langchain/docs/extras/integrations/chat/promptlayer_chatopenai.ipynb

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   "source": [
    "# PromptLayer ChatOpenAI\n",
    "\n",
    "This example showcases how to connect to [PromptLayer](https://www.promptlayer.com) to start recording your ChatOpenAI requests."
   ]
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   "source": [
    "## Install PromptLayer\n",
    "The `promptlayer` package is required to use PromptLayer with OpenAI. Install `promptlayer` using pip."
   ]
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   "source": [
    "pip install promptlayer"
   ]
  },
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   "source": [
    "## Imports"
   ]
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   "source": [
    "import os\n",
    "from langchain.chat_models import PromptLayerChatOpenAI\n",
    "from langchain.schema import HumanMessage"
   ]
  },
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   "source": [
    "## Set the Environment API Key\n",
    "You can create a PromptLayer API Key at [www.promptlayer.com](https://www.promptlayer.com) by clicking the settings cog in the navbar.\n",
    "\n",
    "Set it as an environment variable called `PROMPTLAYER_API_KEY`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "os.environ[\"PROMPTLAYER_API_KEY\"] = \"**********\""
   ]
  },
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   "source": [
    "## Use the PromptLayerOpenAI LLM like normal\n",
    "*You can optionally pass in `pl_tags` to track your requests with PromptLayer's tagging feature.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3acf0069",
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "AIMessage(content='to take a nap in a cozy spot. I search around for a suitable place and finally settle on a soft cushion on the window sill. I curl up into a ball and close my eyes, relishing the warmth of the sun on my fur. As I drift off to sleep, I can hear the birds chirping outside and feel the gentle breeze blowing through the window. This is the life of a contented cat.', additional_kwargs={})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "chat = PromptLayerChatOpenAI(pl_tags=[\"langchain\"])\n",
    "chat([HumanMessage(content=\"I am a cat and I want\")])"
   ]
  },
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   "source": [
    "**The above request should now appear on your [PromptLayer dashboard](https://www.promptlayer.com).**"
   ]
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   "source": [
    "## Using PromptLayer Track\n",
    "If you would like to use any of the [PromptLayer tracking features](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9), you need to pass the argument `return_pl_id` when instantializing the PromptLayer LLM to get the request id.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7d4db01",
   "metadata": {},
   "outputs": [],
   "source": [
    "chat = PromptLayerChatOpenAI(return_pl_id=True)\n",
    "chat_results = chat.generate([[HumanMessage(content=\"I am a cat and I want\")]])\n",
    "\n",
    "for res in chat_results.generations:\n",
    "    pl_request_id = res[0].generation_info[\"pl_request_id\"]\n",
    "    promptlayer.track.score(request_id=pl_request_id, score=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e56507",
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   "source": [
    "Using this allows you to track the performance of your model in the PromptLayer dashboard. If you are using a prompt template, you can attach a template to a request as well.\n",
    "Overall, this gives you the opportunity to track the performance of different templates and models in the PromptLayer dashboard."
   ]
  }
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