---
title: "Serializing Pipelines"
id: serialization
slug: "/serialization"
description: "Save your pipelines into a custom format and explore the serialization options."
---

# Serializing Pipelines

Save your pipelines into a custom format and explore the serialization options.

Serialization means converting a pipeline to a format that you can save on your disk and load later.

Haystack supports YAML format for pipeline serialization.

## Converting a Pipeline to YAML

Use the `dumps()` method to convert a Pipeline object to YAML:

```python
from haystack import Pipeline

pipe = Pipeline()
print(pipe.dumps())

# Prints:
#
# components: {}
# connections: []
# max_runs_per_component: 100
# metadata: {}
```

You can also use `dump()` method to save the YAML representation of a pipeline in a file:

```python
with open("/content/test.yml", "w") as file:
    pipe.dump(file)
```

## Converting a Pipeline Back to Python

You can convert a YAML pipeline back into Python. Use the `loads()` method to convert a string representation of a pipeline (`str`, `bytes` or `bytearray`)  or the `load()` method to convert a pipeline represented in a file-like object into a corresponding Python object.

Both loading methods support callbacks that let you modify components during the deserialization process. Therefore, loading a serialized pipeline or component assumes that the serialized definition originates from a trusted source and has been reviewed by the user.

Here is an example script:

```python
from haystack import Pipeline
from haystack.core.serialization import DeserializationCallbacks
from typing import Type, Dict, Any

# This is the YAML you want to convert to Python:
pipeline_yaml = """
components:
  cleaner:
    init_parameters:
      remove_empty_lines: true
      remove_extra_whitespaces: true
      remove_regex: null
      remove_repeated_substrings: false
      remove_substrings: null
    type: haystack.components.preprocessors.document_cleaner.DocumentCleaner
  converter:
    init_parameters:
      encoding: utf-8
    type: haystack.components.converters.txt.TextFileToDocument
connections:
- receiver: cleaner.documents
  sender: converter.documents
max_runs_per_component: 100
metadata: {}
"""


def component_pre_init_callback(
    component_name: str,
    component_cls: Type,
    init_params: Dict[str, Any],
):
    # This function gets called every time a component is deserialized.
    if component_name == "cleaner":
        assert "DocumentCleaner" in component_cls.__name__
        # Modify the init parameters. The modified parameters are passed to
        # the init method of the component during deserialization.
        init_params["remove_empty_lines"] = False
        print("Modified 'remove_empty_lines' to False in 'cleaner' component")
    else:
        print(f"Not modifying component {component_name} of class {component_cls}")


pipe = Pipeline.loads(
    pipeline_yaml,
    callbacks=DeserializationCallbacks(component_pre_init_callback),
)
```

## Default Serialization Behavior

The serialization system uses `default_to_dict` and `default_from_dict` to handle many object types automatically. You typically do **not** need to implement custom `to_dict`/`from_dict` for:

- **Secrets**: serialized and deserialized automatically so that sensitive values aren't stored in plain text.
- **ComponentDevice**: device configuration is detected and restored automatically.
- **Objects with their own `to_dict`/`from_dict`**: any init parameter whose type defines `to_dict()` is serialized by calling it; any dict in `init_parameters` with a `type` key pointing to a class with `from_dict()` is deserialized automatically.

To serialize or deserialize a single component, you can use `component_to_dict` and `component_from_dict` from `haystack.core.serialization`. They use the default behavior above as a fallback when the component doesn't define custom `to_dict`/`from_dict`:

```python
from haystack import component
from haystack.core.serialization import component_from_dict, component_to_dict


@component
class Greeter:
    def __init__(self, message: str = "Hello"):
        self.message = message

    @component.output_types(greeting=str)
    def run(self, name: str):
        return {"greeting": f"{self.message}, {name}!"}


# Serialize a component instance to a dictionary
greeter = Greeter(message="Hi")
data = component_to_dict(greeter, "my_greeter")

# Deserialize back to a component instance
restored = component_from_dict(Greeter, data, "my_greeter")
assert restored.message == greeter.message
```

:::caution[Init parameters must be stored as instance attributes]

Default serialization only works when there is a **1:1 mapping** between init parameter names and instance attributes. For every argument in `__init__`, the component must assign it to an attribute with the same name. For example, if you have `def __init__(self, prompt: str)`, you must have `self.prompt = prompt` in the class. Otherwise the serialization logic can't find the value to serialize and raises an error or uses the default value if the parameter has one.
:::

## Performing Custom Serialization

Pipelines and components in Haystack can serialize simple components, including custom ones, out of the box. Code like this just works:

```python
from haystack import component


@component
class RepeatWordComponent:
    def __init__(self, times: int):
        self.times = times

    @component.output_types(result=str)
    def run(self, word: str):
        return word * self.times
```

On the other hand, this code doesn't work if the final format is JSON, as the `set` type is not JSON-serializable:

```python
from haystack import component


@component
class SetIntersector:
    def __init__(self, intersect_with: set):
        self.intersect_with = intersect_with

    @component.output_types(result=set)
    def run(self, data: set):
        return data.intersection(self.intersect_with)
```

In such cases, you can provide your own implementation  `from_dict` and `to_dict` to components:

```python
from haystack import component, default_from_dict, default_to_dict


class SetIntersector:
    def __init__(self, intersect_with: set):
        self.intersect_with = intersect_with

    @component.output_types(result=set)
    def run(self, data: set):
        return data.intersect(self.intersect_with)

    def to_dict(self):
        return default_to_dict(self, intersect_with=list(self.intersect_with))

    @classmethod
    def from_dict(cls, data):
        # convert the set into a list for the dict representation,
        # so it can be converted to JSON
        data["intersect_with"] = set(data["intersect_with"])
        return default_from_dict(cls, data)
```

## Saving a Pipeline to a Custom Format

Once a pipeline is available in its dictionary format, the last step of serialization is to convert that dictionary into a format you can store or send over the wire. Haystack supports YAML out of the box, but if you need a different format, you can write a custom Marshaller.

A `Marshaller` is a Python class responsible for converting text to a dictionary and a dictionary to text according to a certain format. Marshallers must respect the `Marshaller` [protocol](https://github.com/deepset-ai/haystack/blob/main/haystack/marshal/protocol.py), providing the methods `marshal` and `unmarshal`.

This is the code for a custom TOML marshaller that relies on the `rtoml` library:

```python
# This code requires a `pip install rtoml`
from typing import Dict, Any, Union
import rtoml


class TomlMarshaller:
    def marshal(self, dict_: Dict[str, Any]) -> str:
        return rtoml.dumps(dict_)

    def unmarshal(self, data_: Union[str, bytes]) -> Dict[str, Any]:
        return dict(rtoml.loads(data_))
```

You can then pass a Marshaller instance to the methods `dump`, `dumps`, `load`, and `loads`:

```python
from haystack import Pipeline
from my_custom_marshallers import TomlMarshaller

pipe = Pipeline()
pipe.dumps(TomlMarshaller())
# prints:
# 'max_runs_per_component = 100\nconnections = []\n\n[metadata]\n\n[components]\n'
```

## Additional References

:notebook: Tutorial: [Serializing LLM Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)
