relativedb 0.1.0

Predictive queries (RelQL) over your own data: GraphQL-style user-defined retrievers, no bundled database connectors.

RelativeDB / RelQL

RelativeDB is an optimized implementation of Relational Transformers, exposed through RelQL, a query language for predicting what happens next:

PREDICT NOT EXISTS(orders.*)
FROM customers

"For every customer, what is the probability they don't place an order."

RelativeDB works best with many tables (10–100) and needs no feature engineering. Subgraphs are discovered automatically, though you can ablate them to find what features really matter. Because it uses a pretrained model, it works in environments with very little data.

# Auto-label a GitHub issue: predict its label from title, body, and history.
PREDICT issues.label
WHERE issues.label IS NULL

# Would customer 42 churn if we moved them to the premium plan?
PREDICT NOT EXISTS(orders.*)
FROM customers c
WHERE c.customer_id = 42
ASSUMING c.plan = 'premium'

# Expected spend per customer over the next quarter.
PREDICT SUM(transactions.price) OVER (90 DAYS FOLLOWING)
FROM customers

# The 12 articles each customer is most likely to buy next.
PREDICT ARRAY_AGG(transactions.article_id) OVER (30 DAYS FOLLOWING RANK TOP 12)
FROM customers

Install

pip install relativedb

Wheels bundle the native inference engine for macOS arm64 (Apple Silicon; CPU and Metal). On other platforms the package installs from source; build the engine from the repository (cpp/ with CMake) and point RELATIVEDB_RT_LIB at the built librt_c.

Quickstart: 90-day churn from your own DataFrames

from relativedb import (Schema, TableDef, LinkDef, ValueType,
                        RetrieverWiring, Engine, ExecutionInput, RtNativeBackend)

schema = (Schema.new_schema()
    .table(TableDef.new_table("customers")
        .column("age", ValueType.NUMBER)
        .column("signup_date", ValueType.DATETIME)
        .primary_key("customer_id").build())
    .table(TableDef.new_table("orders")
        .column("qty", ValueType.NUMBER)
        .column("order_date", ValueType.DATETIME)
        .primary_key("order_id").time_column("order_date").build())
    .link(LinkDef("orders", "customer_id", "customers"))
    .build())

wiring = (RetrieverWiring.new_wiring()
    .entities("customers", lambda table, ids, bound: customer_dao.by_ids(ids))
    .entities("orders",    lambda table, ids, bound: order_dao.by_ids(ids, bound))
    .default_links(lambda link, parent_id, bound, limit:
                   order_dao.recent_by_customer(parent_id, bound.as_of, limit))
    .build())

engine = Engine(schema, wiring, model_backend=RtNativeBackend(schema=schema))
result = engine.execute(ExecutionInput(
    query="PREDICT NOT EXISTS(orders.*) OVER (90 DAYS FOLLOWING) FROM customers "
          "WHERE customers.customer_id IN :ids",
    params={"ids": ["C7"]},   # the cohort; drop the WHERE to score every customer
    anchor_time=t0))

Checkpoints

Model checkpoints resolve through the Hugging Face cache on first use. Set RELATIVEDB_RT_QUANTIZED to f16, q8, or q4 to trade footprint for precision:

Checkpoint	On-disk	Accuracy	Download
fp32	342 MB	reference	—
fp16	172 MB	identical	rt-j-fp16
int8	88 MB	±0.01	rt-j-int8
int4	64 MB	±0.15	rt-j-int4

The model

RelativeDB is based on:

• stanford-star/relational-transformer — RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions
• Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data (arXiv:2510.06377)

Docs

Read the RelQL book.

License

Apache-2.0.