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app.py

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+import streamlit as st
+import os
+from typing import Annotated
+from typing_extensions import TypedDict
+from langchain_community.utilities import ArxivAPIWrapper, WikipediaAPIWrapper
+from langchain_community.tools import ArxivQueryRun, WikipediaQueryRun
+from langgraph.graph.message import add_messages
+from langgraph.graph import StateGraph, START, END
+from langchain_groq import ChatGroq
+from langgraph.prebuilt import ToolNode, tools_condition
+
+# Initialize tools
+arxiv_wrapper = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=300)
+arxiv_tool = ArxivQueryRun(api_wrapper=arxiv_wrapper)
+wiki_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=300)
+wiki_tool = WikipediaQueryRun(api_wrapper=wiki_wrapper)
+tools = [wiki_tool, arxiv_tool]
+
+# Define State
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+# Initialize LLM
+@st.cache_resource
+def initialize_llm():
+    groq_api_key = os.getenv("GROQ_API_KEY")
+    if not groq_api_key:
+        st.error("Please set the GROQ_API_KEY environment variable.")
+        st.stop()
+    return ChatGroq(groq_api_key=groq_api_key, model_name="Gemma2-9b-It")
+
+llm = initialize_llm()
+llm_with_tools = llm.bind_tools(tools=tools)
+
+# Define chatbot function
+def chatbot(state: State):
+    return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+# Build graph
+@st.cache_resource
+def build_graph():
+    graph_builder = StateGraph(State)
+    graph_builder.add_node("chatbot", chatbot)
+    tool_node = ToolNode(tools=tools)
+    graph_builder.add_node("tools", tool_node)
+    graph_builder.add_conditional_edges("chatbot", tools_condition)
+    graph_builder.add_edge("tools", "chatbot")
+    graph_builder.add_edge(START, "chatbot")
+    return graph_builder.compile()
+
+graph = build_graph()
+
+# Streamlit UI
+st.title("WIKXIV AI: Wikipedia and ArXiv Chatbot")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# React to user input
+if prompt := st.chat_input("What is your question?"):
+    # Display user message in chat message container
+    st.chat_message("user").markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    
+    # Get bot response
+    events = graph.stream(
+        {"messages": [("user", prompt)]},
+        stream_mode="values"
+    )
+    
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+        for event in events:
+            message = event["messages"][-1]
+            full_response += message.content
+            message_placeholder.markdown(full_response + "▌")
+        message_placeholder.markdown(full_response)
+    
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": full_response})
+    
+# Display info about the app
+st.sidebar.title("About")
+st.sidebar.info("This is a Streamlit app made by theaimart\nthat uses LangGraph to create a chatbot with access to Wikipedia and ArXiv tools.")
+# st.sidebar.title("made with love by theaimart")

requirements.txt

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+langgraph
+langsmith
+langchain
+langchain_groq
+langchain_community
+arxiv
+wikipedia-api
+streamlit
+wikipedia