Shreyas094 commited on
Commit
b5f8745
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1 Parent(s): 8ac5745

Update app.py

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Files changed (1) hide show
  1. app.py +12 -29
app.py CHANGED
@@ -628,41 +628,24 @@ def rephrase_for_search(query, model):
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  rephrase_prompt = PromptTemplate(
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  input_variables=["query"],
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  template="""
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- Rephrase the following conversational query into a concise, search-engine-friendly format.
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- Maintain key terms and add relevant context if necessary.
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- Provide ONLY the rephrased query without any explanation or additional text.
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-
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  Conversational query: {query}
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-
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- Rephrased query:"""
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  )
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  chain = LLMChain(llm=model, prompt=rephrase_prompt)
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  response = chain.run(query=query).strip()
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- # Extract only the rephrased query using regex
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- match = re.search(r'^(.*?)(?:\n|$)', response)
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- if match:
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- rephrased_query = match.group(1).strip()
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- else:
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- rephrased_query = response.strip()
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-
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- # Remove any "Rephrased query:" prefix if present
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- rephrased_query = re.sub(r'^Rephrased query:\s*', '', rephrased_query, flags=re.IGNORECASE)
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-
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- # Check if the rephrased query is actually a rephrasing and not the original prompt or instructions
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- if (rephrased_query.lower().startswith(("rephrase", "your task")) or
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- len(rephrased_query.split()) > len(query.split()) * 2):
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- # If it's not a proper rephrasing, use a more sophisticated keyword extraction
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- keywords = query.lower()
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- # Remove common stop words and question words
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- stop_words = {'how', 'did', 'the', 'in', 'a', 'an', 'and', 'or', 'but', 'is', 'are', 'was', 'were', 'perform'}
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- keywords = ' '.join(word for word in keywords.split() if word not in stop_words)
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- # Ensure important terms are included
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- if 'kkr' not in keywords:
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- keywords = 'kkr ' + keywords
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- if 'q1 2024' not in keywords:
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- keywords += ' q1 2024'
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  return keywords
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  return rephrased_query
 
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  rephrase_prompt = PromptTemplate(
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  input_variables=["query"],
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  template="""
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+ Your task is to rephrase the given conversational query into a concise, search-engine-friendly format.
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+ Remove any conversational elements and focus on the core information need.
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+
 
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  Conversational query: {query}
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+
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+ Rephrased query: """
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  )
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  chain = LLMChain(llm=model, prompt=rephrase_prompt)
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  response = chain.run(query=query).strip()
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+ # Extract only the rephrased query, ignoring any explanations
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+ rephrased_query = response.split('\n')[0].strip()
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+
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+ # Check if the rephrased query is actually a rephrasing and not the original prompt
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+ if rephrased_query.lower().startswith("rephrase") or len(rephrased_query) > len(query) * 2:
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+ # If it's not a proper rephrasing, use a simple keyword extraction
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+ keywords = ' '.join(query.lower().split())
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  return keywords
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  return rephrased_query