LangChain_Agents.ipynb

{
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    {
     "ename": "ModuleNotFoundError",
     "errorDetails": {
      "actions": [
       {
        "action": "open_url",
        "actionText": "Open Examples",
        "url": "/notebooks/snippets/importing_libraries.ipynb"
       }
      ]
     },
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-cb81d6452b26>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magents\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTool\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magents\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAgentType\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmemory\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mConversationBufferMemory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOpenAI\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutilities\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSerpAPIWrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'langchain'",
      "",
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from langchain.agents import Tool\n",
    "from langchain.agents import AgentType\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain import OpenAI\n",
    "from langchain.utilities import SerpAPIWrapper\n",
    "from langchain.agents import initialize_agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "HF7JhHMmHnYu"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'SerpAPIWrapper' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m search \u001b[38;5;241m=\u001b[39m \u001b[43mSerpAPIWrapper\u001b[49m()\n\u001b[1;32m      2\u001b[0m tools \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m      3\u001b[0m     Tool(\n\u001b[1;32m      4\u001b[0m         name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCurrent Search\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      7\u001b[0m     ),\n\u001b[1;32m      8\u001b[0m ]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'SerpAPIWrapper' is not defined"
     ]
    }
   ],
   "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\"\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MbTvzGWJHo_d"
   },
   "outputs": [],
   "source": [
    "memory = ConversationBufferMemory(memory_key=\"chat_history\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dghSyxzZNTjB"
   },
   "outputs": [],
   "source": [
    "llm=OpenAI(temperature=0)\n",
    "agent_chain = initialize_agent(tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wjhbxIf2Nkc3"
   },
   "outputs": [],
   "source": [
    "agent_chain.run(input=\"hi, i am bob\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "p-BFk7QCNtCn"
   },
   "outputs": [],
   "source": [
    "agent_chain.run(input=\"what's my name?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_8G5XDgcNzDP"
   },
   "outputs": [],
   "source": [
    "agent_chain.run(\"what are some good dinners to make this week, if i like thai food?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BzlGe_iiN5kX"
   },
   "outputs": [],
   "source": [
    "agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1gDzworGN9Af"
   },
   "outputs": [],
   "source": [
    "    > Entering new AgentExecutor chain...\n",
    "\n",
    "    Thought: Do I need to use a tool? Yes\n",
    "    Action: Current Search\n",
    "    Action Input: Who won the World Cup in 1978\n",
    "    Observation: Argentina national football team\n",
    "    Thought: Do I need to use a tool? No\n",
    "    AI: The last letter in your name is \"b\" and the winner of the 1978 World Cup was the Argentina national football team.\n",
    "\n",
    "    > Finished chain.\n",
    "\n",
    "\n",
    "    'The last letter in your name is \"b\" and the winner of the 1978 World Cup was the Argentina national football team.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nssCADO9OCL5"
   },
   "outputs": [],
   "source": [
    "agent_chain.run(input=\"whats the current temperature in pomfret?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8alDo01dPjnE"
   },
   "outputs": [],
   "source": [
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
    "llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY, 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": {
    "id": "VIysZ6nSQiKn"
   },
   "outputs": [],
   "source": [
    "agent_chain.run(input=\"hi, i am bob\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S5H_JAJ9RCsD"
   },
   "outputs": [],
   "source": [
    "agent_chain.run(input=\"what's my name?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bGzvetf9RHr1"
   },
   "outputs": [],
   "source": [
    "agent_chain.run(\"what are some good dinners to make this week, if i like thai food?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lofTlNWhRKQW"
   },
   "outputs": [],
   "source": [
    "agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VhQw5Z1VRQjy"
   },
   "outputs": [],
   "source": [
    "agent_chain.run(input=\"whats the weather like in pomfret?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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