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import streamlit as st
from langchain.chains import create_history_aware_retriever, create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_groq import ChatGroq
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import FAISS
import os
import tempfile
# Configurar o tema para dark
st.set_page_config(page_title="RAG Q&A Conversacional", layout="wide", initial_sidebar_state="expanded", page_icon="🤖", menu_items=None)
# Aplicar o tema dark
st.markdown("""
<style>
.stApp {
background-color: #0e1117;
color: #fafafa;
}
.stSidebar {
background-color: #262730;
}
.stButton>button {
color: #4F8BF9;
border-radius: 20px;
height: 3em;
width: 200px;
}
.stTextInput>div>div>input {
color: #4F8BF9;
}
</style>
""", unsafe_allow_html=True)
# Sidebar com orientações
st.sidebar.title("Menu")
st.sidebar.markdown("""
# ... [O conteúdo do sidebar permanece o mesmo] ...
""")
st.title("RAG conversacional com upload em PDF e histórico de bate-papo")
st.write("Carregue PDFs e converse com o conteúdo deles - aqui é usado o modelo de LLM Gemma2-9b-It e a plataforma de embeddings é all-MiniLM-L6-v2")
# Solicitar as chaves de API
groq_api_key = st.text_input("Insira sua chave de API Groq:", type="password")
huggingface_api_token = st.text_input("Insira seu token de API Hugging Face:", type="password")
if groq_api_key and huggingface_api_token:
# Configurar o token da API do Hugging Face
os.environ["HUGGINGFACEHUB_API_TOKEN"] = huggingface_api_token
# Inicializar o modelo de linguagem e embeddings
llm = ChatGroq(groq_api_key=groq_api_key, model_name="Gemma2-9b-It")
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
session_id = st.text_input("Session ID", value="default_session")
if 'store' not in st.session_state:
st.session_state.store = {}
uploaded_files = st.file_uploader("Faça o upload de um ou mais arquivos PDF: ", type="pdf", accept_multiple_files=True)
if uploaded_files:
documents = []
for uploaded_file in uploaded_files:
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
temp_file.write(uploaded_file.getvalue())
temp_file_path = temp_file.name
loader = PyPDFLoader(temp_file_path)
docs = loader.load()
documents.extend(docs)
os.unlink(temp_file_path) # Remove temporary file
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
splits = text_splitter.split_documents(documents)
# Create FAISS vector store
vectorstore = FAISS.from_documents(splits, embeddings)
st.success(f"Processed {len(splits)} document chunks.")
retriever = vectorstore.as_retriever()
contextualize_q_system_prompt = (
"Given a chat history and the latest user question "
"which might reference context in the chat history, "
"formulate a standalone question which can be understood "
"without the chat history. Do NOT answer the question, "
"just reformulate it if needed and otherwise return it as is."
)
contextualize_q_prompt = ChatPromptTemplate.from_messages([
("system", contextualize_q_system_prompt),
MessagesPlaceholder("chat_history"),
("human", "{input}"),
])
history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
system_prompt = (
"Você é um assistente para tarefas de resposta a perguntas. Responda em Português do Brasil a menos que seja pedido outro idioma"
"Use os seguintes pedaços de contexto recuperado para responder "
"à pergunta. Se você não sabe a resposta, diga que "
"não sabe. Use no máximo três frases e mantenha a "
"resposta concisa."
"\n\n"
"{context}"
)
qa_prompt = ChatPromptTemplate.from_messages([
("system", system_prompt),
MessagesPlaceholder("chat_history"),
("human", "{input}"),
])
question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
def get_session_history(session: str) -> BaseChatMessageHistory:
if session not in st.session_state.store:
st.session_state.store[session] = ChatMessageHistory()
return st.session_state.store[session]
conversational_rag_chain = RunnableWithMessageHistory(
rag_chain, get_session_history,
input_messages_key="input",
history_messages_key="chat_history",
output_messages_key="answer"
)
user_input = st.text_input("Sua pergunta:")
if user_input:
with st.spinner("Processando sua pergunta..."):
session_history = get_session_history(session_id)
response = conversational_rag_chain.invoke(
{"input": user_input},
config={"configurable": {"session_id": session_id}},
)
st.write("Assistente:", response['answer'])
with st.expander("Ver histórico do chat"):
for message in session_history.messages:
st.write(f"**{message.type}:** {message.content}")
else:
st.warning("Por favor, insira tanto a chave da API do Groq quanto o token da API do Hugging Face.")
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