arabic-RAG / app.py
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derek-thomas HF staff
Better MD messaging
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import logging
from functools import partial
from pathlib import Path
from time import perf_counter
import gradio as gr
from gradio_rich_textbox import RichTextbox
from jinja2 import Environment, FileSystemLoader
from transformers import AutoTokenizer
from backend.query_llm import check_endpoint_status, generate
from backend.semantic_search import retriever
proj_dir = Path(__file__).parent
# Setting up the logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Set up the template environment with the templates directory
env = Environment(loader=FileSystemLoader(proj_dir / 'templates'))
# Load the templates directly from the environment
template = env.get_template('template.j2')
template_html = env.get_template('template_html.j2')
# Initialize tokenizer
tokenizer = AutoTokenizer.from_pretrained('derek-thomas/jais-13b-chat-hf')
# Examples
examples = ['ู…ู† ูƒุงู† ุทุฑููŠ ู…ุนุฑูƒุฉ ุงูƒุชูŠูˆู… ุงู„ุจุญุฑูŠุฉุŸ',
'ู„ู… ุงู„ุณู…ุงุก ุฒุฑู‚ุงุกุŸ',
"ู…ู† ูุงุฒ ุจูƒุฃุณ ุงู„ุนุงู„ู… ู„ู„ุฑุฌุงู„ ููŠ ุนุงู… 2014ุŸ", ]
def add_text(history, text):
history = [] if history is None else history
history = history + [(text, None)]
return history, gr.Textbox(value="", interactive=False)
def bot(history, hyde=False):
top_k = 5
query = history[-1][0]
logger.warning('Retrieving documents...')
# Retrieve documents relevant to query
document_start = perf_counter()
if hyde:
hyde_document = generate(f"Write a wikipedia article intro paragraph to answer this query: {query}").split(
'### Response: [|AI|]')[-1]
logger.warning(hyde_document)
documents = retriever(hyde_document, top_k=top_k)
else:
documents = retriever(query, top_k=top_k)
document_time = perf_counter() - document_start
logger.warning(f'Finished Retrieving documents in {round(document_time, 2)} seconds...')
# Function to count tokens
def count_tokens(text):
return len(tokenizer.encode(text))
# Create Prompt
prompt = template.render(documents=documents, query=query)
# Check if the prompt is too long
token_count = count_tokens(prompt)
while token_count > 2048:
# Shorten your documents here. This is just a placeholder for the logic you'd use.
documents.pop() # Remove the last document
prompt = template.render(documents=documents, query=query) # Re-render the prompt
token_count = count_tokens(prompt) # Re-count tokens
prompt_html = template_html.render(documents=documents, query=query)
history[-1][1] = ""
response = generate(prompt)
history[-1][1] = response.split('### Response: [|AI|]')[-1]
return history, prompt_html
intro_md = """
# Arabic RAG
This is a project to demonstrate Retreiver Augmented Generation (RAG) in Arabic and English. It uses
[Arabic Wikipedia](https://ar.wikipedia.org/wiki) as a base to answer questions you have.
A retriever ([sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/discussions/8))
will find the articles relevant to your query and include them in a prompt so the reader ([core42/jais-13b-chat](https://huggingface.co/core42/jais-13b-chat))
can then answer your questions on it.
You can see the prompt clearly displayed below the chatbot to understand what is going to the LLM.
# Read this if you get an error
I'm using [Inference Endpoint's](https://huggingface.co/inference-endpoints)
[Scale to Zero](https://huggingface.co/docs/inference-endpoints/main/en/autoscaling#scaling-to-0) to save money on GPUs.
If the staus is "scaledToZero" click **Wake Up Endpoint** to wake it up. You will get an `error` and it will take
~4 minutes to wake up. This is expected, if you dont like it please give me a free GPU with enough VRAM.
"""
def process_example(text, history=[]):
history = history + [[text, None]]
return bot(history)
# hyde_prompt_html = gr.HTML()
with gr.Blocks() as demo:
gr.Markdown(intro_md)
endpoint_status = RichTextbox(check_endpoint_status, label="Inference Endpoint Status", every=1)
wakeup_endpoint = gr.Button('Click to Wake Up Endpoint')
with gr.Tab("Arabic-RAG"):
chatbot = gr.Chatbot(
[],
elem_id="chatbot",
avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg',
'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'),
bubble_full_width=False,
show_copy_button=True,
show_share_button=True,
)
with gr.Row():
txt = gr.Textbox(
scale=3,
show_label=False,
placeholder="Enter query in Arabic or English and press enter",
container=False,
)
txt_btn = gr.Button(value="Submit text", scale=1)
# gr.Examples(examples, txt)
prompt_html = gr.HTML()
gr.Examples(
examples=examples,
inputs=txt,
outputs=[chatbot, prompt_html],
fn=process_example,
cache_examples=True, )
# prompt_html.render()
# Turn off interactivity while generating if you click
txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
bot, chatbot, [chatbot, prompt_html])
# Turn it back on
txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
# Turn off interactivity while generating if you hit enter
txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
bot, chatbot, [chatbot, prompt_html])
# Turn it back on
txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
# Easy to turn this on when I want to
# with gr.Tab("Arabic-RAG + HyDE"):
# hyde_chatbot = gr.Chatbot(
# [],
# elem_id="chatbot",
# avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg',
# 'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'),
# bubble_full_width=False,
# show_copy_button=True,
# show_share_button=True,
# )
#
# with gr.Row():
# hyde_txt = gr.Textbox(
# scale=3,
# show_label=False,
# placeholder="Enter text and press enter",
# container=False,
# )
# hyde_txt_btn = gr.Button(value="Submit text", scale=1)
#
# hyde_prompt_html = gr.HTML()
# gr.Examples(
# examples=examples,
# inputs=hyde_txt,
# outputs=[hyde_chatbot, hyde_prompt_html],
# fn=process_example,
# cache_examples=True, )
# # prompt_html.render()
# # Turn off interactivity while generating if you click
# hyde_txt_msg = hyde_txt_btn.click(add_text, [hyde_chatbot, hyde_txt], [hyde_chatbot, hyde_txt],
# queue=False).then(
# partial(bot, hyde=True), [hyde_chatbot], [hyde_chatbot, hyde_prompt_html])
#
# # Turn it back on
# hyde_txt_msg.then(lambda: gr.Textbox(interactive=True), None, [hyde_txt], queue=False)
#
# # Turn off interactivity while generating if you hit enter
# hyde_txt_msg = hyde_txt.submit(add_text, [hyde_chatbot, hyde_txt], [hyde_chatbot, hyde_txt], queue=False).then(
# partial(bot, hyde=True), [hyde_chatbot], [hyde_chatbot, hyde_prompt_html])
#
# # Turn it back on
# hyde_txt_msg.then(lambda: gr.Textbox(interactive=True), None, [hyde_txt], queue=False)
wakeup_endpoint.click(partial(generate,'Wakeup'))
demo.queue()
demo.launch(debug=True)