Semantic Layer

The semantic layer is how you teach Marivo what your data means. You declare datasources, entities, dimensions, and metrics in Python, and agents address them by semantic ref (a qualified name like sales.revenue) instead of raw table and column names.

Three rules hold across every object:

• Python declarations are the contract. Decorated functions and builder calls are the source of truth for names, definitions, and shapes.
• Ibis expressions are the execution language. Decorator bodies return ibis expressions, never raw SQL strings.
• SQL text is metadata only. When you need SQL (for parity checking), it lives in provenance=ms.from_sql(sql=..., dialect=...), never in an executable body.

You work through two namespaces:

import marivo.datasource as md   # connections (md.duckdb, md.ref, ...)
import marivo.semantic as ms     # meaning (ms.entity, ms.metric, ...)

How names work

Every object lives under a domain and is addressed by a qualified ref:

• Domain-level objects: <domain>.<object> — e.g. sales.revenue, sales.orders.
• Entity-scoped objects (dimensions and measures): <domain>.<entity>.<field> — e.g. sales.orders.region.

A project is a folder of declaration files. Datasources are declared once under models/datasources/; semantics live under models/semantic/<domain>/, with a _domain.py per domain:

your-project/
  marivo.toml
  models/
    datasources/
      warehouse.py          # md.duckdb(name="warehouse", ...)
    semantic/
      sales/
        _domain.py          # ms.domain(name="sales") + entities, metrics, ...

Semantic refs

Every semantic object is identified by a semantic ref — a typed, immutable handle that carries both the qualified name and the kind of object it refers to. Refs are the same type at authoring time and in the analysis loop: an ms.entity(...) call, a catalog.get(...).ref lookup, and an analysis intent parameter all use the same SemanticRef family.

The SemanticRef base

All refs share two read-only attributes:

Attribute	Type	Meaning
.id	str	Qualified semantic id (e.g. "sales.revenue").
.kind	SemanticKind	Object kind — one of the eight values below.

str(ref) returns .id, so refs can be used wherever a string id is expected. Equality and hashing are by (type, id), so two refs of the same subclass and id are interchangeable.

Per-kind subclasses

Each SemanticKind value has a concrete ref subclass:

Kind	Subclass	Returned by	Callable?
domain	DomainRef	ms.domain(...)	No
datasource	DatasourceRef	md.ref(...)	No
entity	EntityRef	ms.entity(...)	No
dimension	DimensionRef	ms.dimension_column(...) for direct columns, @ms.dimension for expressions	Yes — in metric bodies
time_dimension	TimeDimensionRef	ms.time_dimension_column(...) for direct columns, @ms.time_dimension for expressions	Yes — in metric bodies
measure	MeasureRef	ms.measure_column(...) for direct columns, @ms.measure for expressions	Yes — in metric bodies
metric	MetricRef	ms.aggregate(...), ms.count(...), @ms.metric, ms.ratio(...), …	No
relationship	RelationshipRef	ms.relationship(...)	No

Callable field refs (DimensionRef, MeasureRef, TimeDimensionRef) resolve to an ibis expression when called inside a metric body. All other refs raise a teaching error if accidentally called — they are identity tokens, not decorators.

One family, one accessor

Because authoring refs and catalog refs are the same type family, you can pass an authoring ref directly to an analysis intent without wrapping it:

revenue = ms.aggregate(name="revenue", measure=amount, agg="sum")
# revenue is a MetricRef — pass it directly to observe:
frame = session.observe(revenue, timescope={...})

The catalog’s catalog.get("sales.revenue").ref returns the same MetricRef subclass. There is no .ref.ref chain or type mismatch between authoring and analysis.

Factory and normalizers

• mv.make_ref(id, kind) — internal; removed from the public surface (used internally by the catalog).
• as_ref_id(value) — extract the .id string from a SemanticRef, SemanticObject, or plain str. String-tolerant: raw ids pass through.

ai_context: the human-to-agent contract

Every semantic object and datasource accepts an optional ai_context parameter, constructed with ms.ai_context(...). This is where business meaning and guardrails live — the context an agent reads before it uses the object. All parameters are optional, but unknown keyword arguments are rejected by Python at call time.

Field	Type	Required	Default	Meaning
business_definition	str	No	None	What the object means in business terms, in a sentence or two.
guardrails	list[str]	No	[]	Rules an agent must respect: required filters, exclusions, scope limits.
synonyms	list[str]	No	[]	Alternate names so agents can resolve natural-language references.
examples	list[str]	No	[]	Example questions or phrasings this object answers.
instructions	str	No	None	Direct guidance on how (and how not) to use the object.
owner_notes	str	No	None	Notes from the human owner: provenance, caveats, known issues.

ai_context=ms.ai_context(
    business_definition="Gross order amount before refunds.",
    guardrails=["Validate refund exclusions before using as net revenue."],
    synonyms=["sales", "gmv"],
    examples=["What was revenue by region last week?"],
)

Datasources

Datasources are declared in models/datasources/*.py with a typed helper per backend. The helper registers the connection; it does not return a value. Semantic files refer to a datasource by kind-qualified ref through md.ref("datasource.warehouse").

models/datasources/warehouse.py

import marivo.datasource as md
import marivo.semantic as ms

md.duckdb(
    name="warehouse",
    path="warehouse.duckdb",
    ai_context=ms.ai_context(
        business_definition="Local DuckDB warehouse for sales analysis.",
        guardrails=["Use only for development or approved local analysis."],
    ),
)

Every helper (md.duckdb, md.mysql, md.postgres, md.trino, md.clickhouse) shares these parameters:

Parameter	Type	Required	Default	Meaning
name	str	Yes	—	Global datasource name (letters, digits, _, -). Used by md.ref(name).
ai_context	AiContextValue	No	None	Agent-facing context, via ms.ai_context(...).
extra	dict	No	None	Rare JSON-safe ibis keyword arguments the typed helper does not model.

Backend-specific parameters:

Helper	Required	Optional
md.duckdb	—	path (default ":memory:"), read_only (default False)
md.mysql	host, database	port (3306), autocommit, user_env, password_env
md.postgres	host, database	port (5432), schema, autocommit, user_env, password_env
md.trino	host, catalog	port (8080), schema, source, timezone, http_scheme, client_tags, session_properties, user_env, auth_env
md.clickhouse	host	port (9000 / 9440 secure), database, secure, settings, user_env, password_env

Credentials are references, never literals

Sensitive fields are authored as *_env references that name an environment variable — user_env="TRINO_USER", password_env="MYSQL_PASSWORD". Putting a secret literal in a datasource field raises an error. Marivo resolves *_env values through os.environ → ~/.marivo/secrets.toml and caches resolved secrets there after a validated md.test(...) round-trip.

models/datasources/lake.py

import marivo.datasource as md

md.trino(
    name="lake",
    host="trino.example.internal",
    catalog="hive",
    user_env="TRINO_USER",
    auth_env="TRINO_AUTH",
)

Domain

ms.domain(...) opens a namespace. Call it once per _domain.py. It returns a DomainRef you can pass as domain= to override the active domain for an object declared in a sibling file.

Parameter	Type	Required	Default	Meaning
name	str	Yes	—	Domain namespace, e.g. "sales". Objects become <name>.<object>.
default	bool	No	True	When True, decorators in this file resolve to this domain unless domain= is passed.
ai_context	AiContextValue	No	None	Agent-facing context, via ms.ai_context(...).

import marivo.semantic as ms

ms.domain(name="sales")

Entity

An entity is one physical source (a table or file) plus its primary key. It is the anchor that dimensions, measures, and metrics attach to.

Parameter	Type	Required	Default	Meaning
name	str	Yes	—	Entity name. Becomes <domain>.<name>.
datasource	DatasourceRef	Yes	—	md.ref("datasource.warehouse").
source	source builder	Yes	—	ms.table(...), ms.parquet(...), or ms.csv(...).
primary_key	list[str]	No	None	Column names forming the primary key.
versioning	ms.snapshot | ms.validity	No	None	Snapshot or SCD2 validity versioning (see below).
domain	DomainRef	No	file default	Override the active domain.
ai_context	AiContextValue	No	None	Agent-facing context, via ms.ai_context(...).

warehouse = md.ref("datasource.warehouse")

orders = ms.entity(
    name="orders",
    datasource=warehouse,
    source=ms.table("orders"),
    primary_key=["order_id"],
    ai_context=ms.ai_context(business_definition="One row per order."),
)

Source builders

Builder	Required	Optional	Use for
ms.table(name)	name	database	A table in the datasource (use database="schema" for Trino/MySQL).
ms.parquet(path)	path	hive_partitioning, columns	Parquet files (typically through DuckDB).
ms.csv(path)	path	header, delimiter, columns	CSV files (typically through DuckDB).

Versioning (optional)

For entities whose rows change over time, declare how to read the current state:

• ms.snapshot(partition_field, grain="day", timezone=None, format=None) — daily partitioned snapshots; reads the latest partition.
• ms.validity(valid_from, valid_to, interval, open_end, timezone=None) — SCD2 validity intervals. interval is "closed_open" ([from, to)) or "closed_closed"; open_end lists the sentinel values that mean “still current” (e.g. (None,) for SQL NULL, or ("9999-12-31",)).

Dimension

A dimension is a categorical attribute you group or filter by. For direct physical columns, use ms.dimension_column(...); for expressions (e.g. table.region.upper()), use the @ms.dimension decorator whose body returns a single ibis expression over the entity table.

Parameter	Type	Required	Default	Meaning
name	str	Yes	—	Dimension name. Becomes <domain>.<entity>.<name>.
entity	EntityRef | str	Yes	—	The owning entity.
column	str	Yes	—	Physical column name on the entity table.
domain	DomainRef	No	file default	Override the active domain.
ai_context	AiContextValue	No	None	Agent-facing context, via ms.ai_context(...).

region = ms.dimension_column(
    name="region",
    entity=orders,
    column="region",
    ai_context=ms.ai_context(business_definition="Sales reporting region."),
)

Time dimension

A time dimension is a special dimension that carries grain and parsing metadata. Only time dimensions can serve as the time axis for session.observe.

Parameter	Type	Required	Default	Meaning
name	str	Yes	—	Dimension name.
entity	EntityRef | str	Yes	—	The owning entity.
column	str	Yes	—	Physical column name on the entity table.
granularity	grain literal	Yes	—	year, quarter, month, week, day, hour, minute, or second — the finest grain at which queries are meaningful.
parse	parse variant	No	None	How the source column becomes a time value (see below). Omit for native temporal columns — the parse variant is inferred at analysis time.
is_default	bool	No	False	Marks the default time axis when the entity has several. observe uses it when time_dimension= is omitted.
domain	DomainRef	No	file default	Override the active domain.
ai_context	AiContextValue	No	None	Agent-facing context, via ms.ai_context(...).

Parse variants

The parse= value declares the physical encoding of the column. When omitted, the parse variant is inferred from the column’s ibis dtype at analysis time (native date, datetime, and timestamp columns do not need an explicit parse). For string or integer columns, provide ms.strptime(format) or ms.hour_prefix(prefix). The variant must be compatible with granularity (e.g. an hour grain needs a time-bearing format).

Builder	Source column is…	Key parameters
(omit parse)	a native temporal column	—
ms.datetime()	a native datetime	timezone (IANA), sample_interval
ms.timestamp()	a native timestamp	timezone (IANA), sample_interval
ms.strptime(format)	a string/integer to parse	timezone, sample_interval
ms.hour_prefix(prefix)	an hour-only partition	sample_interval — prefix is the day-grain time-dimension id that supplies the date

timezone defaults to the datasource engine timezone; set it (e.g. "UTC") only when the column’s wall-clock meaning differs. sample_interval like (5, "minute") marks a periodically-sampled axis used by semi-additive folds.

# Day partition stored as the string "20260131"
log_date = ms.time_dimension_column(
    name="log_date",
    entity=orders,
    column="dt",
    granularity="day",
    parse=ms.strptime("%Y%m%d"),
    is_default=True,
)

# Native UTC timestamp, usable for sub-day buckets
event_ts = ms.time_dimension_column(
    name="event_ts",
    entity=orders,
    column="event_ts",
    granularity="minute",
    parse=ms.timestamp(timezone="UTC"),
)

Measure

A measure is a row-level quantitative fact you intend to aggregate (e.g. an amount or quantity). For direct physical columns, use ms.measure_column(...); for expressions over one or more columns, use @ms.measure. Measures carry additivity and an optional unit.

Parameter	Type	Required	Default	Meaning
name	str	Yes	—	Measure name. Becomes <domain>.<entity>.<name>.
entity	EntityRef | str	Yes	—	The owning entity.
column	str	Yes	—	Physical column name on the entity table.
additivity	additivity value	Yes	—	"additive", "non_additive", or ms.semi_additive(...).
unit	str	No	None	UCUM unit token: "USD", "CNY", "%", "ms", "{order}".
domain	DomainRef	No	file default	Override the active domain.
ai_context	AiContextValue	No	None	Agent-facing context, via ms.ai_context(...).

amount = ms.measure_column(
    name="amount",
    entity=orders,
    column="amount",
    additivity="additive",
    unit="CNY",
)

Metrics

A metric is the trusted, analysis-ready number an agent starts from. Marivo has several authoring shapes — pick by how the number is computed.

Simple metric from a measure — ms.aggregate

Aggregates a measure. No body; additivity is inherited from the measure.

Parameter	Type	Required	Default	Meaning
name	str	Yes	—	Metric name.
measure	MeasureRef | str	Yes	—	The measure to aggregate.
agg	aggregation	Yes	—	"sum", "mean", "min", "max", …
fold	fold	No	None	Time-fold override for semi-additive measures.
unit	str	No	inherited	Override the unit derived from the measure.
domain / ai_context	—	No	—	As elsewhere.

revenue = ms.aggregate(name="revenue", measure=amount, agg="sum")

Count metrics — ms.count

Use ms.count instead of declaring a redundant measure just to count entity rows. The helper accepts refs only and infers the metric domain from the entity ref.

order_count = ms.count(name="order_count", entity=orders, ai_context=ms.ai_context(business_definition="Total number of orders."))

Tier-2 custom metric from an ibis body — @ms.metric

Use this escape hatch when the metric cannot be expressed as ms.aggregate(...), ms.count(...), or a derived metric builder. The body returns one ibis aggregation, and you declare additivity directly.

Parameter	Type	Required	Default	Meaning
name	str	No	function name	Metric name.
entities	list[EntityRef | str]	Yes	—	Entities the body reads.
additivity	additivity value	Yes	—	"additive", "non_additive", or ms.semi_additive(...).
root_entity	EntityRef | str	No	the single entity	Required when entities has more than one.
fanout_policy	"block" | "aggregate_then_join"	No	"block"	How to handle join fan-out across entities.
unit	str	No	None	UCUM unit token.
provenance	SqlProvenance	No	None	ms.from_sql(sql=..., dialect=...) for parity checking.
domain / ai_context	—	No	—	As elsewhere.

@ms.metric(
    entities=[orders],
    additivity="additive",
    name="revenue",
    provenance=ms.from_sql(
        sql="SELECT SUM(amount) AS revenue FROM orders",
        dialect="duckdb",
    ),
    ai_context=ms.ai_context(business_definition="Gross order amount before refunds."),
)
def revenue(table):
    return table.amount.sum()

Derived metrics — ms.ratio / ms.weighted_average / ms.linear

Body-free metrics composed from other metrics. The computation comes entirely from the components.

Builder	Required	Computes
ms.ratio(name, numerator, denominator)	both refs	numerator / denominator (e.g. average order value, rates)
ms.weighted_average(name, value, weight)	both refs	weighted average; attribute later splits mix vs rate
ms.linear(name, add, subtract)	add (≥2 terms total)	sum of add minus subtract (e.g. net = gross - refunds)

Each also accepts unit, domain, and ai_context.

net_revenue = ms.linear(name="net_revenue", add=[gross_revenue], subtract=[refunds])
aov = ms.ratio(name="aov", numerator=total_amount, denominator=orders_count)

Additivity and provenance helpers

• ms.semi_additive(over, fold) — for snapshot/status facts that are additive across most axes but folded over a time axis. over is the status time dimension ref returned by @ms.time_dimension(...); fold is "last", "first", "mean", "max", or ("percentile", 0.95).
• ms.from_sql(sql, dialect) — attaches SQL as provenance only, enabling ms.parity_check(...). It is never executed as the metric body.

snapshot_date = ms.time_dimension_column(
    name="snapshot_date",
    entity=inventory_daily,
    column="snapshot_date",
    granularity="day",
)

on_hand_units = ms.measure_column(
    name="on_hand_units",
    entity=inventory_daily,
    column="on_hand_units",
    additivity=ms.semi_additive(over=snapshot_date, fold="last"),
)

Relationship

Declares how two entities join, so metrics and dimensions can reach across them. Keys are dimension refs, not raw column names.

Parameter	Type	Required	Default	Meaning
name	str	Yes	—	Relationship name.
from_entity	EntityRef | str	Yes	—	Source entity.
to_entity	EntityRef | str	Yes	—	Target entity.
keys	list[JoinKey]	Yes	—	One or more ms.join_on(from_key, to_key) pairs.
domain / ai_context	—	No	—	As elsewhere.

ms.relationship(
    name="orders_to_customers",
    from_entity=orders,
    to_entity=customers,
    keys=[ms.join_on(order_customer_id, customer_id)],
)

Loading and the readiness gate

Once declarations are in place, load the catalog and inspect it:

import marivo.semantic as ms

catalog = ms.load()                       # SemanticCatalog
catalog.list().show()                     # everything, grouped
catalog.list(kind="metric").show()        # just metrics
revenue = catalog.get("sales.revenue")           # one object
region = catalog.get("sales.orders.region")      # also an object

Before any analysis, check readiness — the structural gate that keeps half-specified objects out of analysis:

report = ms.readiness()
if report.status == "blocked":
    report.show()                         # blockers, with the next step for each

Readiness is required before analysis

A project can load() successfully while readiness is still blocked. Resolve the blockers report.show() lists before passing any ref to an analysis session.

Two more checks support authoring:

• ms.richness() — advisory coverage/depth report; never blocks.
• ms.parity_check("sales.revenue") — runs the metric against its provenance SQL and compares results. Requires provenance=ms.from_sql(...).

For how readiness decides what is “ready,” see Readiness. For how analysis records what it concludes, see Evidence. Then continue to the Analysis Workflow.