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93 lines
2.5 KiB
Plaintext
93 lines
2.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0c3f1df8",
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"metadata": {},
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"source": [
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"# Self Ask With Search\n",
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"\n",
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"This notebook showcases the Self Ask With Search chain."
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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": 2,
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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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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m 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[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",
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"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",
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"\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
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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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"'El Palmar, Spain'"
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]
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},
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"execution_count": 2,
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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 OpenAI, SerpAPIWrapper\n",
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"from langchain.agents import initialize_agent, Tool\n",
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"from langchain.agents import AgentType\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"search = SerpAPIWrapper()\n",
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"tools = [\n",
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" Tool(\n",
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" name=\"Intermediate Answer\",\n",
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" func=search.run,\n",
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" description=\"useful for when you need to ask with search\"\n",
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" )\n",
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"]\n",
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"\n",
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"self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.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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]
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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": "3.10.9"
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},
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"vscode": {
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"interpreter": {
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"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
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}
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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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