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IntelligenzaArtificiale
43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
import requests
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from retry import retry
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import numpy as np
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import os
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#read from env the hf token
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if os.environ.get("HUGGINGFACEHUB_API_TOKEN") is not None:
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hf_token = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
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else:
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raise Exception("You must provide the huggingface token")
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# model_id = "sentence-transformers/all-MiniLM-L6-v2" NOT WORKING FROM 10/05/2023
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model_id = "obrizum/all-MiniLM-L6-v2"
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api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
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headers = {"Authorization": f"Bearer {hf_token}"}
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def reshape_array(arr):
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# create an array of zeros with shape (1536)
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new_arr = np.zeros((1536,))
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# copy the original array into the new array
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new_arr[:arr.shape[0]] = arr
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# return the new array
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return new_arr
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@retry(tries=3, delay=10)
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def newEmbeddings(texts):
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response = requests.post(api_url, headers=headers, json={"inputs": texts, "options":{"wait_for_model":True}})
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result = response.json()
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if isinstance(result, list):
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return result
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elif list(result.keys())[0] == "error":
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raise RuntimeError(
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"The model is currently loading, please re-run the query."
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)
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def newEmbeddingFunction(texts):
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embeddings = newEmbeddings(texts)
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embeddings = np.array(embeddings, dtype=np.float32)
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shaped_embeddings = reshape_array(embeddings)
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return shaped_embeddings
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