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

local_id · str

App ID

name · str

Name of the app

config · BaseConfig

Configuration of the app

llm · BaseLlm

Configured LLM for the RAG app

db · BaseVectorDB

Configured vector database for the RAG app

embedding_model · BaseEmbedder

Configured embedding model for the RAG app

chunker · ChunkerConfig

Chunker configuration

client · Client

Client object (used to deploy an app to Embedchain platform)

logger · logging.Logger

Logger object

Usage

You can create an app instance using the following methods:

Default setting

Code Example

from embedchain import App
app = App()

Python Dict

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

main.py

from embedchain import App

# load llm configuration from config.yaml file
app = App.from_config(config_path="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

JSON Config

main.py

from embedchain import App

# load llm configuration from config.json file
app = App.from_config(config_path="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"
  }
}