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"""A signal to compute semantic search for a document."""
from typing import Any, Iterable, Optional, Union
import numpy as np
from scipy.interpolate import interp1d
from typing_extensions import override
from ..embeddings.embedding import EmbedFn, get_embed_fn
from ..embeddings.vector_store import VectorStore
from ..schema import Field, Item, RichData, VectorKey, field
from .signal import TextEmbeddingModelSignal
class SemanticSimilaritySignal(TextEmbeddingModelSignal):
"""Compute semantic similarity for a query and a document.
<br>This is done by embedding the query with the same embedding as the document and computing a
a similarity score between them.
"""
name = 'semantic_similarity'
display_name = 'Semantic Similarity'
query: str
_embed_fn: EmbedFn
# Dot products are in the range [-1, 1]. We want to map this to [0, 1] for the similarity score
# with a slight bias towards 1 since dot product of <0.2 is not really relevant.
_interpolate_fn = interp1d([-1, 0.2, 1], [0, 0.5, 1])
_search_text_embedding: Optional[np.ndarray] = None
def __init__(self, query: Union[str, bytes], embedding: str, **kwargs: Any):
if isinstance(query, bytes):
raise ValueError('Image queries are not yet supported for SemanticSimilarity.')
super().__init__(query=query, embedding=embedding, **kwargs)
# TODO(nsthorat): The embedding cls might have arguments. This needs to be resolved.
self._embed_fn = get_embed_fn(embedding)
@override
def fields(self) -> Field:
return field('float32')
def _get_search_embedding(self) -> np.ndarray:
"""Return the embedding for the search text."""
if self._search_text_embedding is None:
self._search_text_embedding = self._embed_fn([self.query])[0].reshape(-1)
return self._search_text_embedding
@override
def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
text_embeddings = self._embed_fn(data)
similarities = text_embeddings.dot(self._get_search_embedding()).reshape(-1)
return similarities.tolist()
@override
def vector_compute(self, keys: Iterable[VectorKey],
vector_store: VectorStore) -> Iterable[Optional[Item]]:
text_embeddings = vector_store.get(keys)
similarities = text_embeddings.dot(self._get_search_embedding()).reshape(-1)
# Clip the similarities since float precision can cause these to be barely outside the range and
# throw an exception with interp1d.
similarities = np.clip(similarities, -1., 1.)
return self._interpolate_fn(similarities).tolist()
@override
def vector_compute_topk(
self,
topk: int,
vector_store: VectorStore,
keys: Optional[Iterable[VectorKey]] = None) -> list[tuple[VectorKey, Optional[Item]]]:
query = self._get_search_embedding()
topk_keys = [key for key, _ in vector_store.topk(query, topk, keys)]
return list(zip(topk_keys, self.vector_compute(topk_keys, vector_store)))
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