Token-based splitting in LangChain - Mini Project: Build & Apply

Token-based splitting with Langchain

📖 Scenario: You are building a text processing tool that splits large documents into smaller chunks based on token count. This helps in managing text for AI models that have token limits.

🎯 Goal: Create a Langchain TokenTextSplitter that splits a long text into chunks of 50 tokens each.

📋 What You'll Learn

Create a variable text with the given sample text.

Create a variable chunk_size set to 50.

Use Langchain's TokenTextSplitter with chunk_size to split text.

Store the result in a variable called chunks.

💡 Why This Matters

🌍 Real World

Token-based splitting is useful when working with language models that have token limits. It helps break down large texts into manageable pieces for processing.

💼 Career

Understanding token-based splitting is important for building efficient AI applications, chatbots, and text analysis tools that use language models.

1

Create the text variable

Create a variable called text and assign it this exact string: "Langchain helps you build applications with language models. It provides tools to manage text and tokens efficiently."

LangChain

# Create the variable text with the given string
# Your code here

Need a hint?

Use a simple assignment to create text with the exact string.

2

Set the chunk size

Create a variable called chunk_size and set it to the integer 50.

LangChain

text = "Langchain helps you build applications with language models. It provides tools to manage text and tokens efficiently."
# Create chunk_size variable and set it to 50
# Your code here

Need a hint?

Just assign the number 50 to chunk_size.

3

Import and create TokenTextSplitter

Import TokenTextSplitter from langchain.text_splitter. Then create a variable called splitter by initializing TokenTextSplitter with chunk_size=chunk_size.

LangChain

text = "Langchain helps you build applications with language models. It provides tools to manage text and tokens efficiently."
chunk_size = 50
# Import TokenTextSplitter and create splitter with chunk_size
# Your code here

Need a hint?

Use the exact import statement and initialize splitter with the chunk_size variable.

4

Split the text into chunks

Use the split_text method of splitter to split the text. Store the result in a variable called chunks.

LangChain

from langchain.text_splitter import TokenTextSplitter

text = "Langchain helps you build applications with language models. It provides tools to manage text and tokens efficiently."
chunk_size = 50

splitter = TokenTextSplitter(chunk_size=chunk_size)
# Split the text using splitter and save to chunks
# Your code here

Need a hint?

Call split_text on splitter with text as argument and assign to chunks.