3. **低相关步骤**（保留度 10-20%）
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- 保留：步骤ID、标题
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- 极简化：一句话总结（或直接省略执行结果）
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- 判断：如果完全无关，可整体省略该步骤
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</compression_workflow>
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<compression_principles>
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- 删繁就简：移除重复、冗长的描述性内容
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- 去粗取精：针对当前任务目标，保留最相关的要素
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- 保数据留结论：优先保留具体数据、关键结论、链接引用
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- 保持时序：按原始顺序输出，不要打乱逻辑
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- 可追溯性：保留必要的步骤标识，确保能理解信息来源
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- 识别共性：如果连续多个步骤结果相似，可合并描述
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</compression_principles>
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<quality_check>
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压缩完成后，请自我检查：
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1. 是否达到了目标压缩比例？
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2. 当前任务所需的关键信息是否都保留了？
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3. 压缩后的内容是否仍能让后续步骤理解发生了什么？
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4. 步骤的时序关系是否清晰？
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</quality_check>`;
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const userPrompt = `请对以下工作流步骤的执行历史进行压缩，保留与当前任务最相关的信息。
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**当前任务目标**：${currentDescription}
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**需要压缩的步骤历史**：
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${stepPrompt}
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**压缩要求**：
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- 原始长度：${tokenCount} tokens
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- 目标长度：约 ${targetTokens} tokens（压缩到原长度的 ${Math.round((targetTokens / tokenCount) * 100)}%）
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**输出格式要求**：
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1. 保留步骤结构：每个步骤使用"# 步骤ID: [id]\\n\\t - 步骤标题: [title]\\n\\t - 执行结果: [精简后的结果]"的格式
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2. 根据相关性分级处理：
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- 与当前任务高度相关的步骤：保留完整的关键信息（数据、结论、链接等）
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- 中等相关的步骤：提炼要点，移除冗余描述
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- 低相关的步骤：仅保留一句话总结或省略执行结果
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3. 保持步骤顺序：按原始顺序输出，不要打乱
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4. 提取共性：如果连续多个步骤结果相似，可以适当合并描述
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**质量标准**：
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- 压缩后的内容能让后续步骤理解前置步骤做了什么、得到了什么结果
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- 保留所有对当前任务有价值的具体数据和关键结论
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- 移除重复、啰嗦的描述性文字
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请直接输出压缩后的步骤历史：`;
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try {
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const { answerText } = await createLLMResponse({
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body: {
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model: modelData,
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messages: [
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{
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role: ChatCompletionRequestMessageRoleEnum.System,
|
||||
content: compressionSystemPrompt
|
||||
},
|
||||
{
|
||||
role: ChatCompletionRequestMessageRoleEnum.User,
|
||||
content: userPrompt
|
||||
}
|
||||
],
|
||||
temperature: 0.1,
|
||||
stream: false
|
||||
}
|
||||
});
|
||||
|
||||
return answerText || stepPrompt;
|
||||
} catch (error) {
|
||||
console.error('压缩 stepPrompt 失败:', error);
|
||||
// 压缩失败时返回原始内容
|
||||
return stepPrompt;
|
||||
}
|
||||
};
|
||||
|
||||
let stepPrompt = steps
|
||||
.filter((item) => step.depends_on && step.depends_on.includes(item.id))
|
||||
.map(
|
||||
(item) =>
|
||||
`# 步骤ID: ${item.id}\n\t - 步骤标题: ${item.title}\n\t - 执行结果: ${item.response}`
|
||||
)
|
||||
.filter(Boolean)
|
||||
.join('\n');
|
||||
addLog.debug(`Step call depends_on (LLM): ${step.id}`, step.depends_on);
|
||||
// 压缩依赖的上下文
|
||||
stepPrompt = await compressStepPrompt(stepPrompt, model, step.description || step.title);
|
||||
|
||||
return `请根据任务背景、之前步骤的执行结果和当前步骤要求选择并调用相应的工具。如果是一个总结性质的步骤，请整合之前步骤的结果进行总结。
|
||||
【任务背景】
|
||||
目标: ${userInput}
|
||||
前置信息: ${background}
|
||||
|
||||
【当前步骤】
|
||||
步骤ID: ${step.id}
|
||||
步骤标题: ${step.title}
|
||||
|
||||
${
|
||||
stepPrompt
|
||||
? `【之前步骤的执行结果】
|
||||
${stepPrompt}`
|
||||
: ''
|
||||
}
|
||||
|
||||
【执行指导】
|
||||
1. 仔细阅读前面步骤的执行结果，理解已经获得的信息
|
||||
2. 根据当前步骤描述和前面的结果，分析需要使用的工具
|
||||
3. 从可用工具列表中选择最合适的工具
|
||||
4. 基于前面步骤的结果为工具生成合理的参数
|
||||
5. 如果需要多个工具，可以同时调用
|
||||
6. 确保当前步骤的执行能够有效利用和整合前面的结果
|
||||
7. 如果是总结的步骤，请利用之前步骤的信息进行全面总结
|
||||
|
||||
请严格按照步骤描述执行，确保完成所有要求的子任务。`;
|
||||
};
|
||||
|
|
@ -0,0 +1,487 @@
|
|||
import type { NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
|
||||
import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
|
||||
import {
|
||||
ConfirmPlanAgentText,
|
||||
DispatchNodeResponseKeyEnum,
|
||||
SseResponseEventEnum
|
||||
} from '@fastgpt/global/core/workflow/runtime/constants';
|
||||
import type {
|
||||
DispatchNodeResultType,
|
||||
ModuleDispatchProps,
|
||||
RuntimeNodeItemType
|
||||
} from '@fastgpt/global/core/workflow/runtime/type';
|
||||
import { getLLMModel } from '../../../../ai/model';
|
||||
import { getNodeErrResponse, getHistories } from '../../utils';
|
||||
import type { AIChatItemValueItemType, ChatItemType } from '@fastgpt/global/core/chat/type';
|
||||
import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
|
||||
import {
|
||||
chats2GPTMessages,
|
||||
chatValue2RuntimePrompt,
|
||||
GPTMessages2Chats
|
||||
} from '@fastgpt/global/core/chat/adapt';
|
||||
import { formatModelChars2Points } from '../../../../../support/wallet/usage/utils';
|
||||
import { filterMemoryMessages } from '../utils';
|
||||
import { systemSubInfo } from './sub/constants';
|
||||
import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
|
||||
import { dispatchPlanAgent, dispatchReplanAgent } from './sub/plan';
|
||||
import type { FlowNodeTemplateType } from '@fastgpt/global/core/workflow/type/node';
|
||||
import { getSubApps, rewriteSubAppsToolset } from './sub';
|
||||
|
||||
import { getFileInputPrompt } from './sub/file/utils';
|
||||
import type { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type';
|
||||
import type { AgentPlanType } from './sub/plan/type';
|
||||
import type { localeType } from '@fastgpt/global/common/i18n/type';
|
||||
import { stepCall } from './master/call';
|
||||
import type { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
|
||||
import { addLog } from '../../../../../common/system/log';
|
||||
import { checkTaskComplexity } from './master/taskComplexity';
|
||||
|
||||
export type DispatchAgentModuleProps = ModuleDispatchProps<{
|
||||
[NodeInputKeyEnum.history]?: ChatItemType[];
|
||||
[NodeInputKeyEnum.userChatInput]: string;
|
||||
|
||||
[NodeInputKeyEnum.fileUrlList]?: string[];
|
||||
[NodeInputKeyEnum.aiModel]: string;
|
||||
[NodeInputKeyEnum.aiSystemPrompt]: string;
|
||||
[NodeInputKeyEnum.aiChatTemperature]?: number;
|
||||
[NodeInputKeyEnum.aiChatTopP]?: number;
|
||||
|
||||
[NodeInputKeyEnum.subApps]?: FlowNodeTemplateType[];
|
||||
[NodeInputKeyEnum.isAskAgent]?: boolean;
|
||||
[NodeInputKeyEnum.isPlanAgent]?: boolean;
|
||||
}>;
|
||||
|
||||
type Response = DispatchNodeResultType<{
|
||||
[NodeOutputKeyEnum.answerText]: string;
|
||||
}>;
|
||||
|
||||
export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise<Response> => {
|
||||
let {
|
||||
node: { nodeId, name, isEntry, version, inputs },
|
||||
lang,
|
||||
runtimeNodes,
|
||||
histories,
|
||||
query,
|
||||
requestOrigin,
|
||||
chatConfig,
|
||||
lastInteractive,
|
||||
runningUserInfo,
|
||||
runningAppInfo,
|
||||
externalProvider,
|
||||
stream,
|
||||
workflowDispatchDeep,
|
||||
workflowStreamResponse,
|
||||
usagePush,
|
||||
params: {
|
||||
model,
|
||||
systemPrompt,
|
||||
userChatInput,
|
||||
history = 6,
|
||||
fileUrlList: fileLinks,
|
||||
temperature,
|
||||
aiChatTopP,
|
||||
subApps = [],
|
||||
isPlanAgent = true,
|
||||
isAskAgent = true
|
||||
}
|
||||
} = props;
|
||||
const agentModel = getLLMModel(model);
|
||||
const chatHistories = getHistories(history, histories);
|
||||
const historiesMessages = chats2GPTMessages({
|
||||
messages: chatHistories,
|
||||
reserveId: false,
|
||||
reserveTool: false
|
||||
});
|
||||
|
||||
const planMessagesKey = `planMessages-${nodeId}`;
|
||||
const replanMessagesKey = `replanMessages-${nodeId}`;
|
||||
const agentPlanKey = `agentPlan-${nodeId}`;
|
||||
|
||||
// 交互模式进来的话，这个值才是交互输入的值
|
||||
const interactiveInput = lastInteractive ? chatValue2RuntimePrompt(query).text : '';
|
||||
|
||||
// Get history messages
|
||||
let { planHistoryMessages, replanMessages, agentPlan } = (() => {
|
||||
const lastHistory = chatHistories[chatHistories.length - 1];
|
||||
if (lastHistory && lastHistory.obj === ChatRoleEnum.AI) {
|
||||
return {
|
||||
planHistoryMessages: (lastHistory.memories?.[planMessagesKey] ||
|
||||
[]) as ChatCompletionMessageParam[],
|
||||
replanMessages: (lastHistory.memories?.[replanMessagesKey] ||
|
||||
[]) as ChatCompletionMessageParam[],
|
||||
agentPlan: (lastHistory.memories?.[agentPlanKey] || []) as AgentPlanType
|
||||
};
|
||||
}
|
||||
return {
|
||||
planHistoryMessages: undefined,
|
||||
replanMessages: undefined,
|
||||
agentPlan: undefined
|
||||
};
|
||||
})();
|
||||
|
||||
try {
|
||||
// Get files
|
||||
const fileUrlInput = inputs.find((item) => item.key === NodeInputKeyEnum.fileUrlList);
|
||||
if (!fileUrlInput || !fileUrlInput.value || fileUrlInput.value.length === 0) {
|
||||
fileLinks = undefined;
|
||||
}
|
||||
const { filesMap, prompt: fileInputPrompt } = getFileInputPrompt({
|
||||
fileUrls: fileLinks,
|
||||
requestOrigin,
|
||||
maxFiles: chatConfig?.fileSelectConfig?.maxFiles || 20,
|
||||
histories: chatHistories
|
||||
});
|
||||
|
||||
// Get sub apps
|
||||
const { subAppList, subAppsMap, getSubAppInfo } = await useSubApps({
|
||||
subApps,
|
||||
lang,
|
||||
filesMap
|
||||
});
|
||||
|
||||
/* ===== AI Start ===== */
|
||||
|
||||
/* ===== Check task complexity ===== */
|
||||
const taskIsComplexity = await (async () => {
|
||||
// Check task complexity: 第一次进入任务时候进行判断。（有 plan了，说明已经开始执行任务了）
|
||||
const isCheckTaskComplexityStep = isPlanAgent && !agentPlan && !planHistoryMessages;
|
||||
// if (isCheckTaskComplexityStep) {
|
||||
// const res = await checkTaskComplexity({
|
||||
// model,
|
||||
// userChatInput
|
||||
// });
|
||||
// if (res.usage) {
|
||||
// usagePush([res.usage]);
|
||||
// }
|
||||
// return res.complex;
|
||||
// }
|
||||
|
||||
// 对轮运行时候，代表都是进入复杂流程
|
||||
return true;
|
||||
})();
|
||||
|
||||
if (taskIsComplexity) {
|
||||
/* ===== Plan Agent ===== */
|
||||
const planCallFn = async () => {
|
||||
// 点了确认。此时肯定有 agentPlans
|
||||
if (
|
||||
lastInteractive?.type === 'agentPlanCheck' &&
|
||||
interactiveInput === ConfirmPlanAgentText &&
|
||||
agentPlan
|
||||
) {
|
||||
planHistoryMessages = undefined;
|
||||
} else {
|
||||
const { answerText, plan, completeMessages, usages, interactiveResponse } =
|
||||
await dispatchPlanAgent({
|
||||
historyMessages: planHistoryMessages || historiesMessages,
|
||||
userInput: lastInteractive ? interactiveInput : userChatInput,
|
||||
interactive: lastInteractive,
|
||||
subAppList,
|
||||
getSubAppInfo,
|
||||
systemPrompt,
|
||||
model,
|
||||
temperature,
|
||||
top_p: aiChatTopP,
|
||||
stream,
|
||||
isTopPlanAgent: workflowDispatchDeep === 1
|
||||
});
|
||||
|
||||
const assistantResponses: AIChatItemValueItemType[] = [
|
||||
...(answerText
|
||||
? [
|
||||
{
|
||||
text: {
|
||||
content: answerText
|
||||
}
|
||||
}
|
||||
]
|
||||
: []),
|
||||
...(plan
|
||||
? [
|
||||
{
|
||||
agentPlan: plan
|
||||
}
|
||||
]
|
||||
: [])
|
||||
];
|
||||
|
||||
// SSE response
|
||||
if (answerText) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: answerText
|
||||
})
|
||||
});
|
||||
}
|
||||
if (plan) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.agentPlan,
|
||||
data: { agentPlan: plan }
|
||||
});
|
||||
}
|
||||
|
||||
agentPlan = plan;
|
||||
|
||||
usagePush(usages);
|
||||
// Sub agent plan 不会有交互响应。Top agent plan 肯定会有。
|
||||
if (interactiveResponse) {
|
||||
return {
|
||||
[DispatchNodeResponseKeyEnum.assistantResponses]: assistantResponses,
|
||||
[DispatchNodeResponseKeyEnum.memories]: {
|
||||
[planMessagesKey]: filterMemoryMessages(completeMessages),
|
||||
[agentPlanKey]: agentPlan
|
||||
},
|
||||
[DispatchNodeResponseKeyEnum.interactive]: interactiveResponse
|
||||
};
|
||||
} else {
|
||||
planHistoryMessages = undefined;
|
||||
}
|
||||
}
|
||||
};
|
||||
const replanCallFn = async ({ plan }: { plan: AgentPlanType }) => {
|
||||
if (!agentPlan) return;
|
||||
|
||||
addLog.debug(`Replan step`);
|
||||
|
||||
const {
|
||||
answerText,
|
||||
plan: rePlan,
|
||||
completeMessages,
|
||||
usages,
|
||||
interactiveResponse
|
||||
} = await dispatchReplanAgent({
|
||||
historyMessages: replanMessages || historiesMessages,
|
||||
userInput: lastInteractive ? interactiveInput : userChatInput,
|
||||
plan,
|
||||
interactive: lastInteractive,
|
||||
subAppList,
|
||||
getSubAppInfo,
|
||||
systemPrompt,
|
||||
model,
|
||||
temperature,
|
||||
top_p: aiChatTopP,
|
||||
stream,
|
||||
isTopPlanAgent: workflowDispatchDeep === 1
|
||||
});
|
||||
|
||||
if (rePlan) {
|
||||
agentPlan.steps.push(...rePlan.steps);
|
||||
agentPlan.replan = rePlan.replan;
|
||||
}
|
||||
|
||||
const assistantResponses: AIChatItemValueItemType[] = [
|
||||
...(answerText
|
||||
? [
|
||||
{
|
||||
text: {
|
||||
content: answerText
|
||||
}
|
||||
}
|
||||
]
|
||||
: []),
|
||||
...(rePlan
|
||||
? [
|
||||
{
|
||||
agentPlan: plan
|
||||
}
|
||||
]
|
||||
: [])
|
||||
];
|
||||
|
||||
// SSE response
|
||||
if (answerText) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: answerText
|
||||
})
|
||||
});
|
||||
}
|
||||
if (rePlan) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.agentPlan,
|
||||
data: { agentPlan: plan }
|
||||
});
|
||||
}
|
||||
|
||||
usagePush(usages);
|
||||
// Sub agent plan 不会有交互响应。Top agent plan 肯定会有。
|
||||
if (interactiveResponse) {
|
||||
return {
|
||||
[DispatchNodeResponseKeyEnum.assistantResponses]: assistantResponses,
|
||||
[DispatchNodeResponseKeyEnum.memories]: {
|
||||
[replanMessagesKey]: filterMemoryMessages(completeMessages),
|
||||
[agentPlanKey]: agentPlan
|
||||
},
|
||||
[DispatchNodeResponseKeyEnum.interactive]: interactiveResponse
|
||||
};
|
||||
} else {
|
||||
replanMessages = undefined;
|
||||
}
|
||||
};
|
||||
|
||||
// Plan step: 需要生成 plan，且还没有完整的 plan
|
||||
const isPlanStep = isPlanAgent && (!agentPlan || planHistoryMessages);
|
||||
// Replan step: 已有 plan，且有 replan 历史消息
|
||||
const isReplanStep = isPlanAgent && agentPlan && replanMessages;
|
||||
|
||||
// 执行 Plan/replan
|
||||
if (isPlanStep) {
|
||||
const result = await planCallFn();
|
||||
// 有 result 代表 plan 有交互响应（check/ask）
|
||||
if (result) return result;
|
||||
} else if (isReplanStep) {
|
||||
const result = await replanCallFn({
|
||||
plan: agentPlan!
|
||||
});
|
||||
if (result) return result;
|
||||
}
|
||||
|
||||
addLog.debug(`Start master agent`, {
|
||||
agentPlan: JSON.stringify(agentPlan, null, 2)
|
||||
});
|
||||
|
||||
/* ===== Master agent, 逐步执行 plan ===== */
|
||||
if (!agentPlan) return Promise.reject('没有 plan');
|
||||
|
||||
let assistantResponses: AIChatItemValueItemType[] = [];
|
||||
|
||||
while (agentPlan.steps!.filter((item) => !item.response)!.length) {
|
||||
for await (const step of agentPlan?.steps) {
|
||||
if (step.response) continue;
|
||||
addLog.debug(`Step call: ${step.id}`, step);
|
||||
|
||||
// Temp code
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: `\n # ${step.id}: ${step.title}\n`
|
||||
})
|
||||
});
|
||||
const tmpAssistantResponses: AIChatItemValueItemType = {
|
||||
text: {
|
||||
content: `\n # ${step.id}: ${step.title}\n`
|
||||
}
|
||||
};
|
||||
assistantResponses.push(tmpAssistantResponses);
|
||||
|
||||
// Step call
|
||||
const result = await stepCall({
|
||||
...props,
|
||||
getSubAppInfo,
|
||||
steps: agentPlan.steps, // 传入所有步骤，而不仅仅是未执行的步骤
|
||||
subAppList,
|
||||
step,
|
||||
filesMap,
|
||||
subAppsMap
|
||||
});
|
||||
|
||||
// Merge response
|
||||
const assistantResponse = GPTMessages2Chats({
|
||||
messages: result.assistantMessages,
|
||||
reserveTool: true,
|
||||
getToolInfo: getSubAppInfo
|
||||
})
|
||||
.map((item) => item.value as AIChatItemValueItemType[])
|
||||
.flat();
|
||||
step.response = result.rawResponse;
|
||||
step.summary = result.summary;
|
||||
assistantResponses.push(...assistantResponse);
|
||||
}
|
||||
|
||||
// Call replan
|
||||
if (agentPlan?.replan === true) {
|
||||
// 内部会修改 agentPlan.steps 的内容，从而使循环重复触发
|
||||
const replanResult = await replanCallFn({
|
||||
plan: agentPlan
|
||||
});
|
||||
// Replan 里有需要用户交互的内容，直接 return
|
||||
if (replanResult) return replanResult;
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
// 目前 Master 不会触发交互
|
||||
// [DispatchNodeResponseKeyEnum.interactive]: interactiveResponse,
|
||||
// TODO: 需要对 memoryMessages 单独建表存储
|
||||
[DispatchNodeResponseKeyEnum.memories]: {
|
||||
[agentPlanKey]: agentPlan,
|
||||
[planMessagesKey]: undefined,
|
||||
[replanMessagesKey]: undefined
|
||||
},
|
||||
[DispatchNodeResponseKeyEnum.assistantResponses]: assistantResponses,
|
||||
[DispatchNodeResponseKeyEnum.nodeResponse]: {
|
||||
// 展示的积分消耗
|
||||
// totalPoints: totalPointsUsage,
|
||||
// toolCallInputTokens: inputTokens,
|
||||
// toolCallOutputTokens: outputTokens,
|
||||
// childTotalPoints: toolTotalPoints,
|
||||
// model: modelName,
|
||||
query: userChatInput,
|
||||
// toolDetail: dispatchFlowResponse,
|
||||
mergeSignId: nodeId
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
// 简单 tool call 模式（一轮对话就结束了，不会多轮，所以不会受到连续对话的 taskIsComplexity 影响）
|
||||
return Promise.reject('目前未支持简单模式');
|
||||
} catch (error) {
|
||||
return getNodeErrResponse({ error });
|
||||
}
|
||||
};
|
||||
|
||||
export const useSubApps = async ({
|
||||
subApps,
|
||||
lang,
|
||||
filesMap
|
||||
}: {
|
||||
subApps: FlowNodeTemplateType[];
|
||||
lang?: localeType;
|
||||
filesMap: Record<string, string>;
|
||||
}) => {
|
||||
// Get sub apps
|
||||
const runtimeSubApps = await rewriteSubAppsToolset({
|
||||
subApps: subApps.map<RuntimeNodeItemType>((node) => {
|
||||
return {
|
||||
nodeId: node.id,
|
||||
name: node.name,
|
||||
avatar: node.avatar,
|
||||
intro: node.intro,
|
||||
toolDescription: node.toolDescription,
|
||||
flowNodeType: node.flowNodeType,
|
||||
showStatus: node.showStatus,
|
||||
isEntry: false,
|
||||
inputs: node.inputs,
|
||||
outputs: node.outputs,
|
||||
pluginId: node.pluginId,
|
||||
version: node.version,
|
||||
toolConfig: node.toolConfig,
|
||||
catchError: node.catchError
|
||||
};
|
||||
}),
|
||||
lang
|
||||
});
|
||||
|
||||
const subAppList = getSubApps({
|
||||
subApps: runtimeSubApps,
|
||||
addReadFileTool: Object.keys(filesMap).length > 0
|
||||
});
|
||||
|
||||
const subAppsMap = new Map(runtimeSubApps.map((item) => [item.nodeId, item]));
|
||||
const getSubAppInfo = (id: string) => {
|
||||
const toolNode = subAppsMap.get(id) || systemSubInfo[id];
|
||||
return {
|
||||
name: toolNode?.name || '',
|
||||
avatar: toolNode?.avatar || '',
|
||||
toolDescription: toolNode?.toolDescription || toolNode?.name || ''
|
||||
};
|
||||
};
|
||||
|
||||
return {
|
||||
subAppList,
|
||||
subAppsMap,
|
||||
getSubAppInfo
|
||||
};
|
||||
};
|
||||
|
|
@ -16,7 +16,7 @@ import {
|
|||
} from '@fastgpt/global/core/workflow/runtime/utils';
|
||||
import { getWorkflowChildResponseWrite } from '../../../utils';
|
||||
import { SubAppIds } from '../sub/constants';
|
||||
import { parseToolArgs } from '../../utils';
|
||||
import { parseToolArgs } from '../../../../../ai/utils';
|
||||
import { dispatchFileRead } from '../sub/file';
|
||||
import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
|
||||
import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
|
||||
|
|
@ -25,8 +25,6 @@ import { dispatchApp, dispatchPlugin } from '../sub/app';
|
|||
import { getErrText } from '@fastgpt/global/common/error/utils';
|
||||
import type { DispatchAgentModuleProps } from '..';
|
||||
import { getLLMModel } from '../../../../../ai/model';
|
||||
import { createLLMResponse } from '../../../../../ai/llm/request';
|
||||
import { addLog } from '../../../../../../common/system/log';
|
||||
import { getStepDependon } from './dependon';
|
||||
import { getResponseSummary } from './responseSummary';
|
||||
|
||||
|
|
@ -47,6 +45,7 @@ export const stepCall = async ({
|
|||
subAppsMap: Map<string, RuntimeNodeItemType>;
|
||||
}) => {
|
||||
const {
|
||||
res,
|
||||
node: { nodeId },
|
||||
runtimeNodes,
|
||||
chatConfig,
|
||||
|
|
@ -55,7 +54,6 @@ export const stepCall = async ({
|
|||
variables,
|
||||
externalProvider,
|
||||
stream,
|
||||
res,
|
||||
workflowStreamResponse,
|
||||
usagePush,
|
||||
params: { userChatInput, systemPrompt, model, temperature, aiChatTopP }
|
||||
|
|
@ -74,15 +72,17 @@ export const stepCall = async ({
|
|||
step.depends_on = depends;
|
||||
}
|
||||
|
||||
// addLog.debug(`Step information`, steps);
|
||||
// Step call system prompt
|
||||
// TODO: 需要把压缩的 usage 返回
|
||||
const systemPromptContent = await getMasterAgentSystemPrompt({
|
||||
steps,
|
||||
step,
|
||||
userInput: userChatInput,
|
||||
model
|
||||
// background: systemPrompt
|
||||
model,
|
||||
background: systemPrompt
|
||||
});
|
||||
|
||||
// Step call request messages
|
||||
const requestMessages = chats2GPTMessages({
|
||||
messages: [
|
||||
{
|
||||
|
|
@ -109,24 +109,22 @@ export const stepCall = async ({
|
|||
// 'Step call requestMessages',
|
||||
// JSON.stringify({ requestMessages, subAppList }, null, 2)
|
||||
// );
|
||||
// TODO: 阶段性推送账单
|
||||
const { assistantResponses, inputTokens, outputTokens, subAppUsages, interactiveResponse } =
|
||||
|
||||
const { assistantMessages, inputTokens, outputTokens, subAppUsages, interactiveResponse } =
|
||||
await runAgentCall({
|
||||
maxRunAgentTimes: 100,
|
||||
currentStep: step,
|
||||
// interactiveEntryToolParams: lastInteractive?.toolParams,
|
||||
body: {
|
||||
messages: requestMessages,
|
||||
model: getLLMModel(model),
|
||||
temperature,
|
||||
stream,
|
||||
top_p: aiChatTopP,
|
||||
subApps: subAppList
|
||||
tools: subAppList
|
||||
},
|
||||
|
||||
userKey: externalProvider.openaiAccount,
|
||||
isAborted: res ? () => res.closed : undefined,
|
||||
getToolInfo: getSubAppInfo,
|
||||
// childrenInteractiveParams
|
||||
|
||||
onReasoning({ text }) {
|
||||
workflowStreamResponse?.({
|
||||
|
|
@ -160,9 +158,9 @@ export const stepCall = async ({
|
|||
}
|
||||
});
|
||||
},
|
||||
onToolParam({ call, params }) {
|
||||
onToolParam({ tool, params }) {
|
||||
workflowStreamResponse?.({
|
||||
id: call.id,
|
||||
id: tool.id,
|
||||
event: SseResponseEventEnum.toolParams,
|
||||
data: {
|
||||
tool: {
|
||||
|
|
@ -172,6 +170,7 @@ export const stepCall = async ({
|
|||
});
|
||||
},
|
||||
|
||||
// TODO: 对齐最新的方案
|
||||
handleToolResponse: async ({ call, messages }) => {
|
||||
const toolId = call.function.name;
|
||||
const childWorkflowStreamResponse = getWorkflowChildResponseWrite({
|
||||
|
|
@ -338,12 +337,34 @@ export const stepCall = async ({
|
|||
|
||||
return {
|
||||
response,
|
||||
assistantMessages: [], // TODO
|
||||
usages
|
||||
};
|
||||
},
|
||||
handleInteractiveTool: async ({ toolParams }) => {
|
||||
return {
|
||||
response: 'Interactive tool not supported',
|
||||
assistantMessages: [], // TODO
|
||||
usages: []
|
||||
};
|
||||
}
|
||||
});
|
||||
|
||||
const answerText = assistantResponses.map((item) => item.text?.content).join('\n');
|
||||
const answerText = assistantMessages
|
||||
.map((item) => {
|
||||
if (item.role === 'assistant' && item.content) {
|
||||
if (typeof item.content === 'string') {
|
||||
return item.content;
|
||||
} else {
|
||||
return item.content
|
||||
.map((content) => (content.type === 'text' ? content.text : ''))
|
||||
.join('\n');
|
||||
}
|
||||
}
|
||||
return '';
|
||||
})
|
||||
.join('\n');
|
||||
// Get step response summary
|
||||
const { answerText: summary, usage: summaryUsage } = await getResponseSummary({
|
||||
response: answerText,
|
||||
model
|
||||
|
|
@ -355,6 +376,6 @@ export const stepCall = async ({
|
|||
return {
|
||||
rawResponse: answerText,
|
||||
summary,
|
||||
assistantResponses
|
||||
assistantMessages
|
||||
};
|
||||
};
|
||||
|
|
@ -2,7 +2,7 @@ import { getLLMModel } from '../../../../../ai/model';
|
|||
import type { AgentPlanStepType } from '../sub/plan/type';
|
||||
import { addLog } from '../../../../../../common/system/log';
|
||||
import { createLLMResponse } from '../../../../../ai/llm/request';
|
||||
import { parseToolArgs } from '../../utils';
|
||||
import { parseToolArgs } from '../../../../../ai/utils';
|
||||
import type { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
|
||||
import { formatModelChars2Points } from '../../../../../../support/wallet/usage/utils';
|
||||
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
import { createLLMResponse } from '../../../../../ai/llm/request';
|
||||
import { parseToolArgs } from '../../utils';
|
||||
import { parseToolArgs } from '../../../../../ai/utils';
|
||||
import { addLog } from '../../../../../../common/system/log';
|
||||
import { formatModelChars2Points } from '../../../../../../support/wallet/usage/utils';
|
||||
import type { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
|
||||
|
|
@ -16,7 +16,7 @@ import type {
|
|||
InteractiveNodeResponseType,
|
||||
WorkflowInteractiveResponseType
|
||||
} from '@fastgpt/global/core/workflow/template/system/interactive/type';
|
||||
import { parseToolArgs } from '../../../utils';
|
||||
import { parseToolArgs } from '../../../../../../ai/utils';
|
||||
import { PlanAgentAskTool, type AskAgentToolParamsType } from './ask/constants';
|
||||
import { PlanCheckInteractive } from './constants';
|
||||
import type { AgentPlanType } from './type';
|
||||
|
|
@ -93,7 +93,7 @@ export const dispatchPlanAgent = async ({
|
|||
tool_call_id: lastMessages.tool_calls[0].id,
|
||||
content: userInput
|
||||
});
|
||||
// TODO: 是否合理
|
||||
// TODO: 是否合理，以及模型兼容性问题
|
||||
requestMessages.push({
|
||||
role: 'assistant',
|
||||
content: '请基于以上收集的用户信息，重新生成完整的计划，严格按照 JSON Schema 输出。'
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
import type { RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type';
|
||||
import type { JSONSchemaInputType } from '@fastgpt/global/core/app/jsonschema';
|
||||
import type { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
|
||||
|
||||
export type ToolNodeItemType = RuntimeNodeItemType & {
|
||||
toolParams: RuntimeNodeItemType['inputs'];
|
||||
jsonSchema?: JSONSchemaInputType;
|
||||
};
|
||||
|
||||
export type DispatchSubAppResponse = {
|
||||
response: string;
|
||||
usages?: ChatNodeUsageType[];
|
||||
};
|
||||
|
||||
export type GetSubAppInfoFnType = (id: string) => {
|
||||
name: string;
|
||||
avatar: string;
|
||||
toolDescription: string;
|
||||
};
|
||||
|
|
@ -0,0 +1,31 @@
|
|||
/*
|
||||
匹配 {{@toolId@}}，转化成: @name 的格式。
|
||||
*/
|
||||
export const parseSystemPrompt = ({
|
||||
systemPrompt,
|
||||
getSubAppInfo
|
||||
}: {
|
||||
systemPrompt?: string;
|
||||
getSubAppInfo: (id: string) => {
|
||||
name: string;
|
||||
avatar: string;
|
||||
toolDescription: string;
|
||||
};
|
||||
}): string => {
|
||||
if (!systemPrompt) return '';
|
||||
|
||||
// Match pattern {{@toolId@}} and convert to @name format
|
||||
const pattern = /\{\{@([^@]+)@\}\}/g;
|
||||
|
||||
const processedPrompt = systemPrompt.replace(pattern, (match, toolId) => {
|
||||
const toolInfo = getSubAppInfo(toolId);
|
||||
if (!toolInfo) {
|
||||
console.warn(`Tool not found for ID: ${toolId}`);
|
||||
return match; // Return original match if tool not found
|
||||
}
|
||||
|
||||
return `@${toolInfo.name}`;
|
||||
});
|
||||
|
||||
return processedPrompt;
|
||||
};
|
||||
|
|
@ -1,197 +1,14 @@
|
|||
import type { AgentPlanStepType } from './sub/plan/type';
|
||||
import type { AgentPlanType } from './sub/plan/type';
|
||||
import { getLLMModel } from '../../../../ai/model';
|
||||
import { countPromptTokens } from '../../../../../common/string/tiktoken/index';
|
||||
import { createLLMResponse } from '../../../../ai/llm/request';
|
||||
import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
|
||||
import { addLog } from '../../../../../common/system/log';
|
||||
import { calculateCompressionThresholds } from '../../../../ai/llm/compress/constants';
|
||||
import { replaceVariable } from '@fastgpt/global/common/string/tools';
|
||||
|
||||
export const getMasterAgentSystemPrompt = async ({
|
||||
steps,
|
||||
step,
|
||||
userInput,
|
||||
background = '',
|
||||
model
|
||||
}: {
|
||||
steps: AgentPlanStepType[];
|
||||
step: AgentPlanStepType;
|
||||
userInput: string;
|
||||
background?: string;
|
||||
model: string;
|
||||
export const getMultiplePrompt = (obj: {
|
||||
fileCount: number;
|
||||
imgCount: number;
|
||||
question: string;
|
||||
}) => {
|
||||
/**
|
||||
* 压缩步骤提示词（Depends on）
|
||||
* 当 stepPrompt 的 token 长度超过模型最大长度的 15% 时，调用 LLM 压缩到 12%
|
||||
*/
|
||||
const compressStepPrompt = async (
|
||||
stepPrompt: string,
|
||||
model: string,
|
||||
currentDescription: string
|
||||
): Promise<string> => {
|
||||
if (!stepPrompt) return stepPrompt;
|
||||
|
||||
const modelData = getLLMModel(model);
|
||||
if (!modelData) return stepPrompt;
|
||||
|
||||
const tokenCount = await countPromptTokens(stepPrompt);
|
||||
const thresholds = calculateCompressionThresholds(modelData.maxContext);
|
||||
const maxTokenThreshold = thresholds.dependsOn.threshold;
|
||||
|
||||
if (tokenCount <= maxTokenThreshold) {
|
||||
return stepPrompt;
|
||||
}
|
||||
|
||||
const targetTokens = thresholds.dependsOn.target;
|
||||
|
||||
const compressionSystemPrompt = `<role>
|
||||
你是工作流步骤历史压缩专家，擅长从多个已执行步骤的结果中提取关键信息。
|
||||
你的任务是对工作流的执行历史进行智能压缩，在保留关键信息的同时，大幅降低 token 消耗。
|
||||
</role>
|
||||
|
||||
<task_context>
|
||||
输入内容是按照"步骤ID → 步骤标题 → 执行结果"格式组织的多个步骤记录。
|
||||
你需要根据当前任务目标，对这些历史记录进行分级压缩。
|
||||
</task_context>
|
||||
|
||||
<compression_workflow>
|
||||
**第一阶段：快速扫描与相关性评估**
|
||||
|
||||
在开始压缩前，请先在内心完成以下思考（不需要输出）：
|
||||
1. 浏览所有步骤，识别每个步骤与当前任务目标的相关性
|
||||
2. 为每个步骤标注相关性等级：
|
||||
- [高]：直接支撑当前任务，包含关键数据或结论
|
||||
- [中]：间接相关，提供背景信息或辅助判断
|
||||
- [低]：弱相关或无关，可大幅精简或省略
|
||||
3. 确定压缩策略：基于相关性等级，决定每个步骤的保留程度
|
||||
|
||||
**第二阶段：执行分级压缩**
|
||||
|
||||
根据第一阶段的评估，按以下策略压缩：
|
||||
|
||||
1. **高相关步骤**（保留度 80-100%）
|
||||
- 完整保留：步骤ID、标题、核心执行结果
|
||||
- 保留所有：具体数据、关键结论、链接引用、重要发现
|
||||
- 仅精简：去除啰嗦的过程描述和冗余表达
|
||||
|
||||
2. **中等相关步骤**（保留度 40-60%）
|
||||
- 保留：步骤ID、标题、核心要点
|
||||
- 提炼：将执行结果浓缩为 2-3 句话
|
||||
- 去除：详细过程、重复信息、次要细节
|
||||
|
||||
3. **低相关步骤**（保留度 10-20%）
|
||||
- 保留：步骤ID、标题
|
||||
- 极简化：一句话总结（或直接省略执行结果）
|
||||
- 判断：如果完全无关，可整体省略该步骤
|
||||
</compression_workflow>
|
||||
|
||||
<compression_principles>
|
||||
- 删繁就简：移除重复、冗长的描述性内容
|
||||
- 去粗取精：针对当前任务目标，保留最相关的要素
|
||||
- 保数据留结论：优先保留具体数据、关键结论、链接引用
|
||||
- 保持时序：按原始顺序输出，不要打乱逻辑
|
||||
- 可追溯性：保留必要的步骤标识，确保能理解信息来源
|
||||
- 识别共性：如果连续多个步骤结果相似，可合并描述
|
||||
</compression_principles>
|
||||
|
||||
<quality_check>
|
||||
压缩完成后，请自我检查：
|
||||
1. 是否达到了目标压缩比例？
|
||||
2. 当前任务所需的关键信息是否都保留了？
|
||||
3. 压缩后的内容是否仍能让后续步骤理解发生了什么？
|
||||
4. 步骤的时序关系是否清晰？
|
||||
</quality_check>`;
|
||||
|
||||
const userPrompt = `请对以下工作流步骤的执行历史进行压缩，保留与当前任务最相关的信息。
|
||||
|
||||
**当前任务目标**：${currentDescription}
|
||||
|
||||
**需要压缩的步骤历史**：
|
||||
${stepPrompt}
|
||||
|
||||
**压缩要求**：
|
||||
- 原始长度：${tokenCount} tokens
|
||||
- 目标长度：约 ${targetTokens} tokens（压缩到原长度的 ${Math.round((targetTokens / tokenCount) * 100)}%）
|
||||
|
||||
**输出格式要求**：
|
||||
1. 保留步骤结构：每个步骤使用"# 步骤ID: [id]\\n\\t - 步骤标题: [title]\\n\\t - 执行结果: [精简后的结果]"的格式
|
||||
2. 根据相关性分级处理：
|
||||
- 与当前任务高度相关的步骤：保留完整的关键信息（数据、结论、链接等）
|
||||
- 中等相关的步骤：提炼要点，移除冗余描述
|
||||
- 低相关的步骤：仅保留一句话总结或省略执行结果
|
||||
3. 保持步骤顺序：按原始顺序输出，不要打乱
|
||||
4. 提取共性：如果连续多个步骤结果相似，可以适当合并描述
|
||||
|
||||
**质量标准**：
|
||||
- 压缩后的内容能让后续步骤理解前置步骤做了什么、得到了什么结果
|
||||
- 保留所有对当前任务有价值的具体数据和关键结论
|
||||
- 移除重复、啰嗦的描述性文字
|
||||
|
||||
请直接输出压缩后的步骤历史：`;
|
||||
|
||||
try {
|
||||
const { answerText } = await createLLMResponse({
|
||||
body: {
|
||||
model: modelData,
|
||||
messages: [
|
||||
{
|
||||
role: ChatCompletionRequestMessageRoleEnum.System,
|
||||
content: compressionSystemPrompt
|
||||
},
|
||||
{
|
||||
role: ChatCompletionRequestMessageRoleEnum.User,
|
||||
content: userPrompt
|
||||
}
|
||||
],
|
||||
temperature: 0.1,
|
||||
stream: false
|
||||
}
|
||||
});
|
||||
|
||||
return answerText || stepPrompt;
|
||||
} catch (error) {
|
||||
console.error('压缩 stepPrompt 失败:', error);
|
||||
// 压缩失败时返回原始内容
|
||||
return stepPrompt;
|
||||
}
|
||||
};
|
||||
|
||||
let stepPrompt = steps
|
||||
.filter((item) => step.depends_on && step.depends_on.includes(item.id))
|
||||
.map(
|
||||
(item) =>
|
||||
`# 步骤ID: ${item.id}\n\t - 步骤标题: ${item.title}\n\t - 执行结果: ${item.response}`
|
||||
)
|
||||
.filter(Boolean)
|
||||
.join('\n');
|
||||
addLog.debug(`Step call depends_on (LLM): ${step.id}`, step.depends_on);
|
||||
// 压缩依赖的上下文
|
||||
stepPrompt = await compressStepPrompt(stepPrompt, model, step.description || step.title);
|
||||
|
||||
return `请根据任务背景、之前步骤的执行结果和当前步骤要求选择并调用相应的工具。如果是一个总结性质的步骤，请整合之前步骤的结果进行总结。
|
||||
【任务背景】
|
||||
目标: ${userInput}
|
||||
前置信息: ${background}
|
||||
|
||||
【当前步骤】
|
||||
步骤ID: ${step.id}
|
||||
步骤标题: ${step.title}
|
||||
|
||||
${
|
||||
stepPrompt
|
||||
? `【之前步骤的执行结果】
|
||||
${stepPrompt}`
|
||||
: ''
|
||||
}
|
||||
|
||||
【执行指导】
|
||||
1. 仔细阅读前面步骤的执行结果，理解已经获得的信息
|
||||
2. 根据当前步骤描述和前面的结果，分析需要使用的工具
|
||||
3. 从可用工具列表中选择最合适的工具
|
||||
4. 基于前面步骤的结果为工具生成合理的参数
|
||||
5. 如果需要多个工具，可以同时调用
|
||||
6. 确保当前步骤的执行能够有效利用和整合前面的结果
|
||||
7. 如果是总结的步骤，请利用之前步骤的信息进行全面总结
|
||||
|
||||
请严格按照步骤描述执行，确保完成所有要求的子任务。`;
|
||||
const prompt = `Number of session file inputs：
|
||||
Document：{{fileCount}}
|
||||
Image：{{imgCount}}
|
||||
------
|
||||
{{question}}`;
|
||||
return replaceVariable(prompt, obj);
|
||||
};
|
||||
|
|
|
|||
|
|
@ -1,20 +1,23 @@
|
|||
import type { NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
|
||||
import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
|
||||
import {
|
||||
ConfirmPlanAgentText,
|
||||
DispatchNodeResponseKeyEnum,
|
||||
SseResponseEventEnum
|
||||
} from '@fastgpt/global/core/workflow/runtime/constants';
|
||||
import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
|
||||
import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
|
||||
import type {
|
||||
ChatDispatchProps,
|
||||
DispatchNodeResultType,
|
||||
ModuleDispatchProps,
|
||||
RuntimeNodeItemType
|
||||
} from '@fastgpt/global/core/workflow/runtime/type';
|
||||
import { getLLMModel } from '../../../../ai/model';
|
||||
import { getNodeErrResponse, getHistories } from '../../utils';
|
||||
import type { AIChatItemValueItemType, ChatItemType } from '@fastgpt/global/core/chat/type';
|
||||
import { filterToolNodeIdByEdges, getNodeErrResponse, getHistories } from '../../utils';
|
||||
import { runToolCall } from './toolCall';
|
||||
import { type DispatchToolModuleProps, type ToolNodeItemType } from './type';
|
||||
import { type ChatItemType, type UserChatItemValueItemType } from '@fastgpt/global/core/chat/type';
|
||||
import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
|
||||
import { chats2GPTMessages, chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
|
||||
import {
|
||||
GPTMessages2Chats,
|
||||
chatValue2RuntimePrompt,
|
||||
chats2GPTMessages,
|
||||
getSystemPrompt_ChatItemType,
|
||||
runtimePrompt2ChatsValue
|
||||
} from '@fastgpt/global/core/chat/adapt';
|
||||
import { formatModelChars2Points } from '../../../../../support/wallet/usage/utils';
|
||||
import { getHistoryPreview } from '@fastgpt/global/core/chat/utils';
|
||||
import { replaceVariable } from '@fastgpt/global/common/string/tools';
|
||||
|
|
@ -34,23 +37,19 @@ type Response = DispatchNodeResultType<{
|
|||
[NodeOutputKeyEnum.answerText]: string;
|
||||
}>;
|
||||
|
||||
export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise<Response> => {
|
||||
export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<Response> => {
|
||||
let {
|
||||
node: { nodeId, name, isEntry, version, inputs },
|
||||
lang,
|
||||
runtimeNodes,
|
||||
runtimeEdges,
|
||||
histories,
|
||||
query,
|
||||
requestOrigin,
|
||||
chatConfig,
|
||||
lastInteractive,
|
||||
runningUserInfo,
|
||||
runningAppInfo,
|
||||
externalProvider,
|
||||
stream,
|
||||
workflowDispatchDeep,
|
||||
workflowStreamResponse,
|
||||
usagePush,
|
||||
usageId,
|
||||
params: {
|
||||
model,
|
||||
systemPrompt,
|
||||
|
|
@ -62,111 +61,49 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise
|
|||
isResponseAnswerText = true
|
||||
}
|
||||
} = props;
|
||||
const agentModel = getLLMModel(model);
|
||||
const chatHistories = getHistories(history, histories);
|
||||
const historiesMessages = chats2GPTMessages({
|
||||
messages: chatHistories,
|
||||
reserveId: false,
|
||||
reserveTool: false
|
||||
});
|
||||
|
||||
const planMessagesKey = `planMessages-${nodeId}`;
|
||||
const replanMessagesKey = `replanMessages-${nodeId}`;
|
||||
const agentPlanKey = `agentPlan-${nodeId}`;
|
||||
|
||||
// 交互模式进来的话，这个值才是交互输入的值
|
||||
const interactiveInput = lastInteractive ? chatValue2RuntimePrompt(query).text : '';
|
||||
|
||||
// Get history messages
|
||||
let { planHistoryMessages, replanMessages, agentPlan } = (() => {
|
||||
const lastHistory = chatHistories[chatHistories.length - 1];
|
||||
if (lastHistory && lastHistory.obj === ChatRoleEnum.AI) {
|
||||
return {
|
||||
planHistoryMessages: (lastHistory.memories?.[planMessagesKey] ||
|
||||
[]) as ChatCompletionMessageParam[],
|
||||
replanMessages: (lastHistory.memories?.[replanMessagesKey] ||
|
||||
[]) as ChatCompletionMessageParam[],
|
||||
agentPlan: (lastHistory.memories?.[agentPlanKey] || []) as AgentPlanType
|
||||
};
|
||||
}
|
||||
return {
|
||||
planHistoryMessages: undefined,
|
||||
replanMessages: undefined,
|
||||
agentPlan: undefined
|
||||
};
|
||||
})();
|
||||
|
||||
// Check task complexity: 第一次进入任务时候进行判断。（有 plan了，说明已经开始执行任务了）
|
||||
const isCheckTaskComplexityStep = isPlanAgent && !agentPlan && !planHistoryMessages;
|
||||
|
||||
try {
|
||||
// Get files
|
||||
const toolModel = getLLMModel(model);
|
||||
const useVision = aiChatVision && toolModel.vision;
|
||||
const chatHistories = getHistories(history, histories);
|
||||
|
||||
props.params.aiChatVision = aiChatVision && toolModel.vision;
|
||||
props.params.aiChatReasoning = aiChatReasoning && toolModel.reasoning;
|
||||
const fileUrlInput = inputs.find((item) => item.key === NodeInputKeyEnum.fileUrlList);
|
||||
if (!fileUrlInput || !fileUrlInput.value || fileUrlInput.value.length === 0) {
|
||||
fileLinks = undefined;
|
||||
}
|
||||
<<<<<<< HEAD
|
||||
const { filesMap, prompt: fileInputPrompt } = getFileInputPrompt({
|
||||
fileUrls: fileLinks,
|
||||
requestOrigin,
|
||||
maxFiles: chatConfig?.fileSelectConfig?.maxFiles || 20,
|
||||
histories: chatHistories
|
||||
});
|
||||
|
||||
// Get sub apps
|
||||
const { subAppList, subAppsMap, getSubAppInfo } = await useSubApps({
|
||||
subApps,
|
||||
lang,
|
||||
filesMap
|
||||
});
|
||||
|
||||
/* ===== AI Start ===== */
|
||||
|
||||
/* ===== Check task complexity ===== */
|
||||
const taskIsComplexity = await (async () => {
|
||||
// if (isCheckTaskComplexityStep) {
|
||||
// const res = await checkTaskComplexity({
|
||||
// model,
|
||||
// userChatInput
|
||||
// });
|
||||
// if (res.usage) {
|
||||
// usagePush([res.usage]);
|
||||
// }
|
||||
// return res.complex;
|
||||
// }
|
||||
|
||||
// 对轮运行时候，代表都是进入复杂流程
|
||||
return true;
|
||||
})();
|
||||
|
||||
if (taskIsComplexity) {
|
||||
/* ===== Plan Agent ===== */
|
||||
const planCallFn = async () => {
|
||||
// 点了确认。此时肯定有 agentPlans
|
||||
if (
|
||||
lastInteractive?.type === 'agentPlanCheck' &&
|
||||
interactiveInput === ConfirmPlanAgentText &&
|
||||
agentPlan
|
||||
) {
|
||||
planHistoryMessages = undefined;
|
||||
} else {
|
||||
// 临时代码
|
||||
const tmpText = '正在进行规划生成...\n';
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: tmpText
|
||||
})
|
||||
});
|
||||
|
||||
<<<<<<<< HEAD:packages/service/core/workflow/dispatch/ai/tool/index.ts
|
||||
const {
|
||||
toolWorkflowInteractiveResponse,
|
||||
toolDispatchFlowResponses, // tool flow response
|
||||
=======
|
||||
|
||||
const toolNodeIds = filterToolNodeIdByEdges({ nodeId, edges: runtimeEdges });
|
||||
const toolNodes = getToolNodesByIds({ toolNodeIds, runtimeNodes });
|
||||
|
||||
// Gets the module to which the tool is connected
|
||||
const toolNodes = toolNodeIds
|
||||
.map((nodeId) => {
|
||||
const tool = runtimeNodes.find((item) => item.nodeId === nodeId);
|
||||
return tool;
|
||||
})
|
||||
.filter(Boolean)
|
||||
.map<ToolNodeItemType>((tool) => {
|
||||
const toolParams: FlowNodeInputItemType[] = [];
|
||||
// Raw json schema(MCP tool)
|
||||
let jsonSchema: JSONSchemaInputType | undefined = undefined;
|
||||
tool?.inputs.forEach((input) => {
|
||||
if (input.toolDescription) {
|
||||
toolParams.push(input);
|
||||
}
|
||||
|
||||
if (input.key === NodeInputKeyEnum.toolData || input.key === 'toolData') {
|
||||
const value = input.value as McpToolDataType;
|
||||
jsonSchema = value.inputSchema;
|
||||
}
|
||||
});
|
||||
|
||||
return {
|
||||
...(tool as RuntimeNodeItemType),
|
||||
toolParams,
|
||||
jsonSchema
|
||||
};
|
||||
});
|
||||
|
||||
// Check interactive entry
|
||||
props.node.isEntry = false;
|
||||
|
|
@ -183,6 +120,7 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise
|
|||
customPdfParse: chatConfig?.fileSelectConfig?.customPdfParse,
|
||||
fileLinks,
|
||||
inputFiles: globalFiles,
|
||||
<<<<<<< HEAD
|
||||
<<<<<<< HEAD
|
||||
hasReadFilesTool,
|
||||
usageId,
|
||||
|
|
@ -192,6 +130,10 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise
|
|||
=======
|
||||
hasReadFilesTool
|
||||
>>>>>>> a48ad2abe (squash: compress all commits into one)
|
||||
=======
|
||||
hasReadFilesTool,
|
||||
usageId
|
||||
>>>>>>> daaea654e (feat: plan response in ui)
|
||||
});
|
||||
|
||||
const concatenateSystemPrompt = [
|
||||
|
|
@ -250,16 +192,11 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise
|
|||
|
||||
const {
|
||||
toolWorkflowInteractiveResponse,
|
||||
dispatchFlowResponse, // tool flow response
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
toolDispatchFlowResponses, // tool flow response
|
||||
toolCallInputTokens,
|
||||
toolCallOutputTokens,
|
||||
completeMessages = [], // The actual message sent to AI(just save text)
|
||||
assistantResponses = [], // FastGPT system store assistant.value response
|
||||
<<<<<<< HEAD
|
||||
=======
|
||||
runTimes,
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
finish_reason
|
||||
} = await (async () => {
|
||||
const adaptMessages = chats2GPTMessages({
|
||||
|
|
@ -267,162 +204,20 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise
|
|||
reserveId: false
|
||||
// reserveTool: !!toolModel.toolChoice
|
||||
});
|
||||
<<<<<<< HEAD
|
||||
|
||||
return runToolCall({
|
||||
...props,
|
||||
=======
|
||||
const requestParams = {
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
runtimeNodes,
|
||||
runtimeEdges,
|
||||
toolNodes,
|
||||
toolModel,
|
||||
messages: adaptMessages,
|
||||
<<<<<<< HEAD
|
||||
childrenInteractiveParams:
|
||||
lastInteractive?.type === 'toolChildrenInteractive' ? lastInteractive.params : undefined
|
||||
========
|
||||
const { answerText, plan, completeMessages, usages, interactiveResponse } =
|
||||
await dispatchPlanAgent({
|
||||
historyMessages: planHistoryMessages || historiesMessages,
|
||||
userInput: lastInteractive ? interactiveInput : userChatInput,
|
||||
interactive: lastInteractive,
|
||||
subAppList,
|
||||
getSubAppInfo,
|
||||
systemPrompt,
|
||||
model,
|
||||
temperature,
|
||||
top_p: aiChatTopP,
|
||||
stream,
|
||||
isTopPlanAgent: workflowDispatchDeep === 1
|
||||
});
|
||||
|
||||
const text = `${answerText}${plan ? `\n\`\`\`json\n${JSON.stringify(plan, null, 2)}\n\`\`\`` : ''}`;
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text
|
||||
})
|
||||
});
|
||||
|
||||
agentPlan = plan;
|
||||
|
||||
usagePush(usages);
|
||||
// Sub agent plan 不会有交互响应。Top agent plan 肯定会有。
|
||||
if (interactiveResponse) {
|
||||
return {
|
||||
[DispatchNodeResponseKeyEnum.answerText]: `${tmpText}${text}`,
|
||||
[DispatchNodeResponseKeyEnum.memories]: {
|
||||
[planMessagesKey]: filterMemoryMessages(completeMessages),
|
||||
[agentPlanKey]: agentPlan
|
||||
},
|
||||
[DispatchNodeResponseKeyEnum.interactive]: interactiveResponse
|
||||
};
|
||||
} else {
|
||||
planHistoryMessages = undefined;
|
||||
}
|
||||
}
|
||||
};
|
||||
const replanCallFn = async ({ plan }: { plan: AgentPlanType }) => {
|
||||
if (!agentPlan) return;
|
||||
|
||||
addLog.debug(`Replan step`);
|
||||
// 临时代码
|
||||
const tmpText = '\n # 正在重新进行规划生成...\n';
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: tmpText
|
||||
})
|
||||
});
|
||||
|
||||
const {
|
||||
answerText,
|
||||
plan: rePlan,
|
||||
completeMessages,
|
||||
usages,
|
||||
interactiveResponse
|
||||
} = await dispatchReplanAgent({
|
||||
historyMessages: replanMessages || historiesMessages,
|
||||
userInput: lastInteractive ? interactiveInput : userChatInput,
|
||||
plan,
|
||||
interactive: lastInteractive,
|
||||
subAppList,
|
||||
getSubAppInfo,
|
||||
systemPrompt,
|
||||
model,
|
||||
temperature,
|
||||
top_p: aiChatTopP,
|
||||
stream,
|
||||
isTopPlanAgent: workflowDispatchDeep === 1
|
||||
});
|
||||
|
||||
if (rePlan) {
|
||||
agentPlan.steps.push(...rePlan.steps);
|
||||
agentPlan.replan = rePlan.replan;
|
||||
}
|
||||
|
||||
const text = `${answerText}${agentPlan ? `\n\`\`\`json\n${JSON.stringify(agentPlan, null, 2)}\n\`\`\`\n` : ''}`;
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text
|
||||
})
|
||||
});
|
||||
|
||||
usagePush(usages);
|
||||
// Sub agent plan 不会有交互响应。Top agent plan 肯定会有。
|
||||
if (interactiveResponse) {
|
||||
return {
|
||||
[DispatchNodeResponseKeyEnum.answerText]: `${tmpText}${text}`,
|
||||
[DispatchNodeResponseKeyEnum.memories]: {
|
||||
[replanMessagesKey]: filterMemoryMessages(completeMessages),
|
||||
[agentPlanKey]: agentPlan
|
||||
},
|
||||
[DispatchNodeResponseKeyEnum.interactive]: interactiveResponse
|
||||
};
|
||||
} else {
|
||||
replanMessages = undefined;
|
||||
}
|
||||
};
|
||||
|
||||
// Plan step: 需要生成 plan，且还没有完整的 plan
|
||||
const isPlanStep = isPlanAgent && (!agentPlan || planHistoryMessages);
|
||||
// Replan step: 已有 plan，且有 replan 历史消息
|
||||
const isReplanStep = isPlanAgent && agentPlan && replanMessages;
|
||||
|
||||
// 执行 Plan/replan
|
||||
if (isPlanStep) {
|
||||
const result = await planCallFn();
|
||||
// 有 result 代表 plan 有交互响应（check/ask）
|
||||
if (result) return result;
|
||||
} else if (isReplanStep) {
|
||||
const result = await replanCallFn({
|
||||
plan: agentPlan!
|
||||
});
|
||||
if (result) return result;
|
||||
}
|
||||
|
||||
addLog.debug(`Start master agent`, {
|
||||
agentPlan: JSON.stringify(agentPlan, null, 2)
|
||||
>>>>>>>> 757253617 (squash: compress all commits into one):packages/service/core/workflow/dispatch/ai/agent/index.ts
|
||||
});
|
||||
|
||||
<<<<<<<< HEAD:packages/service/core/workflow/dispatch/ai/tool/index.ts
|
||||
// Usage computed
|
||||
=======
|
||||
interactiveEntryToolParams: lastInteractive?.toolParams
|
||||
};
|
||||
|
||||
return runToolCall({
|
||||
...props,
|
||||
...requestParams,
|
||||
maxRunToolTimes: 100
|
||||
});
|
||||
})();
|
||||
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
// Usage computed
|
||||
const { totalPoints: modelTotalPoints, modelName } = formatModelChars2Points({
|
||||
model,
|
||||
inputTokens: toolCallInputTokens,
|
||||
|
|
@ -430,29 +225,13 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise
|
|||
});
|
||||
const modelUsage = externalProvider.openaiAccount?.key ? 0 : modelTotalPoints;
|
||||
|
||||
<<<<<<< HEAD
|
||||
const toolUsages = toolDispatchFlowResponses.map((item) => item.flowUsages).flat();
|
||||
const toolTotalPoints = toolUsages.reduce((sum, item) => sum + item.totalPoints, 0);
|
||||
========
|
||||
/* ===== Master agent, 逐步执行 plan ===== */
|
||||
if (!agentPlan) return Promise.reject('没有 plan');
|
||||
|
||||
let assistantResponses: AIChatItemValueItemType[] = [];
|
||||
>>>>>>>> 757253617 (squash: compress all commits into one):packages/service/core/workflow/dispatch/ai/agent/index.ts
|
||||
|
||||
while (agentPlan.steps!.filter((item) => !item.response)!.length) {
|
||||
const pendingSteps = agentPlan?.steps!.filter((item) => !item.response)!;
|
||||
|
||||
<<<<<<<< HEAD:packages/service/core/workflow/dispatch/ai/tool/index.ts
|
||||
// Preview assistant responses
|
||||
=======
|
||||
const toolUsages = dispatchFlowResponse.map((item) => item.flowUsages).flat();
|
||||
const toolTotalPoints = toolUsages.reduce((sum, item) => sum + item.totalPoints, 0);
|
||||
|
||||
// concat tool usage
|
||||
const totalPointsUsage = modelUsage + toolTotalPoints;
|
||||
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
// Preview assistant responses
|
||||
const previewAssistantResponses = filterToolResponseToPreview(assistantResponses);
|
||||
|
||||
return {
|
||||
|
|
@ -462,11 +241,11 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise
|
|||
.map((item) => item.text?.content || '')
|
||||
.join('')
|
||||
},
|
||||
<<<<<<< HEAD
|
||||
[DispatchNodeResponseKeyEnum.runTimes]: toolDispatchFlowResponses.reduce(
|
||||
(sum, item) => sum + item.runTimes,
|
||||
0
|
||||
),
|
||||
<<<<<<< HEAD
|
||||
<<<<<<< HEAD
|
||||
[DispatchNodeResponseKeyEnum.assistantResponses]: isResponseAnswerText
|
||||
? previewAssistantResponses
|
||||
|
|
@ -475,6 +254,8 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise
|
|||
=======
|
||||
[DispatchNodeResponseKeyEnum.runTimes]: runTimes,
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
=======
|
||||
>>>>>>> daaea654e (feat: plan response in ui)
|
||||
[DispatchNodeResponseKeyEnum.assistantResponses]: previewAssistantResponses,
|
||||
>>>>>>> a48ad2abe (squash: compress all commits into one)
|
||||
[DispatchNodeResponseKeyEnum.nodeResponse]: {
|
||||
|
|
@ -490,11 +271,7 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise
|
|||
10000,
|
||||
useVision
|
||||
),
|
||||
<<<<<<< HEAD
|
||||
toolDetail: toolDispatchFlowResponses.map((item) => item.flowResponses).flat(),
|
||||
=======
|
||||
toolDetail: dispatchFlowResponse.map((item) => item.flowResponses).flat(),
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
mergeSignId: nodeId,
|
||||
finishReason: finish_reason
|
||||
},
|
||||
|
|
@ -506,134 +283,17 @@ export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise
|
|||
totalPoints: modelUsage,
|
||||
inputTokens: toolCallInputTokens,
|
||||
outputTokens: toolCallOutputTokens
|
||||
<<<<<<< HEAD
|
||||
========
|
||||
for await (const step of pendingSteps) {
|
||||
addLog.debug(`Step call: ${step.id}`, step);
|
||||
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: `\n # ${step.id}: ${step.title}\n`
|
||||
})
|
||||
});
|
||||
|
||||
const result = await stepCall({
|
||||
...props,
|
||||
getSubAppInfo,
|
||||
steps: agentPlan.steps, // 传入所有步骤，而不仅仅是未执行的步骤
|
||||
subAppList,
|
||||
step,
|
||||
filesMap,
|
||||
subAppsMap
|
||||
});
|
||||
|
||||
step.response = result.rawResponse;
|
||||
step.summary = result.summary;
|
||||
assistantResponses.push(...result.assistantResponses);
|
||||
}
|
||||
|
||||
if (agentPlan?.replan === true) {
|
||||
const replanResult = await replanCallFn({
|
||||
plan: agentPlan
|
||||
});
|
||||
if (replanResult) return replanResult;
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
// 目前 Master 不会触发交互
|
||||
// [DispatchNodeResponseKeyEnum.interactive]: interactiveResponse,
|
||||
// TODO: 需要对 memoryMessages 单独建表存储
|
||||
[DispatchNodeResponseKeyEnum.memories]: {
|
||||
[agentPlanKey]: agentPlan,
|
||||
[planMessagesKey]: undefined,
|
||||
[replanMessagesKey]: undefined
|
||||
>>>>>>>> 757253617 (squash: compress all commits into one):packages/service/core/workflow/dispatch/ai/agent/index.ts
|
||||
},
|
||||
[DispatchNodeResponseKeyEnum.assistantResponses]: assistantResponses,
|
||||
[DispatchNodeResponseKeyEnum.nodeResponse]: {
|
||||
// 展示的积分消耗
|
||||
// totalPoints: totalPointsUsage,
|
||||
// toolCallInputTokens: inputTokens,
|
||||
// toolCallOutputTokens: outputTokens,
|
||||
// childTotalPoints: toolTotalPoints,
|
||||
// model: modelName,
|
||||
query: userChatInput,
|
||||
// toolDetail: dispatchFlowResponse,
|
||||
mergeSignId: nodeId
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
// 简单 tool call 模式（一轮对话就结束了，不会多轮，所以不会受到连续对话的 taskIsComplexity 影响）
|
||||
return Promise.reject('目前未支持简单模式');
|
||||
=======
|
||||
},
|
||||
// 工具的消耗
|
||||
...toolUsages
|
||||
],
|
||||
[DispatchNodeResponseKeyEnum.interactive]: toolWorkflowInteractiveResponse
|
||||
};
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
} catch (error) {
|
||||
return getNodeErrResponse({ error });
|
||||
}
|
||||
};
|
||||
|
||||
<<<<<<< HEAD
|
||||
export const useSubApps = async ({
|
||||
subApps,
|
||||
lang,
|
||||
filesMap
|
||||
}: {
|
||||
subApps: FlowNodeTemplateType[];
|
||||
lang?: localeType;
|
||||
filesMap: Record<string, string>;
|
||||
}) => {
|
||||
// Get sub apps
|
||||
const runtimeSubApps = await rewriteSubAppsToolset({
|
||||
subApps: subApps.map<RuntimeNodeItemType>((node) => {
|
||||
return {
|
||||
nodeId: node.id,
|
||||
name: node.name,
|
||||
avatar: node.avatar,
|
||||
intro: node.intro,
|
||||
toolDescription: node.toolDescription,
|
||||
flowNodeType: node.flowNodeType,
|
||||
showStatus: node.showStatus,
|
||||
isEntry: false,
|
||||
inputs: node.inputs,
|
||||
outputs: node.outputs,
|
||||
pluginId: node.pluginId,
|
||||
version: node.version,
|
||||
toolConfig: node.toolConfig,
|
||||
catchError: node.catchError
|
||||
};
|
||||
}),
|
||||
lang
|
||||
});
|
||||
|
||||
const subAppList = getSubApps({
|
||||
subApps: runtimeSubApps,
|
||||
addReadFileTool: Object.keys(filesMap).length > 0
|
||||
});
|
||||
|
||||
const subAppsMap = new Map(runtimeSubApps.map((item) => [item.nodeId, item]));
|
||||
const getSubAppInfo = (id: string) => {
|
||||
const toolNode = subAppsMap.get(id) || systemSubInfo[id];
|
||||
return {
|
||||
name: toolNode?.name || '',
|
||||
avatar: toolNode?.avatar || '',
|
||||
toolDescription: toolNode?.toolDescription || toolNode?.name || ''
|
||||
};
|
||||
};
|
||||
|
||||
return {
|
||||
subAppList,
|
||||
subAppsMap,
|
||||
getSubAppInfo
|
||||
=======
|
||||
const getMultiInput = async ({
|
||||
runningUserInfo,
|
||||
histories,
|
||||
|
|
@ -642,6 +302,7 @@ const getMultiInput = async ({
|
|||
maxFiles,
|
||||
customPdfParse,
|
||||
inputFiles,
|
||||
<<<<<<< HEAD
|
||||
<<<<<<< HEAD
|
||||
hasReadFilesTool,
|
||||
usageId,
|
||||
|
|
@ -651,6 +312,10 @@ const getMultiInput = async ({
|
|||
=======
|
||||
hasReadFilesTool
|
||||
>>>>>>> a48ad2abe (squash: compress all commits into one)
|
||||
=======
|
||||
hasReadFilesTool,
|
||||
usageId
|
||||
>>>>>>> daaea654e (feat: plan response in ui)
|
||||
}: {
|
||||
runningUserInfo: ChatDispatchProps['runningUserInfo'];
|
||||
histories: ChatItemType[];
|
||||
|
|
@ -660,6 +325,7 @@ const getMultiInput = async ({
|
|||
customPdfParse?: boolean;
|
||||
inputFiles: UserChatItemValueItemType['file'][];
|
||||
hasReadFilesTool: boolean;
|
||||
<<<<<<< HEAD
|
||||
<<<<<<< HEAD
|
||||
usageId?: string;
|
||||
appId: string;
|
||||
|
|
@ -667,6 +333,9 @@ const getMultiInput = async ({
|
|||
uId: string;
|
||||
=======
|
||||
>>>>>>> a48ad2abe (squash: compress all commits into one)
|
||||
=======
|
||||
usageId?: string;
|
||||
>>>>>>> daaea654e (feat: plan response in ui)
|
||||
}) => {
|
||||
// Not file quote
|
||||
if (!fileLinks || hasReadFilesTool) {
|
||||
|
|
@ -693,6 +362,7 @@ const getMultiInput = async ({
|
|||
requestOrigin,
|
||||
maxFiles,
|
||||
customPdfParse,
|
||||
usageId,
|
||||
teamId: runningUserInfo.teamId,
|
||||
tmbId: runningUserInfo.tmbId
|
||||
});
|
||||
|
|
@ -700,6 +370,53 @@ const getMultiInput = async ({
|
|||
return {
|
||||
documentQuoteText: text,
|
||||
userFiles: fileLinks.map((url) => parseUrlToFileType(url)).filter(Boolean)
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
};
|
||||
};
|
||||
|
||||
/*
|
||||
Tool call， auth add file prompt to question。
|
||||
Guide the LLM to call tool.
|
||||
*/
|
||||
const toolCallMessagesAdapt = ({
|
||||
userInput,
|
||||
skip
|
||||
}: {
|
||||
userInput: UserChatItemValueItemType[];
|
||||
skip?: boolean;
|
||||
}): UserChatItemValueItemType[] => {
|
||||
if (skip) return userInput;
|
||||
|
||||
const files = userInput.filter((item) => item.file);
|
||||
|
||||
if (files.length > 0) {
|
||||
const filesCount = files.filter((file) => file.file?.type === 'file').length;
|
||||
const imgCount = files.filter((file) => file.file?.type === 'image').length;
|
||||
|
||||
if (userInput.some((item) => item.text)) {
|
||||
return userInput.map((item) => {
|
||||
if (item.text) {
|
||||
const text = item.text?.content || '';
|
||||
|
||||
return {
|
||||
...item,
|
||||
text: {
|
||||
content: getMultiplePrompt({ fileCount: filesCount, imgCount, question: text })
|
||||
}
|
||||
};
|
||||
}
|
||||
return item;
|
||||
});
|
||||
}
|
||||
|
||||
// Every input is a file
|
||||
return [
|
||||
{
|
||||
text: {
|
||||
content: getMultiplePrompt({ fileCount: filesCount, imgCount, question: '' })
|
||||
}
|
||||
}
|
||||
];
|
||||
}
|
||||
|
||||
return userInput;
|
||||
};
|
||||
|
|
|
|||
|
|
@ -1,7 +0,0 @@
|
|||
import type { DispatchSubAppResponse } from '../../type';
|
||||
|
||||
export const dispatchContextAgent = async (props: {}): Promise<DispatchSubAppResponse> => {
|
||||
return {
|
||||
response: ''
|
||||
};
|
||||
};
|
||||
|
|
@ -1,10 +0,0 @@
|
|||
import type { ChatCompletionTool } from '@fastgpt/global/core/ai/type';
|
||||
import { SubAppIds } from '../constants';
|
||||
|
||||
export const StopAgentTool: ChatCompletionTool = {
|
||||
type: 'function',
|
||||
function: {
|
||||
name: SubAppIds.stop,
|
||||
description: '如果完成了所有的任务，可调用此工具。'
|
||||
}
|
||||
};
|
||||
|
|
@ -1,6 +1,4 @@
|
|||
<<<<<<< HEAD
|
||||
import type { ChatCompletionTool } from '@fastgpt/global/core/ai/type';
|
||||
import { responseWriteController } from '../../../../../common/response';
|
||||
import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
|
||||
import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
|
||||
import { runWorkflow } from '../../index';
|
||||
|
|
@ -18,88 +16,6 @@ import { runAgentCall } from '../../../../ai/llm/agentCall';
|
|||
export const runToolCall = async (props: DispatchToolModuleProps): Promise<RunToolResponse> => {
|
||||
const { messages, toolNodes, toolModel, childrenInteractiveParams, ...workflowProps } = props;
|
||||
const {
|
||||
=======
|
||||
import { filterGPTMessageByMaxContext } from '../../../../ai/llm/utils';
|
||||
import type {
|
||||
ChatCompletionToolMessageParam,
|
||||
ChatCompletionMessageParam,
|
||||
ChatCompletionTool
|
||||
} from '@fastgpt/global/core/ai/type';
|
||||
import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
|
||||
import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
|
||||
import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
|
||||
import { runWorkflow } from '../../index';
|
||||
import type { DispatchToolModuleProps, RunToolResponse, ToolNodeItemType } from './type';
|
||||
import type { DispatchFlowResponse } from '../../type';
|
||||
import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
|
||||
import type { AIChatItemType } from '@fastgpt/global/core/chat/type';
|
||||
import { formatToolResponse, parseToolArgs } from '../utils';
|
||||
import { initToolNodes, initToolCallEdges } from './utils';
|
||||
import { computedMaxToken } from '../../../../ai/utils';
|
||||
import { sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
|
||||
import type { WorkflowInteractiveResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type';
|
||||
import { getErrText } from '@fastgpt/global/common/error/utils';
|
||||
import { createLLMResponse } from '../../../../ai/llm/request';
|
||||
import { toolValueTypeList, valueTypeJsonSchemaMap } from '@fastgpt/global/core/workflow/constants';
|
||||
|
||||
type ToolRunResponseType = {
|
||||
toolRunResponse?: DispatchFlowResponse;
|
||||
toolMsgParams: ChatCompletionToolMessageParam;
|
||||
}[];
|
||||
|
||||
/*
|
||||
调用思路：
|
||||
先Check 是否是交互节点触发
|
||||
|
||||
交互模式：
|
||||
1. 从缓存中获取工作流运行数据
|
||||
2. 运行工作流
|
||||
3. 检测是否有停止信号或交互响应
|
||||
- 无：汇总结果，递归运行工具
|
||||
- 有：缓存结果，结束调用
|
||||
|
||||
非交互模式：
|
||||
1. 组合 tools
|
||||
2. 过滤 messages
|
||||
3. Load request llm messages: system prompt, histories, human question, （assistant responses, tool responses, assistant responses....)
|
||||
4. 请求 LLM 获取结果
|
||||
|
||||
- 有工具调用
|
||||
1. 批量运行工具的工作流，获取结果（工作流原生结果，工具执行结果）
|
||||
2. 合并递归中，所有工具的原生运行结果
|
||||
3. 组合 assistants tool 响应
|
||||
4. 组合本次 request 和 llm response 的 messages，并计算出消耗的 tokens
|
||||
5. 组合本次 request、llm response 和 tool response 结果
|
||||
6. 组合本次的 assistant responses: history assistant + tool assistant + tool child assistant
|
||||
7. 判断是否还有停止信号或交互响应
|
||||
- 无：递归运行工具
|
||||
- 有：缓存结果，结束调用
|
||||
- 无工具调用
|
||||
1. 汇总结果，递归运行工具
|
||||
2. 计算 completeMessages 和 tokens 后返回。
|
||||
|
||||
交互节点额外缓存结果包括：
|
||||
1. 入口的节点 id
|
||||
2. toolCallId: 本次工具调用的 ID，可以找到是调用了哪个工具，入口并不会记录工具的 id
|
||||
3. messages：本次递归中，assistants responses 和 tool responses
|
||||
*/
|
||||
|
||||
export const runToolCall = async (
|
||||
props: DispatchToolModuleProps & {
|
||||
maxRunToolTimes: number;
|
||||
},
|
||||
response?: RunToolResponse
|
||||
): Promise<RunToolResponse> => {
|
||||
const {
|
||||
messages,
|
||||
toolNodes,
|
||||
toolModel,
|
||||
maxRunToolTimes,
|
||||
interactiveEntryToolParams,
|
||||
...workflowProps
|
||||
} = props;
|
||||
let {
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
res,
|
||||
requestOrigin,
|
||||
runtimeNodes,
|
||||
|
|
@ -121,105 +37,7 @@ export const runToolCall = async (
|
|||
}
|
||||
} = workflowProps;
|
||||
|
||||
<<<<<<< HEAD
|
||||
// 构建 tools 参数
|
||||
=======
|
||||
if (maxRunToolTimes <= 0 && response) {
|
||||
return response;
|
||||
}
|
||||
|
||||
// Interactive
|
||||
if (interactiveEntryToolParams) {
|
||||
initToolNodes(runtimeNodes, interactiveEntryToolParams.entryNodeIds);
|
||||
initToolCallEdges(runtimeEdges, interactiveEntryToolParams.entryNodeIds);
|
||||
|
||||
// Run entry tool
|
||||
const toolRunResponse = await runWorkflow({
|
||||
...workflowProps,
|
||||
usageId: undefined,
|
||||
isToolCall: true
|
||||
});
|
||||
const stringToolResponse = formatToolResponse(toolRunResponse.toolResponses);
|
||||
|
||||
// Response to frontend
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.toolResponse,
|
||||
data: {
|
||||
tool: {
|
||||
id: interactiveEntryToolParams.toolCallId,
|
||||
toolName: '',
|
||||
toolAvatar: '',
|
||||
params: '',
|
||||
response: sliceStrStartEnd(stringToolResponse, 5000, 5000)
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
// Check stop signal
|
||||
const hasStopSignal = toolRunResponse.flowResponses?.some((item) => item.toolStop);
|
||||
// Check interactive response(Only 1 interaction is reserved)
|
||||
const workflowInteractiveResponse = toolRunResponse.workflowInteractiveResponse;
|
||||
|
||||
const requestMessages = [
|
||||
...messages,
|
||||
...interactiveEntryToolParams.memoryMessages.map((item) =>
|
||||
item.role === 'tool' && item.tool_call_id === interactiveEntryToolParams.toolCallId
|
||||
? {
|
||||
...item,
|
||||
content: stringToolResponse
|
||||
}
|
||||
: item
|
||||
)
|
||||
];
|
||||
|
||||
if (hasStopSignal || workflowInteractiveResponse) {
|
||||
// Get interactive tool data
|
||||
const toolWorkflowInteractiveResponse: WorkflowInteractiveResponseType | undefined =
|
||||
workflowInteractiveResponse
|
||||
? {
|
||||
...workflowInteractiveResponse,
|
||||
toolParams: {
|
||||
entryNodeIds: workflowInteractiveResponse.entryNodeIds,
|
||||
toolCallId: interactiveEntryToolParams.toolCallId,
|
||||
memoryMessages: interactiveEntryToolParams.memoryMessages
|
||||
}
|
||||
}
|
||||
: undefined;
|
||||
|
||||
return {
|
||||
dispatchFlowResponse: [toolRunResponse],
|
||||
toolCallInputTokens: 0,
|
||||
toolCallOutputTokens: 0,
|
||||
completeMessages: requestMessages,
|
||||
assistantResponses: toolRunResponse.assistantResponses,
|
||||
runTimes: toolRunResponse.runTimes,
|
||||
toolWorkflowInteractiveResponse
|
||||
};
|
||||
}
|
||||
|
||||
return runToolCall(
|
||||
{
|
||||
...props,
|
||||
interactiveEntryToolParams: undefined,
|
||||
maxRunToolTimes: maxRunToolTimes - 1,
|
||||
// Rewrite toolCall messages
|
||||
messages: requestMessages
|
||||
},
|
||||
{
|
||||
dispatchFlowResponse: [toolRunResponse],
|
||||
toolCallInputTokens: 0,
|
||||
toolCallOutputTokens: 0,
|
||||
assistantResponses: toolRunResponse.assistantResponses,
|
||||
runTimes: toolRunResponse.runTimes
|
||||
}
|
||||
);
|
||||
}
|
||||
|
||||
// ------------------------------------------------------------
|
||||
|
||||
const assistantResponses = response?.assistantResponses || [];
|
||||
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
const toolNodesMap = new Map<string, ToolNodeItemType>();
|
||||
const tools: ChatCompletionTool[] = toolNodes.map((item) => {
|
||||
toolNodesMap.set(item.nodeId, item);
|
||||
|
|
@ -271,7 +89,6 @@ export const runToolCall = async (
|
|||
}
|
||||
};
|
||||
});
|
||||
<<<<<<< HEAD
|
||||
const getToolInfo = (name: string) => {
|
||||
const toolNode = toolNodesMap.get(name);
|
||||
return {
|
||||
|
|
@ -280,8 +97,6 @@ export const runToolCall = async (
|
|||
};
|
||||
};
|
||||
|
||||
// SSE 响应实例
|
||||
const write = res ? responseWriteController({ res, readStream: stream }) : undefined;
|
||||
// 工具响应原始值
|
||||
const toolRunResponses: DispatchFlowResponse[] = [];
|
||||
|
||||
|
|
@ -310,74 +125,12 @@ export const runToolCall = async (
|
|||
requestOrigin,
|
||||
retainDatasetCite,
|
||||
useVision: aiChatVision
|
||||
=======
|
||||
|
||||
const max_tokens = computedMaxToken({
|
||||
model: toolModel,
|
||||
maxToken,
|
||||
min: 100
|
||||
});
|
||||
|
||||
// Filter histories by maxToken
|
||||
const filterMessages = (
|
||||
await filterGPTMessageByMaxContext({
|
||||
messages,
|
||||
maxContext: toolModel.maxContext - (max_tokens || 0) // filter token. not response maxToken
|
||||
})
|
||||
).map((item) => {
|
||||
if (item.role === 'assistant' && item.tool_calls) {
|
||||
return {
|
||||
...item,
|
||||
tool_calls: item.tool_calls.map((tool) => ({
|
||||
id: tool.id,
|
||||
type: tool.type,
|
||||
function: tool.function
|
||||
}))
|
||||
};
|
||||
}
|
||||
return item;
|
||||
});
|
||||
|
||||
let {
|
||||
reasoningText: reasoningContent,
|
||||
answerText: answer,
|
||||
toolCalls = [],
|
||||
finish_reason,
|
||||
usage,
|
||||
getEmptyResponseTip,
|
||||
assistantMessage,
|
||||
completeMessages
|
||||
} = await createLLMResponse({
|
||||
body: {
|
||||
model: toolModel.model,
|
||||
stream,
|
||||
messages: filterMessages,
|
||||
tool_choice: 'auto',
|
||||
toolCallMode: toolModel.toolChoice ? 'toolChoice' : 'prompt',
|
||||
tools,
|
||||
parallel_tool_calls: true,
|
||||
temperature,
|
||||
max_tokens,
|
||||
top_p: aiChatTopP,
|
||||
stop: aiChatStopSign,
|
||||
response_format: {
|
||||
type: aiChatResponseFormat as any,
|
||||
json_schema: aiChatJsonSchema
|
||||
},
|
||||
retainDatasetCite,
|
||||
useVision: aiChatVision,
|
||||
requestOrigin
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
},
|
||||
isAborted: () => res?.closed,
|
||||
userKey: externalProvider.openaiAccount,
|
||||
onReasoning({ text }) {
|
||||
if (!aiChatReasoning) return;
|
||||
workflowStreamResponse?.({
|
||||
<<<<<<< HEAD
|
||||
write,
|
||||
=======
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
reasoning_content: text
|
||||
|
|
@ -387,10 +140,6 @@ export const runToolCall = async (
|
|||
onStreaming({ text }) {
|
||||
if (!isResponseAnswerText) return;
|
||||
workflowStreamResponse?.({
|
||||
<<<<<<< HEAD
|
||||
write,
|
||||
=======
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text
|
||||
|
|
@ -400,7 +149,6 @@ export const runToolCall = async (
|
|||
onToolCall({ call }) {
|
||||
if (!isResponseAnswerText) return;
|
||||
const toolNode = toolNodesMap.get(call.function.name);
|
||||
<<<<<<< HEAD
|
||||
if (toolNode) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.toolCall,
|
||||
|
|
@ -420,34 +168,10 @@ export const runToolCall = async (
|
|||
onToolParam({ tool, params }) {
|
||||
if (!isResponseAnswerText) return;
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.toolParams,
|
||||
data: {
|
||||
tool: {
|
||||
id: tool.id,
|
||||
=======
|
||||
if (!toolNode) return;
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.toolCall,
|
||||
data: {
|
||||
tool: {
|
||||
id: call.id,
|
||||
toolName: toolNode.name,
|
||||
toolAvatar: toolNode.avatar,
|
||||
functionName: call.function.name,
|
||||
params: call.function.arguments ?? '',
|
||||
response: ''
|
||||
}
|
||||
}
|
||||
});
|
||||
},
|
||||
onToolParam({ call, params }) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.toolParams,
|
||||
data: {
|
||||
tool: {
|
||||
id: call.id,
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
toolName: '',
|
||||
toolAvatar: '',
|
||||
params,
|
||||
|
|
@ -455,7 +179,6 @@ export const runToolCall = async (
|
|||
}
|
||||
}
|
||||
});
|
||||
<<<<<<< HEAD
|
||||
},
|
||||
handleToolResponse: async ({ call, messages }) => {
|
||||
const toolNode = toolNodesMap.get(call.function?.name);
|
||||
|
|
@ -588,213 +311,4 @@ export const runToolCall = async (
|
|||
finish_reason,
|
||||
toolWorkflowInteractiveResponse: interactiveResponse
|
||||
};
|
||||
=======
|
||||
}
|
||||
});
|
||||
|
||||
if (!answer && !reasoningContent && !toolCalls.length) {
|
||||
return Promise.reject(getEmptyResponseTip());
|
||||
}
|
||||
|
||||
/* Run the selected tool by LLM.
|
||||
Since only reference parameters are passed, if the same tool is run in parallel, it will get the same run parameters
|
||||
*/
|
||||
const toolsRunResponse: ToolRunResponseType = [];
|
||||
for await (const tool of toolCalls) {
|
||||
try {
|
||||
const toolNode = toolNodesMap.get(tool.function?.name);
|
||||
|
||||
if (!toolNode) continue;
|
||||
|
||||
const startParams = parseToolArgs(tool.function.arguments);
|
||||
|
||||
initToolNodes(runtimeNodes, [toolNode.nodeId], startParams);
|
||||
const toolRunResponse = await runWorkflow({
|
||||
...workflowProps,
|
||||
usageId: undefined,
|
||||
isToolCall: true
|
||||
});
|
||||
|
||||
const stringToolResponse = formatToolResponse(toolRunResponse.toolResponses);
|
||||
const toolMsgParams: ChatCompletionToolMessageParam = {
|
||||
tool_call_id: tool.id,
|
||||
role: ChatCompletionRequestMessageRoleEnum.Tool,
|
||||
content: stringToolResponse
|
||||
};
|
||||
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.toolResponse,
|
||||
data: {
|
||||
tool: {
|
||||
id: tool.id,
|
||||
toolName: '',
|
||||
toolAvatar: '',
|
||||
params: '',
|
||||
response: sliceStrStartEnd(stringToolResponse, 5000, 5000)
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
toolsRunResponse.push({
|
||||
toolRunResponse,
|
||||
toolMsgParams
|
||||
});
|
||||
} catch (error) {
|
||||
const err = getErrText(error);
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.toolResponse,
|
||||
data: {
|
||||
tool: {
|
||||
id: tool.id,
|
||||
toolName: '',
|
||||
toolAvatar: '',
|
||||
params: '',
|
||||
response: sliceStrStartEnd(err, 5000, 5000)
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
toolsRunResponse.push({
|
||||
toolRunResponse: undefined,
|
||||
toolMsgParams: {
|
||||
tool_call_id: tool.id,
|
||||
role: ChatCompletionRequestMessageRoleEnum.Tool,
|
||||
content: sliceStrStartEnd(err, 5000, 5000)
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
const flatToolsResponseData = toolsRunResponse
|
||||
.map((item) => item.toolRunResponse)
|
||||
.flat()
|
||||
.filter(Boolean) as DispatchFlowResponse[];
|
||||
// concat tool responses
|
||||
const dispatchFlowResponse = response
|
||||
? response.dispatchFlowResponse.concat(flatToolsResponseData)
|
||||
: flatToolsResponseData;
|
||||
|
||||
const inputTokens = response
|
||||
? response.toolCallInputTokens + usage.inputTokens
|
||||
: usage.inputTokens;
|
||||
const outputTokens = response
|
||||
? response.toolCallOutputTokens + usage.outputTokens
|
||||
: usage.outputTokens;
|
||||
|
||||
if (toolCalls.length > 0) {
|
||||
/*
|
||||
...
|
||||
user
|
||||
assistant: tool data
|
||||
tool: tool response
|
||||
*/
|
||||
const nextRequestMessages: ChatCompletionMessageParam[] = [
|
||||
...completeMessages,
|
||||
...toolsRunResponse.map((item) => item?.toolMsgParams)
|
||||
];
|
||||
|
||||
/*
|
||||
Get tool node assistant response
|
||||
- history assistant
|
||||
- current tool assistant
|
||||
- tool child assistant
|
||||
*/
|
||||
const toolNodeAssistant = GPTMessages2Chats({
|
||||
messages: [...assistantMessage, ...toolsRunResponse.map((item) => item?.toolMsgParams)],
|
||||
getToolInfo: (id) => {
|
||||
const toolNode = toolNodesMap.get(id);
|
||||
return {
|
||||
name: toolNode?.name || '',
|
||||
avatar: toolNode?.avatar || ''
|
||||
};
|
||||
}
|
||||
})[0] as AIChatItemType;
|
||||
const toolChildAssistants = flatToolsResponseData
|
||||
.map((item) => item.assistantResponses)
|
||||
.flat()
|
||||
.filter((item) => !item.interactive); // 交互节点留着下次记录
|
||||
const concatAssistantResponses = [
|
||||
...assistantResponses,
|
||||
...toolNodeAssistant.value,
|
||||
...toolChildAssistants
|
||||
];
|
||||
|
||||
const runTimes =
|
||||
(response?.runTimes || 0) +
|
||||
flatToolsResponseData.reduce((sum, item) => sum + item.runTimes, 0);
|
||||
|
||||
// Check stop signal
|
||||
const hasStopSignal = flatToolsResponseData.some(
|
||||
(item) => !!item.flowResponses?.find((item) => item.toolStop)
|
||||
);
|
||||
// Check interactive response(Only 1 interaction is reserved)
|
||||
const workflowInteractiveResponseItem = toolsRunResponse.find(
|
||||
(item) => item.toolRunResponse?.workflowInteractiveResponse
|
||||
);
|
||||
if (hasStopSignal || workflowInteractiveResponseItem) {
|
||||
// Get interactive tool data
|
||||
const workflowInteractiveResponse =
|
||||
workflowInteractiveResponseItem?.toolRunResponse?.workflowInteractiveResponse;
|
||||
|
||||
// Flashback traverses completeMessages, intercepting messages that know the first user
|
||||
const firstUserIndex = nextRequestMessages.findLastIndex((item) => item.role === 'user');
|
||||
const newMessages = nextRequestMessages.slice(firstUserIndex + 1);
|
||||
|
||||
const toolWorkflowInteractiveResponse: WorkflowInteractiveResponseType | undefined =
|
||||
workflowInteractiveResponse
|
||||
? {
|
||||
...workflowInteractiveResponse,
|
||||
toolParams: {
|
||||
entryNodeIds: workflowInteractiveResponse.entryNodeIds,
|
||||
toolCallId: workflowInteractiveResponseItem?.toolMsgParams.tool_call_id,
|
||||
memoryMessages: newMessages
|
||||
}
|
||||
}
|
||||
: undefined;
|
||||
|
||||
return {
|
||||
dispatchFlowResponse,
|
||||
toolCallInputTokens: inputTokens,
|
||||
toolCallOutputTokens: outputTokens,
|
||||
completeMessages: nextRequestMessages,
|
||||
assistantResponses: concatAssistantResponses,
|
||||
toolWorkflowInteractiveResponse,
|
||||
runTimes,
|
||||
finish_reason
|
||||
};
|
||||
}
|
||||
|
||||
return runToolCall(
|
||||
{
|
||||
...props,
|
||||
maxRunToolTimes: maxRunToolTimes - 1,
|
||||
messages: nextRequestMessages
|
||||
},
|
||||
{
|
||||
dispatchFlowResponse,
|
||||
toolCallInputTokens: inputTokens,
|
||||
toolCallOutputTokens: outputTokens,
|
||||
assistantResponses: concatAssistantResponses,
|
||||
runTimes,
|
||||
finish_reason
|
||||
}
|
||||
);
|
||||
} else {
|
||||
// concat tool assistant
|
||||
const toolNodeAssistant = GPTMessages2Chats({
|
||||
messages: assistantMessage
|
||||
})[0] as AIChatItemType;
|
||||
|
||||
return {
|
||||
dispatchFlowResponse: response?.dispatchFlowResponse || [],
|
||||
toolCallInputTokens: inputTokens,
|
||||
toolCallOutputTokens: outputTokens,
|
||||
|
||||
completeMessages,
|
||||
assistantResponses: [...assistantResponses, ...toolNodeAssistant.value],
|
||||
runTimes: (response?.runTimes || 0) + 1,
|
||||
finish_reason
|
||||
};
|
||||
}
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
};
|
||||
|
|
|
|||
|
|
@ -1,60 +0,0 @@
|
|||
import type {
|
||||
ChatCompletionMessageParam,
|
||||
CompletionFinishReason
|
||||
} from '@fastgpt/global/core/ai/type';
|
||||
import type { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
|
||||
import { NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
|
||||
import type {
|
||||
ModuleDispatchProps,
|
||||
DispatchNodeResponseType
|
||||
} from '@fastgpt/global/core/workflow/runtime/type';
|
||||
import type { RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type';
|
||||
import { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
|
||||
import type { DispatchFlowResponse } from '../../type';
|
||||
import type { AIChatItemValueItemType } from '@fastgpt/global/core/chat/type';
|
||||
import { ChatItemValueItemType } from '@fastgpt/global/core/chat/type';
|
||||
import type { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
|
||||
import type {
|
||||
ToolCallChildrenInteractive,
|
||||
InteractiveNodeResponseType,
|
||||
WorkflowInteractiveResponseType
|
||||
} from '@fastgpt/global/core/workflow/template/system/interactive/type';
|
||||
import type { LLMModelItemType } from '@fastgpt/global/core/ai/model';
|
||||
import type { JSONSchemaInputType } from '@fastgpt/global/core/app/jsonschema';
|
||||
|
||||
export type DispatchToolModuleProps = ModuleDispatchProps<{
|
||||
[NodeInputKeyEnum.history]?: ChatItemType[];
|
||||
[NodeInputKeyEnum.userChatInput]: string;
|
||||
|
||||
[NodeInputKeyEnum.fileUrlList]?: string[];
|
||||
[NodeInputKeyEnum.aiModel]: string;
|
||||
[NodeInputKeyEnum.aiSystemPrompt]: string;
|
||||
[NodeInputKeyEnum.aiChatTemperature]: number;
|
||||
[NodeInputKeyEnum.aiChatMaxToken]: number;
|
||||
[NodeInputKeyEnum.aiChatIsResponseText]: boolean;
|
||||
[NodeInputKeyEnum.aiChatVision]?: boolean;
|
||||
[NodeInputKeyEnum.aiChatReasoning]?: boolean;
|
||||
[NodeInputKeyEnum.aiChatTopP]?: number;
|
||||
[NodeInputKeyEnum.aiChatStopSign]?: string;
|
||||
[NodeInputKeyEnum.aiChatResponseFormat]?: string;
|
||||
[NodeInputKeyEnum.aiChatJsonSchema]?: string;
|
||||
}> & {
|
||||
messages: ChatCompletionMessageParam[];
|
||||
toolNodes: ToolNodeItemType[];
|
||||
toolModel: LLMModelItemType;
|
||||
childrenInteractiveParams?: ToolCallChildrenInteractive['params'];
|
||||
};
|
||||
|
||||
export type RunToolResponse = {
|
||||
toolDispatchFlowResponses: DispatchFlowResponse[];
|
||||
toolCallInputTokens: number;
|
||||
toolCallOutputTokens: number;
|
||||
completeMessages: ChatCompletionMessageParam[];
|
||||
assistantResponses: AIChatItemValueItemType[];
|
||||
finish_reason: CompletionFinishReason;
|
||||
toolWorkflowInteractiveResponse?: ToolCallChildrenInteractive;
|
||||
};
|
||||
export type ToolNodeItemType = RuntimeNodeItemType & {
|
||||
toolParams: RuntimeNodeItemType['inputs'];
|
||||
jsonSchema?: JSONSchemaInputType;
|
||||
};
|
||||
|
|
@ -1,19 +1,48 @@
|
|||
import type {
|
||||
ChatCompletionMessageParam,
|
||||
CompletionFinishReason
|
||||
} from '@fastgpt/global/core/ai/type';
|
||||
import type { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
|
||||
import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
|
||||
import type { RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type';
|
||||
import type { DispatchFlowResponse } from '../../type';
|
||||
import type { AIChatItemValueItemType, ChatItemType } from '@fastgpt/global/core/chat/type';
|
||||
import type { ToolCallChildrenInteractive } from '@fastgpt/global/core/workflow/template/system/interactive/type';
|
||||
import type { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
|
||||
import type { JSONSchemaInputType } from '@fastgpt/global/core/app/jsonschema';
|
||||
import type { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
|
||||
|
||||
export type DispatchToolModuleProps = ModuleDispatchProps<{
|
||||
[NodeInputKeyEnum.history]?: ChatItemType[];
|
||||
[NodeInputKeyEnum.userChatInput]: string;
|
||||
|
||||
[NodeInputKeyEnum.fileUrlList]?: string[];
|
||||
[NodeInputKeyEnum.aiModel]: string;
|
||||
[NodeInputKeyEnum.aiSystemPrompt]: string;
|
||||
[NodeInputKeyEnum.aiChatTemperature]: number;
|
||||
[NodeInputKeyEnum.aiChatMaxToken]: number;
|
||||
[NodeInputKeyEnum.aiChatVision]?: boolean;
|
||||
[NodeInputKeyEnum.aiChatReasoning]?: boolean;
|
||||
[NodeInputKeyEnum.aiChatTopP]?: number;
|
||||
[NodeInputKeyEnum.aiChatStopSign]?: string;
|
||||
[NodeInputKeyEnum.aiChatResponseFormat]?: string;
|
||||
[NodeInputKeyEnum.aiChatJsonSchema]?: string;
|
||||
}> & {
|
||||
messages: ChatCompletionMessageParam[];
|
||||
toolNodes: ToolNodeItemType[];
|
||||
toolModel: LLMModelItemType;
|
||||
childrenInteractiveParams?: ToolCallChildrenInteractive['params'];
|
||||
};
|
||||
|
||||
export type RunToolResponse = {
|
||||
toolDispatchFlowResponses: DispatchFlowResponse[];
|
||||
toolCallInputTokens: number;
|
||||
toolCallOutputTokens: number;
|
||||
completeMessages: ChatCompletionMessageParam[];
|
||||
assistantResponses: AIChatItemValueItemType[];
|
||||
finish_reason: CompletionFinishReason;
|
||||
toolWorkflowInteractiveResponse?: ToolCallChildrenInteractive;
|
||||
};
|
||||
export type ToolNodeItemType = RuntimeNodeItemType & {
|
||||
toolParams: RuntimeNodeItemType['inputs'];
|
||||
jsonSchema?: JSONSchemaInputType;
|
||||
};
|
||||
|
||||
export type DispatchSubAppResponse = {
|
||||
response: string;
|
||||
usages?: ChatNodeUsageType[];
|
||||
};
|
||||
|
||||
export type GetSubAppInfoFnType = (id: string) => {
|
||||
name: string;
|
||||
avatar: string;
|
||||
toolDescription: string;
|
||||
};
|
||||
|
|
|
|||
|
|
@ -1,34 +1,41 @@
|
|||
<<<<<<< HEAD
|
||||
/*
|
||||
匹配 {{@toolId@}}，转化成: @name 的格式。
|
||||
*/
|
||||
export const parseSystemPrompt = ({
|
||||
systemPrompt,
|
||||
getSubAppInfo
|
||||
import { sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
|
||||
import { type AIChatItemValueItemType } from '@fastgpt/global/core/chat/type';
|
||||
import { type FlowNodeInputItemType } from '@fastgpt/global/core/workflow/type/io';
|
||||
import { type RuntimeEdgeItemType } from '@fastgpt/global/core/workflow/type/edge';
|
||||
import { type RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type';
|
||||
|
||||
export const updateToolInputValue = ({
|
||||
params,
|
||||
inputs
|
||||
}: {
|
||||
systemPrompt?: string;
|
||||
getSubAppInfo: (id: string) => {
|
||||
name: string;
|
||||
avatar: string;
|
||||
toolDescription: string;
|
||||
};
|
||||
}): string => {
|
||||
if (!systemPrompt) return '';
|
||||
params: Record<string, any>;
|
||||
inputs: FlowNodeInputItemType[];
|
||||
}) => {
|
||||
return inputs.map((input) => ({
|
||||
...input,
|
||||
value: params[input.key] ?? input.value
|
||||
}));
|
||||
};
|
||||
|
||||
// Match pattern {{@toolId@}} and convert to @name format
|
||||
const pattern = /\{\{@([^@]+)@\}\}/g;
|
||||
|
||||
const processedPrompt = systemPrompt.replace(pattern, (match, toolId) => {
|
||||
const toolInfo = getSubAppInfo(toolId);
|
||||
if (!toolInfo) {
|
||||
console.warn(`Tool not found for ID: ${toolId}`);
|
||||
return match; // Return original match if tool not found
|
||||
export const filterToolResponseToPreview = (response: AIChatItemValueItemType[]) => {
|
||||
return response.map((item) => {
|
||||
if (item.tools) {
|
||||
const formatTools = item.tools?.map((tool) => {
|
||||
return {
|
||||
...tool,
|
||||
response: sliceStrStartEnd(tool.response, 500, 500)
|
||||
};
|
||||
});
|
||||
return {
|
||||
...item,
|
||||
tools: formatTools
|
||||
};
|
||||
}
|
||||
|
||||
return `@${toolInfo.name}`;
|
||||
return item;
|
||||
});
|
||||
};
|
||||
|
||||
<<<<<<<< HEAD:packages/service/core/workflow/dispatch/ai/tool/utils.ts
|
||||
export const formatToolResponse = (toolResponses: any) => {
|
||||
if (typeof toolResponses === 'object') {
|
||||
return JSON.stringify(toolResponses, null, 2);
|
||||
|
|
@ -37,42 +44,6 @@ export const formatToolResponse = (toolResponses: any) => {
|
|||
return toolResponses ? String(toolResponses) : 'none';
|
||||
};
|
||||
|
||||
=======
|
||||
import type { RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type';
|
||||
import type { RuntimeEdgeItemType } from '@fastgpt/global/core/workflow/type/edge';
|
||||
import type { FlowNodeInputItemType } from '@fastgpt/global/core/workflow/type/io';
|
||||
|
||||
export const initToolNodes = (
|
||||
nodes: RuntimeNodeItemType[],
|
||||
entryNodeIds: string[],
|
||||
startParams?: Record<string, any>
|
||||
) => {
|
||||
const updateToolInputValue = ({
|
||||
params,
|
||||
inputs
|
||||
}: {
|
||||
params: Record<string, any>;
|
||||
inputs: FlowNodeInputItemType[];
|
||||
}) => {
|
||||
return inputs.map((input) => ({
|
||||
...input,
|
||||
value: params[input.key] ?? input.value
|
||||
}));
|
||||
};
|
||||
|
||||
nodes.forEach((node) => {
|
||||
if (entryNodeIds.includes(node.nodeId)) {
|
||||
node.isEntry = true;
|
||||
node.isStart = true;
|
||||
if (startParams) {
|
||||
node.inputs = updateToolInputValue({ params: startParams, inputs: node.inputs });
|
||||
}
|
||||
} else {
|
||||
node.isStart = false;
|
||||
}
|
||||
});
|
||||
};
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
// 在原参上改变值，不修改原对象，tool workflow 中，使用的还是原对象
|
||||
export const initToolCallEdges = (edges: RuntimeEdgeItemType[], entryNodeIds: string[]) => {
|
||||
edges.forEach((edge) => {
|
||||
|
|
@ -81,7 +52,6 @@ export const initToolCallEdges = (edges: RuntimeEdgeItemType[], entryNodeIds: st
|
|||
}
|
||||
});
|
||||
};
|
||||
<<<<<<< HEAD
|
||||
|
||||
export const initToolNodes = (
|
||||
nodes: RuntimeNodeItemType[],
|
||||
|
|
@ -96,9 +66,4 @@ export const initToolNodes = (
|
|||
}
|
||||
}
|
||||
});
|
||||
========
|
||||
return processedPrompt;
|
||||
>>>>>>>> 757253617 (squash: compress all commits into one):packages/service/core/workflow/dispatch/ai/agent/utils.ts
|
||||
};
|
||||
=======
|
||||
>>>>>>> 757253617 (squash: compress all commits into one)
|
||||
|
|
|
|||
|
|
@ -139,11 +139,3 @@ export const getToolNodesByIds = ({
|
|||
};
|
||||
});
|
||||
};
|
||||
|
||||
export const parseToolArgs = <T = Record<string, any>>(toolArgs: string) => {
|
||||
try {
|
||||
return json5.parse(sliceJsonStr(toolArgs)) as T;
|
||||
} catch {
|
||||
return;
|
||||
}
|
||||
};
|
||||
|
|
|
|||
|
|
@ -27,8 +27,8 @@
|
|||
"config_input_guide_lexicon": "Set Up Lexicon",
|
||||
"config_input_guide_lexicon_title": "Set Up Lexicon",
|
||||
"confirm_clear_input_value": "Are you sure to clear the form content? \nDefault values will be restored!",
|
||||
"confirm_plan": "Confirm plan",
|
||||
"confirm_to_clear_share_chat_history": "Are you sure you want to clear all chat history?",
|
||||
"confirm_plan_agent": "Please confirm whether the change plan meets expectations. If you need to modify it, you can send the modification requirements in the input box at the bottom.",
|
||||
"content_empty": "No Content",
|
||||
"contextual": "{{num}} Contexts",
|
||||
"contextual_preview": "Contextual Preview {{num}} Items",
|
||||
|
|
@ -72,6 +72,7 @@
|
|||
"not_query": "Missing query content",
|
||||
"not_select_file": "No file selected",
|
||||
"plan_agent": "Plan agent",
|
||||
"plan_check_tip": "Please confirm your plan or enter new requirements in the input box",
|
||||
"plugins_output": "Plugin Output",
|
||||
"press_to_speak": "Hold down to speak",
|
||||
"query_extension_IO_tokens": "Problem Optimization Input/Output Tokens",
|
||||
|
|
|
|||
|
|
@ -27,8 +27,8 @@
|
|||
"config_input_guide_lexicon": "配置词库",
|
||||
"config_input_guide_lexicon_title": "配置词库",
|
||||
"confirm_clear_input_value": "确认清空表单内容？将会恢复默认值！",
|
||||
"confirm_plan": "确认计划",
|
||||
"confirm_to_clear_share_chat_history": "确认清空所有聊天记录？",
|
||||
"confirm_plan_agent": "请确认改计划是否符合预期，如需修改，可在底部输入框中发送修改要求。",
|
||||
"content_empty": "内容为空",
|
||||
"contextual": "{{num}}条上下文",
|
||||
"contextual_preview": "上下文预览 {{num}} 条",
|
||||
|
|
@ -72,6 +72,7 @@
|
|||
"not_query": "缺少查询内容",
|
||||
"not_select_file": "未选择文件",
|
||||
"plan_agent": "任务规划",
|
||||
"plan_check_tip": "请确认计划或在输入框中输入新要求",
|
||||
"plugins_output": "插件输出",
|
||||
"press_to_speak": "按住说话",
|
||||
"query_extension_IO_tokens": "问题优化输入/输出 Tokens",
|
||||
|
|
|
|||
|
|
@ -27,8 +27,8 @@
|
|||
"config_input_guide_lexicon": "設定詞彙庫",
|
||||
"config_input_guide_lexicon_title": "設定詞彙庫",
|
||||
"confirm_clear_input_value": "確認清空表單內容？\n將會恢復默認值！",
|
||||
"confirm_plan": "確認計劃",
|
||||
"confirm_to_clear_share_chat_history": "確認清空所有聊天記錄？",
|
||||
"confirm_plan_agent": "請確認改計劃是否符合預期，如需修改，可在底部輸入框中發送修改要求。",
|
||||
"content_empty": "無內容",
|
||||
"contextual": "{{num}} 筆上下文",
|
||||
"contextual_preview": "上下文預覽 {{num}} 筆",
|
||||
|
|
@ -72,6 +72,7 @@
|
|||
"not_query": "缺少查詢內容",
|
||||
"not_select_file": "尚未選取檔案",
|
||||
"plan_agent": "任務規劃",
|
||||
"plan_check_tip": "請確認計劃或在輸入框中輸入新要求",
|
||||
"plugins_output": "外掛程式輸出",
|
||||
"press_to_speak": "按住說話",
|
||||
"query_extension_IO_tokens": "問題最佳化輸入/輸出 Tokens",
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import { Box, type BoxProps, Card, Flex } from '@chakra-ui/react';
|
||||
import { Box, type BoxProps, Button, Card, Flex } from '@chakra-ui/react';
|
||||
import React, { useMemo } from 'react';
|
||||
import ChatController, { type ChatControllerProps } from './ChatController';
|
||||
import ChatAvatar from './ChatAvatar';
|
||||
|
|
@ -31,6 +31,9 @@ import { addStatisticalDataToHistoryItem } from '@/global/core/chat/utils';
|
|||
import dynamic from 'next/dynamic';
|
||||
import { useMemoizedFn } from 'ahooks';
|
||||
import ChatBoxDivider from '../../../Divider';
|
||||
import { eventBus, EventNameEnum } from '@/web/common/utils/eventbus';
|
||||
import { ConfirmPlanAgentText } from '@fastgpt/global/core/workflow/runtime/constants';
|
||||
import { useMemoEnhance } from '@fastgpt/web/hooks/useMemoEnhance';
|
||||
|
||||
const ResponseTags = dynamic(() => import('./ResponseTags'));
|
||||
|
||||
|
|
@ -57,6 +60,7 @@ type Props = {
|
|||
};
|
||||
questionGuides?: string[];
|
||||
children?: React.ReactNode;
|
||||
hasPlanCheck?: boolean;
|
||||
} & ChatControllerProps;
|
||||
|
||||
const RenderQuestionGuide = ({ questionGuides }: { questionGuides: string[] }) => {
|
||||
|
|
@ -121,33 +125,36 @@ const AIContentCard = React.memo(function AIContentCard({
|
|||
);
|
||||
});
|
||||
|
||||
const ChatItem = (props: Props) => {
|
||||
const ChatItem = ({ hasPlanCheck, ...props }: Props) => {
|
||||
const { avatar, statusBoxData, children, isLastChild, questionGuides = [], chat } = props;
|
||||
|
||||
const { isPc } = useSystem();
|
||||
|
||||
const styleMap: BoxProps = {
|
||||
...(chat.obj === ChatRoleEnum.Human
|
||||
? {
|
||||
order: 0,
|
||||
borderRadius: '8px 0 8px 8px',
|
||||
justifyContent: 'flex-end',
|
||||
textAlign: 'right',
|
||||
bg: 'primary.100'
|
||||
}
|
||||
: {
|
||||
order: 1,
|
||||
borderRadius: '0 8px 8px 8px',
|
||||
justifyContent: 'flex-start',
|
||||
textAlign: 'left',
|
||||
bg: 'myGray.50'
|
||||
}),
|
||||
fontSize: 'mini',
|
||||
fontWeight: '400',
|
||||
color: 'myGray.500'
|
||||
};
|
||||
const { t } = useTranslation();
|
||||
|
||||
const styleMap: BoxProps = useMemoEnhance(
|
||||
() => ({
|
||||
...(chat.obj === ChatRoleEnum.Human
|
||||
? {
|
||||
order: 0,
|
||||
borderRadius: '8px 0 8px 8px',
|
||||
justifyContent: 'flex-end',
|
||||
textAlign: 'right',
|
||||
bg: 'primary.100'
|
||||
}
|
||||
: {
|
||||
order: 1,
|
||||
borderRadius: '0 8px 8px 8px',
|
||||
justifyContent: 'flex-start',
|
||||
textAlign: 'left',
|
||||
bg: 'myGray.50'
|
||||
}),
|
||||
fontSize: 'mini',
|
||||
fontWeight: '400',
|
||||
color: 'myGray.500'
|
||||
}),
|
||||
[chat.obj]
|
||||
);
|
||||
|
||||
const isChatting = useContextSelector(ChatBoxContext, (v) => v.isChatting);
|
||||
const chatType = useContextSelector(ChatBoxContext, (v) => v.chatType);
|
||||
const showNodeStatus = useContextSelector(ChatItemContext, (v) => v.showNodeStatus);
|
||||
|
|
@ -157,7 +164,7 @@ const ChatItem = (props: Props) => {
|
|||
const outLinkAuthData = useContextSelector(WorkflowRuntimeContext, (v) => v.outLinkAuthData);
|
||||
const isShowReadRawSource = useContextSelector(ChatItemContext, (v) => v.isShowReadRawSource);
|
||||
|
||||
const { totalQuoteList: quoteList = [] } = useMemo(
|
||||
const { totalQuoteList: quoteList = [] } = useMemoEnhance(
|
||||
() => addStatisticalDataToHistoryItem(chat),
|
||||
[chat]
|
||||
);
|
||||
|
|
@ -166,7 +173,7 @@ const ChatItem = (props: Props) => {
|
|||
|
||||
const { copyData } = useCopyData();
|
||||
|
||||
const chatStatusMap = useMemo(() => {
|
||||
const chatStatusMap = useMemoEnhance(() => {
|
||||
if (!statusBoxData?.status) return;
|
||||
return colorMap[statusBoxData.status];
|
||||
}, [statusBoxData?.status]);
|
||||
|
|
@ -175,8 +182,12 @@ const ChatItem = (props: Props) => {
|
|||
1. The interactive node is divided into n dialog boxes.
|
||||
2. Auto-complete the last textnode
|
||||
*/
|
||||
const splitAiResponseResults = useMemo(() => {
|
||||
if (chat.obj === ChatRoleEnum.Human) return [chat.value];
|
||||
const { responses: splitAiResponseResults } = useMemo(() => {
|
||||
if (chat.obj === ChatRoleEnum.Human) {
|
||||
return {
|
||||
responses: [chat.value]
|
||||
};
|
||||
}
|
||||
|
||||
if (chat.obj === ChatRoleEnum.AI) {
|
||||
// Remove empty text node
|
||||
|
|
@ -194,7 +205,11 @@ const ChatItem = (props: Props) => {
|
|||
let currentGroup: AIChatItemValueItemType[] = [];
|
||||
|
||||
filterList.forEach((value) => {
|
||||
// 每次遇到交互节点，则推送一个全新的分组
|
||||
if (value.interactive) {
|
||||
if (value.interactive.type === 'agentPlanCheck') {
|
||||
return;
|
||||
}
|
||||
if (currentGroup.length > 0) {
|
||||
groupedValues.push(currentGroup);
|
||||
currentGroup = [];
|
||||
|
|
@ -229,10 +244,14 @@ const ChatItem = (props: Props) => {
|
|||
}
|
||||
}
|
||||
|
||||
return groupedValues;
|
||||
return {
|
||||
responses: groupedValues
|
||||
};
|
||||
}
|
||||
|
||||
return [];
|
||||
return {
|
||||
responses: []
|
||||
};
|
||||
}, [chat.obj, chat.value, isChatting]);
|
||||
|
||||
const setCiteModalData = useContextSelector(ChatItemContext, (v) => v.setCiteModalData);
|
||||
|
|
@ -277,8 +296,6 @@ const ChatItem = (props: Props) => {
|
|||
}
|
||||
);
|
||||
|
||||
const aiSubApps = 'subApps' in chat ? chat.subApps : undefined;
|
||||
|
||||
return (
|
||||
<Box
|
||||
_hover={{
|
||||
|
|
@ -420,6 +437,23 @@ const ChatItem = (props: Props) => {
|
|||
</Box>
|
||||
);
|
||||
})}
|
||||
|
||||
{hasPlanCheck && isLastChild && (
|
||||
<Flex mt={3}>
|
||||
<Button
|
||||
leftIcon={<MyIcon name={'common/check'} w={'16px'} />}
|
||||
variant={'primaryOutline'}
|
||||
onClick={() => {
|
||||
eventBus.emit(EventNameEnum.sendQuestion, {
|
||||
text: ConfirmPlanAgentText,
|
||||
focus: true
|
||||
});
|
||||
}}
|
||||
>
|
||||
{t('chat:confirm_plan')}
|
||||
</Button>
|
||||
</Flex>
|
||||
)}
|
||||
</Box>
|
||||
);
|
||||
};
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ import type {
|
|||
} from '@fastgpt/global/core/chat/type.d';
|
||||
import { useToast } from '@fastgpt/web/hooks/useToast';
|
||||
import { getErrText } from '@fastgpt/global/common/error/utils';
|
||||
import { Box, Checkbox, Flex } from '@chakra-ui/react';
|
||||
import { Box, Button, Checkbox, Flex } from '@chakra-ui/react';
|
||||
import { EventNameEnum, eventBus } from '@/web/common/utils/eventbus';
|
||||
import { chats2GPTMessages } from '@fastgpt/global/core/chat/adapt';
|
||||
import { useForm } from 'react-hook-form';
|
||||
|
|
@ -253,6 +253,7 @@ const ChatBox = ({
|
|||
tool,
|
||||
subAppId,
|
||||
interactive,
|
||||
agentPlan,
|
||||
variables,
|
||||
nodeResponse,
|
||||
durationSeconds,
|
||||
|
|
@ -492,6 +493,14 @@ const ChatBox = ({
|
|||
value: item.value.concat(val)
|
||||
};
|
||||
}
|
||||
if (event === SseResponseEventEnum.agentPlan && agentPlan) {
|
||||
return {
|
||||
...item,
|
||||
value: item.value.concat({
|
||||
agentPlan
|
||||
})
|
||||
};
|
||||
}
|
||||
if (event === SseResponseEventEnum.workflowDuration && durationSeconds) {
|
||||
return {
|
||||
...item,
|
||||
|
|
@ -1151,6 +1160,8 @@ const ChatBox = ({
|
|||
}, [chatType, chatRecords.length, chatStartedWatch]);
|
||||
|
||||
//chat history
|
||||
const hasPlanCheck =
|
||||
lastInteractive?.type === 'agentPlanCheck' && !lastInteractive.params.confirmed;
|
||||
const RecordsBox = useMemo(() => {
|
||||
return (
|
||||
<Box id={'history'}>
|
||||
|
|
@ -1178,6 +1189,7 @@ const ChatBox = ({
|
|||
avatar={appAvatar}
|
||||
chat={item}
|
||||
isLastChild={index === chatRecords.length - 1}
|
||||
hasPlanCheck={hasPlanCheck}
|
||||
{...{
|
||||
showVoiceIcon,
|
||||
statusBoxData,
|
||||
|
|
@ -1249,7 +1261,8 @@ const ChatBox = ({
|
|||
onReadUserDislike,
|
||||
t,
|
||||
showMarkIcon,
|
||||
onCloseCustomFeedback
|
||||
onCloseCustomFeedback,
|
||||
hasPlanCheck
|
||||
]);
|
||||
|
||||
// Child box
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@ import type {
|
|||
import { ChatSiteItemType } from '@fastgpt/global/core/chat/type';
|
||||
import type { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
|
||||
import type { WorkflowInteractiveResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type';
|
||||
import type { AgentPlanType } from '@fastgpt/service/core/workflow/dispatch/ai/agent/sub/plan/type';
|
||||
|
||||
export type generatingMessageProps = {
|
||||
event: SseResponseEventEnum;
|
||||
|
|
@ -19,6 +20,7 @@ export type generatingMessageProps = {
|
|||
status?: 'running' | 'finish';
|
||||
tool?: ToolModuleResponseItemType;
|
||||
interactive?: WorkflowInteractiveResponseType;
|
||||
agentPlan?: AgentPlanType;
|
||||
variables?: Record<string, any>;
|
||||
nodeResponse?: ChatHistoryItemResType;
|
||||
durationSeconds?: number;
|
||||
|
|
|
|||
|
|
@ -26,18 +26,15 @@ import type {
|
|||
import { isEqual } from 'lodash';
|
||||
import { useTranslation } from 'next-i18next';
|
||||
import { eventBus, EventNameEnum } from '@/web/common/utils/eventbus';
|
||||
import {
|
||||
SelectOptionsComponent,
|
||||
FormInputComponent,
|
||||
AgentPlanCheckComponent
|
||||
} from './Interactive/InteractiveComponents';
|
||||
import { SelectOptionsComponent, FormInputComponent } from './Interactive/InteractiveComponents';
|
||||
import { extractDeepestInteractive } from '@fastgpt/global/core/workflow/runtime/utils';
|
||||
import { useContextSelector } from 'use-context-selector';
|
||||
import { type OnOpenCiteModalProps } from '@/web/core/chat/context/chatItemContext';
|
||||
import { WorkflowRuntimeContext } from '../ChatContainer/context/workflowRuntimeContext';
|
||||
import { useCreation } from 'ahooks';
|
||||
import { useSafeTranslation } from '@fastgpt/web/hooks/useSafeTranslation';
|
||||
import { ConfirmPlanAgentText } from '@fastgpt/global/core/workflow/runtime/constants';
|
||||
import type { AgentPlanType } from '@fastgpt/service/core/workflow/dispatch/ai/agent/sub/plan/type';
|
||||
import MyDivider from '@fastgpt/web/components/common/MyDivider';
|
||||
|
||||
const accordionButtonStyle = {
|
||||
w: 'auto',
|
||||
|
|
@ -334,6 +331,33 @@ const RenderPaymentPauseInteractive = React.memo(function RenderPaymentPauseInte
|
|||
);
|
||||
});
|
||||
|
||||
const RenderAgentPlan = React.memo(function RenderAgentPlan({
|
||||
agentPlan
|
||||
}: {
|
||||
agentPlan: AgentPlanType;
|
||||
}) {
|
||||
const { t } = useTranslation();
|
||||
return (
|
||||
<Box>
|
||||
<Box fontSize={'xl'} color={'myGray.900'} fontWeight={'bold'}>
|
||||
{agentPlan.task}
|
||||
</Box>
|
||||
<Box>
|
||||
{agentPlan.steps.map((step, index) => (
|
||||
<Box key={step.id} mt={3}>
|
||||
<Box fontSize={'lg'} fontWeight={'bold'}>
|
||||
{`${index + 1}. ${step.title}`}
|
||||
</Box>
|
||||
<Box>{step.description}</Box>
|
||||
</Box>
|
||||
))}
|
||||
</Box>
|
||||
<MyDivider />
|
||||
<Box>{t('chat:plan_check_tip')}</Box>
|
||||
</Box>
|
||||
);
|
||||
});
|
||||
|
||||
const AIResponseBox = ({
|
||||
chatItemDataId,
|
||||
value,
|
||||
|
|
@ -391,14 +415,7 @@ const AIResponseBox = ({
|
|||
);
|
||||
}
|
||||
if (interactive.type === 'agentPlanCheck') {
|
||||
return (
|
||||
<AgentPlanCheckComponent
|
||||
interactiveParams={interactive.params}
|
||||
onConfirm={() => {
|
||||
onSendPrompt(ConfirmPlanAgentText);
|
||||
}}
|
||||
/>
|
||||
);
|
||||
return null;
|
||||
}
|
||||
if (interactive.type === 'agentPlanAskQuery') {
|
||||
return <Box>{interactive.params.content}</Box>;
|
||||
|
|
@ -407,6 +424,9 @@ const AIResponseBox = ({
|
|||
return <RenderPaymentPauseInteractive interactive={interactive} />;
|
||||
}
|
||||
}
|
||||
if ('agentPlan' in value && value.agentPlan) {
|
||||
return <RenderAgentPlan agentPlan={value.agentPlan} />;
|
||||
}
|
||||
|
||||
// Abandon
|
||||
if ('tools' in value && value.tools) {
|
||||
|
|
|
|||
|
|
@ -253,23 +253,3 @@ export const FormInputComponent = React.memo(function FormInputComponent({
|
|||
</Box>
|
||||
);
|
||||
});
|
||||
|
||||
// Agent interactive
|
||||
export const AgentPlanCheckComponent = React.memo(function AgentPlanCheckComponent({
|
||||
interactiveParams,
|
||||
onConfirm
|
||||
}: {
|
||||
interactiveParams: AgentPlanCheckInteractive['params'];
|
||||
onConfirm: () => void;
|
||||
}) {
|
||||
const { t } = useTranslation();
|
||||
return interactiveParams?.confirmed ? (
|
||||
// TODO：临时 UI
|
||||
<Box>已确认计划</Box>
|
||||
) : (
|
||||
<Box>
|
||||
<Box>{t('chat:confirm_plan_agent')}</Box>
|
||||
<Button onClick={onConfirm}>{t('common:Confirm')}</Button>
|
||||
</Box>
|
||||
);
|
||||
});
|
||||
|
|
|
|||
|
|
@ -87,7 +87,7 @@ const AppListContextProvider = ({ children }: { children: ReactNode }) => {
|
|||
// agent page
|
||||
if (router.pathname.includes('/agent')) {
|
||||
return !type || type === 'all'
|
||||
? [AppTypeEnum.folder, AppTypeEnum.simple, AppTypeEnum.workflow]
|
||||
? [AppTypeEnum.folder, AppTypeEnum.simple, AppTypeEnum.workflow, AppTypeEnum.agent]
|
||||
: [AppTypeEnum.folder, type];
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -2,6 +2,10 @@ import { AppTypeEnum } from '@fastgpt/global/core/app/constants';
|
|||
import { i18nT } from '@fastgpt/web/i18n/utils';
|
||||
|
||||
export const appTypeTagMap = {
|
||||
[AppTypeEnum.agent]: {
|
||||
label: 'Agent',
|
||||
icon: 'core/app/type/mcpTools'
|
||||
},
|
||||
[AppTypeEnum.simple]: {
|
||||
label: i18nT('app:type.Chat_Agent'),
|
||||
icon: 'core/app/type/simple'
|
||||
|
|
@ -28,6 +32,5 @@ export const appTypeTagMap = {
|
|||
},
|
||||
[AppTypeEnum.tool]: undefined,
|
||||
[AppTypeEnum.folder]: undefined,
|
||||
[AppTypeEnum.hidden]: undefined,
|
||||
[AppTypeEnum.agent]: undefined
|
||||
[AppTypeEnum.hidden]: undefined
|
||||
};
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ import { formatTime2YMDHMW } from '@fastgpt/global/common/string/time';
|
|||
import { getWebReqUrl } from '@fastgpt/web/common/system/utils';
|
||||
import type { OnOptimizePromptProps } from '@/components/common/PromptEditor/OptimizerPopover';
|
||||
import type { OnOptimizeCodeProps } from '@/pageComponents/app/detail/WorkflowComponents/Flow/nodes/NodeCode/Copilot';
|
||||
import type { AgentPlanType } from '@fastgpt/service/core/workflow/dispatch/ai/agent/sub/plan/type';
|
||||
|
||||
type StreamFetchProps = {
|
||||
url?: string;
|
||||
|
|
@ -36,6 +37,12 @@ type ResponseQueueItemType =
|
|||
event: SseResponseEventEnum.interactive;
|
||||
[key: string]: any;
|
||||
}
|
||||
| {
|
||||
responseValueId?: string;
|
||||
subAppId?: string;
|
||||
event: SseResponseEventEnum.agentPlan;
|
||||
agentPlan: AgentPlanType;
|
||||
}
|
||||
| {
|
||||
responseValueId?: string;
|
||||
subAppId?: string;
|
||||
|
|
@ -256,6 +263,13 @@ export const streamFetch = ({
|
|||
event,
|
||||
...rest
|
||||
});
|
||||
} else if (event === SseResponseEventEnum.agentPlan) {
|
||||
pushDataToQueue({
|
||||
responseValueId,
|
||||
subAppId,
|
||||
event,
|
||||
agentPlan: rest.agentPlan
|
||||
});
|
||||
} else if (event === SseResponseEventEnum.error) {
|
||||
if (rest.statusText === TeamErrEnum.aiPointsNotEnough) {
|
||||
useSystemStore.getState().setNotSufficientModalType(TeamErrEnum.aiPointsNotEnough);
|
||||
|
|
|
|||
Loading…
Reference in New Issue