ChatGPT/FastGPT
1
0
Fork 0
mirror of https://github.com/labring/FastGPT.git synced 2025-07-22 20:37:48 +00:00
Code Issues Packages Projects Releases Wiki Activity

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

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": {},
"type": "llm"
},
{
"model": "Doubao-pro-128k",
"name": "Doubao-pro-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-pro-32k",
"name": "Doubao-vision-pro-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-embedding-large",
"name": "Doubao-embedding-large",
"defaultToken": 512,
"maxToken": 4096,
"type": "embedding"
},
{
"model": "Doubao-embedding",
"name": "Doubao-embedding",
"defaultToken": 512,
"maxToken": 4096,
"type": "embedding"
}
]
}

107
packages/service/core/ai/config/provider/Ernie.json Normal file
View File

@@ -0,0 +1,107 @@
{
"provider": "Ernie",
"list": [
{
"model": "ERNIE-4.0-8K",
"name": "ERNIE-4.0-8K",
"maxContext": 8000,
"maxResponse": 2048,
"quoteMaxToken": 5000,
"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": "ERNIE-4.0-Turbo-8K",
"name": "ERNIE-4.0-Turbo-8K",
"maxContext": 8000,
"maxResponse": 2048,
"quoteMaxToken": 5000,
"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": "ERNIE-Lite-8K",
"name": "ERNIE-lite-8k",
"maxContext": 8000,
"maxResponse": 2048,
"quoteMaxToken": 6000,
"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": "ERNIE-Speed-128K",
"name": "ERNIE-Speed-128K",
"maxContext": 128000,
"maxResponse": 4096,
"quoteMaxToken": 120000,
"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": "Embedding-V1",
"name": "Embedding-V1",
"defaultToken": 512,
"maxToken": 1000,
"type": "embedding"
},
{
"model": "tao-8k",
"name": "tao-8k",
"defaultToken": 512,
"maxToken": 8000,
"type": "embedding"
}
]
}

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

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

144
packages/service/core/ai/config/provider/Gemini.json Normal file
View File

@@ -0,0 +1,144 @@
{
"provider": "Gemini",
"list": [
{
"model": "gemini-1.5-flash",
"name": "gemini-1.5-flash",
"maxContext": 1000000,
"maxResponse": 8000,
"quoteMaxToken": 60000,
"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": "gemini-1.5-pro",
"name": "gemini-1.5-pro",
"maxContext": 2000000,
"maxResponse": 8000,
"quoteMaxToken": 60000,
"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": "gemini-2.0-flash-exp",
"name": "gemini-2.0-flash-exp",
"maxContext": 1000000,
"maxResponse": 8000,
"quoteMaxToken": 60000,
"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": "gemini-2.0-flash-thinking-exp-1219",
"name": "gemini-2.0-flash-thinking-exp-1219",
"maxContext": 1000000,
"maxResponse": 8000,
"quoteMaxToken": 60000,
"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": "gemini-2.0-flash-thinking-exp-01-21",
"name": "gemini-2.0-flash-thinking-exp-01-21",
"maxContext": 1000000,
"maxResponse": 8000,
"quoteMaxToken": 60000,
"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": "gemini-exp-1206",
"name": "gemini-exp-1206",
"maxContext": 128000,
"maxResponse": 8000,
"quoteMaxToken": 120000,
"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": "text-embedding-004",
"name": "text-embedding-004",
"defaultToken": 512,
"maxToken": 2000,
"type": "embedding"
}
]
}

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

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

47
packages/service/core/ai/config/provider/Groq.json Normal file
View File

@@ -0,0 +1,47 @@
{
"provider": "Groq",
"list": [
{
"model": "llama-3.1-8b-instant",
"name": "Groq-llama-3.1-8b-instant",
"maxContext": 128000,
"maxResponse": 8000,
"quoteMaxToken": 60000,
"maxTemperature": 1.2,
"vision": true,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"type": "llm"
},
{
"model": "llama-3.3-70b-versatile",
"name": "Groq-llama-3.3-70b-versatile",
"maxContext": 128000,
"maxResponse": 8000,
"quoteMaxToken": 60000,
"maxTemperature": 1.2,
"vision": true,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"type": "llm"
}
]
}

166
packages/service/core/ai/config/provider/Hunyuan.json Normal file
View File

@@ -0,0 +1,166 @@
{
"provider": "Hunyuan",
"list": [
{
"model": "hunyuan-large",
"name": "hunyuan-large",
"maxContext": 28000,
"maxResponse": 4000,
"quoteMaxToken": 20000,
"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": "hunyuan-lite",
"name": "hunyuan-lite",
"maxContext": 250000,
"maxResponse": 6000,
"quoteMaxToken": 100000,
"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": "hunyuan-pro",
"name": "hunyuan-pro",
"maxContext": 28000,
"maxResponse": 4000,
"quoteMaxToken": 28000,
"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": "hunyuan-standard",
"name": "hunyuan-standard",
"maxContext": 32000,
"maxResponse": 2000,
"quoteMaxToken": 20000,
"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": "hunyuan-turbo-vision",
"name": "hunyuan-turbo-vision",
"maxContext": 6000,
"maxResponse": 2000,
"quoteMaxToken": 6000,
"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": "hunyuan-turbo",
"name": "hunyuan-turbo",
"maxContext": 28000,
"maxResponse": 4000,
"quoteMaxToken": 20000,
"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": "hunyuan-vision",
"name": "hunyuan-vision",
"maxContext": 6000,
"maxResponse": 2000,
"quoteMaxToken": 4000,
"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": "hunyuan-embedding",
"name": "hunyuan-embedding",
"defaultToken": 512,
"maxToken": 1024,
"type": "embedding"
}
]
}

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

@@ -0,0 +1,49 @@
{
"provider": "Intern",
"list": [
{
"model": "internlm2-pro-chat",
"name": "internlm2-pro-chat",
"maxContext": 32000,
"maxResponse": 8000,
"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": "internlm3-8b-instruct",
"name": "internlm3-8b-instruct",
"maxContext": 32000,
"maxResponse": 8000,
"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"
}
]
}

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

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

240
packages/service/core/ai/config/provider/MiniMax.json Normal file
View File

@@ -0,0 +1,240 @@
{
"provider": "MiniMax",
"list": [
{
"model": "MiniMax-Text-01",
"name": "MiniMax-Text-01",
"maxContext": 1000000,
"maxResponse": 1000000,
"quoteMaxToken": 100000,
"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": "abab6.5s-chat",
"name": "MiniMax-abab6.5s",
"maxContext": 245000,
"maxResponse": 10000,
"quoteMaxToken": 240000,
"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": "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
View File

@@ -0,0 +1,93 @@
{
"provider": "MistralAI",
"list": [
{
"model": "ministral-3b-latest",
"name": "Ministral-3b-latest",
"maxContext": 130000,
"maxResponse": 8000,
"quoteMaxToken": 60000,
"maxTemperature": 1.2,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "ministral-8b-latest",
"name": "Ministral-8b-latest",
"maxContext": 130000,
"maxResponse": 8000,
"quoteMaxToken": 60000,
"maxTemperature": 1.2,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "mistral-large-latest",
"name": "Mistral-large-latest",
"maxContext": 130000,
"maxResponse": 8000,
"quoteMaxToken": 60000,
"maxTemperature": 1.2,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "mistral-small-latest",
"name": "Mistral-small-latest",
"maxContext": 32000,
"maxResponse": 4000,
"quoteMaxToken": 32000,
"maxTemperature": 1.2,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
}
]
}

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

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

71
packages/service/core/ai/config/provider/Moonshot.json Normal file
View File

@@ -0,0 +1,71 @@
{
"provider": "Moonshot",
"list": [
{
"model": "moonshot-v1-8k",
"name": "moonshot-v1-8k",
"maxContext": 8000,
"maxResponse": 4000,
"quoteMaxToken": 6000,
"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": "moonshot-v1-32k",
"name": "moonshot-v1-32k",
"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": "moonshot-v1-128k",
"name": "moonshot-v1-128k",
"maxContext": 128000,
"maxResponse": 4000,
"quoteMaxToken": 60000,
"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"
}
]
}

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

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

252
packages/service/core/ai/config/provider/OpenAI.json Normal file
View File

@@ -0,0 +1,252 @@
{
"provider": "OpenAI",
"list": [
{
"model": "gpt-4o-mini",
"name": "GPT-4o-mini",
"maxContext": 128000,
"maxResponse": 16000,
"quoteMaxToken": 60000,
"maxTemperature": 1.2,
"vision": true,
"toolChoice": true,
"functionCall": true,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "gpt-4o",
"name": "GPT-4o",
"maxContext": 128000,
"maxResponse": 4000,
"quoteMaxToken": 60000,
"maxTemperature": 1.2,
"vision": true,
"toolChoice": true,
"functionCall": true,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "o3-mini",
"name": "o3-mini",
"maxContext": 200000,
"maxResponse": 100000,
"quoteMaxToken": 120000,
"maxTemperature": null,
"vision": false,
"toolChoice": true,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {
"stream": false
},
"fieldMap": {
"max_tokens": "max_completion_tokens"
},
"type": "llm"
},
{
"model": "o1-mini",
"name": "o1-mini",
"maxContext": 128000,
"maxResponse": 4000,
"quoteMaxToken": 120000,
"maxTemperature": null,
"vision": false,
"toolChoice": false,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {
"stream": false
},
"fieldMap": {
"max_tokens": "max_completion_tokens"
},
"type": "llm"
},
{
"model": "o1",
"name": "o1",
"maxContext": 195000,
"maxResponse": 8000,
"quoteMaxToken": 120000,
"maxTemperature": null,
"vision": true,
"toolChoice": false,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {
"stream": false
},
"fieldMap": {
"max_tokens": "max_completion_tokens"
},
"type": "llm"
},
{
"model": "o1-preview",
"name": "o1-preview",
"maxContext": 128000,
"maxResponse": 4000,
"quoteMaxToken": 120000,
"maxTemperature": null,
"vision": false,
"toolChoice": false,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {
"stream": false
},
"fieldMap": {
"max_tokens": "max_completion_tokens"
},
"type": "llm"
},
{
"model": "gpt-3.5-turbo",
"name": "gpt-3.5-turbo",
"maxContext": 16000,
"maxResponse": 4000,
"quoteMaxToken": 13000,
"maxTemperature": 1.2,
"vision": false,
"toolChoice": true,
"functionCall": true,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"type": "llm"
},
{
"model": "gpt-4-turbo",
"name": "gpt-4-turbo",
"maxContext": 128000,
"maxResponse": 4000,
"quoteMaxToken": 60000,
"maxTemperature": 1.2,
"vision": true,
"toolChoice": true,
"functionCall": true,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"type": "llm"
},
{
"model": "text-embedding-3-large",
"name": "text-embedding-3-large",
"defaultToken": 512,
"maxToken": 8000,
"defaultConfig": {
"dimensions": 1024
},
"type": "embedding"
},
{
"model": "text-embedding-3-small",
"name": "text-embedding-3-small",
"defaultToken": 512,
"maxToken": 8000,
"type": "embedding"
},
{
"model": "text-embedding-ada-002",
"name": "text-embedding-ada-002",
"defaultToken": 512,
"maxToken": 8000,
"type": "embedding"
},
{
"model": "tts-1",
"name": "TTS1",
"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"
}
],
"type": "tts"
},
{
"model": "whisper-1",
"name": "whisper-1",
"type": "stt"
}
]
}

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

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

223
packages/service/core/ai/config/provider/Qwen.json Normal file
View File

@@ -0,0 +1,223 @@
{
"provider": "Qwen",
"list": [
{
"model": "qwen-turbo",
"name": "Qwen-turbo",
"maxContext": 128000,
"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": "qwen-plus",
"name": "Qwen-plus",
"maxContext": 64000,
"maxResponse": 8000,
"quoteMaxToken": 60000,
"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": "qwen-vl-plus",
"name": "qwen-vl-plus",
"maxContext": 32000,
"maxResponse": 2000,
"quoteMaxToken": 20000,
"maxTemperature": 1.2,
"vision": true,
"toolChoice": false,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"type": "llm"
},
{
"model": "qwen-max",
"name": "Qwen-max",
"maxContext": 8000,
"maxResponse": 4000,
"quoteMaxToken": 6000,
"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": "qwen-vl-max",
"name": "qwen-vl-max",
"maxContext": 32000,
"maxResponse": 2000,
"quoteMaxToken": 20000,
"maxTemperature": 1.2,
"vision": true,
"toolChoice": false,
"functionCall": false,
"defaultSystemChatPrompt": "",
"datasetProcess": true,
"usedInClassify": true,
"customCQPrompt": "",
"usedInExtractFields": true,
"usedInQueryExtension": true,
"customExtractPrompt": "",
"usedInToolCall": true,
"defaultConfig": {},
"fieldMap": {},
"type": "llm"
},
{
"model": "qwen-coder-turbo",
"name": "qwen-coder-turbo",
"maxContext": 128000,
"maxResponse": 8000,
"quoteMaxToken": 50000,
"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": "qwen2.5-7b-instruct",
"name": "qwen2.5-7b-instruct",
"maxContext": 128000,
"maxResponse": 8000,
"quoteMaxToken": 50000,
"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": "qwen2.5-14b-instruct",
"name": "qwen2.5-14b-instruct",
"maxContext": 128000,
"maxResponse": 8000,
"quoteMaxToken": 50000,
"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": "qwen2.5-32b-instruct",
"name": "qwen2.5-32b-instruct",
"maxContext": 128000,
"maxResponse": 8000,
"quoteMaxToken": 50000,
"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": "qwen2.5-72b-instruct",
"name": "Qwen2.5-72B-instruct",
"maxContext": 128000,
"maxResponse": 8000,
"quoteMaxToken": 50000,
"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"
}
]
}

204
packages/service/core/ai/config/provider/Siliconflow.json Normal file
View File

@@ -0,0 +1,204 @@
{
"provider": "Siliconflow",
"list": [
{
"model": "Qwen/Qwen2.5-72B-Instruct",
"name": "Qwen/Qwen2.5-72B-Instruct",
"maxContext": 128000,
"maxResponse": 8000,
"quoteMaxToken": 50000,
"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": "Qwen/Qwen2-VL-72B-Instruct",
"name": "Qwen/Qwen2-VL-72B-Instruct",
"maxContext": 32000,
"maxResponse": 4000,
"quoteMaxToken": 32000,
"maxTemperature": 1,
"censor": false,
"vision": true,
"datasetProcess": false,
"usedInClassify": false,
"usedInExtractFields": false,
"usedInToolCall": false,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"defaultConfig": {},
"type": "llm"
},
{
"model": "deepseek-ai/DeepSeek-V2.5",
"name": "deepseek-ai/DeepSeek-V2.5",
"maxContext": 32000,
"maxResponse": 4000,
"quoteMaxToken": 32000,
"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": "BAAI/bge-m3",
"name": "BAAI/bge-m3",
"defaultToken": 512,
"maxToken": 8000,
"type": "embedding"
},
{
"model": "FunAudioLLM/CosyVoice2-0.5B",
"name": "FunAudioLLM/CosyVoice2-0.5B",
"voices": [
{
"label": "alex",
"value": "FunAudioLLM/CosyVoice2-0.5B:alex"
},
{
"label": "anna",
"value": "FunAudioLLM/CosyVoice2-0.5B:anna"
},
{
"label": "bella",
"value": "FunAudioLLM/CosyVoice2-0.5B:bella"
},
{
"label": "benjamin",
"value": "FunAudioLLM/CosyVoice2-0.5B:benjamin"
},
{
"label": "charles",
"value": "FunAudioLLM/CosyVoice2-0.5B:charles"
},
{
"label": "claire",
"value": "FunAudioLLM/CosyVoice2-0.5B:claire"
},
{
"label": "david",
"value": "FunAudioLLM/CosyVoice2-0.5B:david"
},
{
"label": "diana",
"value": "FunAudioLLM/CosyVoice2-0.5B:diana"
}
],
"type": "tts"
},
{
"model": "RVC-Boss/GPT-SoVITS",
"name": "RVC-Boss/GPT-SoVITS",
"voices": [
{
"label": "alex",
"value": "RVC-Boss/GPT-SoVITS:alex"
},
{
"label": "anna",
"value": "RVC-Boss/GPT-SoVITS:anna"
},
{
"label": "bella",
"value": "RVC-Boss/GPT-SoVITS:bella"
},
{
"label": "benjamin",
"value": "RVC-Boss/GPT-SoVITS:benjamin"
},
{
"label": "charles",
"value": "RVC-Boss/GPT-SoVITS:charles"
},
{
"label": "claire",
"value": "RVC-Boss/GPT-SoVITS:claire"
},
{
"label": "david",
"value": "RVC-Boss/GPT-SoVITS:david"
},
{
"label": "diana",
"value": "RVC-Boss/GPT-SoVITS:diana"
}
],
"type": "tts"
},
{
"model": "fishaudio/fish-speech-1.5",
"name": "fish-speech-1.5",
"voices": [
{
"label": "alex",
"value": "fishaudio/fish-speech-1.5:alex"
},
{
"label": "anna",
"value": "fishaudio/fish-speech-1.5:anna"
},
{
"label": "bella",
"value": "fishaudio/fish-speech-1.5:bella"
},
{
"label": "benjamin",
"value": "fishaudio/fish-speech-1.5:benjamin"
},
{
"label": "charles",
"value": "fishaudio/fish-speech-1.5:charles"
},
{
"label": "claire",
"value": "fishaudio/fish-speech-1.5:claire"
},
{
"label": "david",
"value": "fishaudio/fish-speech-1.5:david"
},
{
"label": "diana",
"value": "fishaudio/fish-speech-1.5:diana"
}
],
"type": "tts"
},
{
"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
View File

@@ -0,0 +1,129 @@
{
"provider": "SparkDesk",
"list": [
{
"model": "lite",
"name": "SparkDesk-lite",
"maxContext": 32000,
"maxResponse": 4000,
"quoteMaxToken": 32000,
"maxTemperature": 1,
"vision": false,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "generalv3",
"name": "SparkDesk-Pro",
"maxContext": 8000,
"maxResponse": 8000,
"quoteMaxToken": 8000,
"maxTemperature": 1,
"vision": false,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "pro-128k",
"name": "SparkDesk-Pro-128k",
"maxContext": 128000,
"maxResponse": 4000,
"quoteMaxToken": 128000,
"maxTemperature": 1,
"vision": false,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "generalv3.5",
"name": "SparkDesk-max",
"maxContext": 8000,
"maxResponse": 8000,
"quoteMaxToken": 8000,
"maxTemperature": 1,
"vision": false,
"datasetProcess": true,
"usedInClassify": true,
"usedInExtractFields": true,
"usedInToolCall": true,
"usedInQueryExtension": true,
"toolChoice": false,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"type": "llm"
},
{
"model": "max-32k",
"name": "SparkDesk-max-32k",
"maxContext": 32000,
"maxResponse": 8000,
"quoteMaxToken": 32000,
"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": "4.0Ultra",
"name": "SparkDesk-v4.0 Ultra",
"maxContext": 8000,
"maxResponse": 8000,
"quoteMaxToken": 8000,
"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"
}
]
}

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;