SentenceTransformerCrossEncoderRetriever

Retriever

DashAI.back.models.RAG.retrievers.cross_encoder.SentenceTransformerCrossEncoderRetriever

---

Cross-encoder re-ranker powered by SentenceTransformer models.

Retrieves candidates from a single child retriever and re-ranks them using a SentenceTransformer CrossEncoder <https://sbert.net/docs/cross_encoder/pretrained_models.html>_ model that scores (query, chunk) pairs jointly.

The child retriever (the ranker) defines how many candidates are fetched via its own top_k; this cross-encoder only selects top_k of them after re-ranking.

Example usage::

Retrieve 15 candidates with BM25 (child's own top_k), keep top 5

after cross-encoding

config = { "model_name": "cross-encoder/ms-marco-MiniLM-L-6-v2", "top_k": 5, "children": [ { "component": "BM25Retriever", "params": {"...": "...", "top_k": 15}, } ], }

Parameters

model_name : string, default=cross-encoder/ms-marco-MiniLM-L-6-v2

Pre-trained SentenceTransformer cross-encoder model to use.

top_k : integer, default=5

Final number of chunks to return after re-ranking. The candidate set size is determined by the child retriever's own top_k.

children : array, default=[]

The child retriever whose candidates will be re-ranked.

Methods

get_metadata(cls) -> Dict[str, Any]

Defined on SentenceTransformerCrossEncoderRetriever

Return UI metadata including the declarative operation summary.

inject_infra(self, env_RAG_path: str, chunks: Dict[int, Dict[int, DashAI.back.models.RAG.documents.chunk.Chunk]], persistence: Any) -> None

Defined on SentenceTransformerCrossEncoderRetriever

Inject runtime infrastructure and load the cross-encoder model.

load(self, filename: str = '') -> None

Defined on SentenceTransformerCrossEncoderRetriever

Download and load the cross-encoder model from HuggingFace Hub.

add(self, child: DashAI.back.models.RAG.retrievers.base_retriever.BaseRetriever) -> None

Defined on CompositeRetriever

Add a child retriever.

get_children(self) -> List[DashAI.back.models.RAG.retrievers.base_retriever.BaseRetriever]

Defined on CompositeRetriever

Return a copy of the children list.

get_chunk_vectors(self, chunk_ids: List[int]) -> numpy.ndarray

Defined on CrossEncoderRetriever

Return chunk vectors from the child retriever.

get_credential(self, name: str)

Defined on ConfigObject

Resolve a registered credential component by name.

Parameters

name : str

Credential component class name (e.g. "HuggingFaceCredential").

Returns

BaseCredential

An instance of the requested credential component.

get_id(self) -> int | None

Defined on BaseRetriever

Return the database ID of this retriever, or None.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict

Dictionary representing the Json Schema of the component.

init_model(self) -> None

Defined on CrossEncoderRetriever

Initialize the model by loading it.

remove(self, child: DashAI.back.models.RAG.retrievers.base_retriever.BaseRetriever) -> None

Defined on CompositeRetriever

Remove a child retriever.

retrieve(self, query: str, top_k: int | None = None, **kwargs) -> List[DashAI.back.models.RAG.documents.chunk.Chunk]

Defined on CrossEncoderRetriever

Retrieve and re-rank chunks using the cross-encoder.

save(self, filename: str = '') -> None

Defined on CrossEncoderRetriever

Persist the cross-encoder retriever's state.

score_chunks(self, chunk_ids: List[int], query: str) -> List[Tuple[int, float]]

Defined on CrossEncoderRetriever

Score a set of chunk IDs against the query with the cross-encoder.

set_id(self, id: int) -> None

Defined on BaseRetriever

Assign a database ID to this retriever.

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict

A dictionary with the data provided by the user to initialize the model.

Returns

dict

A validated dictionary with the necessary objects.