oatdb 0.4.4

Python client for OAT (Optimization and Analysis Tooling) database

OatDB Python SDK

A Python client library for interacting with the OatDB (Optimization and Analysis Tooling) database backend.

Features

• ✅ Full support for all 30 OatDB API functions
• ✅ Logical operations (AND, OR, XOR, NOT, IMPLY, EQUIV)
• ✅ Cardinality constraints (AtLeast, AtMost, Equal)
• ✅ Linear inequality constraints (GeLineq)
• ✅ Property management
• ✅ DAG operations (sub, sub_many, validate, ranks)
• ✅ Constraint propagation
• ✅ Optimization solver (solve, solve_many)
• ✅ Node deletion and management
• ✅ Alias support for named constraints
• ✅ Type hints for better IDE support

Installation

pip install oat-python-sdk

Or with Poetry:

poetry add oat-python-sdk

Quick Start

from oatdb import OatClient, set_primitive, set_property, set_and, sub, solve

# Initialize client
client = OatClient("http://localhost:7061")

# Create primitives with bounds [min, max]
x = set_primitive("x", bound=1j)  # [0, 1]
y = set_primitive("y", bound=10j)  # [0, 10]

# Add properties
x_name = set_property(x, "name", "Variable X")

# Create constraints
constraint = set_and([x, y], alias="my_constraint")

# Extract DAG and solve
dag = sub(constraint)
solution = solve(
    dag=dag,
    objective={
        x: 1,
        y: 2
    },
    assume={
        # Force constraint to be true
        constraint: 1+1j
    },
    maximize=True
)

# Execute and get results
result = client.execute(solution)
print(result)

Core Concepts

Bounds

Bounds are represented as complex numbers where:

• Real part = lower bound
• Imaginary part = upper bound

# Bound [0, 1]
bound = 1j

# Bound [5, 10]
bound = 5 + 10j

# Access bounds from solution
solution_data = result[solution.out]
x_bounds = solution_data["x"]  # [lower, upper] as list

Function Calls and Execution

All operations return FunctionCall objects that you execute using the client:

from oatdb import OatClient, set_primitive, set_and, sub

client = OatClient("http://localhost:7061")

# Create function calls
x = set_primitive("x", bound=1j)
y = set_primitive("y", bound=1j)
constraint = set_and([x, y])

# Execute a single operation
result = client.execute(constraint)

# Execute multiple operations
dag = sub(constraint)
result = client.execute_many([x, y, constraint, dag])

# Access results by the function call's output key
dag_data = result[dag.out]

Available Functions

All functions return FunctionCall objects that are executed using client.execute() or client.execute_many().

Primitive Operations

• set_primitive(id: str, bound: complex = 1j, alias: Optional[str] = None) - Create a single primitive
• set_primitives(ids: List[str], bound: complex = 1j) - Create multiple primitives
• set_property(id: Union[str, FunctionCall], property: str, value: Any) - Set node property

Logical Operations

• set_and(references: List, alias: Optional[str] = None) - AND operation
• set_or(references: List, alias: Optional[str] = None) - OR operation
• set_xor(references: List, alias: Optional[str] = None) - XOR operation
• set_not(references: List, alias: Optional[str] = None) - NOT operation
• set_imply(lhs, rhs, alias: Optional[str] = None) - Implication (lhs → rhs)
• set_equiv(lhs, rhs, alias: Optional[str] = None) - Equivalence (lhs ↔ rhs)

Cardinality Constraints

• set_atleast(references: List, value: int, alias: Optional[str] = None) - At least N must be true
• set_atmost(references: List, value: int, alias: Optional[str] = None) - At most N must be true
• set_equal(references: List, value: Union[int, str], alias: Optional[str] = None) - Exactly N must be true

Linear Constraints

• set_gelineq(coefficients: Dict, bias: int, alias: Optional[str] = None) - Greater-or-equal linear inequality (ax + b >= 0)

DAG Operations

• sub(root) - Extract sub-DAG from a root node
• sub_many(roots: List) - Extract multiple sub-DAGs
• get_node_ids(dag) - Get all node IDs in a DAG
• get_ids_from_dag(dag) - Get all node IDs from a DAG (alternative)
• validate(dag) - Validate DAG structure
• ranks(dag) - Compute topological ranks

Alias Operations

• get_id_from_alias(alias: str) - Get node ID from alias
• get_alias(id) - Get alias for a node ID
• get_aliases_from_id(id) - Get all aliases for a node ID
• get_ids_from_aliases(aliases: List[str]) - Get IDs for multiple aliases

Node Operations

• get_node(id) - Get a single node
• get_nodes(ids: List) - Get multiple nodes
• get_property_values(property: str) - Get all nodes with a specific property

Propagation

• propagate(assignments: Dict) - Propagate constraints with assignments
• propagate_many(many_assignments: List[Dict]) - Propagate multiple assignment sets

Solver

• solve(dag, objective: Dict, assume: Optional[Dict] = None, maximize: bool = True) - Solve single optimization
• solve_many(dag, objectives: List[Dict], assume: Optional[Dict] = None, maximize: bool = True) - Solve multiple optimizations

Deletion

• delete_node(id) - Delete a single node
• delete_sub(roots: List) - Delete sub-DAGs from roots

Client Methods

• OatClient(url: str) - Initialize client with server URL
• client.execute(call: FunctionCall) - Execute a single function call
• client.execute_many(calls: List[FunctionCall]) - Execute multiple function calls

Complete Example

from oatdb import (
    OatClient, set_primitive, set_property, set_and, set_or,
    set_imply, set_atleast, set_gelineq, sub, solve
)

# Initialize
client = OatClient("http://localhost:7061")

# Create primitives
x = set_primitive("x", bound=10j)
y = set_primitive("y", bound=10j)
z = set_primitive("z", bound=10j)

# Add metadata
x_type = set_property(x, "type", "variable")
x_priority = set_property(x, "priority", 10)

# Create constraints
and_constraint = set_and([x, y], alias="both_xy")
or_constraint = set_or([y, z])
imply_constraint = set_imply(x, y)  # x → y

# Cardinality: at least 2 must be true
atleast_2 = set_atleast([x, y, z], 2)

# Linear constraint: 2x + 3y - z + 5 >= 0
linear = set_gelineq(
    coefficients={x: 2, y: 3, z: -1},
    bias=5
)

# Combine all constraints
root = set_and([atleast_2, linear], alias="root")

# Extract DAG
dag = sub(root)

# Solve optimization: maximize 3x + 2y + z
solution = solve(
    dag=dag,
    objective={
        x: 3,
        y: 2,
        z: 1
    },
    assume={
        root: 1+1j
    },
    maximize=True
)

# Execute and get results
result = client.execute(solution)

print("Solution:")
for var, bounds in result.items():
    if isinstance(bounds, complex):
        print(f"  {var}: [{bounds.real}, {bounds.imag}]")

Working with Aliases

from oatdb import OatClient, set_primitive, set_and, get_id_from_alias, get_alias

client = OatClient("http://localhost:7061")

# Create constraint with alias
x = set_primitive("x", bound=1j)
y = set_primitive("y", bound=1j)
constraint = set_and([x, y], alias="my_constraint")

# Execute creation
client.execute_many([x, y, constraint])

# Query by alias
id_from_alias = get_id_from_alias("my_constraint")
alias_from_id = get_alias(id_from_alias)

result = client.execute_many([id_from_alias, alias_from_id])
print(f"ID: {result[id_from_alias.out]}")
print(f"Alias: {result[alias_from_id.out]}")

Propagation Example

from oatdb import OatClient, set_primitive, set_and, propagate

client = OatClient("http://localhost:7061")

# Create AND constraint
a = set_primitive("a", bound=1j)
b = set_primitive("b", bound=1j)
c = set_primitive("c", bound=1j)
and_gate = set_and([a, b, c], alias="and_gate")

# Propagate: if AND is true, what can we infer?
prop_result = propagate(
    assignments={
        a: 1+1j,
        b: 1+1j,
        c: 1+1j,
        and_gate: 1j  # Upper bound only
    }
)

result = client.execute(prop_result)
# Result will show that a, b, and c must all be [1, 1]
print(f"Inferred bounds: {result}")

Testing

Run the comprehensive test suite:

cd clients/Python
python tests/test_client.py

Or with Poetry:

poetry run python tests/test_client.py

Note: Make sure the OatDB server is running on http://localhost:7061 before running tests.

Run examples:

python examples.py

Requirements

• Python >= 3.10
• requests >= 2.31.0

Development

# Install with Poetry
poetry install

# Run tests
poetry run pytest

# Format code
poetry run black .

# Type checking
poetry run mypy oatdb

License

MIT

Links

• GitHub Repository
• OatDB Documentation
• Rust Client