langchain/docs/modules/agents/implementations/self_ask_with_search.ipynb

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{
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"source": [
"# Self Ask With Search\n",
"\n",
"This notebook showcases the Self Ask With Search chain."
]
},
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{
"cell_type": "code",
"execution_count": 2,
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"id": "7e3b513e",
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"text": [
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"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\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 won the 2022 Men's single title while Poland's Iga Swiatek won the Women's single title defeating Tunisian's Ons Jabeur.\u001b[0m\n",
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"\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",
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"\u001b[32;1m\u001b[1;3mSo the final answer is: El Palmar, Spain\u001b[0m\n",
"\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
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]
},
{
"data": {
"text/plain": [
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"'El Palmar, Spain'"
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]
},
"execution_count": 2,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"from langchain import OpenAI, SerpAPIWrapper\n",
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"from langchain.agents import initialize_agent, Tool\n",
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"\n",
"llm = OpenAI(temperature=0)\n",
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"search = SerpAPIWrapper()\n",
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"tools = [\n",
" Tool(\n",
" name=\"Intermediate Answer\",\n",
" func=search.run,\n",
" description=\"useful for when you need to ask with search\"\n",
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" )\n",
"]\n",
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"\n",
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"self_ask_with_search = initialize_agent(tools, llm, agent=\"self-ask-with-search\", verbose=True)\n",
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"self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
]
}
],
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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