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import gradio as gr | |
import logging, os, sys, threading | |
from dotenv import load_dotenv, find_dotenv | |
from document_model import Listing | |
lock = threading.Lock() | |
_ = load_dotenv(find_dotenv()) | |
RAG_INGESTION = False # load, split, embed, and store documents | |
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: | |
os.environ["OPENAI_API_KEY"] = openai_api_key | |
### | |
prompt = """ | |
I want to stay in a place that's warm and friendly, | |
and not too far from resturants, can you recommend a place? | |
Include a reason as to why you've chosen your selection. | |
""" | |
result = handle_user_prompt(prompt, db, collection) | |
### | |
del os.environ["OPENAI_API_KEY"] | |
""" | |
if (RAG_INGESTION): | |
if (rag_option == RAG_LANGCHAIN): | |
#rag = LangChainRAG() | |
#rag.ingestion(config) | |
elif (rag_option == RAG_LLAMAINDEX): | |
#rag = LlamaIndexRAG() | |
#rag.ingestion(config) | |
try: | |
#rag = LangChainRAG() | |
#completion, callback = rag.rag_chain(config, prompt) | |
#result = completion["result"] | |
elif (rag_option == RAG_LLAMAINDEX): | |
#rag = LlamaIndexRAG() | |
#result, callback = rag.retrieval(config, prompt) | |
else: | |
#rag = LangChainRAG() | |
#completion, callback = rag.llm_chain(config, prompt) | |
#result = completion.generations[0][0].text | |
except Exception as e: | |
err_msg = e | |
raise gr.Error(e) | |
finally: | |
del os.environ["OPENAI_API_KEY"] | |
""" | |
return result | |
gr.close_all() | |
demo = gr.Interface( | |
fn = invoke, | |
inputs = [gr.Textbox(label = "OpenAI API Key", type = "password", lines = 1), | |
gr.Textbox(label = "Prompt", value = "TODO", lines = 1), | |
gr.Radio([RAG_OFF, RAG_NAIVE, RAG_ADVANCED], label = "Retrieval-Augmented Generation", value = RAG_ADVANCED)], | |
outputs = [gr.Textbox(label = "Completion")], | |
title = "Context-Aware Reasoning Application", | |
description = os.environ["DESCRIPTION"] | |
) | |
demo.launch() |