Why chunk size affects retrieval quality in LangChain - See It in Action

Why chunk size affects retrieval quality

📖 Scenario: You are building a simple document retrieval system using LangChain. Documents are split into chunks before being indexed. The size of these chunks can affect how well the system finds relevant information.

🎯 Goal: Learn how to set up document chunks with different sizes and see how chunk size affects retrieval quality in LangChain.

📋 What You'll Learn

Create a list of documents with exact text content

Set a chunk size variable with a specific integer value

Use LangChain's text splitter with the chunk size variable

Create a retriever using the split documents and chunk size

💡 Why This Matters

🌍 Real World

In real applications, documents are split into chunks to make searching faster and more accurate. Choosing the right chunk size helps find relevant information without missing context or returning too much unrelated text.

💼 Career

Understanding chunk size and retrieval quality is important for building effective search engines, chatbots, and AI assistants that rely on document retrieval.

1

Create the initial documents list

Create a list called documents with these exact three strings: 'LangChain helps build LLM apps.', 'Chunk size affects retrieval quality.', and 'Smaller chunks can improve precision.'

LangChain

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

Need a hint?

Use a Python list with the exact strings given.

2

Set the chunk size variable

Create a variable called chunk_size and set it to the integer 20

LangChain

documents = [
    'LangChain helps build LLM apps.',
    'Chunk size affects retrieval quality.',
    'Smaller chunks can improve precision.'
]

# Set chunk_size to 20
# Your code here

Need a hint?

Use a simple assignment statement for chunk_size.

3

Split documents using the chunk size

Import CharacterTextSplitter from langchain.text_splitter. Then create a text_splitter object with chunk_size=chunk_size and chunk_overlap=0. Use text_splitter.split_text on the first document in documents and assign the result to chunks

LangChain

from langchain.text_splitter import CharacterTextSplitter

# Create text_splitter with chunk_size and chunk_overlap=0
# Split the first document into chunks
# Your code here

Need a hint?

Remember to import before using the class. Use the variable chunk_size when creating the splitter.

4

Create a retriever using the chunks

Import InMemoryDocstore from langchain.docstore and VectorStoreRetriever from langchain.retrievers. Create a docstore using InMemoryDocstore with a dictionary mapping str(i) to each chunk in chunks. Then create a retriever using VectorStoreRetriever with docstore=docstore and search_kwargs={'k': 2}

LangChain

from langchain.docstore import InMemoryDocstore
from langchain.retrievers import VectorStoreRetriever

# Create docstore mapping string keys to chunks
# Create retriever with docstore and search_kwargs={'k': 2}
# Your code here

Need a hint?

Use dictionary comprehension to map chunk indexes to chunk texts for the docstore.