Albert
a1a73d8453
Implement the core write-through cache pattern for node creation. This is the architectural foundation of the application. Changes: - Add @google/generative-ai dependency for embeddings - Create lib/db.ts: SurrealDB connection helper with JWT auth - Create lib/ai.ts: AI embedding generation using text-embedding-004 - Create app/api/nodes/route.ts: POST endpoint implementing write-through cache Write-through cache flow: 1. Authenticate user via SurrealDB JWT 2. Publish node to ATproto PDS (source of truth) 3. Generate 768-dimensional embedding via Google AI 4. Cache node + embedding + links in SurrealDB Updated schema to use 768-dimensional embeddings (text-embedding-004) instead of 1536 dimensions. Security: - Row-level permissions enforced via SurrealDB JWT - All secrets server-side only - ATproto OAuth tokens from secure cookies 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
25 lines
797 B
TypeScript
25 lines
797 B
TypeScript
import { GoogleGenerativeAI } from '@google/generative-ai';
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const genAI = new GoogleGenerativeAI(process.env.GOOGLE_API_KEY!);
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const embeddingModel = genAI.getGenerativeModel({
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model: 'text-embedding-004',
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});
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/**
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* Generates a vector embedding for a given text using Google's text-embedding-004 model.
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* The output is a 768-dimension vector (not 1536 as originally specified).
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*
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* @param text - The text to embed
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* @returns A 768-dimension vector (Array<number>)
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*/
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export async function generateEmbedding(text: string): Promise<number[]> {
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try {
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const result = await embeddingModel.embedContent(text);
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return result.embedding.values;
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} catch (error) {
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console.error('Error generating embedding:', error);
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throw new Error('Failed to generate AI embedding.');
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}
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}
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