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https://github.com/hwchase17/langchain.git
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[documentation] documentation revamp (#15281)
needs new versions of langchain-core and langchain --------- Co-authored-by: Nuno Campos <nuno@langchain.dev>
This commit is contained in:
Harrison Chase
GitHub
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cookbook/custom_multi_action_agent.ipynb
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cookbook/custom_multi_action_agent.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ba5f8741",
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"metadata": {},
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"source": [
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"# Custom multi-action agent\n",
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"\n",
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"This notebook goes through how to create your own custom agent.\n",
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"\n",
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"An agent consists of two parts:\n",
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"\n",
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"- Tools: The tools the agent has available to use.\n",
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"- The agent class itself: this decides which action to take.\n",
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" \n",
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" \n",
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"In this notebook we walk through how to create a custom agent that predicts/takes multiple steps at a time."
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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": "9af9734e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import AgentExecutor, BaseMultiActionAgent, Tool\n",
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"from langchain.utilities import SerpAPIWrapper"
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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": "d7c4ebdc",
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"metadata": {},
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"outputs": [],
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"source": [
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"def random_word(query: str) -> str:\n",
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" print(\"\\nNow I'm doing this!\")\n",
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" return \"foo\""
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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": "becda2a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"search = SerpAPIWrapper()\n",
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"tools = [\n",
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" Tool(\n",
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" name=\"Search\",\n",
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" func=search.run,\n",
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" description=\"useful for when you need to answer questions about current events\",\n",
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" ),\n",
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" Tool(\n",
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" name=\"RandomWord\",\n",
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" func=random_word,\n",
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" description=\"call this to get a random word.\",\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": "code",
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"execution_count": 4,
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"id": "a33e2f7e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Any, List, Tuple, Union\n",
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"\n",
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"from langchain.schema import AgentAction, AgentFinish\n",
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"\n",
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"\n",
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"class FakeAgent(BaseMultiActionAgent):\n",
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" \"\"\"Fake Custom Agent.\"\"\"\n",
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"\n",
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" @property\n",
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" def input_keys(self):\n",
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" return [\"input\"]\n",
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"\n",
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" def plan(\n",
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" self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
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" ) -> Union[List[AgentAction], AgentFinish]:\n",
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" \"\"\"Given input, decided what to do.\n",
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"\n",
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" Args:\n",
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" intermediate_steps: Steps the LLM has taken to date,\n",
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" along with observations\n",
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" **kwargs: User inputs.\n",
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"\n",
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" Returns:\n",
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" Action specifying what tool to use.\n",
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" \"\"\"\n",
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" if len(intermediate_steps) == 0:\n",
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" return [\n",
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" AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\"),\n",
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" AgentAction(tool=\"RandomWord\", tool_input=kwargs[\"input\"], log=\"\"),\n",
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" ]\n",
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" else:\n",
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" return AgentFinish(return_values={\"output\": \"bar\"}, log=\"\")\n",
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"\n",
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" async def aplan(\n",
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" self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
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" ) -> Union[List[AgentAction], AgentFinish]:\n",
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" \"\"\"Given input, decided what to do.\n",
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"\n",
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" Args:\n",
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" intermediate_steps: Steps the LLM has taken to date,\n",
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" along with observations\n",
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" **kwargs: User inputs.\n",
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"\n",
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" Returns:\n",
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" Action specifying what tool to use.\n",
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" \"\"\"\n",
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" if len(intermediate_steps) == 0:\n",
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" return [\n",
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" AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\"),\n",
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" AgentAction(tool=\"RandomWord\", tool_input=kwargs[\"input\"], log=\"\"),\n",
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" ]\n",
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" else:\n",
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" return AgentFinish(return_values={\"output\": \"bar\"}, log=\"\")"
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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": 5,
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"id": "655d72f6",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = FakeAgent()"
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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": 6,
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"id": "490604e9",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_executor = AgentExecutor.from_agent_and_tools(\n",
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" agent=agent, tools=tools, 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": "code",
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"execution_count": 7,
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"id": "653b1617",
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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;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",
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"Now I'm doing this!\n",
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"\u001b[33;1m\u001b[1;3mfoo\u001b[0m\u001b[32;1m\u001b[1;3m\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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"'bar'"
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]
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},
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"execution_count": 7,
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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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"agent_executor.run(\"How many people live in canada as of 2023?\")"
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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": "adefb4c2",
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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": "3.11.3"
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},
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"vscode": {
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"interpreter": {
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"hash": "18784188d7ecd866c0586ac068b02361a6896dc3a29b64f5cc957f09c590acef"
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}
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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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