chat_proteomica / app.py
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versão utilizando rag avançado
91c106b
import os
from dotenv import load_dotenv
from langchain_openai import OpenAIEmbeddings
from langchain_qdrant import QdrantVectorStore
from model.chat_agent import ChatAgent
import streamlit as st
load_dotenv(dotenv_path=".env", override=True)
# Interface do ChatBot
st.title('Chat LAMFO x Proteômica')
st.logo('./assets/logo-lamfo.png', size='large')
input = st.chat_input("Digite sua pergunta")
# Configuração ChatBot
# if "messages_for_model" not in st.session_state:
# st.session_state.messages_for_model = []
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
if message["role"] != "system":
with st.chat_message(message["role"]):
st.markdown(message["content"])
def tratar_referencias(source_documents):
texto = "\n\nReferências: \n"
referencias = {}
for source_document in source_documents:
referencia = os.path.splitext(os.path.basename(source_document.metadata['source']))[0]
if referencia not in referencias:
texto += f"* {os.path.splitext(os.path.basename(source_document.metadata['source']))[0]} \n"
referencias[referencia] = True
return texto
embeddings = OpenAIEmbeddings(model="text-embedding-3-large", api_key=os.getenv('OPENAI_KEY'))
vector_store = QdrantVectorStore.from_existing_collection(
url=os.getenv('QDRANT_URL'),
api_key=os.getenv('QDRANT_KEY'),
embedding=embeddings,
collection_name='proteomica',
)
agent = ChatAgent(vector_store=vector_store)
if input:
st.chat_message("user").markdown(input)
st.session_state.messages.append({"role": "user", "content": input})
response = agent.send_message(input)
# st.session_state.messages_for_model.append({"role": "user", "content": prompt})
response["result"] += tratar_referencias(response["source_documents"])
st.session_state.messages.append({"role": "assistant", "content": response["result"]})
with st.chat_message("assistant"):
st.markdown(response["result"])