import type { MemoryEmbeddingProviderCreateOptions } from "openclaw/plugin-sdk/memory-core-host-engine-embeddings";
import { beforeEach, describe, expect, it, vi } from "vitest";

const mocks = vi.hoisted(() => ({
  fetchRemoteEmbeddingVectors: vi.fn(async () => [[1, 0]]),
  resolveRemoteEmbeddingClient: vi.fn(async () => ({
    baseUrl: "https://embeddings.example/v1",
    headers: { Authorization: "Bearer test" },
    model: "text-embedding-3-small",
  })),
}));

vi.mock("openclaw/plugin-sdk/memory-core-host-engine-embeddings", () => ({
  fetchRemoteEmbeddingVectors: mocks.fetchRemoteEmbeddingVectors,
  resolveRemoteEmbeddingClient: mocks.resolveRemoteEmbeddingClient,
}));

import { createOpenAiEmbeddingProvider } from "./embedding-provider.js";

function createOptions(
  overrides: Partial<MemoryEmbeddingProviderCreateOptions> = {},
): MemoryEmbeddingProviderCreateOptions {
  return {
    config: {} as MemoryEmbeddingProviderCreateOptions["config"],
    provider: "openai",
    model: "text-embedding-3-small",
    fallback: "none",
    ...overrides,
  };
}

describe("OpenAI embedding provider", () => {
  beforeEach(() => {
    mocks.fetchRemoteEmbeddingVectors.mockClear();
    mocks.resolveRemoteEmbeddingClient.mockClear();
  });

  it("sends queryInputType on query embeddings", async () => {
    const { provider } = await createOpenAiEmbeddingProvider(
      createOptions({ inputType: "passage", queryInputType: "query" }),
    );

    await provider.embedQuery("hello");

    expect(mocks.fetchRemoteEmbeddingVectors).toHaveBeenCalledWith(
      expect.objectContaining({
        body: {
          model: "text-embedding-3-small",
          input: ["hello"],
          input_type: "query",
        },
      }),
    );
  });

  it("sends documentInputType on document batch embeddings", async () => {
    const { provider } = await createOpenAiEmbeddingProvider(
      createOptions({ inputType: "query", documentInputType: "document" }),
    );

    await provider.embedBatch(["doc one", "doc two"]);

    expect(mocks.fetchRemoteEmbeddingVectors).toHaveBeenCalledWith(
      expect.objectContaining({
        body: {
          model: "text-embedding-3-small",
          input: ["doc one", "doc two"],
          input_type: "document",
        },
      }),
    );
  });

  it("omits input_type unless configured", async () => {
    const { provider } = await createOpenAiEmbeddingProvider(createOptions());

    await provider.embedBatch(["doc"]);

    expect(mocks.fetchRemoteEmbeddingVectors).toHaveBeenCalledWith(
      expect.objectContaining({
        body: {
          model: "text-embedding-3-small",
          input: ["doc"],
        },
      }),
    );
  });

  it("sends outputDimensionality as OpenAI dimensions", async () => {
    const { provider } = await createOpenAiEmbeddingProvider(
      createOptions({ outputDimensionality: 512 }),
    );

    await provider.embedBatch(["doc"]);

    expect(mocks.fetchRemoteEmbeddingVectors).toHaveBeenCalledWith(
      expect.objectContaining({
        body: {
          model: "text-embedding-3-small",
          input: ["doc"],
          dimensions: 512,
        },
      }),
    );
  });
});