langchain/docs/docs/integrations/text_embedding/gradient.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Gradient\n",
    "\n",
    "`Gradient` allows to create `Embeddings` as well fine tune and get completions on LLMs with a simple web API.\n",
    "\n",
    "This notebook goes over how to use Langchain with Embeddings of [Gradient](https://gradient.ai/).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.embeddings import GradientEmbeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set the Environment API Key\n",
    "Make sure to get your API key from Gradient AI. You are given $10 in free credits to test and fine-tune different models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from getpass import getpass\n",
    "\n",
    "if not os.environ.get(\"GRADIENT_ACCESS_TOKEN\", None):\n",
    "    # Access token under https://auth.gradient.ai/select-workspace\n",
    "    os.environ[\"GRADIENT_ACCESS_TOKEN\"] = getpass(\"gradient.ai access token:\")\n",
    "if not os.environ.get(\"GRADIENT_WORKSPACE_ID\", None):\n",
    "    # `ID` listed in `$ gradient workspace list`\n",
    "    # also displayed after login at at https://auth.gradient.ai/select-workspace\n",
    "    os.environ[\"GRADIENT_WORKSPACE_ID\"] = getpass(\"gradient.ai workspace id:\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optional: Validate your environment variables ```GRADIENT_ACCESS_TOKEN``` and ```GRADIENT_WORKSPACE_ID``` to get currently deployed models. Using the `gradientai` Python package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet  gradientai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the Gradient instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "documents = [\n",
    "    \"Pizza is a dish.\",\n",
    "    \"Paris is the capital of France\",\n",
    "    \"numpy is a lib for linear algebra\",\n",
    "]\n",
    "query = \"Where is Paris?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = GradientEmbeddings(model=\"bge-large\")\n",
    "\n",
    "documents_embedded = embeddings.embed_documents(documents)\n",
    "query_result = embeddings.embed_query(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (demo) compute similarity\n",
    "import numpy as np\n",
    "\n",
    "scores = np.array(documents_embedded) @ np.array(query_result).T\n",
    "dict(zip(documents, scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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