File size: 1,639 Bytes
8218847 14b8627 8218847 3723c07 14a5645 8218847 e6f0bd4 bb44bc1 8218847 14b8627 8218847 b603970 8218847 73151db | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | 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=0.7, 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):
predetermined_text = "I want you to take the statement at the start of this query and answer it using information contained in documents in the 'docs' directory. I want you to answer as a highly experienced insurance industry expert."
input_text = input_text + predetermined_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="What would you like to know about insurance?"),
outputs="text",
title="AI Loss Adjuster")
index = construct_index("docs")
iface.launch()
|