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.

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Attributes

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local_id
str

App ID

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name
str

Name of the app

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config
BaseConfig

Configuration of the app

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llm
BaseLlm

Configured LLM for the RAG app

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db
BaseVectorDB

Configured vector database for the RAG app

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embedding_model
BaseEmbedder

Configured embedding model for the RAG app

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chunker
ChunkerConfig

Chunker configuration

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client
Client

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

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logger
logging.Logger

Logger object

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Usage

You can create an app instance using the following methods:

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Default setting

Code Example
from embedchain import App
app = App()

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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)

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YAML Config

from embedchain import App

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

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JSON Config

from embedchain import App

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