Semantic chunking strategies in LangChain - Mini Project: Build & Apply

Semantic Chunking Strategies with LangChain

📖 Scenario: You are building a simple text processing tool using LangChain. The tool will split a long text into smaller, meaningful chunks based on semantic similarity. This helps when you want to analyze or search large documents efficiently.

🎯 Goal: Build a LangChain script that takes a long text, sets a chunk size limit, splits the text into semantic chunks, and prepares the chunks for further processing.

📋 What You'll Learn

Create a variable long_text with the exact given paragraph string.

Create a variable chunk_size set to 1000.

Use LangChain's RecursiveCharacterTextSplitter with chunk_size to split long_text into chunks.

Print the number of chunks created.

💡 Why This Matters

🌍 Real World

Semantic chunking helps break down large documents into meaningful parts for easier searching, summarizing, or feeding into language models.

💼 Career

Understanding text chunking is useful for building AI assistants, chatbots, and document analysis tools that handle large texts efficiently.

1

DATA SETUP: Create the long text variable

Create a variable called long_text and assign it this exact string: "LangChain is a framework for developing applications powered by language models. It helps with managing prompts, chains, and agents to build complex workflows."

LangChain

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

Need a hint?

Use a string variable named long_text and assign the exact text inside double quotes.

2

CONFIGURATION: Set the chunk size

Create a variable called chunk_size and set it to the integer 1000.

LangChain

long_text = "LangChain is a framework for developing applications powered by language models. It helps with managing prompts, chains, and agents to build complex workflows."
# Create the variable chunk_size and set it to 1000
# Your code here

Need a hint?

Define chunk_size as an integer variable with value 1000.

3

CORE LOGIC: Split the text into semantic chunks

Import RecursiveCharacterTextSplitter from langchain.text_splitter. Then create a text_splitter object using RecursiveCharacterTextSplitter(chunk_size=chunk_size). Use text_splitter.split_text(long_text) to create a list called chunks.

LangChain

long_text = "LangChain is a framework for developing applications powered by language models. It helps with managing prompts, chains, and agents to build complex workflows."
chunk_size = 1000
# Import RecursiveCharacterTextSplitter and create text_splitter with chunk_size
# Use text_splitter.split_text(long_text) to create chunks
# Your code here

Need a hint?

Remember to import the splitter class, create an instance with chunk_size, then call split_text on long_text.

4

COMPLETION: Print the number of chunks

Add a line to print the number of chunks by using print(len(chunks)).

LangChain

from langchain.text_splitter import RecursiveCharacterTextSplitter

long_text = "LangChain is a framework for developing applications powered by language models. It helps with managing prompts, chains, and agents to build complex workflows."
chunk_size = 1000

text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size)
chunks = text_splitter.split_text(long_text)
# Print the number of chunks
# Your code here

Need a hint?

Use the len() function on chunks inside print() to show how many chunks were created.