shukdevdatta123 commited on
Commit
e0db20e
·
verified ·
1 Parent(s): f104975

Update generate_answer.py

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Files changed (1) hide show
  1. generate_answer.py +8 -9
generate_answer.py CHANGED
@@ -1,5 +1,3 @@
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- ### generate_answer.py
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-
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  import os
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  from glob import glob
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  import openai
@@ -17,16 +15,15 @@ from langchain.memory import ConversationBufferMemory
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  load_dotenv()
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  api_key = os.getenv("OPENAI_API_KEY")
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- # Corrected line: Set the OpenAI API key correctly
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  openai.api_key = api_key
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  def base_model_chatbot(messages):
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  system_message = [
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- {"role": "system", "content": "You are an helpful AI chatbot, that answers questions asked by User."}
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  ]
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  messages = system_message + messages
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  response = openai.ChatCompletion.create(
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- model="gpt-3.5-turbo", # Ensure the model is specified correctly
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  messages=messages
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  )
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  return response.choices[0].message['content']
@@ -60,7 +57,11 @@ class ConversationalRetrievalChain:
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  self.temperature = temperature
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  def create_chain(self):
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- model = ChatOpenAI(model_name=self.model_name, temperature=self.temperature)
 
 
 
 
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  memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
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  vector_db = VectorDB('docs/')
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  retriever = vector_db.create_vector_db().as_retriever(search_type="similarity", search_kwargs={"k": 2})
@@ -71,9 +72,7 @@ class ConversationalRetrievalChain:
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  )
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  def with_pdf_chatbot(messages):
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- """Main function to execute the QA system."""
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  query = messages[-1]['content'].strip()
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-
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  qa_chain = ConversationalRetrievalChain().create_chain()
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  result = qa_chain({"query": query})
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- return result['result']
 
 
 
1
  import os
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  from glob import glob
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  import openai
 
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  load_dotenv()
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  api_key = os.getenv("OPENAI_API_KEY")
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  openai.api_key = api_key
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  def base_model_chatbot(messages):
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  system_message = [
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+ {"role": "system", "content": "You are a helpful AI chatbot that provides clear, complete, and coherent responses to User's questions. Ensure your answers are in full sentences."}
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  ]
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  messages = system_message + messages
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  response = openai.ChatCompletion.create(
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+ model="gpt-3.5-turbo",
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  messages=messages
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  )
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  return response.choices[0].message['content']
 
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  self.temperature = temperature
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  def create_chain(self):
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+ model = ChatOpenAI(
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+ model_name=self.model_name,
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+ temperature=self.temperature,
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+ system_prompt="You are a knowledgeable AI that answers questions based on provided documents. Always give responses in clear, complete sentences."
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+ )
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  memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
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  vector_db = VectorDB('docs/')
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  retriever = vector_db.create_vector_db().as_retriever(search_type="similarity", search_kwargs={"k": 2})
 
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  )
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  def with_pdf_chatbot(messages):
 
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  query = messages[-1]['content'].strip()
 
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  qa_chain = ConversationalRetrievalChain().create_chain()
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  result = qa_chain({"query": query})
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+ return result['result']