Assume that you want to see the car price based on name of the car, and production year.
Name, production year, price Volvo, 2021, 50000 Volvo, 2012, 16000 Toyota, 2022, 30000 Then if the input data is:
Volvo, 2018 It returns the most similar data maybe as below:
Volvo, 2021, 50000 Volvo, 2012, 16000 So, I can manually choose the right price.
If you want to be choosing something manually, you can just query it using df.query. You can play around with the production_year variable, but in your post it seems rather irrelevant.
data = pd.DataFrame({"name" : ['Volvo', 'Volvo', 'Toyota'], "production_year" : [2021, 2012, 2022], 'price' : [50000, 16000, 30000]}) q = data.query("name == 'Volvo'") Returns :
Volvo, 2021, 50000 Volvo, 2012, 16000 I am unaware whether your data is in text documents or in a table, but it seems like the latter. In that case, you can jump straight to creating a sort of like a recommender system - see https://www.kaggle.com/code/alexanderstefanov/goodreads-search-engine/notebook
In the former case, I would use sentence transformers for semantic search.
from sentence_transformers import SentenceTransformer, util model = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1') query_embedding = model.encode('Volvo made in 2012 with a price of 10000') passage_embedding = model.encode(['Volvo made in 2022 with a price of 500000', 'Volvo made in 2010 with a price of 12000', 'Volvo made in 2013 with a price of 13337', 'London is known for manufacturing Volvos']) print("Similarity:", util.dot_score(query_embedding, passage_embedding)) Returns the following probs: 0.9150, 0.9557, 0.9385, 0.6478
