LLM Chunking & Embedding: Strategies for RAG
Master LLM chunking and embedding strategies for efficient RAG. Learn best practices to optimize your retrieval-augmented generation workflows for AI.
Chunking and Embedding Strategies for LLM Workflows
This document outlines the essential concepts of chunking and embedding in the context of Large Language Model (LLM) workflows, focusing on their importance, various strategies, best practices, and integration within Retrieval-Augmented Generation (RAG) pipelines.
What is Chunking in LLM Workflows?
Chunking is the process of dividing long documents into smaller, semantically meaningful segments, referred to as "chunks." This is crucial because LLMs have limitations on the amount of text they can process at once, known as the context window or token limit.
Why Chunking Matters in LLMs
- Token Limitations: Most LLMs, such as GPT-4, have specific token limits (e.g., 8K to 128K tokens). Chunking allows processing of documents that exceed these limits.
- Improved Semantic Retrieval Accuracy: Smaller, well-defined chunks lead to more precise matching when searching for relevant information.
- Reduced Context Window Overload: Prevents overwhelming the LLM with too much information in a single prompt.
- Cost Efficiency: Reduces API costs by processing only relevant segments of text.
- Enhanced RAG Pipelines: Enables effective input handling for Retrieval-Augmented Generation (RAG) systems, where relevant chunks are retrieved and provided to the LLM.
Types of Chunking Strategies in LLMs
1. Fixed-Size Chunking
Description: Text is split into chunks of a predetermined fixed number of characters or tokens. An optional overlap is included between chunks to maintain context continuity.
Example (using Langchain):
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=500,
chunk_overlap=50
)
chunks = splitter.split_text(document)
Use Case: Ideal for fast, consistent splitting, especially for documents with uniform structure and length.
2. Sentence-Based Chunking
Description: Documents are split by identifying and grouping sentences. Libraries like nltk
or spaCy
can be used for sentence boundary detection.
Example (using NLTK):
import nltk
nltk.download("punkt") # Download the necessary punkt tokenizer
from nltk.tokenize import sent_tokenize
def sentence_chunking(text, max_sentences=3):
sentences = sent_tokenize(text)
chunks = [
" ".join(sentences[i:i + max_sentences])
for i in range(0, len(sentences), max_sentences)
]
return chunks
# Example Usage
text = """Artificial Intelligence is transforming the world. It powers everything from voice assistants to self-driving cars. Machine learning, a subset of AI, helps systems learn from data. Deep learning is a more advanced version of machine learning. It uses neural networks to process information like the human brain."""
chunks = sentence_chunking(text, max_sentences=2)
for i, chunk in enumerate(chunks):
print(f"Chunk {i+1}:\n{chunk}\n")
Use Case: Suitable for articles, essays, and narrative documents where preserving sentence-level coherence is important.
3. Semantic Chunking
Description: Text is segmented based on its meaning, using Natural Language Processing (NLP) techniques such as embeddings or topic modeling. This groups semantically related sentences or paragraphs together.
Tools: BERTopic, Top2Vec, BERT-based similarity clustering.
Use Case: Effective for semantic retrieval and grounding chatbots, as it prioritizes conceptual coherence.
Semantic Chunking Example Code:
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
import numpy as np
# Load a pre-trained sentence transformer model
model = SentenceTransformer('all-MiniLM-L6-v2')
# Example document
text = """
Artificial Intelligence is transforming the world.
It powers everything from voice assistants to self-driving cars.
Machine learning is a subset of AI.
It enables systems to learn patterns from data.
Deep learning is a more complex part of machine learning using neural networks.
Climate change is a global concern.
Governments are working on sustainable energy solutions.
Solar and wind power are growing rapidly.
"""
# Split into sentences
sentences = [s.strip() for s in text.strip().split('\n') if s.strip()]
# Compute sentence embeddings
embeddings = model.encode(sentences)
# Choose the number of clusters (semantic chunks)
num_chunks = 3
# Cluster similar sentences using KMeans
kmeans = KMeans(n_clusters=num_chunks, random_state=0, n_init=10) # Explicitly set n_init
labels = kmeans.fit_predict(embeddings)
# Group sentences by cluster
chunks = {}
for label, sentence in zip(labels, sentences):
chunks.setdefault(label, []).append(sentence)
# Print semantic chunks
for i, chunk in chunks.items():
print(f"\n🔹 Semantic Chunk {i+1}:")
print(" ".join(chunk))
4. Header-based Chunking
Description: Documents are split according to their structural elements, such as markdown headers, sections, or bullet points.
Use Case: Highly useful for technical documentation, manuals, and other structured data formats.
Header-Based Chunking Example Code:
import re
def chunk_by_headers(text, header_pattern=r"^#{1,6} .+"):
lines = text.split("\n")
chunks = []
current_chunk = []
current_header = None
for line in lines:
if re.match(header_pattern, line):
if current_chunk: # If there's content in the previous chunk
chunks.append({"header": current_header, "content": "\n".join(current_chunk)})
current_chunk = [] # Reset for the new chunk
current_header = line.strip() # Set the new header
else:
current_chunk.append(line.strip()) # Add line to current chunk content
# Append the last chunk if it exists
if current_chunk:
chunks.append({"header": current_header, "content": "\n".join(current_chunk)})
return chunks
# Example Markdown document
document = """
# Introduction
This section introduces the topic.
## Background
Some historical context goes here.
## Methods
Details about how the experiments were conducted.
# Results
Key findings are summarized here.
# Conclusion
Final thoughts and implications.
"""
chunks = chunk_by_headers(document)
# Output chunks
for i, chunk in enumerate(chunks):
print(f"\n🔹 Chunk {i+1}: {chunk['header']}")
print(chunk["content"])
Best Practices for Chunking
- Maintain Chunk Overlap: A 10–20% overlap between chunks helps retain contextual information at the boundaries.
- Choose Appropriate Chunk Size: Select chunk sizes (e.g., 100–1000 tokens) based on the LLM's capabilities and the nature of the data.
- Preprocess Data: Clean the text by removing unwanted elements like HTML tags or normalizing spacing before chunking.
- Preserve Semantics: Avoid splitting in the middle of sentences or disrupting coherent ideas.
What is Embedding in LLM Contexts?
Embedding is the process of converting a text chunk into a numerical vector representation. These vectors capture the semantic meaning of the text, enabling LLMs to perform semantic comparisons, similarity searches, and contextual ranking, which are fundamental for RAG pipelines.
Common Embedding Models for LLMs
Embedding Model | Provider | Vector Size | Best Use Case |
---|---|---|---|
text-embedding-ada-002 | OpenAI | 1536 | General-purpose retrieval |
all-MiniLM-L6-v2 | SentenceTransformers | 384 | Lightweight, fast |
Instructor-XL | Hugging Face | 768 | Instruction-rich texts |
Cohere Embed | Cohere | 1024–4096 | Long documents |
Embedding Example Code
from sentence_transformers import SentenceTransformer
# Load a pre-trained sentence transformer model
model = SentenceTransformer("all-MiniLM-L6-v2")
# Encode a piece of text into a vector embedding
embedding = model.encode("Large Language Models power generative AI.")
# Print the first 5 elements of the embedding vector
print(embedding[:5])
Embedding Storage: Vector Databases
To facilitate efficient and rapid similarity searches, embeddings are typically stored in specialized vector databases. Popular options include:
- FAISS (Facebook AI Similarity Search)
- Chroma
- Pinecone
- Weaviate
Example: Similarity Calculation
Cosine similarity is a common metric used to measure the similarity between two embedding vectors.
from sklearn.metrics.pairwise import cosine_similarity
# Assume query_embedding and doc_embedding are pre-computed embedding vectors
# query_embedding = model.encode("What is RAG?")
# doc_embedding = model.encode("Retrieval-Augmented Generation combines retrieval with generation.")
# Calculate cosine similarity
# similarity = cosine_similarity([query_embedding], [doc_embedding])
# print(f"Cosine Similarity: {similarity[0][0]}")
Chunking + Embedding in Retrieval-Augmented Generation (RAG)
RAG is a powerful technique that enhances LLM responses by retrieving relevant information from an external knowledge base before generating an answer. The process involves:
- Chunk Documents: Split documents into manageable chunks using a chosen strategy.
- Embed Chunks: Convert each chunk into a numerical embedding vector.
- Store Embeddings: Store these embeddings in a vector database for efficient searching.
- Embed Query: When a user asks a question, embed the query into a vector.
- Similarity Search: Perform a similarity search in the vector database to find the chunks most relevant to the query.
- Augment Prompt: Inject the retrieved relevant chunks into the LLM's prompt.
- Generate Response: The LLM uses the augmented prompt to generate a more informed and contextually accurate response.
Example with LangChain and FAISS:
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import TextLoader
# 1. Load documents (assuming you have a file named "example.txt")
loader = TextLoader("example.txt")
docs = loader.load()
# 2. Split documents into chunks
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(docs)
# 3. Embed chunks and create a FAISS vector store
embeddings = OpenAIEmbeddings() # Or use SentenceTransformer embeddings
db = FAISS.from_documents(chunks, embeddings)
# 4. Perform a similarity search
query = "What is semantic search?"
retriever = db.as_retriever()
results = retriever.get_relevant_documents(query)
# Print retrieved results
# for result in results:
# print(result.page_content)
Conclusion
Chunking and embedding are foundational techniques for building effective LLM applications, especially for semantic search, RAG, and chatbot functionalities. By carefully selecting chunking strategies and embedding models, you can significantly improve retrieval accuracy and ensure context-aware, high-quality responses from your language models.
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- LangChain FAISS RAG example
Interview Questions
- What is chunking in the context of large language models and why is it important?
- Compare fixed-size chunking and semantic chunking. When would you use each?
- What are some best practices for determining chunk size and overlap?
- How do you perform sentence-based chunking using Python libraries?
- What role do embeddings play in semantic search and RAG pipelines?
- Which embedding models would you choose for lightweight versus instruction-heavy tasks?
- Explain how cosine similarity is used in retrieving semantically similar chunks.
- What are the advantages of using vector databases like FAISS or Pinecone in LLM workflows?
- Describe how chunking and embedding are combined in a typical RAG implementation.
- What factors should be considered when choosing between OpenAI embeddings and SentenceTransformers?
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