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embedding.py

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+import os
+import torch
+import pandas
+import dotenv
+import requests
+from datasets import load_dataset
+from sentence_transformers.util import semantic_search
+
+dotenv.load_dotenv(dotenv.find_dotenv())
+HF_TOKEN = os.environ['YOUR_TOKEN']
+YOUR_API_KEY = os.environ['YOUR_API_KEY']
+REQUEST_SUCESSFULL = 200
+
+def obtener_temas():
+    headers = {"subdomain-X": "demotest", 'X-Subdomain': 'demotest', "Authorization": f"Bearer {YOUR_API_KEY}"}
+    respuesta = requests.request(method="GET", url="https://api.applearnify.es/api/subjects-with-topics", headers=headers)
+    temas = []
+    if respuesta.status_code == REQUEST_SUCESSFULL:
+        for subject in respuesta.json():
+                for topic in subject["topics"]:
+                    for file in topic.get("files", []):
+                        if file["type"] == "temario":
+                            temas.append(file.get("friendly_name"))
+    return temas
+
+def query(api_url, headers, texts):
+    response = requests.post(api_url, headers=headers, json={"inputs": texts, "options":{"wait_for_model":True}})
+    if response.status_code == REQUEST_SUCESSFULL:
+        return response.json()
+    else:
+        raise Exception(response.json())
+
+def main():
+    model_id = ""
+    api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
+    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+    texts = obtener_temas()
+    output = query(api_url, headers, texts)
+    embeddings = pandas.DataFrame(output)
+    embeddings.to_csv("embeddings.csv", index=False)
+    faqs_embeddings = load_dataset('ricitos2001/ecope-dataset')
+    dataset_embeddings = torch.from_numpy(faqs_embeddings["train"].to_pandas().to_numpy()).to(torch.float)
+    question = []
+    text = input("pon algo: ")
+    question.append(text)
+    output = query(api_url, headers, question)
+    query_embeddings = torch.FloatTensor(output)
+    hits = semantic_search(query_embeddings, dataset_embeddings, top_k=5)
+    print([texts[hits[0][i]['corpus_id']] for i in range(len(hits[0]))])
+
+if __name__ == "__main__":
+    main()

embeddings.csv

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+