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# AI assistant with a RAG system to query information from
# the gwIAS search pipline
# using Langchain and deployed with Gradio
from rag import RAG, load_docs
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain.chat_models import ChatOpenAI
import gradio as gr
# Load the documentation
docs = load_docs()
print("Pages loaded:", len(docs))
# LLM model
llm = ChatOpenAI(model="gpt-4o-mini")
# Embeddings
embed_model = "sentence-transformers/multi-qa-distilbert-cos-v1"
# embed_model = "nvidia/NV-Embed-v2"
embeddings = HuggingFaceInstructEmbeddings(model_name=embed_model)
# RAG chain
rag_chain = RAG(llm, docs, embeddings)
# Function to handle prompt and query the RAG chain
def handle_prompt(message, history):
try:
# Stream output
out = ""
for chunk in rag_chain.stream(message):
out += chunk
yield out
except Exception as e:
raise gr.Error(f"An error occurred: {str(e)}")
if __name__ == "__main__":
# Predefined messages and examples
description = "AI powered assistant to help with [gwIAS](https://github.com/JayWadekar/gwIAS-HM) gravitational wave search pipeline."
greetingsmessage = "Hi, I'm the gwIAS Bot, I'm here to assist you with the search pipeline."
example_questions = [
"Can you give me the code for calculating coherent score?",
"Which module in the code is used for collecting coincident triggers?",
"How are template banks constructed?"
]
# Define customized Gradio chatbot
chatbot = gr.Chatbot([{"role": "assistant", "content": greetingsmessage}],
type="messages",
avatar_images=["ims/userpic.png", "ims/gwIASlogo.jpg"],
height="60vh")
# Define Gradio interface
demo = gr.ChatInterface(handle_prompt,
type="messages",
title="gwIAS DocBot",
fill_height=True,
examples=example_questions,
theme=gr.themes.Soft(),
description=description,
# cache_examples=False,
chatbot=chatbot)
demo.launch()
# https://arxiv.org/html/2405.17400v2
# https://arxiv.org/html/2312.06631v1
# https://arxiv.org/html/2310.15233v2
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