added tools

src/langgraphagenticai/graph/graph_builder.py

@@ -3,6 +3,8 @@ from langgraph.prebuilt import tools_condition, tool_node
 from langchain_core.prompts import ChatPromptTemplate
 from src.langgraphagenticai.state.state import State
 from src.langgraphagenticai.nodes.basic_chatbot_node import BasicChatbotNode
+from src.langgraphagenticai.nodes.chatbot_with_tool_node import ChatbotWithToolNode
+from src.langgraphagenticai.tools.search_tools import get_tools, create_tool_node
 
 class GraphBuilder:
     def __init__(self, model):
@@ -21,10 +23,43 @@ class GraphBuilder:
         self.graph_builder.add_edge(START, "chatbot")
         self.graph_builder.add_edge("chatbot", END)
 
+    def chatbot_with_tools_build_graph(self):
+        """
+        Builds an advanced chatbot with tool integration.
+        This method creates a chatbot graph that includes both
+        a chatbot node and tool node. It defines tools, initializes
+        the chatbot with tool capabilities, and sets up conditional
+        and direct edges between nodes. The chatbot node is set as an
+        entry point.
+        """
+        ## Define the tool and tool node
+        tools = get_tools()
+        tool_node = create_tool_node(tools)
+
+        ## Define LLM
+        llm = self.llm
+
+        # Define the chatbot node
+        obj_chatbot_with_node = ChatbotWithToolNode(llm)
+        chatbot_node = obj_chatbot_with_node.create_chatbot(tools)
+
+        # Add nodes
+        self.graph_builder.add_node("chatbot", chatbot_node)
+        self.graph_builder.add_node("tools", tool_node)
+
+        # Define conditional and direct edges
+        self.graph_builder.add_edge(START, "chatbot")
+        self.graph_builder.add_conditional_edges("chatbot", tools_condition)
+        self.graph_builder.add_edge("tools", "chatbot")
+
+
     def setup_graph(self, usecase: str):
         """
         Sets up the graph for the selected use case.
         """
         if usecase == "Basic Chatbot":
             self.basic_chatbot_build_graph()
+
+        if usecase == "Chatbot with Tools":
+            self.chatbot_with_tools_build_graph()
         return self.graph_builder.compile()

src/langgraphagenticai/nodes/chatbot_with_tool_node.py

@@ -0,0 +1,34 @@
+from src.langgraphagenticai.state.state import State
+
+
+class ChatbotWithToolNode:
+    """
+    Chatbot logic enhanced with tool integration
+    """
+    def __init__(self, model):
+        self.llm = model
+
+    def process(self, state: State) -> dict:
+        """
+        Processes the input state and generates a response with tool integration.
+        """
+        user_input = state["messages"] if state["messages"] else ""
+        llm_response = self.llm.invoke([{"role": "user", "content": user_input}])
+
+        # Simulate tool-specific logic
+        tools_response = f"Tool integration for: '{user_input}'"
+        return {"messages": [llm_response, tools_response]}
+
+    def create_chatbot(self, tools):
+        """
+        Returns a chatbot node function.
+        """
+        llm_with_tools = self.llm.bind_tools(tools)
+
+        def chatbot_node(state: State):
+            """
+            Chatbot logic for processing the input state and returning a response
+            """
+            return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+        return chatbot_node

src/langgraphagenticai/tools/search_tools.py

@@ -0,0 +1,13 @@
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langgraph.prebuilt import ToolNode
+
+def get_tools():
+    """
+    Returns the list of tools to be used in the chatbot
+    """
+    tools = [TavilySearchResults(max_results=2)]
+    return tools
+
+def create_tool_node(tools):
+    """creates and returns a tool node for the graph"""
+    return ToolNode(tools=tools)

src/langgraphagenticai/ui/streamlit/display_result.py

@@ -1,5 +1,5 @@
 import streamlit as st
-from langchain_core.messages import HumanMessage, AIMessage
+from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
 import json
 
 class DisplayResultStreamlit:
@@ -12,6 +12,7 @@ class DisplayResultStreamlit:
         usecase = self.usecase
         graph = self.graph
         user_message = self.user_message
+
         if usecase == "Basic Chatbot":
             print(usecase, graph, user_message)
             for event in graph.stream({'messages': ("user", user_message)}):
@@ -21,4 +22,24 @@ class DisplayResultStreamlit:
                     with st.chat_message("user"):
                         st.write(user_message)
                     with st.chat_message("assistant"):
-                        st.write(value["messages"].content)
+                        st.write(value["messages"].content)
+
+        if usecase == "Chatbot with Tools":
+            print(usecase, graph, user_message)
+            # Prepare state and invoke the graph
+            initial_state = {"messages": [user_message]}
+            response = graph.invoke(initial_state)
+            for message in response['messages']:
+                if type(message) == HumanMessage:
+                    with st.chat_message("user"):
+                        st.write(message.content)
+                elif type(message) == ToolMessage:
+                    with st.chat_message("ai"):
+                        st.write("Tool Call Start")
+                        st.write(message.content)
+                        st.write("Tool Call End")
+                elif type(message) == AIMessage and message.content:
+                    with st.chat_message("assistant"):
+                        st.write(message.content)
+
+

src/langgraphagenticai/ui/streamlit/load_ui.py

@@ -59,6 +59,14 @@ class LoadStreamlitUI:
             selected_usecase = st.selectbox("Select Use Case", usecase_options)
             self.user_controls["selected_usecase"] = selected_usecase
 
+            if self.user_controls["selected_usecase"] == "Chatbot with Tools":
+                # APIKey input
+                os.environ["TAVILY_API_KEY"] = self.user_controls["TAVILY_API_KEY"] = st.session_state["TAVILY_API_KEY"] = st.text_input("TAVILY_API_KEY", type="password")
+
+                # Validate API Key
+                if not self.user_controls["TAVILY_API_KEY"]:
+                    st.warning("⚠️ Please enter your TAVILY_API_KEY to proceed. Don't have?, refer: https://app.tavily.com/home")
+
             if "state" not in st.session_state:
                 st.session_state.state = self.initialize_session()
 

src/langgraphagenticai/ui/uiconfigfile.ini

@@ -1,5 +1,5 @@
 [DEFAULT]
 PAGE_TITLE = LangGraph: Build Stateful Agentic AI graph 
 LLM_OPTIONS = Groq, OPENAI
-USECASE_OPTIONS = Basic Chatbot, Voice Analyzer
+USECASE_OPTIONS = Basic Chatbot, Chatbot with Tools
 GROQ_MODEL_OPTIONS = mixtral-8x7b-32768, llama-3.3-70b-versatile, gemma2-9b-it