Search

}
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);

  getUserConversations
} from "../database/queries.ts";
import { generateEmbedding } from "./embeddings.ts";
const app = new Hono();

}
// Fallback text search when embeddings are not available
export async function textSearchMedicalContent(
  query: string,
    
    await sqlite.execute(`
      INSERT OR REPLACE INTO ${TABLES.EMBEDDINGS_CACHE}
      (query_hash, query_text, embedding_vector, last_accessed, access_count)
      VALUES (?, ?, ?, CURRENT_TIMESTAMP, 
        COALESCE((SELECT access_count FROM ${TABLES.EMBEDDINGS_CACHE} WHERE query_hash = ?), 0) + 1)
    `, [queryHash, queryText, JSON.stringify(embedding), queryHash]);
  } catch (error) {
    
    const results = await sqlite.execute(
      `SELECT embedding_vector FROM ${TABLES.EMBEDDINGS_CACHE} WHERE query_hash = ?`,
      [queryHash]
    );
    // Update access count
    await sqlite.execute(
      `UPDATE ${TABLES.EMBEDDINGS_CACHE} 
       SET access_count = access_count + 1, last_accessed = CURRENT_TIMESTAMP
       WHERE query_hash = ?`,

  CONVERSATIONS: 'nelson_conversations_v1',
  USER_PREFERENCES: 'nelson_user_preferences_v1',
  EMBEDDINGS_CACHE: 'nelson_embeddings_cache_v1'
};
    `);
    // Embeddings cache for performance
    await sqlite.execute(`
      CREATE TABLE IF NOT EXISTS ${TABLES.EMBEDDINGS_CACHE} (
        query_hash TEXT PRIMARY KEY,
        query_text TEXT NOT NULL,
    
    await sqlite.execute(`
      CREATE INDEX IF NOT EXISTS idx_embeddings_cache_access 
      ON ${TABLES.EMBEDDINGS_CACHE}(last_accessed DESC)
    `);

import { readFile, serveFile } from "https://esm.town/v/std/utils@85-main/index.ts";
import { chatRoutes } from "./routes/chat.ts";
import { embeddingRoutes } from "./routes/embeddings.ts";
import { runMigrations } from "./database/migrations.ts";
// API Routes
app.route("/api/chat", chatRoutes);
app.route("/api/embeddings", embeddingRoutes);
// Health check endpoint

- **Supabase** with PGVector for vector search
- **OpenAI/Mistral** for AI responses
- **Hugging Face** for embeddings
### Database Schema
```sql
-- Medical content chunks with embeddings
CREATE TABLE medical_content (
  id SERIAL PRIMARY KEY,
- Supabase project with PGVector extension
- OpenAI or Mistral API key
- Hugging Face API key (optional for server-side embeddings)
### Environment Variables
│   ├── routes/
│   │   ├── chat.ts         # Chat API endpoints
│   │   ├── embeddings.ts   # Embedding generation
│   │   └── static.ts       # Static file serving
│   └── index.ts            # Main API entry point

  try {
    // Get embedding
    const embResponse = await fetch("https://api.openai.com/v1/embeddings", {
      method: "POST",
      headers: {

  async getEmbedding(text: string, apiKey: string) {
    const response = await fetch("https://api.openai.com/v1/embeddings", {
      method: "POST",
      headers: {
      },
      body: JSON.stringify({
        model: "text-embedding-3-small", // IMPORTANT: Match your Pinecone embeddings
        input: text,
      }),