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": str(0)*1024} ) 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}")