---
title: "Pipeline"
---

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

<ParamField path="local_id" type="str">
    Pipeline ID
</ParamField>
<ParamField path="name" type="str" optional>
    Name of the pipeline
</ParamField>
<ParamField path="config" type="BaseConfig">
    Configuration of the pipeline
</ParamField>
<ParamField path="llm" type="BaseLlm">
    Configured LLM for the RAG pipeline
</ParamField>
<ParamField path="db" type="BaseVectorDB">
    Configured vector database for the RAG pipeline
</ParamField>
<ParamField path="embedding_model" type="BaseEmbedder">
    Configured embedding model for the RAG pipeline
</ParamField>
<ParamField path="chunker" type="ChunkerConfig">
    Chunker configuration
</ParamField>
<ParamField path="client" type="Client" optional>
    Client object (used to deploy a pipeline to Embedchain platform)
</ParamField>
<ParamField path="logger" type="logging.Logger">
    Logger object
</ParamField>

## Usage

You can create an embedchain pipeline instance using the following methods:

### Default setting

```python Code Example
from embedchain import Pipeline as App
app = App()
```


### Python Dict

```python Code Example
from embedchain import Pipeline as 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

<CodeGroup>

```python main.py
from embedchain import Pipeline as App

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

```yaml 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
```

</CodeGroup>

### JSON Config

<CodeGroup>

```python main.py
from embedchain import Pipeline as App

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

```json 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"
  }
}
```

</CodeGroup>