Search

    "link": "/blog/val-vibes",
    "description":
      "How to build semantic search with embeddings for Val Town within Val Town itself",
    "pubDate": "Tue, 18 Jun 2024 00:00:00 GMT",
    "author": "JP Posma",

```
async function calculateEmbeddings(text) {
    const url = `https://yawnxyz-ai.web.val.run/generate?embed=true&value=${encodeURIComponent(text)}`;
    
        return data;
    } catch (error) {
        console.error('Error calculating embeddings:', error);
        return null;
    }

  // Embedding endpoints
  async generateEmbedding(request: EmbeddingRequest): Promise<ApiResponse<EmbeddingResponse>> {
    return this.request<EmbeddingResponse>('/embeddings/generate', {
      method: 'POST',
      body: JSON.stringify(request),
    threshold?: number;
  }): Promise<ApiResponse<any>> {
    return this.request('/embeddings/search', {
      method: 'POST',
      body: JSON.stringify(query),
  }
  async embeddingsHealthCheck(): Promise<ApiResponse<any>> {
    return this.request('/embeddings/health');
  }

const API_CACHE_PATTERNS = [
  /\/api\/health/,
  /\/api\/embeddings\/health/,
  /\/api\/chat\/health/
];

import { initializeDatabase } from './services/supabase.ts';
import chatRoutes from './routes/chat.ts';
import embeddingRoutes from './routes/embeddings.ts';
const app = new Hono();
// API Routes
app.route('/api/chat', chatRoutes);
app.route('/api/embeddings', embeddingRoutes);
// Health check

import { Hono } from 'https://esm.sh/hono@3.11.7';
import { generateEmbedding, generateEmbeddings } from '../services/embeddings.ts';
import { searchSimilarContent } from '../services/supabase.ts';
import type { EmbeddingRequest, EmbeddingResponse } from '../../shared/types.ts';
const embeddings = new Hono();
// Generate embedding for a single text
embeddings.post('/generate', async (c) => {
  try {
    const body: EmbeddingRequest = await c.req.json();
});
// Generate embeddings for multiple texts
embeddings.post('/batch', async (c) => {
  try {
    const body: { texts: string[] } = await c.req.json();
    }
    const embeddings = await generateEmbeddings(texts);
    return c.json({
      embeddings,
      count: embeddings.length,
      dimensions: embeddings[0]?.length || 0
    });
    console.error('Batch embedding generation error:', error);
    return c.json({ 
      error: 'Failed to generate batch embeddings',
      details: error.message 
    }, 500);
// Search similar content using embedding
embeddings.post('/search', async (c) => {
  try {
    const body: { 
// Health check endpoint
embeddings.get('/health', (c) => {
  return c.json({ 
    status: 'healthy',
    service: 'nelson-gpt-embeddings',
    timestamp: new Date().toISOString()
  });
});
export default embeddings;

import { Hono } from 'https://esm.sh/hono@3.11.7';
import { streamSSE } from 'https://esm.sh/hono@3.11.7/streaming';
import { generateEmbedding } from '../services/embeddings.ts';
import { searchSimilarContent } from '../services/supabase.ts';
import { generateMistralResponse, streamMistralResponse } from '../services/mistral.ts';

}
export async function generateEmbeddings(texts: string[]): Promise<number[][]> {
  try {
    const embeddings = await Promise.all(
      texts.map(text => generateEmbedding(text))
    );
    return embeddings;
  } catch (error) {
    console.error('Error generating batch embeddings:', error);
    throw error;
  }
}
// Utility function to normalize embeddings (optional, for better similarity search)
export function normalizeEmbedding(embedding: number[]): number[] {
  const magnitude = Math.sqrt(embedding.reduce((sum, val) => sum + val * val, 0));
}
// Calculate cosine similarity between two embeddings
export function cosineSimilarity(a: number[], b: number[]): number {
  if (a.length !== b.length) {
    throw new Error('Embeddings must have the same length');
  }
  

- **Database**: Supabase with PGVector for vector search
- **AI**: Mistral API for LLM responses
- **Embeddings**: Hugging Face sentence-transformers
- **Animations**: Lottie
- **PWA**: Service Worker + IndexedDB
│   ├── routes/
│   │   ├── chat.ts        # Chat API endpoints
│   │   └── embeddings.ts  # Embedding generation
│   └── services/
│       ├── supabase.ts    # Supabase client
│       ├── mistral.ts     # Mistral API client
│       └── embeddings.ts  # HuggingFace embeddings
├── frontend/
│   ├── index.html         # Main HTML template
- `VITE_SUPABASE_ANON_KEY`: Supabase anonymous key
- `VITE_SUPABASE_SERVICE_ROLE_KEY`: Supabase service role key
- `VITE_HUGGINFACE_API_KEY`: Hugging Face API key for embeddings
## Getting Started

const app = new Hono();
// Hugging Face API for embeddings
const HF_API_URL = "https://api-inference.huggingface.co/pipeline/feature-extraction/sentence-transformers/all-MiniLM-L6-v2";
}
// Generate embeddings endpoint
app.post("/generate", async (c) => {
  try {
    console.error("Batch embedding error:", error);
    return c.json({ 
      error: "Failed to generate batch embeddings",
      details: error.message 
    }, 500);
    return c.json({
      status: "healthy",
      service: "embeddings",
      model: "sentence-transformers/all-MiniLM-L6-v2",
      dimensions: testEmbedding?.length || 0,
    return c.json({
      status: "unhealthy",
      service: "embeddings",
      error: error.message
    }, 500);