import { describe, expect, it } from "vitest";

import { cosineSimilarity, StubEmbeddingProvider, stubEmbed, STUB_EMBEDDING_DIMENSION } from "./embeddings.js";

describe("stubEmbed", () => {
  it("is deterministic: same text → byte-identical vector", () => {
    expect(stubEmbed("hello world")).toEqual(stubEmbed("hello world"));
  });

  it("is dimension-exact and L2-normalized", () => {
    const v = stubEmbed("the quick brown fox jumps over the lazy dog");
    expect(v).toHaveLength(STUB_EMBEDDING_DIMENSION);
    const norm = Math.sqrt(v.reduce((acc, x) => acc + x * x, 0));
    expect(norm).toBeCloseTo(1, 10);
  });

  it("respects a custom dimension", () => {
    expect(stubEmbed("abc", 16)).toHaveLength(16);
  });

  it("returns a zero vector for empty text", () => {
    const v = stubEmbed("   ");
    expect(v.every((x) => x === 0)).toBe(true);
  });

  it("case- and whitespace-insensitive at the surface", () => {
    expect(stubEmbed("Hello  World")).toEqual(stubEmbed("hello world"));
  });

  it("similar texts score higher than dissimilar ones", () => {
    const base = stubEmbed("quarterly revenue forecast for the sales team");
    const near = stubEmbed("quarterly revenue forecast targets for sales");
    const far = stubEmbed("recipe for chocolate chip cookies with butter");
    expect(cosineSimilarity(base, near)).toBeGreaterThan(cosineSimilarity(base, far));
  });
});

describe("StubEmbeddingProvider", () => {
  it("exposes the contract (model, dimension, embed)", async () => {
    const provider = new StubEmbeddingProvider();
    expect(provider.model).toBe("stub-v1");
    expect(provider.dimension).toBe(STUB_EMBEDDING_DIMENSION);
    const vectors = await provider.embed(["alpha", "beta", "alpha"]);
    expect(vectors).toHaveLength(3);
    expect(vectors[0]).toEqual(vectors[2]); // same text → same vector
    expect(vectors[0]).not.toEqual(vectors[1]);
  });
});

describe("cosineSimilarity", () => {
  it("is 1 for identical unit vectors and 0 for orthogonal", () => {
    expect(cosineSimilarity([1, 0], [1, 0])).toBeCloseTo(1, 12);
    expect(cosineSimilarity([1, 0], [0, 1])).toBeCloseTo(0, 12);
  });

  it("returns 0 for zero vectors (no NaN)", () => {
    expect(cosineSimilarity([0, 0], [1, 1])).toBe(0);
  });

  it("throws on dimension mismatch (the §7.3 structural guard)", () => {
    expect(() => cosineSimilarity([1], [1, 2])).toThrow("dimension mismatch");
  });
});