TransformersSimilarityRanker

Rank documents based on their semantic similarity to the query.

Legacy Component

This component is considered legacy and will no longer receive updates. It may be deprecated in a future release, with removal following after a deprecation period. Consider using SentenceTransformersSimilarityRanker instead, which provides the same functionality along with additional features.

Key Features

• Uses a pre-trained cross-encoder model from Hugging Face to rank documents by semantic relevance.
• Configurable top_k and score_threshold to control number and quality of returned documents.
• Supports score scaling with Sigmoid activation and calibration factor.
• Supports query and document prefix strings for instruction-tuned reranking models.
• Compatible with local model paths or Hugging Face Hub model names.

Configuration

1. Drag the TransformersSimilarityRanker component onto the canvas from the Component Library.
2. Click the component to open the configuration panel.
3. On the General tab:
   1. Enter the model name or local path, such as cross-encoder/ms-marco-MiniLM-L-6-v2.
4. Go to the Advanced tab to configure the device, token, top_k, score threshold, batch size, model kwargs, tokenizer kwargs, scale score, and calibration factor.

Connections

TransformersSimilarityRanker receives a query string and a documents list — typically from a Retriever or DocumentJoiner. It outputs a ranked documents list sorted from most to least relevant. Connect its output to ChatPromptBuilder or AnswerBuilder.

Usage Example

components:
  TransformersSimilarityRanker:
    type: components.rankers.transformers_similarity.TransformersSimilarityRanker
    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 of the raw logit predictions.
calibration_factor	Optional[float]	None	Use this factor to calibrate probabilities with sigmoid(logits * calibration_factor). Used only if scale_score is True.
score_threshold	Optional[float]	None	Use it to return documents only with a score above this threshold.

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, overrides the default device.
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. You can use it to prepend the document with an instruction, as required by embedding models like bge.
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. If False, disables scaling of the raw logit predictions.
calibration_factor	Optional[float]	1.0	Use this factor to calibrate probabilities with sigmoid(logits * calibration_factor). Used only if scale_score is True.
score_threshold	Optional[float]	None	Use it to return documents with a score above this threshold only.
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.
batch_size	int	16	The batch size to use for inference. The higher the batch size, the more memory is required. If you run into memory issues, reduce the batch size.

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 False, disables scaling of the raw logit predictions.
calibration_factor	Optional[float]	None	Use this factor to calibrate probabilities with sigmoid(logits * calibration_factor). Used only if scale_score is True.
score_threshold	Optional[float]	None	Use it to return documents only with a score above this threshold.