mex.backend.graph package¶

Submodules¶

mex.backend.graph.connector module¶

class mex.backend.graph.connector.GraphConnector¶

Bases: BaseConnector

Connector to handle authentication and transactions with the graph database.

__init__() → None¶: Create a new graph database connection.

_check_connectivity_and_authentication() → Result¶: Check the connectivity and authentication to the graph.

_fetch_extracted_or_rule_items(query_string: str | None, stable_target_id: str | None, entity_type: Sequence[str], had_primary_source: Sequence[str] | None, skip: int, limit: int) → Result¶

Query the graph for extracted or rule items.

Parameters:

query_string – Optional full text search query term
stable_target_id – Optional stable target ID filter
entity_type – List of allowed entity types
had_primary_source – Optional merged primary source identifier filter
skip – How many items to skip for pagination
limit – How many items to return at most

Returns:

Graph result instance

_init_driver() → Driver¶: Initialize and return a database driver.

Merge edges into the graph for all relations originating from one model.

All fields containing references will be iterated over. When the referenced node is found and no such relation exists yet, it will be created. A position attribute is added to all edges, that stores the index the reference had in list of references on the originating model. This way, we can preserve the order for example of contact persons referenced on an activity.

Parameters:

model – Model to ensure all edges are created in the graph
session – Active Neo4j driver Session
stable_target_id – Identifier of the connected merged item
extra_refs – Optional extra references to inject into the merge
constraints – Mapping of field names and values to use as constraints when finding the current item

Returns:

Graph result instance

Upsert an extracted or rule model including merged item and nested objects.

The given model is created or updated with all its inline properties. All nested properties (like Text or Link) are created as their own nodes and linked via edges. For multi-valued fields, the position of each nested object is stored as a property on the outbound edge. Any nested objects that are found in the graph, but are not present on the model any more are purged. In addition, a merged item is created (if it does not exist yet) and the extracted item is linked to it via an edge with the label stableTargetId.

Parameters:

model – Model to merge into the graph as a node
session – Active Neo4j driver Session
stable_target_id – Identifier the connected merged item should have
constraints – Mapping of field names and values to use as constraints when finding potential items to update

Returns:

Graph result instance

_seed_constraints() → None¶: Ensure property constraints are created for all entity types.

_seed_data() → None¶: Ensure the primary source mex is seeded and linked to itself.

_seed_indices() → Result¶: Ensure there is a full text search index for all searchable fields.

close() → None¶: Close the connector’s underlying requests session.

commit(query: Query | str, /, session: Session | None = None, **parameters: Any) → Result¶: Send and commit a single graph transaction with retry configuration.

exists_merged_item(stable_target_id: Identifier, stem_types: list[str] | None = None) → bool¶

Validate whether a merged item with the given identifier and type exists.

Parameters:

stable_target_id – Identifier of the to-be-checked merged item
stem_types – Allowed stem types of the to-be-checked merged item

Returns:

Boolean representing the existence of the requested item

fetch_extracted_items(query_string: str | None, stable_target_id: str | None, entity_type: Sequence[str] | None, had_primary_source: Sequence[str] | None, skip: int, limit: int) → Result¶

Query the graph for extracted items.

Parameters:

query_string – Optional full text search query term
stable_target_id – Optional stable target ID filter
entity_type – Optional entity type filter
had_primary_source – Optional merged primary source identifier filter
skip – How many items to skip for pagination
limit – How many items to return at most

Returns:

Graph result instance

fetch_identities(had_primary_source: Identifier | None = None, identifier_in_primary_source: str | None = None, stable_target_id: Identifier | None = None, limit: int = 1000) → Result¶

Search the graph for nodes matching the given ID combination.

Identity queries can be filtered by stable_target_id, had_primary_source or identifier_in_primary_source.

Parameters:

had_primary_source – The stableTargetId of a connected PrimarySource
identifier_in_primary_source – The id the item had in its primary source
stable_target_id – The stableTargetId of an item
limit – How many results to return, defaults to 1000

Returns:

A graph result set containing identities

fetch_merged_items(query_string: str | None, identifier: str | None, entity_type: Sequence[str] | None, had_primary_source: Sequence[str] | None, skip: int, limit: int) → Result¶

Query the graph for merged items.

Parameters:

query_string – Optional full text search query term
identifier – Optional merged item identifier filter
entity_type – Optional merged entity type filter
had_primary_source – Optional merged primary source identifier filter
skip – How many items to skip for pagination
limit – How many items to return at most

Returns:

Graph result instance

fetch_rule_items(query_string: str | None, stable_target_id: str | None, entity_type: Sequence[str] | None, had_primary_source: Sequence[str] | None, skip: int, limit: int) → Result¶

Query the graph for rule items.

Parameters:

query_string – Optional full text search query term
stable_target_id – Optional stable target ID filter
entity_type – Optional entity type filter
had_primary_source – Optional merged primary source identifier filter
skip – How many items to skip for pagination
limit – How many items to return at most

Returns:

Graph result instance

flush() → None¶: Flush the database (only in debug mode).

Ingest a list of models into the graph as nodes and connect all edges.

This is a two-step process: first all extracted and merged items are created along with their nested objects (like Text and Link); then all edges that represent references (like hadPrimarySource, parentUnit, etc.) are added to the graph in a second step.

Parameters:: models – Sequence of extracted models

ingest_v2(models: Sequence[ExtractedAccessPlatform | ExtractedActivity | ExtractedBibliographicResource | ExtractedConsent | ExtractedContactPoint | ExtractedDistribution | ExtractedOrganization | ExtractedOrganizationalUnit | ExtractedPerson | ExtractedPrimarySource | ExtractedResource | ExtractedVariable | ExtractedVariableGroup | AccessPlatformRuleSetResponse | ActivityRuleSetResponse | BibliographicResourceRuleSetResponse | ConsentRuleSetResponse | ContactPointRuleSetResponse | DistributionRuleSetResponse | OrganizationRuleSetResponse | OrganizationalUnitRuleSetResponse | PersonRuleSetResponse | PrimarySourceRuleSetResponse | ResourceRuleSetResponse | VariableRuleSetResponse | VariableGroupRuleSetResponse]) → Generator[None, None, None]¶: Ingest a list of models into the graph as nodes and connect all edges.

ingest_v2_tx(tx: Transaction, data_in: IngestData) → None¶: Ingest a single item in a database transaction.

class mex.backend.graph.connector.MExPrimarySource(*, version: Annotated[str, FieldInfo(annotation=NoneType, required=True, examples=['v1', '2023-01-16', 'Schema 9'])] | None = None, alternativeTitle: list[Text] = [], contact: list[Annotated[MergedOrganizationalUnitIdentifier | MergedPersonIdentifier | MergedContactPointIdentifier, AfterValidator(func=Identifier)]] = [], description: list[Text] = [], documentation: list[Link] = [], locatedAt: list[Link] = [], title: list[Text] = [], unitInCharge: list[MergedOrganizationalUnitIdentifier] = [], entityType: Literal['ExtractedPrimarySource'] = 'ExtractedPrimarySource', hadPrimarySource: MergedPrimarySourceIdentifier = MergedPrimarySourceIdentifier('00000000000000'), identifier: ExtractedPrimarySourceIdentifier = ExtractedPrimarySourceIdentifier('00000000000001'), identifierInPrimarySource: str = 'mex', stableTargetId: MergedPrimarySourceIdentifier = MergedPrimarySourceIdentifier('00000000000000'))¶

Bases: BasePrimarySource

An automatically extracted metadata set describing a primary source.

entityType: Annotated[Literal['ExtractedPrimarySource'], FieldInfo(annotation=NoneType, required=True, alias='$type', alias_priority=2, frozen=True)]¶

hadPrimarySource: MergedPrimarySourceIdentifier¶

identifier: ExtractedPrimarySourceIdentifier¶

identifierInPrimarySource: str¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'extra': 'ignore', 'populate_by_name': True, 'str_max_length': 100000, 'str_min_length': 1, 'str_strip_whitespace': True, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'alternativeTitle': FieldInfo(annotation=list[Text], required=False, default=[]), 'contact': FieldInfo(annotation=list[Annotated[Union[MergedOrganizationalUnitIdentifier, MergedPersonIdentifier, MergedContactPointIdentifier], AfterValidator]], required=False, default=[]), 'description': FieldInfo(annotation=list[Text], required=False, default=[]), 'documentation': FieldInfo(annotation=list[Link], required=False, default=[]), 'entityType': FieldInfo(annotation=Literal['ExtractedPrimarySource'], required=False, default='ExtractedPrimarySource', alias='$type', alias_priority=2, frozen=True), 'hadPrimarySource': FieldInfo(annotation=MergedPrimarySourceIdentifier, required=False, default=MergedPrimarySourceIdentifier("00000000000000")), 'identifier': FieldInfo(annotation=ExtractedPrimarySourceIdentifier, required=False, default=ExtractedPrimarySourceIdentifier("00000000000001")), 'identifierInPrimarySource': FieldInfo(annotation=str, required=False, default='mex'), 'locatedAt': FieldInfo(annotation=list[Link], required=False, default=[]), 'stableTargetId': FieldInfo(annotation=MergedPrimarySourceIdentifier, required=False, default=MergedPrimarySourceIdentifier("00000000000000")), 'title': FieldInfo(annotation=list[Text], required=False, default=[]), 'unitInCharge': FieldInfo(annotation=list[MergedOrganizationalUnitIdentifier], required=False, default=[]), 'version': FieldInfo(annotation=Union[Annotated[str, FieldInfo(annotation=NoneType, required=True, examples=['v1', '2023-01-16', 'Schema 9'])], NoneType], required=False, default=None)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

stableTargetId: MergedPrimarySourceIdentifier¶

mex.backend.graph.exceptions module¶

exception mex.backend.graph.exceptions.InconsistentGraphError¶

Bases: BackendError

Exception raised for inconsistencies found in the graph database.

exception mex.backend.graph.exceptions.IngestionError(*args: Any, errors: Sequence[ErrorDetails] = (), retryable: bool = False)¶

Bases: BackendError

Error for ingestion failures with underlying details.

__init__(*args: Any, errors: Sequence[ErrorDetails] = (), retryable: bool = False) → None¶: Construct a new ingestion failure with underlying details.

errors() → list[ErrorDetails]¶: Details about underlying errors.

is_retryable() → bool¶: Whether the error is retryable.

exception mex.backend.graph.exceptions.MultipleResultsFoundError¶

Bases: BackendError

A single database result was required but more than one were found.

exception mex.backend.graph.exceptions.NoResultFoundError¶

Bases: BackendError

A database result was required but none was found.

mex.backend.graph.models module¶

class mex.backend.graph.models.EdgeExporter¶

Bases: RecordExporter

Transformer class that turns edges into a string of format label {props}.

Full example:: shortName {position: 0}

transform(x: Any) → Any¶: Transform a value, or collection of values.

class mex.backend.graph.models.GraphRel¶

Bases: TypedDict

Type definition for graph relations.

edgeLabel: str¶

edgeProps: dict[str, None | str | int | list[str] | list[int]]¶

nodeLabels: list[str]¶

nodeProps: dict[str, None | str | int | list[str] | list[int]]¶

class mex.backend.graph.models.IngestData(*, stableTargetId: str, identifier: str, entityType: str, nodeProps: dict[str, None | str | int | list[str] | list[int]], linkRels: list[GraphRel] = [], createRels: list[GraphRel] = [])¶

Bases: BaseModel

Type definition for ingestion data.

createRels: list[GraphRel]¶

entityType: str¶

identifier: str¶

linkRels: list[GraphRel]¶

metadata() → dict[str, int | str]¶: Return log-able metadata.

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'createRels': FieldInfo(annotation=list[GraphRel], required=False, default=[]), 'entityType': FieldInfo(annotation=str, required=True), 'identifier': FieldInfo(annotation=str, required=True), 'linkRels': FieldInfo(annotation=list[GraphRel], required=False, default=[]), 'nodeProps': FieldInfo(annotation=dict[str, Union[NoneType, str, int, list[str], list[int]]], required=True), 'stableTargetId': FieldInfo(annotation=str, required=True)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

nodeProps: dict[str, None | str | int | list[str] | list[int]]¶

classmethod sort_create_rels(v: list[GraphRel]) → list[GraphRel]¶: Sort the rels by edge label and position.

classmethod sort_link_rels(v: list[GraphRel]) → list[GraphRel]¶: Sort the rels by edge label and position.

stableTargetId: str¶

class mex.backend.graph.models.IngestParams(*, merged_label: str, node_label: str, all_referenced_labels: list[str], all_nested_labels: list[str], detach_node_edges: list[str], delete_node_edges: list[str], has_link_rels: bool, has_create_rels: bool)¶

Bases: BaseModel

Type definition for query parameters.

all_nested_labels: list[str]¶

all_referenced_labels: list[str]¶

delete_node_edges: list[str]¶

detach_node_edges: list[str]¶

has_create_rels: bool¶

has_link_rels: bool¶

merged_label: str¶

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'all_nested_labels': FieldInfo(annotation=list[str], required=True), 'all_referenced_labels': FieldInfo(annotation=list[str], required=True), 'delete_node_edges': FieldInfo(annotation=list[str], required=True), 'detach_node_edges': FieldInfo(annotation=list[str], required=True), 'has_create_rels': FieldInfo(annotation=bool, required=True), 'has_link_rels': FieldInfo(annotation=bool, required=True), 'merged_label': FieldInfo(annotation=str, required=True), 'node_label': FieldInfo(annotation=str, required=True)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

node_label: str¶

class mex.backend.graph.models.Result(result: Result)¶

Bases: object

Represent a set of graph results.

This class wraps neo4j.Result in an interface akin to sqlalchemy.engine.Result. We do this, to reduce vendor tie-in with neo4j and limit the dependency-scope of the neo4j driver library to the mex.backend.graph submodule.

__init__(result: Result) → None¶: Wrap a neo4j result object in a mex-backend result.

all() → list[dict[str, Any]]¶: Return all records as a list.

get_update_counters() → dict[str, int]¶: Return a summary of counters for operations the query triggered.

log_notifications() → None¶: Log neo4j notifications.

one() → dict[str, Any]¶: Return exactly one record or raise an exception.

one_or_none() → dict[str, Any] | None¶

Return at most one result or raise an exception.

Returns None if the result has no records. Raises MultipleResultsFound if multiple records are returned.

mex.backend.graph.query module¶

class mex.backend.graph.query.Query(name: str, template: Template, kwargs: dict[str, Any])¶

Bases: object

Wrapper for queries that can be rendered.

REPR_MODE = Mode(target_versions=set(), line_length=1024, string_normalization=True, is_pyi=False, is_ipynb=False, skip_source_first_line=False, magic_trailing_comma=True, python_cell_magics=set(), preview=False, unstable=False, enabled_features=set())¶

__init__(name: str, template: Template, kwargs: dict[str, Any]) → None¶: Create a new query instance.

class mex.backend.graph.query.QueryBuilder¶

Bases: BaseConnector

Wrapper around jinja template loading and rendering.

__init__() → None¶: Create a new jinja environment with template loader, filters and globals.

close() → None¶: Clean up the connector.

mex.backend.graph.query.render_constraints(fields: list[~typing.Annotated[str, ~pydantic.types.StringConstraints(strip_whitespace=None, to_upper=None, to_lower=None, strict=None, min_length=None, max_length=None, pattern=^[a-zA-Z]{1,255}$)]]) → str¶: Convert a list of field names into cypher node/edge constraints.

mex.backend.graph.transform module¶

class mex.backend.graph.transform._SearchResultReference¶

Bases: TypedDict

Helper class to show the structure of search result references.

label: str¶

position: int¶

value: str | dict[str, str | None]¶

mex.backend.graph.transform.clean_dict(obj: Any) → Any¶: Clean None and [] from dicts.

mex.backend.graph.transform.expand_references_in_search_result(refs: list[_SearchResultReference]) → dict[str, list[str | dict[str, str | None]]]¶

Expand the _refs collection in a search result item.

Each item in a search result has a collection of _refs in the form of _SearchResultReference. Before parsing them into pydantic, we need to inline the references back into the item dictionary.

mex.backend.graph.transform.get_error_details_from_neo4j_error(data_in: IngestData, error: Neo4jError) → list[ErrorDetails]¶: Convert ingest-data and a neo4j error into error details.

mex.backend.graph.transform.get_graph_rel_id(rel: GraphRel) → tuple[str, int]¶: Returns a string uniquely identifying the GraphRel.

mex.backend.graph.transform.get_ingest_query_for_entity_type(entity_type: str) → str¶: Create a v2 ingest query for the given entity type.

mex.backend.graph.transform.transform_edges_into_expectations_by_edge_locator(start_node_type: str, start_node_constraints: dict[str, str | int | float | None | bool], ref_labels: list[str], ref_identifiers: list[str], ref_positions: list[int]) → dict[str, str]¶: Generate a all expected edges and render a CYPHER-style merge statement.

mex.backend.graph.transform.transform_model_into_ingest_data(model: ExtractedAccessPlatform | ExtractedActivity | ExtractedBibliographicResource | ExtractedConsent | ExtractedContactPoint | ExtractedDistribution | ExtractedOrganization | ExtractedOrganizationalUnit | ExtractedPerson | ExtractedPrimarySource | ExtractedResource | ExtractedVariable | ExtractedVariableGroup) → IngestData¶: Transform the given model into an ingestion instruction.

mex.backend.graph.transform.validate_ingested_data(data_in: IngestData, data_out: IngestData) → list[ErrorDetails]¶: Validate that the ingestion has been executed successfully.

mex.backend.graph package¶

Submodules¶

mex.backend.graph.connector module¶

mex.backend.graph.exceptions module¶

mex.backend.graph.models module¶

mex.backend.graph.query module¶

mex.backend.graph.transform module¶

Module contents¶

mex-backend

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