Embed.py

import numpy as np
import seaborn as sns
import altair as alt
from sklearn.metrics.pairwise import cosine_similarity
import cohere

co = cohere.Client("w9BCnpVENCLMBfaUkuSH1hEGrWNKexfD4N9aq3X3") # Your Cohere API key

texts = ["I like to be in my house",
         "I enjoy staying home",
         "the isotope 238u decays to 206pb"]

response = co.embed(
    texts=texts,
    model='embed-english-v3.0',
    input_type='search_document'
)

embeddings = response.embeddings

[sentence1, sentence2, sentence3] = embeddings

print("Embedding for sentence 1", np.array(sentence1))
print("Embedding for sentence 2", np.array(sentence2))
print("Embedding for sentence 3", np.array(sentence3))
print("\n")
print("Similarity between sentences 1 and 2:", np.dot(sentence1, sentence2))
print("Similarity between sentences 1 and 3:", np.dot(sentence1, sentence3))
print("Similarity between sentences 2 and 3:", np.dot(sentence2, sentence3))
print("\n")
print("Similarity between sentences 1 and 1:", np.dot(sentence1, sentence1))
print("Similarity between sentences 2 and 2:", np.dot(sentence2, sentence2))
print("Similarity between sentences 3 and 3:", np.dot(sentence3, sentence3))
print("\n")
print("Cosine similarity between sentences 1 and 2:", cosine_similarity([sentence1], [sentence2])[0][0])
print("Cosine similarity between sentences 1 and 3:", cosine_similarity([sentence1], [sentence3])[0][0])
print("Cosine similarity between sentences 2 and 3:", cosine_similarity([sentence2], [sentence3])[0][0])
print("\n")
print("Cosine similarity between sentences 1 and 1:", cosine_similarity([sentence1], [sentence1])[0][0])
print("Cosine similarity between sentences 2 and 2:", cosine_similarity([sentence2], [sentence2])[0][0])
print("Cosine similarity between sentences 3 and 3:", cosine_similarity([sentence3], [sentence3])[0][0])