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   "source": [
    "# Jira\n",
    "\n",
    "This notebook goes over how to use the Jira tool.\n",
    "The Jira tool allows agents to interact with a given Jira instance, performing actions such as searching for issues and creating issues, the tool wraps the atlassian-python-api library, for more see: https://atlassian-python-api.readthedocs.io/jira.html\n",
    "\n",
    "To use this tool, you must first set as environment variables:\n",
    "    JIRA_API_TOKEN\n",
    "    JIRA_USERNAME\n",
    "    JIRA_INSTANCE_URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "961b3689",
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     "end_time": "2023-04-17T10:21:20.168639Z"
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   },
   "outputs": [],
   "source": [
    "%pip install atlassian-python-api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34bb5968",
   "metadata": {
    "ExecuteTime": {
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     "end_time": "2023-04-17T10:21:23.730922Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from langchain.agents import AgentType\n",
    "from langchain.agents import initialize_agent\n",
    "from langchain.agents.agent_toolkits.jira.toolkit import JiraToolkit\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.utilities.jira import JiraAPIWrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "os.environ[\"JIRA_API_TOKEN\"] = \"abc\"\n",
    "os.environ[\"JIRA_USERNAME\"] = \"123\"\n",
    "os.environ[\"JIRA_INSTANCE_URL\"] = \"https://jira.atlassian.com\"\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"xyz\""
   ],
   "metadata": {
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    "ExecuteTime": {
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac4910f8",
   "metadata": {
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     "end_time": "2023-04-17T10:22:44.720538Z"
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   },
   "outputs": [],
   "source": [
    "llm = OpenAI(temperature=0)\n",
    "jira = JiraAPIWrapper()\n",
    "toolkit = JiraToolkit.from_jira_api_wrapper(jira)\n",
    "agent = initialize_agent(\n",
    "    toolkit.get_tools(),\n",
    "    llm,\n",
    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
      "\u001B[32;1m\u001B[1;3m I need to create an issue in project PW\n",
      "Action: Create Issue\n",
      "Action Input: {\"summary\": \"Make more fried rice\", \"description\": \"Reminder to make more fried rice\", \"issuetype\": {\"name\": \"Task\"}, \"priority\": {\"name\": \"Low\"}, \"project\": {\"key\": \"PW\"}}\u001B[0m\n",
      "Observation: \u001B[38;5;200m\u001B[1;3mNone\u001B[0m\n",
      "Thought:\u001B[32;1m\u001B[1;3m I now know the final answer\n",
      "Final Answer: A new issue has been created in project PW with the summary \"Make more fried rice\" and description \"Reminder to make more fried rice\".\u001B[0m\n",
      "\n",
      "\u001B[1m> Finished chain.\u001B[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": "'A new issue has been created in project PW with the summary \"Make more fried rice\" and description \"Reminder to make more fried rice\".'"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.run(\"make a new issue in project PW to remind me to make more fried rice\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-17T10:23:33.662454Z",
     "end_time": "2023-04-17T10:23:38.121883Z"
    }
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  }
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