added duckduck searchgo tool

.env

@@ -1,4 +1,9 @@
 AZURE_OPENAI_API_KEY = "3wCRrQmKGc0k5eYxe2USK4yXeuQj7EtEWjF4fw0Z7aICCKDHJKYXJQQJ99BHACHYHv6XJ3w3AAAAACOGALEY"
 AZURE_OPENAI_ENDPOINT = "https://agent-training-foundryres.openai.azure.com/"
 AZURE_OPENAI_DEPLOYMENT_NAME = "gpt-4o"
-OPENAI_API_VERSION = "2025-04-01-preview"
+OPENAI_API_VERSION = "2025-04-01-preview"
+
+# Get keys for your project from the project settings page: https://cloud.langfuse.com
+LANGFUSE_PUBLIC_KEY = "pk-lf-7eab47cc-6608-4d4d-95e9-1e830b60b9e9" 
+LANGFUSE_SECRET_KEY = "sk-lf-438ec200-3367-425a-8283-4f858ba924f1"
+LANGFUSE_HOST = "https://cloud.langfuse.com" # 🇪🇺 EU region

app.py

@@ -21,7 +21,7 @@ class BasicAgent:
 
         message = prompts_lib.system_prompt2 + question
 
-        response = main_agent.graph.invoke({'messages': message})
+        response = main_agent.agent.invoke({'messages': message})
 
         answer = response.get('messages')[1].content
 

main_agent.py

@@ -4,6 +4,8 @@ from langgraph.graph.message import add_messages
 from langchain_core.messages import AnyMessage, SystemMessage
 from langchain_openai import AzureChatOpenAI
 from langgraph.graph import START, StateGraph
+from tools import duckduck_tool
+from langgraph.prebuilt import ToolNode, tools_condition
 
 import prompts_lib as my_prompts
 
@@ -26,7 +28,11 @@ llm = AzureChatOpenAI(
     temperature=0
     )
 
+# bild tools
+tools = [duckduck_tool]
+chat_w_tools = llm.bind_tools(tools)
 
+# load system prompt
 system_prompt = my_prompts.system_prompt
 system_message = SystemMessage(content=system_prompt)
 
@@ -36,15 +42,17 @@ def assistant(state: State):
         "messages": [llm.invoke(state["messages"])]
     }
 
+
 # define graph
 builder = StateGraph(State)
 
 # add nodes
 builder.add_node("assistant", assistant)
+builder.add_node("tools", ToolNode(tools))
 
 # define edges
 builder.add_edge(START, "assistant")
-# No conditional edges in this simple example
-
+builder.add_conditional_edges("assistant", tools_condition)
+builder.add_edge("tools", "assistant")
 # compile gtaph
 agent = builder.compile()

prompts_lib.py

@@ -12,7 +12,8 @@ digits in plain text unless specified otherwise.
 If you are asked for a comma separated list, apply the above rules depending of whether the element
 to be put in the list is a number or a string.'''
 
-system_prompt2 = ''' You are a general AI assistant. I will ask you a question. Report just YOUR FINAL ANSWER.
+system_prompt2 = ''' You are a general AI assistant. I will ask you a question. Use the tools you have available if necessary.
+Report just YOUR FINAL ANSWER.
 YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated
 list of numbers and/or strings.
 If you are asked for a number, don’t use comma to write your number neither use units such as $ or

requirements.txt

@@ -2,4 +2,7 @@ gradio
 requests
 langgraph
 langchain-openai
-dotenv
+dotenv
+langchain-community
+ddgs
+langfuse

test.ipynb

@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 3,
    "id": "bbd5b78b",
    "metadata": {},
    "outputs": [],
@@ -25,61 +25,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "id": "76d480cd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "message = pl.system_prompt2 + 'what is the capital of france'"
+    "message = pl.system_prompt2 + 'How many studio albums were published by Mercedes Sousa between 2000 and 2009 included?'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "62435d86",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response = ma.graph.invoke({'messages': message})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "1ad3fa57",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Paris'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response.get('messages')[1].content"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 18,
    "id": "e097a098",
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langfuse.langchain import CallbackHandler\n",
+    "\n",
+    "# Initialize Langfuse CallbackHandler for LangGraph/Langchain (tracing)\n",
+    "langfuse_handler = CallbackHandler()\n",
+    "langfuse_handler2 = CallbackHandler()\n",
+    "\n",
     "class BasicAgent:\n",
     "    def __init__(self):\n",
-    "        pass\n",
+    "        self.agent = ma.agent\n",
     "    def __call__(self, question: str) -> str:\n",
     "        print(f\"Agent received question (first 50 chars): {question[:50]}...\")\n",
     "\n",
     "        message = pl.system_prompt2 + question\n",
     "\n",
-    "        response = ma.agent.invoke({'messages': message})\n",
+    "        response = self.agent.invoke(\n",
+    "            input={'messages': message},\n",
+    "            config={\"callbacks\": [langfuse_handler2]}\n",
+    "            )\n",
     "\n",
     "        answer = response.get('messages')[1].content\n",
     "\n",
@@ -90,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 19,
    "id": "af68a6a5",
    "metadata": {},
    "outputs": [],
@@ -100,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 20,
    "id": "02c87f0c",
    "metadata": {},
    "outputs": [
@@ -108,13 +86,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Agent received question (first 50 chars): What is the capital of Italy?...\n",
-      "Agent returning answer: Rome\n"
+      "Agent received question (first 50 chars): How many studio albums were published by Mercedes ...\n",
+      "Agent returning answer: Seven\n"
      ]
     }
    ],
    "source": [
-    "answer = agent(\"What is the capital of Italy?\")"
+    "answer = agent(\"How many studio albums were published by Mercedes Sousa between 2000 and 2009 included? use duckduckgo tool you have available to search wikipedia to respond\")"
    ]
   },
   {

tools.py

@@ -0,0 +1,4 @@
+from langchain_community.tools import DuckDuckGoSearchRun
+
+
+duckduck_tool = DuckDuckGoSearchRun()