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#1	{
#2	"cells": [
#3	{
#4	"cell_type": "markdown",
#5	"metadata": {
#6	"id": "b02n_zJ_hl3d"
#7	},
#8	"source": [
#9	"## Cookbook for using LLAMA2 with Embedchain"
#10	]
#11	},
#12	{
#13	"cell_type": "markdown",
#14	"metadata": {
#15	"id": "gyJ6ui2vhtMY"
#16	},
#17	"source": [
#18	"### Step-1: Install embedchain package"
#19	]
#20	},
#21	{
#22	"cell_type": "code",
#23	"execution_count": null,
#24	"metadata": {
#25	"colab": {
#26	"base_uri": "https://localhost:8080/"
#27	},
#28	"id": "-NbXjAdlh0vJ",
#29	"outputId": "86a4a9b2-4ed6-431c-da6f-c3eacb390f42"
#30	},
#31	"outputs": [],
#32	"source": [
#33	"!pip install embedchain[llama2]"
#34	]
#35	},
#36	{
#37	"cell_type": "markdown",
#38	"metadata": {
#39	"id": "nGnpSYAAh2bQ"
#40	},
#41	"source": [
#42	"### Step-2: Set LLAMA2 related environment variables\n",
#43	"\n",
#44	"You can find `OPENAI_API_KEY` on your [OpenAI dashboard](https://platform.openai.com/account/api-keys) and `REPLICATE_API_TOKEN` key on your [Replicate dashboard](https://replicate.com/account/api-tokens)."
#45	]
#46	},
#47	{
#48	"cell_type": "code",
#49	"execution_count": null,
#50	"metadata": {
#51	"id": "0fBdQ9GAiRvK"
#52	},
#53	"outputs": [],
#54	"source": [
#55	"import os\n",
#56	"from embedchain import App\n",
#57	"\n",
#58	"os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\"\n",
#59	"os.environ[\"REPLICATE_API_TOKEN\"] = \"xxx\""
#60	]
#61	},
#62	{
#63	"cell_type": "markdown",
#64	"metadata": {
#65	"id": "PGt6uPLIi1CS"
#66	},
#67	"source": [
#68	"### Step-3 Create embedchain app and define your config"
#69	]
#70	},
#71	{
#72	"cell_type": "code",
#73	"execution_count": null,
#74	"metadata": {
#75	"id": "Amzxk3m-i3tD"
#76	},
#77	"outputs": [],
#78	"source": [
#79	"app = App.from_config(config={\n",
#80	" \"provider\": \"llama2\",\n",
#81	" \"config\": {\n",
#82	" \"model\": \"a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5\",\n",
#83	" \"temperature\": 0.5,\n",
#84	" \"max_tokens\": 1000,\n",
#85	" \"top_p\": 0.5,\n",
#86	" \"stream\": False\n",
#87	" }\n",
#88	"})"
#89	]
#90	},
#91	{
#92	"cell_type": "markdown",
#93	"metadata": {
#94	"id": "XNXv4yZwi7ef"
#95	},
#96	"source": [
#97	"### Step-4: Add data sources to your app"
#98	]
#99	},
#100	{
#101	"cell_type": "code",
#102	"execution_count": null,
#103	"metadata": {
#104	"colab": {
#105	"base_uri": "https://localhost:8080/",
#106	"height": 52
#107	},
#108	"id": "Sn_0rx9QjIY9",
#109	"outputId": "ba158e9c-0f16-4c6b-a876-7543120985a2"
#110	},
#111	"outputs": [],
#112	"source": [
#113	"app.add(\"https://www.forbes.com/profile/elon-musk\")"
#114	]
#115	},
#116	{
#117	"cell_type": "markdown",
#118	"metadata": {
#119	"id": "_7W6fDeAjMAP"
#120	},
#121	"source": [
#122	"### Step-5: All set. Now start asking questions related to your data"
#123	]
#124	},
#125	{
#126	"cell_type": "code",
#127	"execution_count": null,
#128	"metadata": {
#129	"colab": {
#130	"base_uri": "https://localhost:8080/",
#131	"height": 599
#132	},
#133	"id": "cvIK7dWRjN_f",
#134	"outputId": "e2d11a25-a2ed-4034-ec6a-e8a5986c89ae"
#135	},
#136	"outputs": [],
#137	"source": [
#138	"while(True):\n",
#139	" question = input(\"Enter question: \")\n",
#140	" if question in ['q', 'exit', 'quit']:\n",
#141	" break\n",
#142	" answer = app.query(question)\n",
#143	" print(answer)"
#144	]
#145	}
#146	],
#147	"metadata": {
#148	"colab": {
#149	"provenance": []
#150	},
#151	"kernelspec": {
#152	"display_name": "Python 3",
#153	"name": "python3"
#154	},
#155	"language_info": {
#156	"name": "python"
#157	}
#158	},
#159	"nbformat": 4,
#160	"nbformat_minor": 0
#161	}
#162

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