Plugin Data Sources

Plugins extend the source.type discriminator to support custom data sources beyond the builtin csv, parquet, and bigquery types. Any installed Python package that registers in the recotem.datasources entry-point group is automatically discovered at startup — no code changes to Recotem are required.

For the full plugin authoring contract (class shape, FetchContext, testing, packaging, compatibility), see Plugin Authoring.

How plugins are discovered

At both recotem train and recotem serve startup, Recotem scans the recotem.datasources entry-point group in the active Python environment. Each registered class is loaded, its type_name attribute is extracted, and it is added to the source type registry. An unknown source.type in a recipe raises DataSourceError listing all registered type names.

Duplicate type_name values cause hard startup failures

If two installed plugins both declare the same type_name, both recotem train and recotem serve exit with code 3 at startup, listing the conflicting fully-qualified class names. Uninstall one plugin or rename its type_name.

Plugin contract summary

A plugin class must expose the following attributes and methods:

Attribute / Method	Kind	Required	Description
type_name	ClassVar[str]	yes	Discriminator value matched against source.type in the recipe. Must be non-empty and unique across all installed plugins.
Config	pydantic.BaseModel subclass	yes	Describes the recipe sub-fields for this source. All fields appear under source: in the YAML alongside type:. The loader passes the entire source: mapping to Config.model_validate(...).
extras_required	ClassVar[list[str]]	yes	Pip extras to suggest when optional dependencies are missing. Purely documentation — Recotem never auto-installs these. Surface a DataSourceError with the install hint in __init__.
no_expand_fields	ClassVar[frozenset[str]]	yes	Field names inside Config whose string values must never receive ${RECOTEM_RECIPE_*} env-var expansion. Use frozenset() when no fields need protection beyond the global baseline (query, query_parameters, which are always guarded). Missing or wrong-type declaration raises DataSourceError at plugin-discovery time.
__init__(config)	method	yes	Receives an instance of Config. Defer optional dependency imports here; raise DataSourceError with an install hint if imports fail.
fetch(ctx)	method	yes	Returns a pandas.DataFrame containing at least the columns named in recipe.schema. Must raise DataSourceError (not a bare exception) for external or transient failures — bare exceptions become exit 1 instead of exit 3.
probe()	method	optional	Called by recotem validate for a lightweight connectivity / auth check. Should never load full data. Raise DataSourceError on failure. Return value is ignored.

Installing a plugin

# Install from PyPI
pip install recotem-my-source

# Install a local development plugin in editable mode
pip install -e ./my-source-plugin/

Verify discovery after install:

recotem validate my_recipe.yaml

If the plugin is not found, validate reports Unknown DataSource type 'my_type' listing all registered names. Confirm the plugin is installed in the same Python environment as recotem.

Using a plugin in a recipe

A recipe using a plugin looks no different from one using a builtin source. Set source.type to the plugin's type_name and add whichever fields the plugin's Config declares:

name: echo_test

source:
  type: echo          # matches EchoSource.type_name
  n_users: 50
  n_items: 100
  n_rows: 500
  seed: 42

schema:
  user_column: user_id
  item_column: item_id
  time_column: timestamp   # EchoSource emits integer epoch-second timestamps

training:
  algorithms: [TopPop]
  metric: ndcg
  cutoff: 10
  n_trials: 1

output:
  path: ./artifacts/echo_test.recotem

Train as normal:

recotem train recipe.yaml

The echo plugin above is the canonical reference implementation from examples/plugins/echo-source/. It generates a synthetic DataFrame and is useful for CI pipelines that need to exercise the full train-serve path without real data.

JSON Schema and IDE integration

recotem schema builds the JSON Schema at runtime by constructing a discriminated union of every registered DataSource Config class — including plugin-provided ones — and substituting it into the Recipe model. Plugin Config schemas appear in the output, which makes IDE autocompletion work for source.* fields. The plugin must be installed in the same Python environment as recotem for its fields to appear.

Plugin packaging reference

A minimal pyproject.toml for a plugin package:

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "recotem-my-source"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = ["recotem>=2.0,<3", "pandas>=2.2,<4"]

[project.entry-points."recotem.datasources"]
my_source = "recotem_my_source:MySource"

[tool.hatch.build.targets.wheel]
packages = ["src/recotem_my_source"]

The entry-point key (my_source) is used in registry log and error messages but is not the discriminator — Recotem uses the class's type_name attribute. By convention, keep them the same.

Pin recotem>=2.0,<3 in your plugin's dependencies. The type_name / Config / fetch(ctx) contract is stable within the 2.x major version.