Update functions.py

functions.py

@@ -3,29 +3,6 @@ import numpy as np
 import pandas as pd
 import os
 
-def vector_similarity(x: list[float], y: list[float]) -> float:
-    """
-    Returns the similarity between two vectors.
-    
-    Because OpenAI Embeddings are normalized to length 1, the cosine similarity is the same as the dot product.
-    """
-    return np.dot(np.array(x), np.array(y))
-
-def select_document_section_by_query_similarity(query: str, contexts: dict[(str, str), np.array]) -> list[(float, (str, str))]:
-    """
-    Find the query embedding for the supplied query, and compare it against all of the pre-calculated document embeddings
-    to find the most relevant sections. 
-    
-    Return the list of document sections, sorted by relevance in descending order.
-    """
-    query_embedding = get_embedding(query)
-    
-    document_similarities = sorted([
-        (vector_similarity(query_embedding, doc_embedding), doc_index) for doc_index, doc_embedding in contexts.items()
-    ], reverse=True)
-    
-    return document_similarities[0]
-
 def construct_prompt(query: str, context_embeddings: dict, df: pd.DataFrame) -> tuple[str, str]:
     """
     Constructs a prompt for the language model based on the most relevant service description.