---
title: "QdrantEmbeddingRetriever"
id: qdrantembeddingretriever
slug: "/qdrantembeddingretriever"
description: "An embedding-based Retriever compatible with the Qdrant Document Store."
---

# QdrantEmbeddingRetriever

An embedding-based Retriever 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_embedding”: A vector representing the query (a list of floats) |
| **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 `QdrantEmbeddingRetriever` is an embedding-based Retriever compatible with the `QdrantDocumentStore`. It compares the query and Document embeddings and fetches the Documents most relevant to the query from the `QdrantDocumentStore` based on the outcome.

When using the `QdrantEmbeddingRetriever` in your NLP system, make sure it has the query and Document embeddings available. You can add a Document Embedder to your indexing Pipeline and a Text Embedder to your query Pipeline.

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

Some relevant parameters that impact the embedding retrieval must be defined when the corresponding `QdrantDocumentStore` is initialized: these include the embedding dimension (`embedding_dim`), the `similarity` function to use when comparing embeddings and the HNWS configuration (`hnsw_config`).

### 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 QdrantEmbeddingRetriever
from haystack_integrations.document_stores.qdrant import QdrantDocumentStore

document_store = QdrantDocumentStore(
    ":memory:",
    recreate_index=True,
    return_embedding=True,
    wait_result_from_api=True,
)
retriever = QdrantEmbeddingRetriever(document_store=document_store)

## using a fake vector to keep the example simple
retriever.run(query_embedding=[0.1] * 768)
```

#### In a Pipeline

```python
from haystack.document_stores.types import DuplicatePolicy
from haystack import Document
from haystack import Pipeline
from haystack.components.embedders import (
    SentenceTransformersTextEmbedder,
    SentenceTransformersDocumentEmbedder,
)

from haystack_integrations.components.retrievers.qdrant import QdrantEmbeddingRetriever
from haystack_integrations.document_stores.qdrant import QdrantDocumentStore

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

documents = [
    Document(content="There are over 7,000 languages spoken around the world today."),
    Document(
        content="Elephants have been observed to behave in a way that indicates a high level of self-awareness, such as recognizing themselves in mirrors.",
    ),
    Document(
        content="In certain parts of the world, like the Maldives, Puerto Rico, and San Diego, you can witness the phenomenon of bioluminescent waves.",
    ),
]

document_embedder = SentenceTransformersDocumentEmbedder()
document_embedder.warm_up()
documents_with_embeddings = document_embedder.run(documents)

document_store.write_documents(
    documents_with_embeddings.get("documents"),
    policy=DuplicatePolicy.OVERWRITE,
)

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
query_pipeline.add_component(
    "retriever",
    QdrantEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "How many languages are there?"

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

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