mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-29 04:44:34 +00:00
29ae62d2ae
* llama : fix embeddings ggml-ci * llama : do not use KV cache for non-causal models ggml-ci * embeddings : fix llama_batch_init arg * llama : add pooling switch * llama : distinguish token vs sequence embeddings ggml-ci * llama : assert pooling tensor * llama : simplify causal mask condition ggml-ci * llama : assert input batch with pooling enabled * readme : update API changes list
35 lines
940 B
Python
35 lines
940 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(i)*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}")
|
|
|