removed debugging messages now that search is working

app.py

@@ -23,8 +23,6 @@ tfidf_query_docs = create_tfidf_search_function(
     vectorizer_path = tfidf_vectorizer_path,
     model_name = "facebook/fasttext-en-vectors")
 
-print("generated the search functions!")
-
 app = FastAPI()
 
 

src/embeddings_search.py

@@ -50,7 +50,6 @@ def sbert_query_factory(corpus_embeddings_df: pl.DataFrame, model: SentenceTrans
     Returns:
         Callable[[str], pl.DataFrame]: Function to compare the query string to the corpus and return results sorted by the cosine similarity.
     """
-    print("starting factory")
 
     def do_sbert_query(query: str) -> pl.DataFrame:
         """
@@ -127,13 +126,13 @@ def create_embeddings_search_function_from_embeddings_df(model_name: str, embedd
         Callable[[str], pl.DataFrame]: Function to compare the query string to the corpus and return results sorted by the cosine similarity.
 
     """
-    print("starting to build embeddings search")
+
     # Instantiate the sentence-transformer model:
     sentence_model = SentenceTransformer(model_name).to(device = device)
-    print("instantiated sentence-transformers model")
+
     # import the embeddings CSVs
     block_embeddings_df = pl.read_parquet(embeddings_df_path)
-    print("read the embeddings to a data frame")
+
     # call the factory to make the search function and return it
     return sbert_query_factory(corpus_embeddings_df = block_embeddings_df, model = sentence_model)