Document loaders in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a text document using a document loader.

Prompt Engineering / GenAI

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

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

Aexample.txt

Bload_text

Cread_file

Ddocument

Attempts:

3 left

💡 Hint

Common Mistakes

Using a function name instead of a file path.

Forgetting to put the file name in quotes.

The TextLoader requires the file path as a string to load the document correctly.

2fill in blank

medium

Complete the code to load a PDF document using the PDFLoader.

Prompt Engineering / GenAI

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

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

Aimage.png

Bdocument.docx

Cnotes.txt

Dfile.pdf

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-PDF file with PDFLoader.

Providing a file with the wrong extension.

PDFLoader expects a PDF file path to load the document properly.

3fill in blank

hard

Fix the error in the code to load a CSV file using CSVLoader.

Prompt Engineering / GenAI

loader = CSVLoader(file_path='[1]')
data = loader.load()

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

Adata.txt

Bdata.pdf

Cdata.csv

Ddata.docx

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-CSV file with CSVLoader.

Incorrect file extension causing loading failure.

CSVLoader requires a CSV file path to load data correctly.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that loads documents only if their length is greater than 100.

Prompt Engineering / GenAI

filtered_docs = {doc.id: doc for doc in documents if len(doc.[1]) [2] 100}

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

Apage_content

Bmetadata

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'metadata' instead of 'page_content' for length.

Using '<' instead of '>' causing wrong filtering.

We check the length of the document's page_content and filter those greater than 100.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps document titles to their content if the content length is less than 500.

Prompt Engineering / GenAI

doc_map = {doc.[1]: doc.[2] for doc in docs if len(doc.[3]) < 500}

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

Ametadata['title']

Bpage_content

Dmetadata['author']

Attempts:

3 left

💡 Hint

Common Mistakes

Using author instead of title for keys.

Mixing up content and metadata in length check.

We use the document's title from metadata as key, page_content as value, and filter by content length less than 500.

Practice

1. What is the main purpose of a document loader in AI applications?

easy

A. To visualize data in charts and graphs

B. To train AI models directly from raw data

C. To read files and convert their content into a format machines can understand

D. To compress files for storage

Solution

1. Step 1: Understand the role of document loaders

   Document loaders are designed to read files and extract their content in a way that machines can process.

2. Step 2: Differentiate from other tasks

   Training models or visualizing data are separate steps after loading the data.

3. Final Answer:

   To read files and convert their content into a format machines can understand -> Option C

4. Quick Check:

   Document loader = read and convert files [OK]

Hint: Remember: loaders prepare data, not train or visualize [OK]

Common Mistakes:

• Confusing loading with training
• Thinking loaders compress files
• Assuming loaders create visualizations

2. Which of the following is the correct way to load a PDF file using a document loader in Python?

easy

A. loader = ImageLoader('file.pdf')

B. loader = PDFLoader('file.pdf')

C. loader = CSVLoader('file.pdf')

D. loader = TextLoader('file.pdf')

Solution

1. Step 1: Identify the correct loader for PDF files

   PDFLoader is designed specifically to read PDF documents.

2. Step 2: Check other loaders' purposes

   TextLoader is for plain text files, CSVLoader for CSV files, and ImageLoader for images, so they are incorrect for PDFs.

3. Final Answer:

   loader = PDFLoader('file.pdf') -> Option B

4. Quick Check:

   PDF file uses PDFLoader [OK]

Hint: Match loader type to file type exactly [OK]

Common Mistakes:

• Using TextLoader for PDFs
• Confusing CSVLoader with PDFLoader
• Trying to load PDFs as images

3. Given the following Python code snippet, what will be the output type of documents after loading a text file?

from langchain.document_loaders import TextLoader
loader = TextLoader('sample.txt')
documents = loader.load()

medium

A. An integer representing file size

B. A single string with all text combined

C. A dictionary with file metadata

D. A list of Document objects containing the text content

Solution

1. Step 1: Understand what TextLoader.load() returns

   The load() method returns a list of Document objects, each holding part or all of the file's text content.

2. Step 2: Eliminate other options

   It does not return a single string, dictionary, or integer.

3. Final Answer:

   A list of Document objects containing the text content -> Option D

4. Quick Check:

   TextLoader.load() returns list of Documents [OK]

Hint: Loaders return lists of Documents, not raw strings [OK]

Common Mistakes:

• Expecting a single string instead of list
• Thinking output is metadata dictionary
• Confusing output with file size

4. Identify the error in this code snippet for loading a PDF file:

from langchain.document_loaders import PDFLoader
loader = PDFLoader('document.txt')
docs = loader.load()

medium

A. The file extension does not match the loader type

B. Missing parentheses in load method

C. Incorrect import statement for PDFLoader

D. The variable name 'docs' is invalid

Solution

1. Step 1: Check file name and loader compatibility

   PDFLoader expects a PDF file, but the file given is 'document.txt', a text file.

2. Step 2: Verify other code parts

   Parentheses are correct, import is correct, and variable name is valid.

3. Final Answer:

   The file extension does not match the loader type -> Option A

4. Quick Check:

   Loader and file type must match [OK]

Hint: Match file extension to loader type to avoid errors [OK]

Common Mistakes:

• Ignoring file extension mismatch
• Thinking variable names cause errors
• Assuming import is wrong without checking

5. You want to load multiple document types (PDF, TXT, CSV) for an AI model training pipeline. Which approach best handles this using document loaders?

hard

A. Use separate loaders for each file type and combine their outputs into one list

B. Use only TextLoader for all files regardless of type

C. Convert all files to images and use ImageLoader

D. Load only PDF files and ignore others

Solution

1. Step 1: Understand file type differences

   Different file types require different loaders to correctly extract content.

2. Step 2: Combine outputs for unified processing

   Using separate loaders and merging their outputs ensures all data is loaded properly for training.

3. Final Answer:

   Use separate loaders for each file type and combine their outputs into one list -> Option A

4. Quick Check:

   Different loaders + combine outputs = best practice [OK]

Hint: Use correct loader per file, then merge results [OK]

Common Mistakes:

• Using one loader for all file types
• Ignoring non-PDF files
• Converting files unnecessarily to images