@objectstack/embedder-openai

OpenAI-compatible embedder for ObjectStack. Works against any endpoint that speaks the POST /v1/embeddings shape:

| Provider | baseUrl | Typical model | |---|---|---| | OpenAI | https://api.openai.com/v1 | text-embedding-3-small | | Azure OpenAI | https://{resource}.openai.azure.com/openai/deployments/{deployment} | (deployment name) | | 阿里通义 DashScope | https://dashscope.aliyuncs.com/compatible-mode/v1 | text-embedding-v3 | | 智谱 BigModel | https://open.bigmodel.cn/api/paas/v4 | embedding-3 | | 硅基流动 SiliconFlow | https://api.siliconflow.cn/v1 | BAAI/bge-m3 | | 火山引擎 Doubao | https://ark.cn-beijing.volces.com/api/v3 | doubao-embedding-large-text-240915 | | MiniMax | https://api.minimax.chat/v1 | embo-01 | | Ollama (local) | http://localhost:11434/v1 | bge-m3, nomic-embed-text | | LiteLLM / vLLM / 自建网关 | (your endpoint) | (your model) |

Implements IEmbedder from @objectstack/spec/contracts, so it drops into any knowledge adapter that computes vectors. ⚠️ No adapter in this repository does yet — see Using the embedder.

Install

pnpm add @objectstack/embedder-openai

Usage

OpenAI

import { OpenAIEmbedder } from '@objectstack/embedder-openai';

const embedder = new OpenAIEmbedder({
  apiKey: process.env.OPENAI_API_KEY!,
  model: 'text-embedding-3-small', // default
});

阿里通义 DashScope

import { createOpenAIEmbedder } from '@objectstack/embedder-openai';

const embedder = createOpenAIEmbedder({
  preset: 'dashscope',
  apiKey: process.env.DASHSCOPE_API_KEY!,
  model: 'text-embedding-v3',
});

硅基流动 SiliconFlow（推荐桌面端）

const embedder = createOpenAIEmbedder({
  preset: 'siliconflow',
  apiKey: process.env.SILICONFLOW_API_KEY!,
  model: 'BAAI/bge-m3',
});

Ollama（完全离线）

const embedder = createOpenAIEmbedder({
  preset: 'ollama',
  apiKey: 'ollama', // ignored by ollama but required by interface
  model: 'bge-m3',
});

智谱 BigModel

const embedder = createOpenAIEmbedder({
  preset: 'zhipu',
  apiKey: process.env.ZHIPU_API_KEY!,
  model: 'embedding-3',
});

Using the embedder

OpenAIEmbedder is a plain IEmbedder: construct it and hand it to whatever needs vectors.

import { OpenAIEmbedder } from '@objectstack/embedder-openai';

const embedder = new OpenAIEmbedder({ apiKey: process.env.OPENAI_API_KEY! });

const [vector] = await embedder.embed(['ObjectStack turns an app into typed metadata']);
console.log(embedder.id, embedder.dimensions, vector.length);

⚠️ No knowledge adapter in this repository consumes an IEmbedder yet. The two this repo builds — @objectstack/knowledge-memory and @objectstack/knowledge-ragflow — take no embedder option. The vector-computing adapters the contract is written for (@objectstack/knowledge-turso, @objectstack/knowledge-sqlite-vec, named in embedder.ts) are in no directory of this repo, so an earlier revision of this page showed a new KnowledgeTursoPlugin({ embedding }) example that no reader could install or run.

Options

| Option | Default | Description | |---|---|---| | apiKey | — (required) | Bearer token. | | model | 'text-embedding-3-small' | Upstream model id. | | dimensions | (model default) | Override output dim (Matryoshka models only). | | baseUrl | 'https://api.openai.com/v1' | Endpoint root (no /embeddings). | | id | 'openai' | Stable id surfaced as IEmbedder.id. | | headers | {} | Extra request headers. | | fetch | globalThis.fetch | Inject for tests / custom transport. |

Contract

Implements IEmbedder:

interface IEmbedder {
  readonly id: string;
  readonly dimensions: number;
  embed(texts: string[]): Promise<number[][]>;
}

License

Apache-2.0. See LICENSING.md.