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c59fd93 | 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 | 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}")
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