Chunking

Handling Long Documents in Sonamu

You’re building a feature to upload blog posts or manuals to your Sonamu app:

class DocumentModelClass extends BaseModelClass {
  @upload()
  async uploadDocument() {
    const { bufferedFiles } = Sonamu.getContext();
    const file = bufferedFiles?.[0]; // Use first file
    const content = file.buffer.toString();

    // Attempting to generate embedding
    const embedding = await Embedding.embedOne(content, 'voyage', 'document');
    // Error: Token limit exceeded (32,000 tokens)
  }
}

Problem:

Long documents (10,000+ words)
Exceeds embedding API token limit
Cannot embed the entire document at once

Solution: Chunking - splitting documents into smaller pieces

What is Chunking?

Key points:

Long document -> multiple chunks
Each chunk -> individual embedding
During search -> return the most relevant chunks

Why is it Necessary?

1. Token Limits

Voyage AI: 32,000 tokens
OpenAI: 8,191 tokens
Long documents exceed limits

2. Search Accuracy

Shorter chunks yield more accurate results
Searching “refund method” -> returns only the refund section

3. Context Preservation

Keep related information together
Split without breaking sentences

Sonamu’s Chunking Class

import { Chunking } from "sonamu/vector";

const chunking = new Chunking({
  chunkSize: 500,        // chunk size (characters)
  chunkOverlap: 50,      // overlap size
  minChunkSize: 50,      // minimum size
  skipThreshold: 200,    // skip splitting if short
  separators: ['\n\n', '\n', '. '],  // separators
});

const chunks = chunking.chunk("Long text...");

Using in Sonamu Model

Long Document Upload + Chunking

class DocumentModelClass extends BaseModelClass {
  @upload()
  async uploadLongDocument() {
    const { bufferedFiles } = Sonamu.getContext();
    const file = bufferedFiles?.[0]; // Use first file
    const content = file.buffer.toString();

    // 1. Chunking
    const chunking = new Chunking({
      chunkSize: 500,
      chunkOverlap: 50,
    });

    const chunks = chunking.chunk(content);

    // 2. Embedding per chunk
    const embeddings = await Embedding.embed(
      chunks.map(c => c.text),
      'voyage',
      'document'
    );

    // 3. Create parent document
    const parent = await this.saveOne({
      title: file.filename,
      content,
      chunk_count: chunks.length,
    });

    // 4. Save each chunk
    const savedChunks = await Promise.all(
      chunks.map((chunk, i) =>
        DocumentChunkModel.saveOne({
          parent_id: parent.id,
          chunk_index: chunk.index,
          content: chunk.text,
          start_offset: chunk.startOffset,
          end_offset: chunk.endOffset,
          embedding: embeddings[i].embedding,
        })
      )
    );

    return {
      parentId: parent.id,
      chunkCount: chunks.length,
    };
  }
}

Table Structure

-- Parent documents
CREATE TABLE documents (
  id SERIAL PRIMARY KEY,
  title TEXT NOT NULL,
  content TEXT NOT NULL,
  chunk_count INTEGER,
  created_at TIMESTAMP DEFAULT NOW()
);

-- Chunks (store embeddings)
CREATE TABLE document_chunks (
  id SERIAL PRIMARY KEY,
  parent_id INTEGER REFERENCES documents(id),
  chunk_index INTEGER,
  content TEXT NOT NULL,
  start_offset INTEGER,
  end_offset INTEGER,
  embedding vector(1024),
  created_at TIMESTAMP DEFAULT NOW()
);

CREATE INDEX ON document_chunks (parent_id);
CREATE INDEX ON document_chunks USING hnsw (embedding vector_cosine_ops);

Understanding Configuration Options

chunkSize: Chunk Size

const chunking = new Chunking({
  chunkSize: 500,  // 500 characters
});

Recommended values:

Short search: 200-300 characters
General: 400-600 characters
Long context: 800-1000 characters

Considerations:

Korean: ~1 character = ~1 token
English: ~1 character = ~0.7 tokens

chunkOverlap: Overlap Size

const chunking = new Chunking({
  chunkSize: 500,
  chunkOverlap: 50,  // 10%
});

Purpose: Maintain context at chunk boundaries

Chunk 1: [..............................]
Chunk 2:                    [...........]
                           ^ overlap area

Recommended: 10-20% of chunkSize

skipThreshold: Skip Splitting

const chunking = new Chunking({
  skipThreshold: 200,
});

// Don't split if 200 characters or less
const text = "Short text";  // 50 characters
const chunks = chunking.chunk(text);  // [entire text as 1 chunk]

Efficiency: No chunking needed for short documents

separators: Separator Priority

const chunking = new Chunking({
  separators: [
    '\n\n',  // 1st priority: paragraph
    '\n',    // 2nd priority: line
    '. ',    // 3rd priority: sentence
    ', ',    // 4th priority: comma
  ],
});

Behavior: Tries from left to right

Practical Scenarios

Scenario: Technical Documentation Knowledge Base

You’re building a development documentation search system with Sonamu. Step 1: Conditional Chunking

@upload()
async uploadTechDoc() {
  const { bufferedFiles } = Sonamu.getContext();
  const file = bufferedFiles?.[0]; // Use first file
  const content = file.buffer.toString();

  const chunking = new Chunking({
    chunkSize: 500,
    skipThreshold: 300,
  });

  // Keep as-is if short, chunk if long
  if (chunking.needsChunking(content)) {
    return await this.uploadWithChunking(file.filename, content);
  } else {
    return await this.uploadSimple(file.filename, content);
  }
}

private async uploadSimple(title: string, content: string) {
  const embedding = await Embedding.embedOne(
    `${title}\n\n${content}`,
    'voyage',
    'document'
  );

  return await this.saveOne({
    title,
    content,
    embedding: embedding.embedding,
  });
}

private async uploadWithChunking(title: string, content: string) {
  // Same as the example above
  const chunking = new Chunking({ chunkSize: 500 });
  const chunks = chunking.chunk(content);
  // ...
}

Step 2: Search (Chunk-based)

@api({ httpMethod: 'POST' })
async searchDocs(query: string, limit: number = 5) {
  const embedding = await Embedding.embedOne(query, 'voyage', 'query');

  // Search chunks
  const chunks = await this.getPuri().raw(`
    SELECT
      c.id, c.parent_id, c.content, c.chunk_index,
      d.title,
      1 - (c.embedding <=> ?) AS similarity
    FROM document_chunks c
    JOIN documents d ON c.parent_id = d.id
    WHERE c.embedding IS NOT NULL
    ORDER BY c.embedding <=> ?
    LIMIT ?
  `, [
    JSON.stringify(embedding.embedding),
    JSON.stringify(embedding.embedding),
    limit * 2,
  ]);

  // Group by parent document
  const grouped = new Map();

  for (const chunk of chunks.rows) {
    const parentId = chunk.parent_id;

    if (!grouped.has(parentId)) {
      grouped.set(parentId, {
        parentId,
        title: chunk.title,
        bestSimilarity: chunk.similarity,
        relevantChunks: [],
      });
    }

    grouped.get(parentId).relevantChunks.push({
      content: chunk.content,
      chunkIndex: chunk.chunk_index,
      similarity: chunk.similarity,
    });
  }

  return Array.from(grouped.values())
    .sort((a, b) => b.bestSimilarity - a.bestSimilarity)
    .slice(0, limit);
}

Response example:

[
  {
    "parentId": 123,
    "title": "Getting Started with TypeScript",
    "bestSimilarity": 0.89,
    "relevantChunks": [
      {
        "content": "TypeScript is JavaScript with types...",
        "chunkIndex": 2,
        "similarity": 0.89
      }
    ]
  }
]

Optimizing for Markdown Documents

const markdownChunking = new Chunking({
  chunkSize: 600,
  separators: [
    '\n## ',   // Heading 2
    '\n### ',  // Heading 3
    '\n\n',    // Paragraph
    '\n',      // Line
    '. ',      // Sentence
  ],
});

const markdown = `
# Sonamu

## Overview
Sonamu is a TypeScript framework.

## Installation
\`\`\`bash
pnpm add sonamu
\`\`\`
`;

const chunks = markdownChunking.chunk(markdown);

Effect: Splits by headings -> preserves context

Chunking vs Full Document

When is Chunking Necessary?

Document Type	Average Length	Chunking Needed	Reason
FAQ items	< 200 chars	No	Short
Blog posts	1,000 chars	Optional	Medium
Technical docs	5,000 chars	Yes	Long
Manuals	20,000 chars	Required	Very long
Chat messages	< 100 chars	No	Short

Decision in Sonamu

const chunking = new Chunking({
  skipThreshold: 300,  // Skip if 300 chars or less
});

if (chunking.needsChunking(content)) {
  // Process with chunking
} else {
  // Process as-is
}

Cautions

Cautions when using chunking in Sonamu:

Don’t make chunkSize too small

// Too small
chunkSize: 50

// Appropriate
chunkSize: 400-600

Keep chunkOverlap reasonable

// Recommended: 10-20%
chunkSize: 500,
chunkOverlap: 50,

Separator order matters

// Correct: larger units first
separators: ['\n\n', '\n', '. ']

// Wrong: smaller units first
separators: [' ', '.', '\n']

Maintain parent-child relationship

-- Connect with parent_id
CREATE TABLE document_chunks (
  parent_id INTEGER REFERENCES documents(id)
);

Remove duplicates when searching

// Multiple chunks from same document -> group into one
const grouped = new Map();

Store offsets (optional)

// Track original position
start_offset: chunk.startOffset,
end_offset: chunk.endOffset,

Get Started

Core Concepts

Database

API Development

Frontend Integration

Testing

Advanced Features

Tools & CLI

Configuration

API Reference

Troubleshooting

FAQ

Handling Long Documents in Sonamu

What is Chunking?

Why is it Necessary?

Sonamu’s Chunking Class

Using in Sonamu Model

Long Document Upload + Chunking

Table Structure

Understanding Configuration Options

chunkSize: Chunk Size

chunkOverlap: Overlap Size

skipThreshold: Skip Splitting

separators: Separator Priority

Practical Scenarios

Scenario: Technical Documentation Knowledge Base

Optimizing for Markdown Documents

Chunking vs Full Document

When is Chunking Necessary?

Decision in Sonamu

Cautions

Next Steps

Vector Search

Embeddings

​Handling Long Documents in Sonamu

​What is Chunking?

​Why is it Necessary?

​Sonamu’s Chunking Class

​Using in Sonamu Model

​Long Document Upload + Chunking

​Table Structure

​Understanding Configuration Options

​chunkSize: Chunk Size

​chunkOverlap: Overlap Size

​skipThreshold: Skip Splitting

​separators: Separator Priority

​Practical Scenarios

​Scenario: Technical Documentation Knowledge Base

​Optimizing for Markdown Documents

​Chunking vs Full Document

​When is Chunking Necessary?

​Decision in Sonamu

​Cautions

​Next Steps

Vector Search

Embeddings

Handling Long Documents in Sonamu

What is Chunking?

Why is it Necessary?

Sonamu’s Chunking Class

Using in Sonamu Model

Long Document Upload + Chunking

Table Structure

Understanding Configuration Options

chunkSize: Chunk Size

chunkOverlap: Overlap Size

skipThreshold: Skip Splitting

separators: Separator Priority

Practical Scenarios

Scenario: Technical Documentation Knowledge Base

Optimizing for Markdown Documents

Chunking vs Full Document

When is Chunking Necessary?

Decision in Sonamu

Cautions

Next Steps