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Taki (Kieu Dang)
Taki (Kieu Dang)

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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.

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

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Top comments (1)

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ai_joddd profile image
Vinayak Mishra

Really helpful! I also read something on Contextual document embeddings. This might be helpful for everyone

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