llama.cpp/examples/server_embd.py
Georgi Gerganov e235b267a2
py : switch to snake_case (#8305)
* py : switch to snake_case

ggml-ci

* cont

ggml-ci

* cont

ggml-ci

* cont : fix link

* gguf-py : use snake_case in scripts entrypoint export

* py : rename requirements for convert_legacy_llama.py

Needed for scripts/check-requirements.sh

---------

Co-authored-by: Francis Couture-Harpin <git@compilade.net>
2024-07-05 07:53:33 +03:00

34 lines
939 B
Python

import asyncio
import requests
import numpy as np
n = 8
result = []
async def requests_post_async(*args, **kwargs):
return await asyncio.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}")