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import os
import glob
from langchain.document_loaders import UnstructuredMarkdownLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.schema import Document
from transformers import pipeline
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import PeftModel, PeftConfig
import streamlit as st
def main():
# set page title
st.set_page_config(page_title="Project Nexodus Documentation Retreival", page_icon="📖", layout="wide")
st.header("📖 Ask Project Nexodus Docs")
# set description
st.markdown("""
Generates answers to your questions about Project Nexodus by leveraging foundation models to perform search and retreival of Nexodus documentation.\n
Feeling stuck? Here are some examples of questions you can ask:
* How do I run the control plane for Nexodus?
* How do I monitor the Nexodus stack locally?
* How can I contribute to Project Nexodus?
""")
# set sidebar
with st.sidebar:
# create instructions for use
st.markdown("""
# How to use:\n
1. Enter your HuggingFace API token below
2. Select your answer generation strategy from the dropdown menu
3. Ask a question about Linux networking
4. Click on the `Submit` button or optionally, click on the `Feeling Lucky`
""")
# create input box for HF API token
API_KEY = st.text_input('Hugging Face API Token 🤗', type='password',
placeholder='Paste your HuggingFace token here (sk-...)',
help="You can get your API token from https://huggingface.co/docs/hub/security-tokens.")
st.markdown("""
# About
Talk to Project Nexodus is a web application that answers your questions about Nexodus,
with the goal of exploring the capabilities and limitations of Large Language Models (LLMs) for question and
answering tasks. It demonstrates the following strategies for question answering: extractive, abstractive, and
generative.
This project is still in beta and mainly used for research purposes. It is highly unadvised for users to rely on it for Project Nexodus troubleshooting.
Please refer to the [official Nexodus documentation](https://github.com/nexodus-io/nexodus) for help. Proceed at your own risk 💀
""")
if API_KEY:
strategy = st.selectbox('Q&A Strategy', ['Extractive', 'Abstractive', 'Finetuned with LoRA'])
question = st.text_input("Enter your question here:")
col1, col2 = st.columns([1,1])
with col1:
generate_answer = st.button("Generate Answer")
with col2:
feeling_lucky = st.button("Feeling Lucky")
if question != "":
if strategy and generate_answer:
answer = get_answer(question, strategy)
st.write(answer)
elif feeling_lucky:
answer = get_answer(question, 'Generative')
st.write(answer)
def load_db():
# initalize embedder
print('Loading FAISS index...')
embeddings = HuggingFaceEmbeddings()
# load FAISS vector database storing Nexodus documentation
db = FAISS.load_local("nexodus_index.faiss", embeddings)
return db
def load_model():
llm = "deepset/roberta-base-squad2"
return llm
def load_model_tokenizer(strategy):
model_name = "google/flan-t5-base"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name,load_in_8bit=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(model_name)
if strategy == 'Finetuned with LoRA':
print(f"Loading finetuned {model_name} with LoRA...")
model = PeftModel.from_pretrained(model, 'exyou/nexodus-flan-T5')
print("Model sucessfully loaded")
return model, tokenizer
def provide_context(context):
context = [f"<P> {m.page_content}" for m in context]
context = " ".join(context)
return context
def get_answer(question, strategy):
if strategy == 'Generative':
model, tokenizer = load_model_tokenizer(strategy)
context = ""
question_context = f"Question: ## {question} ##\n Context: ## {context} ##"
input_ids = tokenizer(question_context, return_tensors="pt", truncation=True).input_ids.cuda()
outputs = model.generate(input_ids=input_ids, max_new_tokens=1000, do_sample=True, top_p=1)
answer = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
return answer
else:
db = load_db()
if strategy == 'Finetuned with LoRA':
model, tokenizer = load_model_tokenizer(strategy)
# get the top 3 most similar sentences in the docs to the inputted question
top_3 = db.similarity_search(question, k=3)
# set as context for the question
context = provide_context(top_3)
question_context = f"Question: ## {question} ##\n Context: ## {context} ##"
input_ids = tokenizer(question_context, return_tensors="pt", truncation=True).input_ids.cuda()
outputs = model.generate(input_ids=input_ids, max_new_tokens=1000, do_sample=True, top_p=1)
answer = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
return answer
else:
llm = load_model()
if strategy == 'Extractive':
output = db.similarity_search(question, k=1)
answer = output[0].page_content
return answer
elif strategy == 'Abstractive':
top_3 = db.similarity_search(question, k=3)
context = provide_context(top_3)
text2text_generator = pipeline(task='question-answering', tokenizer=llm, model=llm)
output = text2text_generator(question=question, context=context, temperature=1.5, min_length=5, max_length=50)
answer = output["answer"]
return answer
return answer
if __name__ == "__main__":
main()
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