mirror of
https://github.com/hwchase17/langchain.git
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add initial anthropic agent (#8468)
Co-authored-by: Nuno Campos <nuno@boringbits.io>
This commit is contained in:
Harrison Chase
GitHub
parent
a795c3d860
commit
6556a8fcfd
274
docs/extras/modules/agents/agent_types/anthropic_agent.ipynb
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274
docs/extras/modules/agents/agent_types/anthropic_agent.ipynb
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@ -0,0 +1,274 @@
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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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@ -39,6 +39,7 @@ from langchain.agents.react.base import ReActChain, ReActTextWorldAgent
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from langchain.agents.self_ask_with_search.base import SelfAskWithSearchChain
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from langchain.agents.structured_chat.base import StructuredChatAgent
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from langchain.agents.tools import Tool, tool
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from langchain.agents.xml.base import XMLAgent
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__all__ = [
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"Agent",
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@ -78,4 +79,5 @@ __all__ = [
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"load_tools",
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"tool",
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"create_xorbits_agent",
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"XMLAgent",
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]
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0
libs/langchain/langchain/agents/xml/__init__.py
Normal file
0
libs/langchain/langchain/agents/xml/__init__.py
Normal file
118
libs/langchain/langchain/agents/xml/base.py
Normal file
118
libs/langchain/langchain/agents/xml/base.py
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@ -0,0 +1,118 @@
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from typing import Any, List, Tuple, Union
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from langchain.agents.agent import AgentOutputParser, BaseSingleActionAgent
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from langchain.agents.xml.prompt import agent_instructions
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from langchain.callbacks.base import Callbacks
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from langchain.chains.llm import LLMChain
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from langchain.prompts.chat import AIMessagePromptTemplate, ChatPromptTemplate
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from langchain.schema import AgentAction, AgentFinish
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from langchain.tools.base import BaseTool
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class XMLAgentOutputParser(AgentOutputParser):
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def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
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if "</tool>" in text:
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tool, tool_input = text.split("</tool>")
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_tool = tool.split("<tool>")[1]
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_tool_input = tool_input.split("<tool_input>")[1]
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return AgentAction(tool=_tool, tool_input=_tool_input, log=text)
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elif "<final_answer>" in text:
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_, answer = text.split("<final_answer>")
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return AgentFinish(return_values={"output": answer}, log=text)
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else:
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raise ValueError
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def get_format_instructions(self) -> str:
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raise NotImplementedError
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@property
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def _type(self) -> str:
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return "xml-agent"
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class XMLAgent(BaseSingleActionAgent):
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"""Agent that uses XML tags.
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Args:
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tools: list of tools the agent can choose from
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llm_chain: The LLMChain to call to predict the next action
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Examples:
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.. code-block:: python
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from langchain.agents import XMLAgent
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from langchain
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tools = ...
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model =
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"""
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tools: List[BaseTool]
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"""List of tools this agent has access to."""
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llm_chain: LLMChain
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"""Chain to use to predict action."""
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@property
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def input_keys(self) -> List[str]:
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return ["input"]
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@staticmethod
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def get_default_prompt() -> ChatPromptTemplate:
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return ChatPromptTemplate.from_template(
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agent_instructions
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) + AIMessagePromptTemplate.from_template("{intermediate_steps}")
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@staticmethod
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def get_default_output_parser() -> XMLAgentOutputParser:
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return XMLAgentOutputParser()
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def plan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
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log = ""
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for action, observation in intermediate_steps:
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log += (
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f"<tool>{action.tool}</tool><tool_input>{action.tool_input}"
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f"</tool_input><observation>{observation}</observation>"
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)
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tools = ""
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for tool in self.tools:
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tools += f"{tool.name}: {tool.description}\n"
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inputs = {
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"intermediate_steps": log,
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"tools": tools,
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"question": kwargs["input"],
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"stop": ["</tool_input>", "</final_answer>"],
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}
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response = self.llm_chain(inputs, callbacks=callbacks)
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return response[self.llm_chain.output_key]
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async def aplan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
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log = ""
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for action, observation in intermediate_steps:
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log += (
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f"<tool>{action.tool}</tool><tool_input>{action.tool_input}"
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f"</tool_input><observation>{observation}</observation>"
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)
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tools = ""
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for tool in self.tools:
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tools += f"{tool.name}: {tool.description}\n"
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inputs = {
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"intermediate_steps": log,
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"tools": tools,
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"question": kwargs["input"],
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"stop": ["</tool_input>", "</final_answer>"],
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}
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response = await self.llm_chain.acall(inputs, callbacks=callbacks)
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return response[self.llm_chain.output_key]
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21
libs/langchain/langchain/agents/xml/prompt.py
Normal file
21
libs/langchain/langchain/agents/xml/prompt.py
Normal file
@ -0,0 +1,21 @@
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# flake8: noqa
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agent_instructions = """You are a helpful assistant. Help the user answer any questions.
|
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You have access to the following tools:
|
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|
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{tools}
|
||||
|
||||
In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. \
|
||||
You will then get back a response in the form <observation></observation>
|
||||
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:
|
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|
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<tool>search</tool><tool_input>weather in SF</tool_input>
|
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<observation>64 degrees</observation>
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|
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When you are done, respond with a final answer between <final_answer></final_answer>. For example:
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<final_answer>The weather in SF is 64 degrees</final_answer>
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Begin!
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Question: {question}"""
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@ -19,6 +19,7 @@ _EXPECTED = [
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"SelfAskWithSearchChain",
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"StructuredChatAgent",
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"Tool",
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"XMLAgent",
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"ZeroShotAgent",
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"create_csv_agent",
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"create_json_agent",
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File diff suppressed because one or more lines are too long
@ -33,16 +33,38 @@ from langchain.schema.runnable import (
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class FakeTracer(BaseTracer):
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"""Fake tracer that records LangChain execution."""
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"""Fake tracer that records LangChain execution.
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It replaces run ids with deterministic UUIDs for snapshotting."""
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def __init__(self) -> None:
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"""Initialize the tracer."""
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super().__init__()
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self.runs: List[Run] = []
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self.uuids_map: Dict[UUID, UUID] = {}
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self.uuids_generator = (
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UUID(f"00000000-0000-4000-8000-{i:012}", version=4) for i in range(10000)
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)
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def _replace_uuid(self, uuid: UUID) -> UUID:
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if uuid not in self.uuids_map:
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self.uuids_map[uuid] = next(self.uuids_generator)
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return self.uuids_map[uuid]
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||||
|
||||
def _copy_run(self, run: Run) -> Run:
|
||||
return run.copy(
|
||||
update={
|
||||
"id": self._replace_uuid(run.id),
|
||||
"parent_run_id": self.uuids_map[run.parent_run_id]
|
||||
if run.parent_run_id
|
||||
else None,
|
||||
"child_runs": [self._copy_run(child) for child in run.child_runs],
|
||||
}
|
||||
)
|
||||
|
||||
def _persist_run(self, run: Run) -> None:
|
||||
"""Persist a run."""
|
||||
self.runs.append(run)
|
||||
|
||||
self.runs.append(self._copy_run(run))
|
||||
|
||||
|
||||
class FakeRunnable(Runnable[str, int]):
|
||||
@ -78,20 +100,6 @@ class FakeRetriever(BaseRetriever):
|
||||
return [Document(page_content="foo"), Document(page_content="bar")]
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def fixed_uuids(mocker: MockerFixture) -> MockerFixture._Patcher:
|
||||
"""Note this mock only works with `import uuid; uuid.uuid4()`,
|
||||
it does not work with `from uuid import uuid4; uuid4()`."""
|
||||
|
||||
# Disable tracing to avoid fixed UUIDs causing tracing errors.
|
||||
mocker.patch.dict("os.environ", {"LANGCHAIN_TRACING_V2": "false"})
|
||||
|
||||
side_effect = (
|
||||
UUID(f"00000000-0000-4000-8000-{i:012}", version=4) for i in range(10000)
|
||||
)
|
||||
return mocker.patch("uuid.uuid4", side_effect=side_effect)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_default_method_implementations(mocker: MockerFixture) -> None:
|
||||
fake = FakeRunnable()
|
||||
@ -206,13 +214,13 @@ async def test_prompt() -> None:
|
||||
@pytest.mark.asyncio
|
||||
@freeze_time("2023-01-01")
|
||||
async def test_prompt_with_chat_model(
|
||||
mocker: MockerFixture, snapshot: SnapshotAssertion, fixed_uuids: None
|
||||
mocker: MockerFixture, snapshot: SnapshotAssertion
|
||||
) -> None:
|
||||
prompt = (
|
||||
SystemMessagePromptTemplate.from_template("You are a nice assistant.")
|
||||
+ "{question}"
|
||||
)
|
||||
chat = FakeListChatModel(responses=["foo", "bar"])
|
||||
chat = FakeListChatModel(responses=["foo"])
|
||||
|
||||
chain = prompt | chat
|
||||
|
||||
@ -251,7 +259,7 @@ async def test_prompt_with_chat_model(
|
||||
],
|
||||
dict(callbacks=[tracer]),
|
||||
) == [
|
||||
AIMessage(content="bar"),
|
||||
AIMessage(content="foo"),
|
||||
AIMessage(content="foo"),
|
||||
]
|
||||
assert prompt_spy.call_args.args[1] == [
|
||||
@ -272,7 +280,16 @@ async def test_prompt_with_chat_model(
|
||||
]
|
||||
),
|
||||
]
|
||||
assert tracer.runs == snapshot
|
||||
assert (
|
||||
len(
|
||||
[
|
||||
r
|
||||
for r in tracer.runs
|
||||
if r.parent_run_id is None and len(r.child_runs) == 2
|
||||
]
|
||||
)
|
||||
== 2
|
||||
), "Each of 2 outer runs contains exactly two inner runs (1 prompt, 1 chat)"
|
||||
mocker.stop(prompt_spy)
|
||||
mocker.stop(chat_spy)
|
||||
|
||||
@ -282,7 +299,7 @@ async def test_prompt_with_chat_model(
|
||||
tracer = FakeTracer()
|
||||
assert [
|
||||
*chain.stream({"question": "What is your name?"}, dict(callbacks=[tracer]))
|
||||
] == [AIMessage(content="bar")]
|
||||
] == [AIMessage(content="foo")]
|
||||
assert prompt_spy.call_args.args[1] == {"question": "What is your name?"}
|
||||
assert chat_spy.call_args.args[1] == ChatPromptValue(
|
||||
messages=[
|
||||
@ -295,7 +312,7 @@ async def test_prompt_with_chat_model(
|
||||
@pytest.mark.asyncio
|
||||
@freeze_time("2023-01-01")
|
||||
async def test_prompt_with_llm(
|
||||
mocker: MockerFixture, snapshot: SnapshotAssertion, fixed_uuids: None
|
||||
mocker: MockerFixture, snapshot: SnapshotAssertion
|
||||
) -> None:
|
||||
prompt = (
|
||||
SystemMessagePromptTemplate.from_template("You are a nice assistant.")
|
||||
@ -386,7 +403,7 @@ async def test_prompt_with_llm(
|
||||
|
||||
@freeze_time("2023-01-01")
|
||||
def test_prompt_with_chat_model_and_parser(
|
||||
mocker: MockerFixture, snapshot: SnapshotAssertion, fixed_uuids: None
|
||||
mocker: MockerFixture, snapshot: SnapshotAssertion
|
||||
) -> None:
|
||||
prompt = (
|
||||
SystemMessagePromptTemplate.from_template("You are a nice assistant.")
|
||||
@ -424,7 +441,7 @@ def test_prompt_with_chat_model_and_parser(
|
||||
|
||||
@freeze_time("2023-01-01")
|
||||
def test_seq_dict_prompt_llm(
|
||||
mocker: MockerFixture, snapshot: SnapshotAssertion, fixed_uuids: None
|
||||
mocker: MockerFixture, snapshot: SnapshotAssertion
|
||||
) -> None:
|
||||
passthrough = mocker.Mock(side_effect=lambda x: x)
|
||||
|
||||
@ -487,13 +504,16 @@ What is your name?"""
|
||||
]
|
||||
)
|
||||
assert parser_spy.call_args.args[1] == AIMessage(content="foo, bar")
|
||||
assert tracer.runs == snapshot
|
||||
assert len([r for r in tracer.runs if r.parent_run_id is None]) == 1
|
||||
parent_run = next(r for r in tracer.runs if r.parent_run_id is None)
|
||||
assert len(parent_run.child_runs) == 4
|
||||
map_run = parent_run.child_runs[0]
|
||||
assert map_run.name == "RunnableMap"
|
||||
assert len(map_run.child_runs) == 3
|
||||
|
||||
|
||||
@freeze_time("2023-01-01")
|
||||
def test_seq_prompt_dict(
|
||||
mocker: MockerFixture, snapshot: SnapshotAssertion, fixed_uuids: None
|
||||
) -> None:
|
||||
def test_seq_prompt_dict(mocker: MockerFixture, snapshot: SnapshotAssertion) -> None:
|
||||
passthrough = mocker.Mock(side_effect=lambda x: x)
|
||||
|
||||
prompt = (
|
||||
@ -544,13 +564,16 @@ def test_seq_prompt_dict(
|
||||
HumanMessage(content="What is your name?"),
|
||||
]
|
||||
)
|
||||
assert tracer.runs == snapshot
|
||||
assert len([r for r in tracer.runs if r.parent_run_id is None]) == 1
|
||||
parent_run = next(r for r in tracer.runs if r.parent_run_id is None)
|
||||
assert len(parent_run.child_runs) == 3
|
||||
map_run = parent_run.child_runs[2]
|
||||
assert map_run.name == "RunnableMap"
|
||||
assert len(map_run.child_runs) == 2
|
||||
|
||||
|
||||
@freeze_time("2023-01-01")
|
||||
def test_seq_prompt_map(
|
||||
mocker: MockerFixture, snapshot: SnapshotAssertion, fixed_uuids: None
|
||||
) -> None:
|
||||
def test_seq_prompt_map(mocker: MockerFixture, snapshot: SnapshotAssertion) -> None:
|
||||
passthrough = mocker.Mock(side_effect=lambda x: x)
|
||||
|
||||
prompt = (
|
||||
@ -608,7 +631,12 @@ def test_seq_prompt_map(
|
||||
HumanMessage(content="What is your name?"),
|
||||
]
|
||||
)
|
||||
assert tracer.runs == snapshot
|
||||
assert len([r for r in tracer.runs if r.parent_run_id is None]) == 1
|
||||
parent_run = next(r for r in tracer.runs if r.parent_run_id is None)
|
||||
assert len(parent_run.child_runs) == 3
|
||||
map_run = parent_run.child_runs[2]
|
||||
assert map_run.name == "RunnableMap"
|
||||
assert len(map_run.child_runs) == 3
|
||||
|
||||
|
||||
def test_bind_bind() -> None:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user