langchain/docs/extras/modules/agents/how_to/custom_agent.ipynb
Davis Chase 87e502c6bc
Doc refactor (#6300)
Co-authored-by: jacoblee93 <jacoblee93@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-06-16 11:52:56 -07:00

190 lines
5.0 KiB
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{
"cells": [
{
"cell_type": "markdown",
"id": "ba5f8741",
"metadata": {},
"source": [
"# Custom agent\n",
"\n",
"This notebook goes through how to create your own custom agent.\n",
"\n",
"An agent consists of two parts:\n",
" \n",
" - Tools: The tools the agent has available to use.\n",
" - The agent class itself: this decides which action to take.\n",
" \n",
" \n",
"In this notebook we walk through how to create a custom agent."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9af9734e",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import Tool, AgentExecutor, BaseSingleActionAgent\n",
"from langchain import OpenAI, SerpAPIWrapper"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "becda2a1",
"metadata": {},
"outputs": [],
"source": [
"search = SerpAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name=\"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events\",\n",
" return_direct=True,\n",
" )\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a33e2f7e",
"metadata": {},
"outputs": [],
"source": [
"from typing import List, Tuple, Any, Union\n",
"from langchain.schema import AgentAction, AgentFinish\n",
"\n",
"\n",
"class FakeAgent(BaseSingleActionAgent):\n",
" \"\"\"Fake Custom Agent.\"\"\"\n",
"\n",
" @property\n",
" def input_keys(self):\n",
" return [\"input\"]\n",
"\n",
" def plan(\n",
" self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
" ) -> Union[AgentAction, AgentFinish]:\n",
" \"\"\"Given input, decided what to do.\n",
"\n",
" Args:\n",
" intermediate_steps: Steps the LLM has taken to date,\n",
" along with observations\n",
" **kwargs: User inputs.\n",
"\n",
" Returns:\n",
" Action specifying what tool to use.\n",
" \"\"\"\n",
" return AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\")\n",
"\n",
" async def aplan(\n",
" self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
" ) -> Union[AgentAction, AgentFinish]:\n",
" \"\"\"Given input, decided what to do.\n",
"\n",
" Args:\n",
" intermediate_steps: Steps the LLM has taken to date,\n",
" along with observations\n",
" **kwargs: User inputs.\n",
"\n",
" Returns:\n",
" Action specifying what tool to use.\n",
" \"\"\"\n",
" return AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "655d72f6",
"metadata": {},
"outputs": [],
"source": [
"agent = FakeAgent()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "490604e9",
"metadata": {},
"outputs": [],
"source": [
"agent_executor = AgentExecutor.from_agent_and_tools(\n",
" agent=agent, tools=tools, verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "653b1617",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[36;1m\u001b[1;3mThe current population of Canada is 38,669,152 as of Monday, April 24, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
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"'The current population of Canada is 38,669,152 as of Monday, April 24, 2023, based on Worldometer elaboration of the latest United Nations data.'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"How many people live in canada as of 2023?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "adefb4c2",
"metadata": {},
"outputs": [],
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}
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