GHHG10/CodeServer / opencode /packages /llm /test /endpoint.test.ts
GHHG10's picture
download
raw
1.73 kB
import { describe, expect, test } from "bun:test"
import { LLM } from "../src"
import * as OpenAIChat from "../src/protocols/openai-chat"
import { Endpoint } from "../src/route"
import { Model } from "../src/schema"
const request = () =>
LLM.request({
model: Model.make({
id: "model-1",
provider: "test",
route: OpenAIChat.route,
}),
prompt: "hello",
})
describe("Endpoint", () => {
test("appends a static path to the model's baseURL", () => {
const url = Endpoint.render(Endpoint.path("/chat", { baseURL: "https://api.example.test/v1/" }), {
request: request(),
body: {},
})
expect(url.toString()).toBe("https://api.example.test/v1/chat")
})
test("endpoint query params are appended to the rendered URL", () => {
const url = Endpoint.render(
Endpoint.path("/chat?alt=sse", {
baseURL: "https://custom.example.test/root/",
query: { "api-version": "2026-01-01", alt: "json" },
}),
{
request: request(),
body: {},
},
)
expect(url.toString()).toBe("https://custom.example.test/root/chat?alt=json&api-version=2026-01-01")
})
test("path may be a function of the validated body", () => {
const url = Endpoint.render(
Endpoint.path<{ readonly modelId: string }>(
({ body }) => `/model/${encodeURIComponent(body.modelId)}/converse-stream`,
{ baseURL: "https://bedrock-runtime.us-east-1.amazonaws.com" },
),
{
request: request(),
body: { modelId: "us.amazon.nova-micro-v1:0" },
},
)
expect(url.toString()).toBe(
"https://bedrock-runtime.us-east-1.amazonaws.com/model/us.amazon.nova-micro-v1%3A0/converse-stream",
)
})
})

Xet Storage Details

Size:
1.73 kB
·
Xet hash:
30885a69cfc6d28db039015868e4372e93e40056a4953fb44c9e1849132fc36c

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.