feat: integrate vectordb for note embeddings

- Added `vectordb` as a dependency in `package.json`.
- Implemented `embedText` function in `src/main/ai/embeddings.ts` to handle text embeddings using GitHub Copilot OAuth token or OpenAI token.
- Created `vectordb.ts` for managing LanceDB connection and embedding notes with upsert strategy.
- Updated `index.ts` to initialize vector database and migrate existing notes on app ready.
- Modified `router/index.ts` to fire-and-forget embedding calls on note creation and updates.
- Enhanced `progress.txt` with detailed implementation notes and learnings regarding the integration.

src/main/ai/embeddings.ts

@@ -0,0 +1,73 @@
+import fs from 'node:fs';
+import os from 'node:os';
+import path from 'node:path';
+import { getToken } from './token';
+
+interface CopilotConfig {
+  copilot_tokens?: Record<string, string>;
+}
+
+/**
+ * Read the GitHub Copilot OAuth token from the CLI config file.
+ * Stored at ~/.copilot/config.json under copilot_tokens["{host}:{login}"].
+ * Returns the first available token, or null if unavailable.
+ */
+function readCopilotToken(): string | null {
+  try {
+    const raw = fs.readFileSync(
+      path.join(os.homedir(), '.copilot', 'config.json'),
+      'utf-8',
+    );
+    const cfg = JSON.parse(raw) as CopilotConfig;
+    const vals = Object.values(cfg.copilot_tokens ?? {});
+    return vals[0] ?? null;
+  } catch {
+    return null;
+  }
+}
+
+/**
+ * Embed a single text string using the best available credentials.
+ *
+ * Priority:
+ *  1. GitHub Copilot CLI token → OpenAI-compatible embeddings endpoint at
+ *     https://api.githubcopilot.com
+ *  2. Stored OpenAI token → standard OpenAI embeddings API
+ *
+ * Throws if no credentials are available or the API call fails.
+ * Callers must .catch() this and handle the error without rejecting
+ * the surrounding tRPC mutation.
+ */
+export async function embedText(text: string): Promise<number[]> {
+  const { OpenAIEmbeddings } = await import('@langchain/openai');
+
+  const copilotToken = readCopilotToken();
+
+  let embeddingsInstance;
+  if (copilotToken) {
+    embeddingsInstance = new OpenAIEmbeddings({
+      apiKey: copilotToken,
+      model: 'text-embedding-3-small',
+      configuration: { baseURL: 'https://api.githubcopilot.com' },
+    });
+  } else {
+    const openaiToken = await getToken('openai');
+    if (!openaiToken) {
+      throw new Error(
+        '[Embeddings] No credentials available. Authenticate with Copilot CLI or add an OpenAI token in Settings.',
+      );
+    }
+    embeddingsInstance = new OpenAIEmbeddings({
+      apiKey: openaiToken,
+      model: 'text-embedding-3-small',
+    });
+  }
+
+  // embedDocuments returns number[][] — cast explicitly to satisfy strict TS
+  const results = (await embeddingsInstance.embedDocuments([text])) as number[][];
+  const vector = results[0] as number[] | undefined;
+  if (!vector || vector.length === 0) {
+    throw new Error('[Embeddings] Empty vector returned from embedding API');
+  }
+  return vector;
+}