
Most common position in a pipeline	1. After a Text Embedder and before a `PromptBuilder` in a RAG Pipeline 2. The last component in the semantic search pipeline 3. After a Text Embedder and before an `ExtractiveReader` in an extractive QA pipeline
Mandatory init variables	"document_store": An instance of a QdrantDocumentStore
Mandatory run variables	“query_embedding”: A vector representing the query (a list of floats)
Output variables	“documents”: A list of documents
API reference	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:

pip install qdrant-haystack

Usage

On its own

This Retriever needs the QdrantDocumentStore and indexed Documents to run.

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

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])