Why Chunk Text Before Embedding

Chunking text properly before embedding is essential to ensure the best result in embeddings and downstream tasks such as similarity searchers or question-answering. Here's why and how you should approach it:

Why chunk text before embedding

1. Embedding Limits:
Many embedding models, like OpenAi or Hugging Face models, have a maximum token limit (e.g., 512 Tokens). Text exceeding this limit will be truncated or result incomplete embedding.

2. Contextual Understanding:
Smaller chunks allow the model to focus on the local context of each segment, improving the quality of embedding.

3. Efficiency:
Chunking ensures that the embeddings generated are manageable in size and can be indexed efficiently.

4. Improved Embedding Quality:
Long texts can dilute the semantic focus of embeddings. Chunking the text helps retain the specific context of each segment, leading to more precise and relevant embeddings.

How to chunk text

1. Choose a chunk size:
The chunk size should ideally match the token limit of the embedding model you are using. A typical range is 300-500 token, but adjust based on your use cases.

2. Overlap chunk size:
Use overlapping windows (e.g., 20-50 tokens) to maintain context continuity between chunks. This is particularly useful when text is split in the middle of a sentence or paragraph.

3. Semantic Boundaries:
if possible, chunk by logical or semantic boundaries such as sentences or paragraphs, rather than splitting arbitrarily.

4. Preprocessing Tools:
Use libraries like Langchain or NLTK to handle chunking effectively. For example, Langchain has built-in support for chunking and splitting document

Example Using Langchain

import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";

const text = "Load, Chunk, Embed, and Index Documents Using LangChain";
const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 300, // Adjust based on your embedding model's limit
  chunkOverlap: 50, // Ensure overlap for context continuity
});

const chunks = await splitter.splitText(text);

console.log(chunks); // Outputs an array of chunked text

By chunking text effectively, you maximize the utility of embeddings and ensure better performance in downstream tasks like search, retrieval, or classification.