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from functools import cache
import os
import time
import gradio as gr
from langchain.llms import OpenAI
from langchain.chains.summarize import load_summarize_chain
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chains.question_answering import load_qa_chain
import tiktoken
@cache
def tiktoken_len_builder(model_name):
tokenizer = tiktoken.encoding_for_model(model_name)
def token_len(text):
tokens = tokenizer.encode(text, disallowed_special=())
return len(tokens)
return token_len
def split_documents(docs, length_function, chunk_size=400):
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=20,
length_function=length_function,
)
return text_splitter.split_documents(docs)
def summarize_docs(llm, docs):
llm = OpenAI(temperature=temperature, openai_api_key=openai_api_key, model_name=model_name)
chain = load_summarize_chain(llm, chain_type="map_reduce")
return chain.run(docs)
class MdnaQA:
def __init__(self, llm, docs):
self.docs = docs
self.chain = load_qa_chain(llm, chain_type="stuff")
embeddings = OpenAIEmbeddings()
self.docsearch = Chroma.from_documents(docs, embeddings)
def ask(self, question):
input_documents = self.docsearch.similarity_search(question)
return self.chain.run(input_documents=input_documents, question=question)
filename = '2023-05-12_2023_q1_goog_mdna.txt'
loader = TextLoader(filename)
documents = loader.load()
model_name = "text-davinci-003"
tiktoken_len = tiktoken_len_builder(model_name)
docs = split_documents(documents, tiktoken_len)
tokens_sum = sum(tiktoken_len(d.page_content) for d in docs)
title = "Alphabet's Q1 2023 10-Q MD&A"
with gr.Blocks(title=title) as demo:
gr.Markdown(f'# {title}')
gr.Markdown('Video tutorial ')
gr.Markdown('Link to a blog post the video tutorial')
gr.Markdown("You can an API key [from OpenAI](https://platform.openai.com/account/api-keys)")
openai_api_key = gr.Text(
value=os.getenv("OPENAI_API_KEY"),
type="password",
label="OpenAI API key",
)
temperature = gr.Slider(0, 2, value=0, step=0.1, label="Temperature", info="adjusts a model's output from predictable to random")
mdna = gr.State(docs)
tokens_total = gr.Textbox(label="Total input tokens", value=tokens_sum, info='how many tokens will be spent on input / embeddings')
with gr.Tabs(visible=True) as tabs:
with gr.TabItem("Summary"):
summarize = gr.Button("Summarize MD&A", variant='primary', info='On click you spent tokens on input, instructions and output')
summary = gr.TextArea(label='Summary')
def summarize_mdna(docs, api_key, temp):
llm = OpenAI(temperature=temp, openai_api_key=api_key)
# mdna_summary = summarize_docs(llm, docs)
return 'HaHa'
summarize.click(summarize_mdna, inputs=[mdna, openai_api_key, temperature], outputs=[summary])
with gr.TabItem("QA with MD&A"):
start_qa = gr.Button("Start QA with MD&A", variant='primary')
chatbot = gr.Chatbot(label="QA with MD&A", visible=False)
question = gr.Textbox(
label="Your question", interactive=True, visible=False
)
qa_chat = gr.State()
send = gr.Button("Ask question", variant='primary', visible=False)
def start_chat(docs, openai_api_key):
# qa_chat = MdnaQA(docs, openai_api_key)
qa_chat = MDNAQAMock()
return (
qa_chat,
gr.Textbox.update(visible=True),
gr.Textbox.update(visible=True),
gr.Button.update(visible=True)
)
start_qa.click(
start_chat, [openai_api_key], [qa_chat, chatbot, question, send]
)
def respond(qa_chat, question, chat_history):
answer = qa_chat.ask(question)
chat_history.append((question, answer))
time.sleep(3)
return "", chat_history
send.click(respond, [qa_chat, question, chatbot], [question, chatbot])
question.submit(
respond, [qa_chat, question, chatbot], [question, chatbot]
)
demo.launch()
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