Delete rag_functions.py

rag_functions.py

@@ -1,193 +0,0 @@
-# rag_functions.py
-import os
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
-from langchain.chains import ConversationalRetrievalChain
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.llms import HuggingFaceEndpoint
-from langchain.memory import ConversationBufferMemory
-from pathlib import Path
-import chromadb
-from unidecode import unidecode
-import re
-
-# Lista de modelos LLM disponíveis
-list_llm = [
-    "mistralai/Mistral-7B-Instruct-v0.2",
-    "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "mistralai/Mistral-7B-Instruct-v0.1",
-    "google/gemma-7b-it",
-    "google/gemma-2b-it",
-    "HuggingFaceH4/zephyr-7b-beta",
-    "HuggingFaceH4/zephyr-7b-gemma-v0.1",
-    "meta-llama/Llama-2-7b-chat-hf",
-    "microsoft/phi-2",
-    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
-    "mosaicml/mpt-7b-instruct",
-    "tiiuae/falcon-7b-instruct",
-    "google/flan-t5-xxl"
-]
-list_llm_simple = [os.path.basename(llm) for llm in list_llm]
-
-# Função para carregar documentos PDF e dividir em chunks
-def load_doc(list_file_path, chunk_size, chunk_overlap):
-    loaders = [PyPDFLoader(x) for x in list_file_path]
-    pages = []
-    for loader in loaders:
-        pages.extend(loader.load())
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=chunk_size,
-        chunk_overlap=chunk_overlap
-    )
-    doc_splits = text_splitter.split_documents(pages)
-    return doc_splits
-
-# Função para criar o banco de dados vetorial
-def create_db(splits, collection_name):
-    embedding = HuggingFaceEmbeddings()
-    # Usando PersistentClient para persistir o banco de dados
-    new_client = chromadb.PersistentClient(path="./chroma_db")
-    vectordb = Chroma.from_documents(
-        documents=splits,
-        embedding=embedding,
-        client=new_client,
-        collection_name=collection_name,
-    )
-    return vectordb
-
-# Função para inicializar a cadeia de QA com o modelo LLM
-def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=None):
-    if progress:
-        progress(0.1, desc="Inicializando tokenizer da HF...")
-        progress(0.5, desc="Inicializando Hub da HF...")
-    if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model,
-            temperature=temperature,
-            max_new_tokens=max_tokens,
-            top_k=top_k,
-            load_in_8bit=True,
-        )
-    elif llm_model in ["HuggingFaceH4/zephyr-7b-gemma-v0.1", "mosaicml/mpt-7b-instruct"]:
-        raise ValueError("O modelo LLM é muito grande para ser carregado automaticamente no endpoint de inferência gratuito")
-    elif llm_model == "microsoft/phi-2":
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model,
-            temperature=temperature,
-            max_new_tokens=max_tokens,
-            top_k=top_k,
-            trust_remote_code=True,
-            torch_dtype="auto",
-        )
-    elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0":
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model,
-            temperature=temperature,
-            max_new_tokens=250,
-            top_k=top_k,
-        )
-    elif llm_model == "meta-llama/Llama-2-7b-chat-hf":
-        raise ValueError("O modelo Llama-2-7b-chat-hf requer uma assinatura Pro...")
-    else:
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model,
-            temperature=temperature,
-            max_new_tokens=max_tokens,
-            top_k=top_k,
-        )
-
-    if progress:
-        progress(0.75, desc="Definindo memória de buffer...")
-    memory = ConversationBufferMemory(
-        memory_key="chat_history",
-        output_key='answer',
-        return_messages=True
-    )
-    retriever = vector_db.as_retriever()
-    if progress:
-        progress(0.8, desc="Definindo cadeia de recuperação...")
-    qa_chain = ConversationalRetrievalChain.from_llm(
-        llm,
-        retriever=retriever,
-        chain_type="stuff",
-        memory=memory,
-        return_source_documents=True,
-        verbose=False,
-    )
-    if progress:
-        progress(0.9, desc="Concluído!")
-    return qa_chain
-
-# Função para gerar um nome de coleção válido
-def create_collection_name(filepath):
-    collection_name = Path(filepath).stem
-    collection_name = collection_name.replace(" ", "-")
-    collection_name = unidecode(collection_name)
-    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
-    collection_name = collection_name[:50]
-    if len(collection_name) < 3:
-        collection_name = collection_name + 'xyz'
-    if not collection_name[0].isalnum():
-        collection_name = 'A' + collection_name[1:]
-    if not collection_name[-1].isalnum():
-        collection_name = collection_name[:-1] + 'Z'
-    print('Caminho do arquivo: ', filepath)
-    print('Nome da coleção: ', collection_name)
-    return collection_name
-
-# Função para inicializar o banco de dados
-def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=None):
-    list_file_path = [x.name for x in list_file_obj if x is not None]
-    if progress:
-        progress(0.1, desc="Criando nome da coleção...")
-    collection_name = create_collection_name(list_file_path[0])
-    if progress:
-        progress(0.25, desc="Carregando documento...")
-    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
-    if progress:
-        progress(0.5, desc="Gerando banco de dados vetorial...")
-    vector_db = create_db(doc_splits, collection_name)
-    if progress:
-        progress(0.9, desc="Concluído!")
-    return vector_db, collection_name, "Completo!"
-
-# Função para inicializar o modelo LLM
-def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=None):
-    llm_name = list_llm[llm_option]
-    print("Nome do LLM: ", llm_name)
-    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
-    return qa_chain, "Completo!"
-
-# Função para formatar o histórico de conversa
-def format_chat_history(message, chat_history):
-    formatted_chat_history = []
-    for user_message, bot_message in chat_history:
-        formatted_chat_history.append(f"Usuário: {user_message}")
-        formatted_chat_history.append(f"Assistente: {bot_message}")
-    return formatted_chat_history
-
-# Função para realizar a conversa com o chatbot
-def conversation(qa_chain, message, history):
-    formatted_chat_history = format_chat_history(message, history)
-    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
-    response_answer = response["answer"]
-    if response_answer.find("Resposta útil:") != -1:
-        response_answer = response_answer.split("Resposta útil:")[-1]
-    response_sources = response["source_documents"]
-    response_source1 = response_sources[0].page_content.strip()
-    response_source2 = response_sources[1].page_content.strip()
-    response_source3 = response_sources[2].page_content.strip()
-    response_source1_page = response_sources[0].metadata["page"] + 1
-    response_source2_page = response_sources[1].metadata["page"] + 1
-    response_source3_page = response_sources[2].metadata["page"] + 1
-    new_history = history + [(message, response_answer)]
-    return qa_chain, "", new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
-
-# Função para carregar arquivos
-def upload_file(file_obj):
-    list_file_path = []
-    for idx, file in enumerate(file_obj):
-        file_path = file_obj.name
-        list_file_path.append(file_path)
-    return list_file_path