updated tools

agent.py

@@ -4,6 +4,7 @@ from langchain.prompts import PromptTemplate
 from langgraph.graph import START, StateGraph, MessagesState
 from langgraph.prebuilt import ToolNode, tools_condition
 from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.document_loaders import WikipediaLoader
 from langchain_core.messages import HumanMessage
 from langchain.tools import tool
 from langchain_core.prompts import ChatPromptTemplate
@@ -270,6 +271,33 @@ def build_graph():
             str: The weather information.
         """
         return get_weather(location, search_tool)
+    
+    @tool
+    def web_search(query: str) -> str:
+        """Search the web for a given query and return the summary.
+        Args:
+            query (str): The search query.
+        """
+        
+        search_tool = TavilySearchResults()
+        result = search_tool.run(query)
+        return result[0]['content']
+    
+    @tool
+    def wiki_search(query : str) -> str:
+        """Search Wikipedia for a given query and return the summary.
+        Args:
+            query (str): The search query.
+        """
+    
+        search_docs = WikipediaLoader(query=query, load_max_docs=1).load()
+        formatted_search_docs = "\n\n----\n\n".join(
+            [
+                f'<Document Source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}">\n{doc.page_content}\n</Document>'
+                for doc in search_docs
+            ]
+        )
+        return formatted_search_docs
 
     @tool
     def recommendation_tool(weather_condition: str) -> str:
@@ -284,7 +312,8 @@ def build_graph():
         """
         return get_recommendation(weather_condition, recommendation_chain)
 
-    tools = [weather_tool, recommendation_tool, add, subtract, multiply, divide, square, cube, power, factorial, mean, standard_deviation]
+    tools = [weather_tool, recommendation_tool, wiki_search, web_search,
+             add, subtract, multiply, divide, square, cube, power, factorial, mean, standard_deviation]
 
     llm_with_tools = llm.bind_tools(tools)
 
@@ -313,7 +342,7 @@ def build_graph():
 
 if __name__ == "__main__":
     graph = build_graph()
-    question = "What is the factorial of 6 and can you also tell me the weather in Paris?"
+    question = "How many albums were pulished by Mercedes Sosa?"
     messages = [HumanMessage(content=question)]
     result = graph.invoke({"messages": messages})
     for msg in result["messages"]: