2023-07-24 01:00:22 +00:00
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"cells": [
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{
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"cell_type": "markdown",
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"id": "feb31cc6",
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"metadata": {},
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"source": [
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"# Running Agent as an Iterator\n",
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"\n",
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"To demonstrate the `AgentExecutorIterator` functionality, we will set up a problem where an Agent must:\n",
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"\n",
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"- Retrieve three prime numbers from a Tool\n",
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"- Multiply these together. \n",
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"\n",
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"In this simple problem we can demonstrate adding some logic to verify intermediate steps by checking whether their outputs are prime."
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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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"id": "8167db11",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"import dotenv\n",
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"import pydantic\n",
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"from langchain.agents import AgentExecutor, initialize_agent, AgentType\n",
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"from langchain.schema import AgentFinish\n",
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"from langchain.agents.tools import Tool\n",
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2023-09-17 00:22:48 +00:00
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"from langchain.chains import LLMMathChain\n",
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2023-07-24 01:00:22 +00:00
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"from langchain.chat_models import ChatOpenAI"
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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": "7e41b9e6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Uncomment if you have a .env in root of repo contains OPENAI_API_KEY\n",
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"# dotenv.load_dotenv(\"../../../../../.env\")\n",
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"\n",
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"# need to use GPT-4 here as GPT-3.5 does not understand, however hard you insist, that\n",
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"# it should use the calculator to perform the final calculation\n",
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"llm = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
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"llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "81e88aa5",
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"metadata": {},
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"source": [
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"Define tools which provide:\n",
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"- The `n`th prime number (using a small subset for this example) \n",
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2023-09-01 20:28:55 +00:00
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"- The `LLMMathChain` to act as a calculator"
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2023-07-24 01:00:22 +00:00
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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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"id": "86f04b55",
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"metadata": {},
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"outputs": [],
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"source": [
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"primes = {998: 7901, 999: 7907, 1000: 7919}\n",
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"\n",
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"\n",
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"class CalculatorInput(pydantic.BaseModel):\n",
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" question: str = pydantic.Field()\n",
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"\n",
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"\n",
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"class PrimeInput(pydantic.BaseModel):\n",
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" n: int = pydantic.Field()\n",
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"\n",
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"\n",
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"def is_prime(n: int) -> bool:\n",
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" if n <= 1 or (n % 2 == 0 and n > 2):\n",
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" return False\n",
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" for i in range(3, int(n**0.5) + 1, 2):\n",
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" if n % i == 0:\n",
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" return False\n",
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" return True\n",
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"\n",
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"\n",
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"def get_prime(n: int, primes: dict = primes) -> str:\n",
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" return str(primes.get(int(n)))\n",
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"\n",
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"\n",
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"async def aget_prime(n: int, primes: dict = primes) -> str:\n",
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" return str(primes.get(int(n)))\n",
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"\n",
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"\n",
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"tools = [\n",
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" Tool(\n",
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" name=\"GetPrime\",\n",
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" func=get_prime,\n",
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" description=\"A tool that returns the `n`th prime number\",\n",
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" args_schema=PrimeInput,\n",
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" coroutine=aget_prime,\n",
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" ),\n",
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" Tool.from_function(\n",
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" func=llm_math_chain.run,\n",
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" name=\"Calculator\",\n",
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" description=\"Useful for when you need to compute mathematical expressions\",\n",
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" args_schema=CalculatorInput,\n",
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" coroutine=llm_math_chain.arun,\n",
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" ),\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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"id": "0e660ee6",
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"metadata": {},
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"source": [
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"Construct the agent. We will use the default agent type here."
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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": 4,
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"id": "21c775b0",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = initialize_agent(\n",
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" tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, 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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"id": "a233fe4e",
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"metadata": {},
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"source": [
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"Run the iteration and perform a custom check on certain steps:"
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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": 8,
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"id": "582d61f4",
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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 chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mI need to find the 998th, 999th and 1000th prime numbers first.\n",
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"Action: GetPrime\n",
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"Action Input: 998\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m7901\u001b[0m\n",
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"Thought:Checking whether 7901 is prime...\n",
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"Should the agent continue (Y/n)?:\n",
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"Y\n",
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"\u001b[32;1m\u001b[1;3mI have the 998th prime number. Now I need to find the 999th prime number.\n",
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"Action: GetPrime\n",
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"Action Input: 999\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m7907\u001b[0m\n",
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"Thought:Checking whether 7907 is prime...\n",
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"Should the agent continue (Y/n)?:\n",
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"Y\n",
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"\u001b[32;1m\u001b[1;3mI have the 999th prime number. Now I need to find the 1000th prime number.\n",
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"Action: GetPrime\n",
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"Action Input: 1000\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m7919\u001b[0m\n",
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"Thought:Checking whether 7919 is prime...\n",
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"Should the agent continue (Y/n)?:\n",
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"Y\n",
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"\u001b[32;1m\u001b[1;3mI have all three prime numbers. Now I need to calculate the product of these numbers.\n",
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"Action: Calculator\n",
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"Action Input: 7901 * 7907 * 7919\u001b[0m\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"7901 * 7907 * 7919\u001b[32;1m\u001b[1;3m```text\n",
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"7901 * 7907 * 7919\n",
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"```\n",
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"...numexpr.evaluate(\"7901 * 7907 * 7919\")...\n",
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"\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3m494725326233\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\n",
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"Observation: \u001b[33;1m\u001b[1;3mAnswer: 494725326233\u001b[0m\n",
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"Thought:Should the agent continue (Y/n)?:\n",
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"Y\n",
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"\u001b[32;1m\u001b[1;3mI now know the final answer\n",
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"Final Answer: 494725326233\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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"source": [
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"question = \"What is the product of the 998th, 999th and 1000th prime numbers?\"\n",
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"\n",
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"for step in agent.iter(question):\n",
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" if output := step.get(\"intermediate_step\"):\n",
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" action, value = output[0]\n",
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" if action.tool == \"GetPrime\":\n",
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" print(f\"Checking whether {value} is prime...\")\n",
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" assert is_prime(int(value))\n",
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" # Ask user if they want to continue\n",
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" _continue = input(\"Should the agent continue (Y/n)?:\\n\")\n",
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" if _continue != \"Y\":\n",
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" break"
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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": "6934ff8e",
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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": "venv",
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"language": "python",
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"name": "venv"
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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": "3.11.3"
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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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