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

from embedchain import App

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

JSON Config

from embedchain import App

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