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from typing import List, Sequence, Tuple | |
import faiss | |
import numpy as np | |
from core.vectorizer import Vectorizer | |
class PromptSearchEngine: | |
""" | |
The PromptSearchEngine is responsible for finding the most similar prompts to a given query | |
by leveraging vectorized representations of the prompts and a similarity search index. | |
""" | |
def __init__(self, prompts: Sequence[str]) -> None: | |
""" | |
Initialize the PromptSearchEngine with a list of prompts. | |
Args: | |
prompts (Sequence[str]): The sequence of raw corpus prompts to be indexed for similarity search. | |
""" | |
self.vectorizer = Vectorizer() | |
self.corpus_vectors = self.vectorizer.transform(prompts) | |
self.corpus = prompts | |
self.corpus_vectors = self.corpus_vectors / np.linalg.norm( | |
self.corpus_vectors, axis=1, keepdims=True | |
) | |
d = self.corpus_vectors.shape[1] | |
self.index = faiss.IndexFlatIP(d) | |
self.index.add(self.corpus_vectors.astype("float32")) | |
def most_similar(self, query: str, n: int = 5) -> List[Tuple[float, str]]: | |
""" | |
Find the most similar prompts to a given query. | |
Args: | |
query (str): The query prompt to search for similar prompts. | |
n (int, optional): The number of similar prompts to retrieve. Defaults to 5. | |
Returns: | |
List[Tuple[float, str]]: A list of tuples containing the similarity score and the corresponding prompt. | |
""" | |
query_vector = self.vectorizer.transform([query]).astype("float32") | |
query_vector = query_vector / np.linalg.norm(query_vector) | |
distances, indices = self.index.search(query_vector, n) | |
return [(distances[0][i], self.corpus[indices[0][i]]) for i in range(n)] | |