API Reference

Learn

Master Vectorize with our comprehensive tutorials, best practices, and real-world examples. Whether you're new to vector search or building advanced RAG applications, we have resources to help you succeed.

Tutorials

🚀

Getting Started with Your First RAG Pipeline

Beginner 15 minutes

Learn how to create your first RAG pipeline from scratch. This tutorial covers account setup, data source connection, and deploying a working search system.

  • Create a Vectorize account and workspace
  • Connect your first data source (Google Drive)
  • Configure embedding and chunking settings
  • Test your pipeline with sample queries
🔍

Building a Document Q&A System

Intermediate 30 minutes

Build an intelligent document Q&A system that can answer questions about your PDFs, Word documents, and other files with source attribution.

  • Upload and process document collections
  • Optimize chunking strategies for documents
  • Implement query rewriting and re-ranking
  • Add source attribution and confidence scoring
🤖

Creating an AI-Powered Chatbot

Intermediate 45 minutes

Combine Vectorize with OpenAI's GPT to create a chatbot that can answer questions using your organization's knowledge base.

  • Set up retrieval endpoints
  • Integrate with OpenAI's Chat Completions API
  • Handle conversation context and history
  • Implement fallback responses
⚡

Real-time Data Synchronization

Advanced 60 minutes

Set up real-time synchronization to keep your vector indexes updated automatically as your source data changes.

  • Configure webhook-based updates
  • Set up monitoring and alerting
  • Handle incremental updates and deletions
  • Optimize for high-frequency changes

Best Practices

📋 Data Preparation

  • Clean your data: Remove irrelevant metadata, headers, and footers
  • Structure content: Use clear headings and organize information logically
  • Include context: Add metadata like document titles, dates, and categories
  • Remove duplicates: Eliminate redundant content to improve search quality

Chunking Strategy Guidelines

  • Size matters: Use 500-1000 characters for most content types
  • Preserve context: Include 10-20% overlap between adjacent chunks
  • Respect boundaries: Don't split sentences or code blocks
  • Consider content type: Use smaller chunks for technical content, larger for narrative text

Embedding Model Selection

  • Domain specificity: Choose models trained on similar content
  • Language support: Ensure multilingual support if needed
  • Performance vs. quality: Balance between speed and accuracy
  • Test and compare: Use RAG evaluation to compare models

Query Optimization

  • Query expansion: Add synonyms and related terms
  • Rewriting: Rephrase queries for better retrieval
  • Filtering: Use metadata filters to narrow results
  • Re-ranking: Apply post-retrieval ranking for relevance

Code Examples

Basic RAG Pipeline Setup

// Initialize Vectorize client
const vectorize = new VectorizeClient({
  apiKey: 'your-api-key',
  environment: 'production'
});

// Create a new pipeline
const pipeline = await vectorize.pipelines.create({
  name: 'knowledge-base',
  description: 'Company knowledge base search',
  embedding: {
    model: 'text-embedding-ada-002',
    dimensions: 1536
  },
  chunking: {
    strategy: 'recursive',
    chunkSize: 1000,
    overlap: 200
  },
  vectorDatabase: {
    provider: 'pinecone',
    index: 'kb-index'
  }
});

Query Your Knowledge Base

// Search for relevant documents
const results = await vectorize.search({
  query: 'How do I reset my password?',
  pipelineId: pipeline.id,
  limit: 5,
  filters: {
    category: 'user-guides'
  }
});

// Results include content, metadata, and similarity scores
results.forEach(result => {
  console.log(`Score: ${result.score}`);
  console.log(`Content: ${result.content}`);
  console.log(`Source: ${result.metadata.source}`);
});

Real-time Updates

// Set up webhook for real-time updates
await vectorize.webhooks.create({
  pipelineId: pipeline.id,
  url: 'https://your-app.com/webhook',
  events: ['document.created', 'document.updated', 'document.deleted']
});

// Handle webhook in your application
app.post('/webhook', (req, res) => {
  const { event, data } = req.body;
  
  if (event === 'document.updated') {
    console.log(`Document ${data.id} was updated`);
    // Your application logic here
  }
  
  res.status(200).send('OK');
});

Next Steps

Ready to start building? Here are some suggested next steps: