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

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

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

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

Hint

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

Practice

1. What is the main purpose of semantic chunking in langchain?

easy

A. To compress text into shorter summaries

B. To split text into meaningful parts for better understanding by language models

C. To randomly divide text into equal parts without context

D. To translate text into different languages automatically

Solution

1. Step 1: Understand semantic chunking

   Semantic chunking means dividing text into parts that keep meaning intact for better processing.

2. Step 2: Identify the purpose in langchain

   Langchain uses semantic chunking to help language models understand text better by keeping related content together.

3. Final Answer:

   To split text into meaningful parts for better understanding by language models -> Option B

4. Quick Check:

   Semantic chunking = meaningful text parts [OK]

Hint: Semantic chunking means meaningful text parts, not random splits [OK]

Common Mistakes:

• Thinking chunking is random splitting
• Confusing chunking with summarization
• Assuming chunking translates text

2. Which of the following is the correct way to set chunk size and overlap in langchain's semantic chunking?

easy

A. chunk_size=500, chunk_overlap=50

B. chunk_size='large', chunk_overlap='small'

C. chunk_size=0, chunk_overlap=1000

D. chunk_size=-100, chunk_overlap=-10

Solution

1. Step 1: Check valid chunk size and overlap values

   Chunk size and overlap should be positive integers representing text length in characters or tokens.

2. Step 2: Evaluate each option

   chunk_size=500, chunk_overlap=50 uses positive integers (500 and 50), which is valid. Options B uses strings, C and D use zero or negative numbers which are invalid.

3. Final Answer:

   chunk_size=500, chunk_overlap=50 -> Option A

4. Quick Check:

   Valid chunk size and overlap are positive numbers [OK]

Hint: Use positive integers for chunk size and overlap [OK]

Common Mistakes:

• Using strings instead of numbers
• Setting zero or negative values
• Confusing chunk size with overlap

3. Given this code snippet using langchain's semantic chunking:

text = "Hello world. This is a test. Langchain helps chunk text."
chunks = chunk_text(text, chunk_size=12, chunk_overlap=4)
print(chunks)

What is the expected output?

medium

A. SyntaxError due to invalid chunking parameters

B. ["Hello world. This", "This is a test.", "Langchain helps chunk text."]

C. ["Hello", "world.", "This", "is", "a", "test."]

D. ["Hello world.", "world. This is", "This is a test.", "a test. Langchain", "Langchain helps", "helps chunk text."]

Solution

1. Step 1: Understand chunk size and overlap effect

   Chunk size 12 means each chunk has up to 12 characters; overlap 4 means next chunk starts 4 characters before previous chunk ends.

2. Step 2: Trace chunk creation on text

   Chunks will overlap by 4 characters, producing overlapping meaningful parts like "Hello world.", "world. This is", etc.

3. Final Answer:

   ["Hello world.", "world. This is", "This is a test.", "a test. Langchain", "Langchain helps", "helps chunk text."] -> Option D

4. Quick Check:

   Chunk size 12 + overlap 4 = overlapping meaningful chunks [OK]

Hint: Overlap means next chunk starts before previous ends [OK]

Common Mistakes:

• Ignoring overlap and making chunks non-overlapping
• Splitting by words instead of characters
• Assuming syntax error without checking parameters

4. You wrote this code for semantic chunking but get an error:

chunks = chunk_text(text, chunk_size=100, chunk_overlap=150)

What is the likely cause of the error?

medium

A. chunk_overlap is larger than chunk_size, causing invalid overlap

B. chunk_size is too large for the text length

C. Missing import statement for chunk_text function

D. chunk_size and chunk_overlap must be strings, not integers

Solution

1. Step 1: Check relationship between chunk_size and chunk_overlap

   Overlap cannot be larger than chunk size because overlap defines how much chunks share content.

2. Step 2: Identify error cause

   chunk_overlap=150 is greater than chunk_size=100, which is invalid and causes error.

3. Final Answer:

   chunk_overlap is larger than chunk_size, causing invalid overlap -> Option A

4. Quick Check:

   Overlap must be ≤ chunk size [OK]

Hint: Overlap cannot exceed chunk size [OK]

Common Mistakes:

• Setting overlap larger than chunk size
• Assuming large chunk size causes error
• Confusing data types for parameters

5. You want to chunk a long document so that each chunk ends at a sentence boundary and overlaps by one sentence to keep context. Which semantic chunking strategy in langchain best fits this need?

hard

A. Use chunking with chunk_size smaller than sentence length

B. Use fixed-size chunking with chunk_size=1000 and chunk_overlap=0

C. Use a separator-based chunking with '\n\n' as separator and chunk_overlap set to one sentence

D. Use random chunking ignoring sentence boundaries

Solution

1. Step 1: Understand requirement for sentence boundary chunking

   To keep chunks ending at sentence boundaries, use a separator that matches sentence breaks like double newlines.

2. Step 2: Choose chunking strategy with overlap

   Separator-based chunking with overlap of one sentence keeps context by repeating last sentence in next chunk.

3. Final Answer:

   Use a separator-based chunking with '\n\n' as separator and chunk_overlap set to one sentence -> Option C

4. Quick Check:

   Separator chunking + overlap = sentence boundary chunks [OK]

Hint: Use separators matching sentences plus overlap for context [OK]

Common Mistakes:

• Using fixed-size chunking ignoring sentence ends
• Setting zero overlap losing context
• Choosing chunk size smaller than sentence length