Filters documents based on their similarity scores using top-p sampling.
Module top_p
TopPSampler
@component
class TopPSampler()
Implements top-p (nucleus) sampling for document filtering based on cumulative probability scores.
This component provides functionality to filter a list of documents by selecting those whose scores fall within the top 'p' percent of the cumulative distribution. It is useful for focusing on high-probability documents while filtering out less relevant ones based on their assigned scores.
Usage example:
from haystack import Document
from haystack.components.samplers import TopPSampler
sampler = TopPSampler(top_p=0.95, score_field="similarity_score")
docs = [
Document(content="Berlin", meta={"similarity_score": -10.6}),
Document(content="Belgrade", meta={"similarity_score": -8.9}),
Document(content="Sarajevo", meta={"similarity_score": -4.6}),
]
output = sampler.run(documents=docs)
docs = output["documents"]
assert len(docs) == 1
assert docs[0].content == "Sarajevo"
TopPSampler.__init__
def __init__(top_p: float = 1.0, score_field: Optional[str] = None)
Creates an instance of TopPSampler.
Arguments:
top_p
: Float between 0 and 1 representing the cumulative probability threshold for document selection. A value of 1.0 indicates no filtering (all documents are retained).score_field
: Name of the field in each document's metadata that contains the score. If None, the default document score field is used.
TopPSampler.run
@component.output_types(documents=List[Document])
def run(documents: List[Document], top_p: Optional[float] = None)
Filters documents using top-p sampling based on their scores.
If the specified top_p results in no documents being selected (especially in cases of a low top_p value), the method returns the document with the highest similarity score.
Arguments:
documents
: List of Document objects to be filtered.top_p
: Optional. A float to override the cumulative probability threshold set during initialization.
Raises:
ValueError
: If the top_p value is not within the range [0, 1].
Returns:
A dictionary with the following key:
documents
: List of Document objects that have been selected based on the top-p sampling.