|
from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper |
|
from langchain.chat_models import ChatOpenAI |
|
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=ChatOpenAI(temperature=0.5, model_name="gpt-3.5-turbo", 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.components.Textbox(lines=7, label="Enter your text"), |
|
outputs="text", |
|
title="Custom-trained AI Chatbot") |
|
|
|
|
|
index = construct_index("docs") |
|
|
|
|
|
iface.launch() |
