127 lines
4.6 KiB
TypeScript
127 lines
4.6 KiB
TypeScript
import { describe, expect } from "bun:test"
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import { Effect, Layer } from "effect"
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import { HttpClientRequest } from "effect/unstable/http"
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import { Image, ImageClient } from "../src"
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import { OpenAI } from "../src/providers"
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import { it } from "./lib/effect"
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import { dynamicResponse } from "./lib/http"
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describe("Image", () => {
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it.effect("generates images through the OpenAI Images API", () =>
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Effect.gen(function* () {
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const response = yield* Image.generate({
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model: OpenAI.configure({
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apiKey: "test",
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baseURL: "https://api.openai.test/v1",
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queryParams: { "api-version": "v1" },
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http: { body: { deployment: "test" }, headers: { "x-default": "yes" } },
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}).image("gpt-image-2"),
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prompt: "A robot tending a rooftop garden",
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options: {
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n: 2,
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size: "2048x2048",
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quality: "future-quality",
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outputFormat: "jpeg",
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output_format: "avif",
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outputCompression: 30,
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output_compression: 40,
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background: "opaque",
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native_default: true,
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future_option: true,
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},
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http: {
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body: { output_format: "webp", output_compression: 50, future_option: "http", request_metadata: "value" },
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headers: { "x-request": "yes" },
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query: { trace: "1" },
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},
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})
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expect(response.images).toHaveLength(2)
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expect(response.image?.mediaType).toBe("image/webp")
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expect(response.image?.data).toEqual(Uint8Array.from([1, 2, 3]))
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expect(response.image?.providerMetadata).toEqual({ openai: { revisedPrompt: "A precise robot" } })
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expect(response.usage?.totalTokens).toBe(12)
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}).pipe(
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Effect.provide(
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ImageClient.layer.pipe(
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Layer.provide(
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dynamicResponse((input) =>
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Effect.gen(function* () {
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const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
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expect(request.url).toBe("https://api.openai.test/v1/images/generations?api-version=v1&trace=1")
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expect(request.headers.get("authorization")).toBe("Bearer test")
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expect(request.headers.get("x-default")).toBe("yes")
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expect(request.headers.get("x-request")).toBe("yes")
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expect(JSON.parse(input.text)).toEqual({
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model: "gpt-image-2",
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prompt: "A robot tending a rooftop garden",
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n: 2,
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size: "2048x2048",
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quality: "future-quality",
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background: "opaque",
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output_format: "webp",
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output_compression: 50,
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native_default: true,
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future_option: "http",
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deployment: "test",
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request_metadata: "value",
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})
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return input.respond(
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JSON.stringify({
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data: [{ b64_json: "AQID", revised_prompt: "A precise robot" }, { b64_json: "BAUG" }],
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output_format: "webp",
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usage: { input_tokens: 4, output_tokens: 8, total_tokens: 12 },
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}),
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{ headers: { "content-type": "application/json" } },
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)
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}),
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),
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),
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),
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),
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),
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)
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it.effect("preserves native snake_case and unknown request options", () =>
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Image.generate({
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model: OpenAI.configure({
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apiKey: "test",
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baseURL: "https://api.openai.test/v1",
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}).image("future-image-model"),
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prompt: "A lighthouse in fog",
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options: {
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outputFormat: "jpeg",
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output_format: "avif",
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outputCompression: 30,
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output_compression: 40,
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provider_future_option: { enabled: true },
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},
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}).pipe(
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Effect.tap((response) =>
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Effect.sync(() => {
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expect(response.image?.mediaType).toBe("image/avif")
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}),
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),
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Effect.provide(
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ImageClient.layer.pipe(
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Layer.provide(
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dynamicResponse((input) => {
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expect(JSON.parse(input.text)).toEqual({
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model: "future-image-model",
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prompt: "A lighthouse in fog",
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output_format: "avif",
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output_compression: 40,
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provider_future_option: { enabled: true },
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})
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return Effect.succeed(
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input.respond(JSON.stringify({ data: [{ b64_json: "AQID" }] }), {
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headers: { "content-type": "application/json" },
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}),
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)
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}),
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),
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),
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),
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),
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)
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})
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