
Most common position in a pipeline	Used after the main Retriever component, like the `InMemoryEmbeddingRetriever` or any other Retriever.
Mandatory init variables	"document_store": An instance of a Document Store
Mandatory run variables	"retrieved_documents": A list of already retrieved documents for which you want to get a context window
Output variables	“context_windows”: A list of strings
API reference	Retrievers
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/retrievers/sentence_window_retriever.py

Overview

The "sentence window" is a retrieval technique that allows for the retrieval of the context around relevant sentences.

During indexing, documents are broken into smaller chunks or sentences and indexed. During retrieval, the sentences most relevant to a given query, based on a certain similarity metric, are retrieved.

Once we have the relevant sentences, we can retrieve neighboring sentences to provide full context. The number of neighboring sentences to retrieve is defined by a fixed number of sentences before and after the relevant sentence.

This component is meant to be used with other Retrievers, such as the InMemoryEmbeddingRetriever. These Retrievers find relevant sentences by comparing a query against indexed sentences using a similarity metric. Then, the SentenceWindowRetriever component retrieves neighboring sentences around the relevant ones by leveraging metadata stored in the Document object.

Usage

On its own

splitter = DocumentSplitter(split_length=10, split_overlap=5, split_by="word")  
text = ("This is a text with some words. There is a second sentence. And there is also a third sentence. "  
        "It also contains a fourth sentence. And a fifth sentence. And a sixth sentence. And a seventh sentence")
doc = Document(content=text)

docs = splitter.run([doc])
doc_store = InMemoryDocumentStore()
doc_store.write_documents(docs["documents"])

retriever = SentenceWindowRetriever(document_store=doc_store, window_size=3)

In a Pipeline

from haystack import Document, Pipeline  
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever  
from haystack.components.retrievers import SentenceWindowRetriever  
from haystack.components.preprocessors import DocumentSplitter  
from haystack.document_stores.in_memory import InMemoryDocumentStore
    
splitter = DocumentSplitter(split_length=10, split_overlap=5, split_by="word")
text = (
        "This is a text with some words. There is a second sentence. And there is also a third sentence. "
        "It also contains a fourth sentence. And a fifth sentence. And a sixth sentence. And a seventh sentence"
)
doc = Document(content=text)
docs = splitter.run([doc])
doc_store = InMemoryDocumentStore()
doc_store.write_documents(docs["documents"])


rag = Pipeline()
rag.add_component("bm25_retriever", InMemoryBM25Retriever(doc_store, top_k=1))
rag.add_component("sentence_window_retriever", SentenceWindowRetriever(document_store=doc_store, window_size=3))
rag.connect("bm25_retriever", "sentence_window_retriever")

rag.run({'bm25_retriever': {"query":"third"}})

Additional References

📓 Tutorial: Retrieving a Context Window Around a Sentence