Document loading and chunking strategies in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a document from a file path.

Agentic AI

loader = TextLoader('[1]')
docs = loader.load()

Drag options to blanks, or click blank then click option'

Aimage.png

Bdata.txt

Cdocument.pdf

Daudio.mp3

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-text file types like images or audio files.

Providing a file path that does not exist.

The TextLoader expects a text file path to load the document correctly.

2fill in blank

medium

Complete the code to split documents into chunks of 1000 characters.

Agentic AI

text_splitter = CharacterTextSplitter(chunk_size=[1], chunk_overlap=0)
texts = text_splitter.split_documents(docs)

Drag options to blanks, or click blank then click option'

A50

B500

C1000

D2000

Attempts:

3 left

💡 Hint

Common Mistakes

Using too small chunk sizes causing too many chunks.

Using too large chunk sizes causing memory issues.

Setting chunk_size to 1000 splits the document into chunks of 1000 characters each.

3fill in blank

hard

Fix the error in the code to correctly chunk documents with 20 characters overlap.

Agentic AI

text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=[1])
texts = text_splitter.split_documents(docs)

Drag options to blanks, or click blank then click option'

A100

B0

C10

D20

Attempts:

3 left

💡 Hint

Common Mistakes

Setting overlap to zero loses context between chunks.

Setting overlap larger than chunk size causes errors.

Setting chunk_overlap to 20 ensures chunks overlap by 20 characters for better context.

4fill in blank

hard

Complete the code to create a dictionary of chunk lengths for chunks longer than 50 characters.

Agentic AI

chunk_lengths = {chunk.page_content: len(chunk.page_content) for chunk in texts if len(chunk.page_content) [1] 50}

Drag options to blanks, or click blank then click option'

A:

B>

C<

D=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of ':' in dictionary comprehension.

Using '<' instead of '>' in the condition.

The colon separates key and value in the dictionary, and '>' filters chunks longer than 50 characters.

5fill in blank

hard

Fill both blanks to create a list of chunk summaries using a summarizer function.

Agentic AI

summaries = [summarizer(chunk.page_content) for chunk in texts if len(chunk.page_content) [1] 100 and chunk.page_content.count('[2]') > 0]

Drag options to blanks, or click blank then click option'

B>

C'the'

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Adding extra arguments to summarizer function.

Using '==' instead of '>' for length comparison.

Checking for a wrong substring.

No extra argument is needed for summarizer, '>' filters chunks longer than 100, and checking if 'the' appears in chunk.

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