---
title: "QdrantHybridRetriever"
id: qdranthybridretriever
slug: "/qdranthybridretriever"
description: "A Retriever based both on dense and sparse embeddings, compatible with the Qdrant Document Store."
---

# QdrantHybridRetriever

A Retriever based both on dense and 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 a hybrid 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_embedding”: A dense vector representing the query (a list of floats)  <br /> <br />“query_sparse_embedding”: A [`SparseEmbedding`](../../concepts/data-classes.mdx#sparseembedding)  object containing a vectorial representation of the query |
| **Output variables** | “document”: 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 `QdrantHybridRetriever` is a Retriever based both on dense and sparse embeddings, compatible with the [`QdrantDocumentStore`](../../document-stores/qdrant-document-store.mdx).

It compares the query and document’s dense and sparse embeddings and fetches the documents most relevant to the query from the `QdrantDocumentStore`, fusing the scores with Reciprocal Rank Fusion.

:::tip
Hybrid Retrieval Pipeline

If you want additional customization for merging or fusing results, consider creating a hybrid retrieval pipeline with [`DocumentJoiner`](../joiners/documentjoiner.mdx).

You can check out our hybrid retrieval pipeline [tutorial](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval) for detailed steps.
:::

When using the `QdrantHybridRetriever`, make sure it has the query and document with dense and sparse embeddings available. You can do so by:

- Adding a (dense) document Embedder and a sparse document Embedder to your indexing pipeline,
- Adding a (dense) text Embedder and a sparse text Embedder to your query pipeline.

In addition to `query_embedding` and `query_sparse_embedding`, the `QdrantHybridRetriever` 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

```python
from haystack_integrations.components.retrievers.qdrant import QdrantHybridRetriever
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,
    wait_result_from_api=True,
)

doc = Document(
    content="test",
    embedding=[0.5] * 768,
    sparse_embedding=SparseEmbedding(indices=[0, 3, 5], values=[0.1, 0.5, 0.12]),
)

document_store.write_documents([doc])

retriever = QdrantHybridRetriever(document_store=document_store)
embedding = [0.1] * 768
sparse_embedding = SparseEmbedding(indices=[0, 1, 2, 3], values=[0.1, 0.8, 0.05, 0.33])
retriever.run(query_embedding=embedding, query_sparse_embedding=sparse_embedding)
```

### In a pipeline

Currently, you can compute sparse embeddings using Fastembed Sparse Embedders.
First, install the package with:

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

In the example below, we are using Fastembed Embedders to compute dense embeddings as well.

```python
from haystack import Document, Pipeline
from haystack.components.writers import DocumentWriter
from haystack_integrations.components.retrievers.qdrant import QdrantHybridRetriever
from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
from haystack.document_stores.types import DuplicatePolicy
from haystack_integrations.components.embedders.fastembed import (
    FastembedTextEmbedder,
    FastembedDocumentEmbedder,
    FastembedSparseTextEmbedder,
    FastembedSparseDocumentEmbedder,
)

document_store = QdrantDocumentStore(
    ":memory:",
    recreate_index=True,
    use_sparse_embeddings=True,
    embedding_dim=384,
)

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."),
]

indexing = Pipeline()
indexing.add_component(
    "sparse_doc_embedder",
    FastembedSparseDocumentEmbedder(model="prithvida/Splade_PP_en_v1"),
)
indexing.add_component(
    "dense_doc_embedder",
    FastembedDocumentEmbedder(model="BAAI/bge-small-en-v1.5"),
)
indexing.add_component(
    "writer",
    DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE),
)
indexing.connect("sparse_doc_embedder", "dense_doc_embedder")
indexing.connect("dense_doc_embedder", "writer")

indexing.run({"sparse_doc_embedder": {"documents": documents}})

querying = Pipeline()
querying.add_component(
    "sparse_text_embedder",
    FastembedSparseTextEmbedder(model="prithvida/Splade_PP_en_v1"),
)
querying.add_component(
    "dense_text_embedder",
    FastembedTextEmbedder(
        model="BAAI/bge-small-en-v1.5",
        prefix="Represent this sentence for searching relevant passages: ",
    ),
)
querying.add_component(
    "retriever",
    QdrantHybridRetriever(document_store=document_store),
)

querying.connect(
    "sparse_text_embedder.sparse_embedding",
    "retriever.query_sparse_embedding",
)
querying.connect("dense_text_embedder.embedding", "retriever.query_embedding")

question = "Who supports fastembed?"

results = query_mix.run(
    {
        "dense_text_embedder": {"text": question},
        "sparse_text_embedder": {"text": question},
    },
)

print(result["retriever"]["documents"][0])

## Document(id=...,
##  content: 'fastembed is supported by and maintained by Qdrant.',
##  score: 1.0)
```

## Additional References

:notebook: Tutorial: [Creating a Hybrid Retrieval Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval)

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