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#1	{
#2	"cells": [
#3	{
#4	"cell_type": "markdown",
#5	"id": "63ab5e89",
#6	"metadata": {},
#7	"source": [
#8	"## Cookbook for using Azure OpenAI with Embedchain"
#9	]
#10	},
#11	{
#12	"cell_type": "markdown",
#13	"id": "e32a0265",
#14	"metadata": {},
#15	"source": [
#16	"### Step-1: Install embedchain package"
#17	]
#18	},
#19	{
#20	"cell_type": "code",
#21	"execution_count": null,
#22	"id": "b80ff15a",
#23	"metadata": {},
#24	"outputs": [],
#25	"source": [
#26	"!pip install embedchain"
#27	]
#28	},
#29	{
#30	"cell_type": "markdown",
#31	"id": "ac982a56",
#32	"metadata": {},
#33	"source": [
#34	"### Step-2: Set Azure OpenAI related environment variables\n",
#35	"\n",
#36	"You can find these env variables on your Azure OpenAI dashboard."
#37	]
#38	},
#39	{
#40	"cell_type": "code",
#41	"execution_count": null,
#42	"id": "e0a36133",
#43	"metadata": {},
#44	"outputs": [],
#45	"source": [
#46	"import os\n",
#47	"from embedchain import App\n",
#48	"\n",
#49	"os.environ[\"OPENAI_API_TYPE\"] = \"azure\"\n",
#50	"os.environ[\"OPENAI_API_BASE\"] = \"https://xxx.openai.azure.com/\"\n",
#51	"os.environ[\"OPENAI_API_KEY\"] = \"xxx\"\n",
#52	"os.environ[\"OPENAI_API_VERSION\"] = \"xxx\""
#53	]
#54	},
#55	{
#56	"cell_type": "markdown",
#57	"id": "7d7b554e",
#58	"metadata": {},
#59	"source": [
#60	"### Step-3: Define your llm and embedding model config"
#61	]
#62	},
#63	{
#64	"cell_type": "code",
#65	"execution_count": null,
#66	"id": "b9f52fc5",
#67	"metadata": {},
#68	"outputs": [],
#69	"source": [
#70	"config = \"\"\"\n",
#71	"llm:\n",
#72	" provider: azure_openai\n",
#73	" model: gpt-35-turbo\n",
#74	" config:\n",
#75	" deployment_name: ec_openai_azure\n",
#76	" temperature: 0.5\n",
#77	" max_tokens: 1000\n",
#78	" top_p: 1\n",
#79	" stream: false\n",
#80	"\n",
#81	"embedder:\n",
#82	" provider: azure_openai\n",
#83	" config:\n",
#84	" model: text-embedding-ada-002\n",
#85	" deployment_name: ec_embeddings_ada_002\n",
#86	"\"\"\"\n",
#87	"\n",
#88	"# Write the multi-line string to a YAML file\n",
#89	"with open('azure_openai.yaml', 'w') as file:\n",
#90	" file.write(config)"
#91	]
#92	},
#93	{
#94	"cell_type": "markdown",
#95	"id": "98a11130",
#96	"metadata": {},
#97	"source": [
#98	"### Step-4 Create embedchain app based on the config"
#99	]
#100	},
#101	{
#102	"cell_type": "code",
#103	"execution_count": null,
#104	"id": "1ee9bdd9",
#105	"metadata": {},
#106	"outputs": [],
#107	"source": [
#108	"app = App.from_config(config_path=\"azure_openai.yaml\")"
#109	]
#110	},
#111	{
#112	"cell_type": "markdown",
#113	"id": "554dc97b",
#114	"metadata": {},
#115	"source": [
#116	"### Step-5: Add data sources to your app"
#117	]
#118	},
#119	{
#120	"cell_type": "code",
#121	"execution_count": null,
#122	"id": "686ae765",
#123	"metadata": {},
#124	"outputs": [],
#125	"source": [
#126	"app.add(\"https://www.forbes.com/profile/elon-musk\")"
#127	]
#128	},
#129	{
#130	"cell_type": "markdown",
#131	"id": "ccc7d421",
#132	"metadata": {},
#133	"source": [
#134	"### Step-6: All set. Now start asking questions related to your data"
#135	]
#136	},
#137	{
#138	"cell_type": "code",
#139	"execution_count": null,
#140	"id": "27868a7d",
#141	"metadata": {},
#142	"outputs": [],
#143	"source": [
#144	"while(True):\n",
#145	" question = input(\"Enter question: \")\n",
#146	" if question in ['q', 'exit', 'quit']:\n",
#147	" break\n",
#148	" answer = app.query(question)\n",
#149	" print(answer)"
#150	]
#151	}
#152	],
#153	"metadata": {
#154	"kernelspec": {
#155	"display_name": "Python 3 (ipykernel)",
#156	"language": "python",
#157	"name": "python3"
#158	},
#159	"language_info": {
#160	"codemirror_mode": {
#161	"name": "ipython",
#162	"version": 3
#163	},
#164	"file_extension": ".py",
#165	"mimetype": "text/x-python",
#166	"name": "python",
#167	"nbconvert_exporter": "python",
#168	"pygments_lexer": "ipython3",
#169	"version": "3.11.4"
#170	}
#171	},
#172	"nbformat": 4,
#173	"nbformat_minor": 5
#174	}
#175

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