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feat: support OPENAI_BASE_URL and OPENAI_EMBEDDING_MODEL for OpenAI-compatible endpoints #186

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Merged
rohitg00 merged 1 commit into from
Apr 22, 2026
Merged

feat: support OPENAI_BASE_URL and OPENAI_EMBEDDING_MODEL for OpenAI-compatible endpoints #186

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26 changes: 22 additions & 4 deletions src/providers/embedding/openai.ts
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Original file line number Diff line number Diff line change
@@ -1,16 +1,33 @@
import type { EmbeddingProvider } from "../../types.js";
import { getEnvVar } from "../../config.js";

const API_URL = "https://api.openai.com/v1/embeddings";

const DEFAULT_BASE_URL = "https://api.openai.com";
const DEFAULT_MODEL = "text-embedding-3-small";

/**
* OpenAI-compatible embedding provider.
*
* Required env vars:
* OPENAI_API_KEY — API key
*
* Optional:
* OPENAI_BASE_URL — base URL without path (default: https://api.openai.com)
* OPENAI_EMBEDDING_MODEL — model name (default: text-embedding-3-small)
*/
export class OpenAIEmbeddingProvider implements EmbeddingProvider {
readonly name = "openai";
readonly dimensions = 1536;
private apiKey: string;
private baseUrl: string;
private model: string;

constructor(apiKey?: string) {
this.apiKey = apiKey || getEnvVar("OPENAI_API_KEY") || "";
if (!this.apiKey) throw new Error("OPENAI_API_KEY is required");
this.baseUrl =
getEnvVar("OPENAI_BASE_URL") || DEFAULT_BASE_URL;
this.model =
getEnvVar("OPENAI_EMBEDDING_MODEL") || DEFAULT_MODEL;
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@coderabbitai coderabbitai Bot Apr 22, 2026

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⚠️ Potential issue | 🟠 Major

dimensions is hardcoded to 1536 but model is now configurable.

If a user sets OPENAI_EMBEDDING_MODEL=text-embedding-3-large (3072 dims) or text-embedding-ada-002 (1536) or any other compatible model, provider.dimensions will report a value inconsistent with the vectors actually returned by embed(). Downstream consumers (e.g. vector store schemas) relying on dimensions will silently get wrong sizing.

Consider either:

  • deriving dimensions from the configured model (lookup table with a sensible default), or
  • exposing OPENAI_EMBEDDING_DIMENSIONS env var, or
  • at minimum documenting the constraint that callers overriding the model are responsible for ensuring it returns 1536-dim vectors.
🤖 Prompt for AI Agents 
Verify each finding against the current code and only fix it if needed.

In `@src/providers/embedding/openai.ts` around lines 19 - 30, The class currently
hardcodes readonly dimensions = 1536 while allowing model to be configured in
the constructor (via model, getEnvVar("OPENAI_EMBEDDING_MODEL") or
DEFAULT_MODEL), causing a mismatch if a different model (e.g.
text-embedding-3-large) is used; change the implementation so dimensions is
computed from the configured model (use a small lookup map keyed by model name
to set the correct dimension) and allow an override via an environment variable
(OPENAI_EMBEDDING_DIMENSIONS) as a fallback; update the constructor to read
OPENAI_EMBEDDING_DIMENSIONS first, then map model→dimensions, and finally
default to 1536 if neither is present, keeping references to the existing
symbols dimensions, constructor, model, getEnvVar and DEFAULT_MODEL for locating
the change.

}

async embed(text: string): Promise<Float32Array> {
Expand All @@ -19,14 +36,15 @@ export class OpenAIEmbeddingProvider implements EmbeddingProvider {
}

async embedBatch(texts: string[]): Promise<Float32Array[]> {
const response = await fetch(API_URL, {
const url = `${this.baseUrl}/v1/embeddings`;
const response = await fetch(url, {
method: "POST",
headers: {
Authorization: `Bearer ${this.apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "text-embedding-3-small",
model: this.model,
input: texts,
}),
});
Expand Down
57 changes: 57 additions & 0 deletions test/embedding-provider.test.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -47,3 +47,60 @@ describe("createEmbeddingProvider", () => {
expect(provider).toBeInstanceOf(OpenAIEmbeddingProvider);
});
});

describe("OpenAIEmbeddingProvider", () => {
const originalEnv = { ...process.env };

beforeEach(() => {
process.env = { ...originalEnv };
delete process.env["OPENAI_BASE_URL"];
delete process.env["OPENAI_EMBEDDING_MODEL"];
});

afterEach(() => {
process.env = originalEnv;
});

it("uses default base URL and model when env vars are not set", () => {
const provider = new OpenAIEmbeddingProvider("test-key");
expect(provider.name).toBe("openai");
expect(provider.dimensions).toBe(1536);
});

it("throws when no API key is provided", () => {
delete process.env["OPENAI_API_KEY"];
expect(() => new OpenAIEmbeddingProvider()).toThrow("OPENAI_API_KEY is required");
});

it("respects OPENAI_BASE_URL env var", async () => {
process.env["OPENAI_BASE_URL"] = "https://my-proxy.example.com";
const provider = new OpenAIEmbeddingProvider("test-key");

const fetchSpy = vi.spyOn(globalThis, "fetch").mockResolvedValue(
new Response(JSON.stringify({ data: [{ embedding: [0.1, 0.2, 0.3] }] }), { status: 200 }),
);

await provider.embed("hello");
expect(fetchSpy).toHaveBeenCalledWith(
"https://my-proxy.example.com/v1/embeddings",
expect.any(Object),
);

fetchSpy.mockRestore();
});

it("respects OPENAI_EMBEDDING_MODEL env var", async () => {
process.env["OPENAI_EMBEDDING_MODEL"] = "text-embedding-3-large";
const provider = new OpenAIEmbeddingProvider("test-key");

const fetchSpy = vi.spyOn(globalThis, "fetch").mockResolvedValue(
new Response(JSON.stringify({ data: [{ embedding: [0.1, 0.2, 0.3] }] }), { status: 200 }),
);

await provider.embed("hello");
const body = JSON.parse((fetchSpy.mock.calls[0][1] as RequestInit).body as string);
expect(body.model).toBe("text-embedding-3-large");

fetchSpy.mockRestore();
});
});