Sergei Petrov
initial
85d46c2
import subprocess
subprocess.run(["pip", "install", "--upgrade", "transformers[torch,sentencepiece]==4.34.1"])
import logging
from pathlib import Path
from time import perf_counter
import gradio as gr
from jinja2 import Environment, FileSystemLoader
from backend.query_llm import generate_hf, generate_openai
from backend.semantic_search import table, retriever, ranker
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')
# Examples
examples = ['What is the capital of China?',
'Why is the sky blue?',
'Who won the mens world cup in 2014?', ]
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, use_ranker, api_kind):
top_k_rank = 4
top_k_retrieve = 40
query = history[-1][0]
if not query:
gr.Warning("Please submit a non-empty string as a prompt")
raise ValueError("Empty string was submitted")
logger.warning('Retrieving documents...')
# Retrieve documents relevant to query
document_start = perf_counter()
if use_ranker:
query_vec = retriever.encode(query)
documents = table.search(query_vec, vector_column_name="embedding").limit(top_k_retrieve).to_list()
documents = [doc["text"] for doc in documents]
pairs = [(query, doc) for doc in documents]
scores = ranker.predict(pairs, batch_size=8)
documents = [doc for _, doc in sorted(zip(scores, documents))[-top_k_rank:]]
else:
query_vec = retriever.encode(query)
documents = table.search(query_vec, vector_column_name="embedding").limit(top_k_rank).to_list()
documents = [doc["text"] for doc in documents]
document_time = perf_counter() - document_start
logger.warning(f'Finished Retrieving documents in {round(document_time, 2)} seconds...')
# Create Prompt
prompt = template.render(documents=documents, query=query)
prompt_html = template_html.render(documents=documents, query=query)
if api_kind == "HuggingFace":
generate_fn = generate_hf
elif api_kind == "OpenAI":
generate_fn = generate_openai
elif api_kind is None:
gr.Warning("API name was not provided")
raise ValueError("API name was not provided")
else:
gr.Warning(f"API {api_kind} is not supported")
raise ValueError(f"API {api_kind} is not supported")
history[-1][1] = ""
for character in generate_fn(prompt, history[:-1]):
history[-1][1] = character
yield history, prompt_html
with gr.Blocks() as demo:
# with gr.Tab("RAGDemo"):
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 text and press enter",
container=False,
)
txt_btn = gr.Button(value="Submit text", scale=1)
cb = gr.Checkbox(label="Use cross-encoder", info="Rerank after retrieval?")
api_kind = gr.Radio(choices=["HuggingFace", "OpenAI"], value="HuggingFace")
prompt_html = gr.HTML()
# Turn off interactivity while generating if you click
txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
bot, [chatbot, cb, api_kind], [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, cb, api_kind], [chatbot, prompt_html])
# Turn it back on
txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
# Examples
gr.Examples(examples, txt)
demo.queue()
demo.launch(debug=True)