chatbot with tools- langgraph

.github/workflows/main.yml

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+name: Sync to Hugging Face Space
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab 
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: false
+          
+      - name: Ignore large files
+        run : git filter-branch --index-filter 'git rm -rf --cached --ignore-unmatch "Rag_Documents/layout-parser-paper.pdf"' HEAD
+
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push --force https://Dnyaneshb18:[email protected]/spaces/Dnyaneshb18/langgraph-agenticai main
+       
+         

.gitignore

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+venv/
+.DS_Store
+__pycache__/

README.md

@@ -1 +1,17 @@
-# Agentic-AI
+---
+title: LanggraphAgenticAI
+emoji: 🐨
+colorFrom: blue
+colorTo: red
+sdk: streamlit
+sdk_version: 1.42.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Refined langgraphAgenticAI
+---
+
+### End To End Agentic AI Projects
+
+The project is in development
+

app.py

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+from src.langgraphagenticai.main import load_langgraph_agenticai_app
+
+
+if __name__=="__main__":
+    load_langgraph_agenticai_app()

requirements.txt

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+langchain
+langgraph
+langchain_community
+langchain_core
+langchain_groq
+langchain_openai
+faiss-cpu
+streamlit

src/langgraphagenticai/LLMS/groqllm.py

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+import os
+import streamlit as st
+from langchain_groq import ChatGroq
+
+class GroqLLM:
+    def __init__(self,user_controls_input):
+        self.user_controls_input=user_controls_input
+
+    def get_llm_model(self):
+        try:
+            groq_api_key=self.user_controls_input['GROQ_API_KEY']
+            selected_groq_model=self.user_controls_input['selected_groq_model']
+            if groq_api_key=='' and os.environ["GROQ_API_KEY"] =='':
+                st.error("Please Enter the Groq API KEY")
+
+            llm = ChatGroq(api_key =groq_api_key, model=selected_groq_model)
+
+        except Exception as e:
+            raise ValueError(f"Error Occurred with Exception : {e}")
+        return llm

src/langgraphagenticai/graph/.gitignore

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+venv/
+__pycache__/

src/langgraphagenticai/graph/graph_builder.py

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+from langgraph.graph import StateGraph, START,END, MessagesState
+from langgraph.prebuilt import tools_condition,ToolNode
+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.serach_tool import get_tools,create_tool_node
+
+
+
+
+class GraphBuilder:
+
+    def __init__(self,model):
+        self.llm=model
+        self.graph_builder=StateGraph(State)
+
+    def basic_chatbot_build_graph(self):
+        """
+        Builds a basic chatbot graph using LangGraph.
+        This method initializes a chatbot node using the `BasicChatbotNode` class 
+        and integrates it into the graph. The chatbot node is set as both the 
+        entry and exit point of the graph.
+        """
+        self.basic_chatbot_node=BasicChatbotNode(self.llm)
+        self.graph_builder.add_node("chatbot",self.basic_chatbot_node.process)
+        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 graph with tool integration.
+        This method creates a chatbot graph that includes both a chatbot node 
+        and a 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 the entry point.
+        """
+        ## Define the tool and tool node
+
+        tools=get_tools()
+        tool_node=create_tool_node(tools)
+
+        ##Define LLM
+        llm = self.llm
+
+        # Define 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 Tool":
+            self.chatbot_with_tools_build_graph()
+        return self.graph_builder.compile()
+    
+
+
+
+
+    
+

src/langgraphagenticai/main.py

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+import streamlit as st
+import json
+from src.langgraphagenticai.ui.streamlitui.loadui import LoadStreamlitUI
+from src.langgraphagenticai.LLMS.groqllm import GroqLLM
+from src.langgraphagenticai.graph.graph_builder import GraphBuilder
+from src.langgraphagenticai.ui.streamlitui.display_result import DisplayResultStreamlit
+
+# MAIN Function START
+def load_langgraph_agenticai_app():
+    """
+    Loads and runs the LangGraph AgenticAI application with Streamlit UI.
+    This function initializes the UI, handles user input, configures the LLM model,
+    sets up the graph based on the selected use case, and displays the output while 
+    implementing exception handling for robustness.
+    """
+   
+    # Load UI
+    ui = LoadStreamlitUI()
+    user_input = ui.load_streamlit_ui()
+
+    if not user_input:
+        st.error("Error: Failed to load user input from the UI.")
+        return
+
+    # Text input for user message
+    if st.session_state.IsFetchButtonClicked:
+        user_message = st.session_state.timeframe 
+    else :
+        user_message = st.chat_input("Enter your message:")
+
+    if user_message:
+            try:
+                # Configure LLM
+                obj_llm_config = GroqLLM(user_controls_input=user_input)
+                model = obj_llm_config.get_llm_model()
+                
+                if not model:
+                    st.error("Error: LLM model could not be initialized.")
+                    return
+
+                # Initialize and set up the graph based on use case
+                usecase = user_input.get('selected_usecase')
+                if not usecase:
+                    st.error("Error: No use case selected.")
+                    return
+                
+
+                ### Graph Builder
+                graph_builder=GraphBuilder(model)
+                try:
+                    graph = graph_builder.setup_graph(usecase)
+                    DisplayResultStreamlit(usecase,graph,user_message).display_result_on_ui()
+                except Exception as e:
+                    st.error(f"Error: Graph setup failed - {e}")
+                    return
+                
+
+            except Exception as e:
+                 raise ValueError(f"Error Occurred with Exception : {e}")
+            
+
+        
+
+   
+
+    

src/langgraphagenticai/nodes/basic_chatbot_node.py

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+from src.langgraphagenticai.state.state import State
+
+class BasicChatbotNode:
+    """
+    Basic chatbot logic implementation.
+    """
+    def __init__(self,model):
+        self.llm = model
+
+    def process(self, state: State) -> dict:
+        """
+        Processes the input state and generates a chatbot response.
+        """
+        return {"messages":self.llm.invoke(state['messages'])}

src/langgraphagenticai/nodes/chatbot_with_Tool_node.py

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+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"][-1] 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/state/state.py

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+from typing import Annotated, Literal, Optional
+from typing_extensions import TypedDict
+from langgraph.graph.message import add_messages
+from typing import TypedDict, Annotated, List
+from langchain_core.messages import HumanMessage, AIMessage
+
+class State(TypedDict):
+    """
+    Represents the structure of the state used in the graph.
+    """
+    messages: Annotated[list, add_messages]

src/langgraphagenticai/tools/serach_tool.py

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+from langchain_community.tools.tavily_search import TavilySearchResults
+from langgraph.prebuilt import ToolNode
+
+def get_tools():
+    """
+    Return 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/streamlitui/display_result.py

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+import streamlit as st
+from langchain_core.messages import HumanMessage,AIMessage,ToolMessage
+import json
+
+
+class DisplayResultStreamlit:
+    def __init__(self,usecase,graph,user_message):
+        self.usecase= usecase
+        self.graph = graph
+        self.user_message = user_message
+
+    def display_result_on_ui(self):
+        usecase= self.usecase
+        graph = self.graph
+        user_message = self.user_message
+        if usecase =="Basic Chatbot":
+                for event in graph.stream({'messages':("user",user_message)}):
+                    print(event.values())
+                    for value in event.values():
+                        print(value['messages'])
+                        with st.chat_message("user"):
+                            st.write(user_message)
+                        with st.chat_message("assistant"):
+                            st.write(value["messages"].content)
+
+        elif usecase=="Chatbot with Tool":
+             # Prepare state and invoke the graph
+            initial_state = {"messages": [user_message]}
+            res = graph.invoke(initial_state)
+            for message in res['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/streamlitui/loadui.py

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+import streamlit as st
+import os
+from datetime import date
+
+from langchain_core.messages import AIMessage,HumanMessage
+from src.langgraphagenticai.ui.uiconfigfile import Config
+
+
+class LoadStreamlitUI:
+    def __init__(self):
+        self.config =  Config() # config
+        self.user_controls = {}
+
+    def initialize_session(self):
+        return {
+        "current_step": "requirements",
+        "requirements": "",
+        "user_stories": "",
+        "po_feedback": "",
+        "generated_code": "",
+        "review_feedback": "",
+        "decision": None
+    }
+  
+
+
+    def load_streamlit_ui(self):
+        st.set_page_config(page_title= "🤖 " + self.config.get_page_title(), layout="wide")
+        st.header("🤖 " + self.config.get_page_title())
+        st.session_state.timeframe = ''
+        st.session_state.IsFetchButtonClicked = False
+        st.session_state.IsSDLC = False
+        
+        
+
+        with st.sidebar:
+            # Get options from config
+            llm_options = self.config.get_llm_options()
+            usecase_options = self.config.get_usecase_options()
+
+            # LLM selection
+            self.user_controls["selected_llm"] = st.selectbox("Select LLM", llm_options)
+
+            if self.user_controls["selected_llm"] == 'Groq':
+                # Model selection
+                model_options = self.config.get_groq_model_options()
+                self.user_controls["selected_groq_model"] = st.selectbox("Select Model", model_options)
+                # API key input
+                self.user_controls["GROQ_API_KEY"] = st.session_state["GROQ_API_KEY"] = st.text_input("API Key",
+                                                                                                      type="password")
+                # Validate API key
+                if not self.user_controls["GROQ_API_KEY"]:
+                    st.warning("⚠️ Please enter your GROQ API key to proceed. Don't have? refer : https://console.groq.com/keys ")
+                   
+            
+            # Use case selection
+            self.user_controls["selected_usecase"] = st.selectbox("Select Usecases", usecase_options)
+
+            if self.user_controls["selected_usecase"] =="Chatbot with Tool":
+                # API key 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 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()
+            
+            
+        
+        return self.user_controls

src/langgraphagenticai/ui/uiconfigfile.ini

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+[DEFAULT]
+PAGE_TITLE = LangGraph: Build Stateful Agentic AI graph
+LLM_OPTIONS = Groq
+USECASE_OPTIONS = Basic Chatbot, Chatbot with Tool
+GROQ_MODEL_OPTIONS = mixtral-8x7b-32768, llama3-8b-8192, llama3-70b-8192, gemma-7b-i
+

src/langgraphagenticai/ui/uiconfigfile.py

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+from configparser import ConfigParser
+
+class Config:
+    def __init__(self,config_file="./src/langgraphagenticai/ui/uiconfigfile.ini"):
+        self.config=ConfigParser()
+        self.config.read(config_file)
+
+    def get_llm_options(self):
+        return self.config["DEFAULT"].get("LLM_OPTIONS").split(", ")
+    
+    def get_usecase_options(self):
+        return self.config["DEFAULT"].get("USECASE_OPTIONS").split(", ")
+
+    def get_groq_model_options(self):
+        return self.config["DEFAULT"].get("GROQ_MODEL_OPTIONS").split(", ")
+
+    def get_page_title(self):
+        return self.config["DEFAULT"].get("PAGE_TITLE")
+

test_nb.ipynb

@@ -0,0 +1,49 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}