omnigraph/docs/user/schema/index.md

Schema Language (.pg)

Top-level declarations

• interface <Name> { property* } — reusable property contracts.
• node <Name> [implements <Iface>, ...] { property* | constraint* }
• edge <Name>: <FromType> -> <ToType> [@card(min..max)] { property* | constraint* }
• Comments: line // and block /* … */.

Property declarations

<ident>: <TypeRef> [annotation*]

Built-in scalar types

Scalar	Arrow type
String	Utf8
Blob	LargeBinary
Bool	Boolean
I32 / I64	Int32 / Int64
U32 / U64	UInt32 / UInt64
F32 / F64	Float32 / Float64
Date	Date32
DateTime	Date64
Vector(<dim>)	FixedSizeList(Float32, dim), 1 ≤ dim ≤ i32::MAX
[<scalar>]	List(scalar)
enum(v1, v2, …)	Utf8 with sorted/dedup'd set of allowed string values
<scalar>?	Same as scalar but nullable: true

Constraints (body level)

Constraint	On	Effect
@key(p, …)	node	Primary key; implies index on key columns; key_property() returns the first key
@unique(p, …)	node, edge	Uniqueness across listed columns
@index(p, …)	node, edge	Build a scalar (BTREE) index on the columns
@range(p, min..max)	node	Numeric range validation (open ranges allowed)
@check(p, "regex")	node	Regex pattern validation
@card(min..max?)	edge	Edge multiplicity — default 0..*; 0..1, 1..1, 1..*, etc.

Edge bodies only allow @unique and @index.

Annotations

• @<ident> or @<ident>(<literal>) on any declaration or property.
• Known annotations:
  • @embed("source_property") on a Vector property — records which String property is the embedding source for query-time nearest($v, "string") auto-embedding. It is a catalog annotation; it does not populate the vector at ingest (supply vectors in load data, or pre-fill via the offline omnigraph embed pipeline). An optional model="…" kwarg (@embed("source_property", model="openai/text-embedding-3-large")) records the embedding model so a nearest() query whose embedder uses a different model is rejected loudly; model is the only supported kwarg. See search/embeddings.md.
  • @description("…"), @instruction("…") on query declarations (carried through to clients).
• Custom annotations are accepted by the parser and surfaced in catalog metadata; unrecognized annotations don't fail compilation.

Table layout

• Each node type compiles to a table with an id: Utf8 column plus all declared properties (blob columns are stored as LargeBinary); implements clauses expand the interface's properties into the node.
• Each edge type compiles to a table with id: Utf8, src: Utf8, dst: Utf8 plus the edge's own properties. Edge endpoint types (from/to) must exist, and edge names are matched case-insensitively.

Schema migration planning

A migration plan compares the accepted schema against the desired one and reports whether the change is supported plus the ordered steps it requires:

• Add a type
• Rename a type
• Add a property
• Rename a property
• Add a constraint
• Update type or property metadata (annotations)
• Unsupported change (reports the entity and reason; forces the plan to unsupported)

Applying a plan reports whether it was supported, the steps applied, and the resulting manifest version. Concurrent schema applies serialize so they can't interleave.

Destructive drops — --allow-data-loss

DropProperty and DropType steps default to Soft mode: the catalog tombstones the entry but the prior column / dataset remains time-travel-reachable via snapshot_at_version(prev) until omnigraph cleanup runs. Soft drops are reversible.

Pass --allow-data-loss (CLI) or allow_data_loss: true (HTTP POST /schema/apply body, SDK SchemaApplyOptions) to promote every drop in the plan to Hard mode. Hard drops run cleanup_old_versions on the affected dataset immediately after the manifest publish, making the prior column / dataset unreachable. Irreversible.

The flag is honored uniformly across transports — omnigraph schema apply --allow-data-loss, POST /schema/apply { schema_source, allow_data_loss: true }, and apply_schema_with_options(.., SchemaApplyOptions { allow_data_loss: true }) produce identical plans and identical effects.