Colby McHenry
8346440592
Replace native tree-sitter with web-tree-sitter + tree-sitter-wasms for universal cross-platform support. Add node-sqlite3-wasm as a fallback when better-sqlite3 native bindings aren't available. Move better-sqlite3 and sqlite-vss to optionalDependencies so installs never fail. Fix installer to use npx fallback when global npm install fails, so MCP config, hooks, and quick-start instructions all work without the bare codegraph command in PATH. Fix tests: update schema version expectation, fix db test paths and method names, extract MAX_OUTPUT_LENGTH as module constant, normalize Windows path separators in import resolver.
69 lines
2.2 KiB
JavaScript
69 lines
2.2 KiB
JavaScript
#!/usr/bin/env node
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/**
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* Postinstall script - downloads the embedding model to ~/.codegraph/models
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* This runs after `npm install` or `npx @colbymchenry/codegraph`
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*/
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const { existsSync, mkdirSync } = require('fs');
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const { join } = require('path');
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const { homedir } = require('os');
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const CODEGRAPH_DIR = join(homedir(), '.codegraph');
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const MODELS_DIR = join(CODEGRAPH_DIR, 'models');
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const MODEL_ID = 'nomic-ai/nomic-embed-text-v1.5';
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async function downloadModel() {
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// Ensure directories exist
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if (!existsSync(CODEGRAPH_DIR)) {
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mkdirSync(CODEGRAPH_DIR, { recursive: true });
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}
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if (!existsSync(MODELS_DIR)) {
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mkdirSync(MODELS_DIR, { recursive: true });
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}
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// Check if model is already cached
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const modelCachePath = join(MODELS_DIR, MODEL_ID.replace('/', '/'));
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if (existsSync(modelCachePath)) {
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console.log('Embedding model already downloaded.');
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return;
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}
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console.log('Downloading embedding model (~130MB)...');
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console.log('This is a one-time download for semantic code search.\n');
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try {
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// Dynamic import for @xenova/transformers (ESM-only package)
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const { pipeline, env } = await import('@xenova/transformers');
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// Configure cache directory
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env.cacheDir = MODELS_DIR;
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// Download with progress
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await pipeline('feature-extraction', MODEL_ID, {
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progress_callback: (progress) => {
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if (progress.status === 'progress' && progress.file && progress.progress !== undefined) {
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const fileName = progress.file.split('/').pop();
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const percent = Math.round(progress.progress);
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process.stdout.write(`\rDownloading ${fileName}... ${percent}% `);
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} else if (progress.status === 'done') {
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process.stdout.write('\n');
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}
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},
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});
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console.log('\nEmbedding model ready!');
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} catch (error) {
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// Don't fail the install if model download fails
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// User can still use codegraph without semantic search
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console.log('\nNote: Could not download embedding model.');
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console.log('Semantic search will download it on first use.');
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if (process.env.DEBUG) {
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console.error(error);
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}
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}
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}
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downloadModel().catch(() => {
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// Silent exit - don't break npm install
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process.exit(0);
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});
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