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#1	---
#2	title: '⚡ Quickstart'
#3	description: '💡 Create an AI app on your own data in a minute'
#4	---
#5
#6	## Installation
#7
#8	First install the Python package:
#9
#10	```bash
#11	pip install embedchain
#12	```
#13
#14	Once you have installed the package, depending upon your preference you can either use:
#15
#16	<CardGroup cols={2}>
#17	<Card title="Open Source Models" icon="osi" href="#open-source-models">
#18	This includes Open source LLMs like Mistral, Llama, etc.<br/>
#19	Free to use, and runs locally on your machine.
#20	</Card>
#21	<Card title="Paid Models" icon="dollar-sign" href="#paid-models" color="#4A154B">
#22	This includes paid LLMs like GPT 4, Claude, etc.<br/>
#23	Cost money and are accessible via an API.
#24	</Card>
#25	</CardGroup>
#26
#27	## Open Source Models
#28
#29	This section gives a quickstart example of using Mistral as the Open source LLM and Sentence transformers as the Open source embedding model. These models are free and run mostly on your local machine.
#30
#31	We are using Mistral hosted at Hugging Face, so will you need a Hugging Face token to run this example. Its free and you can create one [here](https://huggingface.co/docs/hub/security-tokens).
#32
#33	<CodeGroup>
#34	```python huggingface_demo.py
#35	import os
#36	# Replace this with your HF token
#37	os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "hf_xxxx"
#38
#39	from embedchain import App
#40
#41	config = {
#42	'llm': {
#43	'provider': 'huggingface',
#44	'config': {
#45	'model': 'mistralai/Mistral-7B-Instruct-v0.2',
#46	'top_p': 0.5
#47	}
#48	},
#49	'embedder': {
#50	'provider': 'huggingface',
#51	'config': {
#52	'model': 'sentence-transformers/all-mpnet-base-v2'
#53	}
#54	}
#55	}
#56	app = App.from_config(config=config)
#57	app.add("https://www.forbes.com/profile/elon-musk")
#58	app.add("https://en.wikipedia.org/wiki/Elon_Musk")
#59	app.query("What is the net worth of Elon Musk today?")
#60	# Answer: The net worth of Elon Musk today is $258.7 billion.
#61	```
#62	</CodeGroup>
#63
#64	## Paid Models
#65
#66	In this section, we will use both LLM and embedding model from OpenAI.
#67
#68	```python openai_demo.py
#69	import os
#70	from embedchain import App
#71
#72	# Replace this with your OpenAI key
#73	os.environ["OPENAI_API_KEY"] = "sk-xxxx"
#74
#75	app = App()
#76	app.add("https://www.forbes.com/profile/elon-musk")
#77	app.add("https://en.wikipedia.org/wiki/Elon_Musk")
#78	app.query("What is the net worth of Elon Musk today?")
#79	# Answer: The net worth of Elon Musk today is $258.7 billion.
#80	```
#81
#82	# Next Steps
#83
#84	Now that you have created your first app, you can follow any of the links:
#85
#86	* [Introduction](/get-started/introduction)
#87	* [Customization](/components/introduction)
#88	* [Use cases](/use-cases/introduction)
#89	* [Deployment](/get-started/deployment)
#90

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