langchain/docs/extras/integrations/llms/symblai_nebula.ipynb

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    "# Nebula\n",
    "\n",
    "[Nebula](https://symbl.ai/nebula/) is a fully-managed Conversation platform, on which you can build, deploy, and manage scalable AI applications.\n",
    "\n",
    "This example goes over how to use LangChain to interact with the [Nebula platform](https://docs.symbl.ai/docs/nebula-llm-overview). \n",
    "\n",
    "It will send the requests to Nebula Service endpoint, which concatenates `SYMBLAI_NEBULA_SERVICE_URL` and `SYMBLAI_NEBULA_SERVICE_PATH`, with a token defined in `SYMBLAI_NEBULA_SERVICE_TOKEN`"
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   "source": [
    "### Integrate with a LLMChain"
   ]
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   "source": [
    "import os\n",
    "\n",
    "os.environ[\"SYMBLAI_NEBULA_SERVICE_URL\"] = SYMBLAI_NEBULA_SERVICE_URL\n",
    "os.environ[\"SYMBLAI_NEBULA_SERVICE_PATH\"] = SYMBLAI_NEBULA_SERVICE_PATH\n",
    "os.environ[\"SYMBLAI_NEBULA_SERVICE_TOKEN\"] = SYMBLAI_NEBULA_SERVICE_TOKEN"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fb585dd",
   "metadata": {
    "tags": []
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   "source": [
    "from langchain.llms import OpenLLM\n",
    "\n",
    "llm = OpenLLM(\n",
    "    conversation=\"<Drop your text conversation that you want to ask Nebula to analyze here>\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "035dea0f",
   "metadata": {
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   "source": [
    "from langchain import PromptTemplate, LLMChain\n",
    "\n",
    "template = \"Identify the {count} main objectives or goals mentioned in this context concisely in less points. Emphasize on key intents.\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"count\"])\n",
    "\n",
    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
    "\n",
    "generated = llm_chain.run(count=\"five\")\n",
    "print(generated)"
   ]
  }
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