Delete generate_answer.py

generate_answer.py

@@ -1,95 +0,0 @@
-import os
-from glob import glob
-import openai
-from dotenv import load_dotenv
-
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import Chroma
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-
-from langchain_community.chat_models import ChatOpenAI
-from langchain.chains import RetrievalQA
-from langchain.memory import ConversationBufferMemory
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-openai.api_key = api_key
-
-# Helper function to validate response completeness
-def is_response_complete(response: str) -> bool:
-    return response.strip()[-1] in ".!?"
-
-# Retry mechanism for incomplete responses
-def retry_response(messages):
-    response = openai.ChatCompletion.create(
-        model="gpt-4o-mini",
-        messages=messages
-    ).choices[0].message['content']
-    if not is_response_complete(response):
-        response += " This is the end of the response. Please let me know if you need further clarification."
-    return response
-
-def base_model_chatbot(messages):
-    system_message = [
-        {"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 and complete the thought or idea."}
-    ]
-    messages = system_message + messages
-    response = openai.ChatCompletion.create(
-        model="gpt-4o-mini",
-        messages=messages
-    ).choices[0].message['content']
-    # Validate response completeness
-    if not is_response_complete(response):
-        response = retry_response(messages)
-    return response
-
-class VectorDB:
-    """Class to manage document loading and vector database creation."""
-    
-    def __init__(self, docs_directory: str):
-        self.docs_directory = docs_directory
-
-    def create_vector_db(self):
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-
-        files = glob(os.path.join(self.docs_directory, "*.pdf"))
-
-        loadPDFs = [PyPDFLoader(pdf_file) for pdf_file in files]
-
-        pdf_docs = list()
-        for loader in loadPDFs:
-            pdf_docs.extend(loader.load())
-        chunks = text_splitter.split_documents(pdf_docs)
-            
-        return Chroma.from_documents(chunks, OpenAIEmbeddings()) 
-    
-class ConversationalRetrievalChain:
-    """Class to manage the QA chain setup."""
-
-    def __init__(self, model_name="gpt-3.5-turbo", temperature=0):
-        self.model_name = model_name
-        self.temperature = temperature
-
-    def create_chain(self):
-        model = ChatOpenAI(
-            model_name=self.model_name, 
-            temperature=self.temperature,
-            system_prompt="You are a knowledgeable AI that answers questions based on provided documents. Always give responses in clear, complete sentences."
-        )
-        memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-        vector_db = VectorDB('docs/')
-        retriever = vector_db.create_vector_db().as_retriever(search_type="similarity", search_kwargs={"k": 2})
-        return RetrievalQA.from_chain_type(
-            llm=model,
-            retriever=retriever,
-            memory=memory,
-        )
-    
-def with_pdf_chatbot(messages):
-    query = messages[-1]['content'].strip()
-    qa_chain = ConversationalRetrievalChain().create_chain()
-    result = qa_chain({"query": query})
-    if not is_response_complete(result['result']):
-        result['result'] += " This is the end of the response. Let me know if you need further clarification."
-    return result['result']