mem0/docs/advanced/app_types.mdx

---
title: '📱 App types'
---

## App Types

Embedchain supports a variety of LLMs, embedding functions/models and vector databases.

Our app gives you full control over which components you want to use, you can mix and match them to your hearts content.

<Tip>
Out of the box, if you just use `app = App()`, Embedchain uses what we believe to be the best configuration available. This might include paid/proprietary components. Currently, this is

* LLM: OpenAi (gpt-3.5-turbo)
* Embedder: OpenAi (text-embedding-ada-002)
* Database: ChromaDB
</Tip>

### LLM

#### Choosing an LLM

The following LLM providers are supported by Embedchain:
- OPENAI
- ANTHPROPIC
- VERTEX_AI
- GPT4ALL
- AZURE_OPENAI
- LLAMA2
- JINA
- COHERE

You can choose one by importing it from `embedchain.llm`. E.g.:

```python
from embedchain import App
from embedchain.llm.llama2 import Llama2Llm

app = App(llm=Llama2Llm())
```

#### Configuration

The LLMs can be configured by passing an LlmConfig object.

The config options can be found [here](/advanced/query_configuration#llmconfig)

```python
from embedchain import App
from embedchain.llm.llama2 import Llama2Llm
from embedchain.config import LlmConfig

app = App(llm=Llama2Llm(), llm_config=LlmConfig(number_documents=3, temperature=0))
```

### Embedder

#### Choosing an Embedder

The following providers for embedding functions are supported by Embedchain:
- OPENAI
- HUGGING_FACE
- VERTEX_AI
- GPT4ALL
- AZURE_OPENAI

You can choose one by importing it from `embedchain.embedder`. E.g.:

```python
from embedchain import App
from embedchain.embedder.vertexai import VertexAiEmbedder

app = App(embedder=VertexAiEmbedder())
```

#### Configuration

The LLMs can be configured by passing an EmbedderConfig object.

```python
from embedchain import App
from embedchain.embedder.openai import OpenAiEmbedder
from embedchain.config import EmbedderConfig

app = App(embedder=OpenAiEmbedder(), embedder_config=EmbedderConfig(model="text-embedding-ada-002"))
```

<Tip>
You can also pass an `LlmConfig` instance directly to the `query` or `chat` method.
This creates a temporary config for that request alone, so you could, for example, use a different model (from the same provider) or get more context documents for a specific query.
</Tip>

### Vector Database

#### Choosing a Vector Database

The following vector databases are supported by Embedchain:
- ChromaDB
- Elasticsearch

You can choose one by importing it from `embedchain.vectordb`. E.g.:

```python
from embedchain import App
from embedchain.vectordb.elasticsearch import ElasticsearchDB

app = App(db=ElasticsearchDB())
```

#### Configuration

The vector databases can be configured by passing a specific config object.

These vary greatly between the different vector databases.

```python
from embedchain import App
from embedchain.vectordb.elasticsearch import ElasticsearchDB
from embedchain.config import ElasticsearchDBConfig

app = App(db=ElasticsearchDB(), db_config=ElasticsearchDBConfig())
```

### PersonApp

```python
from embedchain import PersonApp
naval_chat_bot = PersonApp("name_of_person_or_character") #Like "Yoda"
```

- `PersonApp` uses OpenAI's model, so these are paid models. 💸 You will be charged for embedding model usage and LLM usage.
- `PersonApp` uses OpenAI's embedding model to create embeddings for chunks and ChatGPT API as LLM to get answer given the relevant docs. Make sure that you have an OpenAI account and an API key. If you don't have an API key, you can create one by visiting [this link](https://platform.openai.com/account/api-keys).
- Once you have the API key, set it in an environment variable called `OPENAI_API_KEY`

```python
import os
os.environ["OPENAI_API_KEY"] = "sk-xxxx"
```

### Full Configuration Examples

Embedchain previously offered fully configured classes, namely `App`, `OpenSourceApp`, `CustomApp` and `Llama2App`.
We deprecated these apps. The reason for this decision was that it was hard to switch from to a different LLM, embedder or vector db, if you one day decided that that's what you want to do.
The new app allows drop-in replacements, such as changing `App(llm=OpenAiLlm())` to `App(llm=Llama2Llm())`.

To make the switch to our new, fully configurable app easier, we provide you with full examples for what the old classes would look like implemented as a new app.
You can swap these in, and if you decide you want to try a different model one day, you don't have to rewrite your whole bot.

#### App
App without any configuration is still using the best options available, so you can keep using:

```python
from embedchain import App

app = App()
```

#### OpenSourceApp

Use this snippet to run an open source app.

```python
from embedchain import App
from embedchain.llm.gpt4all import GPT4ALLLlm
from embedchain.embedder.gpt4all import GPT4AllEmbedder
from embedchain.vectordb.chroma import ChromaDB

app = App(llm=GPT4ALLLlm(), embedder=GPT4AllEmbedder(), db=ChromaDB())
```

#### Llama2App
```python
from embedchain import App
from embedchain.llm.llama2 import Llama2Llm

app = App(llm=Llama2Llm())
```

#### CustomApp

Every app is a custom app now.
If you were previously using a `CustomApp`, you can now just change it to `App`.

Here's one example, what you could do if we combined everything shown on this page.

```python
from embedchain import App
from embedchain.config import ElasticsearchDBConfig, EmbedderConfig, LlmConfig
from embedchain.embedder.openai import OpenAiEmbedder
from embedchain.llm.llama2 import Llama2Llm
from embedchain.vectordb.elasticsearch import ElasticsearchDB

app = App(
    llm=Llama2Llm(),
    llm_config=LlmConfig(number_documents=3, temperature=0),
    embedder=OpenAiEmbedder(),
    embedder_config=EmbedderConfig(model="text-embedding-ada-002"),
    db=ElasticsearchDB(),
    db_config=ElasticsearchDBConfig(),
)
```

### Compatibility with other apps

- If there is any other app instance in your script or app, you can change the import as

```python
from embedchain import App as EmbedChainApp
from embedchain import PersonApp as EmbedChainPersonApp

# or

from embedchain import App as ECApp
from embedchain import PersonApp as ECPApp
```