Improve docs. (#1096)
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Taranjeet Singh
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---
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title: '⚡ Quickstart'
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description: '💡 Start building ChatGPT like apps in a minute on your own data'
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description: '💡 Create a RAG app on your own data in a minute'
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---
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Install python package:
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## Installation
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First install the python package.
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```bash
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pip install embedchain
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```
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Creating an app involves 3 steps:
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Once you have installed the package, depending upon your preference you can either use:
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<Steps>
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<Step title="⚙️ Import app instance">
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```python
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from embedchain import App
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app = App()
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```
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<Accordion title="Customize your app by a simple YAML config" icon="gear-complex">
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Embedchain provides a wide range of options to customize your app. You can customize the model, data sources, and much more.
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Explore the custom configurations [here](https://docs.embedchain.ai/advanced/configuration).
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<CodeGroup>
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```python yaml_app.py
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from embedchain import App
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app = App.from_config(config_path="config.yaml")
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```
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```python json_app.py
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from embedchain import App
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app = App.from_config(config_path="config.json")
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```
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```python app.py
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from embedchain import App
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config = {} # Add your config here
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app = App.from_config(config=config)
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```
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</CodeGroup>
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</Accordion>
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</Step>
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<Step title="🗃️ Add data sources">
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```python
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app.add("https://en.wikipedia.org/wiki/Elon_Musk")
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app.add("https://www.forbes.com/profile/elon-musk")
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# app.add("path/to/file/elon_musk.pdf")
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```
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<Accordion title="Embedchain supports adding data from many data sources." icon="files">
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Embedchain supports adding data from many data sources including web pages, PDFs, databases, and more.
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Explore the list of supported [data sources](https://docs.embedchain.ai/data-sources/overview).
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</Accordion>
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</Step>
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<Step title="💬 Ask questions, chat, or search through your data with ease">
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```python
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app.query("What is the net worth of Elon Musk today?")
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# Answer: The net worth of Elon Musk today is $258.7 billion.
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```
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<hr />
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<Accordion title="Want to chat with your app?" icon="face-thinking">
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Embedchain provides a wide range of features to interact with your app. You can chat with your app, ask questions, search through your data, and much more.
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```python
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app.chat("How many companies does Elon Musk run? Name those")
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# Answer: Elon Musk runs 3 companies: Tesla, SpaceX, and Neuralink.
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app.chat("What is his net worth today?")
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# Answer: The net worth of Elon Musk today is $258.7 billion.
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```
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To learn about other features, click [here](https://docs.embedchain.ai/get-started/introduction)
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</Accordion>
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</Step>
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</Steps>
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<CardGroup cols={2}>
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<Card title="Open Source Models" icon="osi" href="#open-source-models">
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This includes Open source LLMs like Mistral, Llama, etc.<br/>
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Free to use, and runs locally on your machine.
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</Card>
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<Card title="Paid Models" icon="dollar-sign" href="#paid-models" color="#4A154B">
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This includes paid LLMs like GPT 4, Claude, etc.<br/>
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Cost money and are accessible via an API.
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</Card>
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</CardGroup>
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## Open Source Models
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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.
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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).
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<CodeGroup>
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```python quickstart.py
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import os
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# replace this with your HF key
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os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "hf_xxxx"
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from embedchain import App
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app = App.from_config("mistral.yaml")
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app.add("https://www.forbes.com/profile/elon-musk")
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app.add("https://en.wikipedia.org/wiki/Elon_Musk")
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app.query("What is the net worth of Elon Musk today?")
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# Answer: The net worth of Elon Musk today is $258.7 billion.
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```
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```yaml mistral.yaml
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llm:
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provider: huggingface
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config:
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model: 'mistralai/Mistral-7B-v0.1'
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embedder:
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provider: huggingface
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config:
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model: 'sentence-transformers/all-mpnet-base-v2'
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```
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</CodeGroup>
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## Paid Models
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In this section, we will use both LLM and embedding model from OpenAI.
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```python quickstart.py
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import os
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# replace this with your OpenAI key
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os.environ["OPENAI_API_KEY"] = "sk-xxxx"
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from embedchain import App
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app = App()
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app.add("https://www.forbes.com/profile/elon-musk")
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app.add("https://en.wikipedia.org/wiki/Elon_Musk")
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app.query("What is the net worth of Elon Musk today?")
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# Answer: The net worth of Elon Musk today is $258.7 billion.
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```
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# Next Steps
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Now that you have created your first app, you can follow any of the links:
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* [Introduction](/get-started/introduction)
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* [Customization](/components/introduction)
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* [Use cases](/use-cases/introduction)
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* [Deployment](/get-started/deployment)
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