sdk/python/foundation-models/cohere/cohere-embed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use Cohere Embed in Azure AI and Azure ML\n",
    "\n",
    "Use `cohere` client to consume Cohere Embed model deployments in Azure AI and Azure ML..\n",
    "\n",
    "> Review the [documentation](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-cohere) for the Cohere family of models at for AI Studio and for ML Studio for details on how to provision inference endpoints, regional availability, pricing and inference schema reference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "\n",
    "Before we start, there are certain steps we need to take to deploy the models:\n",
    "\n",
    "* Register for a valid Azure account with subscription \n",
    "* Make sure you have access to [Azure AI Studio](https://learn.microsoft.com/en-us/azure/ai-studio/what-is-ai-studio?tabs=home)\n",
    "* Create a project and resource group\n",
    "* Select `Cohere Embed V3 (English)` or `Cohere Embed V3 (multilingual)`.\n",
    "\n",
    "    > Notice that some models may not be available in all the regions in Azure AI and Azure Machine Learning. On those cases, you can create a workspace or project in the region where the models are available and then consume it with a connection from a different one. To learn more about using connections see [Consume models with connections](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deployments-connections)\n",
    "\n",
    "* Deploy with \"Pay-as-you-go\"\n",
    "\n",
    "Once deployed successfully, you should be assigned for an API endpoint and a security key for inference.\n",
    "\n",
    "For more information, you should consult Azure's official documentation [here](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-cohere) for model deployment and inference.\n",
    "\n",
    "To complete this tutorial, you will need to:\n",
    "\n",
    "* Install `cohere`:\n",
    "\n",
    "    ```bash\n",
    "    pip install cohere\n",
    "    ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example\n",
    "\n",
    "The following is an example about how to use `cohere` with a Cohere Embed model deployed in Azure AI and Azure ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "name": "imports"
   },
   "outputs": [],
   "source": [
    "import cohere"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use `cohere`, create a client and configure it as follows:\n",
    "\n",
    "- `endpoint`: Use the endpoint URL from your deployment. Include `/v1` at the end of the endpoint.\n",
    "- `api_key`: Use your API key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "name": "chat_client"
   },
   "outputs": [],
   "source": [
    "co = cohere.Client(\n",
    "    base_url=\"https://<endpoint>.<region>.inference.ai.azure.com/v1\", api_key=\"<key>\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the client to create the requests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "name": "chat_invoke"
   },
   "outputs": [],
   "source": [
    "response = co.embed(\n",
    "    texts=[\"Who is the most renowned French painter? Provide a short answer.\"],\n",
    "    input_type=\"query\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For image inputs you can try:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import base64\n",
    "\n",
    "# Open the image file in binary mode\n",
    "with open(\"<Path to Image>\", \"rb\") as image_file:\n",
    "    # Read the image data\n",
    "    image_data = image_file.read()\n",
    "\n",
    "    # Encode the image data to base64\n",
    "    base64_encoded_data = base64.b64encode(image_data)\n",
    "\n",
    "    # Convert the base64 bytes to a string\n",
    "    base64_string = base64_encoded_data.decode(\"utf-8\")\n",
    "\n",
    "co.embed(images=[base64_string], input_type=\"image\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aditional resources\n",
    "\n",
    "Here are some additional reference:  \n",
    "\n",
    "* [Plan and manage costs (marketplace)](https://learn.microsoft.com/azure/ai-studio/how-to/costs-plan-manage#monitor-costs-for-models-offered-through-the-azure-marketplace)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "jupyter",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}