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V4.8.20 feature (#3686)

Browse Source 
* Aiproxy (#3649)

* model config

* feat: model config ui

* perf: rename variable

* feat: custom request url

* perf: model buffer

* perf: init model

* feat: json model config

* auto login

* fix: ts

* update packages

* package

* fix: dockerfile

* feat: usage filter & export & dashbord (#3538)

* feat: usage filter & export & dashbord

* adjust ui

* fix tmb scroll

* fix code & selecte all

* merge

* perf: usages list；perf: move components (#3654)

* perf: usages list

* team sub plan load

* perf: usage dashboard code

* perf: dashboard ui

* perf: move components

* add default model config (#3653)

* 4.8.20 test (#3656)

* provider

* perf: model config

* model perf (#3657)

* fix: model

* dataset quote

* perf: model config

* model tag

* doubao model config

* perf: config model

* feat: model test

* fix: POST 500 error on dingtalk bot (#3655)

* feat: default model (#3662)

* move model config

* feat: default model

* fix: false triggerd org selection (#3661)

* export usage csv i18n (#3660)

* export usage csv i18n

* fix build

* feat: markdown extension (#3663)

* feat: markdown extension

* media cros

* rerank test

* default price

* perf: default model

* fix: cannot custom provider

* fix: default model select

* update bg

* perf: default model selector

* fix: usage export

* i18n

* fix: rerank

* update init extension

* perf: ip limit check

* doubao model order

* web default modle

* perf: tts selector

* perf: tts error

* qrcode package

* reload buffer (#3665)

* reload buffer

* reload buffer

* tts selector

* fix: err tip (#3666)

* fix: err tip

* perf: training queue

* doc

* fix interactive edge (#3659)

* fix interactive edge

* fix

* comment

* add gemini model

* fix: chat model select

* perf: supplement assistant empty response (#3669)

* perf: supplement assistant empty response

* check array

* perf: max_token count;feat: support resoner output;fix: member scroll (#3681)

* perf: supplement assistant empty response

* check array

* perf: max_token count

* feat: support resoner output

* member scroll

* update provider order

* i18n

* fix: stream response (#3682)

* perf: supplement assistant empty response

* check array

* fix: stream response

* fix: model config cannot set to null

* fix: reasoning response (#3684)

* perf: supplement assistant empty response

* check array

* fix: reasoning response

* fix: reasoning response

* doc (#3685)

* perf: supplement assistant empty response

* check array

* doc

* lock

* animation

* update doc

* update compose

* doc

* doc

---------

Co-authored-by: heheer <heheer@sealos.io>
Co-authored-by: a.e. <49438478+I-Info@users.noreply.github.com>
This commit is contained in:
Archer
2025-02-05 00:10:47 +08:00
committed by GitHub
parent c393002f1d
commit db2c0a0bdb
496 changed files with 9031 additions and 4726 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
packages/global/common/error/code/user.ts
View File

@@ -2,12 +2,22 @@ import { ErrType } from '../errorCode';
import { i18nT } from '../../../../web/i18n/utils';
/* team: 503000 */
export enum UserErrEnum {
notUser = 'notUser',
userExist = 'userExist',
unAuthRole = 'unAuthRole',
account_psw_error = 'account_psw_error',
balanceNotEnough = 'balanceNotEnough',
unAuthSso = 'unAuthSso'
}
const errList = [
{
statusText: UserErrEnum.notUser,
message: i18nT('common:code_error.account_not_found')
},
{
statusText: UserErrEnum.userExist,
message: i18nT('common:code_error.account_exist')
},
{
statusText: UserErrEnum.account_psw_error,
message: i18nT('common:code_error.account_error')

14
packages/global/common/system/types/index.d.ts vendored
View File

@@ -3,7 +3,7 @@ import type {
ChatModelItemType,
FunctionModelItemType,
LLMModelItemType,
VectorModelItemType,
EmbeddingModelItemType,
AudioSpeechModels,
STTModelType,
ReRankModelItemType
@@ -31,11 +31,13 @@ export type FastGPTConfigFileType = {
feConfigs: FastGPTFeConfigsType;
systemEnv: SystemEnvType;
subPlans?: SubPlanType;
llmModels: ChatModelItemType[];
vectorModels: VectorModelItemType[];
reRankModels: ReRankModelItemType[];
audioSpeechModels: AudioSpeechModelType[];
whisperModel: STTModelType;
// Abandon
llmModels?: ChatModelItemType[];
vectorModels?: EmbeddingModelItemType[];
reRankModels?: ReRankModelItemType[];
audioSpeechModels?: TTSModelType[];
whisperModel?: STTModelType;
};
export type FastGPTFeConfigsType = {

6
packages/global/core/ai/constants.ts
View File

@@ -15,15 +15,13 @@ export enum LLMModelTypeEnum {
all = 'all',
classify = 'classify',
extractFields = 'extractFields',
toolCall = 'toolCall',
queryExtension = 'queryExtension'
toolCall = 'toolCall'
}
export const llmModelTypeFilterMap = {
[LLMModelTypeEnum.all]: 'model',
[LLMModelTypeEnum.classify]: 'usedInClassify',
[LLMModelTypeEnum.extractFields]: 'usedInExtractFields',
[LLMModelTypeEnum.toolCall]: 'usedInToolCall',
[LLMModelTypeEnum.queryExtension]: 'usedInQueryExtension'
[LLMModelTypeEnum.toolCall]: 'usedInToolCall'
};
export enum EmbeddingTypeEnm {

115
packages/global/core/ai/model.d.ts vendored
View File

@@ -1,3 +1,4 @@
import { ModelTypeEnum } from './model';
import type { ModelProviderIdType } from './provider';
type PriceType = {
@@ -7,68 +8,74 @@ type PriceType = {
inputPrice?: number; // 1k tokens=n points
outputPrice?: number; // 1k tokens=n points
};
export type LLMModelItemType = PriceType & {
type BaseModelItemType = {
provider: ModelProviderIdType;
model: string;
name: string;
avatar?: string; // model icon, from provider
maxContext: number;
maxResponse: number;
quoteMaxToken: number;
maxTemperature: number;
censor?: boolean;
vision?: boolean;
isActive?: boolean;
isCustom?: boolean;
isDefault?: boolean;
// diff function model
datasetProcess?: boolean; // dataset
usedInClassify?: boolean; // classify
usedInExtractFields?: boolean; // extract fields
usedInToolCall?: boolean; // tool call
usedInQueryExtension?: boolean; // query extension
functionCall: boolean;
toolChoice: boolean;
customCQPrompt: string;
customExtractPrompt: string;
defaultSystemChatPrompt?: string;
defaultConfig?: Record<string, any>;
fieldMap?: Record<string, string>;
// If has requestUrl, it will request the model directly
requestUrl?: string;
requestAuth?: string;
};
export type VectorModelItemType = PriceType & {
provider: ModelProviderIdType;
model: string; // model name
name: string; // show name
avatar?: string;
defaultToken: number; // split text default token
maxToken: number; // model max token
weight: number; // training weight
hidden?: boolean; // Disallow creation
defaultConfig?: Record<string, any>; // post request config
dbConfig?: Record<string, any>; // Custom parameters for storage
queryConfig?: Record<string, any>; // Custom parameters for query
};
export type LLMModelItemType = PriceType &
BaseModelItemType & {
type: ModelTypeEnum.llm;
maxContext: number;
maxResponse: number;
quoteMaxToken: number;
maxTemperature?: number;
export type ReRankModelItemType = PriceType & {
provider: ModelProviderIdType;
model: string;
name: string;
requestUrl: string;
requestAuth: string;
};
censor?: boolean;
vision?: boolean;
reasoning?: boolean;
export type AudioSpeechModelType = PriceType & {
provider: ModelProviderIdType;
model: string;
name: string;
voices: { label: string; value: string; bufferId: string }[];
};
// diff function model
datasetProcess?: boolean; // dataset
usedInClassify?: boolean; // classify
usedInExtractFields?: boolean; // extract fields
usedInToolCall?: boolean; // tool call
export type STTModelType = PriceType & {
provider: ModelProviderIdType;
model: string;
name: string;
};
functionCall: boolean;
toolChoice: boolean;
customCQPrompt: string;
customExtractPrompt: string;
defaultSystemChatPrompt?: string;
defaultConfig?: Record<string, any>;
fieldMap?: Record<string, string>;
};
export type EmbeddingModelItemType = PriceType &
BaseModelItemType & {
type: ModelTypeEnum.embedding;
defaultToken: number; // split text default token
maxToken: number; // model max token
weight: number; // training weight
hidden?: boolean; // Disallow creation
defaultConfig?: Record<string, any>; // post request config
dbConfig?: Record<string, any>; // Custom parameters for storage
queryConfig?: Record<string, any>; // Custom parameters for query
};
export type ReRankModelItemType = PriceType &
BaseModelItemType & {
type: ModelTypeEnum.rerank;
};
export type TTSModelType = PriceType &
BaseModelItemType & {
type: ModelTypeEnum.tts;
voices: { label: string; value: string }[];
};
export type STTModelType = PriceType &
BaseModelItemType & {
type: ModelTypeEnum.stt;
};

40
packages/global/core/ai/model.ts
View File

@@ -1,9 +1,18 @@
import { i18nT } from '../../../web/i18n/utils';
import type { LLMModelItemType, STTModelType, VectorModelItemType } from './model.d';
import type { LLMModelItemType, STTModelType, EmbeddingModelItemType } from './model.d';
import { getModelProvider, ModelProviderIdType } from './provider';
export enum ModelTypeEnum {
llm = 'llm',
embedding = 'embedding',
tts = 'tts',
stt = 'stt',
rerank = 'rerank'
}
export const defaultQAModels: LLMModelItemType[] = [
{
type: ModelTypeEnum.llm,
provider: 'OpenAI',
model: 'gpt-4o-mini',
name: 'gpt-4o-mini',
@@ -24,8 +33,9 @@ export const defaultQAModels: LLMModelItemType[] = [
}
];
export const defaultVectorModels: VectorModelItemType[] = [
export const defaultVectorModels: EmbeddingModelItemType[] = [
{
type: ModelTypeEnum.embedding,
provider: 'OpenAI',
model: 'text-embedding-3-small',
name: 'Embedding-2',
@@ -36,12 +46,15 @@ export const defaultVectorModels: VectorModelItemType[] = [
}
];
export const defaultWhisperModel: STTModelType = {
provider: 'OpenAI',
model: 'whisper-1',
name: 'whisper-1',
charsPointsPrice: 0
};
export const defaultSTTModels: STTModelType[] = [
{
type: ModelTypeEnum.stt,
provider: 'OpenAI',
model: 'whisper-1',
name: 'whisper-1',
charsPointsPrice: 0
}
];
export const getModelFromList = (
modelList: { provider: ModelProviderIdType; name: string; model: string }[],
@@ -55,15 +68,10 @@ export const getModelFromList = (
};
};
export enum ModelTypeEnum {
chat = 'chat',
embedding = 'embedding',
tts = 'tts',
stt = 'stt'
}
export const modelTypeList = [
{ label: i18nT('common:model.type.chat'), value: ModelTypeEnum.chat },
{ label: i18nT('common:model.type.chat'), value: ModelTypeEnum.llm },
{ label: i18nT('common:model.type.embedding'), value: ModelTypeEnum.embedding },
{ label: i18nT('common:model.type.tts'), value: ModelTypeEnum.tts },
{ label: i18nT('common:model.type.stt'), value: ModelTypeEnum.stt }
{ label: i18nT('common:model.type.stt'), value: ModelTypeEnum.stt },
{ label: i18nT('common:model.type.reRank'), value: ModelTypeEnum.rerank }
];

19
packages/global/core/ai/provider.ts
View File

@@ -7,11 +7,12 @@ export type ModelProviderIdType =
| 'Meta'
| 'MistralAI'
| 'Groq'
| 'Grok'
| 'AliCloud'
| 'Qwen'
| 'Doubao'
| 'ChatGLM'
| 'DeepSeek'
| 'ChatGLM'
| 'Ernie'
| 'Moonshot'
| 'MiniMax'
@@ -20,6 +21,7 @@ export type ModelProviderIdType =
| 'Baichuan'
| 'StepFun'
| 'Yi'
| 'Siliconflow'
| 'Ollama'
| 'BAAI'
| 'FishAudio'
@@ -29,7 +31,7 @@ export type ModelProviderIdType =
export type ModelProviderType = {
id: ModelProviderIdType;
name: string;
name: any;
avatar: string;
};
@@ -59,6 +61,11 @@ export const ModelProviderList: ModelProviderType[] = [
name: 'MistralAI',
avatar: 'model/mistral'
},
{
id: 'Grok',
name: 'Grok',
avatar: 'model/grok'
},
{
id: 'Groq',
name: 'Groq',
@@ -155,6 +162,11 @@ export const ModelProviderList: ModelProviderType[] = [
name: i18nT('common:model_moka'),
avatar: 'model/moka'
},
{
id: 'Siliconflow',
name: i18nT('common:model_siliconflow'),
avatar: 'model/siliconflow'
},
{
id: 'Other',
name: i18nT('common:model_other'),
@@ -165,6 +177,7 @@ export const ModelProviderMap = Object.fromEntries(
ModelProviderList.map((item, index) => [item.id, { ...item, order: index }])
);
export const getModelProvider = (provider: ModelProviderIdType) => {
export const getModelProvider = (provider?: ModelProviderIdType) => {
if (!provider) return ModelProviderMap.Other;
return ModelProviderMap[provider] ?? ModelProviderMap.Other;
};

2
packages/global/core/app/type.d.ts vendored
View File

@@ -80,6 +80,7 @@ export type AppSimpleEditFormType = {
maxToken?: number;
isResponseAnswerText: boolean;
maxHistories: number;
[NodeInputKeyEnum.aiChatReasoning]?: boolean;
};
dataset: {
datasets: SelectedDatasetType;
@@ -117,6 +118,7 @@ export type SettingAIDataType = {
isResponseAnswerText?: boolean;
maxHistories?: number;
[NodeInputKeyEnum.aiChatVision]?: boolean; // Is open vision mode
[NodeInputKeyEnum.aiChatReasoning]?: boolean; // Is open reasoning mode
};
// variable

3
packages/global/core/app/utils.ts
View File

@@ -16,7 +16,8 @@ export const getDefaultAppForm = (): AppSimpleEditFormType => {
temperature: 0,
isResponseAnswerText: true,
maxHistories: 6,
maxToken: 4000
maxToken: 4000,
aiChatReasoning: true
},
dataset: {
datasets: [],

5
packages/global/core/chat/constants.ts
View File

@@ -25,7 +25,8 @@ export enum ChatItemValueTypeEnum {
text = 'text',
file = 'file',
tool = 'tool',
interactive = 'interactive'
interactive = 'interactive',
reasoning = 'reasoning'
}
export enum ChatSourceEnum {
@@ -75,5 +76,3 @@ export enum ChatStatusEnum {
running = 'running',
finish = 'finish'
}
export const MARKDOWN_QUOTE_SIGN = 'QUOTE SIGN';

11
packages/global/core/chat/type.d.ts vendored
View File

@@ -70,14 +70,23 @@ export type SystemChatItemType = {
obj: ChatRoleEnum.System;
value: SystemChatItemValueItemType[];
};
export type AIChatItemValueItemType = {
type: ChatItemValueTypeEnum.text | ChatItemValueTypeEnum.tool | ChatItemValueTypeEnum.interactive;
type:
| ChatItemValueTypeEnum.text
| ChatItemValueTypeEnum.reasoning
| ChatItemValueTypeEnum.tool
| ChatItemValueTypeEnum.interactive;
text?: {
content: string;
};
reasoning?: {
content: string;
};
tools?: ToolModuleResponseItemType[];
interactive?: WorkflowInteractiveResponseType;
};
export type AIChatItemType = {
obj: ChatRoleEnum.AI;
value: AIChatItemValueItemType[];

8
packages/global/core/dataset/type.d.ts vendored
View File

@@ -1,4 +1,4 @@
import type { LLMModelItemType, VectorModelItemType } from '../../core/ai/model.d';
import type { LLMModelItemType, EmbeddingModelItemType } from '../../core/ai/model.d';
import { PermissionTypeEnum } from '../../support/permission/constant';
import { PushDatasetDataChunkProps } from './api';
import {
@@ -152,7 +152,7 @@ export type DatasetSimpleItemType = {
_id: string;
avatar: string;
name: string;
vectorModel: VectorModelItemType;
vectorModel: EmbeddingModelItemType;
};
export type DatasetListItemType = {
_id: string;
@@ -163,14 +163,14 @@ export type DatasetListItemType = {
intro: string;
type: `${DatasetTypeEnum}`;
permission: DatasetPermission;
vectorModel: VectorModelItemType;
vectorModel: EmbeddingModelItemType;
inheritPermission: boolean;
private?: boolean;
sourceMember?: SourceMemberType;
};
export type DatasetItemType = Omit<DatasetSchemaType, 'vectorModel' | 'agentModel'> & {
vectorModel: VectorModelItemType;
vectorModel: EmbeddingModelItemType;
agentModel: LLMModelItemType;
permission: DatasetPermission;
};

2
packages/global/core/workflow/api.d.ts vendored
View File

@@ -1,4 +1,4 @@
import { VectorModelItemType } from '../ai/model.d';
import { EmbeddingModelItemType } from '../ai/model.d';
import { NodeInputKeyEnum } from './constants';
export type SelectedDatasetType = { datasetId: string }[];

4
packages/global/core/workflow/constants.ts
View File

@@ -141,6 +141,7 @@ export enum NodeInputKeyEnum {
aiChatDatasetQuote = 'quoteQA',
aiChatVision = 'aiChatVision',
stringQuoteText = 'stringQuoteText',
aiChatReasoning = 'aiChatReasoning',
// dataset
datasetSelectList = 'datasets',
@@ -220,7 +221,8 @@ export enum NodeOutputKeyEnum {
// common
userChatInput = 'userChatInput',
history = 'history',
answerText = 'answerText', // module answer. the value will be show and save to history
answerText = 'answerText', // node answer. the value will be show and save to history
reasoningText = 'reasoningText', // node reasoning. the value will be show but not save to history
success = 'success',
failed = 'failed',
error = 'error',

1
packages/global/core/workflow/runtime/type.d.ts vendored
View File

@@ -220,6 +220,7 @@ export type AIChatNodeProps = {
[NodeInputKeyEnum.aiChatMaxToken]?: number;
[NodeInputKeyEnum.aiChatIsResponseText]: boolean;
[NodeInputKeyEnum.aiChatVision]?: boolean;
[NodeInputKeyEnum.aiChatReasoning]?: boolean;
[NodeInputKeyEnum.aiChatQuoteRole]?: AiChatQuoteRoleType;
[NodeInputKeyEnum.aiChatQuoteTemplate]?: string;

40
packages/global/core/workflow/runtime/utils.ts
View File

@@ -176,6 +176,7 @@ export const checkNodeRunStatus = ({
}
visited.add(edge.source);
// 递归检测后面的 edge，如果有其中一个成环，则返回 true
const nextEdges = allEdges.filter((item) => item.target === edge.source);
return nextEdges.some((nextEdge) => checkIsCircular(nextEdge, new Set(visited)));
};
@@ -207,7 +208,23 @@ export const checkNodeRunStatus = ({
currentNode: node
});
// check skip（其中一组边，全 skip）
// check active（其中一组边，至少有一个 active，且没有 waiting 即可运行）
if (
commonEdges.length > 0 &&
commonEdges.some((item) => item.status === 'active') &&
commonEdges.every((item) => item.status !== 'waiting')
) {
return 'run';
}
if (
recursiveEdges.length > 0 &&
recursiveEdges.some((item) => item.status === 'active') &&
recursiveEdges.every((item) => item.status !== 'waiting')
) {
return 'run';
}
// check skip（其中一组边，全是 skiped 则跳过运行）
if (commonEdges.length > 0 && commonEdges.every((item) => item.status === 'skipped')) {
return 'skip';
}
@@ -215,14 +232,6 @@ export const checkNodeRunStatus = ({
return 'skip';
}
// check active（有一类边，不全是 wait 即可运行）
if (commonEdges.length > 0 && commonEdges.every((item) => item.status !== 'waiting')) {
return 'run';
}
if (recursiveEdges.length > 0 && recursiveEdges.every((item) => item.status !== 'waiting')) {
return 'run';
}
return 'wait';
};
@@ -355,12 +364,14 @@ export function replaceEditorVariable({
export const textAdaptGptResponse = ({
text,
reasoning_content,
model = '',
finish_reason = null,
extraData = {}
}: {
model?: string;
text: string | null;
text?: string | null;
reasoning_content?: string | null;
finish_reason?: null | 'stop';
extraData?: Object;
}) => {
@@ -372,10 +383,11 @@ export const textAdaptGptResponse = ({
model,
choices: [
{
delta:
text === null
? {}
: { role: ChatCompletionRequestMessageRoleEnum.Assistant, content: text },
delta: {
role: ChatCompletionRequestMessageRoleEnum.Assistant,
content: text,
...(reasoning_content && { reasoning_content })
},
index: 0,
finish_reason
}

11
packages/global/core/workflow/template/system/aiChat/index.ts
View File

@@ -63,14 +63,14 @@ export const AiChatModule: FlowNodeTemplateType = {
key: NodeInputKeyEnum.aiChatTemperature,
renderTypeList: [FlowNodeInputTypeEnum.hidden], // Set in the pop-up window
label: '',
value: 0,
value: undefined,
valueType: WorkflowIOValueTypeEnum.number
},
{
key: NodeInputKeyEnum.aiChatMaxToken,
renderTypeList: [FlowNodeInputTypeEnum.hidden], // Set in the pop-up window
label: '',
value: 2000,
value: undefined,
valueType: WorkflowIOValueTypeEnum.number
},
@@ -91,6 +91,13 @@ export const AiChatModule: FlowNodeTemplateType = {
valueType: WorkflowIOValueTypeEnum.boolean,
value: true
},
{
key: NodeInputKeyEnum.aiChatReasoning,
renderTypeList: [FlowNodeInputTypeEnum.hidden],
label: '',
valueType: WorkflowIOValueTypeEnum.boolean,
value: true
},
// settings modal ---
{
...Input_Template_System_Prompt,

5
packages/global/core/workflow/template/system/queryExtension.ts
View File

@@ -31,10 +31,7 @@ export const AiQueryExtension: FlowNodeTemplateType = {
showStatus: true,
version: '481',
inputs: [
{
...Input_Template_SelectAIModel,
llmModelType: LLMModelTypeEnum.queryExtension
},
Input_Template_SelectAIModel,
{
key: NodeInputKeyEnum.aiSystemPrompt,
renderTypeList: [FlowNodeInputTypeEnum.textarea, FlowNodeInputTypeEnum.reference],

4
packages/global/core/workflow/template/system/tools.ts
View File

@@ -43,14 +43,14 @@ export const ToolModule: FlowNodeTemplateType = {
key: NodeInputKeyEnum.aiChatTemperature,
renderTypeList: [FlowNodeInputTypeEnum.hidden], // Set in the pop-up window
label: '',
value: 0,
value: undefined,
valueType: WorkflowIOValueTypeEnum.number
},
{
key: NodeInputKeyEnum.aiChatMaxToken,
renderTypeList: [FlowNodeInputTypeEnum.hidden], // Set in the pop-up window
label: '',
value: 2000,
value: undefined,
valueType: WorkflowIOValueTypeEnum.number
},
{

16
packages/global/support/wallet/usage/api.d.ts vendored
View File

@@ -6,6 +6,22 @@ export type CreateTrainingUsageProps = {
datasetId: string;
};
export type GetUsageProps = {
dateStart: Date;
dateEnd: Date;
sources?: UsageSourceEnum[];
teamMemberIds?: string[];
projectName?: string;
};
export type GetUsageDashboardProps = GetUsageProps & {
unit: 'day' | 'month';
};
export type GetUsageDashboardResponseItem = {
date: Date;
totalPoints: number;
};
export type ConcatUsageProps = UsageListItemCountType & {
teamId: string;
tmbId: string;

14
packages/global/support/wallet/usage/constants.ts
View File

@@ -18,30 +18,30 @@ export const UsageSourceMap = {
label: i18nT('common:core.chat.logs.online')
},
[UsageSourceEnum.api]: {
label: 'Api'
label: 'API'
},
[UsageSourceEnum.shareLink]: {
label: i18nT('common:core.chat.logs.free_login')
},
[UsageSourceEnum.training]: {
label: 'dataset.Training Name'
label: i18nT('common:dataset.Training Name')
},
[UsageSourceEnum.cronJob]: {
label: i18nT('common:cron_job_run_app')
},
[UsageSourceEnum.feishu]: {
label: i18nT('user:usage.feishu')
label: i18nT('account_usage:feishu')
},
[UsageSourceEnum.official_account]: {
label: i18nT('user:usage.official_account')
label: i18nT('account_usage:official_account')
},
[UsageSourceEnum.share]: {
label: i18nT('user:usage.share')
label: i18nT('account_usage:share')
},
[UsageSourceEnum.wecom]: {
label: i18nT('user:usage.wecom')
label: i18nT('account_usage:wecom')
},
[UsageSourceEnum.dingtalk]: {
label: i18nT('user:usage.dingtalk')
label: i18nT('account_usage:dingtalk')
}
};

3
packages/global/support/wallet/usage/type.d.ts vendored
View File

@@ -1,3 +1,4 @@
import { SourceMemberType } from '../../../support/user/type';
import { CreateUsageProps } from './api';
import { UsageSourceEnum } from './constants';
@@ -10,6 +11,7 @@ export type UsageListItemCountType = {
// deprecated
tokens?: number;
};
export type UsageListItemType = UsageListItemCountType & {
moduleName: string;
amount: number;
@@ -28,4 +30,5 @@ export type UsageItemType = {
source: UsageSchemaType['source'];
totalPoints: number;
list: UsageSchemaType['list'];
sourceMember: SourceMemberType;
};

2
packages/service/common/file/image/controller.ts
View File

@@ -40,7 +40,7 @@ export async function uploadMongoImg({
expiredTime: forever ? undefined : addHours(new Date(), 1)
});
return `${process.env.FE_DOMAIN || ''}${process.env.NEXT_PUBLIC_BASE_URL || ''}${imageBaseUrl}${String(_id)}.${extension}`;
return `${process.env.NEXT_PUBLIC_BASE_URL || ''}${imageBaseUrl}${String(_id)}.${extension}`;
}
const getIdFromPath = (path?: string) => {

17
packages/service/common/middle/reqFrequencyLimit.ts
View File

@@ -1,6 +1,5 @@
import { ApiRequestProps } from '../../type/next';
import requestIp from 'request-ip';
import { ERROR_ENUM } from '@fastgpt/global/common/error/errorCode';
import { authFrequencyLimit } from '../system/frequencyLimit/utils';
import { addSeconds } from 'date-fns';
import { NextApiResponse } from 'next';
@@ -9,7 +8,17 @@ import { jsonRes } from '../response';
// unit: times/s
// how to use?
// export default NextAPI(useQPSLimit(10), handler); // limit 10 times per second for a ip
export function useReqFrequencyLimit(seconds: number, limit: number, force = false) {
export function useIPFrequencyLimit({
id,
seconds,
limit,
force = false
}: {
id: string;
seconds: number;
limit: number;
force?: boolean;
}) {
return async (req: ApiRequestProps, res: NextApiResponse) => {
const ip = requestIp.getClientIp(req);
if (!ip || (process.env.USE_IP_LIMIT !== 'true' && !force)) {
@@ -17,14 +26,14 @@ export function useReqFrequencyLimit(seconds: number, limit: number, force = fal
}
try {
await authFrequencyLimit({
eventId: 'ip-qps-limit' + ip,
eventId: `ip-qps-limit-${id}-` + ip,
maxAmount: limit,
expiredTime: addSeconds(new Date(), seconds)
});
} catch (_) {
jsonRes(res, {
code: 429,
error: ERROR_ENUM.tooManyRequest
error: `Too many request, request ${limit} times every ${seconds} seconds`
});
}
};

10
packages/service/common/response/index.ts
View File

@@ -33,7 +33,15 @@ export const jsonRes = <T = any>(
addLog.error(`Api response error: ${url}`, ERROR_RESPONSE[errResponseKey]);
return res.status(code).json(ERROR_RESPONSE[errResponseKey]);
res.status(code);
if (message) {
res.send(message);
} else {
res.json(ERROR_RESPONSE[errResponseKey]);
}
return;
}
// another error

31
packages/service/common/system/config/controller.ts
View File

@@ -6,8 +6,7 @@ import { FastGPTProUrl } from '../constants';
export const getFastGPTConfigFromDB = async () => {
if (!FastGPTProUrl) {
return {
config: {} as FastGPTConfigFileType,
configId: undefined
config: {} as FastGPTConfigFileType
};
}
@@ -18,9 +17,35 @@ export const getFastGPTConfigFromDB = async () => {
});
const config = res?.value || {};
// 利用配置文件的创建时间（更新时间）来做缓存，如果前端命中缓存，则不需要再返回配置文件
global.systemInitBufferId = res ? res.createTime.getTime().toString() : undefined;
return {
configId: res ? String(res._id) : undefined,
config: config as FastGPTConfigFileType
};
};
export const updateFastGPTConfigBuffer = async () => {
const res = await MongoSystemConfigs.findOne({
type: SystemConfigsTypeEnum.fastgpt
}).sort({
createTime: -1
});
if (!res) return;
res.createTime = new Date();
await res.save();
global.systemInitBufferId = res.createTime.getTime().toString();
};
export const reloadFastGPTConfigBuffer = async () => {
const res = await MongoSystemConfigs.findOne({
type: SystemConfigsTypeEnum.fastgpt
}).sort({
createTime: -1
});
if (!res) return;
global.systemInitBufferId = res.createTime.getTime().toString();
};

9
packages/service/common/system/tools.ts
View File

@@ -13,15 +13,6 @@ export const initFastGPTConfig = (config?: FastGPTConfigFileType) => {
global.feConfigs = config.feConfigs;
global.systemEnv = config.systemEnv;
global.subPlans = config.subPlans;
global.llmModels = config.llmModels;
global.llmModelPriceType = global.llmModels.some((item) => typeof item.inputPrice === 'number')
? 'IO'
: 'Tokens';
global.vectorModels = config.vectorModels;
global.audioSpeechModels = config.audioSpeechModels;
global.whisperModel = config.whisperModel;
global.reRankModels = config.reRankModels;
};
export const systemStartCb = () => {

4
packages/service/common/vectorStore/controller.ts
View File

@@ -2,7 +2,7 @@
import { PgVectorCtrl } from './pg/class';
import { getVectorsByText } from '../../core/ai/embedding';
import { InsertVectorProps } from './controller.d';
import { VectorModelItemType } from '@fastgpt/global/core/ai/model.d';
import { EmbeddingModelItemType } from '@fastgpt/global/core/ai/model.d';
import { MILVUS_ADDRESS, PG_ADDRESS } from './constants';
import { MilvusCtrl } from './milvus/class';
@@ -28,7 +28,7 @@ export const insertDatasetDataVector = async ({
...props
}: InsertVectorProps & {
query: string;
model: VectorModelItemType;
model: EmbeddingModelItemType;
}) => {
const { vectors, tokens } = await getVectorsByText({
model,

28
packages/service/core/ai/audio/speech.ts
View File

@@ -1,5 +1,6 @@
import type { NextApiResponse } from 'next';
import { getAIApi } from '../config';
import { getTTSModel } from '../model';
export async function text2Speech({
res,
@@ -18,15 +19,26 @@ export async function text2Speech({
voice: string;
speed?: number;
}) {
const modelData = getTTSModel(model)!;
const ai = getAIApi();
const response = await ai.audio.speech.create({
model,
// @ts-ignore
voice,
input,
response_format: 'mp3',
speed
});
const response = await ai.audio.speech.create(
{
model,
// @ts-ignore
voice,
input,
response_format: 'mp3',
speed
},
modelData.requestUrl && modelData.requestAuth
? {
path: modelData.requestUrl,
headers: {
Authorization: `Bearer ${modelData.requestAuth}`
}
}
: {}
);
const readableStream = response.body as unknown as NodeJS.ReadableStream;
readableStream.pipe(res);

15
packages/service/core/ai/audio/transcriptions.ts
View File

@@ -2,6 +2,7 @@ import fs from 'fs';
import { getAxiosConfig } from '../config';
import axios from 'axios';
import FormData from 'form-data';
import { getSTTModel } from '../model';
export const aiTranscriptions = async ({
model,
@@ -14,13 +15,21 @@ export const aiTranscriptions = async ({
data.append('model', model);
data.append('file', fileStream);
const modelData = getSTTModel(model);
const aiAxiosConfig = getAxiosConfig();
const { data: result } = await axios<{ text: string }>({
method: 'post',
baseURL: aiAxiosConfig.baseUrl,
url: '/audio/transcriptions',
...(modelData.requestUrl
? { url: modelData.requestUrl }
: {
baseURL: aiAxiosConfig.baseUrl,
url: modelData.requestUrl || '/audio/transcriptions'
}),
headers: {
Authorization: aiAxiosConfig.authorization,
Authorization: modelData.requestAuth
? `Bearer ${modelData.requestAuth}`
: aiAxiosConfig.authorization,
...data.getHeaders()
},
data: data

20
packages/service/core/ai/config.ts
View File

@@ -7,14 +7,14 @@ import { getErrText } from '@fastgpt/global/common/error/utils';
import { addLog } from '../../common/system/log';
import { i18nT } from '../../../web/i18n/utils';
import { OpenaiAccountType } from '@fastgpt/global/support/user/team/type';
import { getLLMModel } from './model';
export const openaiBaseUrl = process.env.OPENAI_BASE_URL || 'https://api.openai.com/v1';
export const getAIApi = (props?: { userKey?: OpenaiAccountType; timeout?: number }) => {
const { userKey, timeout } = props || {};
const baseUrl =
userKey?.baseUrl || global?.systemEnv?.oneapiUrl || process.env.ONEAPI_URL || openaiBaseUrl;
const baseUrl = userKey?.baseUrl || global?.systemEnv?.oneapiUrl || openaiBaseUrl;
const apiKey = userKey?.key || global?.systemEnv?.chatApiKey || process.env.CHAT_API_KEY || '';
return new OpenAI({
@@ -29,8 +29,7 @@ export const getAIApi = (props?: { userKey?: OpenaiAccountType; timeout?: number
export const getAxiosConfig = (props?: { userKey?: OpenaiAccountType }) => {
const { userKey } = props || {};
const baseUrl =
userKey?.baseUrl || global?.systemEnv?.oneapiUrl || process.env.ONEAPI_URL || openaiBaseUrl;
const baseUrl = userKey?.baseUrl || global?.systemEnv?.oneapiUrl || openaiBaseUrl;
const apiKey = userKey?.key || global?.systemEnv?.chatApiKey || process.env.CHAT_API_KEY || '';
return {
@@ -63,12 +62,23 @@ export const createChatCompletion = async <T extends CompletionsBodyType>({
getEmptyResponseTip: () => string;
}> => {
try {
const modelConstantsData = getLLMModel(body.model);
const formatTimeout = timeout ? timeout : body.stream ? 60000 : 600000;
const ai = getAIApi({
userKey,
timeout: formatTimeout
});
const response = await ai.chat.completions.create(body, options);
const response = await ai.chat.completions.create(body, {
...options,
...(modelConstantsData.requestUrl ? { path: modelConstantsData.requestUrl } : {}),
headers: {
...options?.headers,
...(modelConstantsData.requestAuth
? { Authorization: `Bearer ${modelConstantsData.requestAuth}` }
: {})
}
});
const isStreamResponse =
typeof response === 'object' &&

11
packages/service/core/ai/config/embedding/text-embedding-ada-002.json
View File

@@ -1,11 +0,0 @@
{
"provider": "OpenAI",
"model": "text-embedding-ada-002",
"name": "text-embedding-ada-002",
"defaultToken": 512, // 默认分块 token
"maxToken": 3000, // 最大分块 token
"weight": 0, // 权重
"charsPointsPrice": 0 // 积分/1k token
}

33
packages/service/core/ai/config/llm/gpt-4o-mini.json
View File

@@ -1,33 +0,0 @@
{
"provider": "OpenAI",
"model": "gpt-4o-mini",
"name": "GPT-4o-mini", // alias
"maxContext": 125000, // 最大上下文
"maxResponse": 16000, // 最大回复
"quoteMaxToken": 60000, // 最大引用
"maxTemperature": 1.2, // 最大温度
"presencePenaltyRange": [-2, 2], // 惩罚系数范围
"frequencyPenaltyRange": [-2, 2], // 频率惩罚系数范围
"responseFormatList": ["text", "json_object", "json_schema"], // 响应格式
"showStopSign": true, // 是否显示停止符号
"vision": true, // 是否支持图片识别
"toolChoice": true, // 是否支持工具调用
"functionCall": false, // 是否支持函数调用（一般都可以 false 了，基本不用了）
"defaultSystemChatPrompt": "", // 默认系统提示
"datasetProcess": true, // 用于知识库文本处理
"usedInClassify": true, // 用于问题分类
"customCQPrompt": "", // 自定义问题分类提示
"usedInExtractFields": true, // 用于提取字段
"customExtractPrompt": "", // 自定义提取提示
"usedInToolCall": true, // 用于工具调用
"usedInQueryExtension": true, // 用于问题优化
"defaultConfig": {}, // 额外的自定义 body
"fieldMap": {}, // body 字段映射
"censor": false, // 是否开启敏感词过滤
"charsPointsPrice": 0 // n 积分/1k token
}

4
packages/service/core/ai/config/provider/AliCloud.json Normal file
View File

@@ -0,0 +1,4 @@
{
"provider": "AliCloud",
"list": []
}

17
packages/service/core/ai/config/provider/BAAI.json Normal file
View File

@@ -0,0 +1,17 @@
{
"provider": "BAAI",
"list": [
{
"model": "bge-m3",
"name": "bge-m3",
"defaultToken": 512,
"maxToken": 8000,
"type": "embedding"
},
{
"model": "bge-reranker-v2-m3",
"name": "bge-reranker-v2-m3",
"type": "rerank"
}
]
}

4
packages/service/core/ai/config/provider/Baichuan.json Normal file
View File

@@ -0,0 +1,4 @@
{
"provider": "Baichuan",
"list": []
}

147
packages/service/core/ai/config/provider/ChatGLM.json Normal file
View File

@@ -0,0 +1,147 @@
{
"provider": "ChatGLM",
"list": [
{
"model": "glm-4-air",
"name": "glm-4-air",
"maxContext": 128000,
"maxResponse": 4000,
"quoteMaxToken": 120000,
"maxTemperature": 0.99,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "glm-4-flash",
"name": "glm-4-flash",
"maxContext": 128000,
"maxResponse": 4000,
"quoteMaxToken": 120000,
"maxTemperature": 0.99,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "glm-4-long",
"name": "glm-4-long",
"maxContext": 1000000,
"maxResponse": 4000,
"quoteMaxToken": 900000,
"maxTemperature": 0.99,
"vision": false,
"toolChoice": false,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "glm-4-plus",
"name": "GLM-4-plus",
"maxContext": 128000,
"maxResponse": 4000,
"quoteMaxToken": 120000,
"maxTemperature": 0.99,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "glm-4v-flash",
"name": "glm-4v-flash",
"maxContext": 8000,
"maxResponse": 1000,
"quoteMaxToken": 6000,
"maxTemperature": 0.99,
"vision": true,
"toolChoice": false,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "glm-4v-plus",
"name": "GLM-4v-plus",
"maxContext": 8000,
"maxResponse": 1000,
"quoteMaxToken": 6000,
"maxTemperature": 0.99,
"vision": true,
"toolChoice": false,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "embedding-3",
"name": "embedding-3",
"defaultToken": 512,
"maxToken": 8000,
"defaultConfig": {
"dimensions": 1024
},
"type": "embedding"
}
]
}

93
packages/service/core/ai/config/provider/Claude.json Normal file
View File

@@ -0,0 +1,93 @@
{
"provider": "Claude",
"list": [
{
"model": "claude-3-5-haiku-20241022",
"name": "claude-3-5-haiku-20241022",
"maxContext": 200000,
"maxResponse": 8000,
"quoteMaxToken": 100000,
"maxTemperature": 1,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "claude-3-5-sonnet-20240620",
"name": "Claude-3-5-sonnet-20240620",
"maxContext": 200000,
"maxResponse": 8000,
"quoteMaxToken": 100000,
"maxTemperature": 1,
"vision": true,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "claude-3-5-sonnet-20241022",
"name": "Claude-3-5-sonnet-20241022",
"maxContext": 200000,
"maxResponse": 8000,
"quoteMaxToken": 100000,
"maxTemperature": 1,
"vision": true,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "claude-3-opus-20240229",
"name": "claude-3-opus-20240229",
"maxContext": 200000,
"maxResponse": 4096,
"quoteMaxToken": 100000,
"maxTemperature": 1,
"vision": true,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
}
]
}

48
packages/service/core/ai/config/provider/DeepSeek.json Normal file
View File

@@ -0,0 +1,48 @@
{
"provider": "DeepSeek",
"list": [
{
"model": "deepseek-chat",
"name": "Deepseek-chat",
"maxContext": 64000,
"maxResponse": 4096,
"quoteMaxToken": 60000,
"maxTemperature": 1.5,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"type": "llm"
},
{
"model": "deepseek-reasoner",
"name": "Deepseek-reasoner",
"maxContext": 64000,
"maxResponse": 4096,
"quoteMaxToken": 60000,
"maxTemperature": null,
"vision": false,
"reasoning": true,
"toolChoice": false,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
}
]
}

195
packages/service/core/ai/config/provider/Doubao.json Normal file
View File

@@ -0,0 +1,195 @@
{
"provider": "Doubao",
"list": [
{
"model": "Doubao-lite-4k",
"name": "Doubao-lite-4k",
"maxContext": 4000,
"maxResponse": 4000,
"quoteMaxToken": 4000,
"maxTemperature": 1,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "Doubao-lite-32k",
"name": "Doubao-lite-32k",
"maxContext": 32000,
"maxResponse": 4000,
"quoteMaxToken": 32000,
"maxTemperature": 1,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "Doubao-lite-128k",
"name": "Doubao-lite-128k",
"maxContext": 128000,
"maxResponse": 4000,
"quoteMaxToken": 120000,
"maxTemperature": 1,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "Doubao-vision-lite-32k",
"name": "Doubao-vision-lite-32k",
"maxContext": 32000,
"maxResponse": 4000,
"quoteMaxToken": 32000,
"maxTemperature": 1,
"vision": true,
"toolChoice": false,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "Doubao-pro-4k",
"name": "Doubao-pro-4k",
"maxContext": 4000,
"maxResponse": 4000,
"quoteMaxToken": 4000,
"maxTemperature": 1,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "Doubao-pro-32k",
"name": "Doubao-pro-32k",
"maxContext": 32000,
"maxResponse": 4000,
"quoteMaxToken": 32000,
"maxTemperature": 1,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
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{
"model": "Doubao-pro-128k",
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{
"model": "Doubao-vision-pro-32k",
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{
"model": "Doubao-embedding-large",
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},
{
"model": "Doubao-embedding",
"name": "Doubao-embedding",
"defaultToken": 512,
"maxToken": 4096,
"type": "embedding"
}
]
}

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{
"provider": "Ernie",
"list": [
{
"model": "ERNIE-4.0-8K",
"name": "ERNIE-4.0-8K",
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{
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{
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{
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{
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}
]
}

4
packages/service/core/ai/config/provider/FishAudio.json Normal file
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{
"provider": "FishAudio",
"list": []
}

144
packages/service/core/ai/config/provider/Gemini.json Normal file
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{
"provider": "Gemini",
"list": [
{
"model": "gemini-1.5-flash",
"name": "gemini-1.5-flash",
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},
{
"model": "gemini-1.5-pro",
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{
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{
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{
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{
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{
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"maxToken": 2000,
"type": "embedding"
}
]
}

4
packages/service/core/ai/config/provider/Grok.json Normal file
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{
"provider": "Grok",
"list": []
}

47
packages/service/core/ai/config/provider/Groq.json Normal file
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{
"provider": "Groq",
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{
"model": "llama-3.1-8b-instant",
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{
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"defaultConfig": {},
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}
]
}

166
packages/service/core/ai/config/provider/Hunyuan.json Normal file
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{
"provider": "Hunyuan",
"list": [
{
"model": "hunyuan-large",
"name": "hunyuan-large",
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{
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{
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{
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{
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{
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{
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"maxToken": 1024,
"type": "embedding"
}
]
}

49
packages/service/core/ai/config/provider/Intern.json Normal file
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{
"provider": "Intern",
"list": [
{
"model": "internlm2-pro-chat",
"name": "internlm2-pro-chat",
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{
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"fieldMap": {},
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}
]
}

4
packages/service/core/ai/config/provider/Meta.json Normal file
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{
"provider": "Meta",
"list": []
}

240
packages/service/core/ai/config/provider/MiniMax.json Normal file
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{
"provider": "MiniMax",
"list": [
{
"model": "MiniMax-Text-01",
"name": "MiniMax-Text-01",
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"defaultSystemChatPrompt": "",
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"usedInQueryExtension": true,
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"defaultConfig": {},
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},
{
"model": "abab6.5s-chat",
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"defaultConfig": {},
"fieldMap": {},
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},
{
"model": "speech-01-turbo",
"name": "speech-01-turbo",
"voices": [
{
"label": "male-qn-qingse",
"value": "male-qn-qingse"
},
{
"label": "male-qn-jingying",
"value": "male-qn-jingying"
},
{
"label": "male-qn-badao",
"value": "male-qn-badao"
},
{
"label": "male-qn-daxuesheng",
"value": "male-qn-daxuesheng"
},
{
"label": "female-shaonv",
"value": "female-shaonv"
},
{
"label": "female-yujie",
"value": "female-yujie"
},
{
"label": "female-chengshu",
"value": "female-chengshu"
},
{
"label": "female-tianmei",
"value": "female-tianmei"
},
{
"label": "presenter_male",
"value": "presenter_male"
},
{
"label": "presenter_female",
"value": "presenter_female"
},
{
"label": "audiobook_male_1",
"value": "audiobook_male_1"
},
{
"label": "audiobook_male_2",
"value": "audiobook_male_2"
},
{
"label": "audiobook_female_1",
"value": "audiobook_female_1"
},
{
"label": "audiobook_female_2",
"value": "audiobook_female_2"
},
{
"label": "male-qn-qingse-jingpin",
"value": "male-qn-qingse-jingpin"
},
{
"label": "male-qn-jingying-jingpin",
"value": "male-qn-jingying-jingpin"
},
{
"label": "male-qn-badao-jingpin",
"value": "male-qn-badao-jingpin"
},
{
"label": "male-qn-daxuesheng-jingpin",
"value": "male-qn-daxuesheng-jingpin"
},
{
"label": "female-shaonv-jingpin",
"value": "female-shaonv-jingpin"
},
{
"label": "female-yujie-jingpin",
"value": "female-yujie-jingpin"
},
{
"label": "female-chengshu-jingpin",
"value": "female-chengshu-jingpin"
},
{
"label": "female-tianmei-jingpin",
"value": "female-tianmei-jingpin"
},
{
"label": "clever_boy",
"value": "clever_boy"
},
{
"label": "cute_boy",
"value": "cute_boy"
},
{
"label": "lovely_girl",
"value": "lovely_girl"
},
{
"label": "cartoon_pig",
"value": "cartoon_pig"
},
{
"label": "bingjiao_didi",
"value": "bingjiao_didi"
},
{
"label": "junlang_nanyou",
"value": "junlang_nanyou"
},
{
"label": "chunzhen_xuedi",
"value": "chunzhen_xuedi"
},
{
"label": "lengdan_xiongzhang",
"value": "lengdan_xiongzhang"
},
{
"label": "badao_shaoye",
"value": "badao_shaoye"
},
{
"label": "tianxin_xiaoling",
"value": "tianxin_xiaoling"
},
{
"label": "qiaopi_mengmei",
"value": "qiaopi_mengmei"
},
{
"label": "wumei_yujie",
"value": "wumei_yujie"
},
{
"label": "diadia_xuemei",
"value": "diadia_xuemei"
},
{
"label": "danya_xuejie",
"value": "danya_xuejie"
},
{
"label": "Santa_Claus",
"value": "Santa_Claus"
},
{
"label": "Grinch",
"value": "Grinch"
},
{
"label": "Rudolph",
"value": "Rudolph"
},
{
"label": "Arnold",
"value": "Arnold"
},
{
"label": "Charming_Santa",
"value": "Charming_Santa"
},
{
"label": "Charming_Lady",
"value": "Charming_Lady"
},
{
"label": "Sweet_Girl",
"value": "Sweet_Girl"
},
{
"label": "Cute_Elf",
"value": "Cute_Elf"
},
{
"label": "Attractive_Girl",
"value": "Attractive_Girl"
},
{
"label": "Serene_Woman",
"value": "Serene_Woman"
}
],
"type": "tts"
}
]
}

93
packages/service/core/ai/config/provider/MistralAI.json Normal file
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{
"provider": "MistralAI",
"list": [
{
"model": "ministral-3b-latest",
"name": "Ministral-3b-latest",
"maxContext": 130000,
"maxResponse": 8000,
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"maxTemperature": 1.2,
"vision": false,
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"functionCall": false,
"defaultSystemChatPrompt": "",
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"usedInQueryExtension": true,
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"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "ministral-8b-latest",
"name": "Ministral-8b-latest",
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},
{
"model": "mistral-large-latest",
"name": "Mistral-large-latest",
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},
{
"model": "mistral-small-latest",
"name": "Mistral-small-latest",
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"functionCall": false,
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"usedInClassify": true,
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"usedInExtractFields": true,
"usedInQueryExtension": true,
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"defaultConfig": {},
"fieldMap": {},
"type": "llm"
}
]
}

4
packages/service/core/ai/config/provider/Moka.json Normal file
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{
"provider": "Moka",
"list": []
}

71
packages/service/core/ai/config/provider/Moonshot.json Normal file
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{
"provider": "Moonshot",
"list": [
{
"model": "moonshot-v1-8k",
"name": "moonshot-v1-8k",
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{
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{
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}
]
}

4
packages/service/core/ai/config/provider/Ollama.json Normal file
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{
"provider": "Ollama",
"list": []
}

252
packages/service/core/ai/config/provider/OpenAI.json Normal file
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{
"provider": "OpenAI",
"list": [
{
"model": "gpt-4o-mini",
"name": "GPT-4o-mini",
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{
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{
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{
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{
"model": "gpt-3.5-turbo",
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{
"model": "text-embedding-3-large",
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{
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{
"model": "text-embedding-ada-002",
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{
"model": "tts-1",
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"voices": [
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"value": "alloy"
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{
"label": "Echo",
"value": "echo"
},
{
"label": "Fable",
"value": "fable"
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{
"label": "Onyx",
"value": "onyx"
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{
"label": "Nova",
"value": "nova"
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{
"label": "Shimmer",
"value": "shimmer"
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{
"model": "whisper-1",
"name": "whisper-1",
"type": "stt"
}
]
}

4
packages/service/core/ai/config/provider/Other.json Normal file
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{
"provider": "Other",
"list": []
}

223
packages/service/core/ai/config/provider/Qwen.json Normal file
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{
"provider": "Qwen",
"list": [
{
"model": "qwen-turbo",
"name": "Qwen-turbo",
"maxContext": 128000,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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]
}

204
packages/service/core/ai/config/provider/Siliconflow.json Normal file
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{
"provider": "Siliconflow",
"list": [
{
"model": "Qwen/Qwen2.5-72B-Instruct",
"name": "Qwen/Qwen2.5-72B-Instruct",
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{
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{
"model": "deepseek-ai/DeepSeek-V2.5",
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{
"model": "BAAI/bge-m3",
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"type": "embedding"
},
{
"model": "FunAudioLLM/CosyVoice2-0.5B",
"name": "FunAudioLLM/CosyVoice2-0.5B",
"voices": [
{
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"value": "FunAudioLLM/CosyVoice2-0.5B:alex"
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{
"label": "anna",
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{
"label": "bella",
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{
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{
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{
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{
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{
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{
"label": "benjamin",
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{
"label": "charles",
"value": "RVC-Boss/GPT-SoVITS:charles"
},
{
"label": "claire",
"value": "RVC-Boss/GPT-SoVITS:claire"
},
{
"label": "david",
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{
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{
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{
"label": "alex",
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},
{
"label": "anna",
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},
{
"label": "bella",
"value": "fishaudio/fish-speech-1.5:bella"
},
{
"label": "benjamin",
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},
{
"label": "charles",
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},
{
"label": "claire",
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},
{
"label": "david",
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},
{
"label": "diana",
"value": "fishaudio/fish-speech-1.5:diana"
}
],
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},
{
"model": "FunAudioLLM/SenseVoiceSmall",
"name": "FunAudioLLM/SenseVoiceSmall",
"type": "stt"
},
{
"model": "BAAI/bge-reranker-v2-m3",
"name": "BAAI/bge-reranker-v2-m3",
"type": "rerank"
}
]
}

129
packages/service/core/ai/config/provider/SparkDesk.json Normal file
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{
"provider": "SparkDesk",
"list": [
{
"model": "lite",
"name": "SparkDesk-lite",
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},
{
"model": "generalv3",
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{
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{
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{
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"type": "llm"
}
]
}

308
packages/service/core/ai/config/provider/StepFun.json Normal file
View File

@@ -0,0 +1,308 @@
{
"provider": "StepFun",
"list": [
{
"model": "step-1-flash",
"name": "step-1-flash",
"maxContext": 8000,
"maxResponse": 4000,
"quoteMaxToken": 6000,
"maxTemperature": 2,
"vision": false,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "step-1-8k",
"name": "step-1-8k",
"maxContext": 8000,
"maxResponse": 8000,
"quoteMaxToken": 8000,
"maxTemperature": 2,
"vision": false,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "step-1-32k",
"name": "step-1-32k",
"maxContext": 32000,
"maxResponse": 8000,
"quoteMaxToken": 32000,
"maxTemperature": 2,
"vision": false,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "step-1-128k",
"name": "step-1-128k",
"maxContext": 128000,
"maxResponse": 8000,
"quoteMaxToken": 128000,
"maxTemperature": 2,
"vision": false,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "step-1-256k",
"name": "step-1-256k",
"maxContext": 256000,
"maxResponse": 8000,
"quoteMaxToken": 256000,
"maxTemperature": 2,
"vision": false,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "step-1o-vision-32k",
"name": "step-1o-vision-32k",
"maxContext": 32000,
"quoteMaxToken": 32000,
"maxResponse": 8000,
"maxTemperature": 2,
"vision": true,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "step-1v-8k",
"name": "step-1v-8k",
"maxContext": 8000,
"maxResponse": 8000,
"quoteMaxToken": 8000,
"maxTemperature": 2,
"vision": true,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "step-1v-32k",
"name": "step-1v-32k",
"maxContext": 32000,
"quoteMaxToken": 32000,
"maxResponse": 8000,
"maxTemperature": 2,
"vision": true,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "step-2-mini",
"name": "step-2-mini",
"maxContext": 8000,
"maxResponse": 4000,
"quoteMaxToken": 6000,
"maxTemperature": 2,
"vision": false,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "step-2-16k",
"name": "step-2-16k",
"maxContext": 16000,
"maxResponse": 4000,
"quoteMaxToken": 4000,
"maxTemperature": 2,
"vision": false,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "step-2-16k-exp",
"name": "step-2-16k-exp",
"maxContext": 16000,
"maxResponse": 4000,
"quoteMaxToken": 4000,
"maxTemperature": 2,
"vision": false,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "step-tts-mini",
"name": "step-tts-mini",
"voices": [
{
"label": "cixingnansheng",
"value": "cixingnansheng"
},
{
"label": "zhengpaiqingnian",
"value": "zhengpaiqingnian"
},
{
"label": "yuanqinansheng",
"value": "yuanqinansheng"
},
{
"label": "qingniandaxuesheng",
"value": "qingniandaxuesheng"
},
{
"label": "boyinnansheng",
"value": "boyinnansheng"
},
{
"label": "ruyananshi",
"value": "ruyananshi"
},
{
"label": "shenchennanyin",
"value": "shenchennanyin"
},
{
"label": "qinqienvsheng",
"value": "qinqienvsheng"
},
{
"label": "wenrounvsheng",
"value": "wenrounvsheng"
},
{
"label": "jilingshaonv",
"value": "jilingshaonv"
},
{
"label": "yuanqishaonv",
"value": "yuanqishaonv"
},
{
"label": "ruanmengnvsheng",
"value": "ruanmengnvsheng"
},
{
"label": "youyanvsheng",
"value": "youyanvsheng"
},
{
"label": "lengyanyujie",
"value": "lengyanyujie"
},
{
"label": "shuangkuaijiejie",
"value": "shuangkuaijiejie"
},
{
"label": "wenjingxuejie",
"value": "wenjingxuejie"
},
{
"label": "linjiajiejie",
"value": "linjiajiejie"
},
{
"label": "linjiameimei",
"value": "linjiameimei"
},
{
"label": "zhixingjiejie",
"value": "zhixingjiejie"
}
],
"type": "tts"
}
]
}

49
packages/service/core/ai/config/provider/Yi.json Normal file
View File

@@ -0,0 +1,49 @@
{
"provider": "Yi",
"list": [
{
"model": "yi-lightning",
"name": "yi-lightning",
"maxContext": 16000,
"maxResponse": 4000,
"quoteMaxToken": 12000,
"maxTemperature": 1,
"vision": false,
"toolChoice": false,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "yi-vision-v2",
"name": "yi-vision-v2",
"maxContext": 16000,
"maxResponse": 4000,
"quoteMaxToken": 12000,
"maxTemperature": 1,
"vision": true,
"toolChoice": false,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
}
]
}

6
packages/service/core/ai/config/rerank/bge-reranker-v2-m3.json
View File

@@ -1,6 +0,0 @@
{
"provider": "BAAI",
"model": "bge-reranker-v2-m3",
"name": "bge-reranker-v2-m3",
"charsPointsPrice": 0
}

21
packages/service/core/ai/config/schema.ts Normal file
View File

@@ -0,0 +1,21 @@
import { connectionMongo, getMongoModel } from '../../../common/mongo';
const { Schema } = connectionMongo;
import type { SystemModelSchemaType } from '../type';
const SystemModelSchema = new Schema({
model: {
type: String,
required: true,
unique: true
},
metadata: {
type: Object,
required: true,
default: {}
}
});
export const MongoSystemModel = getMongoModel<SystemModelSchemaType>(
'system_models',
SystemModelSchema
);

6
packages/service/core/ai/config/stt/whisper-1.json
View File

@@ -1,6 +0,0 @@
{
"provider": "OpenAI",
"model": "whisper-1",
"name": "whisper-1",
"charsPointsPrice": 0
}

32
packages/service/core/ai/config/tts/tts-1.json
View File

@@ -1,32 +0,0 @@
{
"provider": "OpenAI",
"model": "tts-1",
"name": "TTS1",
"charsPointsPrice": 0,
"voices": [
{
"label": "Alloy",
"value": "alloy"
},
{
"label": "Echo",
"value": "echo"
},
{
"label": "Fable",
"value": "fable"
},
{
"label": "Onyx",
"value": "onyx"
},
{
"label": "Nova",
"value": "nova"
},
{
"label": "Shimmer",
"value": "shimmer"
}
]
}

195
packages/service/core/ai/config/utils.ts Normal file
View File

@@ -0,0 +1,195 @@
import path from 'path';
import * as fs from 'fs';
import { SystemModelItemType } from '../type';
import { ModelTypeEnum } from '@fastgpt/global/core/ai/model';
import { MongoSystemModel } from './schema';
import {
LLMModelItemType,
EmbeddingModelItemType,
TTSModelType,
STTModelType,
ReRankModelItemType
} from '@fastgpt/global/core/ai/model.d';
import { debounce } from 'lodash';
import { ModelProviderType } from '@fastgpt/global/core/ai/provider';
import { findModelFromAlldata } from '../model';
import {
reloadFastGPTConfigBuffer,
updateFastGPTConfigBuffer
} from '../../../common/system/config/controller';
import { delay } from '@fastgpt/global/common/system/utils';
/*
TODO: 分优先级读取：
1. 有外部挂载目录，则读取外部的
2. 没有外部挂载目录，则读取本地的。然后试图拉取云端的进行覆盖。
*/
export const loadSystemModels = async (init = false) => {
const getProviderList = () => {
const currentFileUrl = new URL(import.meta.url);
const modelsPath = path.join(path.dirname(currentFileUrl.pathname), 'provider');
return fs.readdirSync(modelsPath) as string[];
};
const pushModel = (model: SystemModelItemType) => {
global.systemModelList.push(model);
if (model.isActive) {
global.systemActiveModelList.push(model);
if (model.type === ModelTypeEnum.llm) {
global.llmModelMap.set(model.model, model);
global.llmModelMap.set(model.name, model);
if (model.isDefault) {
global.systemDefaultModel.llm = model;
}
} else if (model.type === ModelTypeEnum.embedding) {
global.embeddingModelMap.set(model.model, model);
global.embeddingModelMap.set(model.name, model);
if (model.isDefault) {
global.systemDefaultModel.embedding = model;
}
} else if (model.type === ModelTypeEnum.tts) {
global.ttsModelMap.set(model.model, model);
global.ttsModelMap.set(model.name, model);
if (model.isDefault) {
global.systemDefaultModel.tts = model;
}
} else if (model.type === ModelTypeEnum.stt) {
global.sttModelMap.set(model.model, model);
global.sttModelMap.set(model.name, model);
if (model.isDefault) {
global.systemDefaultModel.stt = model;
}
} else if (model.type === ModelTypeEnum.rerank) {
global.reRankModelMap.set(model.model, model);
global.reRankModelMap.set(model.name, model);
if (model.isDefault) {
global.systemDefaultModel.rerank = model;
}
}
}
};
if (!init && global.systemModelList) return;
global.systemModelList = [];
global.systemActiveModelList = [];
global.llmModelMap = new Map<string, LLMModelItemType>();
global.embeddingModelMap = new Map<string, EmbeddingModelItemType>();
global.ttsModelMap = new Map<string, TTSModelType>();
global.sttModelMap = new Map<string, STTModelType>();
global.reRankModelMap = new Map<string, ReRankModelItemType>();
// @ts-ignore
global.systemDefaultModel = {};
try {
const dbModels = await MongoSystemModel.find({}).lean();
const providerList = getProviderList();
// System model
await Promise.all(
providerList.map(async (name) => {
const fileContent = (await import(`./provider/${name}`))?.default as {
provider: ModelProviderType;
list: SystemModelItemType[];
};
fileContent.list.forEach((fileModel) => {
const dbModel = dbModels.find((item) => item.model === fileModel.model);
const modelData: any = {
...fileModel,
...dbModel?.metadata,
provider: dbModel?.metadata?.provider || fileContent.provider,
type: dbModel?.metadata?.type || fileModel.type,
isCustom: false
};
pushModel(modelData);
});
})
);
// Custom model
dbModels.forEach((dbModel) => {
if (global.systemModelList.find((item) => item.model === dbModel.model)) return;
pushModel({
...dbModel.metadata,
isCustom: true
});
});
// Default model check
if (!global.systemDefaultModel.llm) {
global.systemDefaultModel.llm = Array.from(global.llmModelMap.values())[0];
}
if (!global.systemDefaultModel.embedding) {
global.systemDefaultModel.embedding = Array.from(global.embeddingModelMap.values())[0];
}
if (!global.systemDefaultModel.tts) {
global.systemDefaultModel.tts = Array.from(global.ttsModelMap.values())[0];
}
if (!global.systemDefaultModel.stt) {
global.systemDefaultModel.stt = Array.from(global.sttModelMap.values())[0];
}
if (!global.systemDefaultModel.rerank) {
global.systemDefaultModel.rerank = Array.from(global.reRankModelMap.values())[0];
}
console.log('Load models success', JSON.stringify(global.systemActiveModelList, null, 2));
} catch (error) {
console.error('Load models error', error);
// @ts-ignore
global.systemModelList = undefined;
}
};
export const getSystemModelConfig = async (model: string): Promise<SystemModelItemType> => {
const modelData = findModelFromAlldata(model);
if (!modelData) return Promise.reject('Model is not found');
if (modelData.isCustom) return Promise.reject('Custom model not data');
// Read file
const fileContent = (await import(`./provider/${modelData.provider}`))?.default as {
provider: ModelProviderType;
list: SystemModelItemType[];
};
const config = fileContent.list.find((item) => item.model === model);
if (!config) return Promise.reject('Model config is not found');
return {
...config,
provider: modelData.provider,
isCustom: false
};
};
export const watchSystemModelUpdate = () => {
const changeStream = MongoSystemModel.watch();
changeStream.on(
'change',
debounce(async () => {
try {
// Main node will reload twice
await loadSystemModels(true);
// All node reaload buffer
await reloadFastGPTConfigBuffer();
} catch (error) {}
}, 500)
);
};
// 更新完模型后，需要重载缓存
export const updatedReloadSystemModel = async () => {
// 1. 更新模型（所有节点都会触发）
await loadSystemModels(true);
// 2. 更新缓存（仅主节点触发）
await updateFastGPTConfigBuffer();
// 3. 延迟1秒，等待其他节点刷新
await delay(1000);
};

28
packages/service/core/ai/embedding/index.ts
View File

@@ -1,11 +1,11 @@
import { VectorModelItemType } from '@fastgpt/global/core/ai/model.d';
import { EmbeddingModelItemType } from '@fastgpt/global/core/ai/model.d';
import { getAIApi } from '../config';
import { countPromptTokens } from '../../../common/string/tiktoken/index';
import { EmbeddingTypeEnm } from '@fastgpt/global/core/ai/constants';
import { addLog } from '../../../common/system/log';
type GetVectorProps = {
model: VectorModelItemType;
model: EmbeddingModelItemType;
input: string;
type?: `${EmbeddingTypeEnm}`;
};
@@ -24,13 +24,23 @@ export async function getVectorsByText({ model, input, type }: GetVectorProps) {
// input text to vector
const result = await ai.embeddings
.create({
...model.defaultConfig,
...(type === EmbeddingTypeEnm.db && model.dbConfig),
...(type === EmbeddingTypeEnm.query && model.queryConfig),
model: model.model,
input: [input]
})
.create(
{
...model.defaultConfig,
...(type === EmbeddingTypeEnm.db && model.dbConfig),
...(type === EmbeddingTypeEnm.query && model.queryConfig),
model: model.model,
input: [input]
},
model.requestUrl && model.requestAuth
? {
path: model.requestUrl,
headers: {
Authorization: `Bearer ${model.requestAuth}`
}
}
: {}
)
.then(async (res) => {
if (!res.data) {
addLog.error('Embedding API is not responding', res);

127
packages/service/core/ai/functions/queryExtension.ts
View File

@@ -2,10 +2,12 @@ import { replaceVariable } from '@fastgpt/global/common/string/tools';
import { createChatCompletion } from '../config';
import { ChatItemType } from '@fastgpt/global/core/chat/type';
import { countGptMessagesTokens, countPromptTokens } from '../../../common/string/tiktoken/index';
import { chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
import { chats2GPTMessages } from '@fastgpt/global/core/chat/adapt';
import { getLLMModel } from '../model';
import { llmCompletionsBodyFormat } from '../utils';
import { addLog } from '../../../common/system/log';
import { filterGPTMessageByMaxContext } from '../../chat/utils';
import json5 from 'json5';
/*
query extension - 问题扩展
@@ -13,72 +15,73 @@ import { addLog } from '../../../common/system/log';
*/
const title = global.feConfigs?.systemTitle || 'FastAI';
const defaultPrompt = `作为一个向量检索助手，你的任务是结合历史记录，从不同角度，为“原问题”生成个不同版本的“检索词”，从而提高向量检索的语义丰富度，提高向量检索的精度。
const defaultPrompt = `## 你的任务
你作为一个向量检索助手，你的任务是结合历史记录，从不同角度，为“原问题”生成个不同版本的“检索词”，从而提高向量检索的语义丰富度，提高向量检索的精度。
生成的问题要求指向对象清晰明确，并与“原问题语言相同”。
参考 <Example></Example> 标中的示例来完成任务。
## 参考示例
<Example>
历史记录:
"""
null
"""
原问题: 介绍下剧情。
检索词: ["介绍下故事的背景。","故事的主题是什么？","介绍下故事的主要人物。"]
----------------
历史记录:
"""
Q: 对话背景。
A: 当前对话是关于 Nginx 的介绍和使用等。
user: 对话背景。
assistant: 当前对话是关于 Nginx 的介绍和使用等。
"""
原问题: 怎么下载
检索词: ["Nginx 如何下载？","下载 Nginx 需要什么条件？","有哪些渠道可以下载 Nginx？"]
----------------
历史记录:
"""
Q: 对话背景。
A: 当前对话是关于 Nginx 的介绍和使用等。
Q: 报错 "no connection"
A: 报错"no connection"可能是因为……
user: 对话背景。
assistant: 当前对话是关于 Nginx 的介绍和使用等。
user: 报错 "no connection"
assistant: 报错"no connection"可能是因为……
"""
原问题: 怎么解决
检索词: ["Nginx报错"no connection"如何解决？","造成'no connection'报错的原因。","Nginx提示'no connection'，要怎么办？"]
----------------
历史记录:
"""
Q: 护产假多少天?
A: 护产假的天数根据员工所在的城市而定。请提供您所在的城市，以便我回答您的问题。
user: How long is the maternity leave?
assistant: The number of days of maternity leave depends on the city in which the employee is located. Please provide your city so that I can answer your questions.
"""
原问题: 沈阳
检索词: ["沈阳的护产假多少天？","沈阳的护产假政策。","沈阳的护产假标准。"]
原问题: ShenYang
检索词: ["How many days is maternity leave in Shenyang?","Shenyang's maternity leave policy.","The standard of maternity leave in Shenyang."]
----------------
历史记录:
"""
Q: 作者是谁？
A: ${title} 的作者是 labring。
user: 作者是谁？
assistant: ${title} 的作者是 labring。
"""
原问题: Tell me about him
检索词: ["Introduce labring, the author of ${title}." ," Background information on author labring." "," Why does labring do ${title}?"]
----------------
历史记录:
"""
Q: 对话背景。
A: 关于 ${title} 的介绍和使用等问题。
user: 对话背景。
assistant: 关于 ${title} 的介绍和使用等问题。
"""
原问题: 你好。
检索词: ["你好"]
----------------
历史记录:
"""
Q: ${title} 如何收费？
A: ${title} 收费可以参考……
user: ${title} 如何收费？
assistant: ${title} 收费可以参考……
"""
原问题: 你知道 laf 么？
检索词: ["laf 的官网地址是多少？","laf 的使用教程。","laf 有什么特点和优势。"]
----------------
历史记录:
"""
Q: ${title} 的优势
A: 1. 开源
user: ${title} 的优势
assistant: 1. 开源
2. 简便
3. 扩展性强
"""
@@ -87,18 +90,20 @@ A: 1. 开源
----------------
历史记录:
"""
Q: 什么是 ${title}？
A: ${title} 是一个 RAG 平台。
Q: 什么是 Laf？
A: Laf 是一个云函数开发平台。
user: 什么是 ${title}？
assistant: ${title} 是一个 RAG 平台。
user: 什么是 Laf？
assistant: Laf 是一个云函数开发平台。
"""
原问题: 它们有什么关系？
检索词: ["${title}和Laf有什么关系？","介绍下${title}","介绍下Laf"]
</Example>
-----
## 输出要求
下面是正式的任务：
1. 输出格式为 JSON 数组，数组中每个元素为字符串。无需对输出进行任何解释。
2. 输出语言与原问题相同。原问题为中文则输出中文；原问题为英文则输出英文。
## 开始任务
历史记录:
"""
@@ -125,26 +130,39 @@ export const queryExtension = async ({
outputTokens: number;
}> => {
const systemFewShot = chatBg
? `Q: 对话背景。
A: ${chatBg}
? `user: 对话背景。
assistant: ${chatBg}
`
: '';
const historyFewShot = histories
.map((item) => {
const role = item.obj === 'Human' ? 'Q' : 'A';
return `${role}: ${chatValue2RuntimePrompt(item.value).text}`;
})
.join('\n');
const concatFewShot = `${systemFewShot}${historyFewShot}`.trim();
const modelData = getLLMModel(model);
const filterHistories = await filterGPTMessageByMaxContext({
messages: chats2GPTMessages({ messages: histories, reserveId: false }),
maxContext: modelData.maxContext - 1000
});
const historyFewShot = filterHistories
.map((item) => {
const role = item.role;
const content = item.content;
if ((role === 'user' || role === 'assistant') && content) {
if (typeof content === 'string') {
return `${role}: ${content}`;
} else {
return `${role}: ${content.map((item) => (item.type === 'text' ? item.text : '')).join('\n')}`;
}
}
})
.filter(Boolean)
.join('\n');
const concatFewShot = `${systemFewShot}${historyFewShot}`.trim();
const messages = [
{
role: 'user',
content: replaceVariable(defaultPrompt, {
query: `${query}`,
histories: concatFewShot
histories: concatFewShot || 'null'
})
}
] as any;
@@ -154,7 +172,7 @@ A: ${chatBg}
{
stream: false,
model: modelData.model,
temperature: 0.01,
temperature: 0.1,
messages
},
modelData
@@ -172,22 +190,41 @@ A: ${chatBg}
};
}
const start = answer.indexOf('[');
const end = answer.lastIndexOf(']');
if (start === -1 || end === -1) {
addLog.warn('Query extension failed, not a valid JSON', {
answer
});
return {
rawQuery: query,
extensionQueries: [],
model,
inputTokens: 0,
outputTokens: 0
};
}
// Intercept the content of [] and retain []
answer = answer.match(/\[.*?\]/)?.[0] || '';
answer = answer.replace(/\\"/g, '"');
const jsonStr = answer
.substring(start, end + 1)
.replace(/(\\n|\\)/g, '')
.replace(/ /g, '');
try {
const queries = JSON.parse(answer) as string[];
const queries = json5.parse(jsonStr) as string[];
return {
rawQuery: query,
extensionQueries: Array.isArray(queries) ? queries : [],
extensionQueries: (Array.isArray(queries) ? queries : []).slice(0, 5),
model,
inputTokens: await countGptMessagesTokens(messages),
outputTokens: await countPromptTokens(answer)
};
} catch (error) {
addLog.error(`Query extension error`, error);
addLog.warn('Query extension failed, not a valid JSON', {
answer
});
return {
rawQuery: query,
extensionQueries: [],

65
packages/service/core/ai/model.ts
View File

@@ -1,51 +1,52 @@
import { SystemModelItemType } from './type';
export const getDefaultLLMModel = () => global?.systemDefaultModel.llm!;
export const getLLMModel = (model?: string) => {
return (
global.llmModels.find((item) => item.model === model || item.name === model) ??
global.llmModels[0]
);
if (!model) return getDefaultLLMModel();
return global.llmModelMap.get(model) || getDefaultLLMModel();
};
export const getDatasetModel = (model?: string) => {
return (
global.llmModels
Array.from(global.llmModelMap.values())
?.filter((item) => item.datasetProcess)
?.find((item) => item.model === model || item.name === model) ?? global.llmModels[0]
?.find((item) => item.model === model || item.name === model) ?? getDefaultLLMModel()
);
};
export const getVectorModel = (model?: string) => {
return (
global.vectorModels.find((item) => item.model === model || item.name === model) ||
global.vectorModels[0]
);
export const getDefaultEmbeddingModel = () => global?.systemDefaultModel.embedding!;
export const getEmbeddingModel = (model?: string) => {
if (!model) return getDefaultEmbeddingModel();
return global.embeddingModelMap.get(model) || getDefaultEmbeddingModel();
};
export function getAudioSpeechModel(model?: string) {
return (
global.audioSpeechModels.find((item) => item.model === model || item.name === model) ||
global.audioSpeechModels[0]
);
export const getDefaultTTSModel = () => global?.systemDefaultModel.tts!;
export function getTTSModel(model?: string) {
if (!model) return getDefaultTTSModel();
return global.ttsModelMap.get(model) || getDefaultTTSModel();
}
export function getWhisperModel(model?: string) {
return global.whisperModel;
export const getDefaultSTTModel = () => global?.systemDefaultModel.stt!;
export function getSTTModel(model?: string) {
if (!model) return getDefaultSTTModel();
return global.sttModelMap.get(model) || getDefaultSTTModel();
}
export const getDefaultRerankModel = () => global?.systemDefaultModel.rerank!;
export function getReRankModel(model?: string) {
return global.reRankModels.find((item) => item.model === model);
if (!model) return getDefaultRerankModel();
return global.reRankModelMap.get(model) || getDefaultRerankModel();
}
export enum ModelTypeEnum {
llm = 'llm',
vector = 'vector',
audioSpeech = 'audioSpeech',
whisper = 'whisper',
rerank = 'rerank'
}
export const getModelMap = {
[ModelTypeEnum.llm]: getLLMModel,
[ModelTypeEnum.vector]: getVectorModel,
[ModelTypeEnum.audioSpeech]: getAudioSpeechModel,
[ModelTypeEnum.whisper]: getWhisperModel,
[ModelTypeEnum.rerank]: getReRankModel
export const findAIModel = (model: string): SystemModelItemType | undefined => {
return (
global.llmModelMap.get(model) ||
global.embeddingModelMap.get(model) ||
global.ttsModelMap.get(model) ||
global.sttModelMap.get(model) ||
global.reRankModelMap.get(model)
);
};
export const findModelFromAlldata = (model: string) => {
return global.systemModelList.find((item) => item.model === model);
};

17
packages/service/core/ai/rerank/index.ts
View File

@@ -1,5 +1,8 @@
import { addLog } from '../../../common/system/log';
import { POST } from '../../../common/api/serverRequest';
import { getDefaultRerankModel } from '../model';
import { getAxiosConfig } from '../config';
import { ReRankModelItemType } from '@fastgpt/global/core/ai/model.d';
type PostReRankResponse = {
id: string;
@@ -11,21 +14,23 @@ type PostReRankResponse = {
type ReRankCallResult = { id: string; score?: number }[];
export function reRankRecall({
model = getDefaultRerankModel(),
query,
documents
}: {
model?: ReRankModelItemType;
query: string;
documents: { id: string; text: string }[];
}): Promise<ReRankCallResult> {
const model = global.reRankModels[0];
if (!model || !model?.requestUrl) {
if (!model) {
return Promise.reject('no rerank model');
}
const { baseUrl, authorization } = getAxiosConfig({});
let start = Date.now();
return POST<PostReRankResponse>(
model.requestUrl,
model.requestUrl ? model.requestUrl : `${baseUrl}/rerank`,
{
model: model.model,
query,
@@ -33,7 +38,7 @@ export function reRankRecall({
},
{
headers: {
Authorization: `Bearer ${model.requestAuth}`
Authorization: model.requestAuth ? model.requestAuth : authorization
},
timeout: 30000
}
@@ -53,6 +58,6 @@ export function reRankRecall({
.catch((err) => {
addLog.error('rerank error', err);
return [];
return Promise.reject(err);
});
}

42
packages/service/core/ai/type.d.ts vendored Normal file
View File

@@ -0,0 +1,42 @@
import { ModelTypeEnum } from '@fastgpt/global/core/ai/model';
import {
STTModelType,
ReRankModelItemType,
TTSModelType,
EmbeddingModelItemType,
LLMModelItemType
} from '@fastgpt/global/core/ai/model.d';
export type SystemModelSchemaType = {
_id: string;
model: string;
metadata: SystemModelItemType;
};
export type SystemModelItemType =
| LLMModelItemType
| EmbeddingModelItemType
| TTSModelType
| STTModelType
| ReRankModelItemType;
export type SystemDefaultModelType = {
[ModelTypeEnum.llm]?: LLMModelItemType;
[ModelTypeEnum.embedding]?: EmbeddingModelItemType;
[ModelTypeEnum.tts]?: TTSModelType;
[ModelTypeEnum.stt]?: STTModelType;
[ModelTypeEnum.rerank]?: ReRankModelItemType;
};
declare global {
var systemModelList: SystemModelItemType[];
// var systemModelMap: Map<string, SystemModelItemType>;
var llmModelMap: Map<string, LLMModelItemType>;
var embeddingModelMap: Map<string, EmbeddingModelItemType>;
var ttsModelMap: Map<string, TTSModelType>;
var sttModelMap: Map<string, STTModelType>;
var reRankModelMap: Map<string, ReRankModelItemType>;
var systemActiveModelList: SystemModelItemType[];
var systemDefaultModel: SystemDefaultModelType;
}

31
packages/service/core/ai/utils.ts
View File

@@ -2,32 +2,23 @@ import { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
import {
ChatCompletionCreateParamsNonStreaming,
ChatCompletionCreateParamsStreaming,
ChatCompletionMessageParam
StreamChatType
} from '@fastgpt/global/core/ai/type';
import { countGptMessagesTokens } from '../../common/string/tiktoken';
import { getLLMModel } from './model';
export const computedMaxToken = async ({
/*
Count response max token
*/
export const computedMaxToken = ({
maxToken,
model,
filterMessages = []
model
}: {
maxToken?: number;
model: LLMModelItemType;
filterMessages: ChatCompletionMessageParam[];
}) => {
if (maxToken === undefined) return;
maxToken = Math.min(maxToken, model.maxResponse);
const tokensLimit = model.maxContext;
/* count response max token */
const promptsToken = await countGptMessagesTokens(filterMessages);
maxToken = promptsToken + maxToken > tokensLimit ? tokensLimit - promptsToken : maxToken;
if (maxToken <= 0) {
maxToken = 200;
}
return maxToken;
};
@@ -39,6 +30,7 @@ export const computedTemperature = ({
model: LLMModelItemType;
temperature: number;
}) => {
if (typeof model.maxTemperature !== 'number') return undefined;
temperature = +(model.maxTemperature * (temperature / 10)).toFixed(2);
temperature = Math.max(temperature, 0.01);
@@ -87,3 +79,12 @@ export const llmCompletionsBodyFormat = <T extends CompletionsBodyType>(
return requestBody as InferCompletionsBody<T>;
};
export const llmStreamResponseToText = async (response: StreamChatType) => {
let answer = '';
for await (const part of response) {
const content = part.choices?.[0]?.delta?.content || '';
answer += content;
}
return answer;
};

539
packages/service/core/chat/utils.ts
View File

@@ -1,6 +1,9 @@
import { countGptMessagesTokens } from '../../common/string/tiktoken/index';
import type {
ChatCompletionAssistantMessageParam,
ChatCompletionContentPart,
ChatCompletionContentPartRefusal,
ChatCompletionContentPartText,
ChatCompletionMessageParam,
SdkChatCompletionMessageParam
} from '@fastgpt/global/core/ai/type.d';
@@ -11,36 +14,19 @@ import { serverRequestBaseUrl } from '../../common/api/serverRequest';
import { i18nT } from '../../../web/i18n/utils';
import { addLog } from '../../common/system/log';
export const filterGPTMessageByMaxTokens = async ({
export const filterGPTMessageByMaxContext = async ({
messages = [],
maxTokens
maxContext
}: {
messages: ChatCompletionMessageParam[];
maxTokens: number;
maxContext: number;
}) => {
if (!Array.isArray(messages)) {
return [];
}
const rawTextLen = messages.reduce((sum, item) => {
if (typeof item.content === 'string') {
return sum + item.content.length;
}
if (Array.isArray(item.content)) {
return (
sum +
item.content.reduce((sum, item) => {
if (item.type === 'text') {
return sum + item.text.length;
}
return sum;
}, 0)
);
}
return sum;
}, 0);
// If the text length is less than half of the maximum token, no calculation is required
if (rawTextLen < maxTokens * 0.5) {
if (messages.length < 4) {
return messages;
}
@@ -52,7 +38,7 @@ export const filterGPTMessageByMaxTokens = async ({
const chatPrompts: ChatCompletionMessageParam[] = messages.slice(chatStartIndex);
// reduce token of systemPrompt
maxTokens -= await countGptMessagesTokens(systemPrompts);
maxContext -= await countGptMessagesTokens(systemPrompts);
// Save the last chat prompt(question)
const question = chatPrompts.pop();
@@ -70,9 +56,9 @@ export const filterGPTMessageByMaxTokens = async ({
}
const tokens = await countGptMessagesTokens([assistant, user]);
maxTokens -= tokens;
maxContext -= tokens;
/* 整体 tokens 超出范围，截断 */
if (maxTokens < 0) {
if (maxContext < 0) {
break;
}
@@ -102,223 +88,324 @@ export const loadRequestMessages = async ({
useVision?: boolean;
origin?: string;
}) => {
// Load image to base64
const loadImageToBase64 = async (messages: ChatCompletionContentPart[]) => {
return Promise.all(
messages.map(async (item) => {
if (item.type === 'image_url') {
// Remove url origin
const imgUrl = (() => {
if (origin && item.image_url.url.startsWith(origin)) {
return item.image_url.url.replace(origin, '');
}
return item.image_url.url;
})();
// base64 image
if (imgUrl.startsWith('data:image/')) {
return item;
}
try {
// If imgUrl is a local path, load image from local, and set url to base64
if (imgUrl.startsWith('/') || process.env.MULTIPLE_DATA_TO_BASE64 === 'true') {
addLog.debug('Load image from local server', {
baseUrl: serverRequestBaseUrl,
requestUrl: imgUrl
});
const response = await axios.get(imgUrl, {
baseURL: serverRequestBaseUrl,
responseType: 'arraybuffer',
proxy: false
});
const base64 = Buffer.from(response.data, 'binary').toString('base64');
const imageType =
getFileContentTypeFromHeader(response.headers['content-type']) ||
guessBase64ImageType(base64);
return {
...item,
image_url: {
...item.image_url,
url: `data:${imageType};base64,${base64}`
}
};
}
// 检查下这个图片是否可以被访问，如果不行的话，则过滤掉
const response = await axios.head(imgUrl, {
timeout: 10000
});
if (response.status < 200 || response.status >= 400) {
addLog.info(`Filter invalid image: ${imgUrl}`);
return;
}
} catch (error) {
return;
}
}
return item;
})
).then((res) => res.filter(Boolean) as ChatCompletionContentPart[]);
const replaceLinkUrl = (text: string) => {
const baseURL = process.env.FE_DOMAIN;
if (!baseURL) return text;
// 匹配 /api/system/img/xxx.xx 的图片链接，并追加 baseURL
return text.replace(/(\/api\/system\/img\/[^\s.]*\.[^\s]*)/g, (match, p1) => `${baseURL}${p1}`);
};
// Split question text and image
const parseStringWithImages = (input: string): ChatCompletionContentPart[] => {
if (!useVision || input.length > 500) {
return [{ type: 'text', text: input || '' }];
const parseSystemMessage = (
content: string | ChatCompletionContentPartText[]
): string | ChatCompletionContentPartText[] | undefined => {
if (typeof content === 'string') {
if (!content) return;
return replaceLinkUrl(content);
}
// 正则表达式匹配图片URL
const imageRegex =
/(https?:\/\/[^\s/$.?#].[^\s]*\.(?:png|jpe?g|gif|webp|bmp|tiff?|svg|ico|heic|avif))/gi;
const result: ChatCompletionContentPart[] = [];
// 提取所有HTTPS图片URL并添加到result开头
const httpsImages = [...new Set(Array.from(input.matchAll(imageRegex), (m) => m[0]))];
httpsImages.forEach((url) => {
result.push({
type: 'image_url',
image_url: {
url: url
}
});
});
// Too many images return text
if (httpsImages.length > 4) {
return [{ type: 'text', text: input || '' }];
}
// 添加原始input作为文本
result.push({ type: 'text', text: input });
return result;
const arrayContent = content
.filter((item) => item.text)
.map((item) => ({ ...item, text: replaceLinkUrl(item.text) }));
if (arrayContent.length === 0) return;
return arrayContent;
};
// Parse user content(text and img) Store history => api messages
const parseUserContent = async (content: string | ChatCompletionContentPart[]) => {
if (typeof content === 'string') {
return loadImageToBase64(parseStringWithImages(content));
}
const result = await Promise.all(
content.map(async (item) => {
if (item.type === 'text') return parseStringWithImages(item.text);
if (item.type === 'file_url') return; // LLM not support file_url
if (!item.image_url.url) return item;
return item;
})
);
return loadImageToBase64(result.flat().filter(Boolean) as ChatCompletionContentPart[]);
};
// format GPT messages, concat text messages
const clearInvalidMessages = (messages: ChatCompletionMessageParam[]) => {
return messages
.map((item) => {
if (item.role === ChatCompletionRequestMessageRoleEnum.System && !item.content) {
return;
}
if (item.role === ChatCompletionRequestMessageRoleEnum.User) {
if (item.content === undefined) return;
if (typeof item.content === 'string') {
return {
...item,
content: item.content.trim()
};
}
// array
if (item.content.length === 0) return;
if (item.content.length === 1 && item.content[0].type === 'text') {
return {
...item,
content: item.content[0].text
};
}
}
if (item.role === ChatCompletionRequestMessageRoleEnum.Assistant) {
if (item.content === undefined && !item.tool_calls && !item.function_call) return;
}
return item;
})
.filter(Boolean) as ChatCompletionMessageParam[];
};
/*
Merge data for some consecutive roles
1. Contiguous assistant and both have content, merge content
*/
const mergeConsecutiveMessages = (
messages: ChatCompletionMessageParam[]
): ChatCompletionMessageParam[] => {
return messages.reduce((mergedMessages: ChatCompletionMessageParam[], currentMessage) => {
const lastMessage = mergedMessages[mergedMessages.length - 1];
if (
lastMessage &&
currentMessage.role === ChatCompletionRequestMessageRoleEnum.Assistant &&
lastMessage.role === ChatCompletionRequestMessageRoleEnum.Assistant &&
typeof lastMessage.content === 'string' &&
typeof currentMessage.content === 'string'
) {
lastMessage.content += currentMessage ? `\n${currentMessage.content}` : '';
} else {
mergedMessages.push(currentMessage);
// Split question text and image
const parseStringWithImages = (input: string): ChatCompletionContentPart[] => {
if (!useVision || input.length > 500) {
return [{ type: 'text', text: input }];
}
return mergedMessages;
}, []);
// 正则表达式匹配图片URL
const imageRegex =
/(https?:\/\/[^\s/$.?#].[^\s]*\.(?:png|jpe?g|gif|webp|bmp|tiff?|svg|ico|heic|avif))/gi;
const result: ChatCompletionContentPart[] = [];
// 提取所有HTTPS图片URL并添加到result开头
const httpsImages = [...new Set(Array.from(input.matchAll(imageRegex), (m) => m[0]))];
httpsImages.forEach((url) => {
result.push({
type: 'image_url',
image_url: {
url: url
}
});
});
// Too many images return text
if (httpsImages.length > 4) {
return [{ type: 'text', text: input }];
}
// 添加原始input作为文本
result.push({ type: 'text', text: input });
return result;
};
// Load image to base64
const loadUserContentImage = async (content: ChatCompletionContentPart[]) => {
return Promise.all(
content.map(async (item) => {
if (item.type === 'image_url') {
// Remove url origin
const imgUrl = (() => {
if (origin && item.image_url.url.startsWith(origin)) {
return item.image_url.url.replace(origin, '');
}
return item.image_url.url;
})();
// base64 image
if (imgUrl.startsWith('data:image/')) {
return item;
}
try {
// If imgUrl is a local path, load image from local, and set url to base64
if (imgUrl.startsWith('/') || process.env.MULTIPLE_DATA_TO_BASE64 === 'true') {
addLog.debug('Load image from local server', {
baseUrl: serverRequestBaseUrl,
requestUrl: imgUrl
});
const response = await axios.get(imgUrl, {
baseURL: serverRequestBaseUrl,
responseType: 'arraybuffer',
proxy: false
});
const base64 = Buffer.from(response.data, 'binary').toString('base64');
const imageType =
getFileContentTypeFromHeader(response.headers['content-type']) ||
guessBase64ImageType(base64);
return {
...item,
image_url: {
...item.image_url,
url: `data:${imageType};base64,${base64}`
}
};
}
// 检查下这个图片是否可以被访问，如果不行的话，则过滤掉
const response = await axios.head(imgUrl, {
timeout: 10000
});
if (response.status < 200 || response.status >= 400) {
addLog.info(`Filter invalid image: ${imgUrl}`);
return;
}
} catch (error) {
return;
}
}
return item;
})
).then((res) => res.filter(Boolean) as ChatCompletionContentPart[]);
};
if (content === undefined) return;
if (typeof content === 'string') {
if (content === '') return;
const loadImageContent = await loadUserContentImage(parseStringWithImages(content));
if (loadImageContent.length === 0) return;
return loadImageContent;
}
const result = (
await Promise.all(
content.map(async (item) => {
if (item.type === 'text') {
if (item.text) return parseStringWithImages(item.text);
return;
}
if (item.type === 'file_url') return; // LLM not support file_url
if (item.type === 'image_url') {
// close vision, remove image_url
if (!useVision) return;
// remove empty image_url
if (!item.image_url.url) return;
}
return item;
})
)
)
.flat()
.filter(Boolean) as ChatCompletionContentPart[];
const loadImageContent = await loadUserContentImage(result);
if (loadImageContent.length === 0) return;
return loadImageContent;
};
const formatAssistantItem = (item: ChatCompletionAssistantMessageParam) => {
return {
role: item.role,
content: item.content,
function_call: item.function_call,
name: item.name,
refusal: item.refusal,
tool_calls: item.tool_calls
};
};
const parseAssistantContent = (
content:
| string
| (ChatCompletionContentPartText | ChatCompletionContentPartRefusal)[]
| null
| undefined
) => {
if (typeof content === 'string') {
return content || '';
}
// 交互节点
if (!content) return '';
const result = content.filter((item) => item?.type === 'text');
if (result.length === 0) return '';
return result.map((item) => item.text).join('\n');
};
if (messages.length === 0) {
return Promise.reject(i18nT('common:core.chat.error.Messages empty'));
}
// filter messages file
const filterMessages = messages.map((item) => {
// If useVision=false, only retain text.
if (
item.role === ChatCompletionRequestMessageRoleEnum.User &&
Array.isArray(item.content) &&
!useVision
) {
return {
...item,
content: item.content.filter((item) => item.type === 'text')
};
}
// 合并相邻 role 的内容，只保留一个 role， content 变成数组。 assistant 的话，工具调用不合并。
const mergeMessages = ((messages: ChatCompletionMessageParam[]): ChatCompletionMessageParam[] => {
return messages.reduce((mergedMessages: ChatCompletionMessageParam[], currentMessage) => {
const lastMessage = mergedMessages[mergedMessages.length - 1];
return item;
});
const loadMessages = (await Promise.all(
filterMessages.map(async (item) => {
if (item.role === ChatCompletionRequestMessageRoleEnum.User) {
return {
...item,
content: await parseUserContent(item.content)
};
} else if (item.role === ChatCompletionRequestMessageRoleEnum.Assistant) {
// remove invalid field
return {
role: item.role,
content: item.content,
function_call: item.function_call,
name: item.name,
refusal: item.refusal,
tool_calls: item.tool_calls
};
} else {
return item;
if (!lastMessage) {
return [currentMessage];
}
})
)) as ChatCompletionMessageParam[];
return mergeConsecutiveMessages(
clearInvalidMessages(loadMessages)
) as SdkChatCompletionMessageParam[];
if (
lastMessage.role === ChatCompletionRequestMessageRoleEnum.System &&
currentMessage.role === ChatCompletionRequestMessageRoleEnum.System
) {
const lastContent: ChatCompletionContentPartText[] = Array.isArray(lastMessage.content)
? lastMessage.content
: [{ type: 'text', text: lastMessage.content || '' }];
const currentContent: ChatCompletionContentPartText[] = Array.isArray(
currentMessage.content
)
? currentMessage.content
: [{ type: 'text', text: currentMessage.content || '' }];
lastMessage.content = [...lastContent, ...currentContent];
} // Handle user messages
else if (
lastMessage.role === ChatCompletionRequestMessageRoleEnum.User &&
currentMessage.role === ChatCompletionRequestMessageRoleEnum.User
) {
const lastContent: ChatCompletionContentPart[] = Array.isArray(lastMessage.content)
? lastMessage.content
: [{ type: 'text', text: lastMessage.content }];
const currentContent: ChatCompletionContentPart[] = Array.isArray(currentMessage.content)
? currentMessage.content
: [{ type: 'text', text: currentMessage.content }];
lastMessage.content = [...lastContent, ...currentContent];
} else if (
lastMessage.role === ChatCompletionRequestMessageRoleEnum.Assistant &&
currentMessage.role === ChatCompletionRequestMessageRoleEnum.Assistant
) {
// Content 不为空的对象，或者是交互节点
if (
(typeof lastMessage.content === 'string' ||
Array.isArray(lastMessage.content) ||
lastMessage.interactive) &&
(typeof currentMessage.content === 'string' ||
Array.isArray(currentMessage.content) ||
currentMessage.interactive)
) {
const lastContent: (ChatCompletionContentPartText | ChatCompletionContentPartRefusal)[] =
Array.isArray(lastMessage.content)
? lastMessage.content
: [{ type: 'text', text: lastMessage.content || '' }];
const currentContent: (
| ChatCompletionContentPartText
| ChatCompletionContentPartRefusal
)[] = Array.isArray(currentMessage.content)
? currentMessage.content
: [{ type: 'text', text: currentMessage.content || '' }];
lastMessage.content = [...lastContent, ...currentContent];
} else {
// 有其中一个没有 content，说明不是连续的文本输出
mergedMessages.push(currentMessage);
}
} else {
mergedMessages.push(currentMessage);
}
return mergedMessages;
}, []);
})(messages);
const loadMessages = (
await Promise.all(
mergeMessages.map(async (item, i) => {
if (item.role === ChatCompletionRequestMessageRoleEnum.System) {
const content = parseSystemMessage(item.content);
if (!content) return;
return {
...item,
content
};
} else if (item.role === ChatCompletionRequestMessageRoleEnum.User) {
const content = await parseUserContent(item.content);
if (!content) {
return {
...item,
content: 'null'
};
}
const formatContent = (() => {
if (Array.isArray(content) && content.length === 1 && content[0].type === 'text') {
return content[0].text;
}
return content;
})();
return {
...item,
content: formatContent
};
} else if (item.role === ChatCompletionRequestMessageRoleEnum.Assistant) {
if (item.tool_calls || item.function_call) {
return formatAssistantItem(item);
}
const parseContent = parseAssistantContent(item.content);
// 如果内容为空，且前后不再是 assistant，需要补充成 null，避免丢失 user-assistant 的交互
const formatContent = (() => {
const lastItem = mergeMessages[i - 1];
const nextItem = mergeMessages[i + 1];
if (
parseContent === '' &&
(lastItem?.role === ChatCompletionRequestMessageRoleEnum.Assistant ||
nextItem?.role === ChatCompletionRequestMessageRoleEnum.Assistant)
) {
return;
}
return parseContent || 'null';
})();
if (!formatContent) return;
return {
...formatAssistantItem(item),
content: formatContent
};
} else {
return item;
}
})
)
).filter(Boolean) as ChatCompletionMessageParam[];
return loadMessages as SdkChatCompletionMessageParam[];
};

4
packages/service/core/dataset/collection/controller.ts
View File

@@ -19,7 +19,7 @@ import { predictDataLimitLength } from '../../../../global/core/dataset/utils';
import { mongoSessionRun } from '../../../common/mongo/sessionRun';
import { createTrainingUsage } from '../../../support/wallet/usage/controller';
import { UsageSourceEnum } from '@fastgpt/global/support/wallet/usage/constants';
import { getLLMModel, getVectorModel } from '../../ai/model';
import { getLLMModel, getEmbeddingModel } from '../../ai/model';
import { pushDataListToTrainingQueue } from '../training/controller';
import { MongoImage } from '../../../common/file/image/schema';
import { hashStr } from '@fastgpt/global/common/string/tools';
@@ -93,7 +93,7 @@ export const createCollectionAndInsertData = async ({
tmbId,
appName: usageName,
billSource: UsageSourceEnum.training,
vectorModel: getVectorModel(dataset.vectorModel)?.name,
vectorModel: getEmbeddingModel(dataset.vectorModel)?.name,
agentModel: getLLMModel(dataset.agentModel)?.name,
session
});

13
packages/service/core/dataset/data/dataTextSchema.ts
View File

@@ -33,7 +33,18 @@ const DatasetDataTextSchema = new Schema({
});
try {
DatasetDataTextSchema.index({ teamId: 1, datasetId: 1, fullTextToken: 'text' });
DatasetDataTextSchema.index(
{ teamId: 1, datasetId: 1, fullTextToken: 'text' },
{
name: 'teamId_1_datasetId_1_fullTextToken_text',
default_language: 'none',
collation: {
locale: 'simple', // 使用简单匹配规则
strength: 2, // 忽略大小写
caseLevel: false // 进一步确保大小写不敏感
}
}
);
DatasetDataTextSchema.index({ dataId: 1 }, { unique: true });
} catch (error) {
console.log(error);

122
packages/service/core/dataset/search/controller.ts
View File

@@ -5,10 +5,9 @@ import {
} from '@fastgpt/global/core/dataset/constants';
import { recallFromVectorStore } from '../../../common/vectorStore/controller';
import { getVectorsByText } from '../../ai/embedding';
import { getVectorModel } from '../../ai/model';
import { getEmbeddingModel, getDefaultRerankModel } from '../../ai/model';
import { MongoDatasetData } from '../data/schema';
import {
DatasetDataSchemaType,
DatasetDataTextSchemaType,
SearchDataResponseItemType
} from '@fastgpt/global/core/dataset/type';
@@ -67,7 +66,7 @@ export async function searchDatasetData(props: SearchDatasetDataProps) {
/* init params */
searchMode = DatasetSearchModeMap[searchMode] ? searchMode : DatasetSearchModeEnum.embedding;
usingReRank = usingReRank && global.reRankModels.length > 0;
usingReRank = usingReRank && !!getDefaultRerankModel();
// Compatible with topk limit
let set = new Set<string>();
@@ -253,7 +252,7 @@ export async function searchDatasetData(props: SearchDatasetDataProps) {
filterCollectionIdList?: string[];
}) => {
const { vectors, tokens } = await getVectorsByText({
model: getVectorModel(model),
model: getEmbeddingModel(model),
input: query,
type: 'query'
});
@@ -348,119 +347,6 @@ export async function searchDatasetData(props: SearchDatasetDataProps) {
};
}
const searchResults = (
await Promise.all(
datasetIds.map(async (id) => {
return MongoDatasetData.aggregate(
[
{
$match: {
teamId: new Types.ObjectId(teamId),
datasetId: new Types.ObjectId(id),
$text: { $search: jiebaSplit({ text: query }) },
...(filterCollectionIdList
? {
collectionId: {
$in: filterCollectionIdList.map((id) => new Types.ObjectId(id))
}
}
: {}),
...(forbidCollectionIdList && forbidCollectionIdList.length > 0
? {
collectionId: {
$nin: forbidCollectionIdList.map((id) => new Types.ObjectId(id))
}
}
: {})
}
},
{
$sort: {
score: { $meta: 'textScore' }
}
},
{
$limit: limit
},
{
$project: {
_id: 1,
datasetId: 1,
collectionId: 1,
updateTime: 1,
q: 1,
a: 1,
chunkIndex: 1,
score: { $meta: 'textScore' }
}
}
],
{
...readFromSecondary
}
);
})
)
).flat() as (DatasetDataSchemaType & { score: number })[];
// Get data and collections
const collections = await MongoDatasetCollection.find(
{
_id: { $in: searchResults.map((item) => item.collectionId) }
},
'_id name fileId rawLink externalFileId externalFileUrl',
{ ...readFromSecondary }
).lean();
return {
fullTextRecallResults: searchResults
.map((data, index) => {
const collection = collections.find(
(col) => String(col._id) === String(data.collectionId)
);
if (!collection) {
console.log('Collection is not found', data);
return;
}
return {
id: String(data._id),
datasetId: String(data.datasetId),
collectionId: String(data.collectionId),
updateTime: data.updateTime,
q: data.q,
a: data.a,
chunkIndex: data.chunkIndex,
indexes: data.indexes,
...getCollectionSourceData(collection),
score: [{ type: SearchScoreTypeEnum.fullText, value: data.score ?? 0, index }]
};
})
.filter(Boolean) as SearchDataResponseItemType[],
tokenLen: 0
};
};
const fullTextRecall2 = async ({
query,
limit,
filterCollectionIdList,
forbidCollectionIdList
}: {
query: string;
limit: number;
filterCollectionIdList?: string[];
forbidCollectionIdList: string[];
}): Promise<{
fullTextRecallResults: SearchDataResponseItemType[];
tokenLen: number;
}> => {
if (limit === 0) {
return {
fullTextRecallResults: [],
tokenLen: 0
};
}
const searchResults = (
await Promise.all(
datasetIds.map(async (id) => {
@@ -637,7 +523,7 @@ export async function searchDatasetData(props: SearchDatasetDataProps) {
filterCollectionIdList
}),
// FullText tmp
fullTextRecall2({
fullTextRecall({
query,
limit: fullTextLimit,
filterCollectionIdList,

4
packages/service/core/dataset/training/controller.ts
View File

@@ -7,7 +7,7 @@ import type {
import { TrainingModeEnum } from '@fastgpt/global/core/dataset/constants';
import { simpleText } from '@fastgpt/global/common/string/tools';
import { ClientSession } from '../../../common/mongo';
import { getLLMModel, getVectorModel } from '../../ai/model';
import { getLLMModel, getEmbeddingModel } from '../../ai/model';
import { addLog } from '../../../common/system/log';
import { getCollectionWithDataset } from '../controller';
import { mongoSessionRun } from '../../../common/mongo/sessionRun';
@@ -70,7 +70,7 @@ export async function pushDataListToTrainingQueue({
if (!agentModelData) {
return Promise.reject(`File model ${agentModel} is inValid`);
}
const vectorModelData = getVectorModel(vectorModel);
const vectorModelData = getEmbeddingModel(vectorModel);
if (!vectorModelData) {
return Promise.reject(`Vector model ${vectorModel} is inValid`);
}

3
packages/service/core/workflow/dispatch/agent/classifyQuestion.ts
View File

@@ -13,7 +13,7 @@ import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/
import { replaceVariable } from '@fastgpt/global/common/string/tools';
import { Prompt_CQJson } from '@fastgpt/global/core/ai/prompt/agent';
import { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
import { ModelTypeEnum, getLLMModel } from '../../../ai/model';
import { getLLMModel } from '../../../ai/model';
import { getHistories } from '../utils';
import { formatModelChars2Points } from '../../../../support/wallet/usage/utils';
import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
@@ -22,6 +22,7 @@ import { getHandleId } from '@fastgpt/global/core/workflow/utils';
import { loadRequestMessages } from '../../../chat/utils';
import { llmCompletionsBodyFormat } from '../../../ai/utils';
import { addLog } from '../../../../common/system/log';
import { ModelTypeEnum } from '../../../../../global/core/ai/model';
type Props = ModuleDispatchProps<{
[NodeInputKeyEnum.aiModel]: string;

9
packages/service/core/workflow/dispatch/agent/extract.ts
View File

@@ -1,5 +1,5 @@
import { chats2GPTMessages } from '@fastgpt/global/core/chat/adapt';
import { filterGPTMessageByMaxTokens, loadRequestMessages } from '../../../chat/utils';
import { filterGPTMessageByMaxContext, loadRequestMessages } from '../../../chat/utils';
import type { ChatItemType } from '@fastgpt/global/core/chat/type.d';
import {
countMessagesTokens,
@@ -16,7 +16,7 @@ import { Prompt_ExtractJson } from '@fastgpt/global/core/ai/prompt/agent';
import { replaceVariable, sliceJsonStr } from '@fastgpt/global/common/string/tools';
import { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
import { getHistories } from '../utils';
import { ModelTypeEnum, getLLMModel } from '../../../ai/model';
import { getLLMModel } from '../../../ai/model';
import { formatModelChars2Points } from '../../../../support/wallet/usage/utils';
import json5 from 'json5';
import {
@@ -28,6 +28,7 @@ import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/co
import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
import { chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
import { llmCompletionsBodyFormat } from '../../../ai/utils';
import { ModelTypeEnum } from '../../../../../global/core/ai/model';
type Props = ModuleDispatchProps<{
[NodeInputKeyEnum.history]?: ChatItemType[];
@@ -174,9 +175,9 @@ ${description ? `- ${description}` : ''}
}
];
const adaptMessages = chats2GPTMessages({ messages, reserveId: false });
const filterMessages = await filterGPTMessageByMaxTokens({
const filterMessages = await filterGPTMessageByMaxContext({
messages: adaptMessages,
maxTokens: extractModel.maxContext
maxContext: extractModel.maxContext
});
const requestMessages = await loadRequestMessages({
messages: filterMessages,

17
packages/service/core/workflow/dispatch/agent/runTool/functionCall.ts
View File

@@ -1,5 +1,5 @@
import { createChatCompletion } from '../../../../ai/config';
import { filterGPTMessageByMaxTokens, loadRequestMessages } from '../../../../chat/utils';
import { filterGPTMessageByMaxContext, loadRequestMessages } from '../../../../chat/utils';
import {
ChatCompletion,
StreamChatType,
@@ -172,10 +172,14 @@ export const runToolWithFunctionCall = async (
};
});
const max_tokens = computedMaxToken({
model: toolModel,
maxToken
});
const filterMessages = (
await filterGPTMessageByMaxTokens({
await filterGPTMessageByMaxContext({
messages,
maxTokens: toolModel.maxContext - 300 // filter token. not response maxToken
maxContext: toolModel.maxContext - (max_tokens || 0) // filter token. not response maxToken
})
).map((item) => {
if (item.role === ChatCompletionRequestMessageRoleEnum.Assistant && item.function_call) {
@@ -190,16 +194,11 @@ export const runToolWithFunctionCall = async (
}
return item;
});
const [requestMessages, max_tokens] = await Promise.all([
const [requestMessages] = await Promise.all([
loadRequestMessages({
messages: filterMessages,
useVision: toolModel.vision && aiChatVision,
origin: requestOrigin
}),
computedMaxToken({
model: toolModel,
maxToken,
filterMessages
})
]);
const requestBody = llmCompletionsBodyFormat(

3
packages/service/core/workflow/dispatch/agent/runTool/index.ts
View File

@@ -4,7 +4,7 @@ import type {
DispatchNodeResultType,
RuntimeNodeItemType
} from '@fastgpt/global/core/workflow/runtime/type';
import { ModelTypeEnum, getLLMModel } from '../../../../ai/model';
import { getLLMModel } from '../../../../ai/model';
import { filterToolNodeIdByEdges, getHistories } from '../../utils';
import { runToolWithToolChoice } from './toolChoice';
import { DispatchToolModuleProps, ToolNodeItemType } from './type.d';
@@ -30,6 +30,7 @@ import { parseUrlToFileType } from '@fastgpt/global/common/file/tools';
import { Prompt_DocumentQuote } from '@fastgpt/global/core/ai/prompt/AIChat';
import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
import { postTextCensor } from '../../../../../common/api/requestPlusApi';
import { ModelTypeEnum } from '@fastgpt/global/core/ai/model';
type Response = DispatchNodeResultType<{
[NodeOutputKeyEnum.answerText]: string;

17
packages/service/core/workflow/dispatch/agent/runTool/promptCall.ts
View File

@@ -1,5 +1,5 @@
import { createChatCompletion } from '../../../../ai/config';
import { filterGPTMessageByMaxTokens, loadRequestMessages } from '../../../../chat/utils';
import { filterGPTMessageByMaxContext, loadRequestMessages } from '../../../../chat/utils';
import {
ChatCompletion,
StreamChatType,
@@ -196,21 +196,20 @@ export const runToolWithPromptCall = async (
return Promise.reject('Prompt call invalid input');
}
const filterMessages = await filterGPTMessageByMaxTokens({
const max_tokens = computedMaxToken({
model: toolModel,
maxToken
});
const filterMessages = await filterGPTMessageByMaxContext({
messages,
maxTokens: toolModel.maxContext - 500 // filter token. not response maxToken
maxContext: toolModel.maxContext - (max_tokens || 0) // filter token. not response maxToken
});
const [requestMessages, max_tokens] = await Promise.all([
const [requestMessages] = await Promise.all([
loadRequestMessages({
messages: filterMessages,
useVision: toolModel.vision && aiChatVision,
origin: requestOrigin
}),
computedMaxToken({
model: toolModel,
maxToken,
filterMessages
})
]);
const requestBody = llmCompletionsBodyFormat(

20
packages/service/core/workflow/dispatch/agent/runTool/toolChoice.ts
View File

@@ -1,5 +1,5 @@
import { createChatCompletion } from '../../../../ai/config';
import { filterGPTMessageByMaxTokens, loadRequestMessages } from '../../../../chat/utils';
import { filterGPTMessageByMaxContext, loadRequestMessages } from '../../../../chat/utils';
import {
ChatCompletion,
ChatCompletionMessageToolCall,
@@ -228,11 +228,16 @@ export const runToolWithToolChoice = async (
};
});
const max_tokens = computedMaxToken({
model: toolModel,
maxToken
});
// Filter histories by maxToken
const filterMessages = (
await filterGPTMessageByMaxTokens({
await filterGPTMessageByMaxContext({
messages,
maxTokens: toolModel.maxContext - 300 // filter token. not response maxToken
maxContext: toolModel.maxContext - (max_tokens || 0) // filter token. not response maxToken
})
).map((item) => {
if (item.role === 'assistant' && item.tool_calls) {
@@ -248,16 +253,11 @@ export const runToolWithToolChoice = async (
return item;
});
const [requestMessages, max_tokens] = await Promise.all([
const [requestMessages] = await Promise.all([
loadRequestMessages({
messages: filterMessages,
useVision: toolModel.vision && aiChatVision,
origin: requestOrigin
}),
computedMaxToken({
model: toolModel,
maxToken,
filterMessages
})
]);
const requestBody = llmCompletionsBodyFormat(
@@ -272,7 +272,7 @@ export const runToolWithToolChoice = async (
},
toolModel
);
// console.log(JSON.stringify(requestBody, null, 2), '==requestBody');
// console.log(JSON.stringify(requestMessages, null, 2), '==requestBody');
/* Run llm */
const {
response: aiResponse,

88
packages/service/core/workflow/dispatch/chat/oneapi.ts
View File

@@ -1,5 +1,5 @@
import type { NextApiResponse } from 'next';
import { filterGPTMessageByMaxTokens, loadRequestMessages } from '../../../chat/utils';
import { filterGPTMessageByMaxContext, loadRequestMessages } from '../../../chat/utils';
import type { ChatItemType, UserChatItemValueItemType } from '@fastgpt/global/core/chat/type.d';
import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
@@ -33,7 +33,7 @@ import type { AIChatNodeProps } from '@fastgpt/global/core/workflow/runtime/type
import { replaceVariable } from '@fastgpt/global/common/string/tools';
import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
import { responseWriteController } from '../../../../common/response';
import { getLLMModel, ModelTypeEnum } from '../../../ai/model';
import { getLLMModel } from '../../../ai/model';
import type { SearchDataResponseItemType } from '@fastgpt/global/core/dataset/type';
import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
@@ -47,6 +47,7 @@ import { AiChatQuoteRoleType } from '@fastgpt/global/core/workflow/template/syst
import { getFileContentFromLinks, getHistoryFileLinks } from '../tools/readFiles';
import { parseUrlToFileType } from '@fastgpt/global/common/file/tools';
import { i18nT } from '../../../../../web/i18n/utils';
import { ModelTypeEnum } from '@fastgpt/global/core/ai/model';
export type ChatProps = ModuleDispatchProps<
AIChatNodeProps & {
@@ -57,6 +58,7 @@ export type ChatProps = ModuleDispatchProps<
>;
export type ChatResponse = DispatchNodeResultType<{
[NodeOutputKeyEnum.answerText]: string;
[NodeOutputKeyEnum.reasoningText]?: string;
[NodeOutputKeyEnum.history]: ChatItemType[];
}>;
@@ -86,22 +88,24 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
quoteTemplate,
quotePrompt,
aiChatVision,
aiChatReasoning = true,
fileUrlList: fileLinks, // node quote file links
stringQuoteText //abandon
}
} = props;
const { files: inputFiles } = chatValue2RuntimePrompt(query); // Chat box input files
stream = stream && isResponseAnswerText;
const chatHistories = getHistories(history, histories);
quoteQA = checkQuoteQAValue(quoteQA);
const modelConstantsData = getLLMModel(model);
if (!modelConstantsData) {
return Promise.reject('The chat model is undefined, you need to select a chat model.');
}
stream = stream && isResponseAnswerText;
aiChatReasoning = !!aiChatReasoning && !!modelConstantsData.reasoning;
const chatHistories = getHistories(history, histories);
quoteQA = checkQuoteQAValue(quoteQA);
const [{ datasetQuoteText }, { documentQuoteText, userFiles }] = await Promise.all([
filterDatasetQuote({
quoteQA,
@@ -123,9 +127,15 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
return Promise.reject(i18nT('chat:AI_input_is_empty'));
}
const max_tokens = computedMaxToken({
model: modelConstantsData,
maxToken
});
const [{ filterMessages }] = await Promise.all([
getChatMessages({
model: modelConstantsData,
maxTokens: max_tokens,
histories: chatHistories,
useDatasetQuote: quoteQA !== undefined,
datasetQuoteText,
@@ -136,8 +146,8 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
userFiles,
documentQuoteText
}),
// Censor = true and system key, will check content
(() => {
// censor model and system key
if (modelConstantsData.censor && !externalProvider.openaiAccount?.key) {
return postTextCensor({
text: `${systemPrompt}
@@ -148,18 +158,11 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
})()
]);
const [requestMessages, max_tokens] = await Promise.all([
loadRequestMessages({
messages: filterMessages,
useVision: modelConstantsData.vision && aiChatVision,
origin: requestOrigin
}),
computedMaxToken({
model: modelConstantsData,
maxToken,
filterMessages
})
]);
const requestMessages = await loadRequestMessages({
messages: filterMessages,
useVision: modelConstantsData.vision && aiChatVision,
origin: requestOrigin
});
const requestBody = llmCompletionsBodyFormat(
{
@@ -182,34 +185,41 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
}
});
const { answerText } = await (async () => {
const { answerText, reasoningText } = await (async () => {
if (res && isStreamResponse) {
// sse response
const { answer } = await streamResponse({
const { answer, reasoning } = await streamResponse({
res,
stream: response,
aiChatReasoning,
workflowStreamResponse
});
return {
answerText: answer
answerText: answer,
reasoningText: reasoning
};
} else {
const unStreamResponse = response as ChatCompletion;
const answer = unStreamResponse.choices?.[0]?.message?.content || '';
const reasoning = aiChatReasoning
? // @ts-ignore
unStreamResponse.choices?.[0]?.message?.reasoning_content || ''
: '';
if (stream) {
// Some models do not support streaming
workflowStreamResponse?.({
event: SseResponseEventEnum.fastAnswer,
data: textAdaptGptResponse({
text: answer
text: answer,
reasoning_content: reasoning
})
});
}
return {
answerText: answer
answerText: answer,
reasoningText: reasoning
};
}
})();
@@ -240,6 +250,7 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
return {
answerText,
reasoningText,
[DispatchNodeResponseKeyEnum.nodeResponse]: {
totalPoints: externalProvider.openaiAccount?.key ? 0 : totalPoints,
model: modelName,
@@ -280,11 +291,12 @@ async function filterDatasetQuote({
}) {
function getValue(item: SearchDataResponseItemType, index: number) {
return replaceVariable(quoteTemplate || Prompt_QuoteTemplateList[0].value, {
id: item.id,
q: item.q,
a: item.a,
updateTime: formatTime2YMDHM(item.updateTime),
source: item.sourceName,
sourceId: String(item.sourceId || 'UnKnow'),
sourceId: String(item.sourceId || ''),
index: index + 1
});
}
@@ -365,6 +377,7 @@ async function getMultiInput({
async function getChatMessages({
model,
maxTokens = 0,
aiChatQuoteRole,
datasetQuotePrompt = '',
datasetQuoteText,
@@ -376,6 +389,7 @@ async function getChatMessages({
documentQuoteText
}: {
model: LLMModelItemType;
maxTokens?: number;
// dataset quote
aiChatQuoteRole: AiChatQuoteRoleType; // user: replace user prompt; system: replace system prompt
datasetQuotePrompt?: string;
@@ -442,9 +456,9 @@ async function getChatMessages({
const adaptMessages = chats2GPTMessages({ messages, reserveId: false });
const filterMessages = await filterGPTMessageByMaxTokens({
const filterMessages = await filterGPTMessageByMaxContext({
messages: adaptMessages,
maxTokens: model.maxContext - 300 // filter token. not response maxToken
maxContext: model.maxContext - maxTokens // filter token. not response maxToken
});
return {
@@ -455,33 +469,43 @@ async function getChatMessages({
async function streamResponse({
res,
stream,
workflowStreamResponse
workflowStreamResponse,
aiChatReasoning
}: {
res: NextApiResponse;
stream: StreamChatType;
workflowStreamResponse?: WorkflowResponseType;
aiChatReasoning?: boolean;
}) {
const write = responseWriteController({
res,
readStream: stream
});
let answer = '';
let reasoning = '';
for await (const part of stream) {
if (res.closed) {
stream.controller?.abort();
break;
}
const content = part.choices?.[0]?.delta?.content || '';
answer += content;
const reasoningContent = aiChatReasoning
? part.choices?.[0]?.delta?.reasoning_content || ''
: '';
reasoning += reasoningContent;
workflowStreamResponse?.({
write,
event: SseResponseEventEnum.answer,
data: textAdaptGptResponse({
text: content
text: content,
reasoning_content: reasoningContent
})
});
}
return { answer };
return { answer, reasoning };
}

7
packages/service/core/workflow/dispatch/dataset/search.ts
View File

@@ -6,7 +6,7 @@ import { formatModelChars2Points } from '../../../../support/wallet/usage/utils'
import type { SelectedDatasetType } from '@fastgpt/global/core/workflow/api.d';
import type { SearchDataResponseItemType } from '@fastgpt/global/core/dataset/type';
import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
import { ModelTypeEnum, getLLMModel, getVectorModel } from '../../../ai/model';
import { getLLMModel, getEmbeddingModel } from '../../../ai/model';
import { searchDatasetData } from '../../../dataset/search/controller';
import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
@@ -18,6 +18,7 @@ import { checkTeamReRankPermission } from '../../../../support/permission/teamLi
import { MongoDataset } from '../../../dataset/schema';
import { i18nT } from '../../../../../web/i18n/utils';
import { filterDatasetsByTmbId } from '../../../dataset/utils';
import { ModelTypeEnum } from '@fastgpt/global/core/ai/model';
type DatasetSearchProps = ModuleDispatchProps<{
[NodeInputKeyEnum.datasetSelectList]: SelectedDatasetType;
@@ -110,7 +111,7 @@ export async function dispatchDatasetSearch(
// console.log(concatQueries, rewriteQuery, aiExtensionResult);
// get vector
const vectorModel = getVectorModel(
const vectorModel = getEmbeddingModel(
(await MongoDataset.findById(datasets[0].datasetId, 'vectorModel').lean())?.vectorModel
);
@@ -138,7 +139,7 @@ export async function dispatchDatasetSearch(
const { totalPoints, modelName } = formatModelChars2Points({
model: vectorModel.model,
inputTokens: tokens,
modelType: ModelTypeEnum.vector
modelType: ModelTypeEnum.embedding
});
const responseData: DispatchNodeResponseType & { totalPoints: number } = {
totalPoints,

28
packages/service/core/workflow/dispatch/index.ts
View File

@@ -204,6 +204,7 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
{ inputs = [] }: RuntimeNodeItemType,
{
answerText = '',
reasoningText,
responseData,
nodeDispatchUsages,
toolResponses,
@@ -213,6 +214,7 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
}: Omit<
DispatchNodeResultType<{
[NodeOutputKeyEnum.answerText]?: string;
[NodeOutputKeyEnum.reasoningText]?: string;
[DispatchNodeResponseKeyEnum.nodeResponse]?: ChatHistoryItemResType;
}>,
'nodeResponse'
@@ -239,18 +241,28 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
// Histories store
if (assistantResponses) {
chatAssistantResponse = chatAssistantResponse.concat(assistantResponses);
} else if (answerText) {
// save assistant text response
const isResponseAnswerText =
inputs.find((item) => item.key === NodeInputKeyEnum.aiChatIsResponseText)?.value ?? true;
if (isResponseAnswerText) {
} else {
if (reasoningText) {
chatAssistantResponse.push({
type: ChatItemValueTypeEnum.text,
text: {
content: answerText
type: ChatItemValueTypeEnum.reasoning,
reasoning: {
content: reasoningText
}
});
}
if (answerText) {
// save assistant text response
const isResponseAnswerText =
inputs.find((item) => item.key === NodeInputKeyEnum.aiChatIsResponseText)?.value ?? true;
if (isResponseAnswerText) {
chatAssistantResponse.push({
type: ChatItemValueTypeEnum.text,
text: {
content: answerText
}
});
}
}
}
if (rewriteHistories) {

1
packages/service/core/workflow/dispatch/tools/http468.ts
View File

@@ -244,7 +244,6 @@ export const dispatchHttp468Request = async (props: HttpRequestProps): Promise<H
if (!httpJsonBody) return {};
if (httpContentType === ContentTypes.json) {
httpJsonBody = replaceJsonBodyString(httpJsonBody);
console.log(httpJsonBody);
return json5.parse(httpJsonBody);
}

3
packages/service/core/workflow/dispatch/tools/queryExternsion.ts
View File

@@ -2,12 +2,13 @@ import type { ChatItemType } from '@fastgpt/global/core/chat/type.d';
import type { ModuleDispatchProps } from '@fastgpt/global/core/workflow/runtime/type';
import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
import { ModelTypeEnum, getLLMModel } from '../../../../core/ai/model';
import { getLLMModel } from '../../../../core/ai/model';
import { formatModelChars2Points } from '../../../../support/wallet/usage/utils';
import { queryExtension } from '../../../../core/ai/functions/queryExtension';
import { getHistories } from '../utils';
import { hashStr } from '@fastgpt/global/common/string/tools';
import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
import { ModelTypeEnum } from '@fastgpt/global/core/ai/model';
type Props = ModuleDispatchProps<{
[NodeInputKeyEnum.aiModel]: string;

3
packages/service/support/permission/dataset/auth.ts
View File

@@ -279,6 +279,7 @@ export async function authDatasetData({
return {
...result,
datasetData: data
datasetData: data,
collection: result.collection
};
}

20
packages/service/support/permission/user/auth.ts
View File

@@ -5,6 +5,10 @@ import { TeamErrEnum } from '@fastgpt/global/common/error/code/team';
import { AuthModeType, AuthResponseType } from '../type';
import { NullPermission } from '@fastgpt/global/support/permission/constant';
import { TeamPermission } from '@fastgpt/global/support/permission/user/controller';
import { authCert } from '../auth/common';
import { MongoUser } from '../../user/schema';
import { ERROR_ENUM } from '@fastgpt/global/common/error/errorCode';
import { ApiRequestProps } from '../../../type/next';
/* auth user role */
export async function authUserPer(props: AuthModeType): Promise<
@@ -34,3 +38,19 @@ export async function authUserPer(props: AuthModeType): Promise<
tmb
};
}
export const authSystemAdmin = async ({ req }: { req: ApiRequestProps }) => {
try {
const result = await authCert({ req, authToken: true });
const user = await MongoUser.findOne({
_id: result.userId
});
if (user && user.username !== 'root') {
return Promise.reject(ERROR_ENUM.unAuthorization);
}
return result;
} catch (error) {
throw error;
}
};

3
packages/service/support/wallet/usage/schema.ts
View File

@@ -61,7 +61,8 @@ const UsageSchema = new Schema({
});
try {
UsageSchema.index({ teamId: 1, tmbId: 1, source: 1, time: -1 });
UsageSchema.index({ teamId: 1, time: 1, tmbId: 1, source: 1 });
UsageSchema.index({ teamId: 1, time: 1, appName: 1 });
// timer task. clear dead team
// UsageSchema.index({ teamId: 1, time: -1 });

6
packages/service/support/wallet/usage/utils.ts
View File

@@ -1,5 +1,5 @@
import { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
import { ModelTypeEnum, getModelMap } from '../../../core/ai/model';
import { findAIModel } from '../../../core/ai/model';
import { ModelTypeEnum } from '@fastgpt/global/core/ai/model';
export const formatModelChars2Points = ({
model,
@@ -14,7 +14,7 @@ export const formatModelChars2Points = ({
modelType: `${ModelTypeEnum}`;
multiple?: number;
}) => {
const modelData = getModelMap?.[modelType]?.(model) as LLMModelItemType;
const modelData = findAIModel(model);
if (!modelData) {
return {
totalPoints: 0,

12
packages/service/type.d.ts vendored
View File

@@ -1,9 +1,9 @@
import { FastGPTFeConfigsType, SystemEnvType } from '@fastgpt/global/common/system/types';
import {
AudioSpeechModelType,
TTSModelType,
ReRankModelItemType,
STTModelType,
VectorModelItemType,
EmbeddingModelItemType,
LLMModelItemType
} from '@fastgpt/global/core/ai/model.d';
import { SubPlanType } from '@fastgpt/global/support/wallet/sub/type';
@@ -12,17 +12,11 @@ import { Worker } from 'worker_threads';
declare global {
var systemInitBufferId: string | undefined;
var systemVersion: string;
var feConfigs: FastGPTFeConfigsType;
var systemEnv: SystemEnvType;
var subPlans: SubPlanType | undefined;
var llmModels: LLMModelItemType[];
var llmModelPriceType: 'IO' | 'Tokens';
var vectorModels: VectorModelItemType[];
var audioSpeechModels: AudioSpeechModelType[];
var whisperModel: STTModelType;
var reRankModels: ReRankModelItemType[];
var workerPoll: Record<WorkerNameEnum, WorkerPool>;
}

18
packages/web/components/common/DateRangePicker/index.tsx
View File

@@ -1,4 +1,4 @@
import React, { useState, useMemo, useRef } from 'react';
import React, { useState, useMemo, useRef, useEffect } from 'react';
import { Box, Card, Flex, useTheme, useOutsideClick, Button } from '@chakra-ui/react';
import { addDays, format } from 'date-fns';
import { type DateRange, DayPicker } from 'react-day-picker';
@@ -14,12 +14,14 @@ const DateRangePicker = ({
defaultDate = {
from: addDays(new Date(), -30),
to: new Date()
}
},
dateRange
}: {
onChange?: (date: DateRange) => void;
onSuccess?: (date: DateRange) => void;
position?: 'bottom' | 'top';
defaultDate?: DateRange;
dateRange?: DateRange;
}) => {
const { t } = useTranslation();
const theme = useTheme();
@@ -27,6 +29,12 @@ const DateRangePicker = ({
const [range, setRange] = useState<DateRange | undefined>(defaultDate);
const [showSelected, setShowSelected] = useState(false);
useEffect(() => {
if (dateRange) {
setRange(dateRange);
}
}, [dateRange]);
const formatSelected = useMemo(() => {
if (range?.from && range.to) {
return `${format(range.from, 'y-MM-dd')} ~ ${format(range.to, 'y-MM-dd')}`;
@@ -49,7 +57,7 @@ const DateRangePicker = ({
py={1}
borderRadius={'sm'}
cursor={'pointer'}
bg={'myGray.100'}
bg={'myGray.50'}
fontSize={'sm'}
onClick={() => setShowSelected(true)}
>
@@ -93,7 +101,7 @@ const DateRangePicker = ({
date.to = date.from;
}
setRange(date);
onChange && onChange(date);
onChange?.(date);
}}
footer={
<Flex justifyContent={'flex-end'}>
@@ -108,7 +116,7 @@ const DateRangePicker = ({
<Button
size={'sm'}
onClick={() => {
onSuccess && onSuccess(range || defaultDate);
onSuccess?.(range || defaultDate);
setShowSelected(false);
}}
>

39
packages/web/components/common/Icon/button.tsx
View File

@@ -1,12 +1,14 @@
import React from 'react';
import { Flex, FlexProps } from '@chakra-ui/react';
import MyIcon from './index';
import MyTooltip from '../MyTooltip';
type Props = FlexProps & {
icon: string;
size?: string;
onClick?: () => void;
hoverColor?: string;
tip?: string;
};
const MyIconButton = ({
@@ -14,26 +16,29 @@ const MyIconButton = ({
onClick,
hoverColor = 'primary.600',
size = '1rem',
tip,
...props
}: Props) => {
return (
<Flex
p={1}
color={'myGray.500'}
rounded={'sm'}
alignItems={'center'}
bg={'transparent'}
transition={'background 0.1s'}
cursor={'pointer'}
_hover={{
bg: 'myGray.05',
color: hoverColor
}}
onClick={onClick}
{...props}
>
<MyIcon name={icon as any} w={size} />
</Flex>
<MyTooltip label={tip}>
<Flex
p={1}
color={'myGray.500'}
rounded={'sm'}
alignItems={'center'}
bg={'transparent'}
transition={'background 0.1s'}
cursor={'pointer'}
_hover={{
bg: 'myGray.05',
color: hoverColor
}}
onClick={onClick}
{...props}
>
<MyIcon name={icon as any} w={size} />
</Flex>
</MyTooltip>
);
};

9
packages/web/components/common/Icon/constants.ts
View File

@@ -178,6 +178,7 @@ export const iconPaths = {
'core/chat/sideLine': () => import('./icons/core/chat/sideLine.svg'),
'core/chat/speaking': () => import('./icons/core/chat/speaking.svg'),
'core/chat/stopSpeech': () => import('./icons/core/chat/stopSpeech.svg'),
'core/chat/think': () => import('./icons/core/chat/think.svg'),
'core/dataset/commonDataset': () => import('./icons/core/dataset/commonDataset.svg'),
'core/dataset/commonDatasetColor': () => import('./icons/core/dataset/commonDatasetColor.svg'),
'core/dataset/commonDatasetOutline': () =>
@@ -270,7 +271,6 @@ export const iconPaths = {
import('./icons/core/workflow/template/datasource.svg'),
'core/workflow/template/duckduckgo': () =>
import('./icons/core/workflow/template/duckduckgo.svg'),
'core/workflow/template/sleep': () => import('./icons/core/workflow/template/sleep.svg'),
'core/workflow/template/extractJson': () =>
import('./icons/core/workflow/template/extractJson.svg'),
'core/workflow/template/fetchUrl': () => import('./icons/core/workflow/template/fetchUrl.svg'),
@@ -296,6 +296,7 @@ export const iconPaths = {
'core/workflow/template/reply': () => import('./icons/core/workflow/template/reply.svg'),
'core/workflow/template/runApp': () => import('./icons/core/workflow/template/runApp.svg'),
'core/workflow/template/searxng': () => import('./icons/core/workflow/template/searxng.svg'),
'core/workflow/template/sleep': () => import('./icons/core/workflow/template/sleep.svg'),
'core/workflow/template/stopTool': () => import('./icons/core/workflow/template/stopTool.svg'),
'core/workflow/template/systemConfig': () =>
import('./icons/core/workflow/template/systemConfig.svg'),
@@ -374,21 +375,23 @@ export const iconPaths = {
'model/ernie': () => import('./icons/model/ernie.svg'),
'model/fishaudio': () => import('./icons/model/fishaudio.svg'),
'model/gemini': () => import('./icons/model/gemini.svg'),
'model/grok': () => import('./icons/model/grok.svg'),
'model/groq': () => import('./icons/model/groq.svg'),
'model/huggingface': () => import('./icons/model/huggingface.svg'),
'model/hunyuan': () => import('./icons/model/hunyuan.svg'),
'model/intern': () => import('./icons/model/intern.svg'),
'model/meta': () => import('./icons/model/meta.svg'),
'model/minimax': () => import('./icons/model/minimax.svg'),
'model/mistral': () => import('./icons/model/mistral.svg'),
'model/moka': () => import('./icons/model/moka.svg'),
'model/moonshot': () => import('./icons/model/moonshot.svg'),
'model/ollama': () => import('./icons/model/ollama.svg'),
'model/openai': () => import('./icons/model/openai.svg'),
'model/qwen': () => import('./icons/model/qwen.svg'),
'model/siliconflow': () => import('./icons/model/siliconflow.svg'),
'model/sparkDesk': () => import('./icons/model/sparkDesk.svg'),
'model/stepfun': () => import('./icons/model/stepfun.svg'),
'model/yi': () => import('./icons/model/yi.svg'),
'model/intern': () => import('./icons/model/intern.svg'),
'model/moka': () => import('./icons/model/moka.svg'),
more: () => import('./icons/more.svg'),
moreLine: () => import('./icons/moreLine.svg'),
out: () => import('./icons/out.svg'),

1
packages/web/components/common/Icon/icons/core/chat/think.svg Normal file
View File

@@ -0,0 +1 @@
<svg t="1737983662269" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="6134" width="64" height="64"><path d="M512 512m-91.264 0a91.264 91.264 0 1 0 182.528 0 91.264 91.264 0 1 0-182.528 0Z" fill="" p-id="6135"></path><path d="M256.341333 693.546667l-20.138666-5.12C86.101333 650.496 0 586.112 0 511.829333s86.101333-138.666667 236.202667-176.597333l20.138666-5.077333 5.674667 19.968a1003.946667 1003.946667 0 0 0 58.154667 152.661333l4.309333 9.088-4.309333 9.088a994.432 994.432 0 0 0-58.154667 152.661333l-5.674667 19.925334zM226.858667 381.866667c-114.090667 32.042667-184.106667 81.066667-184.106667 129.962666 0 48.853333 70.016 97.877333 184.106667 129.962667a1064.533333 1064.533333 0 0 1 50.432-129.962667A1056.085333 1056.085333 0 0 1 226.858667 381.866667z m540.8 311.68l-5.674667-20.010667a996.565333 996.565333 0 0 0-58.197333-152.618667l-4.309334-9.088 4.309334-9.088a999.253333 999.253333 0 0 0 58.197333-152.661333l5.674667-19.968 20.181333 5.077333c150.058667 37.930667 236.16 102.314667 236.16 176.64s-86.101333 138.666667-236.16 176.597334l-20.181333 5.12z m-20.949334-181.717334c20.48 44.330667 37.418667 87.893333 50.432 129.962667 114.133333-32.085333 184.106667-81.109333 184.106667-129.962667 0-48.896-70.016-97.877333-184.106667-129.962666a1057.621333 1057.621333 0 0 1-50.432 129.962666z" fill="" p-id="6136"></path><path d="M226.56 381.653333l-5.674667-19.925333C178.688 212.992 191.488 106.410667 256 69.205333c63.274667-36.522667 164.864 6.613333 271.317333 115.882667l14.506667 14.890667-14.506667 14.890666a1004.885333 1004.885333 0 0 0-103.338666 126.592l-5.76 8.234667-10.026667 0.853333a1009.365333 1009.365333 0 0 0-161.493333 26.026667l-20.138667 5.077333z m80.896-282.88c-11.434667 0-21.546667 2.474667-30.08 7.381334-42.410667 24.448-49.92 109.44-20.693333 224.128a1071.872 1071.872 0 0 1 137.941333-21.376 1060.138667 1060.138667 0 0 1 87.552-108.544c-66.56-64.810667-129.578667-101.589333-174.72-101.589334z m409.130667 868.778667c-0.042667 0-0.042667 0 0 0-60.8 0-138.88-45.781333-219.904-128.981333l-14.506667-14.890667 14.506667-14.890667a1003.946667 1003.946667 0 0 0 103.296-126.634666l5.76-8.234667 9.984-0.853333a1008.213333 1008.213333 0 0 0 161.578666-25.984l20.138667-5.077334 5.717333 19.968c42.112 148.650667 29.354667 255.274667-35.157333 292.437334a101.546667 101.546667 0 0 1-51.413333 13.141333z m-174.762667-144.256c66.56 64.810667 129.578667 101.589333 174.72 101.589333h0.042667c11.392 0 21.546667-2.474667 30.037333-7.381333 42.410667-24.448 49.962667-109.482667 20.693333-224.170667a1067.52 1067.52 0 0 1-137.984 21.376 1052.757333 1052.757333 0 0 1-87.509333 108.586667z" fill="" p-id="6137"></path><path d="M797.44 381.653333l-20.138667-5.077333a1001.770667 1001.770667 0 0 0-161.578666-26.026667l-9.984-0.853333-5.76-8.234667a998.997333 998.997333 0 0 0-103.296-126.592l-14.506667-14.890666 14.506667-14.890667C603.093333 75.861333 704.64 32.725333 768 69.205333c64.512 37.205333 77.312 143.786667 35.157333 292.48l-5.717333 19.968zM629.333333 308.906667c48.725333 4.437333 95.018667 11.648 137.984 21.376 29.269333-114.688 21.717333-199.68-20.693333-224.128-42.154667-24.362667-121.386667 12.970667-204.8 94.208A1060.224 1060.224 0 0 1 629.333333 308.906667zM307.456 967.552A101.546667 101.546667 0 0 1 256 954.410667c-64.512-37.162667-77.312-143.744-35.114667-292.437334l5.632-19.968 20.138667 5.077334c49.28 12.416 103.637333 21.162667 161.493333 25.984l10.026667 0.853333 5.717333 8.234667a1006.762667 1006.762667 0 0 0 103.338667 126.634666l14.506667 14.890667-14.506667 14.890667c-80.981333 83.2-159.061333 128.981333-219.776 128.981333z m-50.773333-274.218667c-29.269333 114.688-21.717333 199.722667 20.693333 224.170667 42.112 24.021333 121.301333-13.013333 204.8-94.208a1066.581333 1066.581333 0 0 1-87.552-108.586667 1065.642667 1065.642667 0 0 1-137.941333-21.376z" fill="" p-id="6138"></path><path d="M512 720.128c-35.114667 0-71.210667-1.536-107.349333-4.522667l-10.026667-0.853333-5.76-8.234667a1296.554667 1296.554667 0 0 1-57.6-90.538666 1295.104 1295.104 0 0 1-49.749333-95.061334l-4.266667-9.088 4.266667-9.088a1292.8 1292.8 0 0 1 49.749333-95.061333c17.664-30.549333 37.077333-61.013333 57.6-90.538667l5.76-8.234666 10.026667-0.853334a1270.826667 1270.826667 0 0 1 214.741333 0l9.984 0.853334 5.717333 8.234666a1280.256 1280.256 0 0 1 107.392 185.6l4.309334 9.088-4.309334 9.088a1262.933333 1262.933333 0 0 1-107.392 185.6l-5.717333 8.234667-9.984 0.853333c-36.138667 2.986667-72.277333 4.522667-107.392 4.522667z m-93.738667-46.250667c63.146667 4.736 124.330667 4.736 187.52 0a1237.589333 1237.589333 0 0 0 93.696-162.048 1219.626667 1219.626667 0 0 0-93.738666-162.048 1238.656 1238.656 0 0 0-187.477334 0 1215.018667 1215.018667 0 0 0-93.738666 162.048 1242.197333 1242.197333 0 0 0 93.738666 162.048z" p-id="6139"></path></svg>
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2
packages/web/components/common/Icon/icons/modal/changePer.svg
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@@ -1,4 +1,4 @@
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packages/web/components/common/Input/NumberInput/index.tsx
View File

@@ -26,11 +26,12 @@ const MyNumberInput = (props: Props) => {
{...restProps}
onChange={(e) => {
if (!onChange) return;
if (e === '') {
const numE = Number(e);
if (isNaN(numE)) {
// @ts-ignore
onChange('');
} else {
onChange(Number(e));
onChange(numE);
}
}}
>

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