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import gradio as gr
from functools import partial
from rag_benchmark import get_benchmark
title = "Prototype Temporal Augmented Retrieval (TAR)"
desc = "Database: 22.4k tweets related to finance dated from July 12,2018 to July 19,2018 - know more about the approach: [blog post](https://medium.com/@adam-rida/temporal-augmented-retrieval-tar-dynamic-rag-ad737506dfcc)\ncontact: adrida.github.io"
with gr.Blocks(title=title,theme='nota-ai/theme') as demo:
gr.Markdown(f"# {title}\n{desc}")
with gr.Row():
with gr.Column(scale = 10):
text_area = gr.Textbox(placeholder="Write here", lines=1, label="Ask anything")
with gr.Column(scale = 2):
api_key = gr.Textbox(placeholder="Paste your OpenAI API key here", lines=1)
search_button = gr.Button(value="Ask")
with gr.Row():
with gr.Tab("Dynamic Temporal Augmented Retrieval (ours)"):
gr.Markdown("## Dynamic Temporal Augmented Retrieval (ours)\n---")
tempo = gr.Markdown()
with gr.Tab("Naive Semantic Search"):
gr.Markdown("## Simple Semantic Search\n---")
naive = gr.Markdown()
with gr.Tab("Traditional RAG (Langchain type)"):
gr.Markdown("## Augmented Indexed Retrieval\n---")
classic = gr.Markdown()
search_function = partial(get_benchmark)
search_button.click(fn=search_function, inputs=[text_area, api_key], outputs=[tempo, classic, naive],
)
#demo.queue(concurrency_count=100,status_update_rate=500).launch(max_threads=100, show_error=True, debug = True, inline =False)
demo.launch(max_threads=40)
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