Delete main_class.py

main_class.py

@@ -1,93 +0,0 @@
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_openai import OpenAIEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain_openai import ChatOpenAI
-from langchain.retrievers import ContextualCompressionRetriever
-from langchain.retrievers.document_compressors import LLMChainExtractor
-from langchain.tools.retriever import create_retriever_tool
-from langchain import hub
-from langchain.agents import AgentExecutor, create_openai_tools_agent
-import os
-import gradio as gr
-
-# The Agent retriever is based on: https://python.langchain.com/docs/use_cases/question_answering/conversational_retrieval_agents?ref=blog.langchain.dev
-# The chat history is based on: https://python.langchain.com/docs/use_cases/question_answering/chat_history
-# Inspired by https://github.com/Niez-Gharbi/PDF-RAG-with-Llama2-and-Gradio/tree/master
-# Inspired by https://github.com/mirabdullahyaser/Retrieval-Augmented-Generation-Engine-with-LangChain-and-Streamlit/tree/master
-
-
-class PDFChatBot:
-    # Initialize the class with the api_key and the model_name
-    def __init__(self, api_key):
-        self.processed = False
-        self.final_agent = None
-        self.chat_history = []
-        self.api_key = api_key
-        self.llm = ChatOpenAI(openai_api_key=self.api_key, temperature=0, model_name="gpt-3.5-turbo-0125")
-
-    # add  text to Gradio text block (not needed without Gradio)
-    def add_text(self, history, text):
-        if not text:
-            raise gr.Error("Please enter text.")
-        history.append((text, ''))
-        return history
-
-    # Load a pdf document with langchain textloader
-    def load_document(self, file_name):
-        loader = PyPDFLoader(file_name)
-        raw_document = loader.load()
-        return raw_document
-
-    # Split the document
-    def split_documents(self, raw_document):
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000,
-                                                       chunk_overlap=100,
-                                                       length_function=len,
-                                                       is_separator_regex=False,
-                                                       separators=["\n\n", "\n", " ", ""])
-        chunks = text_splitter.split_documents(raw_document)
-        return chunks
-
-    # Embed the document with OpenAI Embeddings & store it to vectorstore
-    def create_retriever(self, chunks):
-        embedding_func = OpenAIEmbeddings(openai_api_key=self.api_key)
-            # Create a new vectorstore from the chunks
-        vectorstore = FAISS.from_documents(chunks, embedding_func)
-
-        # Create a retriever
-        basic_retriever = vectorstore.as_retriever()
-        compressor = LLMChainExtractor.from_llm(self.llm)
-        compression_retriever = ContextualCompressionRetriever(base_compressor=compressor,
-                                                               base_retriever=basic_retriever)
-        return basic_retriever  # or compression_retriever
-
-    # Create an agent
-    def create_agent(self, retriever):
-        tool = create_retriever_tool(retriever,
-                                     f"search_document",
-                                     f"Searches and returns excerpts from the provided document.")
-        tools = [tool]
-        prompt = hub.pull("hwchase17/openai-tools-agent")
-        agent = create_openai_tools_agent(self.llm, tools, prompt)
-        self.final_agent = AgentExecutor(agent=agent, tools=tools)
-
-    #Process files
-    def process_file(self, file_name):
-        documents = self.load_document(file_name)
-        texts = self.split_documents(documents)
-        db = self.create_retriever(texts)
-        self.create_agent(db)
-        print("Files successfully processed")
-
-    # Generate a response and write to memory
-    def generate_response(self, history, query, path):
-        if not self.processed:
-            self.process_file(path)
-            self.processed = True
-        result = self.final_agent.invoke({'input': query, 'chat_history': self.chat_history})['output']
-        self.chat_history.extend((query, result))
-        for char in result: # history argument and the subsequent code is only for the purpose of Gradio
-            history[-1][1] += char
-        return history, " "
-