langchain/docs/examples/agents/self_ask_with_search.ipynb

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    "# Self Ask With Search\n",
    "\n",
    "This notebook showcases the Self Ask With Search chain."
   ]
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      "\n",
      "\n",
      "\u001b[1m> Entering new SelfAskWithSearchAgent chain...\u001b[0m\n",
      "What is the hometown of the reigning men's U.S. Open champion?\n",
      "Are follow up questions needed here:\u001b[32;1m\u001b[1;3m Yes.\n",
      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
      "Intermediate answer: \u001b[36;1m\u001b[1;3mCarlos Alcaraz\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mFollow up: Where is Carlos Alcaraz from?\u001b[0m\n",
      "Intermediate answer: \u001b[36;1m\u001b[1;3mEl Palmar, Spain\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mSo the final answer is: El Palmar, Spain\u001b[0m\n",
      "\u001b[1m> Finished SelfAskWithSearchAgent chain.\u001b[0m\n"
     ]
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     "data": {
      "text/plain": [
       "'El Palmar, Spain'"
      ]
     },
     "execution_count": 1,
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   ],
   "source": [
    "from langchain import OpenAI, SerpAPIChain\n",
    "from langchain.agents import initialize_agent, Tool\n",
    "\n",
    "llm = OpenAI(temperature=0)\n",
    "search = SerpAPIChain()\n",
    "tools = [\n",
    "    Tool(\n",
    "        name=\"Intermediate Answer\",\n",
    "        func=search.run\n",
    "    )\n",
    "]\n",
    "\n",
    "self_ask_with_search = initialize_agent(tools, llm, agent=\"self-ask-with-search\", verbose=True)\n",
    "\n",
    "self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
   ]
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   "execution_count": null,
   "id": "683d69e7",
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   "outputs": [],
   "source": []
  }
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