import { Pinecone } from '@pinecone-database/pinecone';

// 1. Instantiate the Pinecone client
// Option A: Pass API key directly
const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' });

// Option B: Use environment variable (PINECONE_API_KEY)
// const pc = new Pinecone();

// 2. Create a serverless index
const indexModel = await pc.createIndex({
  name: 'example-index',
  dimension: 1536,
  metric: 'cosine',
  spec: {
    serverless: {
      cloud: 'aws',
      region: 'us-east-1',
    },
  },
});

// 3. Target the index
const index = pc.index({ host: indexModel.host });

// 4. Upsert vectors with metadata
await index.upsert({
  records: [
    {
      id: 'vec1',
      values: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8], // ... dimension should match index (1536)
      metadata: { genre: 'drama', year: 2020 },
    },
    {
      id: 'vec2',
      values: [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
      metadata: { genre: 'action', year: 2021 },
    },
  ],
});

// 5. Query the index
const queryResponse = await index.query({
  vector: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8], // ... query vector
  topK: 3,
  includeMetadata: true,
});

console.log(queryResponse);

import { Pinecone } from '@pinecone-database/pinecone';

// 1. Instantiate the Pinecone client
const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' });

// 2. Create an index configured for use with a particular embedding model
const indexModel = await pc.createIndexForModel({
  name: 'example-index',
  cloud: 'aws',
  region: 'us-east-1',
  embed: {
    model: 'multilingual-e5-large',
    fieldMap: { text: 'chunk_text' },
  },
  waitUntilReady: true,
});

// 3. Target the index
const index = pc.index({ host: indexModel.host });

// 4. Upsert records with raw text data
// Pinecone will automatically generate embeddings using the configured model
await index.upsertRecords({
  records: [
    {
      id: 'rec1',
      chunk_text:
        "Apple's first product, the Apple I, was released in 1976 and was hand-built by co-founder Steve Wozniak.",
      category: 'product',
    },
    {
      id: 'rec2',
      chunk_text:
        'Apples are a great source of dietary fiber, which supports digestion and helps maintain a healthy gut.',
      category: 'nutrition',
    },
    {
      id: 'rec3',
      chunk_text:
        'Apples originated in Central Asia and have been cultivated for thousands of years, with over 7,500 varieties available today.',
      category: 'cultivation',
    },
    {
      id: 'rec4',
      chunk_text:
        'In 2001, Apple released the iPod, which transformed the music industry by making portable music widely accessible.',
      category: 'product',
    },
  ],
});

// 5. Search for similar records using text queries
// Pinecone handles embedding the query and optionally reranking results
const searchResponse = await index.searchRecords({
  query: {
    inputs: { text: 'Apple corporation' },
    topK: 3,
  },
  rerank: {
    model: 'bge-reranker-v2-m3',
    topN: 2,
    rankFields: ['chunk_text'],
  },
});

console.log(searchResponse);

import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();

// Create an assistant
const assistant = await pc.createAssistant({
  name: 'product-assistant',
  instructions: 'You are a helpful product recommendation assistant.',
});

// Target the assistant for data operations
const myAssistant = pc.assistant({ name: 'product-assistant' });

// Upload a file
await myAssistant.uploadFile({
  path: 'product-catalog.txt',
  metadata: { source: 'catalog' },
});

// Chat with the assistant
const response = await myAssistant.chat({
  messages: [
    {
      role: 'user',
      content: 'What products do you recommend for outdoor activities?',
    },
  ],
});

console.log(response.message.content);