/**
* Vector Tools Tests
*
* Tests for SQLite vector similarity search tools.
*/
import { describe, it, expect, beforeEach, afterEach } from 'vitest';
import { SqliteAdapter } from '../../../src/adapters/sqlite/SqliteAdapter.js';
describe('Vector Tools', () => {
let adapter: SqliteAdapter;
let tools: Map<string, (params: unknown) => Promise<unknown>>;
beforeEach(async () => {
adapter = new SqliteAdapter();
await adapter.connect({
type: 'sqlite',
connectionString: ':memory:'
});
tools = new Map();
const toolDefs = adapter.getToolDefinitions();
const context = { scopes: ['read', 'write', 'admin'] };
for (const tool of toolDefs) {
tools.set(tool.name, (params) => tool.handler(params, context as never));
}
});
afterEach(async () => {
await adapter.disconnect();
});
describe('sqlite_vector_create_table', () => {
it('should create vector table', async () => {
const result = await tools.get('sqlite_vector_create_table')?.({
tableName: 'embeddings',
dimensions: 3
});
expect(result).toHaveProperty('success', true);
const tables = await adapter.listTables();
expect(tables.map(t => t.name)).toContain('embeddings');
});
});
describe('sqlite_vector_store', () => {
it('should store a vector', async () => {
await adapter.executeWriteQuery(`
CREATE TABLE vectors (id INTEGER PRIMARY KEY, embedding TEXT)
`);
const result = await tools.get('sqlite_vector_store')?.({
table: 'vectors',
idColumn: 'id',
vectorColumn: 'embedding',
id: 1,
vector: [0.1, 0.2, 0.3]
});
expect(result).toHaveProperty('success', true);
});
});
describe('sqlite_vector_search', () => {
it('should find similar vectors', async () => {
await adapter.executeWriteQuery(`
CREATE TABLE vectors (id INTEGER PRIMARY KEY, embedding TEXT, label TEXT)
`);
// Store some vectors
await adapter.executeWriteQuery(`
INSERT INTO vectors (embedding, label) VALUES
('[1.0, 0.0, 0.0]', 'x-axis'),
('[0.0, 1.0, 0.0]', 'y-axis'),
('[0.707, 0.707, 0.0]', 'xy-diagonal')
`);
// Search for vector similar to x-axis
const result = await tools.get('sqlite_vector_search')?.({
table: 'vectors',
vectorColumn: 'embedding',
queryVector: [0.9, 0.1, 0.0],
metric: 'cosine',
limit: 2
}) as { results: { label: string; _similarity: number }[] };
// x-axis should be most similar
expect(result.results[0]?.label).toBe('x-axis');
});
});
describe('sqlite_vector_distance', () => {
it('should calculate cosine similarity', async () => {
const result = await tools.get('sqlite_vector_distance')?.({
vector1: [1, 0, 0],
vector2: [1, 0, 0],
metric: 'cosine'
}) as { value: number };
expect(result.value).toBe(1); // Same vector = similarity 1
});
it('should calculate euclidean distance', async () => {
const result = await tools.get('sqlite_vector_distance')?.({
vector1: [0, 0],
vector2: [3, 4],
metric: 'euclidean'
}) as { value: number };
expect(result.value).toBe(5); // 3-4-5 triangle
});
});
describe('sqlite_vector_normalize', () => {
it('should normalize a vector', async () => {
const result = await tools.get('sqlite_vector_normalize')?.({
vector: [3, 4]
}) as { normalized: number[] };
expect(result.normalized[0]).toBeCloseTo(0.6, 5);
expect(result.normalized[1]).toBeCloseTo(0.8, 5);
});
});
});
