Update functions.py

functions.py

@@ -47,18 +47,41 @@ def select_document_section_by_query_similarity(query: str, contexts: dict[(str,
     
     return document_similarities[0]
 
-def construct_prompt(query: str, context_embeddings: dict, df: pd.DataFrame) -> str:
+def construct_prompt(query: str, context_embeddings: dict, df: pd.DataFrame) -> tuple[str, str]:
     """
-    Construct the prompt for the ChatCompletion API
+    Constructs a prompt for the language model based on the most relevant service description.
+
+    This function identifies the most relevant service by comparing the query with precomputed 
+    document embeddings. It then formats the prompt to include an introduction, the service 
+    description as context, and the user's question.
+
+    Parameters:
+        query (str): The user's input question.
+        context_embeddings (dict): A dictionary mapping service identifiers to their embeddings.
+        df (pd.DataFrame): A DataFrame containing service descriptions and links.
+
+    Returns:
+        tuple[str, str]: A tuple containing the formatted prompt and the associated service link.
     """
-    _ , chosen_service = select_document_section_by_query_similarity(query, context_embeddings)
+    # Select the most relevant service based on the query
+    _, chosen_service = select_document_section_by_query_similarity(query, context_embeddings)
         
+    # Format the service description and clean up newline characters
     service_description = df.loc[chosen_service].description.replace("\n", " ")
-    introduction = "Answer the question as truthfully as possible using the provided context, and if the answer is not contained within the text below, say "
-    introduction += "I could not find an answer to your question, please reach out to Helpdesk."
+    
+    # Construct the introduction and the full prompt
+    introduction = (
+        "Answer the question as truthfully as possible using the provided context. "
+        "If the answer is not contained within the text below, say: "
+        "'I could not find an answer to your question, please reach out to Helpdesk.'"
+    )
+    
     question = f"\n\nQ: {query}"
-    message = introduction + "\n* " + "\n\nContext:\n" + service_description + question
+    message = f"{introduction}\n* \n\nContext:\n{service_description}{question}"
+    
+    # Get the relevant service link
     link = df.loc[chosen_service].link
+    
     return message, link
 
 def answer_query_with_context(
@@ -87,4 +110,5 @@ def answer_query_with_context(
     end_message += """Helpdesk representatives are also available for a remote chat session during normal hours on Monday - Friday, 8:00 AM - 5:00 PM PST via https://helpdesk.hmc.edu"""
     reply = response["choices"][0]["message"]["content"] + end_message
 
-    return reply
+    return reply
+