mirror of
https://github.com/hwchase17/langchain.git
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add xml agent notebook
This commit is contained in:
Harrison Chase
parent
a8be207ea3
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "9926203f",
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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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"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
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"os.environ[\"LANGCHAIN_API_KEY\"] = \"\""
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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": "45bc4149",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_instructions = \"\"\"You are a helpful assistant. Help the user answer any questions.\n",
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"\n",
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"You have access to the following tools:\n",
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"\n",
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"{tools}\n",
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"\n",
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"In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. \\\n",
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"You will then get back a response in the form <observation></observation>\n",
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"For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:\n",
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"\n",
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"<tool>search</tool><tool_input>weather in SF</tool_input>\n",
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"<observation>64 degrees</observation>\n",
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"\n",
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"When you are done, respond with a final answer between <final_answer></final_answer>. For example:\n",
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"\n",
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"<final_answer>The weather in SF is 64 degrees</final_answer>\n",
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"\n",
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"Begin!\n",
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"\n",
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"Question: {question}\"\"\""
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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": "4da4c0d2",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"from langchain.chat_models import ChatAnthropic\n",
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"from langchain.prompts import ChatPromptTemplate, AIMessagePromptTemplate\n",
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"from langchain.agents import tool"
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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": "b81e9120",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = ChatAnthropic(model=\"claude-2\")"
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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": "5271f612",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt_template = ChatPromptTemplate.from_template(agent_instructions) + AIMessagePromptTemplate.from_template(\"{intermediate_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": 6,
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"id": "83780d81",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = prompt_template | model.bind(stop=[\"</tool_input>\", \"</final_answer>\"])"
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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": "c091d0e1",
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"metadata": {},
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"outputs": [],
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"source": [
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"@tool\n",
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"def search(query: str) -> str:\n",
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" \"\"\"Search things about current events.\"\"\"\n",
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" return \"32 degrees\""
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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": "1e81b05d",
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"metadata": {},
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"outputs": [],
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"source": [
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"tool_list = [search]"
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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": 9,
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"id": "5f0d986f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import Tool, AgentExecutor, BaseSingleActionAgent\n",
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"from typing import List, Tuple, Any, Union\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 AnthropicAgent(BaseSingleActionAgent):\n",
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" \n",
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" tools: List[Tool]\n",
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" chain: Any\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[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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" log = \"\"\n",
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" for action, observation in intermediate_steps:\n",
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" log += f\"<tool>{action.tool}</tool><tool_input>{action.tool_input}</tool_input><observation>{observation}</observation>\"\n",
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" tools = \"\"\n",
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" for tool in self.tools:\n",
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" tools += f\"{tool.name}: {tool.description}\\n\"\n",
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" response = self.chain.invoke({\"intermediate_steps\": log, \"tools\": tools, \"question\": kwargs[\"input\"]})\n",
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" if \"</tool>\" in response.content:\n",
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" t, ti = response.content.split(\"</tool>\")\n",
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" _t = t.split(\"<tool>\")[1]\n",
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" _ti = ti.split(\"<tool_input>\")[1]\n",
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" return AgentAction(tool=_t, tool_input=_ti, log=response.content)\n",
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" elif \"<final_answer>\" in response.content:\n",
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" t, ti = response.content.split(\"<final_answer>\")\n",
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" return AgentFinish(return_values={\"output\": ti}, log=response.content)\n",
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" else:\n",
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" raise ValueError\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[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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" raise ValueError"
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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": 10,
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"id": "315361c5",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = AnthropicAgent(tools=tool_list, chain=chain)"
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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": 11,
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"id": "bca6096f",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_executor = AgentExecutor(agent=agent, tools=tool_list, verbose=True)"
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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": 12,
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"id": "71b872b1",
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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 <tool>search</tool>\n",
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"<tool_input>weather in new york\u001b[0m\u001b[36;1m\u001b[1;3m32 degrees\u001b[0m\u001b[32;1m\u001b[1;3m\n",
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"\n",
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"<final_answer>The weather in New York is 32 degrees\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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"'The weather in New York is 32 degrees'"
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]
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},
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"execution_count": 12,
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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(\"whats the weather in New york?\")"
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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": "cca87246",
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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": "3.9.1"
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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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149
docs/extras/modules/agents/agent_types/xml_agent.ipynb
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149
docs/extras/modules/agents/agent_types/xml_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": "3c284df8",
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"metadata": {},
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"source": [
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"# XML Agent\n",
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"\n",
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"Some language models (like Anthropic's Claude) are particularly good at reasoning/writing XML. This goes over how to use an agent that uses XML when prompting. "
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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": "f9d2ead2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import XMLAgent, tool, AgentExecutor\n",
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"from langchain.chat_models import ChatAnthropic\n",
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"from langchain.chains import LLMChain"
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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": "ebadf04f",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = ChatAnthropic(model=\"claude-2\")"
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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": "6ce9f9a5",
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"metadata": {},
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"outputs": [],
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"source": [
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"@tool\n",
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"def search(query: str) -> str:\n",
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" \"\"\"Search things about current events.\"\"\"\n",
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" return \"32 degrees\""
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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": 10,
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"id": "c589944e",
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"metadata": {},
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"outputs": [],
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"source": [
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"tool_list = [search]"
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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": 11,
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"id": "2d8454be",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = LLMChain(\n",
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" llm=model,\n",
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" prompt=XMLAgent.get_default_prompt(),\n",
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" output_parser=XMLAgent.get_default_output_parser()\n",
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")\n",
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"agent = XMLAgent(tools=tool_list, llm_chain=chain)"
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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": 12,
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"id": "bca6096f",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_executor = AgentExecutor(agent=agent, tools=tool_list, verbose=True)"
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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": 13,
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"id": "71b872b1",
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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 <tool>search</tool>\n",
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"<tool_input>weather in New York\u001b[0m\u001b[36;1m\u001b[1;3m32 degrees\u001b[0m\u001b[32;1m\u001b[1;3m\n",
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"\n",
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"<final_answer>The weather in New York is 32 degrees\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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|
"'The weather in New York is 32 degrees'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 13,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"agent_executor.run(\"whats the weather in New york?\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "cca87246",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.9.1"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue
Block a user