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import gradio as gr
import os, time
from dotenv import load_dotenv, find_dotenv
from rag import run_llm_chain, run_rag_chain, run_rag_batch
from trace import trace_wandb
_ = load_dotenv(find_dotenv())
RUN_RAG_BATCH = False # load, split, embed, and store documents
config = {
"chunk_overlap": 150, # split documents
"chunk_size": 1500, # split documents
"k": 3, # retrieve documents
"model_name": "gpt-4-0314", # llm
"temperature": 0, # llm
}
RAG_OFF = "Off"
RAG_CHROMA = "Chroma"
RAG_MONGODB = "MongoDB"
def invoke(openai_api_key, rag_option, prompt):
if (openai_api_key == ""):
raise gr.Error("OpenAI API Key is required.")
if (rag_option is None):
raise gr.Error("Retrieval Augmented Generation is required.")
if (prompt == ""):
raise gr.Error("Prompt is required.")
if (RUN_RAG_BATCH):
run_rag_batch(config)
chain = None
completion = ""
result = ""
cb = ""
err_msg = ""
try:
start_time_ms = round(time.time() * 1000)
if (rag_option == RAG_OFF):
completion, chain, cb = run_llm_chain(config, openai_api_key, prompt)
if (completion.generations[0] != None and completion.generations[0][0] != None):
result = completion.generations[0][0].text
else:
completion, chain, cb = run_rag_chain(config, openai_api_key, rag_option, prompt)
result = completion["result"]
except Exception as e:
err_msg = e
raise gr.Error(e)
finally:
end_time_ms = round(time.time() * 1000)
trace_wandb(config,
rag_option == RAG_OFF,
prompt,
completion,
result,
chain,
cb,
err_msg,
start_time_ms,
end_time_ms)
return result
gr.close_all()
demo = gr.Interface(fn = invoke,
inputs = [gr.Textbox(label = "OpenAI API Key", type = "password", lines = 1),
gr.Radio([RAG_OFF, RAG_CHROMA, RAG_MONGODB], label = "Retrieval-Augmented Generation", value = RAG_OFF),
gr.Textbox(label = "Prompt", value = "What are GPT-4's media capabilities in 5 emojis and 1 sentence?", lines = 1),
],
outputs = [gr.Textbox(label = "Completion", lines = 1)],
title = "Context-Aware Multimodal Reasoning Application",
description = os.environ["DESCRIPTION"])
demo.launch() |