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Delete generateResponse.py

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  1. generateResponse.py +0 -44
generateResponse.py DELETED
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- import ollama
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- from typing import List, Optional, Dict
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-
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- 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.
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- def generateResponse(query_text: str, collection: Optional[Dict] = None) -> str:
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- """Generates a response to a query based on the Chroma database collection.
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-
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- Args:
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- query_text (str): The query to search for.
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- collection (Optional[Dict]): The Chroma collection object to use for querying.
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-
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- Returns:
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- str: The response generated from the query.
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- """
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- if collection is None:
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- raise ValueError("Collection is not provided")
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-
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- # Query the embedding database for similar documents
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- query_results = collection.query(
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- query_texts=query_text,
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- n_results=N_RESULTS,
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- )
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-
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- # Extract the best recommendations from the query results
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- best_recommendation = query_results.get('documents', [])
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-
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- # Create the prompt for the ollama model
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- 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.
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-
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- This is the piece of context necessary: {best_recommendation}
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-
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- 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.
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-
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- Question: {query_text}
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-
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- """
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- response = ollama.generate(model="llama3", prompt=prompt_template)
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- final_response = response.get('response', 'No response generated')
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- return final_response
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-
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- #from createVectorDB import collection
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- #query = "What protein interacts with ach2?"
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- #result = generateResponse(query_text=query, collection=collection)
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- #print("Response:", result)