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import json
import os
import numpy as np
import openai
import requests
from scipy.spatial.distance import cosine
def get_embedding_from_api(word, model="vicuna-7b-v1.1"):
if "ada" in model:
resp = openai.Embedding.create(
model=model,
input=word,
)
embedding = np.array(resp["data"][0]["embedding"])
return embedding
url = "http://localhost:8000/v1/embeddings"
headers = {"Content-Type": "application/json"}
data = json.dumps({"model": model, "input": word})
response = requests.post(url, headers=headers, data=data)
if response.status_code == 200:
embedding = np.array(response.json()["data"][0]["embedding"])
return embedding
else:
print(f"Error: {response.status_code} - {response.text}")
return None
def cosine_similarity(vec1, vec2):
return 1 - cosine(vec1, vec2)
def print_cosine_similarity(embeddings, texts):
for i in range(len(texts)):
for j in range(i + 1, len(texts)):
sim = cosine_similarity(embeddings[texts[i]], embeddings[texts[j]])
print(f"Cosine similarity between '{texts[i]}' and '{texts[j]}': {sim:.2f}")
texts = [
"The quick brown fox",
"The quick brown dog",
"The fast brown fox",
"A completely different sentence",
]
embeddings = {}
for text in texts:
embeddings[text] = get_embedding_from_api(text)
print("Vicuna-7B:")
print_cosine_similarity(embeddings, texts)
for text in texts:
embeddings[text] = get_embedding_from_api(text, model="text-similarity-ada-001")
print("text-similarity-ada-001:")
print_cosine_similarity(embeddings, texts)
for text in texts:
embeddings[text] = get_embedding_from_api(text, model="text-embedding-ada-002")
print("text-embedding-ada-002:")
print_cosine_similarity(embeddings, texts)
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