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import gradio as gr
import logging, os, re, sys, threading
from custom_utils import connect_to_database, rag_ingestion, rag_retrieval, rag_inference
lock = threading.Lock()
RAG_INGESTION = False
RAG_OFF = "Off"
RAG_NAIVE = "Naive RAG"
RAG_ADVANCED = "Advanced RAG"
logging.basicConfig(stream = sys.stdout, level = logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream = sys.stdout))
def invoke(openai_api_key, prompt, rag_option):
if not openai_api_key:
raise gr.Error("OpenAI API Key is required.")
if not prompt:
raise gr.Error("Prompt is required.")
if not rag_option:
raise gr.Error("Retrieval-Augmented Generation is required.")
with lock:
db, collection = connect_to_database()
if (RAG_INGESTION):
return rag_ingestion(collection)
else:
### Pre-retrieval processing: index filter
### Post-retrieval processing: result filter
"""
match_stage = {
"$match": {
"address.country": re.compile(r"United States"),
"accommodates": { "$gt": 1, "$lt": 3}
}
}
additional_stages = [match_stage]
"""
###
search_results = rag_retrieval(openai_api_key, prompt, db, collection, additional_stages)
return rag_inference(openai_api_key, prompt, search_results)
gr.close_all()
PROMPT = "Recommend a place that's modern, spacious, and within walking distance from restaurants."
demo = gr.Interface(
fn = invoke,
inputs = [gr.Textbox(label = "OpenAI API Key", type = "password", lines = 1),
gr.Textbox(label = "Prompt", value = PROMPT, lines = 1),
gr.Radio([RAG_OFF, RAG_NAIVE, RAG_ADVANCED], label = "Retrieval-Augmented Generation", value = RAG_ADVANCED)],
outputs = [gr.Markdown(label = "Completion")],
title = "Context-Aware Reasoning Application",
description = os.environ["DESCRIPTION"]
)
demo.launch() |