Why document loading is the RAG foundation in LangChain - See It in Action

Why Document Loading is the RAG Foundation

📖 Scenario: You are building a simple Retrieval-Augmented Generation (RAG) system that answers questions based on documents. The first step is to load documents correctly so the system can find the right information.

🎯 Goal: Create a Python script that loads a list of documents, sets a minimum length filter, extracts the text content, and finally prepares the documents for retrieval. This shows why document loading is the foundation of RAG.

📋 What You'll Learn

Create a list called documents with three exact text strings.

Create a variable called min_length set to 50.

Use a list comprehension to filter documents by min_length and extract their text.

Create a final list called prepared_docs that stores the filtered texts.

💡 Why This Matters

🌍 Real World

In real RAG systems, loading and preparing documents correctly ensures the AI can find the right information quickly and accurately.

💼 Career

Understanding document loading is essential for building AI applications that combine search and language generation, a key skill in AI engineering roles.

1

Create the initial documents list

Create a list called documents with these exact strings: 'LangChain helps build LLM apps.', 'Document loading is crucial for RAG.', and 'Proper data setup improves retrieval quality.'

LangChain

# Create the documents list with exact strings
# Your code here

Need a hint?

Use square brackets [] to create a list and include the exact strings inside quotes.

2

Add a minimum length filter variable

Create a variable called min_length and set it to 50 to filter out short documents.

LangChain

documents = [
    'LangChain helps build LLM apps.',
    'Document loading is crucial for RAG.',
    'Proper data setup improves retrieval quality.'
]
# Create min_length variable
# Your code here

Need a hint?

Just assign the number 50 to the variable min_length.

3

Filter documents by minimum length

Use a list comprehension to create a new list called filtered_texts that includes only documents from documents whose length is greater than or equal to min_length.

LangChain

documents = [
    'LangChain helps build LLM apps.',
    'Document loading is crucial for RAG.',
    'Proper data setup improves retrieval quality.'
]
min_length = 50
# Filter documents by length
# Your code here

Need a hint?

Use [doc for doc in documents if len(doc) >= min_length] to filter.

4

Prepare the final documents list

Create a list called prepared_docs and assign it the value of filtered_texts. This represents the documents ready for retrieval in RAG.

LangChain

documents = [
    'LangChain helps build LLM apps.',
    'Document loading is crucial for RAG.',
    'Proper data setup improves retrieval quality.'
]
min_length = 50
filtered_texts = [doc for doc in documents if len(doc) >= min_length]
# Assign filtered_texts to prepared_docs
# Your code here

Need a hint?

Simply assign filtered_texts to prepared_docs.