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   "source": [
    "# Clarifai\n",
    "\n",
    ">[Clarifai](https://www.clarifai.com/) is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference.\n",
    "\n",
    "This example goes over how to use LangChain to interact with `Clarifai` [models](https://clarifai.com/explore/models). Text embedding models in particular can be found [here](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22model_type_id%22%2C%22value%22%3A%5B%22text-embedder%22%5D%7D%5D).\n",
    "\n",
    "To use Clarifai, you must have an account and a Personal Access Token (PAT) key. \n",
    "[Check here](https://clarifai.com/settings/security) to get or create a PAT."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2a773d8d",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91ea14ce-831d-409a-a88f-30353acdabd1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Install required dependencies\n",
    "!pip install clarifai"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "426f1156",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we will be setting the personal access token. You can find your PAT under [settings/security](https://clarifai.com/settings/security) in your Clarifai account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f5dc9d7-65e3-4b5b-9086-3327d016cfe0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " ········\n"
     ]
    }
   ],
   "source": [
    "# Please login and get your API key from  https://clarifai.com/settings/security\n",
    "from getpass import getpass\n",
    "\n",
    "CLARIFAI_PAT = getpass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6fb585dd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import the required modules\n",
    "from langchain.embeddings import ClarifaiEmbeddings\n",
    "from langchain import PromptTemplate, LLMChain"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "16521ed2",
   "metadata": {},
   "source": [
    "# Input\n",
    "Create a prompt template to be used with the LLM Chain:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "035dea0f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's think step by step.\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
   ]
  },
  {
   "attachments": {},
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   "id": "c8905eac",
   "metadata": {},
   "source": [
    "# Setup\n",
    "Set the user id and app id to the application in which the model resides. You can find a list of public models on https://clarifai.com/explore/models\n",
    "\n",
    "You will have to also initialize the model id and if needed, the model version id. Some models have many versions, you can choose the one appropriate for your task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1fe9bf15",
   "metadata": {},
   "outputs": [],
   "source": [
    "USER_ID = \"openai\"\n",
    "APP_ID = \"embed\"\n",
    "MODEL_ID = \"text-embedding-ada\"\n",
    "\n",
    "# You can provide a specific model version as the model_version_id arg.\n",
    "# MODEL_VERSION_ID = \"MODEL_VERSION_ID\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f3458d9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Initialize a Clarifai embedding model\n",
    "embeddings = ClarifaiEmbeddings(\n",
    "    pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a641dbd9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32b4d5f4-2b8e-4681-856f-19a3dd141ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_result = embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "47076457-1880-48ac-970f-872ead6f0d94",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_result = embeddings.embed_documents([text])"
   ]
  }
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