{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bce7335e-f3b2-44f3-90cc-8c0a23a89a21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from langchain.agents import load_tools\n",
    "from langchain.agents import initialize_agent\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.utilities import GoogleSearchAPIWrapper\n",
    "from langchain.schema import (\n",
    "    SystemMessage,\n",
    "    HumanMessage,\n",
    "    AIMessage\n",
    ")\n",
    "\n",
    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
    "# os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
    "# os.environ[\"LANGCHAIN_API_KEY\"] = \"******\"\n",
    "# os.environ[\"LANGCHAIN_PROJECT\"] = \"Jarvis\"\n",
    "\n",
    "\n",
    "prefix_messages = [{\"role\": \"system\", \"content\": \"You are a helpful discord Chatbot.\"}]\n",
    "\n",
    "llm = ChatOpenAI(model_name='gpt-3.5-turbo', \n",
    "             temperature=0.5, \n",
    "             max_tokens = 2000)\n",
    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
    "agent = initialize_agent(tools,\n",
    "                         llm,\n",
    "                         agent=\"zero-shot-react-description\",\n",
    "                         verbose=True,\n",
    "                         handle_parsing_errors=True\n",
    "                         )\n",
    "\n",
    "\n",
    "async def on_ready():\n",
    "    print(f'{bot.user} has connected to Discord!')\n",
    "\n",
    "async def on_message(message):\n",
    "\n",
    "    print(\"Detected bot name in message:\", message.content)\n",
    "\n",
    "    # Capture the output of agent.run() in the response variable\n",
    "    response = agent.run(message.content)\n",
    "\n",
    "    while response:\n",
    "        print(response)\n",
    "        chunk, response = response[:2000], response[2000:]\n",
    "        print(f\"Chunk: {chunk}\")\n",
    "    print(\"Response sent.\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1551ce9f-b6de-4035-b6d6-825722823b48",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "@dataclass\n",
    "class Message:\n",
    "    content: str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6e6859ec-8544-4407-9663-6b53c0092903",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected bot name in message: Hi AI, how are you today?\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThis question is not something that can be answered using the available tools.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to follow the correct format for answering questions.\n",
      "Action: N/A\u001b[0m\n",
      "Observation: Invalid Format: Missing 'Action Input:' after 'Action:'\n",
      "Thought:\u001b[32;1m\u001b[1;3m\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "Agent stopped due to iteration limit or time limit.\n",
      "Chunk: Agent stopped due to iteration limit or time limit.\n",
      "Response sent.\n"
     ]
    }
   ],
   "source": [
    "await on_message(Message(content=\"Hi AI, how are you today?\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b850294c-7f8f-4e79-adcf-47e4e3a898df",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langsmith import Client\n",
    "\n",
    "client = Client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6d089ddc-69bc-45a8-b8db-9962e4f1f5ee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from itertools import islice\n",
    "\n",
    "runs = list(islice(client.list_runs(), 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f0349fac-5a98-400f-ba03-61ed4e1332be",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "runs = sorted(runs, key=lambda x: x.start_time, reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "02f133f0-39ee-4b46-b443-12c1f9b76fff",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ids = [run.id for run in runs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3366dce4-0c38-4a7d-8111-046a58b24917",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "runs2 = list(client.list_runs(id=ids))\n",
    "runs2 = sorted(runs2, key=lambda x: x.start_time, reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "82915b90-39a0-47d6-9121-56a13f210f52",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a36092d2-4ad5-4fb4-9b0d-0dba9a2ed836',\n",
       " '9398e6be-964f-4aa4-8de9-ad78cd4b7074']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[str(x) for x in ids[:2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f610ec91-dc48-4a17-91c5-5c4675c77abc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langsmith.run_helpers import traceable\n",
    "\n",
    "@traceable(run_type=\"llm\", name=\"\"\"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/dQw4w9WgXcQ?start=5\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\"\"\")\n",
    "def foo():\n",
    "    return \"bar\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bd317bd7-8b2a-433a-8ec3-098a84ba8e64",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bar'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b142519b-6885-415c-83b9-4a346fb90589",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.llms import AzureOpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c50bb2b-72b8-4322-9b16-d857ecd9f347",
   "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
