---
title: "MetaFieldRanker"
id: metafieldranker
slug: "/metafieldranker"
description: "`MetaFieldRanker` ranks Documents based on the value of their meta field you specify. It's a lightweight Ranker that can improve your pipeline's results without slowing it down."
---

# MetaFieldRanker

`MetaFieldRanker` ranks Documents based on the value of their meta field you specify. It's a lightweight Ranker that can improve your pipeline's results without slowing it down.

|  |  |
| --- | --- |
| **Most common position in a pipeline** | In a query pipeline, after a component that returns a list of documents, such as a [Retriever](../retrievers.mdx)  |
| **Mandatory init variables** | "meta_field": The name of the meta field to rank by |
| **Mandatory run variables** | “documents”: A list of documents  <br /> <br />”top_k”: The maximum number of documents to return. If not provided, returns all documents it received. |
| **Output variables** | “documents”: A list of documents |
| **API reference** | [Rankers](/reference/rankers-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/rankers/meta_field.py |

## Overview

`MetaFieldRanker` sorts documents based on the value of a specific meta field in descending or ascending order. This means the returned list of `Document` objects are arranged in a selected order, with string values sorted alphabetically or in reverse (for example, Tokyo, Paris, Berlin).

`MetaFieldRanker` comes with the optional parameters  `weight` and `ranking_mode` you can use to combine a document’s score assigned by the Retriever and the value of its meta field for the ranking. The `weight` parameter lets you balance the importance of the Document's content and the meta field in the ranking process. The `ranking_mode` parameter defines how the scores from the Retriever and the Ranker are combined.

This Ranker is useful in query pipelines, like retrieval-augmented generation (RAG) pipelines or document search pipelines. It ensures the documents are ordered by their meta field value. You can also use it after a Retriever (such as the `InMemoryEmbeddingRetriever`) to combine the Retriever’s score with a document’s meta value for improved ranking.

By default, `MetaFieldRanker` sorts documents only based on the meta field. You can adjust this by setting the `weight` to less than 1 when initializing this component. For more details on different initialization settings, check out the API reference for this component.

## Usage

### On its own

You can use this Ranker outside of a pipeline to sort documents.

This example uses the `MetaFieldRanker` to rank two simple documents. When running the Ranker, you pass the  `query`, provide the `documents` and set the number of documents to rank using the `top_k` parameter.

```python
from haystack import Document
from haystack.components.rankers import MetaFieldRanker

docs = [Document(content="Paris", meta={"rating": 1.3}), Document(content="Berlin", meta={"rating": 0.7})]

ranker = MetaFieldRanker(meta_field="rating")

ranker.run(query="City in France", documents=docs, top_k=1)
```

### In a pipeline

Below is an example of a pipeline that retrieves documents from an `InMemoryDocumentStore` based on keyword search (using `InMemoryBM25Retriever`). It then uses the `MetaFieldRanker` to rank the retrieved documents based on the meta field `rating`, using the Ranker's default settings:

```python
from haystack import Document, Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.rankers import MetaFieldRanker

docs = [Document(content="Paris", meta={"rating": 1.3}),
        Document(content="Berlin", meta={"rating": 0.7}),
        Document(content="Barcelona", meta={"rating": 2.1})]
document_store = InMemoryDocumentStore()
document_store.write_documents(docs)

retriever = InMemoryBM25Retriever(document_store = document_store)
ranker = MetaFieldRanker(meta_field="rating")

document_ranker_pipeline = Pipeline()
document_ranker_pipeline.add_component(instance=retriever, name="retriever")
document_ranker_pipeline.add_component(instance=ranker, name="ranker")

document_ranker_pipeline.connect("retriever.documents", "ranker.documents")

query = "Cities in France"
document_ranker_pipeline.run(data={"retriever": {"query": query, "top_k": 3},
                                   "ranker": {"query": query, "top_k": 2}})
```
