SentenceTransformersSparseTextEmbedder

Embed text strings, such as queries, using sparse embedding models from Sentence Transformers.

Basic Information

• Type: haystack.components.embedders.sentence_transformers_sparse_text_embedder.SentenceTransformersSparseTextEmbedder
• Components it can connect with:
  • Any component that produces text. It's usually used in query pipelines to embed the query it receives from the Input component. .
  • Any component that consumes sparse_embedding, such as SparseEmbeddingRetriever.

Inputs

Parameter	Type	Default	Description
text	str		Text to embed.

Outputs

Parameter	Type	Default	Description
sparse_embedding	SparseEmbedding		The sparse embedding of the input text.

Overview

SentenceTransformersSparseTextEmbedder transforms strings into sparse vectors using sparse embedding models from Sentence Transformers. You can use it to embed user queries and send them to a sparse embedding retriever.

When performing sparse retrieval, use SentenceTransformersSparseTextEmbedder to embed your query and send it to a retriever. The retriever then uses the sparse vector to search for relevant documents.

Compatible Models

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.

Compatible models are based on SPLADE (SParse Lexical AnD Expansion), a technique for producing sparse representations for text, where each non-zero value in the embedding is the importance weight of a term in the vocabulary. This approach combines the benefits of learned sparse representations with the efficiency of traditional sparse retrieval methods. For more information, see Pipeline Components.

Authentication

To use private models from Hugging Face, connect deepset AI Platform to Hugging Face first. For details, see Use Hugging Face Models.

Usage Example

components:
  sparse_text_embedder:
    type: haystack.components.embedders.sentence_transformers_sparse_text_embedder.SentenceTransformersSparseTextEmbedder
    init_parameters:
      model: prithivida/Splade_PP_en_v2  # SPLADE model for sparse embeddings
      prefix: ""
      suffix: ""

  sparse_retriever:
    type: haystack_integrations.components.retrievers.qdrant.retriever.QdrantSparseEmbeddingRetriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.qdrant.document_store.QdrantDocumentStore
        init_parameters:
          location: ${QDRANT_HOST}
          api_key: ${QDRANT_API_KEY}
          index: default
          use_sparse_embeddings: true
          return_embedding: false
      top_k: 10
      scale_score: false

  prompt_builder:
    type: haystack.components.builders.prompt_builder.PromptBuilder
    init_parameters:
      template: |-
        You are a helpful assistant.
        Answer the question based on the provided documents.
        If the documents don't contain enough information, say so.
        
        Documents:
        {% for document in documents %}
        Document[{{ loop.index }}]:
        {{ document.content }}
        {% endfor %}
        
        Question: {{ question }}
        Answer:

  llm:
    type: haystack.components.generators.openai.OpenAIGenerator
    init_parameters:
      api_key: {"type": "env_var", "env_vars": ["OPENAI_API_KEY"], "strict": false}
      model: gpt-4o
      generation_kwargs:
        max_tokens: 500
        temperature: 0.0

  answer_builder:
    type: haystack.components.builders.answer_builder.AnswerBuilder
    init_parameters: {}

connections:
- sender: sparse_text_embedder.sparse_embedding
  receiver: sparse_retriever.query_sparse_embedding
- sender: sparse_retriever.documents
  receiver: prompt_builder.documents
- sender: sparse_retriever.documents
  receiver: answer_builder.documents
- sender: prompt_builder.prompt
  receiver: llm.prompt
- sender: llm.replies
  receiver: answer_builder.replies

max_runs_per_component: 100

inputs:
  query:
  - sparse_text_embedder.text
  - prompt_builder.question
  - answer_builder.query
  filters:
  - sparse_retriever.filters

outputs:
  documents: sparse_retriever.documents
  answers: answer_builder.answers

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	str	prithivida/Splade_PP_en_v2	The model to use for calculating sparse embeddings. Specify the path to a local model or the ID of the model on Hugging Face. For available models, check Hugging Face.
device	Optional[ComponentDevice]	None	Overrides the default device used to load the model.
token	Optional[Secret]		An API token to use private models from Hugging Face.
prefix	str	""	A string to add at the beginning of each text to be embedded. Some models may benefit from adding a prefix or suffix to the text before embedding. For example, the prithivida/Splade_PP_en_v2 model may benefit from adding a prefix of "query: " to the text before embedding.
suffix	str	""	A string to add at the end of each text to embed. Some models may benefit from adding a prefix or suffix to the text before embedding.
trust_remote_code	bool	False	If True, permits custom models and scripts.
local_files_only	bool	False	If True, only looks at local files without downloading from Hugging Face Hub.
model_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for AutoModelForSequenceClassification.from_pretrained when loading the model. Refer to specific model documentation for available kwargs.
tokenizer_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for AutoTokenizer.from_pretrained when loading the tokenizer. Refer to specific model documentation for available kwargs.
config_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for AutoConfig.from_pretrained when loading the model configuration.
backend	Literal["torch", "onnx", "openvino"]	torch	The backend to use for the Sentence Transformers model. Choose from torch, onnx, or openvino. Refer to the Sentence Transformers documentation for more information on acceleration and quantization options.
revision	Optional[str]	None	The specific model version to use. It can be a branch name, a tag name, or a commit ID for a stored model on Hugging Face.

Run Method Parameters

These are the parameters you can configure for the component's run() method.

Parameter	Type	Default	Description
text	str		Text to embed.