Custom document loaders in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the base class for creating a custom document loader.

LangChain

from langchain.document_loaders import [1]

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

ABaseDocumentLoader

BCustomLoaderBase

CDocumentLoaderBase

DLoaderBase

Attempts:

3 left

💡 Hint

Common Mistakes

Using a class name that does not exist in the module.

Confusing the order of words in the class name.

The base class to create custom document loaders in LangChain is BaseDocumentLoader.

2fill in blank

medium

Complete the method name to override for loading documents in a custom loader class.

LangChain

class MyLoader(BaseDocumentLoader):
    def [1](self):
        # Your code to load documents
        pass

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Aload_documents

Bfetch_documents

Cload

Dget_docs

Attempts:

3 left

💡 Hint

Common Mistakes

Using a method name that does not match the base class signature.

Using a generic name like 'load' instead of 'load_documents'.

The method to override in a custom document loader is load_documents, which returns the loaded documents.

3fill in blank

hard

Fix the error in the custom loader by completing the return statement to return a list of Document objects.

LangChain

from langchain.schema import Document

class MyLoader(BaseDocumentLoader):
    def load_documents(self):
        text = 'Sample text content'
        return [[1](page_content=text)]

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ADoc

BDocumentLoader

CDocument

DDocObject

Attempts:

3 left

💡 Hint

Common Mistakes

Returning a list of strings instead of Document objects.

Using a wrong class name for the document wrapper.

The load_documents method must return a list of Document objects from langchain.schema.

4fill in blank

hard

Fill both blanks to create a custom loader that reads text from a file path and returns a Document.

LangChain

class FileLoader(BaseDocumentLoader):
    def __init__(self, file_path):
        self.file_path = file_path

    def load_documents(self):
        with open(self.file_path, [1]) as f:
            text = f.read()
        return [Document(page_content=[2])]

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Ar

Btext

Cw

Dfile_path

Attempts:

3 left

💡 Hint

Common Mistakes

Using write mode 'w' instead of read mode 'r'.

Returning the file path string instead of the file content.

Open the file in read mode 'r' and return a Document with the read text.

5fill in blank

hard

Fill all three blanks to create a custom loader that filters lines containing a keyword and returns Documents for each matching line.

LangChain

class KeywordLoader(BaseDocumentLoader):
    def __init__(self, file_path, keyword):
        self.file_path = file_path
        self.keyword = keyword

    def load_documents(self):
        docs = []
        with open(self.file_path, [1]) as f:
            for line in f:
                if [2] in line:
                    docs.append(Document(page_content=[3]))
        return docs

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Ar

Bself.keyword

Cline

Dw

Attempts:

3 left

💡 Hint

Common Mistakes

Opening the file in write mode instead of read mode.

Checking the wrong variable for the keyword.

Appending the keyword instead of the line content.

Open the file in read mode 'r', check if self.keyword is in each line, and create a Document with the matching line.

Practice

1. What is the main purpose of creating a custom document loader in Langchain?

easy

A. To load and preprocess documents from any data source before use

B. To create user interfaces for document display

C. To train machine learning models directly

D. To manage database connections automatically

Solution

1. Step 1: Understand the role of document loaders

   Document loaders bring data into Langchain, preparing it for processing.

2. Step 2: Identify the purpose of custom loaders

   Custom loaders allow loading from any source and preprocessing like cleaning or adding metadata.

3. Final Answer:

   To load and preprocess documents from any data source before use -> Option A

4. Quick Check:

   Custom loaders = load + preprocess documents [OK]

Hint: Custom loaders bring in and prep data from any source [OK]

Common Mistakes:

• Confusing loaders with UI components
• Thinking loaders train models
• Assuming loaders manage databases

2. Which of the following is the correct way to start defining a custom document loader class in Langchain?

easy

A. class MyLoader():\n def load(self):\n pass

B. def MyLoader():\n return BaseLoader.load()

C. class MyLoader(BaseLoader):\n def load(self):\n pass

D. class MyLoader(BaseLoader):\n def read(self):\n pass

Solution

1. Step 1: Check inheritance from BaseLoader

   Custom loaders must inherit from BaseLoader to fit Langchain's pattern.

2. Step 2: Confirm the method name is load

   The required method to implement is load(), not read().

3. Final Answer:

   class MyLoader(BaseLoader):\n def load(self):\n pass -> Option C

4. Quick Check:

   Inherit BaseLoader + define load() method [OK]

Hint: Inherit BaseLoader and implement load() method [OK]

Common Mistakes:

• Not inheriting from BaseLoader
• Using wrong method name like read()
• Defining function instead of class

3. Given this custom loader code snippet, what will print(docs) output?

from langchain.schema import Document
from langchain.document_loaders.base import BaseLoader

class SimpleLoader(BaseLoader):
    def load(self):
        return [Document(page_content='Hello World', metadata={'source': 'test'})]

loader = SimpleLoader()
docs = loader.load()

medium

A. [Document(page_content='Hello World', metadata={'source': 'test'})]

B. ['Hello World']

C. None

D. SyntaxError

Solution

1. Step 1: Analyze the load method return value

   The load method returns a list with one Document object containing content and metadata.

2. Step 2: Understand what docs holds

   docs is assigned the list returned, so printing docs shows the list with the Document instance.

3. Final Answer:

   [Document(page_content='Hello World', metadata={'source': 'test'})] -> Option A

4. Quick Check:

   load() returns list of Document objects [OK]

Hint: load() returns list of Document objects, not strings [OK]

Common Mistakes:

• Expecting a list of strings instead of Document objects
• Thinking load() returns None
• Assuming syntax error due to imports

4. Identify the error in this custom loader code:

from langchain.document_loaders.base import BaseLoader

class FaultyLoader(BaseLoader):
    def load(self):
        docs = []
        docs.append('Just a string')
        return docs

loader = FaultyLoader()
loader.load()

medium

A. The load method must be named read

B. BaseLoader cannot be inherited directly

C. The class must not have a load method

D. The load method should return Document objects, not strings

Solution

1. Step 1: Check what load returns

   The load method returns a list with a string instead of Document objects.

2. Step 2: Recall Langchain expects Document objects

   Custom loaders must return Document instances, not plain strings, for proper processing.

3. Final Answer:

   The load method should return Document objects, not strings -> Option D

4. Quick Check:

   load() must return Document objects [OK]

Hint: load() must return Document objects, not plain strings [OK]

Common Mistakes:

• Returning strings instead of Document objects
• Misnaming the load method
• Thinking BaseLoader can't be inherited

5. You want to create a custom loader that reads text files from a folder, adds the filename as metadata, and returns Document objects. Which approach correctly implements this?

hard

A. Use BaseLoader directly without subclassing, call load() with folder path argument

B. Inherit BaseLoader, implement load() to read files, create Document with content and metadata filename, return list

C. Create a function that returns file contents as strings without Document wrapping

D. Subclass BaseLoader but return raw file paths as strings in load()

Solution

1. Step 1: Confirm inheritance and method implementation

   Custom loaders must subclass BaseLoader and implement load() method.

2. Step 2: Ensure load reads files and returns Document objects with metadata

   Reading files and wrapping content in Document with filename metadata is correct.

3. Final Answer:

   Inherit BaseLoader, implement load() to read files, create Document with content and metadata filename, return list -> Option B

4. Quick Check:

   Subclass + load() returns Document list with metadata [OK]

Hint: Subclass BaseLoader, load files, wrap in Document with metadata [OK]

Common Mistakes:

• Not subclassing BaseLoader
• Returning strings instead of Document objects
• Ignoring metadata addition