6c7e2911a2
- Visual progress bar with filled/empty blocks - Calculate ETA based on bytes processed (larger files = longer time) - Show throughput in bytes/sec - Skip empty documents - Fix UNIQUE constraint with INSERT OR REPLACE 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
1359 lines
48 KiB
TypeScript
Executable File
1359 lines
48 KiB
TypeScript
Executable File
#!/usr/bin/env bun
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import { Database } from "bun:sqlite";
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import { Glob } from "bun";
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import { mkdirSync, existsSync } from "node:fs";
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import { homedir } from "node:os";
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import { resolve } from "node:path";
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import * as sqliteVec from "sqlite-vec";
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// On macOS, use Homebrew's SQLite which supports extensions
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if (process.platform === "darwin") {
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const homebrewSqlitePath = "/opt/homebrew/opt/sqlite/lib/libsqlite3.dylib";
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if (existsSync(homebrewSqlitePath)) {
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Database.setCustomSQLite(homebrewSqlitePath);
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}
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}
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const DEFAULT_EMBED_MODEL = "embeddinggemma";
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const DEFAULT_RERANK_MODEL = "ExpedientFalcon/qwen3-reranker:0.6b-q8_0";
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const DEFAULT_QUERY_MODEL = "qwen3:0.6b";
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const DEFAULT_GLOB = "**/*.md";
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const OLLAMA_URL = process.env.OLLAMA_URL || "http://localhost:11434";
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// Terminal colors (respects NO_COLOR env)
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const useColor = !process.env.NO_COLOR && process.stdout.isTTY;
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const c = {
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reset: useColor ? "\x1b[0m" : "",
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dim: useColor ? "\x1b[2m" : "",
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bold: useColor ? "\x1b[1m" : "",
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cyan: useColor ? "\x1b[36m" : "",
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yellow: useColor ? "\x1b[33m" : "",
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green: useColor ? "\x1b[32m" : "",
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magenta: useColor ? "\x1b[35m" : "",
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blue: useColor ? "\x1b[34m" : "",
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};
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// Global state for --index option
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let customIndexName: string | null = null;
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// Terminal progress bar using OSC 9;4 escape sequence
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const progress = {
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set(percent: number) {
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process.stderr.write(`\x1b]9;4;1;${Math.round(percent)}\x07`);
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},
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clear() {
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process.stderr.write(`\x1b]9;4;0\x07`);
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},
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indeterminate() {
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process.stderr.write(`\x1b]9;4;3\x07`);
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},
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error() {
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process.stderr.write(`\x1b]9;4;2\x07`);
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},
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};
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// Format seconds into human-readable ETA
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function formatETA(seconds: number): string {
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if (seconds < 60) return `${Math.round(seconds)}s`;
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if (seconds < 3600) return `${Math.floor(seconds / 60)}m ${Math.round(seconds % 60)}s`;
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return `${Math.floor(seconds / 3600)}h ${Math.floor((seconds % 3600) / 60)}m`;
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}
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function getDbPath(): string {
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const cacheDir = process.env.XDG_CACHE_HOME || resolve(homedir(), ".cache");
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const qmdCacheDir = resolve(cacheDir, "qmd");
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mkdirSync(qmdCacheDir, { recursive: true });
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const dbName = customIndexName || "index";
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return resolve(qmdCacheDir, `${dbName}.sqlite`);
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}
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function getPwd(): string {
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return process.env.PWD || process.cwd();
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}
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/*
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Schema:
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CREATE TABLE collections (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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pwd TEXT NOT NULL,
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glob_pattern TEXT NOT NULL,
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created_at TEXT NOT NULL,
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UNIQUE(pwd, glob_pattern)
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);
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CREATE TABLE documents (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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collection_id INTEGER NOT NULL,
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name TEXT NOT NULL,
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title TEXT NOT NULL,
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hash TEXT NOT NULL,
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filepath TEXT NOT NULL,
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body TEXT NOT NULL,
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created_at TEXT NOT NULL,
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modified_at TEXT NOT NULL,
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active INTEGER NOT NULL DEFAULT 1,
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FOREIGN KEY (collection_id) REFERENCES collections(id)
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);
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CREATE TABLE content_vectors (
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hash TEXT PRIMARY KEY,
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embedding BLOB NOT NULL,
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model TEXT NOT NULL,
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embedded_at TEXT NOT NULL
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);
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CREATE VIRTUAL TABLE documents_fts USING fts5(...);
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*/
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function getDb(): Database {
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const db = new Database(getDbPath());
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sqliteVec.load(db);
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db.exec("PRAGMA journal_mode = WAL");
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// Collections table
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db.exec(`
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CREATE TABLE IF NOT EXISTS collections (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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pwd TEXT NOT NULL,
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glob_pattern TEXT NOT NULL,
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created_at TEXT NOT NULL,
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UNIQUE(pwd, glob_pattern)
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)
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`);
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// Documents table with collection_id and full filepath
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db.exec(`
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CREATE TABLE IF NOT EXISTS documents (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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collection_id INTEGER NOT NULL,
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name TEXT NOT NULL,
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title TEXT NOT NULL,
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hash TEXT NOT NULL,
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filepath TEXT NOT NULL,
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body TEXT NOT NULL,
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created_at TEXT NOT NULL,
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modified_at TEXT NOT NULL,
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active INTEGER NOT NULL DEFAULT 1,
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FOREIGN KEY (collection_id) REFERENCES collections(id)
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)
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`);
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// Content vectors keyed by hash (UNIQUE)
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db.exec(`
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CREATE TABLE IF NOT EXISTS content_vectors (
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hash TEXT PRIMARY KEY,
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model TEXT NOT NULL,
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embedded_at TEXT NOT NULL
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)
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`);
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// FTS on documents
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db.exec(`
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CREATE VIRTUAL TABLE IF NOT EXISTS documents_fts USING fts5(
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name, body,
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content='documents',
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content_rowid='id',
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tokenize='porter unicode61'
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)
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`);
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db.exec(`
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CREATE TRIGGER IF NOT EXISTS documents_ai AFTER INSERT ON documents BEGIN
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INSERT INTO documents_fts(rowid, name, body) VALUES (new.id, new.name, new.body);
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END
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`);
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db.exec(`
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CREATE TRIGGER IF NOT EXISTS documents_ad AFTER DELETE ON documents BEGIN
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INSERT INTO documents_fts(documents_fts, rowid, name, body) VALUES('delete', old.id, old.name, old.body);
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END
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`);
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db.exec(`
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CREATE TRIGGER IF NOT EXISTS documents_au AFTER UPDATE ON documents BEGIN
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INSERT INTO documents_fts(documents_fts, rowid, name, body) VALUES('delete', old.id, old.name, old.body);
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INSERT INTO documents_fts(rowid, name, body) VALUES (new.id, new.name, new.body);
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END
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`);
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db.exec(`CREATE INDEX IF NOT EXISTS idx_documents_collection ON documents(collection_id, active)`);
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db.exec(`CREATE INDEX IF NOT EXISTS idx_documents_hash ON documents(hash)`);
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db.exec(`CREATE INDEX IF NOT EXISTS idx_documents_filepath ON documents(filepath, active)`);
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return db;
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}
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function ensureVecTable(db: Database, dimensions: number): void {
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const tableInfo = db.prepare(`SELECT sql FROM sqlite_master WHERE type='table' AND name='vectors_vec'`).get() as { sql: string } | null;
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if (tableInfo) {
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const match = tableInfo.sql.match(/float\[(\d+)\]/);
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if (match && parseInt(match[1]) === dimensions) return;
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db.exec("DROP TABLE IF EXISTS vectors_vec");
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}
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db.exec(`CREATE VIRTUAL TABLE vectors_vec USING vec0(hash TEXT PRIMARY KEY, embedding float[${dimensions}])`);
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}
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function getHashesNeedingEmbedding(db: Database): number {
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const result = db.prepare(`
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SELECT COUNT(DISTINCT d.hash) as count
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FROM documents d
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LEFT JOIN content_vectors v ON d.hash = v.hash
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WHERE d.active = 1 AND v.hash IS NULL
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`).get() as { count: number };
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return result.count;
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}
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async function hashContent(content: string): Promise<string> {
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const hash = new Bun.CryptoHasher("sha256");
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hash.update(content);
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return hash.digest("hex");
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}
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// Extract title from first markdown headline, or use filename as fallback
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function extractTitle(content: string, filename: string): string {
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const match = content.match(/^##?\s+(.+)$/m);
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if (match) return match[1].trim();
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return filename.replace(/\.md$/, "").split("/").pop() || filename;
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}
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// Format text for EmbeddingGemma
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function formatQueryForEmbedding(query: string): string {
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return `task: search result | query: ${query}`;
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}
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function formatDocForEmbedding(text: string, title?: string): string {
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return `title: ${title || "none"} | text: ${text}`;
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}
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// Auto-pull model if not found
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async function ensureModelAvailable(model: string): Promise<void> {
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try {
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const response = await fetch(`${OLLAMA_URL}/api/show`, {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({ name: model }),
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});
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if (response.ok) return;
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} catch {
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// Continue to pull attempt
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}
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console.log(`Model ${model} not found. Pulling...`);
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progress.indeterminate();
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const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({ name: model, stream: false }),
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});
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if (!pullResponse.ok) {
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progress.error();
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throw new Error(`Failed to pull model ${model}: ${pullResponse.status} - ${await pullResponse.text()}`);
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}
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progress.clear();
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console.log(`Model ${model} pulled successfully.`);
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}
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async function getEmbedding(text: string, model: string, isQuery: boolean = false, title?: string, retried: boolean = false): Promise<number[]> {
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const input = isQuery ? formatQueryForEmbedding(text) : formatDocForEmbedding(text, title);
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const response = await fetch(`${OLLAMA_URL}/api/embed`, {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({ model, input }),
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});
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if (!response.ok) {
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const errorText = await response.text();
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if (!retried && (errorText.includes("not found") || errorText.includes("does not exist"))) {
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await ensureModelAvailable(model);
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return getEmbedding(text, model, isQuery, title, true);
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}
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throw new Error(`Ollama API error: ${response.status} - ${errorText}`);
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}
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const data = await response.json() as { embeddings: number[][] };
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return data.embeddings[0];
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}
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// Qwen3-Reranker prompt format (trained for yes/no relevance classification)
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const RERANK_SYSTEM = `Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".`;
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function formatRerankPrompt(query: string, title: string, doc: string): string {
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return `<Instruct>: Determine if this document from a Shopify knowledge base is relevant to the search query. The query may reference specific Shopify programs, competitions, features, or named concepts (e.g., "Build a Business" competition, "Shop Pay", "Polaris"). Match documents that discuss the queried topic, even if phrasing differs.
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<Query>: ${query}
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<Document Title>: ${title}
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<Document>: ${doc}`;
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}
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type LogProb = { token: string; logprob: number };
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type RerankResponse = {
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response: string;
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logprobs?: LogProb[];
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};
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async function rerankSingle(prompt: string, model: string, retried: boolean = false): Promise<number> {
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// Use generate with raw template for qwen3-reranker format
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// Include empty <think> tags as per HuggingFace reference implementation
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const fullPrompt = `<|im_start|>system
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${RERANK_SYSTEM}<|im_end|>
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<|im_start|>user
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${prompt}<|im_end|>
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<|im_start|>assistant
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<think>
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</think>
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`;
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const response = await fetch(`${OLLAMA_URL}/api/generate`, {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({
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model,
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prompt: fullPrompt,
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raw: true,
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stream: false,
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logprobs: true,
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options: { num_predict: 1 },
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}),
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});
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if (!response.ok) {
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const errorText = await response.text();
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if (!retried && (errorText.includes("not found") || errorText.includes("does not exist"))) {
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await ensureModelAvailable(model);
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return rerankSingle(prompt, model, true);
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}
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throw new Error(`Ollama API error: ${response.status} - ${errorText}`);
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}
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const data = await response.json() as RerankResponse;
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// Extract score from logprobs - required for proper reranking
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if (!data.logprobs || data.logprobs.length === 0) {
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throw new Error("Reranker response missing logprobs - ensure Ollama supports logprobs");
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}
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const firstToken = data.logprobs[0];
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const token = firstToken.token.toLowerCase().trim();
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const confidence = Math.exp(firstToken.logprob); // 0-1, higher = more confident
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if (token === "yes") {
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// Relevant: return confidence (e.g., 0.93 for high confidence yes)
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return confidence;
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}
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if (token === "no") {
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// Not relevant: return low score, scaled by inverse confidence
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// High confidence "no" → very low score
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return (1 - confidence) * 0.3; // Cap at 0.3 for uncertain "no"
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}
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throw new Error(`Unexpected reranker token: "${token}" (expected "yes" or "no")`);
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}
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async function rerank(query: string, documents: { file: string; text: string }[], model: string = DEFAULT_RERANK_MODEL): Promise<{ file: string; score: number }[]> {
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const results: { file: string; score: number }[] = [];
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const total = documents.length;
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const PARALLEL = 5;
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process.stderr.write(`Reranking ${total} documents with ${model} (parallel: ${PARALLEL})...\n`);
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progress.indeterminate();
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// Process in parallel batches
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for (let i = 0; i < documents.length; i += PARALLEL) {
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const batch = documents.slice(i, i + PARALLEL);
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const batchResults = await Promise.all(
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batch.map(async (doc) => {
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try {
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// Extract title from filename for reranker context
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const title = doc.file.split('/').pop()?.replace(/\.md$/, '') || doc.file;
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const prompt = formatRerankPrompt(query, title, doc.text.slice(0, 4000));
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const score = await rerankSingle(prompt, model);
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return { file: doc.file, score };
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} catch (err) {
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return { file: doc.file, score: 0 };
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}
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})
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);
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results.push(...batchResults);
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const processed = Math.min(i + PARALLEL, total);
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progress.set((processed / total) * 100);
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process.stderr.write(`\rReranking: ${processed}/${total}`);
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}
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progress.clear();
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process.stderr.write("\n");
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return results.sort((a, b) => b.score - a.score);
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}
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function getOrCreateCollection(db: Database, pwd: string, globPattern: string): number {
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const now = new Date().toISOString();
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// Use INSERT OR IGNORE to handle race conditions, then SELECT
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db.prepare(`INSERT OR IGNORE INTO collections (pwd, glob_pattern, created_at) VALUES (?, ?, ?)`).run(pwd, globPattern, now);
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const existing = db.prepare(`SELECT id FROM collections WHERE pwd = ? AND glob_pattern = ?`).get(pwd, globPattern) as { id: number };
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return existing.id;
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}
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function cleanupDuplicateCollections(db: Database): void {
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// Remove duplicate collections keeping the oldest one
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db.exec(`
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DELETE FROM collections WHERE id NOT IN (
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SELECT MIN(id) FROM collections GROUP BY pwd, glob_pattern
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)
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`);
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// Remove bogus "." glob pattern entries (from earlier bug)
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db.exec(`DELETE FROM collections WHERE glob_pattern = '.'`);
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}
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function formatTimeAgo(date: Date): string {
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const seconds = Math.floor((Date.now() - date.getTime()) / 1000);
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if (seconds < 60) return `${seconds}s ago`;
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const minutes = Math.floor(seconds / 60);
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if (minutes < 60) return `${minutes}m ago`;
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const hours = Math.floor(minutes / 60);
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if (hours < 24) return `${hours}h ago`;
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const days = Math.floor(hours / 24);
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return `${days}d ago`;
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}
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function formatBytes(bytes: number): string {
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if (bytes < 1024) return `${bytes} B`;
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if (bytes < 1024 * 1024) return `${(bytes / 1024).toFixed(1)} KB`;
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if (bytes < 1024 * 1024 * 1024) return `${(bytes / (1024 * 1024)).toFixed(1)} MB`;
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return `${(bytes / (1024 * 1024 * 1024)).toFixed(1)} GB`;
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}
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function showStatus(): void {
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const dbPath = getDbPath();
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const db = getDb();
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// Cleanup any duplicate collections
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cleanupDuplicateCollections(db);
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// Index size
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let indexSize = 0;
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try {
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const stat = Bun.file(dbPath).size;
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indexSize = stat;
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} catch {}
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// Collections info
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const collections = db.prepare(`
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SELECT c.id, c.pwd, c.glob_pattern, c.created_at,
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COUNT(d.id) as doc_count,
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SUM(CASE WHEN d.active = 1 THEN 1 ELSE 0 END) as active_count,
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MAX(d.modified_at) as last_modified
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FROM collections c
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LEFT JOIN documents d ON d.collection_id = c.id
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GROUP BY c.id
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ORDER BY c.created_at DESC
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`).all() as { id: number; pwd: string; glob_pattern: string; created_at: string; doc_count: number; active_count: number; last_modified: string | null }[];
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// Overall stats
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const totalDocs = db.prepare(`SELECT COUNT(*) as count FROM documents WHERE active = 1`).get() as { count: number };
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const vectorCount = db.prepare(`SELECT COUNT(*) as count FROM content_vectors`).get() as { count: number };
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const needsEmbedding = getHashesNeedingEmbedding(db);
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// Most recent update across all collections
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const mostRecent = db.prepare(`SELECT MAX(modified_at) as latest FROM documents WHERE active = 1`).get() as { latest: string | null };
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console.log(`${c.bold}QMD Status${c.reset}\n`);
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console.log(`Index: ${dbPath}`);
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console.log(`Size: ${formatBytes(indexSize)}\n`);
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console.log(`${c.bold}Documents${c.reset}`);
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console.log(` Total: ${totalDocs.count} files indexed`);
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console.log(` Vectors: ${vectorCount.count} embedded`);
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if (needsEmbedding > 0) {
|
|
console.log(` ${c.yellow}Pending: ${needsEmbedding} need embedding${c.reset} (run 'qmd embed')`);
|
|
}
|
|
if (mostRecent.latest) {
|
|
const lastUpdate = new Date(mostRecent.latest);
|
|
console.log(` Updated: ${formatTimeAgo(lastUpdate)}`);
|
|
}
|
|
|
|
if (collections.length > 0) {
|
|
console.log(`\n${c.bold}Collections${c.reset}`);
|
|
for (const col of collections) {
|
|
const lastMod = col.last_modified ? formatTimeAgo(new Date(col.last_modified)) : "never";
|
|
console.log(` ${c.cyan}${col.pwd}${c.reset}`);
|
|
console.log(` ${col.glob_pattern} → ${col.active_count} docs (updated ${lastMod})`);
|
|
}
|
|
} else {
|
|
console.log(`\n${c.dim}No collections. Run 'qmd add .' to index markdown files.${c.reset}`);
|
|
}
|
|
|
|
db.close();
|
|
}
|
|
|
|
async function updateAllCollections(): Promise<void> {
|
|
const db = getDb();
|
|
cleanupDuplicateCollections(db);
|
|
const collections = db.prepare(`SELECT id, pwd, glob_pattern FROM collections`).all() as { id: number; pwd: string; glob_pattern: string }[];
|
|
|
|
if (collections.length === 0) {
|
|
console.log(`${c.dim}No collections found. Run 'qmd add .' to index markdown files.${c.reset}`);
|
|
db.close();
|
|
return;
|
|
}
|
|
|
|
db.close();
|
|
|
|
console.log(`${c.bold}Updating ${collections.length} collection(s)...${c.reset}\n`);
|
|
|
|
for (let i = 0; i < collections.length; i++) {
|
|
const col = collections[i];
|
|
console.log(`${c.cyan}[${i + 1}/${collections.length}]${c.reset} ${c.bold}${col.pwd}${c.reset}`);
|
|
console.log(`${c.dim} Pattern: ${col.glob_pattern}${c.reset}`);
|
|
// Temporarily set PWD for indexing
|
|
const originalPwd = process.env.PWD;
|
|
process.env.PWD = col.pwd;
|
|
await indexFiles(col.glob_pattern);
|
|
process.env.PWD = originalPwd;
|
|
console.log("");
|
|
}
|
|
|
|
console.log(`${c.green}✓ All collections updated.${c.reset}`);
|
|
}
|
|
|
|
async function dropCollection(globPattern: string): Promise<void> {
|
|
const db = getDb();
|
|
const pwd = getPwd();
|
|
|
|
const collection = db.prepare(`SELECT id FROM collections WHERE pwd = ? AND glob_pattern = ?`).get(pwd, globPattern) as { id: number } | null;
|
|
|
|
if (!collection) {
|
|
console.log(`No collection found for ${pwd} with pattern ${globPattern}`);
|
|
db.close();
|
|
return;
|
|
}
|
|
|
|
// Delete documents in this collection
|
|
const deleted = db.prepare(`DELETE FROM documents WHERE collection_id = ?`).run(collection.id);
|
|
|
|
// Delete the collection
|
|
db.prepare(`DELETE FROM collections WHERE id = ?`).run(collection.id);
|
|
|
|
console.log(`Dropped collection: ${pwd} (${globPattern})`);
|
|
console.log(`Removed ${deleted.changes} documents`);
|
|
console.log(`(Vectors kept for potential reuse)`);
|
|
|
|
db.close();
|
|
}
|
|
|
|
async function indexFiles(globPattern: string = DEFAULT_GLOB): Promise<void> {
|
|
const db = getDb();
|
|
const pwd = getPwd();
|
|
const now = new Date().toISOString();
|
|
const excludeDirs = ["node_modules", ".git", ".cache", "vendor", "dist", "build"];
|
|
|
|
// Get or create collection for this (pwd, glob)
|
|
const collectionId = getOrCreateCollection(db, pwd, globPattern);
|
|
console.log(`Collection: ${pwd} (${globPattern})`);
|
|
|
|
progress.indeterminate();
|
|
const glob = new Glob(globPattern);
|
|
const files: string[] = [];
|
|
for await (const file of glob.scan({ cwd: pwd, onlyFiles: true, followSymlinks: true })) {
|
|
// Skip node_modules, hidden folders (.*), and other common excludes
|
|
const parts = file.split("/");
|
|
const shouldSkip = parts.some(part =>
|
|
part === "node_modules" ||
|
|
part.startsWith(".") ||
|
|
excludeDirs.includes(part)
|
|
);
|
|
if (!shouldSkip) {
|
|
files.push(file);
|
|
}
|
|
}
|
|
|
|
const total = files.length;
|
|
if (total === 0) {
|
|
progress.clear();
|
|
console.log("No files found matching pattern.");
|
|
db.close();
|
|
return;
|
|
}
|
|
|
|
const insertStmt = db.prepare(`INSERT INTO documents (collection_id, name, title, hash, filepath, body, created_at, modified_at, active) VALUES (?, ?, ?, ?, ?, ?, ?, ?, 1)`);
|
|
const deactivateStmt = db.prepare(`UPDATE documents SET active = 0 WHERE collection_id = ? AND filepath = ? AND active = 1`);
|
|
const findActiveStmt = db.prepare(`SELECT id, hash FROM documents WHERE collection_id = ? AND filepath = ? AND active = 1`);
|
|
|
|
let indexed = 0, updated = 0, unchanged = 0, processed = 0;
|
|
const seenFiles = new Set<string>();
|
|
const startTime = Date.now();
|
|
|
|
for (const relativeFile of files) {
|
|
const filepath = resolve(pwd, relativeFile);
|
|
seenFiles.add(filepath);
|
|
|
|
const content = await Bun.file(filepath).text();
|
|
const hash = await hashContent(content);
|
|
const name = relativeFile.replace(/\.md$/, "").split("/").pop() || relativeFile;
|
|
const title = extractTitle(content, relativeFile);
|
|
const existing = findActiveStmt.get(collectionId, filepath) as { id: number; hash: string } | null;
|
|
|
|
if (existing) {
|
|
if (existing.hash === hash) {
|
|
unchanged++;
|
|
} else {
|
|
deactivateStmt.run(collectionId, filepath);
|
|
updated++;
|
|
const stat = await Bun.file(filepath).stat();
|
|
insertStmt.run(collectionId, name, title, hash, filepath, content, stat ? new Date(stat.birthtime).toISOString() : now, stat ? new Date(stat.mtime).toISOString() : now);
|
|
}
|
|
} else {
|
|
indexed++;
|
|
const stat = await Bun.file(filepath).stat();
|
|
insertStmt.run(collectionId, name, title, hash, filepath, content, stat ? new Date(stat.birthtime).toISOString() : now, stat ? new Date(stat.mtime).toISOString() : now);
|
|
}
|
|
|
|
processed++;
|
|
progress.set((processed / total) * 100);
|
|
const elapsed = (Date.now() - startTime) / 1000;
|
|
const rate = processed / elapsed;
|
|
const remaining = (total - processed) / rate;
|
|
const eta = processed > 2 ? ` ETA: ${formatETA(remaining)}` : "";
|
|
process.stderr.write(`\rIndexing: ${processed}/${total}${eta} `);
|
|
}
|
|
|
|
// Deactivate documents in this collection that no longer exist
|
|
const allActive = db.prepare(`SELECT filepath FROM documents WHERE collection_id = ? AND active = 1`).all(collectionId) as { filepath: string }[];
|
|
let removed = 0;
|
|
for (const row of allActive) {
|
|
if (!seenFiles.has(row.filepath)) {
|
|
deactivateStmt.run(collectionId, row.filepath);
|
|
removed++;
|
|
}
|
|
}
|
|
|
|
// Check if vector index needs updating
|
|
const needsEmbedding = getHashesNeedingEmbedding(db);
|
|
|
|
progress.clear();
|
|
console.log(`\nIndexed: ${indexed} new, ${updated} updated, ${unchanged} unchanged, ${removed} removed`);
|
|
|
|
if (needsEmbedding > 0) {
|
|
console.log(`\nRun 'qmd embed' to update embeddings (${needsEmbedding} unique hashes need vectors)`);
|
|
}
|
|
|
|
db.close();
|
|
}
|
|
|
|
function renderProgressBar(percent: number, width: number = 30): string {
|
|
const filled = Math.round((percent / 100) * width);
|
|
const empty = width - filled;
|
|
const bar = "█".repeat(filled) + "░".repeat(empty);
|
|
return bar;
|
|
}
|
|
|
|
async function vectorIndex(model: string = DEFAULT_EMBED_MODEL, force: boolean = false): Promise<void> {
|
|
const db = getDb();
|
|
const now = new Date().toISOString();
|
|
|
|
// If force, clear all vectors
|
|
if (force) {
|
|
console.log(`${c.yellow}Force re-indexing: clearing all vectors...${c.reset}`);
|
|
db.exec(`DELETE FROM content_vectors`);
|
|
db.exec(`DROP TABLE IF EXISTS vectors_vec`);
|
|
}
|
|
|
|
// Find unique hashes that need embedding (from active documents)
|
|
const hashesToEmbed = db.prepare(`
|
|
SELECT DISTINCT d.hash, d.title, d.body
|
|
FROM documents d
|
|
LEFT JOIN content_vectors v ON d.hash = v.hash
|
|
WHERE d.active = 1 AND v.hash IS NULL
|
|
`).all() as { hash: string; title: string; body: string }[];
|
|
|
|
if (hashesToEmbed.length === 0) {
|
|
console.log(`${c.green}✓ All content hashes already have embeddings.${c.reset}`);
|
|
db.close();
|
|
return;
|
|
}
|
|
|
|
// Calculate total bytes for accurate progress tracking, skip empty files
|
|
const itemsWithSize = hashesToEmbed
|
|
.map(item => ({
|
|
...item,
|
|
bytes: new TextEncoder().encode(item.body).length
|
|
}))
|
|
.filter(item => item.bytes > 0); // Skip empty documents
|
|
|
|
if (itemsWithSize.length === 0) {
|
|
console.log(`${c.green}✓ No non-empty documents to embed.${c.reset}`);
|
|
db.close();
|
|
return;
|
|
}
|
|
|
|
const totalBytes = itemsWithSize.reduce((sum, item) => sum + item.bytes, 0);
|
|
const total = itemsWithSize.length;
|
|
const skipped = hashesToEmbed.length - total;
|
|
|
|
console.log(`${c.bold}Embedding ${total} documents${c.reset} ${c.dim}(${formatBytes(totalBytes)})${c.reset}`);
|
|
if (skipped > 0) {
|
|
console.log(`${c.dim}Skipped ${skipped} empty documents${c.reset}`);
|
|
}
|
|
console.log(`${c.dim}Model: ${model}${c.reset}\n`);
|
|
|
|
progress.indeterminate();
|
|
const firstEmbedding = await getEmbedding(itemsWithSize[0].body, model, false, itemsWithSize[0].title);
|
|
ensureVecTable(db, firstEmbedding.length);
|
|
|
|
const insertVecStmt = db.prepare(`INSERT OR REPLACE INTO vectors_vec (hash, embedding) VALUES (?, ?)`);
|
|
const insertContentVectorStmt = db.prepare(`INSERT OR REPLACE INTO content_vectors (hash, model, embedded_at) VALUES (?, ?, ?)`);
|
|
|
|
let embedded = 0, errors = 0, bytesProcessed = 0;
|
|
const startTime = Date.now();
|
|
|
|
// Insert first
|
|
insertVecStmt.run(itemsWithSize[0].hash, new Float32Array(firstEmbedding));
|
|
insertContentVectorStmt.run(itemsWithSize[0].hash, model, now);
|
|
embedded++;
|
|
bytesProcessed += itemsWithSize[0].bytes;
|
|
|
|
for (let i = 1; i < itemsWithSize.length; i++) {
|
|
const item = itemsWithSize[i];
|
|
try {
|
|
const embedding = await getEmbedding(item.body, model, false, item.title);
|
|
insertVecStmt.run(item.hash, new Float32Array(embedding));
|
|
insertContentVectorStmt.run(item.hash, model, now);
|
|
embedded++;
|
|
bytesProcessed += item.bytes;
|
|
} catch (err) {
|
|
errors++;
|
|
bytesProcessed += item.bytes;
|
|
progress.error();
|
|
console.error(`\n${c.yellow}⚠ Error embedding ${item.hash.slice(0, 8)}...: ${err}${c.reset}`);
|
|
}
|
|
|
|
const processed = embedded + errors;
|
|
const percent = (bytesProcessed / totalBytes) * 100;
|
|
progress.set(percent);
|
|
|
|
const elapsed = (Date.now() - startTime) / 1000;
|
|
const bytesPerSec = bytesProcessed / elapsed;
|
|
const remainingBytes = totalBytes - bytesProcessed;
|
|
const etaSec = remainingBytes / bytesPerSec;
|
|
|
|
const bar = renderProgressBar(percent);
|
|
const percentStr = percent.toFixed(0).padStart(3);
|
|
const throughput = `${formatBytes(bytesPerSec)}/s`;
|
|
const eta = elapsed > 2 ? formatETA(etaSec) : "...";
|
|
const errStr = errors > 0 ? ` ${c.yellow}${errors} err${c.reset}` : "";
|
|
|
|
process.stderr.write(`\r${c.cyan}${bar}${c.reset} ${c.bold}${percentStr}%${c.reset} ${c.dim}${embedded}/${total}${c.reset}${errStr} ${c.dim}${throughput} ETA ${eta}${c.reset} `);
|
|
}
|
|
|
|
progress.clear();
|
|
const totalTime = ((Date.now() - startTime) / 1000).toFixed(1);
|
|
const avgThroughput = formatBytes(totalBytes / parseFloat(totalTime));
|
|
|
|
console.log(`\r${c.green}${renderProgressBar(100)}${c.reset} ${c.bold}100%${c.reset} `);
|
|
console.log(`\n${c.green}✓ Done!${c.reset} Embedded ${c.bold}${embedded}${c.reset} documents in ${c.bold}${totalTime}s${c.reset} ${c.dim}(${avgThroughput}/s)${c.reset}`);
|
|
if (errors > 0) {
|
|
console.log(`${c.yellow}⚠ ${errors} documents failed${c.reset}`);
|
|
}
|
|
db.close();
|
|
}
|
|
|
|
function escapeCSV(value: string): string {
|
|
if (value.includes('"') || value.includes(',') || value.includes('\n')) {
|
|
return `"${value.replace(/"/g, '""')}"`;
|
|
}
|
|
return value;
|
|
}
|
|
|
|
function extractSnippet(body: string, query: string, maxLen = 500): { line: number; snippet: string } {
|
|
const lines = body.split('\n');
|
|
const queryTerms = query.toLowerCase().split(/\s+/).filter(t => t.length > 0);
|
|
let bestLine = 0, bestScore = -1;
|
|
|
|
for (let i = 0; i < lines.length; i++) {
|
|
const lineLower = lines[i].toLowerCase();
|
|
let score = 0;
|
|
for (const term of queryTerms) {
|
|
if (lineLower.includes(term)) score++;
|
|
}
|
|
if (score > bestScore) {
|
|
bestScore = score;
|
|
bestLine = i;
|
|
}
|
|
}
|
|
|
|
const startLine = Math.max(0, bestLine - 1);
|
|
const endLine = Math.min(lines.length, bestLine + 2);
|
|
let snippet = lines.slice(startLine, endLine).join('\n');
|
|
if (snippet.length > maxLen) snippet = snippet.substring(0, maxLen - 3) + "...";
|
|
return { line: bestLine + 1, snippet };
|
|
}
|
|
|
|
type SearchResult = { file: string; body: string; score: number; source: "fts" | "vec" };
|
|
|
|
// Build FTS5 query: phrase-aware with fallback to individual terms
|
|
function buildFTS5Query(query: string): string {
|
|
const terms = query
|
|
.split(/\s+/)
|
|
.filter(term => term.length >= 2); // Skip single chars
|
|
|
|
if (terms.length === 0) return "";
|
|
if (terms.length === 1) return `"${terms[0].replace(/"/g, '""')}"`;
|
|
|
|
// Strategy: exact phrase OR proximity match OR individual terms
|
|
// Exact phrase matches rank highest, then close proximity, then any term
|
|
const phrase = `"${query.replace(/"/g, '""')}"`;
|
|
const quotedTerms = terms.map(t => `"${t.replace(/"/g, '""')}"`);
|
|
|
|
// FTS5 NEAR syntax: NEAR(term1 term2, distance)
|
|
const nearPhrase = `NEAR(${quotedTerms.join(' ')}, 10)`;
|
|
const orTerms = quotedTerms.join(' OR ');
|
|
|
|
// Exact phrase > proximity > any term
|
|
return `(${phrase}) OR (${nearPhrase}) OR (${orTerms})`;
|
|
}
|
|
|
|
// Normalize BM25 score to 0-1 range using sigmoid
|
|
function normalizeBM25(score: number): number {
|
|
// BM25 scores are negative in SQLite (lower = better)
|
|
// Typical range: -15 (excellent) to -2 (weak match)
|
|
// Map to 0-1 where higher is better
|
|
const absScore = Math.abs(score);
|
|
// Sigmoid-ish normalization: maps ~2-15 range to ~0.1-0.95
|
|
return 1 / (1 + Math.exp(-(absScore - 5) / 3));
|
|
}
|
|
|
|
function searchFTS(db: Database, query: string, limit: number = 20): SearchResult[] {
|
|
const ftsQuery = buildFTS5Query(query);
|
|
if (!ftsQuery) return [];
|
|
|
|
// BM25 weights: name=10, body=1 (title matches ranked higher)
|
|
const stmt = db.prepare(`
|
|
SELECT d.filepath, d.body, bm25(documents_fts, 10.0, 1.0) as score
|
|
FROM documents_fts f
|
|
JOIN documents d ON d.id = f.rowid
|
|
WHERE documents_fts MATCH ? AND d.active = 1
|
|
ORDER BY score
|
|
LIMIT ?
|
|
`);
|
|
const results = stmt.all(ftsQuery, limit) as { filepath: string; body: string; score: number }[];
|
|
return results.map(r => ({
|
|
file: r.filepath,
|
|
body: r.body,
|
|
score: normalizeBM25(r.score),
|
|
source: "fts" as const,
|
|
}));
|
|
}
|
|
|
|
async function searchVec(db: Database, query: string, model: string, limit: number = 20): Promise<SearchResult[]> {
|
|
const tableExists = db.prepare(`SELECT name FROM sqlite_master WHERE type='table' AND name='vectors_vec'`).get();
|
|
if (!tableExists) return [];
|
|
|
|
const queryEmbedding = await getEmbedding(query, model, true);
|
|
const queryVec = new Float32Array(queryEmbedding);
|
|
|
|
// Join: documents -> content_vectors -> vectors_vec
|
|
const stmt = db.prepare(`
|
|
SELECT d.filepath, d.body, vec.distance
|
|
FROM vectors_vec vec
|
|
JOIN documents d ON d.hash = vec.hash
|
|
WHERE vec.embedding MATCH ? AND k = ? AND d.active = 1
|
|
ORDER BY vec.distance
|
|
`);
|
|
const results = stmt.all(queryVec, limit) as { filepath: string; body: string; distance: number }[];
|
|
return results.map(r => ({
|
|
file: r.filepath,
|
|
body: r.body,
|
|
score: 1 / (1 + r.distance),
|
|
source: "vec" as const,
|
|
}));
|
|
}
|
|
|
|
function normalizeScores(results: SearchResult[]): SearchResult[] {
|
|
if (results.length === 0) return results;
|
|
const maxScore = Math.max(...results.map(r => r.score));
|
|
const minScore = Math.min(...results.map(r => r.score));
|
|
const range = maxScore - minScore || 1;
|
|
return results.map(r => ({ ...r, score: (r.score - minScore) / range }));
|
|
}
|
|
|
|
// Reciprocal Rank Fusion: combines multiple ranked lists
|
|
// RRF score = sum(1 / (k + rank)) across all lists where doc appears
|
|
// k=60 is standard, provides good balance between top and lower ranks
|
|
type RankedResult = { file: string; body: string; score: number };
|
|
|
|
function reciprocalRankFusion(
|
|
resultLists: RankedResult[][],
|
|
weights: number[] = [], // Weight per result list (default 1.0)
|
|
k: number = 60
|
|
): RankedResult[] {
|
|
const scores = new Map<string, { score: number; body: string; bestRank: number }>();
|
|
|
|
for (let listIdx = 0; listIdx < resultLists.length; listIdx++) {
|
|
const results = resultLists[listIdx];
|
|
const weight = weights[listIdx] ?? 1.0;
|
|
for (let rank = 0; rank < results.length; rank++) {
|
|
const doc = results[rank];
|
|
const rrfScore = weight / (k + rank + 1);
|
|
const existing = scores.get(doc.file);
|
|
if (existing) {
|
|
existing.score += rrfScore;
|
|
existing.bestRank = Math.min(existing.bestRank, rank);
|
|
} else {
|
|
scores.set(doc.file, { score: rrfScore, body: doc.body, bestRank: rank });
|
|
}
|
|
}
|
|
}
|
|
|
|
// Add bonus for best rank: documents that ranked #1-3 in any list get a boost
|
|
// This prevents dilution of exact matches by expansion queries
|
|
return Array.from(scores.entries())
|
|
.map(([file, { score, body, bestRank }]) => {
|
|
let bonus = 0;
|
|
if (bestRank === 0) bonus = 0.05; // Ranked #1 somewhere
|
|
else if (bestRank <= 2) bonus = 0.02; // Ranked top-3 somewhere
|
|
return { file, body, score: score + bonus };
|
|
})
|
|
.sort((a, b) => b.score - a.score);
|
|
}
|
|
|
|
type OutputFormat = "cli" | "csv" | "md" | "xml";
|
|
type OutputOptions = {
|
|
format: OutputFormat;
|
|
full: boolean;
|
|
limit: number;
|
|
minScore: number;
|
|
};
|
|
|
|
// Extract snippet with more context lines for CLI display
|
|
function extractSnippetWithContext(body: string, query: string, contextLines = 3): { line: number; snippet: string; hasMatch: boolean } {
|
|
const lines = body.split('\n');
|
|
const queryTerms = query.toLowerCase().split(/\s+/).filter(t => t.length > 0);
|
|
let bestLine = 0, bestScore = -1;
|
|
|
|
for (let i = 0; i < lines.length; i++) {
|
|
const lineLower = lines[i].toLowerCase();
|
|
let score = 0;
|
|
for (const term of queryTerms) {
|
|
if (lineLower.includes(term)) score++;
|
|
}
|
|
if (score > bestScore) {
|
|
bestScore = score;
|
|
bestLine = i;
|
|
}
|
|
}
|
|
|
|
// No query match found - return beginning of file
|
|
if (bestScore <= 0) {
|
|
const preview = lines.slice(0, contextLines * 2).join('\n').trim();
|
|
return { line: 1, snippet: preview, hasMatch: false };
|
|
}
|
|
|
|
const startLine = Math.max(0, bestLine - contextLines);
|
|
const endLine = Math.min(lines.length, bestLine + contextLines + 1);
|
|
const snippet = lines.slice(startLine, endLine).join('\n').trim();
|
|
return { line: bestLine + 1, snippet, hasMatch: true };
|
|
}
|
|
|
|
// Highlight query terms in text (skip short words < 3 chars)
|
|
function highlightTerms(text: string, query: string): string {
|
|
if (!useColor) return text;
|
|
const terms = query.toLowerCase().split(/\s+/).filter(t => t.length >= 3);
|
|
let result = text;
|
|
for (const term of terms) {
|
|
const regex = new RegExp(`(${term.replace(/[.*+?^${}()|[\]\\]/g, '\\$&')})`, 'gi');
|
|
result = result.replace(regex, `${c.yellow}${c.bold}$1${c.reset}`);
|
|
}
|
|
return result;
|
|
}
|
|
|
|
// Format score with color based on value
|
|
function formatScore(score: number): string {
|
|
const pct = (score * 100).toFixed(0).padStart(3);
|
|
if (!useColor) return `${pct}%`;
|
|
if (score >= 0.7) return `${c.green}${pct}%${c.reset}`;
|
|
if (score >= 0.4) return `${c.yellow}${pct}%${c.reset}`;
|
|
return `${c.dim}${pct}%${c.reset}`;
|
|
}
|
|
|
|
// Shorten filepath for display
|
|
function shortPath(filepath: string): string {
|
|
const cwd = getPwd();
|
|
if (filepath.startsWith(cwd)) {
|
|
return filepath.slice(cwd.length + 1);
|
|
}
|
|
// Show last 2 path components
|
|
const parts = filepath.split('/');
|
|
if (parts.length > 2) {
|
|
return '.../' + parts.slice(-2).join('/');
|
|
}
|
|
return filepath;
|
|
}
|
|
|
|
function outputResults(results: { file: string; body: string; score: number }[], query: string, opts: OutputOptions): void {
|
|
const filtered = results.filter(r => r.score >= opts.minScore).slice(0, opts.limit);
|
|
|
|
if (filtered.length === 0) {
|
|
console.log("No results found above minimum score threshold.");
|
|
return;
|
|
}
|
|
|
|
if (opts.format === "cli") {
|
|
for (let i = 0; i < filtered.length; i++) {
|
|
const row = filtered[i];
|
|
const { line, snippet, hasMatch } = extractSnippetWithContext(row.body, query, 2);
|
|
|
|
// Header: score and filename
|
|
const score = formatScore(row.score);
|
|
const path = shortPath(row.file);
|
|
const lineInfo = hasMatch ? `:${line}` : "";
|
|
console.log(`${c.bold}${score}${c.reset} ${c.cyan}${path}${c.dim}${lineInfo}${c.reset}`);
|
|
|
|
// Snippet with highlighting
|
|
const highlighted = highlightTerms(snippet, query);
|
|
const indented = highlighted.split('\n').map(l => ` ${c.dim}│${c.reset} ${l}`).join('\n');
|
|
console.log(indented);
|
|
|
|
if (i < filtered.length - 1) console.log();
|
|
}
|
|
} else if (opts.format === "md") {
|
|
for (const row of filtered) {
|
|
if (opts.full) {
|
|
console.log(`---\n# ${row.file}\n\n${row.body}\n`);
|
|
} else {
|
|
const { snippet } = extractSnippet(row.body, query);
|
|
console.log(`---\n# ${row.file}\n\n${snippet}\n`);
|
|
}
|
|
}
|
|
} else if (opts.format === "xml") {
|
|
for (const row of filtered) {
|
|
if (opts.full) {
|
|
console.log(`<file name="${row.file}">\n${row.body}\n</file>\n`);
|
|
} else {
|
|
const { snippet } = extractSnippet(row.body, query);
|
|
console.log(`<file name="${row.file}">\n${snippet}\n</file>\n`);
|
|
}
|
|
}
|
|
} else {
|
|
// CSV format
|
|
console.log("score,file,line,snippet");
|
|
for (const row of filtered) {
|
|
const { line, snippet } = extractSnippet(row.body, query);
|
|
const content = opts.full ? row.body : snippet;
|
|
console.log(`${row.score.toFixed(4)},${escapeCSV(row.file)},${line},${escapeCSV(content)}`);
|
|
}
|
|
}
|
|
}
|
|
|
|
function search(query: string, opts: OutputOptions): void {
|
|
const db = getDb();
|
|
const results = searchFTS(db, query, 50);
|
|
db.close();
|
|
|
|
if (results.length === 0) {
|
|
console.log("No results found.");
|
|
return;
|
|
}
|
|
outputResults(results, query, opts);
|
|
}
|
|
|
|
async function vectorSearch(query: string, opts: OutputOptions, model: string = DEFAULT_EMBED_MODEL): Promise<void> {
|
|
const db = getDb();
|
|
|
|
const tableExists = db.prepare(`SELECT name FROM sqlite_master WHERE type='table' AND name='vectors_vec'`).get();
|
|
if (!tableExists) {
|
|
console.error("Vector index not found. Run 'qmd embed' first to create embeddings.");
|
|
db.close();
|
|
return;
|
|
}
|
|
|
|
// Expand query to multiple variations
|
|
const queries = await expandQuery(query);
|
|
process.stderr.write(`Searching with ${queries.length} query variations...\n`);
|
|
|
|
// Collect results from all query variations
|
|
const allResults = new Map<string, { file: string; body: string; score: number }>();
|
|
|
|
for (const q of queries) {
|
|
const vecResults = await searchVec(db, q, model, 20);
|
|
for (const r of vecResults) {
|
|
const existing = allResults.get(r.file);
|
|
if (!existing || r.score > existing.score) {
|
|
allResults.set(r.file, { file: r.file, body: r.body, score: r.score });
|
|
}
|
|
}
|
|
}
|
|
|
|
db.close();
|
|
|
|
// Sort by max score and limit to requested count
|
|
const results = Array.from(allResults.values())
|
|
.sort((a, b) => b.score - a.score)
|
|
.slice(0, opts.limit);
|
|
|
|
if (results.length === 0) {
|
|
console.log("No results found.");
|
|
return;
|
|
}
|
|
outputResults(results, query, { ...opts, limit: results.length }); // Already limited
|
|
}
|
|
|
|
async function expandQuery(query: string, model: string = DEFAULT_QUERY_MODEL): Promise<string[]> {
|
|
process.stderr.write("Generating query variations...\n");
|
|
|
|
const prompt = `Generate 3 search query variations to find documents about this topic.
|
|
|
|
IMPORTANT: Keep multi-word phrases intact if they look like names (e.g., "Build a Business" should stay as "Build a Business", not "create a company").
|
|
|
|
Query: "${query}"
|
|
|
|
Output 3 variations, one per line:`;
|
|
|
|
const response = await fetch(`${OLLAMA_URL}/api/generate`, {
|
|
method: "POST",
|
|
headers: { "Content-Type": "application/json" },
|
|
body: JSON.stringify({
|
|
model,
|
|
prompt,
|
|
stream: false,
|
|
think: false, // Disable thinking mode for qwen3 models
|
|
options: { num_predict: 150 },
|
|
}),
|
|
});
|
|
|
|
if (!response.ok) {
|
|
const errorText = await response.text();
|
|
if (errorText.includes("not found") || errorText.includes("does not exist")) {
|
|
await ensureModelAvailable(model);
|
|
return expandQuery(query, model);
|
|
}
|
|
// Fall back to original query if expansion fails
|
|
return [query];
|
|
}
|
|
|
|
const data = await response.json() as { response: string };
|
|
const lines = data.response.trim().split('\n')
|
|
.map(l => l.replace(/^[\d\.\-\*\"\s]+/, '').replace(/["\s]+$/, '').trim())
|
|
.filter(l => l.length > 0 && !l.startsWith('<'))
|
|
.slice(0, 1); // Only 1 expanded query to preserve original query signal
|
|
|
|
// Original query + expansions (original gets 2x weight in RRF)
|
|
const allQueries = [query, ...lines];
|
|
process.stderr.write(`Queries:\n - ${allQueries.join('\n - ')}\n`);
|
|
return allQueries;
|
|
}
|
|
|
|
async function querySearch(query: string, opts: OutputOptions, embedModel: string = DEFAULT_EMBED_MODEL, rerankModel: string = DEFAULT_RERANK_MODEL): Promise<void> {
|
|
const db = getDb();
|
|
|
|
// Expand query to multiple variations
|
|
const queries = await expandQuery(query);
|
|
process.stderr.write(`Searching with ${queries.length} query variations...\n`);
|
|
|
|
// Collect ranked result lists for RRF fusion
|
|
const rankedLists: RankedResult[][] = [];
|
|
const hasVectors = !!db.prepare(`SELECT name FROM sqlite_master WHERE type='table' AND name='vectors_vec'`).get();
|
|
|
|
for (const q of queries) {
|
|
// FTS search - get ranked results
|
|
const ftsResults = searchFTS(db, q, 20);
|
|
if (ftsResults.length > 0) {
|
|
rankedLists.push(ftsResults.map(r => ({ file: r.file, body: r.body, score: r.score })));
|
|
}
|
|
|
|
// Vector search - get ranked results
|
|
if (hasVectors) {
|
|
const vecResults = await searchVec(db, q, embedModel, 20);
|
|
if (vecResults.length > 0) {
|
|
rankedLists.push(vecResults.map(r => ({ file: r.file, body: r.body, score: r.score })));
|
|
}
|
|
}
|
|
}
|
|
|
|
// Apply Reciprocal Rank Fusion to combine all ranked lists
|
|
// Give 2x weight to original query results (first 2 lists: FTS + vector)
|
|
const weights = rankedLists.map((_, i) => i < 2 ? 2.0 : 1.0);
|
|
const fused = reciprocalRankFusion(rankedLists, weights);
|
|
const candidates = fused.slice(0, 30); // Over-retrieve for reranking
|
|
|
|
if (candidates.length === 0) {
|
|
console.log("No results found.");
|
|
db.close();
|
|
return;
|
|
}
|
|
|
|
// Rerank with the original query
|
|
const reranked = await rerank(
|
|
query,
|
|
candidates.map(c => ({ file: c.file, text: c.body })),
|
|
rerankModel
|
|
);
|
|
|
|
db.close();
|
|
|
|
// Blend RRF position score with reranker score using position-aware weights
|
|
// Top retrieval results get more protection from reranker disagreement
|
|
const bodyMap = new Map(candidates.map(c => [c.file, c.body]));
|
|
const rrfRankMap = new Map(candidates.map((c, i) => [c.file, i + 1])); // 1-indexed rank
|
|
|
|
const finalResults = reranked.map(r => {
|
|
const rrfRank = rrfRankMap.get(r.file) || 30;
|
|
// Position-aware blending: top retrieval results preserved more
|
|
// Rank 1-3: 75% RRF, 25% reranker (trust retrieval for exact matches)
|
|
// Rank 4-10: 60% RRF, 40% reranker
|
|
// Rank 11+: 40% RRF, 60% reranker (trust reranker for lower-ranked)
|
|
let rrfWeight: number;
|
|
if (rrfRank <= 3) {
|
|
rrfWeight = 0.75;
|
|
} else if (rrfRank <= 10) {
|
|
rrfWeight = 0.60;
|
|
} else {
|
|
rrfWeight = 0.40;
|
|
}
|
|
const rrfScore = 1 / rrfRank; // Position-based: 1, 0.5, 0.33...
|
|
const blendedScore = rrfWeight * rrfScore + (1 - rrfWeight) * r.score;
|
|
return {
|
|
file: r.file,
|
|
body: bodyMap.get(r.file) || "",
|
|
score: blendedScore,
|
|
};
|
|
}).sort((a, b) => b.score - a.score);
|
|
|
|
outputResults(finalResults, query, opts);
|
|
}
|
|
|
|
// Parse CLI options
|
|
function parseOptions(args: string[], defaultMinScore: number = 0): { opts: OutputOptions; query: string } {
|
|
let format: OutputFormat = "cli";
|
|
let full = false;
|
|
let limit = 5;
|
|
let minScore = defaultMinScore;
|
|
const queryParts: string[] = [];
|
|
|
|
for (let i = 0; i < args.length; i++) {
|
|
const arg = args[i];
|
|
if (arg === "-n" && i + 1 < args.length) {
|
|
limit = parseInt(args[++i], 10) || 5;
|
|
} else if (arg === "--min-score" && i + 1 < args.length) {
|
|
minScore = parseFloat(args[++i]) || defaultMinScore;
|
|
} else if (arg === "--full") {
|
|
full = true;
|
|
} else if (arg === "-csv" || arg === "--csv") {
|
|
format = "csv";
|
|
} else if (arg === "-md" || arg === "--md") {
|
|
format = "md";
|
|
} else if (arg === "-xml" || arg === "--xml") {
|
|
format = "xml";
|
|
} else if (!arg.startsWith("-")) {
|
|
queryParts.push(arg);
|
|
}
|
|
}
|
|
|
|
return {
|
|
opts: { format, full, limit, minScore },
|
|
query: queryParts.join(" "),
|
|
};
|
|
}
|
|
|
|
// Parse global options and extract remaining args
|
|
function parseGlobalOptions(args: string[]): string[] {
|
|
const remaining: string[] = [];
|
|
for (let i = 0; i < args.length; i++) {
|
|
if (args[i] === "--index" && i + 1 < args.length) {
|
|
customIndexName = args[++i];
|
|
} else {
|
|
remaining.push(args[i]);
|
|
}
|
|
}
|
|
return remaining;
|
|
}
|
|
|
|
// Main CLI
|
|
const rawArgs = process.argv.slice(2);
|
|
const args = parseGlobalOptions(rawArgs);
|
|
|
|
if (args.length === 0) {
|
|
console.log("Usage:");
|
|
console.log(" qmd add [--drop] [glob] - Add/update collection from $PWD (default: **/*.md)");
|
|
console.log(" qmd status - Show index status and collections");
|
|
console.log(" qmd update-all - Re-index all collections");
|
|
console.log(" qmd embed [-f] - Create vector embeddings for all content");
|
|
console.log(" qmd search <query> - Full-text search (BM25)");
|
|
console.log(" qmd vsearch <query> - Vector similarity search");
|
|
console.log(" qmd query <query> - Combined search with query expansion + reranking");
|
|
console.log("");
|
|
console.log("Global options:");
|
|
console.log(" --index <name> - Use custom index name (default: index)");
|
|
console.log("");
|
|
console.log("Search options:");
|
|
console.log(" -n <num> - Number of results (default: 5)");
|
|
console.log(" --min-score <num> - Minimum similarity score");
|
|
console.log(" --full - Output full document instead of snippet");
|
|
console.log(" -csv - CSV output (default is colorized CLI)");
|
|
console.log(" -md - Markdown output");
|
|
console.log(" -xml - XML output");
|
|
console.log("");
|
|
console.log("Environment:");
|
|
console.log(" OLLAMA_URL - Ollama server URL (default: http://localhost:11434)");
|
|
console.log("");
|
|
console.log("Models:");
|
|
console.log(` Embedding: ${DEFAULT_EMBED_MODEL}`);
|
|
console.log(` Reranking: ${DEFAULT_RERANK_MODEL}`);
|
|
console.log("");
|
|
console.log(`Index: ${getDbPath()}`);
|
|
process.exit(1);
|
|
}
|
|
|
|
const cmd = args[0];
|
|
|
|
if (cmd === "add") {
|
|
const addArgs = args.slice(1);
|
|
const drop = addArgs.includes("--drop");
|
|
const globArg = addArgs.find(a => !a.startsWith("-"));
|
|
// Treat "." as "use default glob in current directory"
|
|
const globPattern = (!globArg || globArg === ".") ? DEFAULT_GLOB : globArg;
|
|
|
|
if (drop) {
|
|
await dropCollection(globPattern);
|
|
} else {
|
|
await indexFiles(globPattern);
|
|
}
|
|
} else if (cmd === "status") {
|
|
showStatus();
|
|
} else if (cmd === "update-all") {
|
|
await updateAllCollections();
|
|
} else if (cmd === "embed") {
|
|
const embedArgs = args.slice(1);
|
|
const force = embedArgs.includes("-f") || embedArgs.includes("--force");
|
|
await vectorIndex(DEFAULT_EMBED_MODEL, force);
|
|
} else if (cmd === "search") {
|
|
const { opts, query } = parseOptions(args.slice(1), 0);
|
|
if (!query) {
|
|
console.error("Usage: qmd search [-n num] [--min-score num] [--full] [-csv|-md|-xml] <query>");
|
|
process.exit(1);
|
|
}
|
|
search(query, opts);
|
|
} else if (cmd === "vsearch") {
|
|
const { opts, query } = parseOptions(args.slice(1), 0.3);
|
|
if (!query) {
|
|
console.error("Usage: qmd vsearch [-n num] [--min-score num] [--full] [-csv|-md|-xml] <query>");
|
|
process.exit(1);
|
|
}
|
|
await vectorSearch(query, opts);
|
|
} else if (cmd === "query") {
|
|
const { opts, query } = parseOptions(args.slice(1), 0);
|
|
if (!query) {
|
|
console.error("Usage: qmd query [-n num] [--min-score num] [--full] [-csv|-md|-xml] <query>");
|
|
process.exit(1);
|
|
}
|
|
await querySearch(query, opts);
|
|
} else {
|
|
console.error(`Unknown command: ${cmd}`);
|
|
console.error("Run 'qmd' without arguments for usage.");
|
|
process.exit(1);
|
|
}
|