Document loading and chunking strategies in Agentic AI

Introduction

We split big documents into smaller parts to help AI understand and work with them better.

When you have a long report and want the AI to find specific information quickly.

When feeding text into an AI model that can only handle limited text size at once.

When you want to organize a book into chapters for easier searching.

When preparing documents for AI to summarize or answer questions about.

When you want to improve AI speed by processing smaller text pieces.

Syntax

Agentic AI

loader = DocumentLoader('file_path')
documents = loader.load()
chunks = Chunker(documents, chunk_size=500, overlap=50).chunk()

DocumentLoader reads the full document from a file or source.

Chunker splits the document into smaller pieces with optional overlap to keep context.

Examples

This loads a report and splits it into chunks of 1000 characters with 100 characters overlapping.

Agentic AI

loader = DocumentLoader('report.txt')
docs = loader.load()
chunker = Chunker(docs, chunk_size=1000, overlap=100)
chunks = chunker.chunk()

This loads a PDF book and splits it into chunks of 300 characters without overlap.

Agentic AI

loader = DocumentLoader('book.pdf')
docs = loader.load()
chunks = Chunker(docs, chunk_size=300, overlap=0).chunk()

Sample Model

This program loads a text file, splits it into chunks of 100 characters with 20 characters overlapping, then prints how many chunks were made and shows the start of the first chunk.

Agentic AI

class DocumentLoader:
    def __init__(self, file_path):
        self.file_path = file_path

    def load(self):
        with open(self.file_path, 'r', encoding='utf-8') as f:
            return f.read()

class Chunker:
    def __init__(self, text, chunk_size=500, overlap=50):
        self.text = text
        self.chunk_size = chunk_size
        self.overlap = overlap

    def chunk(self):
        chunks = []
        start = 0
        text_length = len(self.text)
        while start < text_length:
            end = min(start + self.chunk_size, text_length)
            chunks.append(self.text[start:end])
            start += self.chunk_size - self.overlap
        return chunks

# Sample usage
loader = DocumentLoader('sample.txt')
text = loader.load()
chunker = Chunker(text, chunk_size=100, overlap=20)
chunks = chunker.chunk()

print(f'Total chunks created: {len(chunks)}')
print('First chunk preview:', chunks[0][:50])

Important Notes

Overlap helps keep context between chunks but increases total data size.

Chunk size depends on the AI model's input limits and the document's nature.

Always check the chunk boundaries to avoid cutting sentences awkwardly.

Summary

Chunking breaks big documents into smaller, manageable pieces.

Overlap keeps context between chunks for better AI understanding.

Choosing chunk size and overlap depends on your AI model and task.

Practice

1. What is the main purpose of chunking in document loading for AI?

easy

A. To translate documents into different languages

B. To combine multiple documents into one large file

C. To break large documents into smaller, manageable pieces

D. To remove all punctuation from the text

Solution

1. Step 1: Understand chunking concept

   Chunking means splitting big documents into smaller parts so AI can handle them easily.

2. Step 2: Identify the main goal

   The goal is to make documents manageable, not to combine or translate them.

3. Final Answer:

   To break large documents into smaller, manageable pieces -> Option C

4. Quick Check:

   Chunking = breaking big documents [OK]

Hint: Chunking means splitting big text into small parts [OK]

Common Mistakes:

• Thinking chunking combines documents
• Confusing chunking with translation
• Assuming chunking removes punctuation

2. Which of the following is the correct way to specify chunk size and overlap in a document loader?

easy

A. loader.load(size=500, overlap=50)

B. loader.load(chunk_size=500, overlap=50)

C. loader.load(chunk=500, overlap=50)

D. loader.load(chunk_size=50, overlap=500)

Solution

1. Step 1: Check parameter names

   The standard parameters are usually named chunk_size and overlap.

2. Step 2: Verify values make sense

   Chunk size should be larger than overlap, so 500 and 50 is logical.

3. Final Answer:

   <code>loader.load(chunk_size=500, overlap=50)</code> -> Option B

4. Quick Check:

   Correct params = chunk_size and overlap [OK]

Hint: Chunk size param is chunk_size, overlap param is overlap [OK]

Common Mistakes:

• Using wrong parameter names like size or chunk
• Swapping chunk size and overlap values
• Using overlap larger than chunk size

3. Given this code snippet:

chunks = loader.load(chunk_size=100, overlap=20)
print(len(chunks))

If the original document has 250 characters, what will be the output?

medium

A. 4

B. 3

C. 2

D. 5

Solution

1. Step 1: Calculate chunk positions

   Chunks start every (chunk_size - overlap) = 80 characters: positions 0, 80, 160, 240.

2. Step 2: Count chunks covering 250 characters

   Chunks at 0, 80, 160, and 240 cover the document. The last chunk at 240 covers 240-340, overlapping document end.

3. Final Answer:

   4 -> Option A

4. Quick Check:

   Chunks = ceil((250 - overlap) / (chunk_size - overlap)) = ceil((250 - 20) / 80) = ceil(230 / 80) = 3, but since the last chunk starts at 240, total chunks = 4 [OK]

Hint: Chunks start every chunk_size - overlap characters [OK]

Common Mistakes:

• Ignoring overlap when counting chunks
• Assuming chunks equal document length divided by chunk size
• Not counting last partial chunk

4. You wrote this code but get an error:

chunks = loader.load(chunk_size=100, overlap=150)

What is the likely cause?

medium

A. Chunk size must be zero or negative

B. Chunk size and overlap must be equal

C. Missing import statement for loader

D. Overlap is larger than chunk size, causing invalid chunking

Solution

1. Step 1: Check parameter relationship

   Overlap cannot be larger than chunk size because chunks would overlap more than their length.

2. Step 2: Identify error cause

   Setting overlap=150 with chunk_size=100 is invalid and causes error.

3. Final Answer:

   Overlap is larger than chunk size, causing invalid chunking -> Option D

4. Quick Check:

   Overlap <= chunk size [OK]

Hint: Overlap must be smaller or equal to chunk size [OK]

Common Mistakes:

• Setting overlap larger than chunk size
• Assuming chunk size can be zero
• Ignoring parameter constraints

5. You want to load a very long document for an AI model that understands context well but has a token limit of 512. Which chunking strategy is best?

hard

A. Use chunk size 256 with overlap 128 to keep context between chunks

B. Use chunk size 100 with overlap 0 to create many small chunks

C. Use chunk size 512 with zero overlap to maximize chunk length

D. Use chunk size 600 with overlap 100 to exceed token limit

Solution

1. Step 1: Consider model token limit

   Model can handle max 512 tokens, so chunk size must be ≤512.

2. Step 2: Choose overlap for context

   Overlap keeps context between chunks; 128 overlap with 256 chunk size balances size and context.

3. Step 3: Evaluate other options

   Zero overlap loses context; chunk size >512 exceeds limit; very small chunks increase overhead.

4. Final Answer:

   Use chunk size 256 with overlap 128 to keep context between chunks -> Option A

5. Quick Check:

   Chunk size ≤ token limit + overlap for context [OK]

Hint: Balance chunk size and overlap to fit token limit and context [OK]

Common Mistakes:

• Ignoring token limit and using too large chunks
• Using zero overlap losing context
• Choosing too small chunks causing inefficiency