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"""
Credit to Derek Thomas, [email protected]
"""
import os
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 retrieve
import itertools
emb_models = ["bge", "minilm"]
splitters = ['ct', 'rct', 'nltk']
chunk_sizes = ["500", "2000"]
sub_vectors = ["8", "16", "32"]
# Create all combinations of the provided arrays
combinations = itertools.product(emb_models, splitters, chunk_sizes, sub_vectors)
TOP_K = int(os.getenv("TOP_K", 4))
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')
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, api_kind,
num_docs, model_kind, sub_vector_size, chunk_size, splitter_type, all_at_once):
query = history[-1][0]
if not query:
raise gr.Warning("Please submit a non-empty string as a prompt")
logger.info('Retrieving documents...')
# Retrieve documents relevant to query
document_start = perf_counter()
documents = retrieve(query, int(num_docs), model_kind, sub_vector_size, chunk_size, splitter_type)
document_time = perf_counter() - document_start
logger.info(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
else:
raise gr.Error(f"API {api_kind} is not supported")
history[-1][1] = ""
if all_at_once:
for model_name, doc, size, sub_vector in combinations:
documents_i = retrieve(query, int(num_docs), model_name, sub_vector, size, doc)
prompt_i = template.render(documents=documents_i, query=query)
prompt_html = template_html.render(documents=documents, query=query)
hist_chunk = ""
prev_hist = history[-1][1] + f"\nmodel {model_name}, splitter {doc}, size {size}, sub vector {sub_vector}\n"
for character in generate_fn(prompt_i, history[:-1]):
hist_chunk = character
history[-1][1] = prev_hist + hist_chunk
yield history, prompt_html
else:
for character in generate_fn(prompt, history[:-1]):
history[-1][1] = character
yield history, prompt_html
with gr.Blocks() as demo:
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)
with gr.Row():
num_docs = gr.Slider(1, 20, label="number of docs", step=1, value=4)
model_kind = gr.Radio(choices=emb_models, value="bge", label="embedding model")
sub_vector_size = gr.Radio(choices=sub_vectors, value="32", label="sub-vector size")
all_at_once = gr.Checkbox(value=False, label="Run all at once")
with gr.Row():
api_kind = gr.Radio(choices=["HuggingFace", "OpenAI"], value="HuggingFace", label="Chat model engine")
chunk_size = gr.Radio(choices=chunk_sizes, value="2000", label="chunk size")
splitter_type = gr.Radio(choices=splitters, value="nltk", label="splitter")
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, api_kind,
num_docs, model_kind, sub_vector_size, chunk_size, splitter_type, all_at_once
], [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, api_kind,
num_docs, model_kind, sub_vector_size, chunk_size, splitter_type
], [chatbot, prompt_html])
# Turn it back on
txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
demo.queue()
demo.launch(debug=True)
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