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- /**
- * llm.test.ts - Unit tests for the LLM abstraction layer (node-llama-cpp)
- *
- * Run with: bun test src/llm.test.ts
- *
- * These tests require the actual models to be downloaded. Run the embed or
- * rerank functions first to trigger model downloads.
- */
- import { describe, test, expect, beforeAll, afterAll } from "bun:test";
- import {
- LlamaCpp,
- getDefaultLlamaCpp,
- setDefaultLlamaCpp,
- type RerankDocument,
- } from "./llm.js";
- // =============================================================================
- // Singleton Tests (no model loading required)
- // =============================================================================
- describe("Default LlamaCpp Singleton", () => {
- // Don't dispose - let process exit handle Metal cleanup naturally
- test("getDefaultLlamaCpp creates instance on first call", () => {
- setDefaultLlamaCpp(null);
- const llm = getDefaultLlamaCpp();
- expect(llm).toBeInstanceOf(LlamaCpp);
- });
- test("getDefaultLlamaCpp returns same instance on subsequent calls", () => {
- setDefaultLlamaCpp(null);
- const llm1 = getDefaultLlamaCpp();
- const llm2 = getDefaultLlamaCpp();
- expect(llm1).toBe(llm2);
- });
- test("setDefaultLlamaCpp allows replacing the singleton", () => {
- const custom = new LlamaCpp({ embedModel: "custom-model" });
- setDefaultLlamaCpp(custom);
- const result = getDefaultLlamaCpp();
- expect(result).toBe(custom);
- });
- test("setDefaultLlamaCpp with null resets singleton", () => {
- const original = getDefaultLlamaCpp();
- setDefaultLlamaCpp(null);
- const newInstance = getDefaultLlamaCpp();
- expect(newInstance).not.toBe(original);
- });
- });
- // =============================================================================
- // Model Existence Tests
- // =============================================================================
- describe("LlamaCpp.modelExists", () => {
- test("returns exists:true for HuggingFace model URIs", async () => {
- const llm = getDefaultLlamaCpp();
- const result = await llm.modelExists("hf:org/repo/model.gguf");
- expect(result.exists).toBe(true);
- expect(result.name).toBe("hf:org/repo/model.gguf");
- });
- test("returns exists:false for non-existent local paths", async () => {
- const llm = getDefaultLlamaCpp();
- const result = await llm.modelExists("/nonexistent/path/model.gguf");
- expect(result.exists).toBe(false);
- expect(result.name).toBe("/nonexistent/path/model.gguf");
- });
- });
- // =============================================================================
- // Integration Tests (require actual models)
- // =============================================================================
- describe("LlamaCpp Integration", () => {
- // Use the singleton to avoid multiple Metal contexts
- const llm = getDefaultLlamaCpp();
- describe("embed", () => {
- test("returns embedding with correct dimensions", async () => {
- const result = await llm.embed("Hello world");
- expect(result).not.toBeNull();
- expect(result!.embedding).toBeInstanceOf(Array);
- expect(result!.embedding.length).toBeGreaterThan(0);
- // embeddinggemma outputs 768 dimensions
- expect(result!.embedding.length).toBe(768);
- });
- test("returns consistent embeddings for same input", async () => {
- const result1 = await llm.embed("test text");
- const result2 = await llm.embed("test text");
- expect(result1).not.toBeNull();
- expect(result2).not.toBeNull();
- // Embeddings should be identical for the same input
- for (let i = 0; i < result1!.embedding.length; i++) {
- expect(result1!.embedding[i]).toBeCloseTo(result2!.embedding[i], 5);
- }
- });
- test("returns different embeddings for different inputs", async () => {
- const result1 = await llm.embed("cats are great");
- const result2 = await llm.embed("database optimization");
- expect(result1).not.toBeNull();
- expect(result2).not.toBeNull();
- // Calculate cosine similarity - should be less than 1.0 (not identical)
- let dotProduct = 0;
- let norm1 = 0;
- let norm2 = 0;
- for (let i = 0; i < result1!.embedding.length; i++) {
- dotProduct += result1!.embedding[i] * result2!.embedding[i];
- norm1 += result1!.embedding[i] ** 2;
- norm2 += result2!.embedding[i] ** 2;
- }
- const similarity = dotProduct / (Math.sqrt(norm1) * Math.sqrt(norm2));
- expect(similarity).toBeLessThan(0.95); // Should be meaningfully different
- });
- });
- describe("embedBatch", () => {
- test("returns embeddings for multiple texts", async () => {
- const texts = ["Hello world", "Test text", "Another document"];
- const results = await llm.embedBatch(texts);
- expect(results).toHaveLength(3);
- for (const result of results) {
- expect(result).not.toBeNull();
- expect(result!.embedding.length).toBe(768);
- }
- });
- test("returns same results as individual embed calls", async () => {
- const texts = ["cats are great", "dogs are awesome"];
- // Get batch embeddings
- const batchResults = await llm.embedBatch(texts);
- // Get individual embeddings
- const individualResults = await Promise.all(texts.map(t => llm.embed(t)));
- // Compare - should be identical
- for (let i = 0; i < texts.length; i++) {
- expect(batchResults[i]).not.toBeNull();
- expect(individualResults[i]).not.toBeNull();
- for (let j = 0; j < batchResults[i]!.embedding.length; j++) {
- expect(batchResults[i]!.embedding[j]).toBeCloseTo(individualResults[i]!.embedding[j], 5);
- }
- }
- });
- test("handles empty array", async () => {
- const results = await llm.embedBatch([]);
- expect(results).toHaveLength(0);
- });
- test("batch is faster than sequential", async () => {
- const texts = Array(10).fill(null).map((_, i) => `Document number ${i} with content`);
- // Time batch
- const batchStart = Date.now();
- await llm.embedBatch(texts);
- const batchTime = Date.now() - batchStart;
- // Time sequential
- const seqStart = Date.now();
- for (const text of texts) {
- await llm.embed(text);
- }
- const seqTime = Date.now() - seqStart;
- console.log(`Batch: ${batchTime}ms, Sequential: ${seqTime}ms`);
- // Batch should be faster (or at least not much slower)
- // Allow some variance since first call may load the model
- expect(batchTime).toBeLessThan(seqTime * 1.5);
- });
- });
- describe("rerank", () => {
- test("scores capital of France question correctly", async () => {
- const query = "What is the capital of France?";
- const documents: RerankDocument[] = [
- { file: "butterflies.txt", text: "Butterflies indeed fly through the garden." },
- { file: "france.txt", text: "The capital of France is Paris." },
- { file: "canada.txt", text: "The capital of Canada is Ottawa." },
- ];
- const result = await llm.rerank(query, documents);
- expect(result.results).toHaveLength(3);
- // The France document should score highest
- expect(result.results[0].file).toBe("france.txt");
- expect(result.results[0].score).toBeGreaterThan(0.7);
- // Canada should be somewhat relevant (also about capitals)
- expect(result.results[1].file).toBe("canada.txt");
- // Butterflies should score lowest
- expect(result.results[2].file).toBe("butterflies.txt");
- expect(result.results[2].score).toBeLessThan(0.6);
- });
- test("scores authentication query correctly", async () => {
- const query = "How do I configure authentication?";
- const documents: RerankDocument[] = [
- { file: "weather.md", text: "The weather today is sunny with mild temperatures." },
- { file: "auth.md", text: "Authentication can be configured by setting the AUTH_SECRET environment variable." },
- { file: "pizza.md", text: "Our restaurant serves the best pizza in town." },
- { file: "jwt.md", text: "JWT authentication requires a secret key and expiration time." },
- ];
- const result = await llm.rerank(query, documents);
- expect(result.results).toHaveLength(4);
- // Auth documents should score highest
- const topTwo = result.results.slice(0, 2).map((r) => r.file);
- expect(topTwo).toContain("auth.md");
- expect(topTwo).toContain("jwt.md");
- // Irrelevant documents should score lowest
- const bottomTwo = result.results.slice(2).map((r) => r.file);
- expect(bottomTwo).toContain("weather.md");
- expect(bottomTwo).toContain("pizza.md");
- });
- test("handles programming queries correctly", async () => {
- const query = "How do I handle errors in JavaScript?";
- const documents: RerankDocument[] = [
- { file: "cooking.md", text: "To make a good pasta, boil water and add salt." },
- { file: "errors.md", text: "Use try-catch blocks to handle JavaScript errors gracefully." },
- { file: "python.md", text: "Python uses try-except for exception handling." },
- ];
- const result = await llm.rerank(query, documents);
- // JavaScript errors doc should score highest
- expect(result.results[0].file).toBe("errors.md");
- expect(result.results[0].score).toBeGreaterThan(0.7);
- // Python doc might be somewhat relevant (same concept, different language)
- // Cooking should be least relevant
- expect(result.results[2].file).toBe("cooking.md");
- });
- test("handles empty document list", async () => {
- const result = await llm.rerank("test query", []);
- expect(result.results).toHaveLength(0);
- });
- test("handles single document", async () => {
- const result = await llm.rerank("test", [{ file: "doc.md", text: "content" }]);
- expect(result.results).toHaveLength(1);
- expect(result.results[0].file).toBe("doc.md");
- });
- test("preserves original file paths", async () => {
- const documents: RerankDocument[] = [
- { file: "path/to/doc1.md", text: "content one" },
- { file: "another/path/doc2.md", text: "content two" },
- ];
- const result = await llm.rerank("query", documents);
- const files = result.results.map((r) => r.file).sort();
- expect(files).toEqual(["another/path/doc2.md", "path/to/doc1.md"]);
- });
- test("returns scores between 0 and 1", async () => {
- const documents: RerankDocument[] = [
- { file: "a.md", text: "The quick brown fox jumps over the lazy dog." },
- { file: "b.md", text: "Machine learning algorithms process data efficiently." },
- { file: "c.md", text: "React components use JSX syntax for rendering." },
- ];
- const result = await llm.rerank("Tell me about animals", documents);
- for (const doc of result.results) {
- expect(doc.score).toBeGreaterThanOrEqual(0);
- expect(doc.score).toBeLessThanOrEqual(1);
- }
- });
- test("batch reranks multiple documents efficiently", async () => {
- // Create 10 documents to verify batch processing works
- const documents: RerankDocument[] = Array(10)
- .fill(null)
- .map((_, i) => ({
- file: `doc${i}.md`,
- text: `Document number ${i} with some content about topic ${i % 3}`,
- }));
- const start = Date.now();
- const result = await llm.rerank("topic 1", documents);
- const elapsed = Date.now() - start;
- expect(result.results).toHaveLength(10);
- // Verify all documents are returned with valid scores
- for (const doc of result.results) {
- expect(doc.score).toBeGreaterThanOrEqual(0);
- expect(doc.score).toBeLessThanOrEqual(1);
- }
- // Log timing for monitoring batch performance
- console.log(`Batch rerank of 10 docs took ${elapsed}ms`);
- });
- });
- describe("expandQuery", () => {
- test("returns at least the original query", async () => {
- const result = await llm.expandQuery("test query");
- expect(result).toContain("test query");
- expect(result.length).toBeGreaterThanOrEqual(1);
- }, 30000); // 30s timeout for model loading
- test("returns original query first", async () => {
- const result = await llm.expandQuery("authentication setup");
- expect(result[0]).toBe("authentication setup");
- });
- });
- });
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