FastembedSparseTextEmbedder

Compute sparse text embeddings using Fastembed sparse models.

Basic Information

• Type: haystack_integrations.components.embedders.fastembed.fastembed_sparse_text_embedder.FastembedSparseTextEmbedder
• Components it can connect with:
  • Input:Receives a query string as input in a query pipeline.
  • Retrievers: Sends the computed sparse embedding to a sparse retriever.

Inputs

Parameter	Type	Default	Description
text	str		A string to embed.

Outputs

Parameter	Type	Default	Description
sparse_embedding	SparseEmbedding		A sparse embedding representing the input text.

Overview

FastembedSparseTextEmbedder computes sparse text embeddings using Fastembed sparse models like SPLADE. Sparse embeddings are useful for hybrid search scenarios where you want to combine semantic search with keyword-based retrieval.

Use this component in query pipelines alongside a dense text embedder for sparse retrieval. Make sure to use the same sparse embedding model as the one used to embed the documents in the document store.

Compatible Models

You can find the supported models in the FastEmbed documentation.

Embedding Models in Query Pipelines and Indexes

The embedding model you use to embed documents in your indexing pipeline must be the same as the embedding model you use to embed the query in your query pipeline.

This means the embedders for your indexing and query pipelines must match. For example, if you use CohereDocumentEmbedder to embed your documents, you should use CohereTextEmbedder with the same model to embed your queries.

Usage Example

This query pipeline uses FastembedSparseTextEmbedder for sparse retrieval:

components:
  FastembedSparseTextEmbedder:
    type: haystack_integrations.components.embedders.fastembed.fastembed_sparse_text_embedder.FastembedSparseTextEmbedder
    init_parameters:
      model: prithivida/Splade_PP_en_v1
      cache_dir:
      threads:
      progress_bar: true
      parallel:
      local_files_only: false
      model_kwargs:

  bm25_retriever:
    type: haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: 'default'
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      top_k: 20

  ChatPromptBuilder:
    type: haystack.components.builders.chat_prompt_builder.ChatPromptBuilder
    init_parameters:
      template:
        - role: system
          content: "You are a helpful assistant answering questions based on the provided documents."
        - role: user
          content: "Documents:\n{% for doc in documents %}\n{{ doc.content }}\n{% endfor %}\n\nQuestion: {{ query }}"

  OpenAIChatGenerator:
    type: haystack.components.generators.chat.openai.OpenAIChatGenerator
    init_parameters:
      api_key:
        type: env_var
        env_vars:
          - OPENAI_API_KEY
        strict: false
      model: gpt-4o-mini

  OutputAdapter:
    type: haystack.components.converters.output_adapter.OutputAdapter
    init_parameters:
      template: '{{ replies[0] }}'
      output_type: List[str]

  answer_builder:
    type: deepset_cloud_custom_nodes.augmenters.deepset_answer_builder.DeepsetAnswerBuilder
    init_parameters:
      reference_pattern: acm

connections:
  - sender: bm25_retriever.documents
    receiver: ChatPromptBuilder.documents
  - sender: ChatPromptBuilder.prompt
    receiver: OpenAIChatGenerator.messages
  - sender: OpenAIChatGenerator.replies
    receiver: OutputAdapter.replies
  - sender: OutputAdapter.output
    receiver: answer_builder.replies
  - sender: bm25_retriever.documents
    receiver: answer_builder.documents

inputs:
  query:
    - bm25_retriever.query
    - ChatPromptBuilder.query
    - answer_builder.query
  filters:
    - bm25_retriever.filters

outputs:
  documents: bm25_retriever.documents
  answers: answer_builder.answers

max_runs_per_component: 100

metadata: {}

Parameters

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 Fastembed'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.
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.
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 run() method. You can pass these parameters at query time through the API, in Playground, or when running a job.

Parameter	Type	Default	Description
text	str		A string to embed.