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

packages/service/core/ai/config/embedding/text-embedding-3-small.json

@@ -0,0 +1,10 @@
+{
+  "provider": "OpenAI",
+  "model": "text-embedding-3-small",
+  "name": "text-embedding-3-small",
+
+  "defaultToken": 512,
+  "maxToken": 3000,
+
+  "charsPointsPrice": 0
+}

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

@@ -3,9 +3,8 @@
   "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
+  "defaultToken": 512,
+  "maxToken": 3000,
 
-  "charsPointsPrice": 0 // 积分/1k token
+  "charsPointsPrice": 0
 }

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

@@ -1,33 +1,28 @@
 {
   "provider": "OpenAI",
   "model": "gpt-4o-mini",
-  "name": "GPT-4o-mini", // alias
+  "name": "GPT-4o-mini",
 
-  "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
+  "censor": false,
+  "charsPointsPrice": 0,
 
-  "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
+  "maxContext": 125000,
+  "maxResponse": 16000,
+  "quoteMaxToken": 60000,
+  "maxTemperature": 1.2,
 
-  "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
+  "vision": true,
+  "toolChoice": true,
+  "functionCall": false,
+  "defaultSystemChatPrompt": "",
 
-  "defaultConfig": {}, // 额外的自定义 body
-  "fieldMap": {}, // body 字段映射
+  "datasetProcess": true,
+  "usedInClassify": true,
+  "customCQPrompt": "",
+  "usedInExtractFields": true,
+  "customExtractPrompt": "",
+  "usedInToolCall": true,
 
-  "censor": false, // 是否开启敏感词过滤
-  "charsPointsPrice": 0 // n 积分/1k token
+  "defaultConfig": {},
+  "fieldMap": {}
 }

packages/service/core/ai/config/schema.ts

@@ -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
+);

packages/service/core/ai/config/utils.ts

@@ -0,0 +1,118 @@
+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';
+
+type FolderBaseType = `${ModelTypeEnum}`;
+
+export const loadSystemModels = async (init = false) => {
+  const getModelNameList = (base: FolderBaseType) => {
+    const currentFileUrl = new URL(import.meta.url);
+    const modelsPath = path.join(path.dirname(currentFileUrl.pathname), base);
+
+    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);
+      } else if (model.type === ModelTypeEnum.embedding) {
+        global.embeddingModelMap.set(model.model, model);
+        global.embeddingModelMap.set(model.name, model);
+      } else if (model.type === ModelTypeEnum.tts) {
+        global.ttsModelMap.set(model.model, model);
+        global.ttsModelMap.set(model.name, model);
+      } else if (model.type === ModelTypeEnum.stt) {
+        global.sttModelMap.set(model.model, model);
+        global.sttModelMap.set(model.name, model);
+      } else if (model.type === ModelTypeEnum.rerank) {
+        global.reRankModelMap.set(model.model, model);
+        global.reRankModelMap.set(model.name, model);
+      }
+    }
+  };
+
+  if (!init && global.systemModelList && global.systemModelList.length > 0) return;
+
+  const dbModels = await MongoSystemModel.find({}).lean();
+
+  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>();
+
+  const baseList: FolderBaseType[] = [
+    ModelTypeEnum.llm,
+    ModelTypeEnum.embedding,
+    ModelTypeEnum.tts,
+    ModelTypeEnum.stt,
+    ModelTypeEnum.rerank
+  ];
+
+  // System model
+  await Promise.all(
+    baseList.map(async (base) => {
+      const modelList = getModelNameList(base);
+      const nameList = modelList.map((name) => `${base}/${name}`);
+
+      await Promise.all(
+        nameList.map(async (name) => {
+          const fileContent = (await import(`./${name}`))?.default as SystemModelItemType;
+
+          const dbModel = dbModels.find((item) => item.model === fileContent.model);
+
+          const model: any = {
+            ...fileContent,
+            ...dbModel?.metadata,
+            type: dbModel?.metadata?.type || base,
+            isCustom: false
+          };
+
+          pushModel(model);
+        })
+      );
+    })
+  );
+
+  // Custom model
+  dbModels.forEach((dbModel) => {
+    if (global.systemModelList.find((item) => item.model === dbModel.model)) return;
+
+    pushModel({
+      ...dbModel.metadata,
+      isCustom: true
+    });
+  });
+
+  console.log('Load models success', JSON.stringify(global.systemActiveModelList, null, 2));
+};
+
+export const watchSystemModelUpdate = () => {
+  const changeStream = MongoSystemModel.watch();
+
+  changeStream.on(
+    'change',
+    debounce(async () => {
+      try {
+        await loadSystemModels(true);
+      } catch (error) {}
+    }, 500)
+  );
+};