langchain/docs/extras/modules/agents/how_to/agent_iter.ipynb

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"# 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",
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"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",
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"id": "86f04b55",
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"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"
]
},
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