---
title: "QdrantSparseEmbeddingRetriever"
id: qdrantsparseembeddingretriever
slug: "/qdrantsparseembeddingretriever"
description: "A Retriever based on sparse embeddings, compatible with the Qdrant Document Store."
---

# QdrantSparseEmbeddingRetriever

A Retriever based on sparse embeddings, compatible with the Qdrant Document Store.

|  |  |
| --- | --- |
| **Most common position in a pipeline** | 1\. After a Text Embedder and before a [`PromptBuilder`](../builders/promptbuilder.mdx)  in a RAG pipeline  <br /> <br />2. The last component in the semantic search pipeline      3. After a Text Embedder and before an [`ExtractiveReader`](../readers/extractivereader.mdx)  in an extractive QA pipeline |
| **Mandatory init variables** | "document_store": An instance of a [QdrantDocumentStore](../../document-stores/qdrant-document-store.mdx) |
| **Mandatory run variables** | “query_sparse_embedding”: A [`SparseEmbedding`](../../concepts/data-classes.mdx#sparseembedding)  object containing a vectorial representation of the query |
| **Output variables** | “documents”: A list of documents |
| **API reference** | [Qdrant](/reference/integrations-qdrant) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/qdrant |

## Overview

The `QdrantSparseEmbeddingRetriever` is a Retriever based on sparse embeddings, compatible with the [`QdrantDocumentStore`](../../document-stores/qdrant-document-store.mdx).

It compares the query and document sparse embeddings and, based on the outcome, fetches the documents most relevant to the query from the `QdrantDocumentStore`.

When using the `QdrantSparseEmbeddingRetriever`, make sure it has the query and document sparse embeddings available. You can do so by adding a sparse document Embedder to your indexing pipeline and a sparse text Embedder to your query pipeline.

In addition to the `query_sparse_embedding`, the `QdrantSparseEmbeddingRetriever` accepts other optional parameters, including `top_k` (the maximum number of documents to retrieve) and `filters` to narrow down the search space.

:::note
Sparse Embedding Support

To use Sparse Embedding support, you need to initialize the `QdrantDocumentStore` with `use_sparse_embeddings=True`, which is `False` by default.

If you want to use Document Store or collection previously created with this feature disabled, you must migrate the existing data. You can do this by taking advantage of the `migrate_to_sparse_embeddings_support` utility function.
:::

### Installation

To start using Qdrant with Haystack, first install the package with:

```shell
pip install qdrant-haystack
```

## Usage

### On its own

This Retriever needs the `QdrantDocumentStore` and indexed documents to run.

```python
from haystack_integrations.components.retrievers.qdrant import QdrantSparseEmbeddingRetriever
from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
from haystack.dataclasses import Document, SparseEmbedding

document_store = QdrantDocumentStore(
    ":memory:",
    use_sparse_embeddings=True,
    recreate_index=True,
    return_embedding=True,
)

doc = Document(content="test", sparse_embedding=SparseEmbedding(indices=[0, 3, 5], values=[0.1, 0.5, 0.12]))
document_store.write_documents([doc])

retriever = QdrantSparseEmbeddingRetriever(document_store=document_store)
sparse_embedding = SparseEmbedding(indices=[0, 1, 2, 3], values=[0.1, 0.8, 0.05, 0.33])
retriever.run(query_sparse_embedding=sparse_embedding)
```

### In a pipeline

In Haystack, you can compute sparse embeddings using Fastembed Embedders.

First, install the package with:

```shell
pip install fastembed-haystack
```

Then, try out this pipeline:

```python
from haystack import Document, Pipeline
from haystack.components.writers import DocumentWriter
from haystack_integrations.components.retrievers.qdrant import QdrantSparseEmbeddingRetriever
from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
from haystack.document_stores.types import DuplicatePolicy
from haystack_integrations.components.embedders.fastembed import FastembedDocumentEmbedder, FastembedTextEmbedder

document_store = QdrantDocumentStore(
    ":memory:",
    recreate_index=True,
    use_sparse_embeddings=True
)

documents = [
    Document(content="My name is Wolfgang and I live in Berlin"),
    Document(content="I saw a black horse running"),
    Document(content="Germany has many big cities"),
    Document(content="fastembed is supported by and maintained by Qdrant."),
]

sparse_document_embedder = FastembedSparseDocumentEmbedder()
writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE)

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("sparse_document_embedder", sparse_document_embedder)
indexing_pipeline.add_component("writer", writer)
indexing_pipeline.connect("sparse_document_embedder", "writer")

indexing_pipeline.run({"sparse_document_embedder": {"documents": documents}})

query_pipeline = Pipeline()
query_pipeline.add_component("sparse_text_embedder", FastembedSparseTextEmbedder())
query_pipeline.add_component("sparse_retriever", QdrantSparseEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("sparse_text_embedder.sparse_embedding", "sparse_retriever.query_sparse_embedding")

query = "Who supports fastembed?"

result = query_pipeline.run({"sparse_text_embedder": {"text": query}})

print(result["sparse_retriever"]["documents"][0])  # noqa: T201

## Document(id=...,
##  content: 'fastembed is supported by and maintained by Qdrant.',
##  score: 0.758..)
```

## Additional References

🧑‍🍳 Cookbook: [Sparse Embedding Retrieval with Qdrant and FastEmbed](https://haystack.deepset.ai/cookbook/sparse_embedding_retrieval)
