Spaces:
Runtime error
Runtime error
File size: 4,437 Bytes
ac493ec |
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 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 |
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)
|