FastembedSparseDocumentEmbedder

Compute sparse document embeddings using Fastembed sparse models.

Key Features

• Uses Fastembed sparse models such as SPLADE for sparse embedding generation.
• Stores the computed sparse embedding in the sparse_embedding field of each document.
• Supports hybrid search scenarios combining sparse and dense retrieval.
• Configurable batch size and parallel processing for large datasets.
• Supports embedding metadata fields alongside document content.

Configuration

1. Drag the FastembedSparseDocumentEmbedder component onto the canvas from the Component Library.
2. Click on the component to open the configuration panel.
3. On the General tab:
   • Select the sparse embedding model. You can find supported models in the FastEmbed documentation.
4. Go to the Advanced tab to configure additional settings such as batch_size, parallel, meta_fields_to_embed, and model_kwargs.

Connections

FastembedSparseDocumentEmbedder receives a list of documents from converters or DocumentSplitter in an indexing pipeline. It outputs the same documents with their sparse_embedding field populated. Connect its output to DocumentWriter to store the embedded documents.

Source Code

To check this component's source code, open fastembed_sparse_document_embedder.py in the Haystack Core Integrations repository.

Connections

FastembedSparseDocumentEmbedder accepts a list of documents as input. In an indexing pipeline, connect it to converters such as TextFileToDocument or preprocessors such as DocumentSplitter.

It outputs a list of documents with the sparse_embedding field populated. Connect its documents output to DocumentWriter to store the embedded documents.

Usage Examples

Basic Configuration

  FastembedSparseDocumentEmbedder:
    type: 
      haystack_integrations.components.embedders.fastembed.fastembed_sparse_document_embedder.FastembedSparseDocumentEmbedder
    init_parameters:
      model: prithivida/Splade_PP_en_v1
      batch_size: 32
      progress_bar: true
      local_files_only: false
      embedding_separator: "\n"

This index uses FastembedSparseDocumentEmbedder to create sparse embeddings:

# haystack-pipeline
components:
  TextFileToDocument:
    type: haystack.components.converters.txt.TextFileToDocument
    init_parameters:
      encoding: utf-8
      store_full_path: false

  DocumentSplitter:
    type: haystack.components.preprocessors.document_splitter.DocumentSplitter
    init_parameters:
      split_by: sentence
      split_length: 5
      split_overlap: 1

  FastembedSparseDocumentEmbedder:
    type: haystack_integrations.components.embedders.fastembed.fastembed_sparse_document_embedder.FastembedSparseDocumentEmbedder
    init_parameters:
      model: prithivida/Splade_PP_en_v1
      cache_dir:
      threads:
      batch_size: 32
      progress_bar: true
      parallel:
      local_files_only: false
      meta_fields_to_embed:
      embedding_separator: "\n"
      model_kwargs:

  DocumentWriter:
    type: haystack.components.writers.document_writer.DocumentWriter
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: 'sparse-index'
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      policy: OVERWRITE

connections:
  - sender: TextFileToDocument.documents
    receiver: DocumentSplitter.documents
  - sender: DocumentSplitter.documents
    receiver: FastembedSparseDocumentEmbedder.documents
  - sender: FastembedSparseDocumentEmbedder.documents
    receiver: DocumentWriter.documents

inputs:
  files:
    - TextFileToDocument.sources

max_runs_per_component: 100

metadata: {}

Parameters

Inputs

Parameter	Type	Default	Description
documents	List[Document]		List of Documents to embed.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		List of Documents with each Document's sparse_embedding field set to the computed embeddings.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	str	prithivida/Splade_PP_en_v1	Local path or name of the model in Hugging Face's model hub, such as prithivida/Splade_PP_en_v1.
cache_dir	Optional[str]	None	The path to the cache directory. Can be set using the FASTEMBED_CACHE_PATH env variable. Defaults to fastembed_cache in the system's temp directory.
threads	Optional[int]	None	The number of threads single onnxruntime session can use.
batch_size	int	32	Number of strings to encode at once.
progress_bar	bool	True	If True, displays progress bar during embedding.
parallel	Optional[int]	None	If > 1, data-parallel encoding is used, recommended for offline encoding of large datasets. If 0, use all available cores. If None, don't use data-parallel processing, use default onnxruntime threading instead.
local_files_only	bool	False	If True, only use the model files in the cache_dir.
meta_fields_to_embed	Optional[List[str]]	None	List of meta fields that should be embedded along with the Document content.
embedding_separator	str	\n	Separator used to concatenate the meta fields to the Document content.
model_kwargs	Optional[Dict[str, Any]]	None	Dictionary containing model parameters such as k, b, avg_len, language.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
documents	List[Document]		List of Documents to embed.