---
title: "DocumentLengthRouter"
id: documentlengthrouter
slug: "/documentlengthrouter"
description: "Routes documents to different output connections based on the length of their `content` field."
---

# DocumentLengthRouter

Routes documents to different output connections based on the length of their `content` field.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Flexible |
| **Mandatory run variables** | `documents`: A list of documents |
| **Output variables** | `short_documents`: A list of documents where `content` is None or the length of `content` is less than or equal to the threshold.  <br /> <br />`long_documents`: A list of documents where the length of `content` is greater than the threshold. |
| **API reference** | [Routers](/reference/routers-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/routers/document_length_router.py |

</div>

## Overview

`DocumentLengthRouter` routes documents to different output connections based on the length of their `content` field.

It allows to set a `threshold` init parameter. Documents where `content` is None, or the length of `content` is less than or equal to the threshold are routed to "short_documents". Others are routed to "long_documents".

A common use case for `DocumentLengthRouter` is handling documents obtained from PDFs that contain non-text content, such as scanned pages or images. This component can detect empty or low-content documents and route them to components that perform OCR, generate captions, or compute image embeddings.

## Usage

### On its own

```python
from haystack.components.routers import DocumentLengthRouter
from haystack.dataclasses import Document

docs = [
    Document(content="Short"),
    Document(content="Long document "*20),
]

router = DocumentLengthRouter(threshold=10)

result = router.run(documents=docs)
print(result)

## {
## "short_documents": [Document(content="Short", ...)],
## "long_documents": [Document(content="Long document ...", ...)],
## }
```

### In a pipeline

In the following indexing pipeline, the `PyPDFToDocument` Converter extracts text from PDF files. Documents are then split by pages using a `DocumentSplitter`. Next, the `DocumentLengthRouter` routes short documents to `LLMDocumentContentExtractor` to extract text, which is particularly useful for non-textual, image-based pages. Finally, all documents are collected using `DocumentJoiner` and written to the Document Store.

```python
from haystack import Pipeline
from haystack.components.converters import PyPDFToDocument
from haystack.components.extractors.image import LLMDocumentContentExtractor
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.joiners import DocumentJoiner
from haystack.components.preprocessors import DocumentSplitter
from haystack.components.routers import DocumentLengthRouter
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore

document_store = InMemoryDocumentStore()

indexing_pipe = Pipeline()
indexing_pipe.add_component(
    "pdf_converter",
    PyPDFToDocument(store_full_path=True)
)
## setting skip_empty_documents=False is important here because the
## LLMDocumentContentExtractor can extract text from non-textual documents
## that otherwise would be skipped
indexing_pipe.add_component(
    "pdf_splitter",
    DocumentSplitter(
        split_by="page",
        split_length=1,
        skip_empty_documents=False
    )
)
indexing_pipe.add_component(
    "doc_length_router",
    DocumentLengthRouter(threshold=10)
)
indexing_pipe.add_component(
    "content_extractor",
    LLMDocumentContentExtractor(
        chat_generator=OpenAIChatGenerator(model="gpt-4.1-mini")
    )
)
indexing_pipe.add_component(
    "doc_joiner",
    DocumentJoiner(sort_by_score=False)
)
indexing_pipe.add_component(
    "document_writer",
    DocumentWriter(document_store=document_store)
)

indexing_pipe.connect("pdf_converter.documents", "pdf_splitter.documents")
indexing_pipe.connect("pdf_splitter.documents", "doc_length_router.documents")
## The short PDF pages will be enriched/captioned
indexing_pipe.connect(
    "doc_length_router.short_documents",
    "content_extractor.documents"
)
indexing_pipe.connect(
    "doc_length_router.long_documents",
    "doc_joiner.documents"
)
indexing_pipe.connect(
    "content_extractor.documents",
    "doc_joiner.documents"
)
indexing_pipe.connect("doc_joiner.documents", "document_writer.documents")

## Run the indexing pipeline with sources
indexing_result = indexing_pipe.run(
    data={"sources": ["textual_pdf.pdf", "non_textual_pdf.pdf"]}
)

## Inspect the documents
indexed_documents = document_store.filter_documents()
print(f"Indexed {len(indexed_documents)} documents:\n")
for doc in indexed_documents:
    print("file_path: ", doc.meta["file_path"])
    print("page_number: ", doc.meta["page_number"])
    print("content: ", doc.content)
    print("-" * 100 + "\n")

## Indexed 3 documents:
##
## file_path:  textual_pdf.pdf
## page_number:  1
## content:  A sample PDF ﬁle...
## ----------------------------------------------------------------------------------------------------
##
## file_path:  textual_pdf.pdf
## page_number:  2
## content:  Page 2 of Sample PDF...
## ----------------------------------------------------------------------------------------------------
##
## file_path:  non_textual_pdf.pdf
## page_number:  1
## content:  Content extracted from non-textual PDF using a LLM...
## ----------------------------------------------------------------------------------------------------
```
