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---
title: "App"
---
Create a RAG app object on Embedchain. This is the main entrypoint for a developer to interact with Embedchain APIs. An app configures the llm, vector database, embedding model, and retrieval strategy of your choice.
### Attributes
<ParamField path="local_id" type="str">
App ID
</ParamField>
<ParamField path="name" type="str" optional>
Name of the app
</ParamField>
<ParamField path="config" type="BaseConfig">
Configuration of the app
</ParamField>
<ParamField path="llm" type="BaseLlm">
Configured LLM for the RAG app
</ParamField>
<ParamField path="db" type="BaseVectorDB">
Configured vector database for the RAG app
</ParamField>
<ParamField path="embedding_model" type="BaseEmbedder">
Configured embedding model for the RAG app
</ParamField>
<ParamField path="chunker" type="ChunkerConfig">
Chunker configuration
</ParamField>
<ParamField path="client" type="Client" optional>
Client object (used to deploy an app to Embedchain platform)
</ParamField>
<ParamField path="logger" type="logging.Logger">
Logger object
</ParamField>
## Usage
You can create an app instance using the following methods:
### Default setting
```python Code Example
from embedchain import App
app = App()
```
### Python Dict
```python Code Example
from embedchain import App
config_dict = {
'llm': {
'provider': 'gpt4all',
'config': {
'model': 'orca-mini-3b-gguf2-q4_0.gguf',
'temperature': 0.5,
'max_tokens': 1000,
'top_p': 1,
'stream': False
}
},
'embedder': {
'provider': 'gpt4all'
}
}
# load llm configuration from config dict
app = App.from_config(config=config_dict)
```
### YAML Config
<CodeGroup>
```python main.py
from embedchain import App
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: gpt4all
config:
model: 'orca-mini-3b-gguf2-q4_0.gguf'
temperature: 0.5
max_tokens: 1000
top_p: 1
stream: false
embedder:
provider: gpt4all
```
</CodeGroup>
### JSON Config
<CodeGroup>
```python main.py
from embedchain import App
# load llm configuration from config.json file
app = App.from_config(config_path="config.json")
```
```json config.json
{
"llm": {
"provider": "gpt4all",
"config": {
"model": "orca-mini-3b-gguf2-q4_0.gguf",
"temperature": 0.5,
"max_tokens": 1000,
"top_p": 1,
"stream": false
}
},
"embedder": {
"provider": "gpt4all"
}
}
```
</CodeGroup>
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