{ "cells": [ { "cell_type": "markdown", "id": "feb31cc6", "metadata": {}, "source": [ "# Running Agent as an Iterator\n", "\n", "To demonstrate the `AgentExecutorIterator` functionality, we will set up a problem where an Agent must:\n", "\n", "- Retrieve three prime numbers from a Tool\n", "- Multiply these together. \n", "\n", "In this simple problem we can demonstrate adding some logic to verify intermediate steps by checking whether their outputs are prime." ] }, { "cell_type": "code", "execution_count": 1, "id": "8167db11", "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import dotenv\n", "import pydantic\n", "from langchain.agents import AgentExecutor, initialize_agent, AgentType\n", "from langchain.schema import AgentFinish\n", "from langchain.agents.tools import Tool\n", "from langchain import LLMMathChain\n", "from langchain.chat_models import ChatOpenAI" ] }, { "cell_type": "code", "execution_count": 2, "id": "7e41b9e6", "metadata": {}, "outputs": [], "source": [ "# Uncomment if you have a .env in root of repo contains OPENAI_API_KEY\n", "# dotenv.load_dotenv(\"../../../../../.env\")\n", "\n", "# need to use GPT-4 here as GPT-3.5 does not understand, however hard you insist, that\n", "# it should use the calculator to perform the final calculation\n", "llm = ChatOpenAI(temperature=0, model=\"gpt-4\")\n", "llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)" ] }, { "cell_type": "markdown", "id": "81e88aa5", "metadata": {}, "source": [ "Define tools which provide:\n", "- The `n`th prime number (using a small subset for this example) \n", "- The LLMMathChain to act as a calculator" ] }, { "cell_type": "code", "execution_count": 3, "id": "86f04b55", "metadata": {}, "outputs": [], "source": [ "primes = {998: 7901, 999: 7907, 1000: 7919}\n", "\n", "\n", "class CalculatorInput(pydantic.BaseModel):\n", " question: str = pydantic.Field()\n", "\n", "\n", "class PrimeInput(pydantic.BaseModel):\n", " n: int = pydantic.Field()\n", "\n", "\n", "def is_prime(n: int) -> bool:\n", " if n <= 1 or (n % 2 == 0 and n > 2):\n", " return False\n", " for i in range(3, int(n**0.5) + 1, 2):\n", " if n % i == 0:\n", " return False\n", " return True\n", "\n", "\n", "def get_prime(n: int, primes: dict = primes) -> str:\n", " return str(primes.get(int(n)))\n", "\n", "\n", "async def aget_prime(n: int, primes: dict = primes) -> str:\n", " return str(primes.get(int(n)))\n", "\n", "\n", "tools = [\n", " Tool(\n", " name=\"GetPrime\",\n", " func=get_prime,\n", " description=\"A tool that returns the `n`th prime number\",\n", " args_schema=PrimeInput,\n", " coroutine=aget_prime,\n", " ),\n", " Tool.from_function(\n", " func=llm_math_chain.run,\n", " name=\"Calculator\",\n", " description=\"Useful for when you need to compute mathematical expressions\",\n", " args_schema=CalculatorInput,\n", " coroutine=llm_math_chain.arun,\n", " ),\n", "]" ] }, { "cell_type": "markdown", "id": "0e660ee6", "metadata": {}, "source": [ "Construct the agent. We will use the default agent type here." ] }, { "cell_type": "code", "execution_count": 4, "id": "21c775b0", "metadata": {}, "outputs": [], "source": [ "agent = initialize_agent(\n", " tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n", ")" ] }, { "cell_type": "markdown", "id": "a233fe4e", "metadata": {}, "source": [ "Run the iteration and perform a custom check on certain steps:" ] }, { "cell_type": "code", "execution_count": 8, "id": "582d61f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\u001b[1m> Entering new chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3mI need to find the 998th, 999th and 1000th prime numbers first.\n", "Action: GetPrime\n", "Action Input: 998\u001b[0m\n", "Observation: \u001b[36;1m\u001b[1;3m7901\u001b[0m\n", "Thought:Checking whether 7901 is prime...\n", "Should the agent continue (Y/n)?:\n", "Y\n", "\u001b[32;1m\u001b[1;3mI have the 998th prime number. Now I need to find the 999th prime number.\n", "Action: GetPrime\n", "Action Input: 999\u001b[0m\n", "Observation: \u001b[36;1m\u001b[1;3m7907\u001b[0m\n", "Thought:Checking whether 7907 is prime...\n", "Should the agent continue (Y/n)?:\n", "Y\n", "\u001b[32;1m\u001b[1;3mI have the 999th prime number. Now I need to find the 1000th prime number.\n", "Action: GetPrime\n", "Action Input: 1000\u001b[0m\n", "Observation: \u001b[36;1m\u001b[1;3m7919\u001b[0m\n", "Thought:Checking whether 7919 is prime...\n", "Should the agent continue (Y/n)?:\n", "Y\n", "\u001b[32;1m\u001b[1;3mI have all three prime numbers. Now I need to calculate the product of these numbers.\n", "Action: Calculator\n", "Action Input: 7901 * 7907 * 7919\u001b[0m\n", "\n", "\u001b[1m> Entering new chain...\u001b[0m\n", "7901 * 7907 * 7919\u001b[32;1m\u001b[1;3m```text\n", "7901 * 7907 * 7919\n", "```\n", "...numexpr.evaluate(\"7901 * 7907 * 7919\")...\n", "\u001b[0m\n", "Answer: \u001b[33;1m\u001b[1;3m494725326233\u001b[0m\n", "\u001b[1m> Finished chain.\u001b[0m\n", "\n", "Observation: \u001b[33;1m\u001b[1;3mAnswer: 494725326233\u001b[0m\n", "Thought:Should the agent continue (Y/n)?:\n", "Y\n", "\u001b[32;1m\u001b[1;3mI now know the final answer\n", "Final Answer: 494725326233\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] } ], "source": [ "question = \"What is the product of the 998th, 999th and 1000th prime numbers?\"\n", "\n", "for step in agent.iter(question):\n", " if output := step.get(\"intermediate_step\"):\n", " action, value = output[0]\n", " if action.tool == \"GetPrime\":\n", " print(f\"Checking whether {value} is prime...\")\n", " assert is_prime(int(value))\n", " # Ask user if they want to continue\n", " _continue = input(\"Should the agent continue (Y/n)?:\\n\")\n", " if _continue != \"Y\":\n", " break" ] }, { "cell_type": "code", "execution_count": null, "id": "6934ff8e", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "venv", "language": "python", "name": "venv" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.3" } }, "nbformat": 4, "nbformat_minor": 5 }