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import gradio as gr
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.llms import HuggingFaceHub
# Configurações
EMBEDDING_MODEL = "sentence-transformers/all-mpnet-base-v2"
LLM_REPO_ID = "google/flan-t5-large" # Modelo de linguagem da Hugging Face
# Função para carregar e processar PDFs
def load_and_process_pdf(pdf_path):
# Carrega o PDF
loader = PyPDFLoader(pdf_path)
documents = loader.load()
# Divide o texto em chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = text_splitter.split_documents(documents)
# Cria embeddings e armazena no vetor store
embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
db = FAISS.from_documents(texts, embeddings)
return db
# Função para gerar respostas usando RAG
def generate_response(pdf_file, query):
if pdf_file is None:
return "Erro: Nenhum arquivo PDF foi carregado."
# Carrega e processa o PDF
db = load_and_process_pdf(pdf_file.name)
# Configura o modelo de linguagem
llm = HuggingFaceHub(repo_id=LLM_REPO_ID, model_kwargs={"temperature": 0.7, "max_length": 512})
# Cria a cadeia de RAG
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=db.as_retriever(search_kwargs={"k": 3}),
return_source_documents=True
)
# Executa a consulta
result = qa_chain({"query": query})
return result["result"]
# Interface Gradio
iface = gr.Interface(
fn=generate_response,
inputs=[
gr.File(label="Upload PDF", type="file"),
gr.Textbox(label="Sua Pergunta")
],
outputs=gr.Textbox(label="Resposta Gerada"),
title="Sistema de RAG com LangChain",
description="Faça upload de um PDF e faça perguntas sobre o conteúdo."
)
iface.launch(share=True) |