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from typing import Type
import streamlit as st
from dotenv import load_dotenv
from langchain_community.document_loaders.blob_loaders import Blob
from rag.generator import MODELS, get_generator
from rag.generator.abstract import AbstractGenerator
from rag.generator.prompt import Prompt
from rag.retriever.retriever import Retriever
@st.cache_resource
def load_retriever() -> Retriever:
return Retriever()
@st.cache_resource
def load_generator(model: str) -> Type[AbstractGenerator]:
return get_generator(model)
@st.cache_data(show_spinner=False)
def upload(files):
with st.spinner("Indexing documents..."):
for file in files:
source = file.name
blob = Blob.from_data(file.read())
retriever.add_pdf(blob=blob, source=source)
if __name__ == "__main__":
load_dotenv()
retriever = load_retriever()
with st.sidebar:
st.header("Grouding")
st.markdown(
(
"These files will be uploaded to the knowledge base and used "
"as groudning if they are relevant to the question."
)
)
files = st.file_uploader(
"Choose pdfs to add to the knowledge base",
type="pdf",
accept_multiple_files=True,
)
upload(files)
st.header("Generative Model")
st.markdown("Select the model that will be used for generating the answer.")
model = st.selectbox("Generative Model", options=MODELS)
generator = load_generator(model)
st.title("Retrieval Augmented Generation")
with st.form(key="query"):
query = st.text_area(
"query",
key="query",
height=100,
placeholder="Enter query here",
help="",
label_visibility="collapsed",
disabled=False,
)
submit = st.form_submit_button("Generate")
(result_column, context_column) = st.columns(2)
if submit and query:
with st.spinner("Searching for documents..."):
documents = retriever.retrieve(query)
prompt = Prompt(query, documents)
with context_column:
st.markdown("### Context")
for i, doc in enumerate(documents):
st.markdown(f"### Document {i}")
st.markdown(f"**Title: {doc.title}**")
st.markdown(doc.text)
st.markdown("---")
with result_column:
st.markdown("### Answer")
st.write_stream(generator.generate(prompt))
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