{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b02n_zJ_hl3d"
      },
      "source": [
        "## Cookbook for using Cohere with Embedchain"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gyJ6ui2vhtMY"
      },
      "source": [
        "### Step-1: Install embedchain package"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-NbXjAdlh0vJ",
        "outputId": "fae77912-4e6a-4c78-fcb7-fbbe46f7a9c7"
      },
      "outputs": [],
      "source": [
        "!pip install embedchain[cohere]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGnpSYAAh2bQ"
      },
      "source": [
        "### Step-2: Set Cohere related environment variables\n",
        "\n",
        "You can find `OPENAI_API_KEY` on your [OpenAI dashboard](https://platform.openai.com/account/api-keys) and `COHERE_API_KEY` key on your [Cohere dashboard](https://dashboard.cohere.com/api-keys)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0fBdQ9GAiRvK"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from embedchain import App\n",
        "\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\"\n",
        "os.environ[\"COHERE_API_KEY\"] = \"xxx\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGt6uPLIi1CS"
      },
      "source": [
        "### Step-3 Create embedchain app and define your config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "Amzxk3m-i3tD",
        "outputId": "afe8afde-5cb8-46bc-c541-3ad26cc3fa6e"
      },
      "outputs": [],
      "source": [
        "app = App.from_config(config={\n",
        "    \"provider\": \"cohere\",\n",
        "    \"config\": {\n",
        "        \"model\": \"gptd-instruct-tft\",\n",
        "        \"temperature\": 0.5,\n",
        "        \"max_tokens\": 1000,\n",
        "        \"top_p\": 1,\n",
        "        \"stream\": False\n",
        "    }\n",
        "})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNXv4yZwi7ef"
      },
      "source": [
        "### Step-4: Add data sources to your app"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "Sn_0rx9QjIY9",
        "outputId": "2f2718a4-3b7e-4844-fd46-3e0857653ca0"
      },
      "outputs": [],
      "source": [
        "app.add(\"https://www.forbes.com/profile/elon-musk\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_7W6fDeAjMAP"
      },
      "source": [
        "### Step-5: All set. Now start asking questions related to your data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvIK7dWRjN_f",
        "outputId": "79e873c8-9594-45da-f5a3-0a893511267f"
      },
      "outputs": [],
      "source": [
        "while(True):\n",
        "    question = input(\"Enter question: \")\n",
        "    if question in ['q', 'exit', 'quit']:\n",
        "        break\n",
        "    answer = app.query(question)\n",
        "    print(answer)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}