Spaces:
Paused
Paused
File size: 2,866 Bytes
234eac0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
import numpy as np
from collections import defaultdict
from typing import List, Tuple, Callable
from aimakerspace.openai_utils.embedding import EmbeddingModel
import asyncio
def cosine_similarity(vector_a: np.array, vector_b: np.array) -> float:
"""Computes the cosine similarity between two vectors."""
dot_product = np.dot(vector_a, vector_b)
norm_a = np.linalg.norm(vector_a)
norm_b = np.linalg.norm(vector_b)
return dot_product / (norm_a * norm_b)
class VectorDatabase:
def __init__(self, embedding_model: EmbeddingModel = None):
self.vectors = defaultdict(np.array)
self.embedding_model = embedding_model or EmbeddingModel()
def insert(self, key: str, vector: np.array) -> None:
self.vectors[key] = vector
def search(
self,
query_vector: np.array,
k: int,
distance_measure: Callable = cosine_similarity,
) -> List[Tuple[str, float]]:
scores = [
(key, distance_measure(query_vector, vector))
for key, vector in self.vectors.items()
]
return sorted(scores, key=lambda x: x[1], reverse=True)[:k]
def search_by_text(
self,
query_text: str,
k: int,
distance_measure: Callable = cosine_similarity,
return_as_text: bool = False,
) -> List[Tuple[str, float]]:
query_vector = self.embedding_model.get_embedding(query_text)
results = self.search(query_vector, k, distance_measure)
return [result[0] for result in results] if return_as_text else results
def retrieve_from_key(self, key: str) -> np.array:
return self.vectors.get(key, None)
async def abuild_from_list(self, list_of_text: List[str]) -> "VectorDatabase":
embeddings = await self.embedding_model.async_get_embeddings(list_of_text)
for text, embedding in zip(list_of_text, embeddings):
self.insert(text, np.array(embedding))
return self
if __name__ == "__main__":
list_of_text = [
"I like to eat broccoli and bananas.",
"I ate a banana and spinach smoothie for breakfast.",
"Chinchillas and kittens are cute.",
"My sister adopted a kitten yesterday.",
"Look at this cute hamster munching on a piece of broccoli.",
]
vector_db = VectorDatabase()
vector_db = asyncio.run(vector_db.abuild_from_list(list_of_text))
k = 2
searched_vector = vector_db.search_by_text("I think fruit is awesome!", k=k)
print(f"Closest {k} vector(s):", searched_vector)
retrieved_vector = vector_db.retrieve_from_key(
"I like to eat broccoli and bananas."
)
print("Retrieved vector:", retrieved_vector)
relevant_texts = vector_db.search_by_text(
"I think fruit is awesome!", k=k, return_as_text=True
)
print(f"Closest {k} text(s):", relevant_texts)
|