hnswsqlite/dist/cli/index.js

Vector search with HNSWlib and SQLite in TypeScript.

#!/usr/bin/env node
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const commander_1 = require("commander");
const add_1 = require("./commands/add");
const search_1 = require("./commands/search");
const delete_1 = require("./commands/delete");
const vectorStore_1 = require("../vectorStore");
const db_1 = require("./utils/db");
const program = new commander_1.Command();
program
    .name('hnswsqlite')
    .description('CLI for HNSWSQLite vector store')
    .version('0.1.0');
// Global options
program
    .option('-d, --database <path>', 'path to the SQLite database', 'vectors.db')
    .option('--dim <dimension>', 'dimension of the vectors', '1536')
    .option('--verbose', 'enable verbose output', false);
// Add command
program
    .command('add <text> [embedding...]')
    .description('Add a document with optional embedding')
    .action(async (text, embedding, options) => {
    const { database, dim } = options.parent.opts();
    const store = new vectorStore_1.VectorStore(database, parseInt(dim, 10));
    try {
        const docId = await (0, add_1.addDocument)(store, text, embedding);
        console.log(`✅ Document added with ID: ${docId}`);
    }
    catch (error) {
        if (error instanceof Error) {
            console.error('❌ Error adding document:', error.message);
        }
        else {
            console.error('❌ Error adding document:', error);
        }
        process.exit(1);
    }
    finally {
        store.close();
    }
});
// Search command
program
    .command('search <query>')
    .description('Search for similar documents')
    .option('-k, --k <number>', 'number of results to return', '5')
    .action(async (query, options) => {
    const { database, dim } = options.parent.opts();
    const store = new vectorStore_1.VectorStore(database, parseInt(dim, 10));
    try {
        // In a real app, you'd generate an embedding from the query
        // For now, we'll use a dummy embedding
        const dummyEmbedding = new Array(parseInt(dim, 10)).fill(0.1);
        const results = await (0, search_1.searchDocuments)(store, dummyEmbedding, parseInt(options.k, 10));
        if (results.length === 0) {
            console.log('No matching documents found.');
            return;
        }
        console.log('\nSearch Results:');
        results.forEach((doc, index) => {
            console.log(`\n${index + 1}. ID: ${doc.id}`);
            console.log(`   Text: ${doc.text}`);
            console.log('   ---');
        });
    }
    catch (error) {
        if (error instanceof Error) {
            console.error('❌ Error searching documents:', error.message);
        }
        else {
            console.error('❌ Error searching documents:', error);
        }
        process.exit(1);
    }
    finally {
        store.close();
    }
});
// Delete command
program
    .command('delete <id>')
    .description('Delete a document by ID')
    .action(async (id, options) => {
    const { database, dim } = options.parent.opts();
    const store = new vectorStore_1.VectorStore(database, parseInt(dim, 10));
    try {
        const success = await (0, delete_1.deleteDocument)(store, parseInt(id, 10));
        if (success) {
            console.log(`✅ Document ${id} deleted successfully`);
        }
        else {
            console.log(`❌ Document ${id} not found or could not be deleted`);
        }
    }
    catch (error) {
        if (error instanceof Error) {
            console.error('❌ Error deleting document:', error.message);
        }
        else {
            console.error('❌ Error deleting document:', error);
        }
        process.exit(1);
    }
    finally {
        store.close();
    }
});
// List command
program
    .command('list')
    .description('List all documents')
    .action(async (options) => {
    const { database, dim } = options.parent.opts();
    const store = new vectorStore_1.VectorStore(database, parseInt(dim, 10));
    try {
        const docs = await (0, delete_1.listDocuments)(store);
        if (docs.length === 0) {
            console.log('No documents found.');
            return;
        }
        console.log('\nDocuments:');
        docs.forEach((doc, index) => {
            console.log(`\n${index + 1}. ID: ${doc.id}`);
            console.log(`   Text: ${doc.text}`);
            console.log('   ---');
        });
    }
    catch (error) {
        if (error instanceof Error) {
            console.error('❌ Error listing documents:', error.message);
        }
        else {
            console.error('❌ Error listing documents:', error);
        }
        process.exit(1);
    }
    finally {
        store.close();
    }
});
// Initialize command
program
    .command('init')
    .description('Initialize a new database')
    .action((options) => {
    const { database, dim } = options.parent.opts();
    (0, db_1.initDatabase)(database, parseInt(dim, 10));
    console.log(`✅ Database initialized at ${database}`);
});
// Handle unknown commands
program.on('command:*', () => {
    console.error('Invalid command. Use --help for a list of commands.');
    process.exit(1);
});
// Parse command line arguments
program.parse(process.argv);
// Show help if no arguments
if (!process.argv.slice(2).length) {
    program.outputHelp();
}