RAG Sentence Window Retrieval works by expanding the context around a retrieved document chunk to include surrounding sentences, ensuring the LLM has a richer understanding of the retrieved information.
Here’s how it looks in action. Imagine you have a document:
"The quick brown fox jumps over the lazy dog. This is a classic pangram used for testing typefaces. Pangrams contain every letter of the alphabet. The dog, however, remained unimpressed and continued to nap."
If a search query matches "pangram," a standard retrieval might just return "This is a classic pangram used for testing typefaces." The LLM only sees that single sentence.
With sentence window retrieval, configured to retrieve 1 sentence before and 1 sentence after, the retrieved context becomes:
"The quick brown fox jumps over the lazy dog. This is a classic pangram used for testing typefaces. Pangrams contain every letter of the alphabet."
The LLM now has the preceding sentence about the fox and the subsequent sentence defining pangrams, providing much more context for answering questions about the pangram or the fox.
This technique addresses the common problem in RAG where a single retrieved sentence, though relevant, lacks the surrounding narrative or explanatory context needed for the LLM to accurately interpret and utilize the information. It’s a way to get more signal from each retrieved document chunk without drastically increasing the overall amount of text processed.
The core idea is that relevance isn’t always confined to the exact sentence that contains the keywords. The meaning and intent often spill into neighboring sentences, either providing background, definition, or subsequent actions. By fetching a "window" of sentences around the most relevant hit, you’re essentially giving the LLM a small, focused paragraph that’s much more likely to be coherent and useful.
Here’s how you might configure this in a retrieval system, using a hypothetical example with a library like LangChain:
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import Document
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.llms import OpenAI
from langchain_community.vectorstores import Chroma
from langchain_core.runnables import RunnablePassthrough
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Assume you have your documents loaded and embedded into a vector store
# For demonstration, let's create a simple one
texts = [
"The quick brown fox jumps over the lazy dog. This is a classic pangram used for testing typefaces. Pangrams contain every letter of the alphabet. The dog, however, remained unimpressed and continued to nap.",
"Artificial intelligence is transforming industries. Machine learning, a subset of AI, enables systems to learn from data. Deep learning, a further subset, uses neural networks with many layers.",
"The stock market experienced significant volatility. Investors reacted to the latest economic indicators. Inflation concerns continued to weigh on market sentiment."
]
text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=0)
docs = text_splitter.create_documents(texts)
# In a real scenario, you'd use your actual vector store
# For this example, we'll simulate a retrieval that returns a document
# which we'll then expand.
# For actual implementation, you'd use a retriever that supports metadata or similar.
# Let's simulate a retriever that returns a Document object with metadata
# In many vector stores, the original text is stored and can be retrieved.
# For sentence window, we need to ensure we can access surrounding text.
# A common pattern is to store documents with their original, un-chunked source,
# or to chunk intelligently to preserve sentence boundaries.
# For true sentence window retrieval, you'd typically:
# 1. Chunk your documents into sentences.
# 2. Embed each sentence.
# 3. When a query matches a sentence, retrieve that sentence and N sentences before and after it.
# Let's simulate this expansion logic.
class SentenceWindowRetriever:
def __init__(self, docs, window_size=1):
self.docs = docs
self.window_size = window_size
# In a real system, you'd have an index/embedding model here
# to perform the actual search. We are simulating the retrieval part.
def retrieve(self, query):
# Simulate finding the best matching document chunk
# For simplicity, let's assume the first sentence of the first doc matches 'pangram'
# In reality, this would be an embedding-based similarity search.
best_match_doc_content = self.docs[0].page_content
best_match_doc_metadata = {"source": "doc1.txt", "original_index": 0} # Hypothetical metadata
# Find the index of the best matching chunk within its original document
# This is a simplification. Real systems track document structure.
# We need to know which *original* document and which *part* of it was matched.
# Let's assume for `texts[0]`, the first sentence is the hit.
all_sentences = self.docs[0].page_content.split('. ') # Simple sentence split
hit_sentence_index = -1
for i, sentence in enumerate(all_sentences):
if "pangram" in sentence.lower(): # Simple keyword match
hit_sentence_index = i
break
if hit_sentence_index == -1:
return [Document(page_content=best_match_doc_content, metadata=best_match_doc_metadata)]
start_index = max(0, hit_sentence_index - self.window_size)
end_index = min(len(all_sentences), hit_sentence_index + self.window_size + 1) # +1 for slice end
expanded_sentences = all_sentences[start_index:end_index]
# Re-join sentences, ensuring proper punctuation. This is tricky.
# A robust solution would parse sentences properly.
expanded_text = ". ".join(expanded_sentences)
if not expanded_text.endswith('.'): # Add period if missing from last sentence
expanded_text += '.'
return [Document(page_content=expanded_text, metadata={"source": "doc1.txt", "expanded": True})]
# Instantiate the simulated retriever
retriever = SentenceWindowRetriever(docs, window_size=1)
# Simulate a query
query = "What is a pangram?"
retrieved_docs = retriever.retrieve(query)
print("Retrieved Document(s):")
for doc in retrieved_docs:
print(f"- Content: {doc.page_content}")
print(f" Metadata: {doc.metadata}")
The window_size parameter is key here. A window_size of 1 means we fetch the sentence before and the sentence after the one that directly matched the query. This provides immediate context. You can increase this to 2 or 3 for more expansive context, but you’ll quickly hit token limits and dilute the focus.
The mechanism relies on having access to the original, larger text blocks from which your chunks were generated, or on a retrieval strategy that can identify adjacent sentences. Many vector databases store the original document text alongside embeddings, or you can use text splitters that preserve sentence boundaries and then re-assemble them. The core operation is: perform a standard retrieval to find the most relevant sentence or chunk, then locate that sentence within its original context and pull in the specified number of surrounding sentences. This expanded text is what’s then passed to the LLM.
A subtle but critical point is how sentence boundaries are detected and re-joined. Simple .split('. ') is a naive approach that breaks with abbreviations (e.g., "Mr. Smith") or sentences ending in other punctuation. Robust implementations use natural language processing libraries (like spaCy or NLTK) to accurately segment text into sentences before applying the window logic. The re-joining also needs to be careful about punctuation and spacing to produce a natural-looking block of text.
The next challenge you’ll face is dynamically adjusting the window size based on the query or the nature of the retrieved content.