Create agent.py

agent.py

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+from langchain_community.chat_models import ChatOllama
+from langgraph.graph import MessagesState, StateGraph, START, END
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_core.tools import tool
+from langgraph.prebuilt import ToolNode
+from langchain_community.document_loaders import WikipediaLoader
+from langgraph.prebuilt import tools_condition
+from langchain_huggingface import HuggingFaceEndpoint
+import os
+from huggingface_hub import login
+from dotenv import load_dotenv
+load_dotenv()  
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HF_TOKEN")
+
+@tool
+def use_search_tool(query: str) -> str:
+    """Use the search tool to find information.
+    
+    Args: query (str): The search query.
+    Returns: str: The search result.
+    """
+    search_result = DuckDuckGoSearchRun(verbose=0).run(query)
+    return {"search_result": search_result}
+
+@tool
+def use_wikipedia_tool(query: str) -> str:
+    """Fetch a summary from Wikipedia.
+    
+    Args:
+        query (str): The topic to search on Wikipedia.
+    Returns:
+        str: A summary of the topic from Wikipedia.
+    """
+    result = WikipediaLoader(query=query, load_max_docs=2).load()
+    if result:
+        return {"Wikipedia_summary": result}
+    else:
+        return f"Sorry, I couldn't find any information on '{query}' in Wikipedia."
+
+def build_agent():
+    # llm = ChatOllama(model="llama3.1")
+    llm = HuggingFaceEndpoint(
+        endpoint_url="https://api-inference.huggingface.co/models/deepseek-ai/DeepSeek-Prover-V2-671B",
+        huggingfacehub_api_token=os.getenv("HUGGINGFACEHUB_API_TOKEN")
+    )
+    tools = [use_wikipedia_tool, use_search_tool]
+
+    system_template = (
+        "You are a helpful, friendly, and respectful AI assistant. "
+        "Always address the user politely and answer their questions in a positive manner.\n"
+        "When reasoning, always use the following format:\n"
+        "Thought: [your reasoning here]\n"
+        "Action: [the action to take, should be one of [{tool_names}]]\n"
+        "Action Input: [the input to the action]\n"
+        "If you know the answer without using a tool, respond with:\n"
+        "Thought: [your reasoning here]\n"
+        "Final Answer: [your answer here]\n"
+        "Always ensure your responses are polite, accurate, and helpful."
+    )
+    system_prompt = SystemMessage(content=system_template.format(
+        tool_names=", ".join([tool.name for tool in tools])
+    ))
+
+    def call_model(state: MessagesState):
+        """Call the LLM with the given state."""
+        messages = [system_prompt] + state["messages"]
+        response = llm.invoke(messages)
+        return {"messages" : response} 
+    
+    workflow = StateGraph(MessagesState)
+    workflow.add_node("Assistent", call_model)
+    workflow.add_node("tools", ToolNode(tools))
+    workflow.add_edge(START, "Assistent")
+    workflow.add_conditional_edges("Assistent", tools_condition)
+    workflow.add_edge("tools", "Assistent")
+    workflow.add_edge("Assistent", END)
+    return workflow.compile()
+
+if __name__ == "__main__":
+    
+
+    graph = build_agent()
+    input =  HumanMessage(content="Hello, how are you?")
+    response = graph.invoke(input)
+
+    print(response) 
+
+
+