This code is can be used to create a basic QA chatbot

semantic_bot.py

from sentence_transformers import SentenceTransformer, util
import torch

if __name__ == "__main__":
  questions = ["hi", "What is the product", "what is the cost of the product"]
  answers = ["Hi there, How can I help you", "The product is an QA chatbot", "The product is available for free"]
  
  ## Load the pretrained model
  model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
  model.max_seq_length = 512
  
  ## Generate the embeddings
  embeddings = model.encode(
        questions,
        show_progress_bar=True,
        convert_to_tensor=True,
    )
  
  while True:
    query = input("Enter query: ")
    query_embeddings = model.encode(query, convert_to_tensor=True)
    cosine_scores = util.cos_sim(query_embeddings, embeddings)
    similarity_score = torch.max(cosine_scores)
    if similarity_score > 0.75:
        answer_idx = torch.argmax(cosine_scores)
        result = answers[answer_idx]
    else:
      result = "Please refer to the documentation ..."
    print(result)