Update embedding.py

embedding.py

@@ -1,38 +1,48 @@
-import os
-import pandas
-import dotenv
-import requests
-
-dotenv.load_dotenv(dotenv.find_dotenv())
-HF_TOKEN = os.environ['YOUR_TOKEN']
-
-def query(api_url, headers, texts):
-    response = requests.post(api_url, headers=headers, json={"inputs": texts, "options":{"wait_for_model":True}})
-    return response.json()
-
-def main():
-    model_id = "sentence-transformers/all-MiniLM-L6-v2"
-    api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
-    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-    texts = ["How do I get a replacement Medicare card?",
-        "What is the monthly premium for Medicare Part B?",
-        "How do I terminate my Medicare Part B (medical insurance)?",
-        "How do I sign up for Medicare?",
-        "Can I sign up for Medicare Part B if I am working and have health insurance through an employer?",
-        "How do I sign up for Medicare Part B if I already have Part A?",
-        "What are Medicare late enrollment penalties?",
-        "What is Medicare and who can get it?",
-        "How can I get help with my Medicare Part A and Part B premiums?",
-        "What are the different parts of Medicare?",
-        "Will my Medicare premiums be higher because of my higher income?",
-        "What is TRICARE ?",
-        "Should I sign up for Medicare Part B if I have Veterans' Benefits?"]
-
-    output = query(api_url, headers, texts)
-
-    embeddings = pandas.DataFrame(output)
-    embeddings.to_csv("embeddings.csv", index=False)
-
-if __name__ == "__main__":
-    main()
-
+import os
+import torch
+import numpy
+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']
+
+def query(api_url, headers, texts):
+    response = requests.post(api_url, headers=headers, json={"inputs": texts, "options":{"wait_for_model":True}})
+    return response.json()
+
+def main():
+    model_id = "sentence-transformers/all-MiniLM-L6-v2"
+    api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
+    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+    texts = ["How do I get a replacement Medicare card?",
+        "What is the monthly premium for Medicare Part B?",
+        "How do I terminate my Medicare Part B (medical insurance)?",
+        "How do I sign up for Medicare?",
+        "Can I sign up for Medicare Part B if I am working and have health insurance through an employer?",
+        "How do I sign up for Medicare Part B if I already have Part A?",
+        "What are Medicare late enrollment penalties?",
+        "What is Medicare and who can get it?",
+        "How can I get help with my Medicare Part A and Part B premiums?",
+        "What are the different parts of Medicare?",
+        "Will my Medicare premiums be higher because of my higher income?",
+        "What is TRICARE ?",
+        "Should I sign up for Medicare Part B if I have Veterans' Benefits?"]
+    output = query(api_url, headers, texts)
+    embeddings = pandas.DataFrame(output)
+    embeddings.to_csv("embeddings.csv", index=False)
+    
+    faqs_embeddings = load_dataset('ricitos2001/OMEGAI')
+    dataset_embeddings = torch.from_numpy(faqs_embeddings["train"].to_pandas().to_numpy()).to(torch.float)
+    question = ["How can Medicare help me?"]
+    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()