v4.6 -1 (#459)

projects/app/src/service/core/ai/model.ts

@@ -1,15 +1,17 @@
 import {
+  defaultAudioSpeechModels,
   defaultChatModels,
   defaultCQModels,
   defaultExtractModels,
   defaultQAModels,
   defaultQGModels,
   defaultVectorModels
-} from '@/constants/model';
+} from '@fastgpt/global/core/ai/model';
 
 export const getChatModel = (model?: string) => {
   return (
     (global.chatModels || defaultChatModels).find((item) => item.model === model) ||
+    global.chatModels?.[0] ||
     defaultChatModels[0]
   );
 };
@@ -50,6 +52,14 @@ export const getVectorModel = (model?: string) => {
   );
 };
 
+export function getAudioSpeechModel(model?: string) {
+  return (
+    global.audioSpeechModels.find((item) => item.model === model) ||
+    global.audioSpeechModels?.[0] ||
+    defaultAudioSpeechModels[0]
+  );
+}
+
 export enum ModelTypeEnum {
   chat = 'chat',
   qa = 'qa',

projects/app/src/service/core/ai/vector.ts

@@ -0,0 +1,70 @@
+import { getAIApi } from '@fastgpt/service/core/ai/config';
+
+export type GetVectorProps = {
+  model: string;
+  input: string | string[];
+};
+
+// text to vector
+export async function getVectorsByText({
+  model = 'text-embedding-ada-002',
+  input
+}: GetVectorProps) {
+  try {
+    if (typeof input === 'string' && !input) {
+      return Promise.reject({
+        code: 500,
+        message: 'input is empty'
+      });
+    } else if (Array.isArray(input)) {
+      for (let i = 0; i < input.length; i++) {
+        if (!input[i]) {
+          return Promise.reject({
+            code: 500,
+            message: 'input array is empty'
+          });
+        }
+      }
+    }
+
+    // 获取 chatAPI
+    const ai = getAIApi();
+
+    // 把输入的内容转成向量
+    const result = await ai.embeddings
+      .create({
+        model,
+        input
+      })
+      .then(async (res) => {
+        if (!res.data) {
+          return Promise.reject('Embedding API 404');
+        }
+        if (!res?.data?.[0]?.embedding) {
+          console.log(res?.data);
+          // @ts-ignore
+          return Promise.reject(res.data?.err?.message || 'Embedding API Error');
+        }
+        return {
+          tokenLen: res.usage.total_tokens || 0,
+          vectors: await Promise.all(res.data.map((item) => unityDimensional(item.embedding)))
+        };
+      });
+
+    return result;
+  } catch (error) {
+    console.log(`Embedding Error`, error);
+
+    return Promise.reject(error);
+  }
+}
+
+function unityDimensional(vector: number[]) {
+  if (vector.length > 1536) return Promise.reject('向量维度不能超过 1536');
+  let resultVector = vector;
+  const vectorLen = vector.length;
+
+  const zeroVector = new Array(1536 - vectorLen).fill(0);
+
+  return resultVector.concat(zeroVector);
+}