RecursiveCharacterTextSplitter in LangChain - Mini Project: Build & Apply

Using RecursiveCharacterTextSplitter in Langchain

📖 Scenario: You are building a text processing tool that breaks a long text into smaller chunks for easier analysis. This is useful when working with large documents in language models.

🎯 Goal: Learn how to use the RecursiveCharacterTextSplitter from Langchain to split a long text into manageable pieces.

📋 What You'll Learn

Create a variable text with a multi-sentence string.

Create a chunk_size variable to set the maximum size of each chunk.

Use RecursiveCharacterTextSplitter with the chunk_size to split the text into chunks.

Store the result in a variable called chunks.

💡 Why This Matters

🌍 Real World

Splitting large documents into smaller parts helps language models process text efficiently without losing context.

💼 Career

Understanding text splitting is useful for building chatbots, search engines, and AI applications that handle large texts.

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 prompts, chains, and memory. RecursiveCharacterTextSplitter is useful for splitting long texts."

LangChain

# Create the text variable
# Your code here

Hint

Use a string variable named text and assign the exact text given.

2

Set the chunk size

Create a variable called chunk_size and set it to 50 to define the maximum size of each text chunk.

LangChain

text = "Langchain helps you build applications with language models. It provides tools to manage prompts, chains, and memory. RecursiveCharacterTextSplitter is useful for splitting long texts."
# Set the chunk_size variable
# Your code here

Hint

Define chunk_size as an integer with value 50.

3

Split the text using RecursiveCharacterTextSplitter

Import RecursiveCharacterTextSplitter from langchain.text_splitter. Then create an instance called text_splitter with chunk_size=chunk_size. Use text_splitter.split_text(text) to split the text and assign the result to a variable called chunks.

LangChain

text = "Langchain helps you build applications with language models. It provides tools to manage prompts, chains, and memory. RecursiveCharacterTextSplitter is useful for splitting long texts."
chunk_size = 50
# Import RecursiveCharacterTextSplitter and split the text
# Your code here

Hint

Remember to import the class, create an instance with the chunk size, and call split_text on the text.

4

Complete by checking the chunks variable

Add a comment line that says # chunks now contains the split text pieces to indicate completion.

LangChain

from langchain.text_splitter import RecursiveCharacterTextSplitter

text = "Langchain helps you build applications with language models. It provides tools to manage prompts, chains, and memory. RecursiveCharacterTextSplitter is useful for splitting long texts."
chunk_size = 50

text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size)
chunks = text_splitter.split_text(text)
# Add a comment to indicate chunks are ready
# Your code here

Hint

Add a comment line exactly as shown to mark the end of the code.

Practice

1. What is the main purpose of the RecursiveCharacterTextSplitter in langchain?

easy

A. To split long text into smaller chunks using multiple separators while keeping context.

B. To merge multiple texts into one large string.

C. To translate text into different languages.

D. To encrypt text for secure storage.

Solution

1. Step 1: Understand the function of RecursiveCharacterTextSplitter

   This tool is designed to break down long text into smaller pieces using various separators like paragraphs, sentences, or characters.

2. Step 2: Recognize the benefit of overlapping chunks

   It keeps some overlap between chunks to maintain context, which helps language models understand the text better.

3. Final Answer:

   To split long text into smaller chunks using multiple separators while keeping context. -> Option A

4. Quick Check:

   Splitting text with context = A [OK]

Hint: Remember: It splits text into chunks with overlap [OK]

Common Mistakes:

• Thinking it merges text instead of splitting
• Confusing it with translation or encryption tools
• Ignoring the overlap feature

2. Which of the following is the correct way to create a RecursiveCharacterTextSplitter with a chunk size of 100 and chunk overlap of 20?

easy

A. RecursiveCharacterTextSplitter(chunkSize=100, chunkOverlap=20)

B. RecursiveCharacterTextSplitter(size=100, overlap=20)

C. RecursiveCharacterTextSplitter(chunk=100, overlap=20)

D. RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=20)

Solution

1. Step 1: Recall the parameter names for RecursiveCharacterTextSplitter

   The correct parameters are chunk_size and chunk_overlap to set chunk length and overlap.

2. Step 2: Match the correct syntax

   RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=20) uses the exact parameter names and values correctly.

3. Final Answer:

   RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=20) -> Option D

4. Quick Check:

   Correct parameter names = D [OK]

Hint: Use exact parameter names: chunk_size and chunk_overlap [OK]

Common Mistakes:

• Using incorrect parameter names like size or chunkSize
• Mixing camelCase and snake_case
• Omitting required parameters

3. Given the following code snippet, what will be the length of the first chunk if the text is "Hello world! This is a test." and chunk_size=12, chunk_overlap=4?

splitter = RecursiveCharacterTextSplitter(chunk_size=12, chunk_overlap=4)
chunks = splitter.split_text("Hello world! This is a test.")
print(len(chunks[0]))

medium

A. 12

B. 16

C. 8

D. 20

Solution

1. Step 1: Understand chunk size and overlap effect

   The chunk size is 12, so each chunk will be at most 12 characters long. Overlap affects next chunks, not the first chunk length.

2. Step 2: Determine first chunk length

   The first chunk will be exactly 12 characters long, as it starts from the beginning and takes up to chunk_size.

3. Final Answer:

   12 -> Option A

4. Quick Check:

   First chunk length = chunk_size = 12 [OK]

Hint: First chunk length equals chunk_size, overlap affects next chunks [OK]

Common Mistakes:

• Adding overlap to first chunk length
• Assuming first chunk is shorter than chunk_size
• Confusing chunk size with total text length

4. What is the error in the following code snippet using RecursiveCharacterTextSplitter?

splitter = RecursiveCharacterTextSplitter(chunk_size=50, chunk_overlap=60)
chunks = splitter.split_text("Some long text here...")

medium

A. Missing import statement for RecursiveCharacterTextSplitter

B. split_text method does not exist

C. chunk_overlap cannot be larger than chunk_size

D. chunk_size must be a string, not an integer

Solution

1. Step 1: Check parameter constraints

   chunk_overlap must be less than or equal to chunk_size to avoid logical errors in splitting.

2. Step 2: Identify the error in given values

   chunk_overlap=60 is greater than chunk_size=50, which is invalid and will cause an error.

3. Final Answer:

   chunk_overlap cannot be larger than chunk_size -> Option C

4. Quick Check:

   Overlap ≤ chunk size rule = B [OK]

Hint: Overlap must never exceed chunk size [OK]

Common Mistakes:

• Ignoring parameter value relationships
• Assuming method names are incorrect without checking
• Confusing data types for parameters

5. You want to split a large document into chunks of 200 characters with 50 characters overlapping, prioritizing splitting first by paragraphs, then sentences, then words. Which setup of RecursiveCharacterTextSplitter correctly achieves this?

hard

A. RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50, separators=[" ", ". ", "\n\n"])

B. RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50, separators=["\n\n", ". ", " "])

C. RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50, separators=[". ", "\n\n", " "])

D. RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50, separators=[" ", "\n\n", ". "])

Solution

1. Step 1: Understand separator priority order

   The splitter tries separators in order; to prioritize paragraphs, then sentences, then words, the list must start with paragraph separator "\n\n", then sentence ". ", then space " ".

2. Step 2: Match the correct separators list

   RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50, separators=["\n\n", ". ", " "]) correctly lists separators in the desired priority order.

3. Final Answer:

   RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50, separators=["\n\n", ". ", " "]) -> Option B

4. Quick Check:

   Separator order matches priority = A [OK]

Hint: List separators from largest to smallest text unit [OK]

Common Mistakes:

• Reversing separator order
• Using wrong separator strings
• Ignoring overlap or chunk size settings