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.
Serialization formatsHaystack 2.0 only supports YAML format at this time. We will be rolling out more formats gradually.
Converting a Pipeline to YAML
Use the dumps() method to convert a Pipeline object to YAML:
from haystack import Pipeline
pipe = Pipeline()
print(pipe.dumps())
# Prints:
#
# components: {}
# connections: []
# max_loops_allowed: 100
# metadata: {}Converting a Pipeline YAML to Python
You can convert a YAML pipeline back into Python. Use the loads() method to convert a valid YAML representation of a pipeline into a corresponding Python object:
from haystack import Pipeline
# 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_loops_allowed: 100
metadata: {}
"""
pipe = Pipeline.loads(pipeline_yaml)Performing Custom Serialization
Pipelines and components in Haystack can serialize simple components, including custom ones, out of the box. Code like this just works:
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.timesOn the other hand, this code doesn't work if the final format is JSON, as the set type is not JSON-serializable:
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:
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, providing the methods marshal and unmarshal.
This is the code for a custom TOML marshaller that relies on the rtoml library:
# 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:
from haystack import Pipeline
from my_custom_marshallers import TomlMarshaller
pipe = Pipeline()
pipe.dumps(TomlMarshaller())
# prints:
# 'max_loops_allowed = 100\nconnections = []\n\n[metadata]\n\n[components]\n'Updated 8 days ago
