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import asyncio

import asyncio.threads

import requests

import numpy as np

n = 8

result = []

async def requests_post_async(*args, **kwargs):

return await asyncio.threads.to_thread(requests.post, *args, **kwargs)

async def main():

model_url = "http://127.0.0.1:6900"

responses: list[requests.Response] = await asyncio.gather(*[requests_post_async(

url= f"{model_url}/embedding",

json= {"content": "a "*1022}

) for i in range(n)])

for response in responses:

embedding = response.json()["embedding"]

print(embedding[-8:])

result.append(embedding)

asyncio.run(main())

# compute cosine similarity

for i in range(n-1):

for j in range(i+1, n):

embedding1 = np.array(result[i])

embedding2 = np.array(result[j])

similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))

print(f"Similarity between {i} and {j}: {similarity:.2f}")