Update langgraph_agent.py

langgraph_agent.py

@@ -16,7 +16,8 @@ from langchain_core.messages import SystemMessage, HumanMessage
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_core.tools import tool
 
-from langchain_google_community.tools.Google Search import GoogleSearchResults
+# Correct import for GoogleSearchAPIWrapper
+from langchain_google_community import GoogleSearchAPIWrapper
 
 @tool
 def multiply(a: int, b: int) -> int:
@@ -55,18 +56,17 @@ def wiki_search(query: str) -> dict:
         print(f"Error in wiki_search tool: {e}")
         return {"wiki_results": f"Error occurred while searching Wikipedia for '{query}'. Details: {str(e)}"}
 
-Google Search_tool = GoogleSearchResults(
-    api_key=os.getenv("GOOGLE_API_KEY"),
-    engine_id=os.getenv("GOOGLE_CSE_ID")
-)
+# Instantiate GoogleSearchAPIWrapper
+search = GoogleSearchAPIWrapper()
+
 @tool
-def google_web_search(query: str) -> dict:
+def google_web_search(query: str) -> str:
     try:
-        docs = Google Search_tool.invoke(query)
-        return {"google_web_results": docs}
+        # Use the run method of the GoogleSearchAPIWrapper instance
+        return search.run(query)
     except Exception as e:
         print(f"Error in google_web_search tool: {e}")
-        return {"google_web_results": f"Error occurred while searching the web for '{query}'. Details: {str(e)}"}
+        return f"Error occurred while searching the web for '{query}'. Details: {str(e)}"
 
 @tool
 def arvix_search(query: str) -> dict:
@@ -191,10 +191,10 @@ def build_graph(provider: str = "gemini"):
     llm_with_tools = llm.bind_tools(tools)
 
     def assistant(state: MessagesState):
-        messages_to_send = [sys_msg] + state["messages"] # This line was moved right
-        llm_response = llm_with_tools.invoke(messages_to_send) # This line was moved right
-        print(f"LLM Raw Response: {llm_response}") # This line was moved right
-        return {"messages": [llm_response]} # This line was moved right
+        messages_to_send = [sys_msg] + state["messages"]
+        llm_response = llm_with_tools.invoke(messages_to_send)
+        print(f"LLM Raw Response: {llm_response}")
+        return {"messages": [llm_response]}
 
     builder = StateGraph(MessagesState)
     builder.add_node("assistant", assistant)