Initial commit: RA-H Open Source Edition

Local-first knowledge management system with BYO API keys.

Features:
- 3-panel UI (Nodes | Focus | Helpers)
- SQLite + sqlite-vec for vector search
- Agent system (Easy/Hard mode orchestrators)
- Content extraction (YouTube, PDF, web)
- Integrate workflow for connection discovery
- Dimension system with auto-assignment

Tech stack:
- Next.js 15 + TypeScript + Tailwind CSS
- Anthropic (Claude) + OpenAI (GPT) via Vercel AI SDK

Setup:
  npm install && npm rebuild better-sqlite3
  scripts/dev/bootstrap-local.sh
  npm run dev

MIT License

src/services/embeddings.ts

@@ -0,0 +1,44 @@
+import OpenAI from 'openai';
+import { apiKeyService } from './storage/apiKeys';
+
+// Initialize OpenAI client with dynamic API key support
+function getOpenAiClient(): OpenAI {
+  const apiKey = apiKeyService.getOpenAiKey();
+  if (!apiKey) {
+    throw new Error('OpenAI API key required. Please:\n1. Click the Settings icon (⚙️) in the bottom left\n2. Go to API Keys tab\n3. Add your OpenAI API key\n\nGet your key at: https://platform.openai.com/api-keys');
+  }
+  return new OpenAI({ apiKey });
+}
+
+export class EmbeddingService {
+  /**
+   * Generate embedding for a search query using OpenAI's text-embedding-3-small model
+   * This matches the same model used in embed_universal.py for consistency
+   */
+  static async generateQueryEmbedding(query: string): Promise<number[]> {
+    try {
+      const openai = getOpenAiClient();
+      const response = await openai.embeddings.create({
+        model: "text-embedding-3-small",
+        input: query.trim(),
+        encoding_format: "float"
+      });
+
+      if (!response.data?.[0]?.embedding) {
+        throw new Error('No embedding returned from OpenAI API');
+      }
+
+      return response.data[0].embedding;
+    } catch (error) {
+      console.error('Failed to generate query embedding:', error);
+      throw new Error(`Embedding generation failed: ${error instanceof Error ? error.message : 'Unknown error'}`);
+    }
+  }
+
+  /**
+   * Validate embedding dimensions match expected size (1536 for text-embedding-3-small)
+   */
+  static validateEmbedding(embedding: number[]): boolean {
+    return Array.isArray(embedding) && embedding.length === 1536;
+  }
+}