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+ }, + "bin": { + "why-is-node-running": "cli.js" + }, + "engines": { + "node": ">=8" + } + }, + "node_modules/yallist": { + "version": "5.0.0", + "resolved": "https://registry.npmjs.org/yallist/-/yallist-5.0.0.tgz", + "integrity": "sha512-YgvUTfwqyc7UXVMrB+SImsVYSmTS8X/tSrtdNZMImM+n7+QTriRXyXim0mBrTXNeqzVF0KWGgHPeiyViFFrNDw==", + "license": "BlueOak-1.0.0", + "engines": { + "node": ">=18" + } + } + } +} diff --git a/package.json b/package.json new file mode 100644 index 0000000..9b33397 --- /dev/null +++ b/package.json @@ -0,0 +1,24 @@ +{ + "name": "smart-notes", + "version": "1.0.0", + "description": "AI-powered, privacy-focused desktop app for personal knowledge management", + "private": true, + "type": "module", + "scripts": { + "test": "vitest run", + "test:watch": "vitest", + "build": "tsc -p tsconfig.build.json", + "typecheck": "tsc --noEmit" + }, + "dependencies": { + "@huggingface/transformers": "^3.4.0" + }, + "devDependencies": { + "typescript": "^5.7.0", + "vitest": "^3.0.0", + "@types/node": "^22.0.0" + }, + "engines": { + "node": ">=18.0.0" + } +} \ No newline at end of file diff --git a/src/ai/__tests__/embeddings.test.ts b/src/ai/__tests__/embeddings.test.ts new file mode 100644 index 0000000..e88847f --- /dev/null +++ b/src/ai/__tests__/embeddings.test.ts @@ -0,0 +1,141 @@ +import { describe, it, expect, vi } from "vitest"; +import { EmbeddingService } from "../embeddings.js"; +import type { EmbedFn } from "../embeddings.js"; +import type { TextChunk } from "../types.js"; + +/** Mock that generates deterministic vectors from text via char codes. */ +function createMockEmbedFn(dimensions: number = 384): EmbedFn { + return async (texts: string[]): Promise => { + return texts.map((text) => { + const vector = new Array(dimensions).fill(0); + for (let i = 0; i < text.length; i++) { + vector[i % dimensions] += text.charCodeAt(i) / 1000; + } + const norm = Math.sqrt(vector.reduce((sum, v) => sum + v * v, 0)); + return norm > 0 ? vector.map((v) => v / norm) : vector; + }); + }; +} + +describe("EmbeddingService", () => { + describe("constructor", () => { + it("uses default config when none provided", () => { + const service = new EmbeddingService({}, createMockEmbedFn()); + expect(service.modelId).toBe("Xenova/all-MiniLM-L6-v2"); + }); + + it("accepts custom config", () => { + const service = new EmbeddingService( + { modelId: "custom/model", maxTokenLength: 512 }, + createMockEmbedFn(), + ); + expect(service.modelId).toBe("custom/model"); + }); + }); + + describe("embed", () => { + it("throws if not initialized and no custom embedFn", async () => { + const service = new EmbeddingService(); + await expect(service.embed(["test"])).rejects.toThrow( + "EmbeddingService not initialized", + ); + }); + + it("works immediately with injected embedFn", async () => { + const service = new EmbeddingService({}, createMockEmbedFn()); + const result = await service.embed(["hello world"]); + + expect(result).toHaveLength(1); + expect(result[0]).toHaveLength(384); + }); + + it("returns empty array for empty input", async () => { + const service = new EmbeddingService({}, createMockEmbedFn()); + expect(await service.embed([])).toEqual([]); + }); + + it("produces different vectors for different texts", async () => { + const service = new EmbeddingService({}, createMockEmbedFn()); + const results = await service.embed(["hello", "goodbye"]); + + expect(results).toHaveLength(2); + const areDifferent = results[0].some( + (v, i) => Math.abs(v - results[1][i]) > 0.001, + ); + expect(areDifferent).toBe(true); + }); + + it("is deterministic for the same input", async () => { + const service = new EmbeddingService({}, createMockEmbedFn()); + const a = await service.embed(["test text"]); + const b = await service.embed(["test text"]); + expect(a[0]).toEqual(b[0]); + }); + + it("truncates long texts based on maxTokenLength", async () => { + const spy = vi.fn(createMockEmbedFn()); + const service = new EmbeddingService( + { modelId: "test", maxTokenLength: 10 }, + spy, + ); + + await service.embed(["a".repeat(100)]); + // 10 tokens * 4 chars = 40 char max + expect(spy.mock.calls[0][0][0].length).toBe(40); + }); + }); + + describe("embedSingle", () => { + it("returns a single vector", async () => { + const service = new EmbeddingService({}, createMockEmbedFn()); + const vector = await service.embedSingle("hello"); + + expect(vector).toHaveLength(384); + expect(Array.isArray(vector)).toBe(true); + }); + }); + + describe("embedChunks", () => { + it("preserves original chunk metadata", async () => { + const service = new EmbeddingService({}, createMockEmbedFn()); + const chunks: TextChunk[] = [ + { id: "n1:0", noteId: "n1", content: "Hello world", chunkIndex: 0 }, + { id: "n1:1", noteId: "n1", content: "Goodbye world", chunkIndex: 1 }, + ]; + + const results = await service.embedChunks(chunks); + + expect(results).toHaveLength(2); + expect(results[0].chunk).toEqual(chunks[0]); + expect(results[0].vector).toHaveLength(384); + }); + + it("prepends heading to content when available", async () => { + const spy = vi.fn(createMockEmbedFn()); + const service = new EmbeddingService({}, spy); + + await service.embedChunks([ + { + id: "n1:0", + noteId: "n1", + content: "Some details", + heading: "Introduction", + chunkIndex: 0, + }, + ]); + expect(spy.mock.calls[0][0][0]).toBe("Introduction: Some details"); + }); + + it("returns empty for empty input", async () => { + const service = new EmbeddingService({}, createMockEmbedFn()); + expect(await service.embedChunks([])).toEqual([]); + }); + }); + + describe("initialize", () => { + it("is a no-op when custom embedFn is provided", async () => { + const service = new EmbeddingService({}, createMockEmbedFn()); + await expect(service.initialize()).resolves.toBeUndefined(); + }); + }); +}); diff --git a/src/ai/__tests__/semanticSearch.test.ts b/src/ai/__tests__/semanticSearch.test.ts new file mode 100644 index 0000000..7877b2c --- /dev/null +++ b/src/ai/__tests__/semanticSearch.test.ts @@ -0,0 +1,231 @@ +import { describe, it, expect, beforeEach, afterEach } from "vitest"; +import { mkdtemp, rm } from "node:fs/promises"; +import { tmpdir } from "node:os"; +import { join } from "node:path"; +import { SemanticSearchService, defaultChunker } from "../semanticSearch.js"; +import { EmbeddingService } from "../embeddings.js"; +import { VectorStore } from "../vectorStore.js"; +import type { EmbedFn } from "../embeddings.js"; +import type { TextChunk } from "../types.js"; + +/** + * Mock embeddings that put similar topics near each other in vector space. + * ML/AI content clusters around dimensions 0-1, cooking around 2-3, etc. + */ +function createSemanticMockEmbedFn(dimensions: number = 384): EmbedFn { + return async (texts: string[]): Promise => { + return texts.map((text) => { + const vector = new Array(dimensions).fill(0); + const lower = text.toLowerCase(); + + if (lower.includes("machine learning") || lower.includes("neural")) { vector[0] = 0.8; vector[1] = 0.5; } + if (lower.includes("cooking") || lower.includes("recipe")) { vector[2] = 0.8; vector[3] = 0.5; } + if (lower.includes("typescript") || lower.includes("javascript")) { vector[4] = 0.8; vector[5] = 0.5; } + if (lower.includes("ai") || lower.includes("artificial")) { vector[0] = 0.6; vector[6] = 0.7; } + + vector[10] = text.length / 1000; + + const norm = Math.sqrt(vector.reduce((s, v) => s + v * v, 0)); + return norm > 0 ? vector.map((v) => v / norm) : vector; + }); + }; +} + +describe("defaultChunker", () => { + it("splits text by double newlines", () => { + const chunks = defaultChunker("n1", "paragraph one\n\nparagraph two"); + expect(chunks).toHaveLength(2); + expect(chunks[0].content).toBe("paragraph one"); + expect(chunks[1].content).toBe("paragraph two"); + }); + + it("assigns correct IDs and indices", () => { + const chunks = defaultChunker("my-note", "a\n\nb\n\nc"); + expect(chunks[0].id).toBe("my-note:0"); + expect(chunks[1].id).toBe("my-note:1"); + expect(chunks[2].id).toBe("my-note:2"); + expect(chunks.every((c) => c.noteId === "my-note")).toBe(true); + }); + + it("filters out empty paragraphs", () => { + const chunks = defaultChunker("n1", "text\n\n\n\n\n\nmore text"); + expect(chunks).toHaveLength(2); + }); + + it("returns empty array for empty/whitespace content", () => { + expect(defaultChunker("n1", "")).toEqual([]); + expect(defaultChunker("n1", " ")).toEqual([]); + }); +}); + +describe("SemanticSearchService", () => { + let tempDir: string; + let service: SemanticSearchService; + + beforeEach(async () => { + tempDir = await mkdtemp(join(tmpdir(), "smartnotes-ss-")); + + const embeddingService = new EmbeddingService( + {}, + createSemanticMockEmbedFn(), + ); + const vectorStore = new VectorStore({ + persistDir: tempDir, + indexFilename: "search-index.json", + }); + + service = new SemanticSearchService(embeddingService, vectorStore); + await service.initialize(); + }); + + afterEach(async () => { + await rm(tempDir, { recursive: true, force: true }); + }); + + describe("indexNote", () => { + it("indexes a note and reports chunk count", async () => { + const count = await service.indexNote( + "n1", + "First paragraph about machine learning.\n\n" + + "Second paragraph about neural networks.", + ); + expect(count).toBe(2); + expect(service.indexSize).toBe(2); + }); + + it("replaces old chunks when re-indexing", async () => { + await service.indexNote("n1", "old content\n\nmore old content"); + expect(service.indexSize).toBe(2); + + await service.indexNote("n1", "new single paragraph"); + expect(service.indexSize).toBe(1); + }); + + it("handles empty notes", async () => { + expect(await service.indexNote("n1", "")).toBe(0); + }); + }); + + describe("search", () => { + beforeEach(async () => { + await service.indexNote( + "ml-note", + "Introduction to machine learning and neural networks.\n\n" + + "Deep learning is a subset of machine learning.", + ); + await service.indexNote( + "cooking-note", + "A delicious pasta recipe for dinner.\n\n" + + "Cooking tips and kitchen techniques.", + ); + await service.indexNote( + "ts-note", + "TypeScript fundamentals and type safety.\n\n" + + "Advanced JavaScript patterns.", + ); + }); + + it("ranks relevant results higher", async () => { + const results = await service.search("machine learning AI"); + expect(results.length).toBeGreaterThan(0); + expect(results[0].chunk.noteId).toBe("ml-note"); + }); + + it("respects topK option", async () => { + const results = await service.search("general topic", { + topK: 2, + minScore: 0, + }); + expect(results.length).toBeLessThanOrEqual(2); + }); + + it("filters by noteId", async () => { + const results = await service.search("any query", { + noteId: "cooking-note", + minScore: 0, + }); + // Assert non-empty first — results.every() is vacuously true on [] + expect(results.length).toBeGreaterThan(0); + expect(results.every((r) => r.chunk.noteId === "cooking-note")).toBe(true); + }); + + it("returns empty when nothing meets minScore", async () => { + const results = await service.search("completely unrelated xyz", { + minScore: 0.99, + }); + expect(results).toHaveLength(0); + }); + }); + + describe("removeNote", () => { + it("removes a note from the index", async () => { + await service.indexNote("n1", "some content\n\nmore content"); + expect(service.removeNote("n1")).toBe(2); + expect(service.indexSize).toBe(0); + }); + }); + + describe("findRelatedNotes", () => { + it("finds notes with similar content", async () => { + await service.indexNote("ml-intro", "Introduction to machine learning."); + await service.indexNote("ml-advanced", "Advanced machine learning and neural networks."); + await service.indexNote("cooking", "How to make the perfect pasta recipe."); + + const related = await service.findRelatedNotes("ml-intro", 5); + const noteIds = related.map((r) => r.chunk.noteId); + + // Unconditional — must return results, not guarded by if() + expect(related.length).toBeGreaterThan(0); + expect(noteIds).toContain("ml-advanced"); + expect(noteIds).not.toContain("cooking"); + }); + }); + + describe("isNoteIndexed", () => { + it("returns true for indexed notes", async () => { + await service.indexNote("n1", "some content"); + expect(service.isNoteIndexed("n1")).toBe(true); + expect(service.isNoteIndexed("n2")).toBe(false); + }); + }); + + describe("persistence", () => { + it("saves and restores the index across instances", async () => { + await service.indexNote("n1", "content about machine learning"); + await service.save(); + + const service2 = new SemanticSearchService( + new EmbeddingService({}, createSemanticMockEmbedFn()), + new VectorStore({ + persistDir: tempDir, + indexFilename: "search-index.json", + }), + ); + await service2.initialize(); + + expect(service2.indexSize).toBe(1); + expect(service2.isNoteIndexed("n1")).toBe(true); + }); + }); + + describe("custom chunker", () => { + it("accepts a custom chunk function", async () => { + const singleChunker = (noteId: string, content: string): TextChunk[] => [ + { id: `${noteId}:custom`, noteId, content, chunkIndex: 0 }, + ]; + + const customService = new SemanticSearchService( + new EmbeddingService({}, createSemanticMockEmbedFn()), + new VectorStore({ + persistDir: tempDir, + indexFilename: "custom-index.json", + }), + singleChunker, + ); + await customService.initialize(); + + // singleChunker always returns one chunk regardless of content + expect(await customService.indexNote("n1", "hello\n\nworld")).toBe(1); + }); + }); +}); diff --git a/src/ai/__tests__/vectorStore.test.ts b/src/ai/__tests__/vectorStore.test.ts new file mode 100644 index 0000000..9836fac --- /dev/null +++ b/src/ai/__tests__/vectorStore.test.ts @@ -0,0 +1,233 @@ +import { describe, it, expect, beforeEach, afterEach } from "vitest"; +import { mkdtemp, rm, readFile } from "node:fs/promises"; +import { tmpdir } from "node:os"; +import { join } from "node:path"; +import { VectorStore, cosineSimilarity } from "../vectorStore.js"; +import type { EmbeddedChunk, VectorStoreConfig } from "../types.js"; + +function makeChunk( + noteId: string, + index: number, + vector: number[], +): EmbeddedChunk { + return { + chunk: { + id: `${noteId}:${index}`, + noteId, + content: `Chunk ${index} of note ${noteId}`, + chunkIndex: index, + }, + vector, + }; +} + +function unitVector(angle: number): number[] { + return [Math.cos(angle), Math.sin(angle), 0]; +} + +describe("cosineSimilarity", () => { + it("returns 1 for identical vectors", () => { + expect(cosineSimilarity([1, 0, 0], [1, 0, 0])).toBeCloseTo(1.0); + }); + + it("returns 0 for orthogonal vectors", () => { + expect(cosineSimilarity([1, 0, 0], [0, 1, 0])).toBeCloseTo(0.0); + }); + + it("returns -1 for opposite vectors", () => { + expect(cosineSimilarity([1, 0], [-1, 0])).toBeCloseTo(-1.0); + }); + + it("handles non-unit vectors", () => { + expect(cosineSimilarity([2, 0, 0], [3, 0, 0])).toBeCloseTo(1.0); + }); + + it("returns 0 for zero vectors", () => { + expect(cosineSimilarity([0, 0, 0], [1, 2, 3])).toBe(0); + }); + + it("throws for mismatched dimensions", () => { + expect(() => cosineSimilarity([1, 2], [1, 2, 3])).toThrow( + "Vector dimension mismatch", + ); + }); +}); + +describe("VectorStore", () => { + let tempDir: string; + let config: VectorStoreConfig; + let store: VectorStore; + + beforeEach(async () => { + tempDir = await mkdtemp(join(tmpdir(), "smartnotes-vs-")); + config = { persistDir: tempDir, indexFilename: "test-index.json" }; + store = new VectorStore(config); + }); + + afterEach(async () => { + await rm(tempDir, { recursive: true, force: true }); + }); + + describe("add and search", () => { + it("finds added chunks by similarity", () => { + store.add([ + makeChunk("n1", 0, [1, 0, 0]), + makeChunk("n2", 0, [0, 1, 0]), + ]); + + const results = store.search([1, 0, 0], 10, 0); + expect(results).toHaveLength(2); + expect(results[0].chunk.noteId).toBe("n1"); + expect(results[0].score).toBeCloseTo(1.0); + }); + + it("respects minScore threshold", () => { + store.add([ + makeChunk("n1", 0, [1, 0, 0]), + makeChunk("n2", 0, [0, 1, 0]), + ]); + + const results = store.search([1, 0, 0], 10, 0.5); + expect(results).toHaveLength(1); + }); + + it("respects topK limit", () => { + store.add([ + makeChunk("n1", 0, [1, 0, 0]), + makeChunk("n2", 0, [0.9, 0.1, 0]), + makeChunk("n3", 0, [0.8, 0.2, 0]), + ]); + + expect(store.search([1, 0, 0], 2, 0)).toHaveLength(2); + }); + + it("filters by noteId", () => { + store.add([ + makeChunk("n1", 0, [1, 0, 0]), + makeChunk("n1", 1, [0.9, 0.1, 0]), + makeChunk("n2", 0, [0.95, 0.05, 0]), + ]); + + const results = store.search([1, 0, 0], 10, 0, "n1"); + expect(results).toHaveLength(2); + expect(results.every((r) => r.chunk.noteId === "n1")).toBe(true); + }); + + it("overwrites chunks with the same ID", () => { + store.add([makeChunk("n1", 0, [1, 0, 0])]); + store.add([makeChunk("n1", 0, [0, 1, 0])]); + + expect(store.size).toBe(1); + const results = store.search([0, 1, 0], 1, 0); + expect(results[0].score).toBeCloseTo(1.0); + }); + }); + + describe("removeByNoteId", () => { + it("removes all chunks for a note", () => { + store.add([ + makeChunk("n1", 0, [1, 0, 0]), + makeChunk("n1", 1, [0.9, 0.1, 0]), + makeChunk("n2", 0, [0, 1, 0]), + ]); + + expect(store.removeByNoteId("n1")).toBe(2); + expect(store.size).toBe(1); + }); + + it("returns 0 for unknown note", () => { + store.add([makeChunk("n1", 0, [1, 0, 0])]); + expect(store.removeByNoteId("nonexistent")).toBe(0); + }); + }); + + describe("findRelated", () => { + it("finds related chunks, excluding the query chunk", () => { + store.add([ + makeChunk("n1", 0, unitVector(0)), + makeChunk("n2", 0, unitVector(0.2)), + makeChunk("n3", 0, unitVector(Math.PI / 2)), + ]); + + const results = store.findRelated("n1:0", 5, 0); + expect(results).toHaveLength(2); + expect(results.every((r) => r.chunk.id !== "n1:0")).toBe(true); + expect(results[0].chunk.id).toBe("n2:0"); + }); + + it("returns empty for unknown chunk", () => { + expect(store.findRelated("unknown:0")).toEqual([]); + }); + }); + + describe("persistence", () => { + it("round-trips save and load", async () => { + store.add([ + makeChunk("n1", 0, [1, 0, 0]), + makeChunk("n2", 0, [0, 1, 0]), + ]); + await store.save(); + + const store2 = new VectorStore(config); + expect(await store2.load()).toBe(true); + expect(store2.size).toBe(2); + + const results = store2.search([1, 0, 0], 1, 0); + expect(results[0].chunk.noteId).toBe("n1"); + }); + + it("returns false when no file exists", async () => { + expect(await store.load()).toBe(false); + }); + + it("skips write when nothing changed", async () => { + store.add([makeChunk("n1", 0, [1, 0, 0])]); + await store.save(); + await store.save(); // second save should be no-op + + const content = await readFile( + join(tempDir, "test-index.json"), + "utf-8", + ); + const data = JSON.parse(content); + expect(data.version).toBe(1); + expect(data.entries).toHaveLength(1); + }); + + it("creates directories recursively", async () => { + const nestedStore = new VectorStore({ + persistDir: join(tempDir, "deep", "nested", "dir"), + indexFilename: "index.json", + }); + nestedStore.add([makeChunk("n1", 0, [1, 0, 0])]); + await expect(nestedStore.save()).resolves.toBeUndefined(); + }); + }); + + describe("utility methods", () => { + it("tracks indexed note IDs", () => { + store.add([ + makeChunk("n1", 0, [1, 0, 0]), + makeChunk("n1", 1, [0.9, 0.1, 0]), + makeChunk("n2", 0, [0, 1, 0]), + ]); + + const noteIds = store.getIndexedNoteIds(); + expect(noteIds.size).toBe(2); + expect(noteIds.has("n1")).toBe(true); + expect(noteIds.has("n2")).toBe(true); + }); + + it("checks if a note is indexed", () => { + store.add([makeChunk("n1", 0, [1, 0, 0])]); + expect(store.hasNote("n1")).toBe(true); + expect(store.hasNote("n2")).toBe(false); + }); + + it("clears all entries", () => { + store.add([makeChunk("n1", 0, [1, 0, 0])]); + store.clear(); + expect(store.size).toBe(0); + }); + }); +}); diff --git a/src/ai/embeddings.ts b/src/ai/embeddings.ts new file mode 100644 index 0000000..2fc006b --- /dev/null +++ b/src/ai/embeddings.ts @@ -0,0 +1,111 @@ +// Wraps Hugging Face Transformers.js for on-device text embedding. +// Default model: Xenova/all-MiniLM-L6-v2 (384-dim, runs via ONNX Runtime). + +import type { TextChunk, EmbeddingConfig, EmbeddedChunk } from "./types.js"; +import { DEFAULT_EMBEDDING_CONFIG } from "./types.js"; + +// Lazy-import to avoid loading ONNX runtime until actually needed. +let pipelineFn: typeof import("@huggingface/transformers").pipeline | null = + null; + +async function getPipeline() { + if (!pipelineFn) { + const module = await import("@huggingface/transformers"); + pipelineFn = module.pipeline; + } + return pipelineFn; +} + +/** Pluggable embedding function — allows swapping backends or mocking in tests. */ +export type EmbedFn = (texts: string[]) => Promise; + +export class EmbeddingService { + private config: EmbeddingConfig; + private embedFn: EmbedFn | null = null; + private initPromise: Promise | null = null; + + constructor( + config: Partial = {}, + embedFn?: EmbedFn, + ) { + this.config = { ...DEFAULT_EMBEDDING_CONFIG, ...config }; + if (embedFn) { + this.embedFn = embedFn; + } + } + + /** Load and cache the model. No-op if a custom embedFn was provided. */ + async initialize(): Promise { + if (this.embedFn) return; + if (this.initPromise) return this.initPromise; + + this.initPromise = (async () => { + try { + const pipeline = await getPipeline(); + const extractor = await pipeline( + "feature-extraction", + this.config.modelId, + { dtype: "fp32" }, + ); + + this.embedFn = async (texts: string[]): Promise => { + const output = await extractor(texts, { + pooling: "mean", + normalize: true, + }); + return output.tolist() as number[][]; + }; + } catch (error) { + // Clear so the next initialize() call can retry instead of + // returning this cached rejected Promise forever. + this.initPromise = null; + throw error; + } + })(); + + return this.initPromise; + } + + /** Embed multiple texts. Throws if not initialized. */ + async embed(texts: string[]): Promise { + if (!this.embedFn) { + throw new Error( + "EmbeddingService not initialized. Call initialize() first, " + + "or provide a custom embedFn in the constructor.", + ); + } + if (texts.length === 0) return []; + + // ~4 chars per token as rough estimate + const maxChars = this.config.maxTokenLength * 4; + const truncated = texts.map((t) => + t.length > maxChars ? t.slice(0, maxChars) : t, + ); + + return this.embedFn(truncated); + } + + async embedSingle(text: string): Promise { + const results = await this.embed([text]); + return results[0]; + } + + /** Embed TextChunks, prepending heading context when available. */ + async embedChunks(chunks: TextChunk[]): Promise { + if (chunks.length === 0) return []; + + const texts = chunks.map((c) => + c.heading ? `${c.heading}: ${c.content}` : c.content, + ); + const vectors = await this.embed(texts); + + return chunks.map((chunk, i) => ({ + chunk, + vector: vectors[i], + })); + } + + get modelId(): string { + return this.config.modelId; + } +} diff --git a/src/ai/index.ts b/src/ai/index.ts new file mode 100644 index 0000000..4a37da2 --- /dev/null +++ b/src/ai/index.ts @@ -0,0 +1,25 @@ +export { EmbeddingService } from "./embeddings.js"; +export type { EmbedFn } from "./embeddings.js"; + +export { VectorStore, cosineSimilarity } from "./vectorStore.js"; + +export { + SemanticSearchService, + defaultChunker, +} from "./semanticSearch.js"; +export type { ChunkFn } from "./semanticSearch.js"; + +export type { + TextChunk, + EmbeddedChunk, + SearchResult, + EmbeddingConfig, + VectorStoreConfig, + SearchOptions, + SerializedVectorStore, +} from "./types.js"; + +export { + DEFAULT_EMBEDDING_CONFIG, + DEFAULT_SEARCH_OPTIONS, +} from "./types.js"; diff --git a/src/ai/semanticSearch.ts b/src/ai/semanticSearch.ts new file mode 100644 index 0000000..7188a09 --- /dev/null +++ b/src/ai/semanticSearch.ts @@ -0,0 +1,125 @@ +// High-level semantic search API. +// Pipeline: Notes -> Chunking -> Embedding -> Vector Index -> Search + +import type { TextChunk, SearchResult, SearchOptions } from "./types.js"; +import { DEFAULT_SEARCH_OPTIONS } from "./types.js"; +import { EmbeddingService } from "./embeddings.js"; +import { VectorStore } from "./vectorStore.js"; + +/** Splits a note into chunks. Signature compatible with PR #12's chunker. */ +export type ChunkFn = (noteId: string, content: string) => TextChunk[]; + +/** Simple paragraph-based fallback chunker. */ +export function defaultChunker(noteId: string, content: string): TextChunk[] { + const paragraphs = content + .split(/\n\n+/) + .map((p) => p.trim()) + .filter((p) => p.length > 0); + + return paragraphs.map((text, i) => ({ + id: `${noteId}:${i}`, + noteId, + content: text, + chunkIndex: i, + })); +} + +export class SemanticSearchService { + private embeddingService: EmbeddingService; + private vectorStore: VectorStore; + private chunkFn: ChunkFn; + + constructor( + embeddingService: EmbeddingService, + vectorStore: VectorStore, + chunkFn: ChunkFn = defaultChunker, + ) { + this.embeddingService = embeddingService; + this.vectorStore = vectorStore; + this.chunkFn = chunkFn; + } + + /** Load embedding model + persisted index. Call before anything else. */ + async initialize(): Promise { + await this.embeddingService.initialize(); + await this.vectorStore.load(); + } + + /** Index a note. Replaces any previously indexed chunks for this noteId. */ + async indexNote(noteId: string, content: string): Promise { + const chunks = this.chunkFn(noteId, content); + if (chunks.length === 0) { + this.vectorStore.removeByNoteId(noteId); + return 0; + } + + // Embed first — if this throws, the old index is left intact. + const embeddedChunks = await this.embeddingService.embedChunks(chunks); + this.vectorStore.removeByNoteId(noteId); + this.vectorStore.add(embeddedChunks); + + return chunks.length; + } + + removeNote(noteId: string): number { + return this.vectorStore.removeByNoteId(noteId); + } + + /** Search indexed notes with a natural language query. */ + async search( + query: string, + options: Partial = {}, + ): Promise { + const opts = { ...DEFAULT_SEARCH_OPTIONS, ...options }; + const queryVector = await this.embeddingService.embedSingle(query); + + return this.vectorStore.search( + queryVector, + opts.topK, + opts.minScore, + opts.noteId, + ); + } + + /** Find notes semantically related to a given note, deduplicated by noteId. */ + async findRelatedNotes( + noteId: string, + topK: number = 5, + ): Promise { + // Map keeps the highest-scoring chunk per note — the old Set approach + // kept the first hit, which could discard a better score from a later + // source chunk iteration. + const bestByNoteId = new Map(); + + for (const entry of this.getAllChunksForNote(noteId)) { + const related = this.vectorStore.findRelated(entry.id, topK * 2, 0.4); + for (const result of related) { + if (result.chunk.noteId === noteId) continue; + const existing = bestByNoteId.get(result.chunk.noteId); + if (!existing || result.score > existing.score) { + bestByNoteId.set(result.chunk.noteId, result); + } + } + } + + return Array.from(bestByNoteId.values()) + .sort((a, b) => b.score - a.score) + .slice(0, topK); + } + + async save(): Promise { + await this.vectorStore.save(); + } + + get indexSize(): number { + return this.vectorStore.size; + } + + isNoteIndexed(noteId: string): boolean { + return this.vectorStore.hasNote(noteId); + } + + private getAllChunksForNote(noteId: string): TextChunk[] { + return this.vectorStore.getChunksByNoteId(noteId); + } +} diff --git a/src/ai/types.ts b/src/ai/types.ts new file mode 100644 index 0000000..511c433 --- /dev/null +++ b/src/ai/types.ts @@ -0,0 +1,62 @@ +// Shared types for the AI pipeline. + +export interface TextChunk { + id: string; + noteId: string; + content: string; + heading?: string; + chunkIndex: number; +} + +export interface EmbeddedChunk { + chunk: TextChunk; + vector: number[]; +} + +export interface SearchResult { + chunk: TextChunk; + /** Cosine similarity score (-1 to 1; typically >= 0 for normalized embeddings). */ + score: number; +} + +export interface EmbeddingConfig { + modelId: string; + /** Max tokens before truncation. */ + maxTokenLength: number; +} + +export interface VectorStoreConfig { + persistDir: string; + indexFilename: string; + /** Used to detect incompatible index files at load time. */ + modelId?: string; +} + +export interface SerializedVectorStore { + version: number; + /** Model that produced the stored vectors. */ + modelId?: string; + /** Embedding dimension — used to detect model changes at load time. */ + vectorDim?: number; + entries: Array<{ + chunk: TextChunk; + vector: number[]; + }>; +} + +export interface SearchOptions { + topK: number; + minScore: number; + /** Filter results to a single note. */ + noteId?: string; +} + +export const DEFAULT_EMBEDDING_CONFIG: EmbeddingConfig = { + modelId: "Xenova/all-MiniLM-L6-v2", + maxTokenLength: 256, +}; + +export const DEFAULT_SEARCH_OPTIONS: SearchOptions = { + topK: 10, + minScore: 0.3, +}; diff --git a/src/ai/vectorStore.ts b/src/ai/vectorStore.ts new file mode 100644 index 0000000..72d7b36 --- /dev/null +++ b/src/ai/vectorStore.ts @@ -0,0 +1,221 @@ +// In-memory vector index with cosine similarity search and JSON persistence. +// Pure TypeScript — no native deps, works in Electron without platform builds. +// Brute-force search is fine for personal note collections (< 100K vectors). + +import { readFile, writeFile, mkdir } from "node:fs/promises"; +import { join, dirname } from "node:path"; +import { existsSync } from "node:fs"; +import type { + TextChunk, + EmbeddedChunk, + SearchResult, + VectorStoreConfig, + SerializedVectorStore, +} from "./types.js"; + +const CURRENT_VERSION = 1; + +export class VectorStore { + private entries: Map = new Map(); + private config: VectorStoreConfig; + private dirty = false; + + constructor(config: VectorStoreConfig) { + this.config = config; + } + + /** Add chunks. Overwrites existing entries with the same ID. */ + add(embeddedChunks: EmbeddedChunk[]): void { + for (const ec of embeddedChunks) { + this.entries.set(ec.chunk.id, ec); + } + this.dirty = true; + } + + /** Remove all chunks for a note. Returns count removed. */ + removeByNoteId(noteId: string): number { + let removed = 0; + for (const [id, entry] of this.entries) { + if (entry.chunk.noteId === noteId) { + this.entries.delete(id); + removed++; + } + } + if (removed > 0) this.dirty = true; + return removed; + } + + /** Find the most similar chunks to a query vector. */ + search( + queryVector: number[], + topK: number = 10, + minScore: number = 0.3, + noteId?: string, + ): SearchResult[] { + const results: SearchResult[] = []; + + for (const entry of this.entries.values()) { + if (noteId && entry.chunk.noteId !== noteId) continue; + + const score = cosineSimilarity(queryVector, entry.vector); + if (score >= minScore) { + results.push({ chunk: entry.chunk, score }); + } + } + + results.sort((a, b) => b.score - a.score); + return results.slice(0, topK); + } + + /** Find chunks related to a given chunk (excludes itself). */ + findRelated( + chunkId: string, + topK: number = 5, + minScore: number = 0.4, + ): SearchResult[] { + const entry = this.entries.get(chunkId); + if (!entry) return []; + + const results: SearchResult[] = []; + for (const other of this.entries.values()) { + if (other.chunk.id === chunkId) continue; + + const score = cosineSimilarity(entry.vector, other.vector); + if (score >= minScore) { + results.push({ chunk: other.chunk, score }); + } + } + + results.sort((a, b) => b.score - a.score); + return results.slice(0, topK); + } + + /** Save to disk as JSON. Skips write if nothing changed. */ + async save(): Promise { + if (!this.dirty) return; + + const allEntries = Array.from(this.entries.values()); + const serialized: SerializedVectorStore = { + version: CURRENT_VERSION, + modelId: this.config.modelId, + vectorDim: allEntries.length > 0 ? allEntries[0].vector.length : undefined, + entries: allEntries.map((e) => ({ + chunk: e.chunk, + vector: e.vector, + })), + }; + + const filePath = join(this.config.persistDir, this.config.indexFilename); + const dir = dirname(filePath); + if (!existsSync(dir)) { + await mkdir(dir, { recursive: true }); + } + + await writeFile(filePath, JSON.stringify(serialized), "utf-8"); + this.dirty = false; + } + + /** Load from disk. Returns false if no index file exists. Throws on model mismatch. */ + async load(): Promise { + const filePath = join(this.config.persistDir, this.config.indexFilename); + if (!existsSync(filePath)) return false; + + const raw = await readFile(filePath, "utf-8"); + const data: SerializedVectorStore = JSON.parse(raw); + + if (data.version !== CURRENT_VERSION) { + console.warn( + `VectorStore: version mismatch (got ${data.version}, ` + + `expected ${CURRENT_VERSION}). Rebuild recommended.`, + ); + } + + // Fail fast on model mismatch rather than propagating bad vectors to search. + if (data.modelId && this.config.modelId && data.modelId !== this.config.modelId) { + throw new Error( + `VectorStore: index was built with model "${data.modelId}" ` + + `but current model is "${this.config.modelId}". ` + + `Delete the index file to rebuild.`, + ); + } + if (data.vectorDim !== undefined && data.entries.length > 0) { + const actualDim = data.entries[0].vector.length; + if (actualDim !== data.vectorDim) { + throw new Error( + `VectorStore: index vectorDim mismatch ` + + `(stored ${data.vectorDim}, found ${actualDim}). Index may be corrupted.`, + ); + } + } + + this.entries.clear(); + for (const entry of data.entries) { + this.entries.set(entry.chunk.id, { + chunk: entry.chunk, + vector: entry.vector, + }); + } + + this.dirty = false; + return true; + } + + get size(): number { + return this.entries.size; + } + + getIndexedNoteIds(): Set { + const noteIds = new Set(); + for (const entry of this.entries.values()) { + noteIds.add(entry.chunk.noteId); + } + return noteIds; + } + + hasNote(noteId: string): boolean { + for (const entry of this.entries.values()) { + if (entry.chunk.noteId === noteId) return true; + } + return false; + } + + /** Return all chunks for a note without any vector math. */ + getChunksByNoteId(noteId: string): TextChunk[] { + const chunks: TextChunk[] = []; + for (const entry of this.entries.values()) { + if (entry.chunk.noteId === noteId) { + chunks.push(entry.chunk); + } + } + return chunks; + } + + clear(): void { + this.entries.clear(); + this.dirty = true; + } +} + +/** Cosine similarity between two vectors. Returns -1 to 1. */ +export function cosineSimilarity(a: number[], b: number[]): number { + if (a.length !== b.length) { + throw new Error( + `Vector dimension mismatch: ${a.length} vs ${b.length}`, + ); + } + + let dotProduct = 0; + let normA = 0; + let normB = 0; + + for (let i = 0; i < a.length; i++) { + dotProduct += a[i] * b[i]; + normA += a[i] * a[i]; + normB += b[i] * b[i]; + } + + const denominator = Math.sqrt(normA) * Math.sqrt(normB); + if (denominator === 0) return 0; + + return dotProduct / denominator; +} diff --git a/tsconfig.build.json b/tsconfig.build.json new file mode 100644 index 0000000..11391c0 --- /dev/null +++ b/tsconfig.build.json @@ -0,0 +1,12 @@ +{ + "extends": "./tsconfig.json", + "compilerOptions": { + "rootDir": "./src" + }, + "exclude": [ + "node_modules", + "dist", + "**/__tests__/**", + "*.ts" + ] +} \ No newline at end of file diff --git a/tsconfig.json b/tsconfig.json new file mode 100644 index 0000000..56b7ade --- /dev/null +++ b/tsconfig.json @@ -0,0 +1,27 @@ +{ + "compilerOptions": { + "target": "ES2022", + "module": "ES2022", + "moduleResolution": "bundler", + "lib": [ + "ES2022" + ], + "outDir": "./dist", + "strict": true, + "esModuleInterop": true, + "skipLibCheck": true, + "forceConsistentCasingInFileNames": true, + "resolveJsonModule": true, + "declaration": true, + "declarationMap": true, + "sourceMap": true + }, + "include": [ + "src/**/*.ts", + "*.ts" + ], + "exclude": [ + "node_modules", + "dist" + ] +} \ No newline at end of file diff --git a/vitest.config.ts b/vitest.config.ts new file mode 100644 index 0000000..672b516 --- /dev/null +++ b/vitest.config.ts @@ -0,0 +1,14 @@ +import { defineConfig } from "vitest/config"; + +export default defineConfig({ + test: { + globals: true, + environment: "node", + include: ["src/**/__tests__/**/*.test.ts"], + coverage: { + provider: "v8", + include: ["src/ai/**/*.ts"], + exclude: ["src/ai/__tests__/**", "src/ai/index.ts"], + }, + }, +});