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import streamlit as st | |
from llama_index import SimpleDirectoryReader, GPTVectorStoreIndex | |
#from langchain import OpenAI | |
import os | |
#import shutil | |
def ingest(docs_dir): | |
documents = SimpleDirectoryReader(docs_dir).load_data() | |
index = GPTVectorStoreIndex.from_documents(documents) | |
return index | |
def get_answer(index, message): | |
response = query(index, message) | |
return [('Chatbot', ''.join(response.response))] | |
def query(index, query_text): | |
query_engine = index.as_query_engine() | |
response = query_engine.query(query_text) | |
return response | |
os.environ['OPENAI_API_KEY'] = "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" | |
if os.environ['OPENAI_API_KEY']: | |
# Initialize chatbot history | |
chatbot = [] | |
index = ingest('temp_docs') | |
# Display message input component | |
message = st.text_input('Ingrese su consulta') | |
# If message is entered, ingest documents and get chatbot response | |
if message: | |
chatbot.append(('You', message)) | |
chatbot += get_answer(index, message) | |
# Display chat history | |
st.text_area('Chatbot:', value='\n'.join( | |
[f'{x[0]}: {x[1]}' for x in chatbot]), height=250) | |