mirror of
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V4.14.2 fearured (#5922)
* fix: chat agent template create (#5912) * doc * template market ui (#5917) * Compress tool (#5919) * Compress tool (#5914) * rename file * feat: agent call request * perf: Agent call (#5916) * fix: interactive in tool call * doc * fix: merge node response * fix: test * fix：修改 message 对话中的压缩提示词 (#5918) Co-authored-by: xxyyh <2289112474@qq> * perf: compress code * perf: agent call comment --------- Co-authored-by: YeYuheng <57035043+YYH211@users.noreply.github.com> Co-authored-by: xxyyh <2289112474@qq> * remove pr * feat: auto password * perf: app template cache * fix template market ui (#5921) --------- Co-authored-by: heheer <heheer@sealos.io> Co-authored-by: YeYuheng <57035043+YYH211@users.noreply.github.com> Co-authored-by: xxyyh <2289112474@qq>
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commit
48c0c150eb
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@ -16,7 +16,7 @@ description: FastGPT OpenAPI 对话接口
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{/* * 对话现在有`v1`和`v2`两个接口，可以按需使用，v2 自 4.9.4 版本新增，v1 接口同时不再维护 */}
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## 请求简易应用和工作流
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## 请求对话 Agent 和工作流
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`v1`对话接口兼容`GPT`的接口！如果你的项目使用的是标准的`GPT`官方接口，可以直接通过修改`BaseUrl`和 `Authorization`来访问 FastGpt 应用，不过需要注意下面几个规则：
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@ -112,6 +112,7 @@ description: FastGPT 文档目录
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- [/docs/upgrading/4-13/4132](/docs/upgrading/4-13/4132)
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- [/docs/upgrading/4-14/4140](/docs/upgrading/4-14/4140)
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- [/docs/upgrading/4-14/4141](/docs/upgrading/4-14/4141)
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- [/docs/upgrading/4-14/4142](/docs/upgrading/4-14/4142)
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- [/docs/upgrading/4-8/40](/docs/upgrading/4-8/40)
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- [/docs/upgrading/4-8/41](/docs/upgrading/4-8/41)
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- [/docs/upgrading/4-8/42](/docs/upgrading/4-8/42)
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@ -0,0 +1,20 @@
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---
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title: 'V4.14.2(进行中)'
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description: 'FastGPT V4.14.2 更新说明'
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---
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## 🚀 新增内容
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1. 封装底层 Agent Call 方式，支持工具连续调用时上下文的压缩，以及单个工具长响应的压缩。
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2. 模板市场新 UI。
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## ⚙️ 优化
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1. 30 分钟模板市场缓存时长。
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## 🐛 修复
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1. 简易应用模板未正常转化。
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2. 工具调用中，包含两个以上连续用户选择时候，第二个用户选择异常。
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@ -1,5 +1,5 @@
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{
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"title": "4.14.x",
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"description": "",
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"pages": ["4141", "4140"]
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"pages": ["4142", "4141", "4140"]
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}
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@ -101,7 +101,7 @@
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"document/content/docs/protocol/terms.en.mdx": "2025-08-03T22:37:45+08:00",
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"document/content/docs/protocol/terms.mdx": "2025-08-03T22:37:45+08:00",
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"document/content/docs/toc.en.mdx": "2025-08-04T13:42:36+08:00",
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"document/content/docs/toc.mdx": "2025-11-06T14:47:55+08:00",
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"document/content/docs/toc.mdx": "2025-11-13T13:36:41+08:00",
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"document/content/docs/upgrading/4-10/4100.mdx": "2025-08-02T19:38:37+08:00",
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"document/content/docs/upgrading/4-10/4101.mdx": "2025-09-08T20:07:20+08:00",
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"document/content/docs/upgrading/4-11/4110.mdx": "2025-08-05T23:20:39+08:00",
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@ -115,7 +115,8 @@
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"document/content/docs/upgrading/4-13/4131.mdx": "2025-09-30T15:47:06+08:00",
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"document/content/docs/upgrading/4-13/4132.mdx": "2025-10-21T11:46:53+08:00",
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"document/content/docs/upgrading/4-14/4140.mdx": "2025-11-06T15:43:00+08:00",
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"document/content/docs/upgrading/4-14/4141.mdx": "2025-11-11T14:05:02+08:00",
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"document/content/docs/upgrading/4-14/4141.mdx": "2025-11-12T12:19:02+08:00",
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"document/content/docs/upgrading/4-14/4142.mdx": "2025-11-13T20:49:04+08:00",
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"document/content/docs/upgrading/4-8/40.mdx": "2025-08-02T19:38:37+08:00",
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"document/content/docs/upgrading/4-8/41.mdx": "2025-08-02T19:38:37+08:00",
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"document/content/docs/upgrading/4-8/42.mdx": "2025-08-02T19:38:37+08:00",
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@ -143,10 +143,7 @@ export const getRegQueryStr = (text: string, flags = 'i') => {
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/* slice json str */
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export const sliceJsonStr = (str: string) => {
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str = str
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.trim()
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.replace(/(\\n|\\)/g, '')
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.replace(/ /g, '');
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str = str.trim();
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// Find first opening bracket
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let start = -1;
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@ -80,7 +80,8 @@ export type CompletionFinishReason =
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| 'tool_calls'
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| 'content_filter'
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| 'function_call'
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| null;
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| null
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| undefined;
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export default openai;
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export * from 'openai';
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@ -230,6 +230,8 @@ export type AppTemplateSchemaType = {
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type: string;
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author?: string;
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isActive?: boolean;
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isPromoted?: boolean;
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recommendText?: string;
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userGuide?: {
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type: 'markdown' | 'link';
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content?: string;
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@ -237,6 +239,7 @@ export type AppTemplateSchemaType = {
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};
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isQuickTemplate?: boolean;
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order?: number;
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// TODO: 对于建议应用，是另一个格式
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workflow: WorkflowTemplateBasicType;
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};
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@ -213,21 +213,10 @@ export const getChatSourceByPublishChannel = (publishChannel: PublishChannelEnum
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export const mergeChatResponseData = (
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responseDataList: ChatHistoryItemResType[]
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): ChatHistoryItemResType[] => {
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// Merge children reponse data(Children has interactive response)
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const responseWithMergedPlugins = responseDataList.map((item) => {
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if (item.pluginDetail && item.pluginDetail.length > 1) {
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return {
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...item,
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pluginDetail: mergeChatResponseData(item.pluginDetail)
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};
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}
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return item;
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});
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const result: ChatHistoryItemResType[] = [];
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const mergeMap = new Map<string, number>(); // mergeSignId -> result index
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for (const item of responseWithMergedPlugins) {
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for (const item of responseDataList) {
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if (item.mergeSignId && mergeMap.has(item.mergeSignId)) {
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// Merge with existing item
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const existingIndex = mergeMap.get(item.mergeSignId)!;
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@ -238,9 +227,18 @@ export const mergeChatResponseData = (
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runningTime: +((existing.runningTime || 0) + (item.runningTime || 0)).toFixed(2),
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totalPoints: (existing.totalPoints || 0) + (item.totalPoints || 0),
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childTotalPoints: (existing.childTotalPoints || 0) + (item.childTotalPoints || 0),
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toolDetail: [...(existing.toolDetail || []), ...(item.toolDetail || [])],
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loopDetail: [...(existing.loopDetail || []), ...(item.loopDetail || [])],
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pluginDetail: [...(existing.pluginDetail || []), ...(item.pluginDetail || [])]
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toolDetail: mergeChatResponseData([
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...(existing.toolDetail || []),
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...(item.toolDetail || [])
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]),
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loopDetail: mergeChatResponseData([
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...(existing.loopDetail || []),
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...(item.loopDetail || [])
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]),
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pluginDetail: mergeChatResponseData([
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...(existing.pluginDetail || []),
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...(item.pluginDetail || [])
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])
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};
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} else {
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// Add new item
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@ -2,9 +2,9 @@ import type { ChatNodeUsageType } from '../../../support/wallet/bill/type';
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import type {
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ChatItemType,
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ToolRunResponseItemType,
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AIChatItemValueItemType
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AIChatItemValueItemType,
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ChatHistoryItemResType
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} from '../../chat/type';
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import { NodeOutputItemType } from '../../chat/type';
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import type { FlowNodeInputItemType, FlowNodeOutputItemType } from '../type/io.d';
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import type { NodeToolConfigType, StoreNodeItemType } from '../type/node';
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import type { DispatchNodeResponseKeyEnum } from './constants';
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@ -112,7 +112,6 @@ export type RuntimeNodeItemType = {
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flowNodeType: StoreNodeItemType['flowNodeType'];
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showStatus?: StoreNodeItemType['showStatus'];
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isEntry?: boolean;
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isStart?: boolean;
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version?: string;
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inputs: FlowNodeInputItemType[];
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@ -20,6 +20,7 @@ import type { StoreNodeItemType } from '../type/node';
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import { isValidReferenceValueFormat } from '../utils';
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import type { RuntimeEdgeItemType, RuntimeNodeItemType } from './type';
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import { isSecretValue } from '../../../common/secret/utils';
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import { isChildInteractive } from '../template/system/interactive/constants';
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export const extractDeepestInteractive = (
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interactive: WorkflowInteractiveResponseType
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@ -28,11 +29,7 @@ export const extractDeepestInteractive = (
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let current = interactive;
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let depth = 0;
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while (
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depth < MAX_DEPTH &&
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(current?.type === 'childrenInteractive' || current?.type === 'loopInteractive') &&
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current.params?.childrenResponse
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) {
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while (depth < MAX_DEPTH && 'childrenResponse' in current.params) {
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current = current.params.childrenResponse;
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depth++;
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}
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@ -181,10 +178,7 @@ export const getLastInteractiveValue = (
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return;
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}
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if (
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lastValue.interactive.type === 'childrenInteractive' ||
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lastValue.interactive.type === 'loopInteractive'
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) {
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if (isChildInteractive(lastValue.interactive.type)) {
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return lastValue.interactive;
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}
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@ -297,7 +291,6 @@ export const checkNodeRunStatus = ({
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node: RuntimeNodeItemType;
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runtimeEdges: RuntimeEdgeItemType[];
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}) => {
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const filterRuntimeEdges = filterWorkflowEdges(runtimeEdges);
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const isStartNode = (nodeType: string) => {
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const map: Record<any, boolean> = {
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[FlowNodeTypeEnum.workflowStart]: true,
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@ -310,7 +303,7 @@ export const checkNodeRunStatus = ({
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const commonEdges: RuntimeEdgeItemType[] = [];
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const recursiveEdgeGroupsMap = new Map<string, RuntimeEdgeItemType[]>();
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const sourceEdges = filterRuntimeEdges.filter((item) => item.target === targetNode.nodeId);
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const sourceEdges = runtimeEdges.filter((item) => item.target === targetNode.nodeId);
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sourceEdges.forEach((sourceEdge) => {
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const stack: Array<{
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@ -333,7 +326,7 @@ export const checkNodeRunStatus = ({
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const sourceNode = nodesMap.get(edge.source);
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if (!sourceNode) continue;
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if (isStartNode(sourceNode.flowNodeType) || sourceNode.isStart) {
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if (isStartNode(sourceNode.flowNodeType) || sourceEdge.sourceHandle === 'selectedTools') {
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commonEdges.push(sourceEdge);
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continue;
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}
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@ -355,7 +348,7 @@ export const checkNodeRunStatus = ({
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newVisited.add(edge.source);
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// 查找目标节点的 source edges 并加入栈中
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const nextEdges = filterRuntimeEdges.filter((item) => item.target === edge.source);
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const nextEdges = runtimeEdges.filter((item) => item.target === edge.source);
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for (const nextEdge of nextEdges) {
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stack.push({
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@ -0,0 +1,12 @@
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import type { InteractiveNodeResponseType } from './type';
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export const isChildInteractive = (type: InteractiveNodeResponseType['type']) => {
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if (
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type === 'childrenInteractive' ||
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type === 'toolChildrenInteractive' ||
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type === 'loopInteractive'
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) {
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return true;
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}
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return false;
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};
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@ -9,11 +9,6 @@ type InteractiveBasicType = {
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memoryEdges: RuntimeEdgeItemType[];
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nodeOutputs: NodeOutputItemType[];
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skipNodeQueue?: { id: string; skippedNodeIdList: string[] }[]; // 需要记录目前在 queue 里的节点
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toolParams?: {
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entryNodeIds: string[]; // 记录工具中，交互节点的 Id，而不是起始工作流的入口
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memoryMessages: ChatCompletionMessageParam[]; // 这轮工具中，产生的新的 messages
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toolCallId: string; // 记录对应 tool 的id，用于后续交互节点可以替换掉 tool 的 response
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};
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usageId?: string;
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};
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@ -27,7 +22,17 @@ type InteractiveNodeType = {
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type ChildrenInteractive = InteractiveNodeType & {
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type: 'childrenInteractive';
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params: {
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childrenResponse?: WorkflowInteractiveResponseType;
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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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type: 'toolChildrenInteractive';
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params: {
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childrenResponse: WorkflowInteractiveResponseType;
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toolParams: {
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memoryRequestMessages: ChatCompletionMessageParam[]; // 这轮工具中，产生的新的 messages
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toolCallId: string; // 记录对应 tool 的id，用于后续交互节点可以替换掉 tool 的 response
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};
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};
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};
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|
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@ -94,6 +99,7 @@ export type InteractiveNodeResponseType =
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| UserSelectInteractive
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| UserInputInteractive
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| ChildrenInteractive
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| ToolCallChildrenInteractive
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| LoopInteractive
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| PaymentPauseInteractive;
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|
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@ -0,0 +1,313 @@
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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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CompletionFinishReason
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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 type {
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ToolCallChildrenInteractive,
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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 { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
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import { compressRequestMessages } from '../compress';
|
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import { computedMaxToken } from '../../utils';
|
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import { filterGPTMessageByMaxContext } from '../utils';
|
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import { getLLMModel } from '../../model';
|
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import { filterEmptyAssistantMessages } from './utils';
|
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|
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type RunAgentCallProps = {
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maxRunAgentTimes: number;
|
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compressTaskDescription?: string;
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|
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body: CreateLLMResponseProps['body'] & {
|
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tools: ChatCompletionTool[];
|
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|
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temperature?: number;
|
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top_p?: number;
|
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stream?: boolean;
|
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};
|
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|
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userKey?: CreateLLMResponseProps['userKey'];
|
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isAborted?: CreateLLMResponseProps['isAborted'];
|
||||
|
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childrenInteractiveParams?: ToolCallChildrenInteractive['params'];
|
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handleInteractiveTool: (e: ToolCallChildrenInteractive['params']) => Promise<{
|
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response: string;
|
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assistantMessages: ChatCompletionMessageParam[];
|
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usages: ChatNodeUsageType[];
|
||||
interactive?: WorkflowInteractiveResponseType;
|
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stop?: boolean;
|
||||
}>;
|
||||
|
||||
handleToolResponse: (e: {
|
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call: ChatCompletionMessageToolCall;
|
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messages: ChatCompletionMessageParam[];
|
||||
}) => Promise<{
|
||||
response: string;
|
||||
assistantMessages: ChatCompletionMessageParam[];
|
||||
usages: ChatNodeUsageType[];
|
||||
interactive?: WorkflowInteractiveResponseType;
|
||||
stop?: boolean;
|
||||
}>;
|
||||
} & ResponseEvents;
|
||||
|
||||
type RunAgentResponse = {
|
||||
completeMessages: ChatCompletionMessageParam[]; // Step request complete messages
|
||||
assistantMessages: ChatCompletionMessageParam[]; // Step assistant response messages
|
||||
interactiveResponse?: ToolCallChildrenInteractive;
|
||||
|
||||
// Usage
|
||||
inputTokens: number;
|
||||
outputTokens: number;
|
||||
subAppUsages: ChatNodeUsageType[];
|
||||
|
||||
finish_reason: CompletionFinishReason | undefined;
|
||||
};
|
||||
|
||||
/*
|
||||
一个循环进行工具调用的 LLM 请求封装。
|
||||
|
||||
AssistantMessages 组成：
|
||||
1. 调用 AI 时生成的 messages
|
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2. tool 内部调用产生的 messages
|
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3. tool 响应的值，role=tool，content=tool response
|
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|
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RequestMessages 为模型请求的消息，组成:
|
||||
1. 历史对话记录
|
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2. 调用 AI 时生成的 messages
|
||||
3. tool 响应的值，role=tool，content=tool response
|
||||
|
||||
memoryRequestMessages 为上一轮中断时，requestMessages 的内容
|
||||
*/
|
||||
export const runAgentCall = async ({
|
||||
maxRunAgentTimes,
|
||||
body: { model, messages, max_tokens, tools, ...body },
|
||||
userKey,
|
||||
isAborted,
|
||||
|
||||
childrenInteractiveParams,
|
||||
handleInteractiveTool,
|
||||
handleToolResponse,
|
||||
|
||||
onReasoning,
|
||||
onStreaming,
|
||||
onToolCall,
|
||||
onToolParam
|
||||
}: RunAgentCallProps): Promise<RunAgentResponse> => {
|
||||
const modelData = getLLMModel(model);
|
||||
|
||||
let runTimes = 0;
|
||||
let interactiveResponse: ToolCallChildrenInteractive | undefined;
|
||||
|
||||
// Init messages
|
||||
const maxTokens = computedMaxToken({
|
||||
model: modelData,
|
||||
maxToken: max_tokens || 8000,
|
||||
min: 100
|
||||
});
|
||||
|
||||
// 本轮产生的 assistantMessages，包括 tool 内产生的
|
||||
const assistantMessages: ChatCompletionMessageParam[] = [];
|
||||
// 多轮运行时候的请求 messages
|
||||
let requestMessages = (
|
||||
await filterGPTMessageByMaxContext({
|
||||
messages,
|
||||
maxContext: modelData.maxContext - (maxTokens || 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 inputTokens: number = 0;
|
||||
let outputTokens: number = 0;
|
||||
let finish_reason: CompletionFinishReason | undefined;
|
||||
const subAppUsages: ChatNodeUsageType[] = [];
|
||||
|
||||
// 处理 tool 里的交互
|
||||
if (childrenInteractiveParams) {
|
||||
const {
|
||||
response,
|
||||
assistantMessages: toolAssistantMessages,
|
||||
usages,
|
||||
interactive,
|
||||
stop
|
||||
} = await handleInteractiveTool(childrenInteractiveParams);
|
||||
|
||||
// 将 requestMessages 复原成上一轮中断时的内容，并附上 tool response
|
||||
requestMessages = childrenInteractiveParams.toolParams.memoryRequestMessages.map((item) =>
|
||||
item.role === 'tool' && item.tool_call_id === childrenInteractiveParams.toolParams.toolCallId
|
||||
? {
|
||||
...item,
|
||||
content: response
|
||||
}
|
||||
: item
|
||||
);
|
||||
|
||||
// 只需要推送本轮产生的 assistantMessages
|
||||
assistantMessages.push(...filterEmptyAssistantMessages(toolAssistantMessages));
|
||||
subAppUsages.push(...usages);
|
||||
|
||||
// 相同 tool 触发了多次交互, 调用的 toolId 认为是相同的
|
||||
if (interactive) {
|
||||
// console.dir(interactive, { depth: null });
|
||||
interactiveResponse = {
|
||||
type: 'toolChildrenInteractive',
|
||||
params: {
|
||||
childrenResponse: interactive,
|
||||
toolParams: {
|
||||
memoryRequestMessages: requestMessages,
|
||||
toolCallId: childrenInteractiveParams.toolParams.toolCallId
|
||||
}
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
if (interactiveResponse || stop) {
|
||||
return {
|
||||
inputTokens: 0,
|
||||
outputTokens: 0,
|
||||
subAppUsages,
|
||||
completeMessages: requestMessages,
|
||||
assistantMessages,
|
||||
interactiveResponse,
|
||||
finish_reason: 'stop'
|
||||
};
|
||||
}
|
||||
|
||||
// 正常完成该工具的响应，继续进行工具调用
|
||||
}
|
||||
|
||||
// 自循环运行
|
||||
while (runTimes < maxRunAgentTimes) {
|
||||
// TODO: 费用检测
|
||||
|
||||
runTimes++;
|
||||
|
||||
// 1. Compress request messages
|
||||
const result = await compressRequestMessages({
|
||||
messages: requestMessages,
|
||||
model: modelData
|
||||
});
|
||||
requestMessages = result.messages;
|
||||
inputTokens += result.usage?.inputTokens || 0;
|
||||
outputTokens += result.usage?.outputTokens || 0;
|
||||
|
||||
// 2. Request LLM
|
||||
let {
|
||||
reasoningText: reasoningContent,
|
||||
answerText: answer,
|
||||
toolCalls = [],
|
||||
usage,
|
||||
getEmptyResponseTip,
|
||||
assistantMessage: llmAssistantMessage,
|
||||
finish_reason: finishReason
|
||||
} = await createLLMResponse({
|
||||
body: {
|
||||
...body,
|
||||
model,
|
||||
messages: requestMessages,
|
||||
tool_choice: 'auto',
|
||||
toolCallMode: modelData.toolChoice ? 'toolChoice' : 'prompt',
|
||||
tools,
|
||||
parallel_tool_calls: true
|
||||
},
|
||||
userKey,
|
||||
isAborted,
|
||||
onReasoning,
|
||||
onStreaming,
|
||||
onToolCall,
|
||||
onToolParam
|
||||
});
|
||||
|
||||
finish_reason = finishReason;
|
||||
|
||||
if (!answer && !reasoningContent && !toolCalls.length) {
|
||||
return Promise.reject(getEmptyResponseTip());
|
||||
}
|
||||
|
||||
// 3. 更新 messages
|
||||
const cloneRequestMessages = requestMessages.slice();
|
||||
// 推送 AI 生成后的 assistantMessages
|
||||
assistantMessages.push(...llmAssistantMessage);
|
||||
requestMessages.push(...llmAssistantMessage);
|
||||
|
||||
// 4. Call tools
|
||||
let toolCallStep = false;
|
||||
for await (const tool of toolCalls) {
|
||||
const {
|
||||
response,
|
||||
assistantMessages: toolAssistantMessages,
|
||||
usages,
|
||||
interactive,
|
||||
stop
|
||||
} = await handleToolResponse({
|
||||
call: tool,
|
||||
messages: cloneRequestMessages
|
||||
});
|
||||
|
||||
const toolMessage: ChatCompletionMessageParam = {
|
||||
tool_call_id: tool.id,
|
||||
role: ChatCompletionRequestMessageRoleEnum.Tool,
|
||||
content: response
|
||||
};
|
||||
|
||||
// 5. Add tool response to messages
|
||||
assistantMessages.push(toolMessage);
|
||||
assistantMessages.push(...filterEmptyAssistantMessages(toolAssistantMessages)); // 因为 toolAssistantMessages 也需要记录成 AI 响应，所以这里需要推送。
|
||||
requestMessages.push(toolMessage); // 请求的 Request 只需要工具响应，不需要工具中 assistant 的内容，所以不推送 toolAssistantMessages
|
||||
|
||||
subAppUsages.push(...usages);
|
||||
|
||||
if (interactive) {
|
||||
interactiveResponse = {
|
||||
type: 'toolChildrenInteractive',
|
||||
params: {
|
||||
childrenResponse: interactive,
|
||||
toolParams: {
|
||||
memoryRequestMessages: [],
|
||||
toolCallId: tool.id
|
||||
}
|
||||
}
|
||||
};
|
||||
}
|
||||
if (stop) {
|
||||
toolCallStep = true;
|
||||
}
|
||||
}
|
||||
|
||||
// 6 Record usage
|
||||
inputTokens += usage.inputTokens;
|
||||
outputTokens += usage.outputTokens;
|
||||
|
||||
if (toolCalls.length === 0 || !!interactiveResponse || toolCallStep) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (interactiveResponse) {
|
||||
interactiveResponse.params.toolParams.memoryRequestMessages = requestMessages;
|
||||
}
|
||||
|
||||
return {
|
||||
inputTokens,
|
||||
outputTokens,
|
||||
subAppUsages,
|
||||
completeMessages: requestMessages,
|
||||
assistantMessages,
|
||||
interactiveResponse,
|
||||
finish_reason
|
||||
};
|
||||
};
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
import type { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type';
|
||||
|
||||
export const filterEmptyAssistantMessages = (messages: ChatCompletionMessageParam[]) => {
|
||||
return messages.filter((item) => {
|
||||
if (item.role === 'assistant') {
|
||||
if (!item.content) return false;
|
||||
if (item.content.length === 0) return false;
|
||||
}
|
||||
return true;
|
||||
});
|
||||
};
|
||||
|
|
@ -0,0 +1,103 @@
|
|||
/**
|
||||
* Agent 上下文压缩配置常量
|
||||
*
|
||||
* ## 设计原则
|
||||
*
|
||||
* 1. **空间分配**
|
||||
* - 输出预留：30%（模型生成答案 + 缓冲）
|
||||
* - 系统提示词（Depends on）：15%
|
||||
* - Agent 对话历史：55%
|
||||
*
|
||||
* 2. **压缩策略**
|
||||
* - 触发阈值：接近空间上限时触发
|
||||
* - 压缩目标：激进压缩，预留增长空间
|
||||
* - 约束机制：单个 tool 有绝对大小限制
|
||||
*
|
||||
* 3. **协调关系**
|
||||
* - Depends on 使用完整 response，需要较大空间（15%）
|
||||
* - Agent 历史包含所有 tool responses，是动态主体（55%）
|
||||
* - 单个 tool 不能过大，避免挤占其他空间（10%）
|
||||
*/
|
||||
|
||||
export const COMPRESSION_CONFIG = {
|
||||
/**
|
||||
* === Depends on（系统提示词中的步骤历史）===
|
||||
*
|
||||
* 触发场景：拼接依赖步骤的完整 response 后，token 数超过阈值
|
||||
* 内容特点：包含多个步骤的完整执行结果（使用 response 而非 summary）
|
||||
*
|
||||
* 示例（maxContext=100k）：
|
||||
* - 依赖 3 个步骤，每个 4k → 12k (12%) ✅ 不触发
|
||||
* - 依赖 5 个步骤，每个 4k → 20k (20%) ⚠️ 触发压缩 → 12k
|
||||
*/
|
||||
DEPENDS_ON_THRESHOLD: 0.15, // 15% 触发压缩
|
||||
DEPENDS_ON_TARGET: 0.12, // 压缩到 12%（预留 3% 缓冲）
|
||||
|
||||
/**
|
||||
* === 对话历史 ===
|
||||
*
|
||||
* 触发场景：对话历史（含所有 user/assistant/tool 消息）超过阈值
|
||||
* 内容特点：动态累积，包含所有 tool responses
|
||||
*
|
||||
* 示例（maxContext=100k）：
|
||||
* - 初始 20k + 6 轮对话(34k) = 54k (54%) ✅ 不触发
|
||||
* - 再 1 轮 = 60k (60%) ⚠️ 触发压缩 → 30k
|
||||
* - 预留：55k - 30k = 25k（还能跑 4 轮）
|
||||
*/
|
||||
MESSAGE_THRESHOLD: 0.8, // 55% 触发压缩
|
||||
MESSAGE_TARGET_RATIO: 0.5, // 压缩到 50%（即原 55% → 27.5%）
|
||||
|
||||
/**
|
||||
* === 单个 tool response ===
|
||||
*
|
||||
* 触发场景：单个 tool 返回的内容超过绝对大小限制
|
||||
* 内容特点：单次 tool 调用的响应（如搜索结果、文件内容等）
|
||||
*
|
||||
* 示例（maxContext=100k）：
|
||||
* - tool response = 8k (8%) ✅ 不触发
|
||||
* - tool response = 15k (15%) ⚠️ 触发压缩 → 7k
|
||||
*/
|
||||
SINGLE_TOOL_MAX: 0.5,
|
||||
SINGLE_TOOL_TARGET: 0.25,
|
||||
|
||||
/**
|
||||
* === 分块压缩 ===
|
||||
*
|
||||
* 触发场景：当内容需要分块处理时（超过 LLM 单次处理能力）
|
||||
* 用途：将超大内容切分成多个块，分别压缩后合并
|
||||
*
|
||||
* 示例（maxContext=100k）：
|
||||
* - 单块最大：40k tokens
|
||||
* - 50k 内容 → 切分成 2 块，每块约 25k
|
||||
*/
|
||||
CHUNK_SIZE_RATIO: 0.5 // 40%（单块不超过此比例）
|
||||
} as const;
|
||||
|
||||
/**
|
||||
* 计算各场景的压缩阈值
|
||||
* @param maxContext - 模型的最大上下文长度
|
||||
* @returns 各场景的具体 token 数阈值
|
||||
*/
|
||||
export const calculateCompressionThresholds = (maxContext: number) => {
|
||||
return {
|
||||
// Depends on 压缩阈值
|
||||
dependsOn: {
|
||||
threshold: Math.floor(maxContext * COMPRESSION_CONFIG.DEPENDS_ON_THRESHOLD),
|
||||
target: Math.floor(maxContext * COMPRESSION_CONFIG.DEPENDS_ON_TARGET)
|
||||
},
|
||||
// 对话历史压缩阈值
|
||||
messages: {
|
||||
threshold: Math.floor(maxContext * COMPRESSION_CONFIG.MESSAGE_THRESHOLD),
|
||||
targetRatio: COMPRESSION_CONFIG.MESSAGE_TARGET_RATIO
|
||||
},
|
||||
|
||||
// 单个 tool response 压缩阈值
|
||||
singleTool: {
|
||||
threshold: Math.floor(maxContext * COMPRESSION_CONFIG.SINGLE_TOOL_MAX),
|
||||
target: Math.floor(maxContext * COMPRESSION_CONFIG.SINGLE_TOOL_TARGET)
|
||||
},
|
||||
|
||||
// 分块大小
|
||||
chunkSize: Math.floor(maxContext * COMPRESSION_CONFIG.CHUNK_SIZE_RATIO)
|
||||
};
|
||||
};
|
||||
|
|
@ -0,0 +1,140 @@
|
|||
import type { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
|
||||
import { countGptMessagesTokens } from '../../../../common/string/tiktoken';
|
||||
import { addLog } from '../../../../common/system/log';
|
||||
import { calculateCompressionThresholds } from './constants';
|
||||
import { createLLMResponse } from '../request';
|
||||
import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
|
||||
import type { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type';
|
||||
import { getCompressRequestMessagesPrompt } from './prompt';
|
||||
import type { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
|
||||
import { formatModelChars2Points } from '../../../../support/wallet/usage/utils';
|
||||
import { i18nT } from '../../../../../web/i18n/utils';
|
||||
import { parseToolArgs } from '../../utils';
|
||||
|
||||
/**
|
||||
* 压缩 对话历史
|
||||
* 当 messages 的 token 长度超过阈值时，调用 LLM 进行压缩
|
||||
*/
|
||||
export const compressRequestMessages = async ({
|
||||
messages,
|
||||
model
|
||||
}: {
|
||||
messages: ChatCompletionMessageParam[];
|
||||
model: LLMModelItemType;
|
||||
}): Promise<{
|
||||
messages: ChatCompletionMessageParam[];
|
||||
usage?: ChatNodeUsageType;
|
||||
}> => {
|
||||
if (!messages || messages.length === 0) {
|
||||
return {
|
||||
messages
|
||||
};
|
||||
}
|
||||
|
||||
// Save the system messages
|
||||
const [systemMessages, otherMessages]: [
|
||||
ChatCompletionMessageParam[],
|
||||
ChatCompletionMessageParam[]
|
||||
] = [[], []];
|
||||
messages.forEach((message) => {
|
||||
if (message.role === ChatCompletionRequestMessageRoleEnum.System) {
|
||||
systemMessages.push(message);
|
||||
} else {
|
||||
otherMessages.push(message);
|
||||
}
|
||||
});
|
||||
|
||||
const messageTokens = await countGptMessagesTokens(otherMessages);
|
||||
const thresholds = calculateCompressionThresholds(model.maxContext).messages;
|
||||
|
||||
if (messageTokens < thresholds.threshold) {
|
||||
return {
|
||||
messages
|
||||
};
|
||||
}
|
||||
|
||||
addLog.info('[Compression messages] Start', {
|
||||
tokens: messageTokens
|
||||
});
|
||||
|
||||
const compressPrompt = await getCompressRequestMessagesPrompt({
|
||||
messages: otherMessages,
|
||||
rawTokens: messageTokens,
|
||||
model
|
||||
});
|
||||
|
||||
const userPrompt = '请执行压缩操作，严格按照JSON格式返回结果。';
|
||||
|
||||
try {
|
||||
const { answerText, usage } = await createLLMResponse({
|
||||
body: {
|
||||
model,
|
||||
messages: [
|
||||
{
|
||||
role: ChatCompletionRequestMessageRoleEnum.System,
|
||||
content: compressPrompt
|
||||
},
|
||||
{
|
||||
role: ChatCompletionRequestMessageRoleEnum.User,
|
||||
content: userPrompt
|
||||
}
|
||||
],
|
||||
temperature: 0.1,
|
||||
stream: true
|
||||
}
|
||||
});
|
||||
|
||||
if (!answerText) {
|
||||
addLog.warn('[Compression messages] failed: empty response, return original messages');
|
||||
return { messages };
|
||||
}
|
||||
|
||||
const { totalPoints, modelName } = formatModelChars2Points({
|
||||
model: model.model,
|
||||
inputTokens: usage.inputTokens,
|
||||
outputTokens: usage.outputTokens
|
||||
});
|
||||
const compressedUsage = {
|
||||
moduleName: i18nT('account_usage:compress_llm_messages'),
|
||||
model: modelName,
|
||||
totalPoints,
|
||||
inputTokens: usage.inputTokens,
|
||||
outputTokens: usage.outputTokens
|
||||
};
|
||||
|
||||
const compressResult = parseToolArgs<{
|
||||
compressed_messages: ChatCompletionMessageParam[];
|
||||
compression_summary: string;
|
||||
}>(answerText);
|
||||
|
||||
if (
|
||||
!compressResult ||
|
||||
!Array.isArray(compressResult) ||
|
||||
compressResult.compressed_messages.length === 0
|
||||
) {
|
||||
addLog.warn('[Compression messages] failed: cannot parse JSON, return original messages', {
|
||||
messages: compressResult?.compressed_messages
|
||||
});
|
||||
return { messages, usage: compressedUsage };
|
||||
}
|
||||
|
||||
const compressedTokens = usage.outputTokens;
|
||||
addLog.info('[Compression messages] successfully', {
|
||||
originalTokens: messageTokens,
|
||||
compressedTokens,
|
||||
actualRatio: (compressedTokens / messageTokens).toFixed(2),
|
||||
summary: compressResult.compression_summary
|
||||
});
|
||||
|
||||
// 如果之前提取了 system 消息，现在插回去
|
||||
const finalMessages = [...systemMessages, ...compressResult.compressed_messages];
|
||||
|
||||
return {
|
||||
messages: finalMessages,
|
||||
usage: compressedUsage
|
||||
};
|
||||
} catch (error) {
|
||||
addLog.error('[Compression messages] failed', error);
|
||||
return { messages };
|
||||
}
|
||||
};
|
||||
|
|
@ -0,0 +1,296 @@
|
|||
import type { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
|
||||
import type { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type';
|
||||
import { calculateCompressionThresholds } from './constants';
|
||||
|
||||
export const getCompressRequestMessagesPrompt = async ({
|
||||
rawTokens,
|
||||
messages,
|
||||
model
|
||||
}: {
|
||||
messages: ChatCompletionMessageParam[];
|
||||
rawTokens: number;
|
||||
model: LLMModelItemType;
|
||||
}) => {
|
||||
const thresholds = calculateCompressionThresholds(model.maxContext);
|
||||
const targetTokens = Math.round(rawTokens * thresholds.messages.targetRatio);
|
||||
|
||||
return `你是 Agent 对话历史压缩专家。你的任务是将对话历史压缩到目标 token 数，同时确保对话逻辑连贯性和工具调用的 ID 映射关系完全正确。
|
||||
|
||||
## 核心原则（最高优先级）
|
||||
|
||||
### ⚠️ 忠实性铁律
|
||||
**你只能对原始内容进行删除和截断，绝不能添加、推测、改写或创造任何不存在的内容。**
|
||||
|
||||
**绝对禁止的行为**：
|
||||
- ❌ 添加原文中不存在的信息、数据或结论
|
||||
- ❌ 推测或补充用户可能的意图
|
||||
- ❌ 修改数字、日期、人名、地名等任何事实性信息
|
||||
- ❌ 更改 tool_call 的参数值（即使看起来更合理）
|
||||
|
||||
**允许的操作**：
|
||||
- ✅ 删除整条消息（但要保持 tool_call 原子性）
|
||||
- ✅ 截断消息的 content（删除部分句子或段落）
|
||||
- ✅ 删除冗余的重复表达（保留其中一次）
|
||||
- ✅ 删除寒暄、过程描述等低价值内容
|
||||
- ✅ 改写或重新表述原文，总结或概括原文内容
|
||||
|
||||
**验证方法**：压缩后的每个词、每个数字、每个 ID 都必须能在原始消息中找到。
|
||||
|
||||
---
|
||||
|
||||
## 压缩目标
|
||||
- **原始 token 数**: ${rawTokens} tokens
|
||||
- **目标 token 数**: ${targetTokens} tokens (压缩比例: ${Math.round(thresholds.messages.targetRatio * 100)}%)
|
||||
- **约束**: 输出的 JSON 内容必须接近 ${targetTokens} tokens
|
||||
|
||||
---
|
||||
|
||||
## 三阶段压缩工作流
|
||||
|
||||
### 【第一阶段：扫描与标注】（内部思考，不输出）
|
||||
|
||||
在开始压缩前，请先在内心完成以下分析：
|
||||
|
||||
1. **构建 ID 映射表**
|
||||
- 扫描所有 assistant 消息中的 tool_calls，提取每个 tool_call 的 id
|
||||
- 找到对应的 tool 消息的 tool_call_id
|
||||
- 建立一一对应的映射关系表，例如：
|
||||
\`\`\`
|
||||
call_abc123 → tool 消息 #5
|
||||
call_def456 → tool 消息 #7
|
||||
\`\`\`
|
||||
|
||||
2. **评估消息价值**
|
||||
基于以下四个维度，为每条消息标注价值等级：
|
||||
|
||||
**维度 1：信息密度**
|
||||
- **[高密度]**: 包含数据、结论、决策、关键引用、成功的执行结果
|
||||
- **[中密度]**: 提供背景信息、过程性描述
|
||||
- **[低密度]**: 寒暄、重复、冗余表达、调试日志
|
||||
- **[负价值]**: 空内容、纯错误信息、失败的尝试、无意义的响应
|
||||
|
||||
**维度 2：对话连贯性**
|
||||
- **[关键节点]**: 话题转折点、问题解决的关键步骤、问题定位的关键错误
|
||||
- **[上下文依赖]**: 被后续消息引用或依赖的内容
|
||||
- **[独立片段]**: 与上下文关联较弱的内容
|
||||
- **[断裂节点]**: 失败后被重试的操作、未被引用的错误、中间步骤的失败
|
||||
|
||||
**维度 3：时间权重**
|
||||
- **[近期]**: 越接近对话尾部，权重越高（保留完整度优先）
|
||||
- **[早期]**: 早期消息可适度精简，但需保留关键定义/约束
|
||||
|
||||
**维度 4：工具调用有效性**
|
||||
- **[成功响应]**: tool 消息返回了有效数据或成功执行的确认
|
||||
- **[有价值错误]**: 错误信息帮助定位问题或被后续消息引用分析
|
||||
- **[无价值错误]**: 纯粹的失败尝试，后续有成功重试，未被引用
|
||||
- **[空响应]**: content 为空、null 或仅包含"无结果"、"未找到"等无效信息
|
||||
|
||||
**错误和空响应识别标准**：
|
||||
判断 tool 消息是否为错误或空响应：
|
||||
- content 包含"失败"、"错误"、"Error"、"Exception"、"超时"、"Timeout"
|
||||
- content 为空字符串、null、"无结果"、"未找到"、"No results"
|
||||
- 检查后续是否有 assistant 引用该错误来调整策略
|
||||
- 如果是孤立的错误且后续有成功重试 → 标记为负价值，优先删除
|
||||
- **关键**：删除错误消息时，必须同时删除对应的 tool_call（保持原子性）
|
||||
|
||||
3. **确定压缩策略**
|
||||
综合三个维度，制定压缩策略：
|
||||
- **tool_call 相关消息**：作为原子单元，必须成对保留（见第二阶段的原子性约束）
|
||||
- **高价值消息**（高密度 或 关键节点 或 近期消息）：保留 70-90% 内容
|
||||
- **中等价值消息**（中密度 + 有上下文依赖）：保留 40-60% 内容
|
||||
- **低价值消息**（低密度 + 独立片段 + 早期）：保留 10-20% 或删除
|
||||
|
||||
---
|
||||
|
||||
### 【第二阶段：执行压缩】
|
||||
|
||||
基于第一阶段的分析，执行压缩操作：
|
||||
|
||||
**压缩原则**：
|
||||
1. **工具调用原子性（最高优先级）**:
|
||||
- ⚠️ **强制约束**：assistant 的 tool_calls 消息和对应的 tool 响应消息必须作为**不可分割的原子单元**
|
||||
- 如果要删除某个工具调用，必须**同时删除** assistant 消息中的 tool_call 和对应的 tool 消息
|
||||
- **绝不允许**出现以下情况：
|
||||
- ❌ 保留 tool_call 但删除 tool 响应
|
||||
- ❌ 保留 tool 响应但删除 tool_call
|
||||
- ❌ tool_call 的 id 与 tool 的 tool_call_id 不匹配
|
||||
- 验证方法：遍历所有 tool_call 的 id，确保每个 id 都有且仅有一个对应的 tool 消息
|
||||
|
||||
2. **ID 不可变**: 所有 tool_call 的 id 和 tool_call_id 必须原样保留，绝不修改
|
||||
|
||||
3. **结构完整**: 每个 tool_call 对象必须包含 \`id\`, \`type\`, \`function\` 字段
|
||||
|
||||
4. **顺序保持**: 严格保持对话的时间顺序，assistant 的 tool_calls 和对应的 tool 响应按原始顺序出现
|
||||
|
||||
5. **逻辑连贯**: 确保压缩后的对话仍然能体现完整的逻辑流程（问题→分析→工具调用→结论）
|
||||
|
||||
6. **大幅精简 content**:
|
||||
- tool 消息的 content：删除冗长描述、重复信息，只保留核心结论和关键数据
|
||||
- user/assistant 消息：精简表达，但保留关键信息和逻辑转折
|
||||
- 可合并相似的工具结果（但必须保留各自的 tool_call_id）
|
||||
|
||||
**压缩技巧**：
|
||||
- **删除类内容**：详细过程描述、重复信息、失败尝试、调试日志、冗余寒暄
|
||||
- **保留类内容**：具体数据、关键结论、错误信息、链接引用、决策依据
|
||||
- **精简技巧**：
|
||||
- 用"核心发现：A、B、C"代替长篇叙述
|
||||
- 用"报错：具体错误"代替详细堆栈
|
||||
- 用"已完成：操作X"代替冗长确认
|
||||
|
||||
---
|
||||
|
||||
### 【第三阶段：自校验】
|
||||
|
||||
输出前，必须检查：
|
||||
|
||||
1. **ID 一致性校验**
|
||||
- 每个 assistant 消息中的 tool_calls[i].id 是否有对应的 tool 消息？
|
||||
- 每个 tool 消息的 tool_call_id 是否能在前面的 assistant 消息中找到？
|
||||
- 是否所有 ID 都原样保留，没有修改或生成新 ID？
|
||||
|
||||
2. **逻辑连贯性校验**
|
||||
- 对话流程是否完整？（提问→分析→执行→结论）
|
||||
- 是否存在突兀的跳跃或缺失关键步骤？
|
||||
- 工具调用的上下文是否清晰？
|
||||
|
||||
3. **压缩比例校验**
|
||||
- 估算输出的 JSON 字符串长度，是否接近 ${targetTokens} tokens？
|
||||
- 如果超出目标，需进一步精简 content 字段（优先精简低价值消息）
|
||||
|
||||
4. **格式完整性校验**
|
||||
- 所有 tool_call 对象是否包含完整的 \`id\`, \`type\`, \`function\` 字段？
|
||||
- JSON 结构是否正确？
|
||||
|
||||
---
|
||||
|
||||
## 输出格式
|
||||
|
||||
请按照以下 JSON 格式输出（必须使用 \`\`\`json 代码块）：
|
||||
|
||||
\`\`\`json
|
||||
{
|
||||
"compressed_messages": [
|
||||
{"role": "user", "content": "用户请求（精简表达）"},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "分析说明（精简但保留逻辑）",
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "call_原始ID",
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "工具名",
|
||||
"arguments": "{\\"param\\":\\"精简后的值\\"}"
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_原始ID",
|
||||
"content": "工具返回的核心结果（已大幅精简，只保留关键信息）"
|
||||
},
|
||||
{"role": "assistant", "content": "基于工具结果的结论（精简表达）"}
|
||||
],
|
||||
"compression_summary": "原始${rawTokens}tokens → 约X tokens (压缩比例Y%)。操作：删除了Z条低价值消息，精简了N个工具响应，M条用户/助手消息。对话逻辑保持完整，ID映射关系已验证正确。"
|
||||
}
|
||||
\`\`\`
|
||||
|
||||
---
|
||||
|
||||
## 压缩示例
|
||||
|
||||
**示例 1：忠实性压缩（只删除，不改写）**
|
||||
|
||||
原始（约 500 tokens）：
|
||||
\`\`\`json
|
||||
[
|
||||
{"role": "user", "content": "你好，我想了解一下 Python 性能优化的相关技术和最佳实践，能帮我搜索一些资料吗？"},
|
||||
{"role": "assistant", "content": "当然可以！我会帮您搜索 Python 性能优化相关的资料。让我先搜索相关文章和教程。"},
|
||||
{"role": "assistant", "tool_calls": [{"id": "call_abc", "type": "function", "function": {"name": "search", "arguments": "{\\"query\\":\\"Python性能优化完整指南\\",\\"max_results\\":10}"}}]},
|
||||
{"role": "tool", "tool_call_id": "call_abc", "content": "找到10篇文章：\\n1. 标题：Python性能优化完整指南\\n 作者：张三\\n 发布时间：2024-01-15\\n 摘要：本文详细介绍了Python性能优化的各种技巧，包括使用Cython进行编译优化，NumPy向量化计算，以及内存优化技术...（此处省略400字详细内容）\\n URL: https://example.com/article1\\n\\n2. 标题：高性能Python编程实战\\n 作者：李四\\n ..."},
|
||||
{"role": "assistant", "content": "根据搜索结果，我为您总结了Python性能优化的主要技术..."}
|
||||
]
|
||||
\`\`\`
|
||||
|
||||
压缩后（约 200 tokens，注意：所有内容都直接来自原文，只是删除了冗余部分）：
|
||||
\`\`\`json
|
||||
[
|
||||
{"role": "user", "content": "我想了解 Python 性能优化的相关技术和最佳实践"},
|
||||
{"role": "assistant", "tool_calls": [{"id": "call_abc", "type": "function", "function": {"name": "search", "arguments": "{\\"query\\":\\"Python性能优化完整指南\\",\\"max_results\\":10}"}}]},
|
||||
{"role": "tool", "tool_call_id": "call_abc", "content": "找到10篇文章：\\n1. 标题：Python性能优化完整指南\\n 摘要：使用Cython进行编译优化，NumPy向量化计算，以及内存优化技术"},
|
||||
{"role": "assistant", "content": "根据搜索结果，我为您总结了Python性能优化的主要技术"}
|
||||
]
|
||||
\`\`\`
|
||||
|
||||
**关键**：压缩后的每个词都能在原文找到，只是删除了"你好"、"能帮我搜索"、"作者"、"发布时间"等冗余信息。
|
||||
|
||||
**示例 2：删除失败的工具调用**
|
||||
|
||||
原始（约 600 tokens）：
|
||||
\`\`\`json
|
||||
[
|
||||
{"role": "user", "content": "搜索北京的五星级酒店"},
|
||||
{"role": "assistant", "tool_calls": [{"id": "call_fail1", "type": "function", "function": {"name": "search", "arguments": "{\\"query\\":\\"北京五星级酒店\\",\\"location\\":\\"Beijing\\"}"}}]},
|
||||
{"role": "tool", "tool_call_id": "call_fail1", "content": "Error: 网络超时，请重试"},
|
||||
{"role": "assistant", "content": "搜索遇到网络问题，让我重试"},
|
||||
{"role": "assistant", "tool_calls": [{"id": "call_fail2", "type": "function", "function": {"name": "search", "arguments": "{\\"query\\":\\"北京酒店\\"}"}}]},
|
||||
{"role": "tool", "tool_call_id": "call_fail2", "content": "未找到相关结果"},
|
||||
{"role": "assistant", "content": "没找到结果，我换个搜索方式"},
|
||||
{"role": "assistant", "tool_calls": [{"id": "call_ok", "type": "function", "function": {"name": "search", "arguments": "{\\"query\\":\\"北京五星酒店推荐\\"}"}}]},
|
||||
{"role": "tool", "tool_call_id": "call_ok", "content": "找到5家酒店：1. 北京王府半岛酒店 2. 北京四季酒店..."},
|
||||
{"role": "assistant", "content": "为您找到了5家五星级酒店推荐"}
|
||||
]
|
||||
\`\`\`
|
||||
|
||||
压缩后（约 120 tokens）：
|
||||
\`\`\`json
|
||||
[
|
||||
{"role": "user", "content": "搜索北京的五星级酒店"},
|
||||
{"role": "assistant", "tool_calls": [{"id": "call_ok", "type": "function", "function": {"name": "search", "arguments": "{\\"query\\":\\"北京五星酒店推荐\\"}"}}]},
|
||||
{"role": "tool", "tool_call_id": "call_ok", "content": "找到5家酒店：1. 北京王府半岛酒店 2. 北京四季酒店..."},
|
||||
{"role": "assistant", "content": "为您找到5家五星级酒店"}
|
||||
]
|
||||
\`\`\`
|
||||
|
||||
**示例 3：多轮对话合并（通过删除中间过程）**
|
||||
|
||||
原始（约 400 tokens）：
|
||||
\`\`\`json
|
||||
[
|
||||
{"role": "user", "content": "帮我创建一个新文件"},
|
||||
{"role": "assistant", "content": "好的，我需要知道文件名和内容。请问文件名是什么？"},
|
||||
{"role": "user", "content": "文件名叫 test.txt"},
|
||||
{"role": "assistant", "content": "明白了，文件名是 test.txt。那么您想在文件中写入什么内容呢？"},
|
||||
{"role": "user", "content": "写入 'Hello World'"},
|
||||
{"role": "assistant", "content": "收到！我现在帮您创建文件 test.txt，并写入内容 'Hello World'"},
|
||||
{"role": "assistant", "tool_calls": [{"id": "call_xyz", "type": "function", "function": {"name": "write_file", "arguments": "{\\"path\\":\\"test.txt\\",\\"content\\":\\"Hello World\\"}"}}]},
|
||||
{"role": "tool", "tool_call_id": "call_xyz", "content": "文件创建成功。文件路径：/workspace/test.txt。文件大小：11 bytes。创建时间：2024-01-15 10:30:00"},
|
||||
{"role": "assistant", "content": "太好了！文件 test.txt 已经成功创建，内容为 'Hello World'。"}
|
||||
]
|
||||
\`\`\`
|
||||
|
||||
压缩后（约 150 tokens，删除了询问过程，保留最终状态）：
|
||||
\`\`\`json
|
||||
[
|
||||
{"role": "user", "content": "帮我创建一个新文件"},
|
||||
{"role": "user", "content": "文件名叫 test.txt"},
|
||||
{"role": "user", "content": "写入 'Hello World'"},
|
||||
{"role": "assistant", "tool_calls": [{"id": "call_xyz", "type": "function", "function": {"name": "write_file", "arguments": "{\\"path\\":\\"test.txt\\",\\"content\\":\\"Hello World\\"}"}}]},
|
||||
{"role": "tool", "tool_call_id": "call_xyz", "content": "文件创建成功。文件路径：/workspace/test.txt。文件大小：11 bytes"},
|
||||
{"role": "assistant", "content": "文件 test.txt 已经成功创建，内容为 'Hello World'"}
|
||||
]
|
||||
\`\`\`
|
||||
|
||||
**关键**：删除了 assistant 的询问消息，但保留了所有 user 消息和最终结果，所有内容都来自原文。
|
||||
|
||||
---
|
||||
|
||||
## 待压缩的对话历史
|
||||
|
||||
${JSON.stringify(messages, null, 2)}
|
||||
|
||||
---
|
||||
|
||||
请严格按照三阶段工作流执行，确保对话逻辑连贯、ID 映射关系完全正确，输出接近目标 token 数。`;
|
||||
};
|
||||
|
|
@ -15,7 +15,7 @@ import { removeDatasetCiteText } from '@fastgpt/global/core/ai/llm/utils';
|
|||
import { getAIApi } from '../config';
|
||||
import type { OpenaiAccountType } from '@fastgpt/global/support/user/team/type';
|
||||
import { getNanoid } from '@fastgpt/global/common/string/tools';
|
||||
import { parsePromptToolCall, promptToolCallMessageRewrite } from './promptToolCall';
|
||||
import { parsePromptToolCall, promptToolCallMessageRewrite } from './promptCall';
|
||||
import { getLLMModel } from '../model';
|
||||
import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
|
||||
import { countGptMessagesTokens } from '../../../common/string/tiktoken/index';
|
||||
|
|
@ -26,14 +26,14 @@ import { i18nT } from '../../../../web/i18n/utils';
|
|||
import { getErrText } from '@fastgpt/global/common/error/utils';
|
||||
import json5 from 'json5';
|
||||
|
||||
type ResponseEvents = {
|
||||
export type ResponseEvents = {
|
||||
onStreaming?: ({ text }: { text: string }) => void;
|
||||
onReasoning?: ({ text }: { text: string }) => void;
|
||||
onToolCall?: ({ call }: { call: ChatCompletionMessageToolCall }) => void;
|
||||
onToolParam?: ({ tool, params }: { tool: ChatCompletionMessageToolCall; params: string }) => void;
|
||||
};
|
||||
|
||||
type CreateLLMResponseProps<T extends CompletionsBodyType> = {
|
||||
export type CreateLLMResponseProps<T extends CompletionsBodyType = CompletionsBodyType> = {
|
||||
userKey?: OpenaiAccountType;
|
||||
body: LLMRequestBodyType<T>;
|
||||
isAborted?: () => boolean | undefined;
|
||||
|
|
@ -86,7 +86,7 @@ export const createLLMResponse = async <T extends CompletionsBodyType>(
|
|||
messages: rewriteMessages
|
||||
});
|
||||
|
||||
// console.log(JSON.stringify(requestBody, null, 2));
|
||||
// console.dir(requestBody, { depth: null });
|
||||
const { response, isStreamResponse, getEmptyResponseTip } = await createChatCompletion({
|
||||
body: requestBody,
|
||||
userKey,
|
||||
|
|
|
|||
|
|
@ -2,6 +2,8 @@ import { type LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
|
|||
import type { CompletionFinishReason, CompletionUsage } from '@fastgpt/global/core/ai/type';
|
||||
import { getLLMDefaultUsage } from '@fastgpt/global/core/ai/constants';
|
||||
import { removeDatasetCiteText } from '@fastgpt/global/core/ai/llm/utils';
|
||||
import json5 from 'json5';
|
||||
import { sliceJsonStr } from '@fastgpt/global/common/string/tools';
|
||||
|
||||
/*
|
||||
Count response max token
|
||||
|
|
@ -317,3 +319,11 @@ export const parseLLMStreamResponse = () => {
|
|||
updateFinishReason
|
||||
};
|
||||
};
|
||||
|
||||
export const parseToolArgs = <T = Record<string, any>>(toolArgs: string) => {
|
||||
try {
|
||||
return json5.parse(sliceJsonStr(toolArgs)) as T;
|
||||
} catch {
|
||||
return;
|
||||
}
|
||||
};
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ import { AppToolSourceEnum } from '@fastgpt/global/core/app/tool/constants';
|
|||
import { type AppTemplateSchemaType } from '@fastgpt/global/core/app/type';
|
||||
import { MongoAppTemplate } from './templateSchema';
|
||||
import { pluginClient } from '../../../thirdProvider/fastgptPlugin';
|
||||
import { addMinutes } from 'date-fns';
|
||||
|
||||
const getFileTemplates = async (): Promise<AppTemplateSchemaType[]> => {
|
||||
const res = await pluginClient.workflow.getTemplateList();
|
||||
|
|
@ -11,9 +12,15 @@ const getFileTemplates = async (): Promise<AppTemplateSchemaType[]> => {
|
|||
};
|
||||
|
||||
const getAppTemplates = async () => {
|
||||
const communityTemplates = await getFileTemplates();
|
||||
const originCommunityTemplates = await getFileTemplates();
|
||||
const communityTemplates = originCommunityTemplates.map((template) => {
|
||||
return {
|
||||
...template,
|
||||
templateId: `${AppToolSourceEnum.community}-${template.templateId.split('.')[0]}`
|
||||
};
|
||||
});
|
||||
|
||||
const dbTemplates = await MongoAppTemplate.find();
|
||||
const dbTemplates = await MongoAppTemplate.find().lean();
|
||||
|
||||
// Merge db data to community templates
|
||||
const communityTemplateConfig = communityTemplates.map((template) => {
|
||||
|
|
@ -22,17 +29,12 @@ const getAppTemplates = async () => {
|
|||
if (config) {
|
||||
return {
|
||||
...template,
|
||||
isActive: config.isActive ?? template.isActive,
|
||||
tags: config.tags ?? template.tags,
|
||||
userGuide: config.userGuide ?? template.userGuide,
|
||||
isQuickTemplate: config.isQuickTemplate ?? template.isQuickTemplate,
|
||||
order: config.order ?? template.order
|
||||
...config
|
||||
};
|
||||
}
|
||||
|
||||
return template;
|
||||
});
|
||||
|
||||
const res = [
|
||||
...communityTemplateConfig,
|
||||
...dbTemplates.filter((t) => isCommercialTemaplte(t.templateId))
|
||||
|
|
@ -42,20 +44,31 @@ const getAppTemplates = async () => {
|
|||
};
|
||||
|
||||
export const getAppTemplatesAndLoadThem = async (refresh = false) => {
|
||||
if (isProduction && global.appTemplates && global.appTemplates.length > 0 && !refresh)
|
||||
return global.appTemplates;
|
||||
|
||||
// 首次强制刷新
|
||||
if (!global.templatesRefreshTime) {
|
||||
global.templatesRefreshTime = Date.now() - 10000;
|
||||
}
|
||||
if (!global.appTemplates) {
|
||||
global.appTemplates = [];
|
||||
}
|
||||
|
||||
if (
|
||||
isProduction &&
|
||||
// 有模板缓存
|
||||
global.appTemplates.length > 0 &&
|
||||
// 缓存时间未过期
|
||||
global.templatesRefreshTime > Date.now() &&
|
||||
!refresh
|
||||
) {
|
||||
return global.appTemplates;
|
||||
}
|
||||
|
||||
try {
|
||||
const appTemplates = await getAppTemplates();
|
||||
global.appTemplates = appTemplates;
|
||||
global.templatesRefreshTime = addMinutes(new Date(), 30).getTime(); // 缓存30分钟
|
||||
return appTemplates;
|
||||
} catch (error) {
|
||||
// @ts-ignore
|
||||
global.appTemplates = undefined;
|
||||
return [];
|
||||
}
|
||||
};
|
||||
|
|
@ -66,4 +79,5 @@ export const isCommercialTemaplte = (templateId: string) => {
|
|||
|
||||
declare global {
|
||||
var appTemplates: AppTemplateSchemaType[];
|
||||
var templatesRefreshTime: number;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -19,6 +19,8 @@ const AppTemplateSchema = new Schema({
|
|||
},
|
||||
type: String,
|
||||
isActive: Boolean,
|
||||
isPromoted: Boolean,
|
||||
recommendText: String,
|
||||
userGuide: Object,
|
||||
isQuickTemplate: Boolean,
|
||||
order: {
|
||||
|
|
|
|||
|
|
@ -178,12 +178,11 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
|
|||
|
||||
const {
|
||||
toolWorkflowInteractiveResponse,
|
||||
dispatchFlowResponse, // tool flow response
|
||||
toolDispatchFlowResponses, // tool flow response
|
||||
toolCallInputTokens,
|
||||
toolCallOutputTokens,
|
||||
completeMessages = [], // The actual message sent to AI(just save text)
|
||||
assistantResponses = [], // FastGPT system store assistant.value response
|
||||
runTimes,
|
||||
finish_reason
|
||||
} = await (async () => {
|
||||
const adaptMessages = chats2GPTMessages({
|
||||
|
|
@ -191,22 +190,20 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
|
|||
reserveId: false
|
||||
// reserveTool: !!toolModel.toolChoice
|
||||
});
|
||||
const requestParams = {
|
||||
|
||||
return runToolCall({
|
||||
...props,
|
||||
runtimeNodes,
|
||||
runtimeEdges,
|
||||
toolNodes,
|
||||
toolModel,
|
||||
messages: adaptMessages,
|
||||
interactiveEntryToolParams: lastInteractive?.toolParams
|
||||
};
|
||||
|
||||
return runToolCall({
|
||||
...props,
|
||||
...requestParams,
|
||||
maxRunToolTimes: 100
|
||||
childrenInteractiveParams:
|
||||
lastInteractive?.type === 'toolChildrenInteractive' ? lastInteractive.params : undefined
|
||||
});
|
||||
})();
|
||||
|
||||
// Usage computed
|
||||
const { totalPoints: modelTotalPoints, modelName } = formatModelChars2Points({
|
||||
model,
|
||||
inputTokens: toolCallInputTokens,
|
||||
|
|
@ -214,12 +211,13 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
|
|||
});
|
||||
const modelUsage = externalProvider.openaiAccount?.key ? 0 : modelTotalPoints;
|
||||
|
||||
const toolUsages = dispatchFlowResponse.map((item) => item.flowUsages).flat();
|
||||
const toolUsages = toolDispatchFlowResponses.map((item) => item.flowUsages).flat();
|
||||
const toolTotalPoints = toolUsages.reduce((sum, item) => sum + item.totalPoints, 0);
|
||||
|
||||
// concat tool usage
|
||||
const totalPointsUsage = modelUsage + toolTotalPoints;
|
||||
|
||||
// Preview assistant responses
|
||||
const previewAssistantResponses = filterToolResponseToPreview(assistantResponses);
|
||||
|
||||
return {
|
||||
|
|
@ -229,7 +227,10 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
|
|||
.map((item) => item.text?.content || '')
|
||||
.join('')
|
||||
},
|
||||
[DispatchNodeResponseKeyEnum.runTimes]: runTimes,
|
||||
[DispatchNodeResponseKeyEnum.runTimes]: toolDispatchFlowResponses.reduce(
|
||||
(sum, item) => sum + item.runTimes,
|
||||
0
|
||||
),
|
||||
[DispatchNodeResponseKeyEnum.assistantResponses]: previewAssistantResponses,
|
||||
[DispatchNodeResponseKeyEnum.nodeResponse]: {
|
||||
// 展示的积分消耗
|
||||
|
|
@ -244,7 +245,7 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
|
|||
10000,
|
||||
useVision
|
||||
),
|
||||
toolDetail: dispatchFlowResponse.map((item) => item.flowResponses).flat(),
|
||||
toolDetail: toolDispatchFlowResponses.map((item) => item.flowResponses).flat(),
|
||||
mergeSignId: nodeId,
|
||||
finishReason: finish_reason
|
||||
},
|
||||
|
|
|
|||
|
|
@ -1,85 +1,22 @@
|
|||
import { filterGPTMessageByMaxContext } from '../../../../ai/llm/utils';
|
||||
import type {
|
||||
ChatCompletionToolMessageParam,
|
||||
ChatCompletionMessageParam,
|
||||
ChatCompletionTool
|
||||
} from '@fastgpt/global/core/ai/type';
|
||||
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 { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
|
||||
import { runWorkflow } from '../../index';
|
||||
import type { DispatchToolModuleProps, RunToolResponse, ToolNodeItemType } from './type';
|
||||
import json5 from 'json5';
|
||||
import type { DispatchFlowResponse } from '../../type';
|
||||
import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
|
||||
import type { AIChatItemType } from '@fastgpt/global/core/chat/type';
|
||||
import { chats2GPTMessages, GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
|
||||
import type { AIChatItemValueItemType } from '@fastgpt/global/core/chat/type';
|
||||
import { formatToolResponse, initToolCallEdges, initToolNodes } from './utils';
|
||||
import { computedMaxToken } from '../../../../ai/utils';
|
||||
import { parseToolArgs } from '../../../../ai/utils';
|
||||
import { sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
|
||||
import type { WorkflowInteractiveResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type';
|
||||
import { ChatItemValueTypeEnum } from '@fastgpt/global/core/chat/constants';
|
||||
import { getErrText } from '@fastgpt/global/common/error/utils';
|
||||
import { createLLMResponse } from '../../../../ai/llm/request';
|
||||
import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
|
||||
import { toolValueTypeList, valueTypeJsonSchemaMap } from '@fastgpt/global/core/workflow/constants';
|
||||
import { runAgentCall } from '../../../../ai/llm/agentCall';
|
||||
|
||||
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> => {
|
||||
export const runToolCall = async (props: DispatchToolModuleProps): Promise<RunToolResponse> => {
|
||||
const { messages, toolNodes, toolModel, childrenInteractiveParams, ...workflowProps } = props;
|
||||
const {
|
||||
messages,
|
||||
toolNodes,
|
||||
toolModel,
|
||||
maxRunToolTimes,
|
||||
interactiveEntryToolParams,
|
||||
...workflowProps
|
||||
} = props;
|
||||
let {
|
||||
res,
|
||||
requestOrigin,
|
||||
runtimeNodes,
|
||||
|
|
@ -100,101 +37,7 @@ export const runToolCall = async (
|
|||
}
|
||||
} = workflowProps;
|
||||
|
||||
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 || [];
|
||||
|
||||
// 构建 tools 参数
|
||||
const toolNodesMap = new Map<string, ToolNodeItemType>();
|
||||
const tools: ChatCompletionTool[] = toolNodes.map((item) => {
|
||||
toolNodesMap.set(item.nodeId, item);
|
||||
|
|
@ -246,64 +89,44 @@ export const runToolCall = async (
|
|||
}
|
||||
};
|
||||
});
|
||||
const getToolInfo = (name: string) => {
|
||||
const toolNode = toolNodesMap.get(name);
|
||||
return {
|
||||
name: toolNode?.name || '',
|
||||
avatar: toolNode?.avatar || ''
|
||||
};
|
||||
};
|
||||
|
||||
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;
|
||||
});
|
||||
|
||||
// SSE 响应实例
|
||||
const write = res ? responseWriteController({ res, readStream: stream }) : undefined;
|
||||
// 工具响应原始值
|
||||
const toolRunResponses: DispatchFlowResponse[] = [];
|
||||
|
||||
let {
|
||||
reasoningText: reasoningContent,
|
||||
answerText: answer,
|
||||
toolCalls = [],
|
||||
finish_reason,
|
||||
usage,
|
||||
getEmptyResponseTip,
|
||||
assistantMessage,
|
||||
completeMessages
|
||||
} = await createLLMResponse({
|
||||
const {
|
||||
inputTokens,
|
||||
outputTokens,
|
||||
completeMessages,
|
||||
assistantMessages,
|
||||
interactiveResponse,
|
||||
finish_reason
|
||||
} = await runAgentCall({
|
||||
maxRunAgentTimes: 50,
|
||||
body: {
|
||||
model: toolModel.model,
|
||||
stream,
|
||||
messages: filterMessages,
|
||||
tool_choice: 'auto',
|
||||
toolCallMode: toolModel.toolChoice ? 'toolChoice' : 'prompt',
|
||||
messages,
|
||||
tools,
|
||||
parallel_tool_calls: true,
|
||||
model: toolModel.model,
|
||||
max_tokens: maxToken,
|
||||
stream,
|
||||
temperature,
|
||||
max_tokens,
|
||||
top_p: aiChatTopP,
|
||||
stop: aiChatStopSign,
|
||||
response_format: {
|
||||
type: aiChatResponseFormat as any,
|
||||
type: aiChatResponseFormat,
|
||||
json_schema: aiChatJsonSchema
|
||||
},
|
||||
requestOrigin,
|
||||
retainDatasetCite,
|
||||
useVision: aiChatVision,
|
||||
requestOrigin
|
||||
useVision: aiChatVision
|
||||
},
|
||||
isAborted: () => res?.closed,
|
||||
userKey: externalProvider.openaiAccount,
|
||||
|
|
@ -358,52 +181,39 @@ export const runToolCall = async (
|
|||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
});
|
||||
},
|
||||
handleToolResponse: async ({ call, messages }) => {
|
||||
const toolNode = toolNodesMap.get(call.function?.name);
|
||||
|
||||
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 = (() => {
|
||||
try {
|
||||
return json5.parse(tool.function.arguments);
|
||||
} catch (error) {
|
||||
return {};
|
||||
}
|
||||
})();
|
||||
if (!toolNode) {
|
||||
return {
|
||||
response: 'Call tool not found',
|
||||
assistantMessages: [],
|
||||
usages: [],
|
||||
interactive: undefined
|
||||
};
|
||||
}
|
||||
|
||||
// Init tool params and run
|
||||
const startParams = parseToolArgs(call.function.arguments);
|
||||
initToolNodes(runtimeNodes, [toolNode.nodeId], startParams);
|
||||
initToolCallEdges(runtimeEdges, [toolNode.nodeId]);
|
||||
|
||||
const toolRunResponse = await runWorkflow({
|
||||
...workflowProps,
|
||||
runtimeNodes,
|
||||
usageId: undefined,
|
||||
isToolCall: true
|
||||
});
|
||||
|
||||
// Format tool response
|
||||
const stringToolResponse = formatToolResponse(toolRunResponse.toolResponses);
|
||||
|
||||
const toolMsgParams: ChatCompletionToolMessageParam = {
|
||||
tool_call_id: tool.id,
|
||||
role: ChatCompletionRequestMessageRoleEnum.Tool,
|
||||
name: tool.function.name,
|
||||
content: stringToolResponse
|
||||
};
|
||||
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.toolResponse,
|
||||
data: {
|
||||
tool: {
|
||||
id: tool.id,
|
||||
id: call.id,
|
||||
toolName: '',
|
||||
toolAvatar: '',
|
||||
params: '',
|
||||
|
|
@ -412,166 +222,91 @@ export const runToolCall = async (
|
|||
}
|
||||
});
|
||||
|
||||
toolsRunResponse.push({
|
||||
toolRunResponse,
|
||||
toolMsgParams
|
||||
toolRunResponses.push(toolRunResponse);
|
||||
|
||||
const assistantMessages = chats2GPTMessages({
|
||||
messages: [
|
||||
{
|
||||
obj: ChatRoleEnum.AI,
|
||||
value: toolRunResponse.assistantResponses
|
||||
}
|
||||
],
|
||||
reserveId: false
|
||||
});
|
||||
} catch (error) {
|
||||
const err = getErrText(error);
|
||||
|
||||
return {
|
||||
response: stringToolResponse,
|
||||
assistantMessages,
|
||||
usages: toolRunResponse.flowUsages,
|
||||
interactive: toolRunResponse.workflowInteractiveResponse,
|
||||
stop: toolRunResponse.flowResponses?.some((item) => item.toolStop)
|
||||
};
|
||||
},
|
||||
childrenInteractiveParams,
|
||||
handleInteractiveTool: async ({ childrenResponse, toolParams }) => {
|
||||
initToolNodes(runtimeNodes, childrenResponse.entryNodeIds);
|
||||
initToolCallEdges(runtimeEdges, childrenResponse.entryNodeIds);
|
||||
|
||||
const toolRunResponse = await runWorkflow({
|
||||
...workflowProps,
|
||||
lastInteractive: childrenResponse,
|
||||
runtimeNodes,
|
||||
runtimeEdges,
|
||||
usageId: undefined,
|
||||
isToolCall: true
|
||||
});
|
||||
// console.dir(runtimeEdges, { depth: null });
|
||||
const stringToolResponse = formatToolResponse(toolRunResponse.toolResponses);
|
||||
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.toolResponse,
|
||||
data: {
|
||||
tool: {
|
||||
id: tool.id,
|
||||
id: toolParams.toolCallId,
|
||||
toolName: '',
|
||||
toolAvatar: '',
|
||||
params: '',
|
||||
response: sliceStrStartEnd(err, 5000, 5000)
|
||||
response: sliceStrStartEnd(stringToolResponse, 5000, 5000)
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
toolsRunResponse.push({
|
||||
toolRunResponse: undefined,
|
||||
toolMsgParams: {
|
||||
tool_call_id: tool.id,
|
||||
role: ChatCompletionRequestMessageRoleEnum.Tool,
|
||||
name: tool.function.name,
|
||||
content: sliceStrStartEnd(err, 5000, 5000)
|
||||
}
|
||||
toolRunResponses.push(toolRunResponse);
|
||||
const assistantMessages = chats2GPTMessages({
|
||||
messages: [
|
||||
{
|
||||
obj: ChatRoleEnum.AI,
|
||||
value: toolRunResponse.assistantResponses
|
||||
}
|
||||
],
|
||||
reserveId: false
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
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.type !== ChatItemValueTypeEnum.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
|
||||
response: stringToolResponse,
|
||||
assistantMessages,
|
||||
usages: toolRunResponse.flowUsages,
|
||||
interactive: toolRunResponse.workflowInteractiveResponse,
|
||||
stop: toolRunResponse.flowResponses?.some((item) => item.toolStop)
|
||||
};
|
||||
}
|
||||
});
|
||||
|
||||
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;
|
||||
const assistantResponses = GPTMessages2Chats({
|
||||
messages: assistantMessages,
|
||||
reserveTool: true,
|
||||
getToolInfo
|
||||
})
|
||||
.map((item) => item.value as AIChatItemValueItemType[])
|
||||
.flat();
|
||||
|
||||
return {
|
||||
dispatchFlowResponse: response?.dispatchFlowResponse || [],
|
||||
toolCallInputTokens: inputTokens,
|
||||
toolCallOutputTokens: outputTokens,
|
||||
|
||||
completeMessages,
|
||||
assistantResponses: [...assistantResponses, ...toolNodeAssistant.value],
|
||||
runTimes: (response?.runTimes || 0) + 1,
|
||||
finish_reason
|
||||
};
|
||||
}
|
||||
return {
|
||||
toolDispatchFlowResponses: toolRunResponses,
|
||||
toolCallInputTokens: inputTokens,
|
||||
toolCallOutputTokens: outputTokens,
|
||||
completeMessages,
|
||||
assistantResponses,
|
||||
finish_reason,
|
||||
toolWorkflowInteractiveResponse: interactiveResponse
|
||||
};
|
||||
};
|
||||
|
|
|
|||
|
|
@ -14,7 +14,11 @@ 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 { WorkflowInteractiveResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type';
|
||||
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';
|
||||
|
||||
|
|
@ -37,18 +41,17 @@ export type DispatchToolModuleProps = ModuleDispatchProps<{
|
|||
messages: ChatCompletionMessageParam[];
|
||||
toolNodes: ToolNodeItemType[];
|
||||
toolModel: LLMModelItemType;
|
||||
interactiveEntryToolParams?: WorkflowInteractiveResponseType['toolParams'];
|
||||
childrenInteractiveParams?: ToolCallChildrenInteractive['params'];
|
||||
};
|
||||
|
||||
export type RunToolResponse = {
|
||||
dispatchFlowResponse: DispatchFlowResponse[];
|
||||
toolDispatchFlowResponses: DispatchFlowResponse[];
|
||||
toolCallInputTokens: number;
|
||||
toolCallOutputTokens: number;
|
||||
completeMessages?: ChatCompletionMessageParam[];
|
||||
assistantResponses?: AIChatItemValueItemType[];
|
||||
toolWorkflowInteractiveResponse?: WorkflowInteractiveResponseType;
|
||||
[DispatchNodeResponseKeyEnum.runTimes]: number;
|
||||
finish_reason?: CompletionFinishReason;
|
||||
completeMessages: ChatCompletionMessageParam[];
|
||||
assistantResponses: AIChatItemValueItemType[];
|
||||
finish_reason: CompletionFinishReason;
|
||||
toolWorkflowInteractiveResponse?: ToolCallChildrenInteractive;
|
||||
};
|
||||
export type ToolNodeItemType = RuntimeNodeItemType & {
|
||||
toolParams: RuntimeNodeItemType['inputs'];
|
||||
|
|
|
|||
|
|
@ -62,12 +62,9 @@ export const initToolNodes = (
|
|||
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;
|
||||
}
|
||||
});
|
||||
};
|
||||
|
|
|
|||
|
|
@ -747,6 +747,7 @@ export const runWorkflow = async (data: RunWorkflowProps): Promise<DispatchFlowR
|
|||
|
||||
// Get next source edges and update status
|
||||
const skipHandleId = result[DispatchNodeResponseKeyEnum.skipHandleId] || [];
|
||||
|
||||
const targetEdges = filterWorkflowEdges(runtimeEdges).filter(
|
||||
(item) => item.source === node.nodeId
|
||||
);
|
||||
|
|
@ -957,6 +958,7 @@ export const runWorkflow = async (data: RunWorkflowProps): Promise<DispatchFlowR
|
|||
entryNodeIds,
|
||||
memoryEdges: runtimeEdges.map((edge) => ({
|
||||
...edge,
|
||||
// 入口前面的边全部激活，保证下次进来一定能执行。
|
||||
status: entryNodeIds.includes(edge.target) ? 'active' : edge.status
|
||||
})),
|
||||
nodeOutputs,
|
||||
|
|
|
|||
|
|
@ -36,6 +36,7 @@ export const dispatchAnswer = (props: Record<string, any>): AnswerResponse => {
|
|||
[DispatchNodeResponseKeyEnum.answerText]: responseText,
|
||||
[DispatchNodeResponseKeyEnum.nodeResponse]: {
|
||||
textOutput: formatText
|
||||
}
|
||||
},
|
||||
[DispatchNodeResponseKeyEnum.toolResponses]: responseText
|
||||
};
|
||||
};
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@
|
|||
"app_name": "Application name",
|
||||
"auto_index": "Auto index",
|
||||
"billing_module": "Deduction module",
|
||||
"compress_llm_messages": "AI history compression",
|
||||
"confirm_export": "A total of {{total}} pieces of data were filtered out. Are you sure to export?",
|
||||
"count": "Number of runs",
|
||||
"current_filter_conditions": "Current filter conditions",
|
||||
|
|
|
|||
|
|
@ -319,13 +319,14 @@
|
|||
"template.hard_strict_des": "Based on the question and answer template, stricter requirements are imposed on the model's answers.",
|
||||
"template.qa_template": "Q&A template",
|
||||
"template.qa_template_des": "A knowledge base suitable for QA question and answer structure, which allows AI to answer strictly according to preset content",
|
||||
"template.recommended": "Promoted",
|
||||
"template.simple_robot": "Simple robot",
|
||||
"template.standard_strict": "Standard strict template",
|
||||
"template.standard_strict_des": "Based on the standard template, stricter requirements are imposed on the model's answers.",
|
||||
"template.standard_template": "Standard template",
|
||||
"template.standard_template_des": "Standard prompt words for knowledge bases with unfixed structures.",
|
||||
"templateMarket.Search_template": "Search Template",
|
||||
"templateMarket.Use": "Use",
|
||||
"templateMarket.Use": "Build now",
|
||||
"templateMarket.no_intro": "No introduction yet~",
|
||||
"templateMarket.templateTags.Recommendation": "Recommendation",
|
||||
"templateMarket.template_guide": "Guide",
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@
|
|||
"app_name": "应用名",
|
||||
"auto_index": "索引增强",
|
||||
"billing_module": "扣费模块",
|
||||
"compress_llm_messages": "AI 历史记录压缩",
|
||||
"confirm_export": "共筛选出 {{total}} 条数据，是否确认导出？",
|
||||
"count": "运行次数",
|
||||
"current_filter_conditions": "当前筛选条件：",
|
||||
|
|
|
|||
|
|
@ -333,16 +333,17 @@
|
|||
"template.hard_strict_des": "在问答模板基础上，对模型的回答做更严格的要求。",
|
||||
"template.qa_template": "问答模板",
|
||||
"template.qa_template_des": "适合 QA 问答结构的知识库，可以让AI较为严格的按预设内容回答",
|
||||
"template.recommended": "精选",
|
||||
"template.simple_robot": "简易机器人",
|
||||
"template.standard_strict": "标准严格模板",
|
||||
"template.standard_strict_des": "在标准模板基础上，对模型的回答做更严格的要求。",
|
||||
"template.standard_template": "标准模板",
|
||||
"template.standard_template_des": "标准提示词，用于结构不固定的知识库。",
|
||||
"templateMarket.Search_template": "搜索模板",
|
||||
"templateMarket.Use": "使用",
|
||||
"templateMarket.Use": "立即搭建",
|
||||
"templateMarket.no_intro": "还没有介绍～",
|
||||
"templateMarket.templateTags.Recommendation": "推荐",
|
||||
"templateMarket.template_guide": "模板说明",
|
||||
"templateMarket.template_guide": "说明",
|
||||
"template_market": "模板市场",
|
||||
"template_market_description": "在模板市场探索更多玩法，配置教程与使用引导，带你理解并上手各种应用",
|
||||
"template_market_empty_data": "找不到合适的模板",
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@
|
|||
"app_name": "應用程式名",
|
||||
"auto_index": "索引增強",
|
||||
"billing_module": "扣費模組",
|
||||
"compress_llm_messages": "AI 歷史記錄壓縮",
|
||||
"confirm_export": "共篩選出 {{total}} 條資料，是否確認匯出？",
|
||||
"count": "運行次數",
|
||||
"current_filter_conditions": "目前篩選條件：",
|
||||
|
|
|
|||
|
|
@ -318,16 +318,17 @@
|
|||
"template.hard_strict_des": "在問答範本基礎上，對模型的回答做出更嚴格的要求。",
|
||||
"template.qa_template": "問答範本",
|
||||
"template.qa_template_des": "適合問答結構的知識庫，可以讓 AI 較為嚴格地按照預設內容回答",
|
||||
"template.recommended": "精選",
|
||||
"template.simple_robot": "簡易機器人",
|
||||
"template.standard_strict": "標準嚴格範本",
|
||||
"template.standard_strict_des": "在標準範本基礎上，對模型的回答做出更嚴格的要求。",
|
||||
"template.standard_template": "標準範本",
|
||||
"template.standard_template_des": "標準提示詞，用於結構不固定的知識庫。",
|
||||
"templateMarket.Search_template": "搜尋範本",
|
||||
"templateMarket.Use": "使用",
|
||||
"templateMarket.Use": "立即搭建",
|
||||
"templateMarket.no_intro": "還沒有介紹～",
|
||||
"templateMarket.templateTags.Recommendation": "推薦",
|
||||
"templateMarket.template_guide": "範本說明",
|
||||
"templateMarket.template_guide": "說明",
|
||||
"template_market": "範本市集",
|
||||
"template_market_description": "在範本市集探索更多玩法，設定教學與使用指引，帶您理解並上手各種應用程式",
|
||||
"template_market_empty_data": "找不到合適的範本",
|
||||
|
|
|
|||
|
|
@ -0,0 +1,57 @@
|
|||
<svg width="366" height="190" viewBox="0 0 366 190" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<g clip-path="url(#clip0_29654_18158)">
|
||||
<path d="M0 8C0 3.58172 3.58172 0 8 0H357.667C362.085 0 365.667 3.58172 365.667 8V182C365.667 186.418 362.085 190 357.667 190H7.99999C3.58171 190 0 186.418 0 182V8Z" fill="white"/>
|
||||
<path d="M0 8C0 3.58172 3.58172 0 8 0H357.667C362.085 0 365.667 3.58172 365.667 8V182C365.667 186.418 362.085 190 357.667 190H7.99999C3.58171 190 0 186.418 0 182V8Z" fill="url(#paint0_linear_29654_18158)" fill-opacity="0.16"/>
|
||||
<path d="M0 8C0 3.58172 3.58172 0 8 0H357.667C362.085 0 365.667 3.58172 365.667 8V182C365.667 186.418 362.085 190 357.667 190H7.99999C3.58171 190 0 186.418 0 182V8Z" fill="url(#paint1_linear_29654_18158)" fill-opacity="0.07"/>
|
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<path d="M0 8C0 3.58172 3.58172 0 8 0H357.667C362.085 0 365.667 3.58172 365.667 8V182C365.667 186.418 362.085 190 357.667 190H7.99999C3.58171 190 0 186.418 0 182V8Z" fill="url(#paint2_radial_29654_18158)" fill-opacity="0.1"/>
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<circle cx="278.5" cy="68.5" r="1.5" fill="white"/>
|
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<circle cx="250.5" cy="52.5" r="0.5" fill="white"/>
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<stop offset="1" stop-color="#2643FF"/>
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<linearGradient id="paint1_linear_29654_18158" x1="256.071" y1="66.4159" x2="297.946" y2="-40.2204" gradientUnits="userSpaceOnUse">
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<stop offset="1" stop-color="#2643FF"/>
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<stop stop-color="#E39DFF"/>
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<stop offset="1" stop-color="white"/>
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<stop offset="1" stop-color="white"/>
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<stop offset="1" stop-color="white"/>
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<stop stop-color="white"/>
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<stop offset="1" stop-color="#EFF2FF"/>
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<stop offset="1" stop-color="white"/>
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<clipPath id="clip0_29654_18158">
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<path d="M0 8C0 3.58172 3.58172 0 8 0H357.667C362.085 0 365.667 3.58172 365.667 8V182C365.667 186.418 362.085 190 357.667 190H7.99999C3.58171 190 0 186.418 0 182V8Z" fill="white"/>
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</clipPath>
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</defs>
|
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</svg>
|
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|
After Width: | Height: | Size: 5.8 KiB |
|
|
@ -0,0 +1,45 @@
|
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<svg width="308" height="98" viewBox="0 0 308 98" fill="none" xmlns="http://www.w3.org/2000/svg">
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<g clip-path="url(#clip0_29676_18182)">
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<path d="M0 8C0 3.58172 3.58172 0 8 0H299.667C304.085 0 307.667 3.58172 307.667 8V90C307.667 94.4183 304.085 98 299.667 98H8C3.58172 98 0 94.4183 0 90V8Z" fill="white"/>
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<path d="M0 8C0 3.58172 3.58172 0 8 0H299.667C304.085 0 307.667 3.58172 307.667 8V90C307.667 94.4183 304.085 98 299.667 98H8C3.58172 98 0 94.4183 0 90V8Z" fill="url(#paint0_linear_29676_18182)" fill-opacity="0.16"/>
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<path d="M0 8C0 3.58172 3.58172 0 8 0H299.667C304.085 0 307.667 3.58172 307.667 8V90C307.667 94.4183 304.085 98 299.667 98H8C3.58172 98 0 94.4183 0 90V8Z" fill="url(#paint1_linear_29676_18182)" fill-opacity="0.07"/>
|
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<path d="M0 8C0 3.58172 3.58172 0 8 0H299.667C304.085 0 307.667 3.58172 307.667 8V90C307.667 94.4183 304.085 98 299.667 98H8C3.58172 98 0 94.4183 0 90V8Z" fill="url(#paint2_radial_29676_18182)" fill-opacity="0.1"/>
|
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<path d="M0 8C0 3.58172 3.58172 0 8 0H299.667C304.085 0 307.667 3.58172 307.667 8V90C307.667 94.4183 304.085 98 299.667 98H8C3.58172 98 0 94.4183 0 90V8Z" fill="url(#paint3_radial_29676_18182)"/>
|
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<stop offset="1" stop-color="#2643FF"/>
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|
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<stop stop-color="#E39DFF"/>
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|
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<stop offset="1" stop-color="white" stop-opacity="0"/>
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|
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|
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<stop offset="1" stop-color="white"/>
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|
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<linearGradient id="paint5_linear_29676_18182" x1="246.358" y1="36.1857" x2="252.554" y2="48.3697" gradientUnits="userSpaceOnUse">
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<stop stop-color="white"/>
|
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<stop offset="1" stop-color="white"/>
|
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</linearGradient>
|
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<linearGradient id="paint6_linear_29676_18182" x1="289.133" y1="18.0851" x2="291.304" y2="22.352" gradientUnits="userSpaceOnUse">
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|
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<stop offset="1" stop-color="white"/>
|
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</linearGradient>
|
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<clipPath id="clip0_29676_18182">
|
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<path d="M0 8C0 3.58172 3.58172 0 8 0H299.667C304.085 0 307.667 3.58172 307.667 8V90C307.667 94.4183 304.085 98 299.667 98H8C3.58172 98 0 94.4183 0 90V8Z" fill="white"/>
|
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|
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</defs>
|
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</svg>
|
||||
|
After Width: | Height: | Size: 4.3 KiB |
|
|
@ -123,7 +123,7 @@ const Navbar = ({ unread }: { unread: number }) => {
|
|||
w={'100%'}
|
||||
userSelect={'none'}
|
||||
pb={2}
|
||||
bg={isSecondNavbarPage ? 'myGray.50' : 'transparent'}
|
||||
bg={isSecondNavbarPage ? 'white' : 'transparent'}
|
||||
>
|
||||
{/* logo */}
|
||||
<Box flex={'0 0 auto'} mb={3}>
|
||||
|
|
|
|||
|
|
@ -232,14 +232,13 @@ const DashboardContainer = ({
|
|||
position={'fixed'}
|
||||
left={isPc ? navbarWidth : 0}
|
||||
top={0}
|
||||
bg={'myGray.25'}
|
||||
bg={'white'}
|
||||
w={`220px`}
|
||||
h={'full'}
|
||||
borderLeft={'1px solid'}
|
||||
borderRight={'1px solid'}
|
||||
borderColor={'myGray.200'}
|
||||
pt={4}
|
||||
px={2.5}
|
||||
pb={2.5}
|
||||
zIndex={100}
|
||||
userSelect={'none'}
|
||||
|
|
@ -247,7 +246,12 @@ const DashboardContainer = ({
|
|||
flexDirection={'column'}
|
||||
justifyContent={'space-between'}
|
||||
>
|
||||
<Box>
|
||||
<Box
|
||||
flex={1}
|
||||
overflowY={'auto'}
|
||||
px={2.5}
|
||||
sx={{ '&::-webkit-scrollbar': { width: '4px' } }}
|
||||
>
|
||||
{groupList.map((group) => {
|
||||
const selected = currentTab === group.groupId;
|
||||
|
||||
|
|
@ -339,7 +343,7 @@ const DashboardContainer = ({
|
|||
</MyBox>
|
||||
)}
|
||||
|
||||
<Box h={'100%'} pl={isPc ? `220px` : 0} position={'relative'} bg={'myGray.25'}>
|
||||
<Box h={'100%'} pl={isPc ? `220px` : 0} position={'relative'} bg={'white'}>
|
||||
{children({
|
||||
templateTags,
|
||||
templateList,
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ import {
|
|||
SkeletonCircle,
|
||||
useBreakpointValue
|
||||
} from '@chakra-ui/react';
|
||||
import type { AppTypeEnum } from '@fastgpt/global/core/app/constants';
|
||||
import { AppTypeEnum } from '@fastgpt/global/core/app/constants';
|
||||
import Avatar from '@fastgpt/web/components/common/Avatar';
|
||||
import { useRequest2 } from '@fastgpt/web/hooks/useRequest';
|
||||
import MyIcon from '@fastgpt/web/components/common/Icon';
|
||||
|
|
@ -22,13 +22,16 @@ import MyBox from '@fastgpt/web/components/common/MyBox';
|
|||
import { useLocalStorageState } from 'ahooks';
|
||||
import { useState } from 'react';
|
||||
import { getWebReqUrl } from '@fastgpt/web/common/system/utils';
|
||||
import { form2AppWorkflow } from '@/web/core/app/utils';
|
||||
import { webPushTrack } from '@/web/common/middle/tracks/utils';
|
||||
import { appTypeTagMap } from '../constant';
|
||||
|
||||
const TemplateCreatePanel = ({ type }: { type: AppTypeEnum | 'all' }) => {
|
||||
const { t } = useTranslation();
|
||||
const router = useRouter();
|
||||
|
||||
const randomNumber =
|
||||
useBreakpointValue({ base: 3, sm: 3, md: 4, lg: 4, xl: 5 }, { ssr: false }) || 4;
|
||||
useBreakpointValue({ base: 2, sm: 2, md: 3, lg: 3, xl: 4 }, { ssr: false }) || 3;
|
||||
|
||||
const [isHoverMoreButton, setIsHoverMoreButton] = useState(false);
|
||||
const [isCollapsed, setIsCollapsed] = useLocalStorageState<boolean>(
|
||||
|
|
@ -43,12 +46,20 @@ const TemplateCreatePanel = ({ type }: { type: AppTypeEnum | 'all' }) => {
|
|||
data: templateData,
|
||||
loading: isFetchingTemplates
|
||||
} = useRequest2(
|
||||
(excludeIds?: string[]) =>
|
||||
getTemplateMarketItemList({
|
||||
(ids?: string[]) => {
|
||||
const excludeIds = (() => {
|
||||
try {
|
||||
return JSON.stringify(ids);
|
||||
} catch (error) {
|
||||
return '';
|
||||
}
|
||||
})();
|
||||
return getTemplateMarketItemList({
|
||||
type,
|
||||
randomNumber,
|
||||
excludeIds
|
||||
}),
|
||||
});
|
||||
},
|
||||
{
|
||||
manual: false,
|
||||
refreshDeps: [type, randomNumber]
|
||||
|
|
@ -62,6 +73,11 @@ const TemplateCreatePanel = ({ type }: { type: AppTypeEnum | 'all' }) => {
|
|||
setCreatingTemplateId(templateId);
|
||||
const templateDetail = await getTemplateMarketItemDetail(templateId);
|
||||
|
||||
if (templateDetail.type === AppTypeEnum.simple) {
|
||||
const completeWorkflow = form2AppWorkflow(templateDetail.workflow, t);
|
||||
templateDetail.workflow = completeWorkflow;
|
||||
}
|
||||
|
||||
return postCreateApp({
|
||||
avatar: templateDetail.avatar,
|
||||
name: templateDetail.name,
|
||||
|
|
@ -70,6 +86,13 @@ const TemplateCreatePanel = ({ type }: { type: AppTypeEnum | 'all' }) => {
|
|||
edges: templateDetail.workflow.edges || [],
|
||||
chatConfig: templateDetail.workflow.chatConfig || {},
|
||||
templateId: templateDetail.templateId
|
||||
}).then((res) => {
|
||||
webPushTrack.useAppTemplate({
|
||||
id: res,
|
||||
name: templateDetail.name
|
||||
});
|
||||
|
||||
return res;
|
||||
});
|
||||
},
|
||||
{
|
||||
|
|
@ -141,66 +164,167 @@ const TemplateCreatePanel = ({ type }: { type: AppTypeEnum | 'all' }) => {
|
|||
in={!isCollapsed}
|
||||
animateOpacity
|
||||
transition={{ enter: { duration: 0.2 }, exit: { duration: 0.2 } }}
|
||||
style={{ overflow: 'visible' }}
|
||||
>
|
||||
<Box
|
||||
display={'grid'}
|
||||
gridTemplateColumns={[
|
||||
'repeat(2, 1fr) 160px',
|
||||
'repeat(2, 1fr) 160px',
|
||||
'repeat(3, 1fr) 160px',
|
||||
'repeat(3, 1fr) 160px',
|
||||
'repeat(4, 1fr) 160px',
|
||||
'repeat(4, 1fr) 160px',
|
||||
'repeat(5, 1fr) 160px'
|
||||
'repeat(4, 1fr) 160px'
|
||||
]}
|
||||
gap={4}
|
||||
gap={5}
|
||||
>
|
||||
{isFetchingTemplates && !templateData?.list?.length
|
||||
? Array.from({ length: randomNumber }).map((_, index) => (
|
||||
<Box
|
||||
key={`skeleton-${index}`}
|
||||
bg={'white'}
|
||||
p={'19px'}
|
||||
p={6}
|
||||
borderRadius={'10px'}
|
||||
border={'1px solid'}
|
||||
borderColor={'myGray.200'}
|
||||
>
|
||||
<Flex alignItems={'center'} gap={2} mb={2}>
|
||||
<SkeletonCircle size={'24px'} />
|
||||
<Skeleton height={'16px'} flex={1} />
|
||||
<SkeletonCircle size={'40px'} />
|
||||
<Flex flexDirection={'column'} gap={2} flex={1}>
|
||||
<Skeleton height={4} />
|
||||
<Skeleton height={4} />
|
||||
</Flex>
|
||||
</Flex>
|
||||
<Skeleton height={'12px'} />
|
||||
</Box>
|
||||
))
|
||||
: templateData?.list.map((item, index) => (
|
||||
<MyBox
|
||||
key={index}
|
||||
bg={'white'}
|
||||
p={4}
|
||||
borderRadius={'10px'}
|
||||
border={'1px solid'}
|
||||
borderColor={'myGray.200'}
|
||||
cursor={'pointer'}
|
||||
_hover={{
|
||||
borderColor: 'primary.500',
|
||||
boxShadow: 'md'
|
||||
}}
|
||||
isLoading={creatingTemplateId === item.templateId}
|
||||
onClick={() => {
|
||||
if (!creatingTemplateId) {
|
||||
handleCreateFromTemplate(item.templateId);
|
||||
}
|
||||
}}
|
||||
>
|
||||
<Flex alignItems={'center'} gap={2} mb={2}>
|
||||
<Avatar src={item.avatar} w={'24px'} h={'24px'} borderRadius={'4px'} />
|
||||
<Box fontSize={'16px'} fontWeight={'medium'} color={'myGray.900'} noOfLines={1}>
|
||||
{item.name}
|
||||
</Box>
|
||||
</Flex>
|
||||
<Box fontSize={'12px'} color={'myGray.500'} noOfLines={1}>
|
||||
{item.intro || ''}
|
||||
</Box>
|
||||
</MyBox>
|
||||
))}
|
||||
: templateData?.list.map((item, index) => {
|
||||
return (
|
||||
<MyBox
|
||||
key={index}
|
||||
bg={'white'}
|
||||
p={6}
|
||||
borderRadius={'10px'}
|
||||
border={'1px solid'}
|
||||
borderColor={'myGray.200'}
|
||||
boxShadow={'none'}
|
||||
cursor={'pointer'}
|
||||
position={'relative'}
|
||||
overflow={'hidden'}
|
||||
bgImage={item.isPromoted ? "url('/imgs/app/templateCreateBg.svg')" : 'none'}
|
||||
bgSize={'105% auto'}
|
||||
bgPosition={'top'}
|
||||
bgRepeat={'no-repeat'}
|
||||
_hover={{
|
||||
boxShadow:
|
||||
'0 1px 2px 0 rgba(19, 51, 107, 0.10), 0 0 1px 0 rgba(19, 51, 107, 0.15)',
|
||||
'& .template-content': {
|
||||
filter: 'blur(5px)'
|
||||
},
|
||||
'& .hover-text': {
|
||||
opacity: 1
|
||||
}
|
||||
}}
|
||||
isLoading={creatingTemplateId === item.templateId}
|
||||
onClick={() => {
|
||||
if (!creatingTemplateId) {
|
||||
handleCreateFromTemplate(item.templateId);
|
||||
}
|
||||
}}
|
||||
display={'flex'}
|
||||
gap={2}
|
||||
alignItems={'center'}
|
||||
>
|
||||
<Flex
|
||||
className="template-content"
|
||||
gap={2}
|
||||
alignItems={'center'}
|
||||
transition={'filter 0.1s ease-in-out'}
|
||||
w={'full'}
|
||||
>
|
||||
<Avatar src={item.avatar} w={10} h={10} borderRadius={'4px'} />
|
||||
<Box flex={1} minW={0} h={12}>
|
||||
<Flex
|
||||
fontSize={'16px'}
|
||||
fontWeight={'medium'}
|
||||
color={'myGray.900'}
|
||||
alignItems={'center'}
|
||||
gap={1}
|
||||
justifyContent={'space-between'}
|
||||
>
|
||||
<Flex alignItems={'center'} gap={'7px'} flex={1} minW={0}>
|
||||
<Box className="textEllipsis2" whiteSpace={'nowrap'}>
|
||||
{item.name}
|
||||
</Box>
|
||||
{item.isPromoted && (
|
||||
<Box
|
||||
p={'1px'}
|
||||
bgGradient={'linear(201deg, #E6B3FF 13.74%, #006AFF 89.76%)'}
|
||||
borderRadius={'full'}
|
||||
flexShrink={0}
|
||||
>
|
||||
<Box
|
||||
px={1.5}
|
||||
fontSize={'10px'}
|
||||
bg={'myGray.25'}
|
||||
borderRadius={'full'}
|
||||
color={'myGray.900'}
|
||||
>
|
||||
{t('app:template.recommended')}
|
||||
</Box>
|
||||
</Box>
|
||||
)}
|
||||
</Flex>
|
||||
<MyIcon
|
||||
name={
|
||||
appTypeTagMap[item.type as keyof typeof appTypeTagMap]?.icon as any
|
||||
}
|
||||
w={4}
|
||||
color={'myGray.900'}
|
||||
flexShrink={0}
|
||||
/>
|
||||
</Flex>
|
||||
<Box
|
||||
fontSize={item.isPromoted ? '16px' : '14px'}
|
||||
fontWeight={item.isPromoted ? 'medium' : 'normal'}
|
||||
color={'myGray.500'}
|
||||
noOfLines={1}
|
||||
mt={0.5}
|
||||
>
|
||||
{(item.isPromoted ? item.recommendText || item.intro : item.intro) ||
|
||||
t('app:templateMarket.no_intro')}
|
||||
</Box>
|
||||
</Box>
|
||||
</Flex>
|
||||
<Flex
|
||||
className="hover-text"
|
||||
position={'absolute'}
|
||||
top={0}
|
||||
left={0}
|
||||
right={0}
|
||||
bottom={0}
|
||||
alignItems={'center'}
|
||||
justifyContent={'center'}
|
||||
opacity={0}
|
||||
bg={' linear-gradient(180deg, rgba(255, 255, 255, 0.00) 0%, #FFF 100%)'}
|
||||
transition={'opacity 0.1s ease-in-out'}
|
||||
cursor={'pointer'}
|
||||
>
|
||||
<Flex
|
||||
fontSize={'14px'}
|
||||
fontWeight={'medium'}
|
||||
color={'primary.700'}
|
||||
rounded={'sm'}
|
||||
px={5}
|
||||
py={2.5}
|
||||
_hover={{
|
||||
bg: 'rgba(17, 24, 36, 0.05)'
|
||||
}}
|
||||
>
|
||||
{t('app:templateMarket.Use')}
|
||||
</Flex>
|
||||
</Flex>
|
||||
</MyBox>
|
||||
);
|
||||
})}
|
||||
<Box
|
||||
borderRadius={'10px'}
|
||||
overflow={'hidden'}
|
||||
|
|
@ -218,6 +342,8 @@ const TemplateCreatePanel = ({ type }: { type: AppTypeEnum | 'all' }) => {
|
|||
p={0}
|
||||
onMouseEnter={() => setIsHoverMoreButton(true)}
|
||||
onMouseLeave={() => setIsHoverMoreButton(false)}
|
||||
minH={20}
|
||||
maxW={160}
|
||||
>
|
||||
<Box
|
||||
as="img"
|
||||
|
|
|
|||
|
|
@ -1,44 +1,14 @@
|
|||
import React, { useRef } from 'react';
|
||||
import { AppTypeEnum } from '@fastgpt/global/core/app/constants';
|
||||
import type { AppTypeEnum } from '@fastgpt/global/core/app/constants';
|
||||
import MyIcon from '@fastgpt/web/components/common/Icon';
|
||||
import { Box, Flex } from '@chakra-ui/react';
|
||||
import { useTranslation } from 'next-i18next';
|
||||
import { appTypeTagMap } from '../constant';
|
||||
|
||||
const AppTypeTag = ({ type }: { type: AppTypeEnum }) => {
|
||||
const { t } = useTranslation();
|
||||
|
||||
const map = useRef({
|
||||
[AppTypeEnum.simple]: {
|
||||
label: t('app:type.Chat_Agent'),
|
||||
icon: 'core/app/type/simple'
|
||||
},
|
||||
[AppTypeEnum.workflow]: {
|
||||
label: t('app:type.Workflow bot'),
|
||||
icon: 'core/app/type/workflow'
|
||||
},
|
||||
[AppTypeEnum.workflowTool]: {
|
||||
label: t('app:toolType_workflow'),
|
||||
icon: 'core/app/type/plugin'
|
||||
},
|
||||
[AppTypeEnum.httpPlugin]: {
|
||||
label: t('app:type.Http plugin'),
|
||||
icon: 'core/app/type/httpPlugin'
|
||||
},
|
||||
[AppTypeEnum.httpToolSet]: {
|
||||
label: t('app:toolType_http'),
|
||||
icon: 'core/app/type/httpPlugin'
|
||||
},
|
||||
[AppTypeEnum.mcpToolSet]: {
|
||||
label: t('app:toolType_mcp'),
|
||||
icon: 'core/app/type/mcpTools'
|
||||
},
|
||||
[AppTypeEnum.tool]: undefined,
|
||||
[AppTypeEnum.folder]: undefined,
|
||||
[AppTypeEnum.hidden]: undefined,
|
||||
[AppTypeEnum.agent]: undefined
|
||||
});
|
||||
|
||||
const data = map.current[type as keyof typeof map.current];
|
||||
const data = appTypeTagMap[type as keyof typeof appTypeTagMap];
|
||||
|
||||
return data ? (
|
||||
<Flex
|
||||
|
|
@ -53,7 +23,7 @@ const AppTypeTag = ({ type }: { type: AppTypeEnum }) => {
|
|||
>
|
||||
<MyIcon name={data.icon as any} w={'14px'} color={'myGray.500'} />
|
||||
<Box ml={1} fontSize={'mini'}>
|
||||
{data.label}
|
||||
{t(data.label)}
|
||||
</Box>
|
||||
</Flex>
|
||||
) : null;
|
||||
|
|
|
|||
|
|
@ -0,0 +1,33 @@
|
|||
import { AppTypeEnum } from '@fastgpt/global/core/app/constants';
|
||||
import { i18nT } from '@fastgpt/web/i18n/utils';
|
||||
|
||||
export const appTypeTagMap = {
|
||||
[AppTypeEnum.simple]: {
|
||||
label: i18nT('app:type.Chat_Agent'),
|
||||
icon: 'core/app/type/simple'
|
||||
},
|
||||
[AppTypeEnum.workflow]: {
|
||||
label: i18nT('app:type.Workflow bot'),
|
||||
icon: 'core/app/type/workflow'
|
||||
},
|
||||
[AppTypeEnum.workflowTool]: {
|
||||
label: i18nT('app:toolType_workflow'),
|
||||
icon: 'core/app/type/plugin'
|
||||
},
|
||||
[AppTypeEnum.httpPlugin]: {
|
||||
label: i18nT('app:type.Http plugin'),
|
||||
icon: 'core/app/type/httpPlugin'
|
||||
},
|
||||
[AppTypeEnum.httpToolSet]: {
|
||||
label: i18nT('app:toolType_http'),
|
||||
icon: 'core/app/type/httpPlugin'
|
||||
},
|
||||
[AppTypeEnum.mcpToolSet]: {
|
||||
label: i18nT('app:toolType_mcp'),
|
||||
icon: 'core/app/type/mcpTools'
|
||||
},
|
||||
[AppTypeEnum.tool]: undefined,
|
||||
[AppTypeEnum.folder]: undefined,
|
||||
[AppTypeEnum.hidden]: undefined,
|
||||
[AppTypeEnum.agent]: undefined
|
||||
};
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
import React, { type Dispatch } from 'react';
|
||||
import React, { useEffect, type Dispatch } from 'react';
|
||||
import { FormControl, Flex, Input, Button, Box } from '@chakra-ui/react';
|
||||
import { useForm } from 'react-hook-form';
|
||||
import { LoginPageTypeEnum } from '@/web/support/user/login/constants';
|
||||
|
|
@ -10,6 +10,10 @@ import { useTranslation } from 'next-i18next';
|
|||
import FormLayout from './FormLayout';
|
||||
import { useRequest2 } from '@fastgpt/web/hooks/useRequest';
|
||||
import PolicyTip from './PolicyTip';
|
||||
import { useSearchParams } from 'next/navigation';
|
||||
import { UserErrEnum } from '@fastgpt/global/common/error/code/user';
|
||||
import { useRouter } from 'next/router';
|
||||
import { useMount } from 'ahooks';
|
||||
|
||||
interface Props {
|
||||
setPageType: Dispatch<`${LoginPageTypeEnum}`>;
|
||||
|
|
@ -25,6 +29,9 @@ const LoginForm = ({ setPageType, loginSuccess }: Props) => {
|
|||
const { t } = useTranslation();
|
||||
const { toast } = useToast();
|
||||
const { feConfigs } = useSystemStore();
|
||||
const query = useSearchParams();
|
||||
const router = useRouter();
|
||||
|
||||
const {
|
||||
register,
|
||||
handleSubmit,
|
||||
|
|
@ -41,13 +48,28 @@ const LoginForm = ({ setPageType, loginSuccess }: Props) => {
|
|||
code
|
||||
})
|
||||
);
|
||||
toast({
|
||||
title: t('login:login_success'),
|
||||
status: 'success'
|
||||
});
|
||||
},
|
||||
{
|
||||
refreshDeps: [loginSuccess]
|
||||
refreshDeps: [loginSuccess],
|
||||
successToast: t('login:login_success'),
|
||||
onError: (error: any) => {
|
||||
// 密码错误，需要清空 query 参数
|
||||
if (error.statusText === UserErrEnum.account_psw_error) {
|
||||
router.replace(
|
||||
router.pathname,
|
||||
{
|
||||
query: {
|
||||
...router.query,
|
||||
u: '',
|
||||
p: ''
|
||||
}
|
||||
},
|
||||
{
|
||||
shallow: false
|
||||
}
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
);
|
||||
|
||||
|
|
@ -71,6 +93,17 @@ const LoginForm = ({ setPageType, loginSuccess }: Props) => {
|
|||
.join('/');
|
||||
})();
|
||||
|
||||
useMount(() => {
|
||||
const username = query.get('u');
|
||||
const password = query.get('p');
|
||||
if (username && password) {
|
||||
onclickLogin({
|
||||
username,
|
||||
password
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
return (
|
||||
<FormLayout setPageType={setPageType} pageType={LoginPageTypeEnum.passwordLogin}>
|
||||
<Box
|
||||
|
|
|
|||
|
|
@ -11,7 +11,6 @@ import {
|
|||
import { LoginPageTypeEnum } from '@/web/support/user/login/constants';
|
||||
import { useSystemStore } from '@/web/common/system/useSystemStore';
|
||||
import type { LoginSuccessResponse } from '@/global/support/api/userRes.d';
|
||||
import { useUserStore } from '@/web/support/user/useUserStore';
|
||||
import { useChatStore } from '@/web/core/chat/context/useChatStore';
|
||||
import dynamic from 'next/dynamic';
|
||||
import Script from 'next/script';
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ export type ListParams = {
|
|||
isQuickTemplate?: boolean;
|
||||
randomNumber?: number;
|
||||
type?: AppTypeEnum | 'all';
|
||||
excludeIds?: string[];
|
||||
excludeIds?: string;
|
||||
};
|
||||
|
||||
export type ListResponse = {
|
||||
|
|
@ -24,20 +24,30 @@ async function handler(
|
|||
): Promise<ListResponse> {
|
||||
await authCert({ req, authToken: true });
|
||||
|
||||
const { isQuickTemplate = false, randomNumber = 0, type = 'all', excludeIds = [] } = req.query;
|
||||
const { isQuickTemplate = false, randomNumber = 0, type = 'all', excludeIds } = req.query;
|
||||
|
||||
const parsedExcludeIds: string[] = (() => {
|
||||
if (!excludeIds) return [];
|
||||
try {
|
||||
return JSON.parse(excludeIds);
|
||||
} catch (error) {
|
||||
console.error('Failed to parse excludeIds:', error);
|
||||
return [];
|
||||
}
|
||||
})();
|
||||
const templateMarketItems = await getAppTemplatesAndLoadThem();
|
||||
|
||||
let filteredItems = templateMarketItems.filter((item) => {
|
||||
if (!item.isActive) return false;
|
||||
if (type === 'all' && !ToolTypeList.includes(item.type as AppTypeEnum)) return true;
|
||||
if (type === 'all' && !(ToolTypeList.includes(item.type as AppTypeEnum) && randomNumber > 0))
|
||||
return true;
|
||||
if (item.type === type) return true;
|
||||
return false;
|
||||
});
|
||||
const total = filteredItems.length;
|
||||
|
||||
if (excludeIds && excludeIds.length > 0) {
|
||||
filteredItems = filteredItems.filter((item) => !excludeIds.includes(item.templateId));
|
||||
if (parsedExcludeIds && parsedExcludeIds.length > 0) {
|
||||
filteredItems = filteredItems.filter((item) => !parsedExcludeIds.includes(item.templateId));
|
||||
}
|
||||
|
||||
if (isQuickTemplate) {
|
||||
|
|
@ -63,6 +73,8 @@ async function handler(
|
|||
templateId: item.templateId,
|
||||
name: item.name,
|
||||
intro: item.intro,
|
||||
recommendText: item.recommendText,
|
||||
isPromoted: item.isPromoted,
|
||||
avatar: item.avatar,
|
||||
tags: item.tags,
|
||||
type: item.type,
|
||||
|
|
|
|||
|
|
@ -146,7 +146,7 @@ const ToolKitProvider = ({ MenuIcon }: { MenuIcon: JSX.Element }) => {
|
|||
}, [tools, searchText, selectedTagIds, installedFilter, tags, i18n.language]);
|
||||
|
||||
return (
|
||||
<Box h={'full'} pr={6}>
|
||||
<Box h={'full'}>
|
||||
<MyBox
|
||||
bg={'white'}
|
||||
h={'full'}
|
||||
|
|
|
|||
|
|
@ -12,19 +12,23 @@ import {
|
|||
type AppTemplateSchemaType,
|
||||
type TemplateTypeSchemaType
|
||||
} from '@fastgpt/global/core/app/type';
|
||||
import { appWorkflow2Form } from '@fastgpt/global/core/app/utils';
|
||||
import { form2AppWorkflow } from '@/web/core/app/utils';
|
||||
import MyBox from '@fastgpt/web/components/common/MyBox';
|
||||
import { useRequest2 } from '@fastgpt/web/hooks/useRequest';
|
||||
import { getTemplateMarketItemDetail } from '@/web/core/app/api/template';
|
||||
import { postCreateApp } from '@/web/core/app/api';
|
||||
import { webPushTrack } from '@/web/common/middle/tracks/utils';
|
||||
import Avatar from '@fastgpt/web/components/common/Avatar';
|
||||
import AppTypeTag from '@/pageComponents/dashboard/agent/TypeTag';
|
||||
|
||||
import dynamic from 'next/dynamic';
|
||||
import SearchInput from '@fastgpt/web/components/common/Input/SearchInput';
|
||||
import MySelect from '@fastgpt/web/components/common/MySelect';
|
||||
import EmptyTip from '@fastgpt/web/components/common/EmptyTip';
|
||||
import { useSystem } from '@fastgpt/web/hooks/useSystem';
|
||||
import MyIcon from '@fastgpt/web/components/common/Icon';
|
||||
import { appTypeTagMap } from '@/pageComponents/dashboard/constant';
|
||||
import MyTooltip from '@fastgpt/web/components/common/MyTooltip';
|
||||
const UseGuideModal = dynamic(() => import('@/components/common/Modal/UseGuideModal'), {
|
||||
ssr: false
|
||||
});
|
||||
|
|
@ -40,7 +44,6 @@ const TemplateMarket = ({
|
|||
}) => {
|
||||
const router = useRouter();
|
||||
const { t } = useTranslation();
|
||||
const { feConfigs } = useSystemStore();
|
||||
const { isPc } = useSystem();
|
||||
const containerRef = useRef<HTMLDivElement>(null);
|
||||
|
||||
|
|
@ -51,7 +54,7 @@ const TemplateMarket = ({
|
|||
} = router.query as { parentId?: ParentIdType; type?: string; appType?: AppTypeEnum | 'all' };
|
||||
const [searchKey, setSearchKey] = useState('');
|
||||
|
||||
const filterTemplateTags = useMemo(() => {
|
||||
const tagsWithTemplates = useMemo(() => {
|
||||
return templateTags
|
||||
.map((tag) => {
|
||||
const templates = templateList.filter((template) => template.tags.includes(tag.typeId));
|
||||
|
|
@ -67,6 +70,11 @@ const TemplateMarket = ({
|
|||
async (template: AppTemplateSchemaType) => {
|
||||
const templateDetail = await getTemplateMarketItemDetail(template.templateId);
|
||||
|
||||
if (template.type === AppTypeEnum.simple) {
|
||||
const completeWorkflow = form2AppWorkflow(templateDetail.workflow, t);
|
||||
templateDetail.workflow = completeWorkflow;
|
||||
}
|
||||
|
||||
return postCreateApp({
|
||||
parentId,
|
||||
avatar: template.avatar,
|
||||
|
|
@ -97,99 +105,132 @@ const TemplateMarket = ({
|
|||
const TemplateCard = useCallback(
|
||||
({ item }: { item: AppTemplateSchemaType }) => {
|
||||
const { t } = useTranslation();
|
||||
const icon = appTypeTagMap[item.type as keyof typeof appTypeTagMap]?.icon;
|
||||
|
||||
return (
|
||||
<MyBox
|
||||
key={item.templateId}
|
||||
lineHeight={1.5}
|
||||
h="100%"
|
||||
pt={4}
|
||||
pb={3}
|
||||
px={4}
|
||||
border={'base'}
|
||||
boxShadow={'2'}
|
||||
bg={'white'}
|
||||
borderRadius={'10px'}
|
||||
position={'relative'}
|
||||
w={'100%'}
|
||||
minWidth={0}
|
||||
py={3}
|
||||
px={6}
|
||||
border={'1px solid'}
|
||||
borderColor={'myGray.250'}
|
||||
borderRadius={'lg'}
|
||||
display={'flex'}
|
||||
flexDirection={'column'}
|
||||
gap={4}
|
||||
position={'relative'}
|
||||
overflow={'hidden'}
|
||||
bgImage={item.isPromoted ? "url('/imgs/app/templateBg.svg')" : 'none'}
|
||||
bgSize={'105% auto'}
|
||||
bgPosition={'top'}
|
||||
bgRepeat={'no-repeat'}
|
||||
_hover={{
|
||||
borderColor: 'primary.300',
|
||||
boxShadow: '1.5',
|
||||
'& .buttons': {
|
||||
display: 'flex'
|
||||
}
|
||||
boxShadow: '0 1px 2px 0 rgba(19, 51, 107, 0.10), 0 0 1px 0 rgba(19, 51, 107, 0.15)'
|
||||
}}
|
||||
>
|
||||
<HStack>
|
||||
<Avatar src={item.avatar} borderRadius={'sm'} w={'1.5rem'} h={'1.5rem'} />
|
||||
<Box flex={'1 0 0'} color={'myGray.900'} fontWeight={500}>
|
||||
{item.name}
|
||||
</Box>
|
||||
<Box mr={'-1rem'}>
|
||||
<AppTypeTag type={item.type as AppTypeEnum} />
|
||||
</Box>
|
||||
<Avatar src={item.avatar} borderRadius={'4px'} w={10} h={10} />
|
||||
<Box flex={1} />
|
||||
<Flex w={10} h={10} justifyContent={'center'} alignItems={'center'}>
|
||||
<MyIcon name={icon as any} w={4} color={'myGray.900'} />
|
||||
</Flex>
|
||||
</HStack>
|
||||
<Box
|
||||
flex={['1 0 48px', '1 0 56px']}
|
||||
mt={3}
|
||||
pr={1}
|
||||
textAlign={'justify'}
|
||||
wordBreak={'break-all'}
|
||||
fontSize={'xs'}
|
||||
color={'myGray.500'}
|
||||
>
|
||||
<Box className={'textEllipsis2'}>{item.intro || t('app:templateMarket.no_intro')}</Box>
|
||||
<Box w={'100%'} minWidth={0}>
|
||||
<Flex
|
||||
color={'myGray.900'}
|
||||
fontWeight={'medium'}
|
||||
fontSize={'18px'}
|
||||
alignItems={'center'}
|
||||
gap={'7px'}
|
||||
>
|
||||
{item.name}
|
||||
{item.isPromoted && (
|
||||
<Box
|
||||
p={'1px'}
|
||||
bgGradient={'linear(201deg, #E6B3FF 13.74%, #006AFF 89.76%)'}
|
||||
borderRadius={'full'}
|
||||
flexShrink={0}
|
||||
>
|
||||
<Box
|
||||
px={1.5}
|
||||
fontSize={'10px'}
|
||||
bg={'myGray.25'}
|
||||
borderRadius={'full'}
|
||||
color={'myGray.900'}
|
||||
>
|
||||
{t('app:template.recommended')}
|
||||
</Box>
|
||||
</Box>
|
||||
)}
|
||||
</Flex>
|
||||
<MyTooltip
|
||||
label={item.isPromoted ? item.recommendText || item.intro : item.intro}
|
||||
shouldWrapChildren={false}
|
||||
placement={'top'}
|
||||
hasArrow={false}
|
||||
offset={[0, 3]}
|
||||
>
|
||||
<Box
|
||||
w={'100%'}
|
||||
minWidth={0}
|
||||
color={'myGray.500'}
|
||||
fontSize={item.isPromoted ? '16px' : '14px'}
|
||||
fontWeight={item.isPromoted ? 'medium' : 'normal'}
|
||||
overflow={'hidden'}
|
||||
textOverflow={'ellipsis'}
|
||||
whiteSpace={'nowrap'}
|
||||
>
|
||||
{(item.isPromoted ? item.recommendText || item.intro : item.intro) ||
|
||||
t('app:templateMarket.no_intro')}
|
||||
</Box>
|
||||
</MyTooltip>
|
||||
</Box>
|
||||
|
||||
<Box w={'full'} fontSize={'mini'}>
|
||||
<Box color={'myGray.500'}>{`by ${item.author || feConfigs.systemTitle}`}</Box>
|
||||
<Flex
|
||||
className="buttons"
|
||||
display={'none'}
|
||||
justifyContent={'center'}
|
||||
alignItems={'center'}
|
||||
position={'absolute'}
|
||||
borderRadius={'lg'}
|
||||
w={'full'}
|
||||
h={'full'}
|
||||
left={0}
|
||||
right={0}
|
||||
bottom={1}
|
||||
height={'40px'}
|
||||
bg={'white'}
|
||||
zIndex={1}
|
||||
gap={2}
|
||||
>
|
||||
{((item.userGuide?.type === 'markdown' && item.userGuide?.content) ||
|
||||
(item.userGuide?.type === 'link' && item.userGuide?.link)) && (
|
||||
<UseGuideModal
|
||||
title={item.name}
|
||||
iconSrc={item.avatar}
|
||||
text={item.userGuide?.content}
|
||||
link={item.userGuide?.link}
|
||||
>
|
||||
{({ onClick }) => (
|
||||
<Button variant={'whiteBase'} h={6} rounded={'sm'} onClick={onClick}>
|
||||
{t('app:templateMarket.template_guide')}
|
||||
</Button>
|
||||
)}
|
||||
</UseGuideModal>
|
||||
)}
|
||||
<Button
|
||||
variant={'whiteBase'}
|
||||
h={6}
|
||||
rounded={'sm'}
|
||||
onClick={() => onUseTemplate(item)}
|
||||
<Flex justifyContent={'space-between'} alignItems={'center'}>
|
||||
{(item.userGuide?.type === 'markdown' && item.userGuide?.content) ||
|
||||
(item.userGuide?.type === 'link' && item.userGuide?.link) ? (
|
||||
<UseGuideModal
|
||||
title={item.name}
|
||||
iconSrc={item.avatar}
|
||||
text={item.userGuide?.content}
|
||||
link={item.userGuide?.link}
|
||||
>
|
||||
{t('app:templateMarket.Use')}
|
||||
</Button>
|
||||
</Flex>
|
||||
</Box>
|
||||
{({ onClick }) => (
|
||||
<Flex
|
||||
cursor={'pointer'}
|
||||
color={'myGray.500'}
|
||||
gap={1}
|
||||
fontSize={'14px'}
|
||||
onClick={onClick}
|
||||
_hover={{
|
||||
color: 'primary.600'
|
||||
}}
|
||||
>
|
||||
<MyIcon name="book" w={4} />
|
||||
{t('app:templateMarket.template_guide')}
|
||||
</Flex>
|
||||
)}
|
||||
</UseGuideModal>
|
||||
) : (
|
||||
<Box></Box>
|
||||
)}
|
||||
<Button
|
||||
variant={'transparentBase'}
|
||||
px={5}
|
||||
py={2.5}
|
||||
rounded={'sm'}
|
||||
color={'primary.700'}
|
||||
onClick={() => onUseTemplate(item)}
|
||||
>
|
||||
{t('app:templateMarket.Use')}
|
||||
</Button>
|
||||
</Flex>
|
||||
</MyBox>
|
||||
);
|
||||
},
|
||||
[onUseTemplate, feConfigs.systemTitle]
|
||||
[onUseTemplate]
|
||||
);
|
||||
|
||||
// Scroll to the selected template type
|
||||
|
|
@ -204,20 +245,20 @@ const TemplateMarket = ({
|
|||
|
||||
return (
|
||||
<MyBox ref={containerRef} h={'100%'} isLoading={isCreating}>
|
||||
<Flex flexDirection={'column'} h={'100%'} py={5}>
|
||||
<Flex mb={4} alignItems={'center'} px={5}>
|
||||
<Flex flexDirection={'column'} h={'100%'} py={6}>
|
||||
<Flex alignItems={'center'} px={6} mb={5}>
|
||||
{isPc ? (
|
||||
<Box fontSize={'lg'} color={'myGray.900'} fontWeight={500}>
|
||||
<Box fontSize={'lg'} color={'myGray.900'} fontWeight={'medium'}>
|
||||
{t('app:template_market')}
|
||||
</Box>
|
||||
) : (
|
||||
MenuIcon
|
||||
)}
|
||||
|
||||
<Box flex={1} />
|
||||
<Box mr={3}>
|
||||
<Box mr={2}>
|
||||
<SearchInput
|
||||
h={'34px'}
|
||||
h={9}
|
||||
w={240}
|
||||
bg={'white'}
|
||||
placeholder={t('app:templateMarket.Search_template')}
|
||||
value={searchKey}
|
||||
|
|
@ -225,7 +266,8 @@ const TemplateMarket = ({
|
|||
/>
|
||||
</Box>
|
||||
<MySelect
|
||||
h={'34px'}
|
||||
h={9}
|
||||
w={124}
|
||||
bg={'white'}
|
||||
value={appType}
|
||||
list={[
|
||||
|
|
@ -258,7 +300,7 @@ const TemplateMarket = ({
|
|||
/>
|
||||
</Flex>
|
||||
|
||||
<Box flex={'1 0 0'} px={5} overflow={'auto'}>
|
||||
<Box flex={'1 0 0'} px={6} overflow={'auto'}>
|
||||
{searchKey ? (
|
||||
<>
|
||||
<Box fontSize={'lg'} color={'myGray.900'} mb={4}>
|
||||
|
|
@ -294,11 +336,17 @@ const TemplateMarket = ({
|
|||
})()}
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
{filterTemplateTags.map((item) => {
|
||||
<Flex flexDirection={'column'} gap={5}>
|
||||
{tagsWithTemplates.map((item) => {
|
||||
return (
|
||||
<Box key={item.typeId}>
|
||||
<Box id={item.typeId} color={'myGray.900'} mb={4} fontWeight={500} pt={2}>
|
||||
<Box
|
||||
id={item.typeId}
|
||||
color={'myGray.900'}
|
||||
mb={4}
|
||||
fontWeight={'medium'}
|
||||
fontSize={'14px'}
|
||||
>
|
||||
{t(item.typeName as any)}
|
||||
</Box>
|
||||
<Grid
|
||||
|
|
@ -307,11 +355,10 @@ const TemplateMarket = ({
|
|||
'repeat(2,1fr)',
|
||||
'repeat(3,1fr)',
|
||||
'repeat(3,1fr)',
|
||||
'repeat(4,1fr)'
|
||||
'repeat(4,1fr)',
|
||||
'repeat(5,1fr)'
|
||||
]}
|
||||
gridGap={4}
|
||||
alignItems={'stretch'}
|
||||
pb={5}
|
||||
>
|
||||
{item.templates.map((item) => (
|
||||
<TemplateCard key={item.templateId} item={item} />
|
||||
|
|
@ -320,7 +367,7 @@ const TemplateMarket = ({
|
|||
</Box>
|
||||
);
|
||||
})}
|
||||
</>
|
||||
</Flex>
|
||||
)}
|
||||
</Box>
|
||||
</Flex>
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import type { ListParams, ListResponse } from '@/pages/api/core/app/template/list';
|
||||
import { GET } from '@/web/common/api/request';
|
||||
import { GET, POST } from '@/web/common/api/request';
|
||||
import { useSystemStore } from '@/web/common/system/useSystemStore';
|
||||
import type { AppTemplateSchemaType, TemplateTypeSchemaType } from '@fastgpt/global/core/app/type';
|
||||
import { defaultTemplateTypes } from '@fastgpt/web/core/workflow/constants';
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import {
|
||||
parsePromptToolCall,
|
||||
promptToolCallMessageRewrite
|
||||
} from '@fastgpt/service/core/ai/llm/promptToolCall';
|
||||
} from '@fastgpt/service/core/ai/llm/promptCall/index';
|
||||
import type { ChatCompletionMessageParam, ChatCompletionTool } from '@fastgpt/global/core/ai/type';
|
||||
import { describe, expect, it } from 'vitest';
|
||||
|
||||
|
|
|
|||
|
|
@ -1187,3 +1187,167 @@ describe('checkNodeRunStatus - 边界情况测试', () => {
|
|||
expect(checkNodeRunStatus({ nodesMap, node: nodeA, runtimeEdges: edges })).toBe('run');
|
||||
});
|
||||
});
|
||||
|
||||
describe('checkNodeRunStatus - 工具调用场景测试', () => {
|
||||
it('工具调用1: Tool节点作为入口节点 (无workflowStart时)', () => {
|
||||
// 场景：当工作流中没有 workflowStart/pluginInput 节点时，tool 节点可以作为入口节点
|
||||
// Tool → Process → End
|
||||
const toolNode = createNode('tool1', FlowNodeTypeEnum.tool);
|
||||
const processNode = createNode('process');
|
||||
const endNode = createNode('end');
|
||||
|
||||
const nodesMap = new Map<string, RuntimeNodeItemType>([
|
||||
['tool1', toolNode],
|
||||
['process', processNode],
|
||||
['end', endNode]
|
||||
]);
|
||||
|
||||
// 场景1: Tool节点作为入口，无输入边
|
||||
const edges1: RuntimeEdgeItemType[] = [
|
||||
createEdge('tool1', 'process', 'waiting'),
|
||||
createEdge('process', 'end', 'waiting')
|
||||
];
|
||||
|
||||
// Tool节点作为入口节点应该可以运行
|
||||
expect(checkNodeRunStatus({ nodesMap, node: toolNode, runtimeEdges: edges1 })).toBe('run');
|
||||
// 注意：由于tool节点没有输入边（是入口），process节点也会没有可追溯到start的边
|
||||
// 因此process节点在这个场景下也会返回'run'（因为commonEdges和recursiveEdgeGroups都为空）
|
||||
expect(checkNodeRunStatus({ nodesMap, node: processNode, runtimeEdges: edges1 })).toBe('run');
|
||||
|
||||
// 场景2: Tool节点执行完成后,process可以运行但end仍需等待
|
||||
const edges2: RuntimeEdgeItemType[] = [
|
||||
createEdge('tool1', 'process', 'active'),
|
||||
createEdge('process', 'end', 'waiting')
|
||||
];
|
||||
|
||||
expect(checkNodeRunStatus({ nodesMap, node: processNode, runtimeEdges: edges2 })).toBe('run');
|
||||
// end节点的输入边是waiting状态,需要等待process完成
|
||||
expect(checkNodeRunStatus({ nodesMap, node: endNode, runtimeEdges: edges2 })).toBe('wait');
|
||||
|
||||
// 场景2.1: process完成后,end可以运行
|
||||
const edges2_1: RuntimeEdgeItemType[] = [
|
||||
createEdge('tool1', 'process', 'active'),
|
||||
createEdge('process', 'end', 'active')
|
||||
];
|
||||
|
||||
expect(checkNodeRunStatus({ nodesMap, node: endNode, runtimeEdges: edges2_1 })).toBe('run');
|
||||
|
||||
// 场景3: 有workflowStart时，tool节点不再是入口节点
|
||||
const startNode = createNode('start', FlowNodeTypeEnum.workflowStart);
|
||||
const nodesMapWithStart = new Map<string, RuntimeNodeItemType>([
|
||||
['start', startNode],
|
||||
['tool1', toolNode],
|
||||
['process', processNode],
|
||||
['end', endNode]
|
||||
]);
|
||||
|
||||
const edges3: RuntimeEdgeItemType[] = [
|
||||
createEdge('start', 'tool1', 'active'),
|
||||
createEdge('tool1', 'process', 'waiting'),
|
||||
createEdge('process', 'end', 'waiting')
|
||||
];
|
||||
|
||||
// 此时tool节点不再是入口节点，需要start激活才能运行
|
||||
expect(
|
||||
checkNodeRunStatus({ nodesMap: nodesMapWithStart, node: toolNode, runtimeEdges: edges3 })
|
||||
).toBe('run');
|
||||
expect(
|
||||
checkNodeRunStatus({ nodesMap: nodesMapWithStart, node: processNode, runtimeEdges: edges3 })
|
||||
).toBe('wait');
|
||||
|
||||
// Tool执行完成后，process可以运行
|
||||
const edges4: RuntimeEdgeItemType[] = [
|
||||
createEdge('start', 'tool1', 'active'),
|
||||
createEdge('tool1', 'process', 'active'),
|
||||
createEdge('process', 'end', 'waiting')
|
||||
];
|
||||
|
||||
expect(
|
||||
checkNodeRunStatus({ nodesMap: nodesMapWithStart, node: processNode, runtimeEdges: edges4 })
|
||||
).toBe('run');
|
||||
});
|
||||
|
||||
it('工具调用2: ToolSet节点与条件分支和循环组合 (Agent → ToolSet → Tool1/Tool2 → Result → Agent)', () => {
|
||||
// 场景：Agent调用工具集，工具集根据条件选择不同工具执行，并支持循环调用
|
||||
// Start → Agent → ToolSet → (Tool1 | Tool2) → Result → Agent (循环)
|
||||
const nodeStart = createNode('start', FlowNodeTypeEnum.workflowStart);
|
||||
const agentNode = createNode('agent', FlowNodeTypeEnum.agent);
|
||||
const toolSetNode = createNode('toolSet', FlowNodeTypeEnum.toolSet);
|
||||
const tool1Node = createNode('tool1', FlowNodeTypeEnum.tool);
|
||||
const tool2Node = createNode('tool2', FlowNodeTypeEnum.tool);
|
||||
const resultNode = createNode('result');
|
||||
|
||||
const nodesMap = new Map<string, RuntimeNodeItemType>([
|
||||
['start', nodeStart],
|
||||
['agent', agentNode],
|
||||
['toolSet', toolSetNode],
|
||||
['tool1', tool1Node],
|
||||
['tool2', tool2Node],
|
||||
['result', resultNode]
|
||||
]);
|
||||
|
||||
// 场景1: 第一次执行，Agent选择Tool1
|
||||
const edges1: RuntimeEdgeItemType[] = [
|
||||
createEdge('start', 'agent', 'active'),
|
||||
createEdge('agent', 'toolSet', 'active'),
|
||||
createEdge('toolSet', 'tool1', 'active'), // 选择Tool1
|
||||
createEdge('toolSet', 'tool2', 'skipped'), // Tool2未选择
|
||||
createEdge('tool1', 'result', 'waiting'),
|
||||
createEdge('tool2', 'result', 'skipped'),
|
||||
createEdge('result', 'agent', 'waiting') // 循环边等待
|
||||
];
|
||||
|
||||
expect(checkNodeRunStatus({ nodesMap, node: agentNode, runtimeEdges: edges1 })).toBe('wait');
|
||||
expect(checkNodeRunStatus({ nodesMap, node: toolSetNode, runtimeEdges: edges1 })).toBe('run');
|
||||
expect(checkNodeRunStatus({ nodesMap, node: tool1Node, runtimeEdges: edges1 })).toBe('run');
|
||||
expect(checkNodeRunStatus({ nodesMap, node: tool2Node, runtimeEdges: edges1 })).toBe('skip');
|
||||
expect(checkNodeRunStatus({ nodesMap, node: resultNode, runtimeEdges: edges1 })).toBe('wait');
|
||||
|
||||
// 场景2: Tool1执行完成，Result处理结果
|
||||
const edges2: RuntimeEdgeItemType[] = [
|
||||
createEdge('start', 'agent', 'active'),
|
||||
createEdge('agent', 'toolSet', 'active'),
|
||||
createEdge('toolSet', 'tool1', 'active'),
|
||||
createEdge('toolSet', 'tool2', 'skipped'),
|
||||
createEdge('tool1', 'result', 'active'), // Tool1完成
|
||||
createEdge('tool2', 'result', 'skipped'),
|
||||
createEdge('result', 'agent', 'waiting')
|
||||
];
|
||||
|
||||
expect(checkNodeRunStatus({ nodesMap, node: resultNode, runtimeEdges: edges2 })).toBe('run');
|
||||
|
||||
// 场景3: 循环回Agent，第二次调用选择Tool2
|
||||
const edges3: RuntimeEdgeItemType[] = [
|
||||
createEdge('start', 'agent', 'active'),
|
||||
createEdge('agent', 'toolSet', 'active'),
|
||||
createEdge('toolSet', 'tool1', 'skipped'), // Tool1未选择
|
||||
createEdge('toolSet', 'tool2', 'active'), // 选择Tool2
|
||||
createEdge('tool1', 'result', 'skipped'),
|
||||
createEdge('tool2', 'result', 'active'), // Tool2完成
|
||||
createEdge('result', 'agent', 'active') // 循环边激活
|
||||
];
|
||||
|
||||
// Agent有来自start和result的两条active边
|
||||
expect(checkNodeRunStatus({ nodesMap, node: agentNode, runtimeEdges: edges3 })).toBe('run');
|
||||
expect(checkNodeRunStatus({ nodesMap, node: tool1Node, runtimeEdges: edges3 })).toBe('skip');
|
||||
expect(checkNodeRunStatus({ nodesMap, node: tool2Node, runtimeEdges: edges3 })).toBe('run');
|
||||
expect(checkNodeRunStatus({ nodesMap, node: resultNode, runtimeEdges: edges3 })).toBe('run');
|
||||
|
||||
// 场景4: 循环退出，不再调用工具
|
||||
const edges4: RuntimeEdgeItemType[] = [
|
||||
createEdge('start', 'agent', 'active'),
|
||||
createEdge('agent', 'toolSet', 'skipped'), // 不再调用工具集
|
||||
createEdge('toolSet', 'tool1', 'skipped'),
|
||||
createEdge('toolSet', 'tool2', 'skipped'),
|
||||
createEdge('tool1', 'result', 'skipped'),
|
||||
createEdge('tool2', 'result', 'skipped'),
|
||||
createEdge('result', 'agent', 'skipped') // 循环退出
|
||||
];
|
||||
|
||||
expect(checkNodeRunStatus({ nodesMap, node: agentNode, runtimeEdges: edges4 })).toBe('run');
|
||||
expect(checkNodeRunStatus({ nodesMap, node: toolSetNode, runtimeEdges: edges4 })).toBe('skip');
|
||||
expect(checkNodeRunStatus({ nodesMap, node: tool1Node, runtimeEdges: edges4 })).toBe('skip');
|
||||
expect(checkNodeRunStatus({ nodesMap, node: tool2Node, runtimeEdges: edges4 })).toBe('skip');
|
||||
expect(checkNodeRunStatus({ nodesMap, node: resultNode, runtimeEdges: edges4 })).toBe('skip');
|
||||
});
|
||||
});
|
||||
|
|
|
|||
Loading…
Reference in New Issue