Update agent.py

agent.py

@@ -4,7 +4,10 @@ from langgraph.graph import START, StateGraph, MessagesState
 from langgraph.prebuilt import tools_condition
 from langgraph.prebuilt import ToolNode
 from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_groq import ChatGroq
+from langchain_openai import ChatOpenAI
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_huggingface import ChatHuggingFace
+from langchain_core.llms import HuggingFaceEndpoint
 from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
 from langchain_community.tools.tavily_search import TavilySearchResults
 from langchain_community.document_loaders import WikipediaLoader
@@ -150,17 +153,17 @@ tools = [
 ]
 
 # Build graph function
-def build_graph(provider: str = "groq"):
+def build_graph(provider: str = "openai"):
     """Build the graph"""
     # Load environment variables from .env file
     if provider == "google":
         # Google Gemini
         llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
-    elif provider == "groq":
-        # Groq https://console.groq.com/docs/models
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+    elif provider == "openai":
+        # OpenAI (e.g., GPT-4 or GPT-3.5)
+        llm = ChatOpenAI(model="gpt-4", temperature=0)
     elif provider == "huggingface":
-        # TODO: Add huggingface endpoint
+        # Hugging Face endpoint
         llm = ChatHuggingFace(
             llm=HuggingFaceEndpoint(
                 url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
@@ -168,9 +171,11 @@ def build_graph(provider: str = "groq"):
             ),
         )
     else:
-        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
+        raise ValueError("Invalid provider. Choose 'google', 'openai' or 'huggingface'.")
+        
     # Bind tools to LLM
     llm_with_tools = llm.bind_tools(tools)
+    return llm_with_tools
 
     # Node
     def assistant(state: MessagesState):