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app1.py

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+from typing import TypedDict, Annotated
+from langgraph.graph.message import add_messages
+from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
+from langgraph.prebuilt import ToolNode
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import tools_condition
+from langgraph.checkpoint.memory import InMemorySaver  
+from langchain_ollama import ChatOllama
+from tool import DuckDuckGoSearchRun,web_search_tool,latest_news_tool, get_weather_tool as weather_info_tool, hub_stats_tool
+from retriever import guest_info_tool_1
+
+
+
+
+model = ChatOllama(
+        model="qwen2.5:1.5b",  # Or try other Ollama-supported models
+        base_url="http://127.0.0.1:11434",  # Default Ollama local server
+            num_predict=256
+        )
+
+tools=[weather_info_tool,web_search_tool,weather_info_tool,hub_stats_tool,guest_info_tool_1]
+model_with_tool = model.bind_tools(tools)
+
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage],add_messages]
+
+
+def assistant(state: AgentState):
+    return {
+        "messages": [model_with_tool.invoke(state["messages"])],
+    }
+
+builder = StateGraph(AgentState)
+
+builder.add_node("assistant",assistant)
+builder.add_node('tools',ToolNode(tools))
+
+builder.add_edge(START,'assistant')
+builder.add_conditional_edges(
+    'assistant',
+    tools_condition
+)
+builder.add_edge('tools','assistant')
+
+checkpointer = InMemorySaver()
+
+alfred = builder.compile(checkpointer=checkpointer)
+thread_config = {"configurable": {"thread_id": "1"}}
+
+print("🎩 Alfred: Hello, I am Alfred. How can I assist you today?")
+print("Type 'exit' or 'quit' to stop.\n")
+
+while True:
+    user_input = input("You: ")
+
+    if user_input.lower() in ["exit", "quit"]:
+        print("Alfred: Goodbye.")
+        break
+
+    response = alfred.invoke(
+        {"messages": [HumanMessage(content=user_input)]},
+        thread_config
+    )
+
+    ai_reply = response["messages"][-1].content
+
+    print("\n🎩 Alfred:", ai_reply)
+    print("-" * 40)

tool.py

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+from langchain_core.tools import tool
+from langchain_community.tools import DuckDuckGoSearchRun
+import random
+from huggingface_hub import list_models
+
+search = DuckDuckGoSearchRun()
+
+@tool
+def web_search_tool(query: str) -> str:
+    """Search the web for information about unfamiliar guests."""
+    return search.run(query)
+
+@tool
+def latest_news_tool(topic: str) -> str:
+    """
+    Get the latest news about a specific topic.
+    """
+    
+    query = f"latest news about {topic}"
+    
+    results = search.run(query)
+    
+    return results
+
+
+@tool
+def get_weather_tool(location:str)-> str:
+    """Fetches weather information for a given location."""
+
+    weather_condition =[
+         {"condition": "Rainy", "temp_c": 15},
+        {"condition": "Clear", "temp_c": 25},
+        {"condition": "Windy", "temp_c": 20}
+    ]
+    data = random.choice(weather_condition)
+    return f"Wrather in {location} is  {data['condition']} with temp : {data['temp_c']}c"
+
+
+@tool
+def hub_stats_tool(author: str) -> str:
+    """Fetches the most downloaded model from a specific author on the Hugging Face Hub."""
+    try:
+        # List models from the specified author, sorted by downloads
+        models = list(list_models(author=author, sort="downloads", direction=-1, limit=1))
+
+        if models:
+            model = models[0]
+            return f"The most downloaded model by {author} is {model.id} with {model.downloads:,} downloads."
+        else:
+            return f"No models found for author {author}."
+    except Exception as e:
+        return f"Error fetching models for {author}: {str(e)}"
+    
+