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133 lines
3.6 KiB
Plaintext
133 lines
3.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Human as a tool\n",
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"\n",
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"Human are AGI so they can certainly be used as a tool to help out AI agent \n",
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"when it is confused."
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.agents import load_tools, initialize_agent\n",
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"\n",
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"llm = ChatOpenAI(temperature=0.0)\n",
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"math_llm = OpenAI(temperature=0.0)\n",
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"tools = load_tools(\n",
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" [\"human\", \"llm-math\"], \n",
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" llm=math_llm,\n",
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")\n",
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"\n",
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"agent_chain = initialize_agent(\n",
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" tools,\n",
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" llm,\n",
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" agent=\"zero-shot-react-description\",\n",
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" verbose=True,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In the above code you can see the tool takes input directly from command line.\n",
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"You can customize `prompt_func` and `input_func` according to your need."
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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": 3,
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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;3mI don't know Eric Zhu, so I should ask a human for guidance.\n",
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"Action: Human\n",
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"Action Input: \"Do you know when Eric Zhu's birthday is?\"\u001b[0m\n",
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"\n",
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"Do you know when Eric Zhu's birthday is?\n",
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"last week\n",
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"\n",
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"Observation: \u001b[36;1m\u001b[1;3mlast week\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3mThat's not very helpful. I should ask for more information.\n",
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"Action: Human\n",
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"Action Input: \"Do you know the specific date of Eric Zhu's birthday?\"\u001b[0m\n",
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"\n",
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"Do you know the specific date of Eric Zhu's birthday?\n",
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"august 1st\n",
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"\n",
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"Observation: \u001b[36;1m\u001b[1;3maugust 1st\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3mNow that I have the date, I can check if it's a leap year or not.\n",
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"Action: Calculator\n",
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"Action Input: \"Is 2021 a leap year?\"\u001b[0m\n",
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"Observation: \u001b[33;1m\u001b[1;3mAnswer: False\n",
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"\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3mI have all the information I need to answer the original question.\n",
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"Final Answer: Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished 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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"\"Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\""
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]
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},
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"execution_count": 3,
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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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"\n",
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"agent_chain.run(\"What is Eric Zhu's birthday?\")\n",
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"# Answer with \"last week\""
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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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"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": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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