AashitaK commited on
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84bfec9
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1 Parent(s): f48549c

Update functions.py

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  1. functions.py +30 -13
functions.py CHANGED
@@ -7,8 +7,7 @@ import os
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  openai.api_key = os.getenv("OPENAI_API_KEY")
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  COMPLETIONS_MODEL = "gpt-4o-mini"# "gpt-3.5-turbo-instruct" used earlier .
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- # EMBEDDING_MODEL = "text-embedding-3-small"
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- EMBEDDING_MODEL = "text-embedding-3-large"
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  COMPLETIONS_API_PARAMS = {
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  # We use temperature of 0.0 because it gives the most predictable, factual answer.
@@ -90,13 +89,26 @@ def answer_query_with_context(
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  document_embeddings: dict[(str, str), np.array],
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  show_prompt: bool = False
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  ) -> str:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- prompt, link = construct_prompt(
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- query,
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- document_embeddings,
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- df
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- )
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-
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  response = openai.ChatCompletion.create(
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  model = COMPLETIONS_MODEL, # "gpt-3.5-turbo",
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  messages=[
@@ -105,10 +117,15 @@ def answer_query_with_context(
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  ]
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  )
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- end_message = "\n\nPlease check out the relevant HMC service catalogue for more details: "+ link
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- end_message += """\n\nIf not satisfied with the answer, please email helpdesk@hmc.edu, call 909.607.7777 or visit the Helpdesk located on the Sprague first floor. """
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- 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"""
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- reply = response["choices"][0]["message"]["content"] + end_message
 
 
 
 
 
 
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  return reply
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-
 
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  openai.api_key = os.getenv("OPENAI_API_KEY")
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  COMPLETIONS_MODEL = "gpt-4o-mini"# "gpt-3.5-turbo-instruct" used earlier .
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+ EMBEDDING_MODEL = "text-embedding-3-large" # "text-embedding-3-small"
 
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  COMPLETIONS_API_PARAMS = {
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  # We use temperature of 0.0 because it gives the most predictable, factual answer.
 
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  document_embeddings: dict[(str, str), np.array],
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  show_prompt: bool = False
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  ) -> str:
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+ """
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+ Generates a response to a user's query using the most relevant service description.
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+
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+ This function constructs a prompt by attaching the most relevant service description to the user's query,
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+ sends this prompt to the language model to generate a response, and appends additional service-related
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+ information to this response.
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+
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+ Parameters:
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+ query (str): The user's input question.
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+ df (pd.DataFrame): A DataFrame containing service descriptions and links.
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+ document_embeddings (dict): A dictionary mapping service identifiers to their embeddings.
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+ show_prompt (bool, optional): If True, displays the constructed prompt (for debugging). Defaults to False.
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+
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+ Returns:
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+ str: The final response from the chatbot, including the generated answer and additional service details.
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+ """
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+ # Construct the prompt and retrieve the service link
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+ prompt, link = construct_prompt(query, document_embeddings, df)
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+ # Get the response from the language model
 
 
 
 
 
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  response = openai.ChatCompletion.create(
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  model = COMPLETIONS_MODEL, # "gpt-3.5-turbo",
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  messages=[
 
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  ]
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  )
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+ # Append additional service-related information
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+ end_message = (
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+ f"\n\nPlease check out the relevant HMC service catalogue for more details: {link} "
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+ "\n\nIf not satisfied with the answer, please email helpdesk@hmc.edu, call 909.607.7777, "
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+ "or visit the Helpdesk located on the Sprague first floor. "
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+ "Helpdesk representatives are also available for a remote chat session during normal hours (Monday - Friday, "
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+ "8:00 AM - 5:00 PM PST) via https://helpdesk.hmc.edu"
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+ )
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+
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+ reply = response + end_message
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  return reply