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# coding=utf8 | |
from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper | |
from langchain import OpenAI | |
import gradio as gr | |
import sys | |
import os | |
os.environ["OPENAI_API_KEY"] = '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' | |
def construct_index(directory_path): | |
max_input_size = 4096 | |
num_outputs = 512 | |
max_chunk_overlap = 20 | |
chunk_size_limit = 600 | |
prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) | |
llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.7, model_name="text-davinci-003", max_tokens=num_outputs)) | |
documents = SimpleDirectoryReader(directory_path).load_data() | |
index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper) | |
index.save_to_disk('index.json') | |
return index | |
def chatbot(input_text): | |
index = GPTSimpleVectorIndex.load_from_disk('index.json') | |
response = index.query(input_text, response_mode="compact") | |
return response.response | |
iface = gr.Interface(fn=chatbot, | |
inputs=gr.inputs.Textbox(lines=3, label="O quê gostaria de saber?"), | |
outputs=gr.inputs.Textbox(lines=3, label="Veja o que encontrei"), | |
title="CD2 IA") | |
index = construct_index("docs") | |
iface.launch(share=True) |