Merge branch 'main' of https://huggingface.co/spaces/zhengogo/LangChainGo

agent_chat_memory.ipynb

@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import Tool\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.utilities import SerpAPIWrapper\n",
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.agents import AgentType"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = SerpAPIWrapper()\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name = \"Current Search\",\n",
+    "        func=search.run,\n",
+    "        description=\"useful for when you need to answer questions about current events or the current state of the world. the input to this should be a single search term.\"\n",
+    "    ),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm=ChatOpenAI(temperature=0)\n",
+    "agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_chain.run(input=\"你好，我叫李振\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_chain.run(input=\"今天是几号\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_chain.run(input=\"回答对吗\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_chain.run(input=\"我叫什么\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.10.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

agent_csv.ipynb

@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import create_csv_agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = create_csv_agent(OpenAI(temperature=0), '.inner/xbxb.csv', verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to count the rows\n",
+      "Action: python_repl_ast\n",
+      "Action Input: len(df)\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m59867\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: There are 59867 rows.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'There are 59867 rows.'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"how many rows are there?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to find out what values are in the domain释义 column\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df['domain释义'].unique()\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m[nan '歌曲点播' '天气查询' '笑话' '诗词背诵' '故事点播' '音量控制' '电量查询' '屏幕亮度' '绕口令' '十万个为什么'\n",
+      " '儿童聊天' '维基百科' '音量调节' '日期查询' '四则运算' '拍照' '知识库' '意图不明确' '闹钟' '休眠']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: domain释义这一列有nan '歌曲点播' '天气查询' '笑话' '诗词背诵' '故事点播' '音量控制' '电量查询' '屏幕亮度' '绕口令' '十万个为什么' '儿童聊天' '维基百科' '音量调节' '日期查询' '四则运算' '拍照' '知识库' '意图不明确' '闹钟' '休眠'\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"domain释义这一列有nan '歌曲点播' '天气查询' '笑话' '诗词背诵' '故事点播' '音量控制' '电量查询' '屏幕亮度' '绕口令' '十万个为什么' '儿童聊天' '维基百科' '音量调节' '日期查询' '四则运算' '拍照' '知识库' '意图不明确' '闹钟' '休眠'\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"domain释义这一列，有哪些内容\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to find out what type of domain释义 is most common\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df['domain释义'].value_counts()\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m儿童聊天      9037\n",
+      "意图不明确     4335\n",
+      "十万个为什么    3585\n",
+      "日期查询      2492\n",
+      "诗词背诵      2174\n",
+      "知识库       1127\n",
+      "天气查询      1125\n",
+      "四则运算      1072\n",
+      "维基百科       735\n",
+      "笑话         531\n",
+      "绕口令        250\n",
+      "故事点播       245\n",
+      "歌曲点播       226\n",
+      "闹钟          64\n",
+      "音量调节        58\n",
+      "屏幕亮度        41\n",
+      "电量查询        41\n",
+      "音量控制        41\n",
+      "拍照          16\n",
+      "休眠           5\n",
+      "Name: domain释义, dtype: int64\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m 儿童聊天类型最多\n",
+      "Final Answer: 儿童聊天类型最多\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'儿童聊天类型最多'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"domain释义这一列，哪种类型最多\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.10.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

agent_custom.ipynb

@@ -0,0 +1,150 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import Tool, AgentExecutor, BaseSingleActionAgent\n",
+    "from langchain import OpenAI, SerpAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List, Tuple, Any, Union\n",
+    "from langchain.schema import AgentAction, AgentFinish\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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = FakeAgent()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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 38,680,894 as of Sunday, May 7, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The current population of Canada is 38,680,894 as of Sunday, May 7, 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?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.10.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

agent_custom_lizhen.ipynb

@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'22222, lizhen'"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 测试，自定义tool，一个输入\n",
+    "from langchain.agents import load_tools, AgentExecutor\n",
+    "from langchain.agents import AgentType\n",
+    "from pydantic import BaseModel, Field\n",
+    "from langchain.tools import Tool\n",
+    "\n",
+    "class SingleInput(BaseModel):\n",
+    "    msg: str = Field()\n",
+    "\n",
+    "def singleInputAddLizhen(msg):\n",
+    "    return msg+\", lizhen\"\n",
+    "\n",
+    "singleTool = Tool.from_function(\n",
+    "    name = \"SingleInput\",\n",
+    "        func=singleInputAddLizhen,\n",
+    "        description=\"数字相加\",\n",
+    "        args_schema=SingleInput,\n",
+    "        return_direct=False\n",
+    ")\n",
+    "\n",
+    "singleTool.run(tool_input=\"22222\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'result=3'"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 测试，自定义tool，多个输入\n",
+    "from langchain.tools import StructuredTool\n",
+    "\n",
+    "class MultiInput(BaseModel):\n",
+    "    a: int = Field()\n",
+    "    b: int = Field()\n",
+    "    \n",
+    "def multiInput(a, b) -> str:\n",
+    "    return f\"result={a+b}\"\n",
+    "\n",
+    "multiTool = StructuredTool.from_function(\n",
+    "    name = \"MultiInput\",\n",
+    "        func=multiInput,\n",
+    "        description=\"多个输入，数字相加\",\n",
+    "        args_schema=MultiInput,\n",
+    "        return_direct=True\n",
+    ")\n",
+    "\n",
+    "multiTool.run(tool_input={\"a\":1, \"b\":2})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "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;3mresult=3\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'result=3'"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#自定义代理\n",
+    "from langchain.agents import Tool, AgentExecutor, BaseSingleActionAgent\n",
+    "from typing import List, Tuple, Any, Union\n",
+    "from langchain.schema import AgentAction, AgentFinish\n",
+    "\n",
+    "class LizhenAgent(BaseSingleActionAgent):\n",
+    "    \"\"\"自定义代理\"\"\"\n",
+    "    \n",
+    "    @property\n",
+    "    def input_keys(self):\n",
+    "        return [\"input\"]\n",
+    "    \n",
+    "    def plan(\n",
+    "        self,\n",
+    "        intermediate_steps: List[Tuple[AgentAction, str]],\n",
+    "        **kwargs: Any,\n",
+    "    ) -> Union[AgentAction, AgentFinish]:\n",
+    "        return AgentAction(tool=\"MultiInput\", tool_input=kwargs[\"input\"], log=\"\")\n",
+    "    \n",
+    "    def aplan(\n",
+    "        self,\n",
+    "        intermediate_steps: List[Tuple[AgentAction, str]],\n",
+    "        **kwargs: Any,\n",
+    "    ) -> Union[AgentAction, AgentFinish]:\n",
+    "        return AgentAction(tool=\"MultiInput\", tool_input=kwargs[\"input\"], log=\"\")\n",
+    "    \n",
+    "agent = LizhenAgent()\n",
+    "tools = [multiTool, singleTool]\n",
+    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)\n",
+    "agent_executor.run(input={\"a\":1, \"b\":2})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'BaseMultiActionAgent , run tool1'"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.tools import tool\n",
+    "\n",
+    "@tool\n",
+    "def tool1(event) -> str:\n",
+    "    \"\"\"这里必须添加描述，不然AssertionError: Function must have a docstring if description not provided.\"\"\"\n",
+    "    return f\"BaseMultiActionAgent , {event}\"\n",
+    "\n",
+    "tool1.run(\"run tool1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'BaseMultiActionAgent , run tool2'"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.tools import tool\n",
+    "\n",
+    "@tool\n",
+    "def tool2(event) -> str:\n",
+    "    \"\"\"这里必须添加描述，不然AssertionError: Function must have a docstring if description not provided.\"\"\"\n",
+    "    return f\"BaseMultiActionAgent , {event}\"\n",
+    "\n",
+    "tool2.run(\"run tool2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "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[0mMultiInput is not a valid tool, try another one.\u001b[32;1m\u001b[1;3m\u001b[0mSingleInput is not a valid tool, try another one.\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'bar'"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#自定义代理\n",
+    "from langchain.agents import Tool, AgentExecutor, BaseMultiActionAgent\n",
+    "from typing import List, Tuple, Any, Union\n",
+    "from langchain.schema import AgentAction, AgentFinish\n",
+    "\n",
+    "class LizhenMultiAgent(BaseMultiActionAgent):\n",
+    "    \"\"\"自定义代理\"\"\"\n",
+    "    \n",
+    "    @property\n",
+    "    def input_keys(self):\n",
+    "        return [\"input\"]\n",
+    "    \n",
+    "    def plan(\n",
+    "        self,\n",
+    "        intermediate_steps: List[Tuple[AgentAction, str]],\n",
+    "        **kwargs: Any,\n",
+    "    ) -> Union[AgentAction, AgentFinish]:\n",
+    "        if len(intermediate_steps) == 0:\n",
+    "            return [AgentAction(tool=\"MultiInput\", tool_input=kwargs[\"input\"], log=\"\"), AgentAction(tool=\"SingleInput\", tool_input=kwargs[\"input\"], log=\"\")]\n",
+    "        else:\n",
+    "            return AgentFinish(return_values={\"output\": \"bar\"}, log=\"\")\n",
+    "    def aplan(\n",
+    "        self,\n",
+    "        intermediate_steps: List[Tuple[AgentAction, str]],\n",
+    "        **kwargs: Any,\n",
+    "    ) -> Union[AgentAction, AgentFinish]:\n",
+    "        if len(intermediate_steps) == 0:\n",
+    "            return [AgentAction(tool=\"MultiInput\", tool_input=kwargs[\"input\"], log=\"\"), AgentAction(tool=\"SingleInput\", tool_input=kwargs[\"input\"], log=\"\")]\n",
+    "        else:\n",
+    "            return AgentFinish(return_values={\"output\": \"bar\"}, log=\"\")\n",
+    "    \n",
+    "multiAgent = LizhenMultiAgent()\n",
+    "multiTools = [tool1, tool2]\n",
+    "multi_agent_executor = AgentExecutor.from_agent_and_tools(agent=multiAgent, tools=multiTools, verbose=True)\n",
+    "multi_agent_executor.run(input={\"a\":1, \"b\":2})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.10.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

agent_multi_action.ipynb

@@ -0,0 +1,209 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import Tool, AgentExecutor, BaseMultiActionAgent\n",
+    "from langchain import OpenAI, SerpAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def random_word(query: str) -> str:\n",
+    "    print(\"\\nNow I'm doing this!\")\n",
+    "    return f\"{query}, foo\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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",
+    "    ),\n",
+    "    Tool(\n",
+    "        name = \"RandomWord\",\n",
+    "        func=random_word,\n",
+    "        description=\"call this to get a random word.\",\n",
+    "    )\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.tools import tool\n",
+    "\n",
+    "@tool\n",
+    "def tool1(event) -> str:\n",
+    "    \"\"\"这里必须添加描述，不然AssertionError: Function must have a docstring if description not provided.\"\"\"\n",
+    "    return \"111\"\n",
+    "\n",
+    "from langchain.tools import tool\n",
+    "\n",
+    "@tool\n",
+    "def tool2(event) -> str:\n",
+    "    \"\"\"这里必须添加描述，不然AssertionError: Function must have a docstring if description not provided.\"\"\"\n",
+    "    return \"222\"\n",
+    "\n",
+    "tools = [tool1, tool2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List, Tuple, Any, Union\n",
+    "from langchain.schema import AgentAction, AgentFinish\n",
+    "\n",
+    "class FakeAgent(BaseMultiActionAgent):\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[List[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",
+    "        print(\"lizhen0\",intermediate_steps)\n",
+    "        if len(intermediate_steps) == 0:\n",
+    "            print(\"lizhen1\",intermediate_steps)\n",
+    "            return [\n",
+    "                AgentAction(tool=\"tool1\", tool_input=kwargs[\"input\"], log=\"\"),\n",
+    "                AgentAction(tool=\"tool2\", tool_input=kwargs[\"input\"], log=\"\"),\n",
+    "            ]\n",
+    "        else:\n",
+    "            for tuple in intermediate_steps:\n",
+    "                print(tuple[0].tool, tuple[1])\n",
+    "            return AgentFinish(return_values={\"output\": \"bar\"}, log=\"\")\n",
+    "\n",
+    "    async def aplan(\n",
+    "        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
+    "    ) -> Union[List[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",
+    "        if len(intermediate_steps) == 0:\n",
+    "            return [\n",
+    "                AgentAction(tool=\"tool1\", tool_input=kwargs[\"input\"], log=\"\"),\n",
+    "                AgentAction(tool=\"tool2\", tool_input=kwargs[\"input\"], log=\"\"),\n",
+    "            ]\n",
+    "        else:\n",
+    "            return AgentFinish(return_values={\"output\": \"bar\"}, log=\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = FakeAgent()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "lizhen0 []\n",
+      "lizhen1 []\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[36;1m\u001b[1;3m111\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[33;1m\u001b[1;3m222\u001b[0mlizhen0 [(AgentAction(tool='tool1', tool_input='How many people live in canada as of 2023?', log=''), '111'), (AgentAction(tool='tool2', tool_input='How many people live in canada as of 2023?', log=''), '222')]\n",
+      "tool1 111\n",
+      "tool2 222\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'bar'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"How many people live in canada as of 2023?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.10.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

agent_self_ask.ipynb

@@ -0,0 +1,81 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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 Yes.\n",
+      "Follow up: 谁是最著名的导演？\u001b[0m\n",
+      "Intermediate answer: \u001b[36;1m\u001b[1;3mTOP1： 史蒂文·斯皮尔伯格于1946年12月18日生于美国俄亥俄州的辛辛那提市，犹太人血统，电影导演、bai编剧和电影制作人。 代表作品：《辛德勒的名单》、《拯救大兵瑞恩》、《侏罗纪公园》、《林肯》等。 代表作：《蝴蝶梦》、《惊魂记》、《西北偏北》等。\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "Follow up: 是男的还是女的？\u001b[0m\n",
+      "Intermediate answer: \u001b[36;1m\u001b[1;3mEnglish translation of 是男的还是女的- Translations, examples and discussions from LingQ.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mIs it a man or a woman?\n",
+      "\n",
+      "So the final answer is: 男\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'男'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain import OpenAI, SerpAPIWrapper\n",
+    "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents import AgentType\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "search = SerpAPIWrapper()\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name=\"Intermediate Answer\",\n",
+    "        func=search.run,\n",
+    "        description=\"useful for when you need to ask with search\"\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)\n",
+    "self_ask_with_search.run(\"最著名导演是男的还是女的？\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.10.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}