Add openai embeddings

README.md

@@ -120,7 +120,7 @@ TEXT_EMBEDDING_MODELS = `[
 ```
 
 The required fields are `name`, `chunkCharLength` and `endpoints`.
-Supported text embedding backends are: [`transformers.js`](https://huggingface.co/docs/transformers.js) and [`TEI`](https://github.com/huggingface/text-embeddings-inference). `transformers.js` models run locally as part of `chat-ui`, whereas `TEI` models run in a different environment & accessed through an API endpoint.
+Supported text embedding backends are: [`transformers.js`](https://huggingface.co/docs/transformers.js), [`TEI`](https://github.com/huggingface/text-embeddings-inference) and [`OpenAI`](https://platform.openai.com/docs/guides/embeddings). `transformers.js` models run locally as part of `chat-ui`, whereas `TEI` models run in a different environment & accessed through an API endpoint. `openai` models are accessed through the [OpenAI API](https://platform.openai.com/docs/guides/embeddings).
 
 When more than one embedding models are supplied in `.env.local` file, the first will be used by default, and the others will only be used on LLM's which configured `embeddingModel` to the name of the model.
 

src/lib/server/embeddingEndpoints/embeddingEndpoints.ts

@@ -7,6 +7,10 @@ import {
  embeddingEndpointTransformersJS,
  embeddingEndpointTransformersJSParametersSchema,
 } from "./transformersjs/embeddingEndpoints";
+import {
+ embeddingEndpointOpenAI,
+ embeddingEndpointOpenAIParametersSchema,
+} from "./openai/embeddingEndpoints";
 
 // parameters passed when generating text
 interface EmbeddingEndpointParameters {
@@ -21,6 +25,7 @@ export type EmbeddingEndpoint = (params: EmbeddingEndpointParameters) => Promise
 export const embeddingEndpointSchema = z.discriminatedUnion("type", [
  embeddingEndpointTeiParametersSchema,
  embeddingEndpointTransformersJSParametersSchema,
+ embeddingEndpointOpenAIParametersSchema,
 ]);
 
 type EmbeddingEndpointTypeOptions = z.infer<typeof embeddingEndpointSchema>["type"];
@@ -36,6 +41,7 @@ export const embeddingEndpoints: {
 } = {
  tei: embeddingEndpointTei,
  transformersjs: embeddingEndpointTransformersJS,
+ openai: embeddingEndpointOpenAI,
 };
 
 export default embeddingEndpoints;

src/lib/server/embeddingEndpoints/openai/embeddingEndpoints.ts

@@ -0,0 +1,51 @@
+import { z } from "zod";
+import type { EmbeddingEndpoint, Embedding } from "../embeddingEndpoints";
+import { chunk } from "$lib/utils/chunk";
+import { OPENAI_API_KEY } from "$env/static/private";
+
+export const embeddingEndpointOpenAIParametersSchema = z.object({
+ weight: z.number().int().positive().default(1),
+ model: z.any(),
+ type: z.literal("openai"),
+ url: z.string().url().default("https://api.openai.com/v1/embeddings"),
+ apiKey: z.string().default(OPENAI_API_KEY),
+});
+
+export async function embeddingEndpointOpenAI(
+ input: z.input<typeof embeddingEndpointOpenAIParametersSchema>
+): Promise<EmbeddingEndpoint> {
+ const { url, model, apiKey } = embeddingEndpointOpenAIParametersSchema.parse(input);
+
+ const maxBatchSize = model.maxBatchSize || 100;
+
+ return async ({ inputs }) => {
+ const requestURL = new URL(url);
+
+ const batchesInputs = chunk(inputs, maxBatchSize);
+
+ const batchesResults = await Promise.all(
+ batchesInputs.map(async (batchInputs) => {
+ const response = await fetch(requestURL, {
+ method: "POST",
+ headers: {
+ Accept: "application/json",
+ "Content-Type": "application/json",
+ ...(apiKey ? { Authorization: `Bearer ${apiKey}` } : {}),
+ },
+ body: JSON.stringify({ input: batchInputs, model: model.name }),
+ });
+
+ const embeddings: Embedding[] = [];
+ const responseObject = await response.json();
+ for (const embeddingObject of responseObject.data) {
+ embeddings.push(embeddingObject.embedding);
+ }
+ return embeddings;
+ })
+ );
+
+ const flatAllEmbeddings = batchesResults.flat();
+
+ return flatAllEmbeddings;
+ };
+}

src/lib/server/embeddingModels.ts

@@ -22,6 +22,7 @@ const modelConfig = z.object({
  modelUrl: z.string().url().optional(),
  endpoints: z.array(embeddingEndpointSchema).nonempty(),
  chunkCharLength: z.number().positive(),
+ maxBatchSize: z.number().positive().optional(),
  preQuery: z.string().default(""),
  prePassage: z.string().default(""),
 });
@@ -70,6 +71,8 @@ const addEndpoint = (m: Awaited<ReturnType<typeof processEmbeddingModel>>) => ({
  return embeddingEndpoints.tei(args);
  case "transformersjs":
  return embeddingEndpoints.transformersjs(args);
+ case "openai":
+ return embeddingEndpoints.openai(args);
  }
  }