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import streamlit as st |
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from utils import st_load_retriever, st_load_llm, StreamHandler |
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from langchain.chains import RetrievalQAWithSourcesChain |
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from langchain.callbacks import StreamlitCallbackHandler |
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from langchain.agents import AgentType, initialize_agent, load_tools |
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RETRIEVAL_METHOD_MAP = { |
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"Vector Database": "vectordb", |
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"Google Search": "google search", |
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"DuckDuckGo Search": "duckduckgo search", |
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} |
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st.title("AIxplorer - A Smarter Google Scholar 🌐📚") |
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st.write( |
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"AIxplorer aims to revolutionize academic research by combining the capabilities of traditional search engines like Google Scholar with an advanced retrieval augmented generation (RAG) system. Built on Python and Langchain, this application provides highly relevant and context-aware academic papers, journals, and articles, elevating the standard of academic research." |
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) |
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st.info("Inference on this Huggingface Space is extremely slow due to it being entirely processed on the 2 CPU cores. For quicker inference, please clone the repository and run the inference locally or run it from Colab using a GPU runtime: https://colab.research.google.com/drive/1GQigpg9leftwxgp7ScFeils1-Ge0tY8Z?usp=sharing",icon="ℹ️",) |
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st.divider() |
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st.subheader("Settings") |
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col1, col2, col3 = st.columns(3) |
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with col1: |
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retrieval_method = st.selectbox( |
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"Retrieval Mode", |
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RETRIEVAL_METHOD_MAP.keys(), |
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index=0, |
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help="The retrieval method used to retrieve supporting documents.", |
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) |
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st.divider() |
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llm = st_load_llm() |
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if retrieval_method in ("Vector Database", "Google Search"): |
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retriever = st_load_retriever(llm, RETRIEVAL_METHOD_MAP[retrieval_method]) |
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qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llm, retriever=retriever) |
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user_input = st.text_area( |
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"Enter your query here", |
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help="Query should be on computer science as the RAG system is tuned to that domain.", |
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) |
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if st.button("Generate"): |
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st.divider() |
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st.subheader("Answer:") |
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with st.spinner("Generating..."): |
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container = st.empty() |
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stream_handler = StreamHandler(container) |
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response = qa_chain({"question": user_input}, callbacks=[stream_handler]) |
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else: |
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tools = load_tools(["ddg-search"]) |
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agent = initialize_agent( |
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tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True |
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) |
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if prompt := st.chat_input(): |
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st.chat_message("user").write(prompt) |
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with st.chat_message("assistant"): |
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st_callback = StreamlitCallbackHandler(st.container()) |
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response = agent.run(prompt, callbacks=[st_callback]) |
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st.write(response) |
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