---
title: "SentenceWindowRetriever"
id: sentencewindowretrieval
slug: "/sentencewindowretrieval"
description: "Use this component to retrieve neighboring sentences around relevant sentences to get the full context."
---

# SentenceWindowRetriever

Use this component to retrieve neighboring sentences around relevant sentences to get the full context.

<div className="key-value-table">

|  |  |
| --- | --- |
| **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  <br /> <br />`context_documents`: A list of documents ordered by `split_idx_start` |
| **API reference** | [Retrievers](/reference/retrievers-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/retrievers/sentence_window_retriever.py |
| **Package name** | `haystack-ai` |

</div>

## 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

```python
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

```python
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

:notebook: Tutorial: [Retrieving a Context Window Around a Sentence](https://haystack.deepset.ai/tutorials/42_sentence_window_retriever)
