echodict/llama.cpp

1import asyncio2import asyncio.threads3import requests4import numpy as np5 6 7n = 88 9result = []10 11async def requests_post_async(*args, **kwargs):12    return await asyncio.threads.to_thread(requests.post, *args, **kwargs)13 14async def main():15    model_url = "http://127.0.0.1:6900"16    responses: list[requests.Response] = await asyncio.gather(*[requests_post_async(17        url= f"{model_url}/embedding",18        json= {"content": "a "*1022}19    ) for i in range(n)])20 21    for response in responses:22        embedding = response.json()["embedding"]23        print(embedding[-8:])24        result.append(embedding)25 26asyncio.run(main())27 28# compute cosine similarity29 30for i in range(n-1):31    for j in range(i+1, n):32        embedding1 = np.array(result[i])33        embedding2 = np.array(result[j])34        similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))35        print(f"Similarity between {i} and {j}: {similarity:.2f}")36