{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "b02n_zJ_hl3d" }, "source": [ "## Cookbook for using ElasticSearchDB with Embedchain" ] }, { "cell_type": "markdown", "metadata": { "id": "gyJ6ui2vhtMY" }, "source": [ "### Step-1: Install embedchain package" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-NbXjAdlh0vJ" }, "outputs": [], "source": [ "!pip install embedchain[elasticsearch]" ] }, { "cell_type": "markdown", "metadata": { "id": "nGnpSYAAh2bQ" }, "source": [ "### Step-2: Set OpenAI environment variables.\n", "\n", "You can find this env variable on your [OpenAI dashboard](https://platform.openai.com/account/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\"" ] }, { "cell_type": "markdown", "metadata": { "id": "PGt6uPLIi1CS" }, "source": [ "### Step-3 Create embedchain app and define your config" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Amzxk3m-i3tD" }, "outputs": [], "source": [ "app = App.from_config(config={\n", " \"provider\": \"elasticsearch\",\n", " \"config\": {\n", " \"collection_name\": \"es-index\",\n", " \"es_url\": \"your-elasticsearch-url.com\",\n", " \"allow_reset\": True,\n", " \"api_key\": \"xxx\"\n", " }\n", "})" ] }, { "cell_type": "markdown", "metadata": { "id": "XNXv4yZwi7ef" }, "source": [ "### Step-4: Add data sources to your app" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Sn_0rx9QjIY9" }, "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": { "id": "cvIK7dWRjN_f" }, "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 }