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import streamlit as st
from config import CONFIG
from model.main import process_query, prepare_retriever
st.title("RAG Question Answering System")
# Instructions
st.write("""
Welcome to the Retrieval-Augmented Generation (RAG) Question Answering System.
### What does this system do?
- Searches through a collection of the first 50,000 documents of the dataset to find the most relevant information based on your question using **BM25** and **Semantic Search**.
- Generates accurate answers using the retrieved documents with the power of **OpenAI API GPT-4o-mini**.
- Provides citations for every piece of information to ensure transparency and trustworthiness.
### Instructions
1. **Enter your OpenAI API Key**: You can use your own key.
2. **Ask Your Question**: Type your question in the input box.
3. **Choose a Retrieval Method**:
- **BM25**: A keyword-based retrieval method.
- **Semantic Search**: A context-based retrieval method powered by embeddings.
4. **Generate the Answer**: Click the "Generate Answer" button to retrieve relevant documents and generate a detailed answer.
Feel free to experiment with different questions and retrieval methods to explore how the system performs!
""")
llm_key = st.text_input("Enter your LLM API Key", type="password")
# if st.checkbox("Use Test API Key"):
# llm_key = CONFIG['LLM_API_key']
if not llm_key:
st.warning("Please provide your LLM API Key to proceed.")
st.stop()
query = st.text_input("Enter your question")
retrieval_method = st.radio(
"Select Retrieval Method",
("BM25", "Semantic Search")
)
if st.button("Generate Answear"):
if not query.strip():
st.warning("Please enter a question to process.")
else:
with st.spinner("Processing your query..."):
try:
retrieved_docs, answer = process_query(llm_key, query, retrieval_method)
st.subheader("Retrieved Documents")
for doc in retrieved_docs:
st.write(f"- {doc}")
st.subheader("Generated Answer")
st.text_area("Generated Answer", value=answer, height=CONFIG['TEXTAREA_HEIGHT'])
except Exception as e:
st.error(f"An error occurred: {e}")
# if st.button("Prepare Retriever"):
# with st.spinner("Preparing retriever..."):
# try:
# prepare_retriever()
# st.success("Retriever prepared successfully!")
# except Exception as e:
# st.error(f"Failed to prepare retriever: {e}")
st.markdown(
"""
<style>
.stTextArea {
border: 2px solid #4CAF50;
border-radius: 8px;
padding: 10px;
font-family: Arial, sans-serif;
font-size: 14px;
box-shadow: 2px 2px 5px rgba(0, 0, 0, 0.1);
}
</style>
""",
unsafe_allow_html=True
)
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