graph nodes and llm functionality added

src/llms/groq_llms.py

@@ -0,0 +1,30 @@
+import os
+import sys
+from langchain_groq import ChatGroq
+import streamlit as st
+
+class GroqLLMS:
+    def __init__(self, user_controls_input):
+        """
+        This class will validate the API key & Return the Required Groq Model as per user request
+        Args:
+            user_controls_input (dict): User Controls
+        """
+        self.user_controls= user_controls_input
+
+        def get_groq_model(self):
+            try:
+                api_key= self.user_controls["GROQ_API_KEY"]
+                model_name= self.user_controls["selected_model"]
+                if not api_key and os.environ["GROQ_API_KEY"]:
+                    st.error("Please enter your GROQ API key to proceed. Don't have? refer : https://console.groq.com/keys ", icon="🚨")
+                llm = ChatGroq(model=model_name, api_key= api_key)
+            except Exception as e:
+                st.error("Error in fetching the model " +str(e))
+            return llm
+                   
+
+       
+
+
+

src/nodes/basic_chatbot_nodes.py

@@ -0,0 +1,17 @@
+import os 
+import sys
+
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
+from src.state.state import graphstate
+from langgraph.graph import START, END, StateGraph
+
+class BasicChatbotNodes:
+    def __init__(self, model):
+        self.llm = model
+    
+    def peocess(self, state: graphstate):
+        return {"messages":
+                self.llm.invoke(state["messages"])}
+      
+
+

src/nodes/chabot_with_tool_nodes.py

@@ -0,0 +1,29 @@
+import sys
+import os
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
+from src.state.state import graphstate
+
+class ChatbotWithToolNodes:
+    def __init__(self, model):
+        self.llm = model
+    
+    def peocess(self, state: graphstate):
+        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: graphstate):
+            """
+            Chatbot logic for processing the input state and returning a response.
+            """
+            return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+        return chatbot_node

src/state/state.py

@@ -0,0 +1,18 @@
+from langgraph.graph import StateGraph
+from typing import Literal, TypedDict, List, Annotated, Optional
+from langgraph.graph.message import add_messages
+from langchain_core.messages import AIMessage, HumanMessage
+
+
+class graphstate(TypedDict):
+    """ 
+    Represents the state of the graph, any new message will be added to the messages list 
+    """
+    messages: Annotated[list, add_messages]
+    # current_step: Literal["requirements", "user_stories", "po_feedback", "generated_code", "review_feedback"]
+    # requirements: str
+    # user_stories: str
+    # po_feedback: str
+    # generated_code: str
+    # review_feedback: str
+    # decision: Optional[bool]

src/tools/search_tool.py

@@ -0,0 +1,17 @@
+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/user_interface/streamlitui/loadui.py

@@ -63,12 +63,13 @@ class LoadStreamlitUI:
             
             if "state" not in st.session_state:
                 st.session_state.state = self.initialize_session()
+        return self.user_controls
 
 
-
-if __name__ == "__main__":
-    a= LoadStreamlitUI()
-    a.load_streamlit_ui()
-    print(a.config.get_page_title())
+## UI testing code
+# if __name__ == "__main__":
+#     a= LoadStreamlitUI()
+#     a.load_streamlit_ui()
+#     print(a.config.get_page_title())