QdrantSparseEmbeddingRetriever
A Retriever based on sparse embeddings, compatible with the Qdrant Document Store.
Name | QdrantSparseEmbeddingRetriever |
Folder Path | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/qdrant |
Most common Position in a Pipeline | 1. After a sparse text Embedder and before a PromptBuilder in a RAG pipeline2. The last component in the semantic search pipeline 3. After a sparse text Embedder and before an ExtractiveReader in an extractive QA pipeline |
Mandatory Input variables | “query_sparse_embedding”: A SparseEmbedding object containing a vectorial representation of the query |
Output variables | “document”: A list of documents |
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.
Sparse Embedding Support
To use Sparse Embedding support, you need to initialize the
QdrantDocumentStore
withuse_sparse_embeddings=True
, which isFalse
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..)
Updated 7 months ago
Check out the API reference in the GitHub repo or in our docs: