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The Problem with Sonamu Search API

You’re building a knowledge base app with Sonamu: 
class DocumentModelClass extends BaseModel {
  @api({ httpMethod: 'POST' })
  async search(query: string) {
    // Keyword search
    const results = await this.findMany({
      wq: [
        ['title', 'LIKE', `%${query}%`],
        ['content', 'LIKE', `%${query}%`],
      ],
    });
    return results;
  }
}
What happens when a user searches for β€œTypeScript framework”?
  • β€œSonamu is a TypeScript framework” - Found
  • β€œSonamu is a Node.js API library” - Not found (different keywords)
  • β€œSonamu is a TS framework” - Not found (abbreviation)
  • β€œTypeScript framwork” - Not found (typo)
Limitations of keyword search:
  • Doesn’t handle synonyms
  • Fails when expressions differ
  • Vulnerable to typos
  • Fails when meaning is the same but words differ

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Semantic Search is Needed

β€œTypeScript framework” and β€œNode.js API library” are semantically similar, even though the keywords are different. How can computers understand this meaning? Embeddings

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What are Embeddings?

Embeddings convert text into high-dimensional numerical arrays (vectors). Semantically similar texts are placed close together in vector space. Key points:
  • Converting to numbers enables distance calculation
  • Close vectors = semantically similar texts
  • Distant vectors = semantically different texts

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Flow in Sonamu

// 1. When saving a document: text -> embedding -> DB
await DocumentModel.saveOne({
  title: "Getting Started with Sonamu",
  content: "...",
  embedding: [0.2, 0.8, -0.3, ...],  // 1024 dimensions
});

// 2. When searching: query -> embedding -> similarity calculation
const queryEmbedding = [0.3, 0.7, -0.2, ...];
const results = await searchSimilar(queryEmbedding);

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The Embedding Class

Sonamu provides an Embedding class to easily create embeddings: 
import { Embedding } from "sonamu/vector";

// Single text embedding
const result = await Embedding.embedOne(
  "Sonamu is a TypeScript framework",
  'voyage'  // 'voyage' | 'openai'
);

console.log(result.embedding);  // [0.123, -0.456, ...] (1024 dimensions)
console.log(result.model);      // "voyage-3"
console.log(result.tokenCount); // 8

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Which Provider Should You Choose?

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Voyage AI vs OpenAI

Sonamu supports two embedding providers:
ItemVoyage AIOpenAI
Korean performanceExcellentGood
English performanceExcellentExcellent
Dimensions10241536
Max tokens32,0008,191
Batch size128100
Asymmetric embeddingsYes (document/query distinction)No
Sonamu recommendationHighly recommendedRecommended

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Selection Criteria for Sonamu Projects

Recommend Voyage AI:
  • Korean services (excellent Korean performance)
  • Long document processing (32,000 tokens)
  • Search accuracy matters (asymmetric embeddings)
Recommend OpenAI:
  • Global services (balanced multilingual support)
  • Already using OpenAI API

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Environment Setup

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1. Install Packages

pnpm add voyageai
# or
pnpm add @ai-sdk/openai

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2. Configure API Keys

# Voyage AI (recommended)
VOYAGE_API_KEY=pa-...

# OpenAI (alternative)
OPENAI_API_KEY=sk-...
Get API keys from each provider’s website:

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Using in Sonamu Model

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Generating Embeddings When Saving Documents

import { BaseModel, api } from "sonamu";
import { Embedding } from "sonamu/vector";

class DocumentModelClass extends BaseModel {
  @api({ httpMethod: 'POST' })
  async createDocument(title: string, content: string) {
    // 1. Generate embedding
    const result = await Embedding.embedOne(
      `${title}\n\n${content}`,
      'voyage',
      'document'  // document embedding
    );

    // 2. Save to DB (Sonamu's saveOne)
    const doc = await this.saveOne({
      title,
      content,
      embedding: result.embedding,  // array of 1024 numbers
      token_count: result.tokenCount,
    });

    return doc;
  }
}
Flow:
  1. User uploads document via POST /documents
  2. Sonamu API generates embedding via Voyage AI
  3. PostgreSQL stores text + embedding together

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Search API (to be implemented later)

@api({ httpMethod: 'POST' })
async search(query: string) {
  // 1. Generate query embedding
  const result = await Embedding.embedOne(
    query,
    'voyage',
    'query'  // search embedding
  );

  // 2. Vector search (see vector-search.mdx)
  const results = await this.getPuri().raw(`
    SELECT title, content,
      1 - (embedding <=> ?) AS similarity
    FROM documents
    WHERE embedding IS NOT NULL
    ORDER BY similarity DESC
    LIMIT 10
  `, [JSON.stringify(result.embedding)]);

  return results.rows;
}

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Asymmetric Embeddings (Voyage AI)

Voyage AI distinguishes between document and query embeddings.

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Why the Distinction?

Document:
  • Long text
  • Detailed information
  • Storage purpose
Query:
  • Short text
  • Search terms
  • Search purpose
Documents and queries have different characteristics. Voyage AI considers this to provide more accurate search results (10-15% improvement).

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Usage in Sonamu

// When saving documents: document
const docEmbedding = await Embedding.embedOne(
  "Sonamu is a TypeScript full-stack framework. It provides API, DB, authentication, and more.",
  'voyage',
  'document'  // for documents
);

// When searching: query
const queryEmbedding = await Embedding.embedOne(
  "TypeScript framework",
  'voyage',
  'query'  // for search
);
OpenAI does not support asymmetric embeddings: 
// OpenAI ignores inputType
await Embedding.embedOne(text, 'openai', 'document');  // 'document' is ignored

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Batch Processing

When processing multiple documents at once: 
@api({ httpMethod: 'POST' })
async batchCreateDocuments(documents: Array<{
  title: string;
  content: string;
}>) {
  // 1. Prepare text array
  const texts = documents.map(doc =>
    `${doc.title}\n\n${doc.content}`
  );

  // 2. Batch embedding (automatically splits into batches of 128)
  const embeddings = await Embedding.embed(
    texts,
    'voyage',
    'document'
  );

  // 3. Save to DB
  const savedDocs = await Promise.all(
    documents.map((doc, i) =>
      this.saveOne({
        title: doc.title,
        content: doc.content,
        embedding: embeddings[i].embedding,
        token_count: embeddings[i].tokenCount,
      })
    )
  );

  return savedDocs;
}
Automatic splitting:
  • Voyage AI: 128 at a time
  • OpenAI: 100 at a time
Even 1000 documents are automatically split and processed.

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Progress Display

You can show progress when processing many documents: 
@api({ httpMethod: 'POST' })
async importDocuments(files: string[]) {
  const texts = files.map(f => readFile(f));

  const embeddings = await Embedding.embed(
    texts,
    'voyage',
    'document',
    (processed, total) => {
      const percent = Math.round((processed / total) * 100);
      console.log(`Progress: ${processed}/${total} (${percent}%)`);
    }
  );

  // Progress: 128/1000 (13%)
  // Progress: 256/1000 (26%)
  // ...
  // Progress: 1000/1000 (100%)
}

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Practical Scenario

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Scenario: Customer Support Knowledge Base

You’re building a customer support system with Sonamu. Step 1: Document Upload API 
class KnowledgeBaseModelClass extends BaseModel {
  @upload({ mode: 'single' })
  @api({ httpMethod: 'POST' })
  async uploadDocument() {
    const { files } = Sonamu.getContext();
    const file = files?.[0]; // Use first file

    // Read file
    const content = await file.toBuffer().then(b => b.toString());

    // Generate embedding
    const embedding = await Embedding.embedOne(
      content,
      'voyage',
      'document'
    );

    // Save
    return await this.saveOne({
      title: file.filename,
      content,
      embedding: embedding.embedding,
    });
  }
}
Step 2: Search API 
@api({ httpMethod: 'POST' })
async searchSimilar(query: string) {
  // Query embedding
  const embedding = await Embedding.embedOne(query, 'voyage', 'query');

  // Search similar documents
  const results = await this.getPuri().raw(`
    SELECT id, title, content,
      1 - (embedding <=> ?) AS similarity
    FROM knowledge_base
    WHERE embedding IS NOT NULL
    ORDER BY similarity DESC
    LIMIT 5
  `, [JSON.stringify(embedding.embedding)]);

  return results.rows;
}
Step 3: Handling User Requests 
POST /api/knowledge-base/search
{ "query": "What is the refund policy?" }

-> Returns 5 similar documents:
1. "Refund and Exchange Guide" (similarity: 0.89)
2. "How to Cancel a Purchase" (similarity: 0.82)
3. "Refund Periods by Payment Method" (similarity: 0.78)
...

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Error Handling

@api({ httpMethod: 'POST' })
async createDocument(title: string, content: string) {
  try {
    const embedding = await Embedding.embedOne(
      `${title}\n\n${content}`,
      'voyage',
      'document'
    );

    return await this.saveOne({
      title,
      content,
      embedding: embedding.embedding,
    });
  } catch (error) {
    if (error.message.includes('API_KEY')) {
      throw new Error('Please set your Voyage API key: VOYAGE_API_KEY');
    } else if (error.message.includes('token')) {
      throw new Error('Text is too long (max 32,000 tokens)');
    } else {
      throw new Error(`Failed to generate embedding: ${error.message}`);
    }
  }
}

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Cost Considerations

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Token Calculation

const result = await Embedding.embedOne(
  "Sonamu is a TypeScript full-stack framework",
  'voyage'
);

console.log(`Tokens: ${result.tokenCount}`);  // 12

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Cost Estimation

Voyage AI ($0.13 per 1M tokens): 
// 1000 documents, average 500 tokens
// = 500,000 tokens
// = $0.065
OpenAI ($0.02 per 1M tokens): 
// 1000 documents, average 500 tokens
// = 500,000 tokens
// = $0.01

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Cost Reduction Tips

1. Caching 
const cache = new Map<string, number[]>();

async function getCachedEmbedding(text: string) {
  if (cache.has(text)) {
    return cache.get(text)!;
  }

  const result = await Embedding.embedOne(text, 'voyage');
  cache.set(text, result.embedding);
  return result.embedding;
}
2. Deduplication 
// Embed identical texts only once
const uniqueTexts = [...new Set(texts)];
const embeddings = await Embedding.embed(uniqueTexts, 'voyage');

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Cautions

Cautions when using embeddings in Sonamu:
  1. API key required: Set environment variables 
    VOYAGE_API_KEY=pa-...
  2. Dimension match: Must match DB schema 
    -- Voyage AI
CREATE TABLE docs (embedding vector(1024));

-- OpenAI
CREATE TABLE docs (embedding vector(1536));
  3. document vs query: Voyage distinguishes, OpenAI ignores 
    // When saving
await Embedding.embedOne(text, 'voyage', 'document');

// When searching
await Embedding.embedOne(text, 'voyage', 'query');
  4. Token limits: Chunking needed for very long texts
    • Voyage AI: 32,000 tokens
    • OpenAI: 8,191 tokens
  5. NULL handling: Can store NULL on embedding failure 
    embedding: result?.embedding || null
  6. Cost monitoring: Track token usage 
    console.log(`Total tokens: ${result.tokenCount}`);

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Next Steps

Embedding generation is complete. Now it’s time to implement the search API in Sonamu Model.

pgvector Setup

Creating PostgreSQL vector tables

Vector Search

Implementing search API in Sonamu Model

Chunking

Splitting long documents