import { describe, it, expect } from "vitest";
import { cosineSimilarity, vectorSearch } from "./vectorSearch";
import type { EmbeddedChunk } from "../types";

// ---------------------------------------------------------------------------
// Helper to create an EmbeddedChunk with a given embedding
// ---------------------------------------------------------------------------
function makeEmbeddedChunk(
  embedding: number[],
  docId = "doc1",
  chunkIndex = 0,
): EmbeddedChunk {
  return {
    docId,
    chunkIndex,
    text: `chunk ${chunkIndex} of ${docId}`,
    startChar: 0,
    title: "Test",
    embedding: new Float32Array(embedding),
  };
}

// ---------------------------------------------------------------------------
// cosineSimilarity
// ---------------------------------------------------------------------------
describe("cosineSimilarity", () => {
  it("returns 1 for identical vectors", () => {
    const v = new Float32Array([1, 2, 3]);
    expect(cosineSimilarity(v, v)).toBeCloseTo(1.0, 5);
  });

  it("returns -1 for opposite vectors", () => {
    const a = new Float32Array([1, 0, 0]);
    const b = new Float32Array([-1, 0, 0]);
    expect(cosineSimilarity(a, b)).toBeCloseTo(-1.0, 5);
  });

  it("returns 0 for orthogonal vectors", () => {
    const a = new Float32Array([1, 0, 0]);
    const b = new Float32Array([0, 1, 0]);
    expect(cosineSimilarity(a, b)).toBeCloseTo(0.0, 5);
  });

  it("is symmetric", () => {
    const a = new Float32Array([1, 2, 3]);
    const b = new Float32Array([4, 5, 6]);
    expect(cosineSimilarity(a, b)).toBeCloseTo(cosineSimilarity(b, a), 10);
  });

  it("is scale-invariant", () => {
    const a = new Float32Array([1, 2, 3]);
    const b = new Float32Array([2, 4, 6]); // 2x scale of a
    expect(cosineSimilarity(a, b)).toBeCloseTo(1.0, 5);
  });

  it("computes correct value for known vectors", () => {
    // a = [3, 4], b = [4, 3]
    // dot = 12 + 12 = 24, |a| = 5, |b| = 5 → cos = 24/25 = 0.96
    const a = new Float32Array([3, 4]);
    const b = new Float32Array([4, 3]);
    expect(cosineSimilarity(a, b)).toBeCloseTo(0.96, 2);
  });
});

// ---------------------------------------------------------------------------
// vectorSearch
// ---------------------------------------------------------------------------
describe("vectorSearch", () => {
  // Create chunks with embeddings at known angles from the query
  const query = new Float32Array([1, 0, 0]);

  const chunks: EmbeddedChunk[] = [
    makeEmbeddedChunk([0, 1, 0], "orthogonal", 0), // cos = 0
    makeEmbeddedChunk([1, 0, 0], "identical", 0), // cos = 1
    makeEmbeddedChunk([0.7, 0.7, 0], "similar", 0), // cos ≈ 0.707
    makeEmbeddedChunk([-1, 0, 0], "opposite", 0), // cos = -1
    makeEmbeddedChunk([0.9, 0.1, 0], "very-similar", 0), // cos ≈ 0.994
  ];

  it("returns results sorted by descending score", () => {
    const results = vectorSearch(query, chunks);
    for (let i = 1; i < results.length; i++) {
      expect(results[i].score).toBeLessThanOrEqual(results[i - 1].score);
    }
  });

  it("ranks identical vector highest", () => {
    const results = vectorSearch(query, chunks);
    expect(results[0].chunk.docId).toBe("identical");
  });

  it("ranks opposite vector lowest", () => {
    const results = vectorSearch(query, chunks);
    expect(results[results.length - 1].chunk.docId).toBe("opposite");
  });

  it("all results have source 'vector'", () => {
    const results = vectorSearch(query, chunks);
    for (const r of results) {
      expect(r.source).toBe("vector");
    }
  });

  it("respects topK parameter", () => {
    const results = vectorSearch(query, chunks, 2);
    expect(results.length).toBe(2);
    expect(results[0].chunk.docId).toBe("identical");
    expect(results[1].chunk.docId).toBe("very-similar");
  });

  it("returns all when topK exceeds chunk count", () => {
    const results = vectorSearch(query, chunks, 100);
    expect(results.length).toBe(chunks.length);
  });

  it("handles empty chunks array", () => {
    const results = vectorSearch(query, []);
    expect(results).toEqual([]);
  });

  it("handles single chunk", () => {
    const single = [makeEmbeddedChunk([0.5, 0.5, 0], "only", 0)];
    const results = vectorSearch(query, single);
    expect(results.length).toBe(1);
    expect(results[0].source).toBe("vector");
    expect(results[0].score).toBeGreaterThan(0);
  });
});