import streamlit as st from langchain_groq import ChatGroq from langchain_community.utilities import ArxivAPIWrapper, WikipediaAPIWrapper from langchain_community.tools import ArxivQueryRun, WikipediaQueryRun from langchain.agents import initialize_agent, AgentType from langchain.callbacks import StreamlitCallbackHandler ## Initialize Tools arxiv_wrapper = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200) arxiv = ArxivQueryRun(api_wrapper=arxiv_wrapper) wiki_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=200) wiki = WikipediaQueryRun(api_wrapper=wiki_wrapper) ## Streamlit UI st.title("🔎 LangChain - Chat with search") st.sidebar.title("Settings") api_key = st.sidebar.text_input("Enter your Groq API Key:", type="password") if "messages" not in st.session_state: st.session_state["messages"] = [ {"role": "assistant", "content": "Hi, I'm a chatbot who can search the web. How can I help you?"} ] for msg in st.session_state.messages: st.chat_message(msg["role"]).write(msg['content']) if prompt := st.chat_input(placeholder="What is machine learning?"): st.session_state.messages.append({"role": "user", "content": prompt}) st.chat_message("user").write(prompt) llm = ChatGroq(groq_api_key=api_key, model_name="Llama3-8b-8192", streaming=True) tools = [search, arxiv, wiki] search_agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True ) with st.chat_message("assistant"): st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False) user_query = st.session_state.messages[-1]["content"] try: response = search_agent.run(user_query, callbacks=[st_cb]) response = response if response else "Sorry, I couldn't find any relevant information." except Exception as e: response = f"An error occurred: {str(e)}" st.session_state.messages.append({'role': 'assistant', "content": response}) st.write(response)