Implemented tool calling chatbot node [skip ci]

src/basicchatbot/llms/__init__.py

@@ -1,12 +1,12 @@
 import streamlit as st
 from typing import Dict, Optional, Type, Union
-
+from langchain_core.language_models.chat_models import BaseChatModel
 from src.basicchatbot.llms.base_llm import BaseLLMProvider
 from src.basicchatbot.llms.groq_llm import GroqLLM
 from src.basicchatbot.llms.openai_llm import OpenAILLM
 
 
-def get_llm(llm_name: str, user_input: Dict[str, str]) -> Optional[BaseLLMProvider]:
+def get_llm(llm_name: str, user_input: Dict[str, str]) -> Optional[BaseChatModel]:
     """
     Function to get the appropriate LLM model instance.
     

src/basicchatbot/llms/groq_llm.py

@@ -2,12 +2,13 @@ import os
 import streamlit as st
 from typing import Dict, Optional
 from langchain_groq import ChatGroq
+from langchain_core.language_models.chat_models import BaseChatModel
 from src.basicchatbot.llms.base_llm import BaseLLMProvider
 
 
 class GroqLLM(BaseLLMProvider):
 
-    def get_llm_model(self) -> Optional[ChatGroq]:
+    def get_llm_model(self) -> Optional[BaseChatModel]:
         try:
             # Clear previous error messages
             self.error_messages = [] 

src/basicchatbot/llms/openai_llm.py

@@ -1,12 +1,13 @@
 import os
 import streamlit as st
 from typing import Dict, Optional
+from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_openai import ChatOpenAI
 from src.basicchatbot.llms.base_llm import BaseLLMProvider
 
 class OpenAILLM(BaseLLMProvider):
 
-    def get_llm_model(self) -> Optional[ChatOpenAI]:
+    def get_llm_model(self) -> Optional[BaseChatModel]:
         try:
             # Clear previous error messages
             self.error_messages = []

src/basicchatbot/nodes/__init__.py

@@ -0,0 +1,2 @@
+from src.basicchatbot.nodes.basic_chatbot_node import BasicChatBotNode
+from src.basicchatbot.nodes.chatbot_with_tools import ChatbotWithToolsNode

src/basicchatbot/nodes/chatbot_with_tools.py

@@ -0,0 +1,41 @@
+from typing import Any, List
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import AIMessage
+from src.basicchatbot.state.state import BasicChatBotState
+
+
+class ChatbotWithToolsNode:
+    """Handles chatbot interactions using an LLM and associated tools."""
+
+    def __init__(self, model: BaseChatModel, tools: List[Any]) -> None:
+        """
+        Initialize the chatbot node with a model and tools.
+
+        Args:
+            model (BaseChatModel): The language model used for processing messages.
+            tools (List[Any]): A list of tools that can be used with the chatbot.
+        """
+        self.llm = model
+        self.tools = tools
+
+    def node(self, state: BasicChatBotState) -> dict:
+        """
+        Processes the chatbot state and generates a response.
+
+        Args:
+            state (BasicChatBotState): The current chatbot state containing messages.
+
+        Returns:
+            dict: A dictionary containing the chatbot's response messages.
+        """
+        try:
+            messages = state.get("messages", [])
+            if not messages:
+                return {"messages": [AIMessage(content="ERROR: `messages` key is missing in the state. Contact developer to fix.")]}
+
+            response = self.llm.bind_tools(self.tools).invoke(input=messages)
+
+            return {"messages": [response] }
+
+        except Exception as e:
+            return {"messages": [AIMessage(content=f"Error processing request: {str(e)}")]}