| --- |
| title: '⚡ Quickstart' |
| description: '💡 Create an AI app on your own data in a minute' |
| --- |
|
|
| ## Installation |
|
|
| First install the Python package: |
|
|
| ```bash |
| pip install embedchain |
| ``` |
|
|
| Once you have installed the package, depending upon your preference you can either use: |
|
|
| <CardGroup cols={2}> |
| <Card title="Open Source Models" icon="osi" href="#open-source-models"> |
| This includes Open source LLMs like Mistral, Llama, etc.<br/> |
| Free to use, and runs locally on your machine. |
| </Card> |
| <Card title="Paid Models" icon="dollar-sign" href="#paid-models" color="#4A154B"> |
| This includes paid LLMs like GPT 4, Claude, etc.<br/> |
| Cost money and are accessible via an API. |
| </Card> |
| </CardGroup> |
|
|
| ## Open Source Models |
|
|
| 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. |
|
|
| 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: |
|
|
| <CodeGroup> |
| ```python huggingface_demo.py |
| import os |
| # Replace this with your HF token |
| os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "hf_xxxx" |
|
|
| from embedchain import App |
|
|
| config = { |
| 'llm': { |
| 'provider': 'huggingface', |
| 'config': { |
| 'model': 'mistralai/Mistral-7B-Instruct-v0.2', |
| 'top_p': 0.5 |
| } |
| }, |
| 'embedder': { |
| 'provider': 'huggingface', |
| 'config': { |
| 'model': 'sentence-transformers/all-mpnet-base-v2' |
| } |
| } |
| } |
| app = App.from_config(config=config) |
| app.add("https://www.forbes.com/profile/elon-musk") |
| app.add("https://en.wikipedia.org/wiki/Elon_Musk") |
| app.query("What is the net worth of Elon Musk today?") |
| # Answer: The net worth of Elon Musk today is $258.7 billion. |
| ``` |
| </CodeGroup> |
|
|
| ## Paid Models |
|
|
| In this section, we will use both LLM and embedding model from OpenAI. |
|
|
| ```python openai_demo.py |
| import os |
| from embedchain import App |
|
|
| # Replace this with your OpenAI key |
| os.environ["OPENAI_API_KEY"] = "sk-xxxx" |
|
|
| app = App() |
| app.add("https://www.forbes.com/profile/elon-musk") |
| app.add("https://en.wikipedia.org/wiki/Elon_Musk") |
| app.query("What is the net worth of Elon Musk today?") |
| # Answer: The net worth of Elon Musk today is $258.7 billion. |
| ``` |
|
|
| # Next Steps |
|
|
| Now that you have created your first app, you can follow any of the links: |
|
|
| * [Introduction](/get-started/introduction) |
| * [Customization](/components/introduction) |
| * [Use cases](/use-cases/introduction) |
| * [Deployment](/get-started/deployment) |
|
|