Code-aware text splitting in LangChain - Mini Project: Build & Apply

Code-aware Text Splitting with Langchain

📖 Scenario: You are building a tool to split a long text document that contains code snippets and regular text. You want to split the text so that code blocks stay intact and are not broken in the middle.

🎯 Goal: Create a Python script using Langchain's CodeAwareTextSplitter to split a given text into chunks without breaking code blocks.

📋 What You'll Learn

Create a variable text with a sample string containing both code blocks and normal text.

Create a CodeAwareTextSplitter instance with a chunk size of 50 characters.

Use the splitter's split_text method on the text variable.

Store the result in a variable called chunks.

💡 Why This Matters

🌍 Real World

This technique is useful when processing documents or notes that mix code and text, such as programming tutorials or technical documentation, ensuring code snippets remain whole.

💼 Career

Developers and data scientists often need to preprocess mixed content for tasks like summarization, search indexing, or chatbot input, making code-aware splitting a valuable skill.

1

Create the text variable with code and normal text

Create a variable called text and assign it this exact string including newlines and code blocks: """Here is some introduction text. ```python def greet(): print('Hello, world!') ``` This is the conclusion."""

LangChain

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

Hint

Use triple quotes """ to create a multi-line string exactly as shown.

2

Create a CodeAwareTextSplitter instance

Import CodeAwareTextSplitter from langchain.text_splitter. Then create a variable called splitter and assign it a CodeAwareTextSplitter instance with chunk_size=50.

LangChain

text = """Here is some introduction text.

```python
def greet():
    print('Hello, world!')
```

This is the conclusion."""

# Import CodeAwareTextSplitter and create splitter with chunk_size=50
# Your code here

Hint

Use the exact import statement and create the splitter with the parameter chunk_size=50.

3

Split the text using the splitter

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

LangChain

from langchain.text_splitter import CodeAwareTextSplitter

text = """Here is some introduction text.

```python
def greet():
    print('Hello, world!')
```

This is the conclusion."""

splitter = CodeAwareTextSplitter(chunk_size=50)

# Use splitter.split_text on text and assign to chunks
# Your code here

Hint

Call split_text on the splitter object passing text as argument.

4

Complete by adding a comment about the chunks variable

Add a comment line that says exactly: # chunks now contains the split text preserving code blocks below the chunks assignment.

LangChain

from langchain.text_splitter import CodeAwareTextSplitter

text = """Here is some introduction text.

```python
def greet():
    print('Hello, world!')
```

This is the conclusion."""

splitter = CodeAwareTextSplitter(chunk_size=50)

chunks = splitter.split_text(text)

# Add a comment about chunks preserving code blocks
# Your code here

Hint

Just add the exact comment line below the chunks assignment.

Practice

1. What is the main purpose of code-aware text splitting in langchain?

easy

A. To convert code into plain text

B. To merge multiple code files into one

C. To remove comments from code automatically

D. To split text into smaller parts without breaking code blocks

Solution

1. Step 1: Understand the goal of text splitting

   Text splitting breaks large text into smaller pieces for easier handling.

2. Step 2: Recognize the importance of code awareness

   Code-aware splitting avoids breaking inside code blocks to keep code valid and readable.

3. Final Answer:

   To split text into smaller parts without breaking code blocks -> Option D

4. Quick Check:

   Code-aware splitting = split without breaking code blocks [OK]

Hint: Remember: split text but keep code blocks intact [OK]

Common Mistakes:

• Thinking it merges code files
• Assuming it removes comments
• Believing it converts code to plain text

2. Which of the following is the correct way to create a code-aware text splitter in langchain?

easy

A. TextSplitter(chunk_size=1000, overlap=100)

B. CodeAwareTextSplitter(chunk_size=1000, chunk_overlap=100)

C. CodeSplitter(chunk=1000, overlap=100)

D. CodeAwareSplitter(size=1000, overlap=100)

Solution

1. Step 1: Identify the correct class name

   The langchain class for code-aware splitting is CodeAwareTextSplitter.

2. Step 2: Check parameter names

   It uses chunk_size and chunk_overlap as parameters.

3. Final Answer:

   CodeAwareTextSplitter(chunk_size=1000, chunk_overlap=100) -> Option B

4. Quick Check:

   Correct class and parameters = CodeAwareTextSplitter(chunk_size=1000, chunk_overlap=100) [OK]

Hint: Look for exact class and parameter names [OK]

Common Mistakes:

• Using wrong class names like CodeSplitter
• Using incorrect parameter names like overlap
• Confusing with generic TextSplitter

3. Given the following code snippet using langchain's CodeAwareTextSplitter:

splitter = CodeAwareTextSplitter(chunk_size=50, chunk_overlap=10)
text = '''def add(a, b):\n    return a + b\n\nprint(add(2, 3))'''
chunks = splitter.split_texts([text])
print(len(chunks))

What will be the output?

medium

A. 1

B. 2

C. 3

D. 4

Solution

1. Step 1: Understand chunk size and overlap

   The chunk size is 50 characters, overlap 10 characters, so text splits into chunks of about 50 chars with 10 chars repeated.

2. Step 2: Analyze the text length and splitting

   The text has 49 characters including the '\n' sequences. It will split into 1 chunk because 49 <= 50.

3. Final Answer:

   1 -> Option A

4. Quick Check:

   Text length <= chunk_size, so splits into 1 chunk [OK]

Hint: Compare text length to chunk size to estimate chunks [OK]

Common Mistakes:

• Assuming only 1 chunk regardless of size
• Ignoring chunk overlap effect
• Counting lines instead of characters

4. You wrote this code to split Python code using langchain's CodeAwareTextSplitter:

splitter = CodeAwareTextSplitter(chunk_size=40, chunk_overlap=5)
text = 'def foo():\n    print("Hello")\n    return 42'
chunks = splitter.split_text(text)
print(chunks)

But you get an error: AttributeError: 'CodeAwareTextSplitter' object has no attribute 'split_text'. What is the likely cause?

medium

A. CodeAwareTextSplitter does not support Python code

B. The chunk_size value is too small

C. The method name should be split_documents instead of split_text

D. The text variable is not a list

Solution

1. Step 1: Check method names in CodeAwareTextSplitter

   The correct method to split text is split_documents or split_texts, not split_text.

2. Step 2: Confirm method usage

   Using split_text causes AttributeError because it does not exist.

3. Final Answer:

   The method name should be split_documents instead of split_text -> Option C

4. Quick Check:

   Method name mismatch causes AttributeError [OK]

Hint: Check exact method names in docs or autocomplete [OK]

Common Mistakes:

• Using wrong method name
• Assuming chunk_size causes error
• Passing string instead of list without checking method

5. You want to split a large Python script into chunks for analysis, but you must ensure no code block is broken and each chunk overlaps by 20 characters. Which approach using langchain's CodeAwareTextSplitter is best?

hard

A. Set chunk_size to 500 and chunk_overlap to 20, then call split_documents on the script wrapped as a Document

B. Set chunk_size to 20 and chunk_overlap to 500, then call split_text on the raw string

C. Use TextSplitter with chunk_size=500 and chunk_overlap=20 on the script string

D. Split the script manually by lines and ignore chunk_overlap

Solution

1. Step 1: Choose correct splitter and parameters

   Use CodeAwareTextSplitter to avoid breaking code blocks, with chunk_size=500 and chunk_overlap=20 for desired chunk size and overlap.

2. Step 2: Use correct input and method

   Wrap the script as a Document and call split_documents to get properly split chunks.

3. Final Answer:

   Set chunk_size to 500 and chunk_overlap to 20, then call split_documents on the script wrapped as a Document -> Option A

4. Quick Check:

   Use CodeAwareTextSplitter + split_documents + proper chunk sizes [OK]

Hint: Use CodeAwareTextSplitter with Documents and split_documents method [OK]

Common Mistakes:

• Using wrong method like split_text
• Setting chunk_overlap larger than chunk_size
• Using generic TextSplitter instead of code-aware