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75 lines
2.1 KiB
Plaintext
75 lines
2.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "7e3b513e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"What is the hometown of the reigning men's U.S. Open champion?\n",
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"Are follow up questions needed here:\u001b[102m Yes.\n",
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"Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
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"Intermediate answer: \u001b[106mCarlos Alcaraz\u001b[0m.\u001b[102m\n",
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"Follow up: Where is Carlos Alcaraz from?\u001b[0m\n",
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"Intermediate answer: \u001b[106mEl Palmar, Murcia, Spain\u001b[0m.\u001b[102m\n",
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"So the final answer is: El Palmar, Murcia, Spain\u001b[0m"
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]
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},
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{
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"data": {
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"text/plain": [
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"\"What is the hometown of the reigning men's U.S. Open champion?\\nAre follow up questions needed here: Yes.\\nFollow up: Who is the reigning men's U.S. Open champion?\\nIntermediate answer: Carlos Alcaraz.\\nFollow up: Where is Carlos Alcaraz from?\\nIntermediate answer: El Palmar, Murcia, Spain.\\nSo the final answer is: El Palmar, Murcia, Spain\""
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain import SelfAskWithSearchChain, OpenAI, SerpAPIChain\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"search = SerpAPIChain()\n",
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"\n",
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"self_ask_with_search = SelfAskWithSearchChain(llm=llm, search_chain=search)\n",
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"\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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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6195fc82",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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