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import streamlit as st
from langchain_community.document_loaders.blob_loaders import Blob
from dotenv import load_dotenv
from rag.generator import get_generator, MODELS
from generator.prompt import Prompt
from retriever.retriever import Retriever
if __name__ == "__main__":
load_dotenv()
retriever = Retriever()
ss = st.session_state
st.header("Retrieval Augmented Generation")
model = st.selectbox("Model", options=MODELS)
files = st.file_uploader(
"Choose pdfs to add to the knowledge base",
type="pdf",
accept_multiple_files=True,
)
if 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)
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")
(b,) = st.columns(1)
(result_column, context_column) = st.columns(2)
if submit and model:
if not query:
st.stop()
query = ss.get("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:
generator = get_generator(model)
st.markdown("### Answer")
st.write_stream(generator.generate(prompt))
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