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import logging
import os
from typing import Optional, Tuple
import gradio as gr
import pandas as pd
from buster.completers import Completion
from buster.utils import extract_zip
import cfg
from cfg import setup_buster
# Create a handler to control where log messages go (e.g., console, file)
handler = (
logging.StreamHandler()
) # Console output, you can change it to a file handler if needed
# Set the handler's level to INFO
handler.setLevel(logging.INFO)
logging.basicConfig(level=logging.INFO)
# Check if an openai key is set as an env. variable
if os.getenv("OPENAI_API_KEY") is None:
print(
"Warning: No openai key detected. You can set it with 'export OPENAI_API_KEY=sk-...'."
)
# Typehint for chatbot history
ChatHistory = list[list[Optional[str], Optional[str]]]
buster = setup_buster(cfg.buster_cfg)
def add_user_question(
user_question: str, chat_history: Optional[ChatHistory] = None
) -> ChatHistory:
"""Adds a user's question to the chat history.
If no history is provided, the first element of the history will be the user conversation.
"""
if chat_history is None:
chat_history = []
chat_history.append([user_question, None])
return chat_history
def format_sources(matched_documents: pd.DataFrame) -> str:
if len(matched_documents) == 0:
return ""
matched_documents.similarity_to_answer = (
matched_documents.similarity_to_answer * 100
)
# drop duplicate pages (by title), keep highest ranking ones
matched_documents = matched_documents.sort_values(
"similarity_to_answer", ascending=False
).drop_duplicates("title", keep="first")
documents_answer_template: str = "📝 Here are the sources I used to answer your question:\n\n{documents}\n\n{footnote}"
document_template: str = "[🔗 {document.title}]({document.url}), relevance: {document.similarity_to_answer:2.1f} %"
documents = "\n".join(
[
document_template.format(document=document)
for _, document in matched_documents.iterrows()
]
)
footnote: str = "I'm a bot 🤖 and not always perfect."
return documents_answer_template.format(documents=documents, footnote=footnote)
def add_sources(history, completion):
if completion.answer_relevant:
formatted_sources = format_sources(completion.matched_documents)
history.append([None, formatted_sources])
return history
def chat(chat_history: ChatHistory) -> Tuple[ChatHistory, Completion]:
"""Answer a user's question using retrieval augmented generation."""
# We assume that the question is the user's last interaction
user_input = chat_history[-1][0]
# Do retrieval + augmented generation with buster
completion = buster.process_input(user_input)
# Stream tokens one at a time to the user
chat_history[-1][1] = ""
for token in completion.answer_generator:
chat_history[-1][1] += token
yield chat_history, completion
demo = gr.Blocks()
with demo:
with gr.Row():
gr.Markdown("<h3><center>RAGTheDocs</center></h3>")
chatbot = gr.Chatbot()
with gr.Row():
question = gr.Textbox(
label="What's your question?",
placeholder="Type your question here...",
lines=1,
)
submit = gr.Button(value="Send", variant="secondary")
examples = gr.Examples(
examples=[
"How can I install the library?",
"How do I deal with noisy data?",
"How do I deal with noisy data in 2 words?",
],
inputs=question,
)
gr.Markdown(
"This application uses GPT to search the docs for relevant info and answer questions."
)
response = gr.State()
# fmt: off
submit.click(
add_user_question,
inputs=[question],
outputs=[chatbot]
).then(
chat,
inputs=[chatbot],
outputs=[chatbot, response]
).then(
add_sources,
inputs=[chatbot, response],
outputs=[chatbot]
)
question.submit(
add_user_question,
inputs=[question],
outputs=[chatbot],
).then(
chat,
inputs=[chatbot],
outputs=[chatbot, response]
).then(
add_sources,
inputs=[chatbot, response],
outputs=[chatbot]
)
# fmt: on
demo.queue(concurrency_count=16)
demo.launch(share=False)
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