rag_functions.py

# 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