Delete generateResponse.py

generateResponse.py

@@ -1,44 +0,0 @@
-import ollama
-from typing import List, Optional, Dict
-
-N_RESULTS = 20 #10 sections was derived based on the segments of the BioModel. Based on tests conducted, 10 sections provided the most optimal output.
-def generateResponse(query_text: str, collection: Optional[Dict] = None) -> str:
-    """Generates a response to a query based on the Chroma database collection.
-
-    Args:
-        query_text (str): The query to search for.
-        collection (Optional[Dict]): The Chroma collection object to use for querying.
-
-    Returns:
-        str: The response generated from the query.
-    """
-    if collection is None:
-        raise ValueError("Collection is not provided")
-
-    # Query the embedding database for similar documents
-    query_results = collection.query(
-        query_texts=query_text,
-        n_results=N_RESULTS,
-    )
-
-    # Extract the best recommendations from the query results
-    best_recommendation = query_results.get('documents', [])
-
-    # Create the prompt for the ollama model
-    prompt_template = f"""Use the following pieces of context to answer the question at the end. If you don't know the answer, say so.
-
-    This is the piece of context necessary: {best_recommendation}
-
-    Cross-reference all pieces of context to define variables and other unknown entities. Calculate mathematical values based on provided matching variables. Remember previous responses if asked a follow-up question.
-
-    Question: {query_text}
-
-    """
-    response = ollama.generate(model="llama3", prompt=prompt_template)
-    final_response = response.get('response', 'No response generated')
-    return final_response
-
-#from createVectorDB import collection
-#query = "What protein interacts with ach2?"
-#result = generateResponse(query_text=query, collection=collection)
-#print("Response:", result)