SentenceTransformersSimilarityRanker

Rank documents based on their semantic similarity to the query.

Nvidia Models Recommendation

We recommend using models available through the DeepsetNvidia components instead of the Sentence Transformers models.. Add a DeepsetNvidia component to your pipeline and choose an appropriate model from the list.

Key Features

• Uses a pre-trained cross-encoder model from Hugging Face to rank documents by semantic similarity to the query.
• Configurable number of results returned via top_k.
• Supports score scaling via Sigmoid activation for normalized similarity scores.
• Supports filtering by score threshold.
• Supports multiple backends: torch, ONNX, and OpenVINO.

Configuration

1. Drag the SentenceTransformersSimilarityRanker component onto the canvas from the Component Library.
2. Click on the component to open the configuration panel.
3. On the General tab:
   • Set the ranking model. Pass a local path or the Hugging Face model name of a cross-encoder model.
   • Set top_k to control how many documents to return.
4. Go to the Advanced tab to configure scale_score, score_threshold, batch_size, backend, and meta_fields_to_embed.

Connections

SentenceTransformersSimilarityRanker accepts a query string and a list of documents as inputs. Connect it after a retriever or DocumentJoiner in a query pipeline.

It outputs a ranked list of documents sorted from most to least relevant to the query. Connect its documents output to ChatPromptBuilder, AnswerBuilder, or another downstream component.

Source Code

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

Usage Examples

Basic Configuration

  SentenceTransformersSimilarityRanker:
    type: components.rankers.sentence_transformers_similarity.SentenceTransformersSimilarityRanker
    init_parameters: {}

components:
  SentenceTransformersSimilarityRanker:
    type: components.rankers.sentence_transformers_similarity.SentenceTransformersSimilarityRanker
    init_parameters:

Parameters

Inputs

Parameter	Type	Default	Description
query	str		The input query to compare the documents to.
documents	List[Document]		A list of documents to be ranked.
top_k	Optional[int]	None	The maximum number of documents to return.
scale_score	Optional[bool]	None	If True, scales the raw logit predictions using a Sigmoid activation function. If False, disables scaling. If set, overrides the value set at initialization.
score_threshold	Optional[float]	None	Return documents only with a score above this threshold. If set, overrides the value set at initialization.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		A list of documents closest to the query, sorted from most similar to least similar.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	Union[str, Path]	cross-encoder/ms-marco-MiniLM-L-6-v2	The ranking model. Pass a local path or the Hugging Face model name of a cross-encoder model.
device	Optional[ComponentDevice]	None	The device on which the model is loaded. If None, the default device is automatically selected.
token	Optional[Secret]	Secret.from_env_var(['HF_API_TOKEN', 'HF_TOKEN'], strict=False)	The API token to download private models from Hugging Face.
top_k	int	10	The maximum number of documents to return per query.
query_prefix	str		A string to add at the beginning of the query text before ranking. Use it to prepend the text with an instruction, as required by reranking models like bge.
document_prefix	str		A string to add at the beginning of each document before ranking.
meta_fields_to_embed	Optional[List[str]]	None	List of metadata fields to embed with the document.
embedding_separator	str	\n	Separator to concatenate metadata fields to the document.
scale_score	bool	True	If True, scales the raw logit predictions using a Sigmoid activation function.
score_threshold	Optional[float]	None	Return documents with a score above this threshold only.
trust_remote_code	bool	False	If False, allows only Hugging Face verified model architectures. If True, allows custom models and scripts.
model_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for AutoModelForSequenceClassification.from_pretrained when loading the model.
tokenizer_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for AutoTokenizer.from_pretrained when loading the tokenizer.
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. Refer to the Sentence Transformers documentation for more information.
batch_size	int	16	The batch size to use for inference. The higher the batch size, the more memory is required.

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
query	str		The input query to compare the documents to.
documents	List[Document]		A list of documents to be ranked.
top_k	Optional[int]	None	The maximum number of documents to return.
scale_score	Optional[bool]	None	If True, scales the raw logit predictions using a Sigmoid activation function. If set, overrides the value set at initialization.
score_threshold	Optional[float]	None	Return documents only with a score above this threshold. If set, overrides the value set at initialization.