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QdrantSparseEmbeddingRetriever

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

Suggest Edits
Most common position in a pipeline1. 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_sparse_embedding”: A SparseEmbedding object containing a vectorial representation of the query
Output variables“documents”: A list of documents
API referenceQdrant
GitHub linkhttps://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.

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.

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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:

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 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:

pip install fastembed-haystack

Then, try out this pipeline:

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

Updated about 1 month ago