Pipeline - Embedchain

Create a RAG pipeline object on Embedchain. This is the main entrypoint for a developer to interact with Embedchain APIs. A pipeline configures the llm, vector database, embedding model, and retrieval strategy of your choice.

Attributes

str

Pipeline ID

str

Name of the pipeline

BaseConfig

Configuration of the pipeline

BaseLlm

Configured LLM for the RAG pipeline

BaseVectorDB

Configured vector database for the RAG pipeline

BaseEmbedder

Configured embedding model for the RAG pipeline

ChunkerConfig

Chunker configuration

Client

Client object (used to deploy a pipeline to Embedchain platform)

logging.Logger

Logger object

Usage

You can create an embedchain pipeline 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")

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

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"
  }
}

​Attributes

​Usage

​Default setting

​Python Dict

​YAML Config

​JSON Config