# OMMX Python SDK 3.0.x

```{note}
Python SDK 3.0.0 contains breaking API changes. A migration guide is available in the [Python SDK v2 to v3 Migration Guide](https://github.com/Jij-Inc/ommx/blob/main/PYTHON_SDK_MIGRATION_GUIDE.md).
```

## Unreleased

Changes merged after the most recent release will be appended here as they land, and promoted to a new version section when the next release is cut.

## 3.0.0 Alpha 5

[![Static Badge](https://img.shields.io/badge/GitHub_Release-Python_SDK_3.0.0a5-orange?logo=github)](https://github.com/Jij-Inc/ommx/releases/tag/python-3.0.0a5)

See the GitHub Release above for full details. The following summarizes the main changes. This is a pre-release version. APIs may change before the final release.

### 🆕 Run-scoped Experiment trace storage ([#910](https://github.com/Jij-Inc/ommx/pull/910), [#916](https://github.com/Jij-Inc/ommx/pull/916))

{class}`~ommx.experiment.Experiment`, {meth}`~ommx.experiment.Experiment.with_temp_local_registry`, and {meth}`~ommx.experiment.Experiment.fork` now accept `store_trace=True`. When enabled, each `with experiment.run()` context captures the OpenTelemetry spans emitted inside that Run and stores one trace on the closed {class}`~ommx.experiment.SealedRun`. The stored trace is returned as {class}`~ommx.tracing.TraceResult` from {attr}`~ommx.experiment.SealedRun.trace`, and is carried through commit, load, and fork.

See [Tracing and Profiling](../user_guide/tracing.ipynb) for the full tracing workflow, renderers, and OpenTelemetry setup notes.

```python
from ommx.experiment import Experiment
from ommx.tracing import render_text_tree
from ommx_highs_adapter import OMMXHighsAdapter

with Experiment.with_temp_local_registry(store_trace=True) as experiment:
    with experiment.run() as run:
        run.log_solve(OMMXHighsAdapter, instance)

loaded = Experiment.from_artifact(experiment.artifact)
trace = loaded.runs[0].trace
if trace is not None:
    print(render_text_tree(trace))
```

The stored payload is OTLP protobuf, so {class}`~ommx.tracing.TraceResult` now owns the exported request, exposes flattened `spans`, and can round-trip with `otlp_protobuf()` / `from_otlp_protobuf()`. Text and Chrome trace renderers also use domain-oriented span names such as `Run`, `solve`, `convert`, `call`, and `decode`, and surface instrumentation scope while hiding debug-only source attributes.

### ⚠ Experiment attachments are now name-indexed ([#924](https://github.com/Jij-Inc/ommx/pull/924))

Experiment and Run attachments are now stored as name-indexed tables in the Experiment config. The public Python API is name-oriented: use `attachment_names`, `attachment_media_type(name)`, `get_attachment(name)`, the typed getters such as `get_json(name)` and `get_instance(name)`, `get_blob(name)`, `get_with_codec(...)`, and `write_attachment(...)`.

```python
loaded = Experiment.from_artifact(experiment.artifact)

for name in loaded.attachment_names:
    print(name, loaded.attachment_media_type(name))
    value = loaded.get_attachment(name)
```

Descriptor-oriented attachment views from earlier 3.0 alphas, including `Experiment.experiment_attachments` and `SealedRun.attachments`, are removed. Registry-backed descriptors remain internal so attachment names, media types, file export names, and checkpoint metadata stay in the Experiment config instead of descriptor annotations.

### 🆕 Experiment checkpoints and restore from interrupted sessions ([#917](https://github.com/Jij-Inc/ommx/pull/917))

{class}`~ommx.experiment.Experiment` now publishes local checkpoints for partial experiment state. Closing a {class}`~ommx.experiment.Run` writes a best-effort draft checkpoint, and exiting an Experiment with an exception writes a failed or interrupted checkpoint instead of advancing the successful Experiment image reference. Closed Runs keep their attachments, solves, traces, and run parameters, including Runs closed as `"failed"` or `"interrupted"` after exceptions such as `KeyboardInterrupt`.

See [Experiment Recovery and Cleanup](../user_guide/experiment.md) for Run close boundaries, checkpoint restoration, and Local Registry cleanup behavior.

Use {meth}`~ommx.experiment.Experiment.restore_from_checkpoint` with the original Experiment image name to resume from the latest checkpoint:

```python
from ommx.experiment import Experiment

image_name = "ghcr.io/example/team/experiment:notebook"

try:
    with Experiment(image_name) as experiment:
        with experiment.run() as run:
            run.log_parameter("solver", "highs")
            raise KeyboardInterrupt
except KeyboardInterrupt:
    pass

experiment = Experiment.restore_from_checkpoint(image_name)
assert experiment.image_name == image_name
```

Successful `commit()` still publishes only the requested image reference and removes the local checkpoint when present. Checkpoint Artifact handles and checkpoint image names are intentionally not exposed in the Python API; users restore by remembering the original Experiment image name.

### 🆕 Local Registry cleanup ([#919](https://github.com/Jij-Inc/ommx/pull/919))

The `ommx` CLI now provides Local Registry maintenance commands for the SQLite-backed Artifact registry. Use `ommx gc` to report blobs that are unreachable from SQLite refs, including Experiment checkpoint refs. The command protects recently written unreachable blobs with a grace period so active Experiment writes are not deleted accidentally.

Destructive cleanup commands report by default and mutate the registry only when `--delete` is passed:

```bash
ommx prune-anonymous
ommx gc
ommx prune-anonymous --delete
ommx gc --delete
```

Normal reports show counts and sizes rather than raw digests. Pass `--show-digests` when low-level diagnostics are needed.

The same cleanup operations are also exposed from the Python SDK as
{func}`ommx.artifact.prune_anonymous` and {func}`ommx.artifact.gc`. These
functions are report-only by default, mutate the registry with `delete=True`,
and return structured report objects for notebook and script use.

### 🆕 Typed attachment codecs for Experiments ([#921](https://github.com/Jij-Inc/ommx/pull/921))

The new {class}`ommx.experiment.attachments.AttachmentCodec` protocol lets packages that own Python payload types define how those values are stored as Experiment attachments. A codec class provides a media type plus `encode` / `decode` methods, and OMMX calls it through `log_with_codec` and `get_with_codec` on both Experiment-level and Run-level attachments.

See the {ref}`Attachable Data Formats <experiment-management-attachable-data-formats>` section of the Experiment management tutorial for a JijModeling `Problem` codec example.

```python
from ommx.experiment import Experiment


class TextCodec:
    media_type = "text/plain"

    @staticmethod
    def encode(value: str) -> bytes:
        return value.encode()

    @staticmethod
    def decode(data: bytes) -> str:
        return data.decode()


with Experiment.with_temp_local_registry() as experiment:
    experiment.log_with_codec(TextCodec, "note", "created outside OMMX")

loaded = Experiment.from_artifact(experiment.artifact)
assert loaded.get_with_codec(TextCodec, "note") == "created outside OMMX"
```

The stored attachment media type is validated before decoding, so using the wrong codec for an attachment fails before the codec's `decode` method is called.

### 🆕 File attachments for Experiments ([#922](https://github.com/Jij-Inc/ommx/pull/922))

{class}`~ommx.experiment.Experiment` and {class}`~ommx.experiment.Run` can now attach files that were produced outside OMMX. Use `log_file` to copy an existing file into the Experiment Artifact. OMMX stores the file bytes as an attachment blob, records the original basename for later export, and uses an explicitly provided media type or Rust SDK content-based inference with an `application/octet-stream` fallback.

Committed experiment and run views now also provide `write_attachment` to restore an attachment blob back to disk. For libraries that accept a binary file-like object, wrap the existing `get_blob` result with `io.BytesIO`.

```python
import io
from pathlib import Path

from ommx.experiment import Experiment

with Experiment.with_temp_local_registry() as experiment:
    experiment.log_file("input-spreadsheet", "input.xlsx")

loaded = Experiment.from_artifact(experiment.artifact)
spreadsheet_file = io.BytesIO(loaded.get_blob("input-spreadsheet"))
Path("restored").mkdir(parents=True, exist_ok=True)
loaded.write_attachment("input-spreadsheet", "restored/input.xlsx")
```

## 3.0.0 Alpha 4

[![Static Badge](https://img.shields.io/badge/GitHub_Release-Python_SDK_3.0.0a4-orange?logo=github)](https://github.com/Jij-Inc/ommx/releases/tag/python-3.0.0a4)

See the GitHub Release above for full details. The following summarizes the main changes. This is a pre-release version. APIs may change before the final release.

### ⚠ SQLite-based Local Registry ([#871](https://github.com/Jij-Inc/ommx/pull/871), [#872](https://github.com/Jij-Inc/ommx/pull/872))

In v3, local Artifact storage is organized around the SQLite-based Local Registry. Artifact blobs are stored in content-addressed storage, while image-name references and registry metadata are managed in SQLite. APIs that depended on the old disk OCI directory cache are removed; the user-facing flow is now to commit an Artifact into the Local Registry, then `save` / `push` / `load` that committed Artifact.

Alongside this storage model and the new `Experiment` API, the old `ArtifactBuilder` is reshaped as {class}`~ommx.artifact.ArtifactDraft`. An `ArtifactDraft` represents an uncommitted Artifact draft; after it is committed to the Local Registry, the resulting {class}`~ommx.artifact.Artifact` can be saved or pushed. `.ommx` archives are import/export exchange formats for the Local Registry. The main breaking changes are:

- `ArtifactBuilder.new_archive` → {func}`ArtifactDraft.new <ommx.artifact.ArtifactDraft.new>` + {func}`Artifact.save <ommx.artifact.Artifact.save>` (new method).
- `ArtifactBuilder.new_archive_unnamed` → {func}`ArtifactDraft.new_anonymous <ommx.artifact.ArtifactDraft.new_anonymous>` + `Artifact.save(path)`. In v2, an unnamed archive literally had no image name and was read back as `None`. In v3, an anonymous Artifact gets an automatically generated `<registry-id8>.ommx.local/anonymous:<timestamp>-<nonce>` image name from the Local Registry, so it can still be saved, loaded again, and cleaned up.
- {func}`Artifact.load_archive <ommx.artifact.Artifact.load_archive>` raises a migration error pointing at the two replacement methods: {func}`Artifact.import_archive <ommx.artifact.Artifact.import_archive>` (imports the archive into the user's persistent SQLite Local Registry — the v3 successor with registry-write semantics) and {func}`Artifact.inspect_archive <ommx.artifact.Artifact.inspect_archive>` (side-effect-free read of the manifest + layer descriptors, returns a new {class}`ArchiveManifest <ommx.artifact.ArchiveManifest>` view). v2's `load_archive` opened archives in place with no registry side effect, so the rename makes the semantic shift explicit instead of silently writing into the registry on upgrade. `import_archive` accepts v2 archives produced by `ArtifactBuilder.new_archive_unnamed` (no `org.opencontainers.image.ref.name` annotation) by synthesizing an anonymous name on the fly; `inspect_archive` reads such archives back with `ArchiveManifest.image_name = None` (no registry context for synthesis).
- CLI `ommx push <archive>` and `ommx push <oci-dir>` removed — load into the registry first, then push by image name.
- New CLI `ommx prune-anonymous [--delete]` reports accumulated anonymous-commit entries by default and removes them only when `--delete` is passed.
- `ommx.get_image_dir(...)` and the CLI `ommx image-dir <name>` subcommand are removed. The return value was a v2 disk-cache path (`<root>/<image_name>/<tag>/`) that no longer corresponds to any v3 storage location — the SQLite Local Registry stores blobs content-addressed and refs in SQLite — so pointing users at it was actively misleading. Existing v2 caches still migrate via `ommx import-legacy`.

See the [Python SDK v2→v3 Migration Guide §13](https://github.com/Jij-Inc/ommx/blob/main/PYTHON_SDK_MIGRATION_GUIDE.md#13-artifact-api-archive-becomes-an-exchange-format) for the full before/after code and migration checklist.

### 🆕 Artifact-backed experiment management API: `ommx.experiment` ([#882](https://github.com/Jij-Inc/ommx/pull/882), [#885](https://github.com/Jij-Inc/ommx/pull/885), [#886](https://github.com/Jij-Inc/ommx/pull/886), [#903](https://github.com/Jij-Inc/ommx/pull/903))

The new `ommx.experiment` module records experiment inputs, run conditions, and Solver/Sampler results as one OMMX Artifact. Use {class}`~ommx.experiment.Experiment`, {class}`~ommx.experiment.Run`, and {class}`~ommx.experiment.Solve` to store per-run comparison parameters, attachments, and solve input/output data in the Local Registry.

See the [Experiment management tutorial](../tutorial/experiment_management.md) for the basic workflow, sharing an Experiment, loading a committed Experiment, and creating derived experiments with fork.

### 🆕 `Run.log_solve` records solve input/output and adapter options ([#902](https://github.com/Jij-Inc/ommx/pull/902))

{meth}`~ommx.experiment.Run.log_solve` is now available. Pass a subclass of `ommx.adapter.SolverAdapter` and an {class}`~ommx.v1.Instance`; OMMX calls the adapter's `solve`, then stores the input Instance, output Solution, adapter class name, and JSON-serializable keyword arguments as a {class}`~ommx.experiment.Solve`.

```python
from ommx.experiment import Experiment
from ommx_highs_adapter import OMMXHighsAdapter
from ommx.v1 import Instance, Solution

with Experiment() as experiment:
    with experiment.run() as run:
        solution = run.log_solve(OMMXHighsAdapter, instance, verbose=False)
        run.log_parameter("objective", solution.objective)

solve = experiment.runs[0].solves[0]
assert solve.adapter.endswith("OMMXHighsAdapter")
assert isinstance(solve.input, Instance)
assert isinstance(solve.output, Solution)
assert solve.output.feasible
assert solve.adapter_options == {"verbose": False}
```

Adapter options are solve-scoped metadata, so they do not appear in {meth}`~ommx.experiment.Experiment.run_parameters_df`, which is the table for comparing runs. Record values explicitly with {meth}`~ommx.experiment.Run.log_parameter` when you want them in that DataFrame.

### 🆕 Experiment fork and lineage ([#905](https://github.com/Jij-Inc/ommx/pull/905))

{meth}`~ommx.experiment.Experiment.fork` starts a new uncommitted Experiment from a committed one. The child inherits the parent's attachments, Runs, Solves, and Run parameters, while the parent remains unchanged. When the child is committed after adding new Runs or attachments, the parent manifest descriptor is recorded as the OCI `subject`.

```python
from ommx.experiment import Experiment
from ommx_highs_adapter import OMMXHighsAdapter

loaded = Experiment.load("ghcr.io/jij-inc/ommx/tutorial/experiment:baseline")

with loaded.fork("ghcr.io/jij-inc/ommx/tutorial/experiment:capacity-64") as child:
    with child.run() as run:
        run.log_parameter("capacity", 64)
        run.log_solve(OMMXHighsAdapter, instance, verbose=False)
```

Forking creates a new Artifact Manifest, but Instance / Solution / attachment payloads continue to reference content-addressed blobs in the Local Registry, so the data bodies are not duplicated. Saving or pushing the fork shares the complete forked Experiment, including Runs and Solves inherited from the parent.

### 🆕 `Instance.substitute` / `ParametricInstance.substitute` ([#891](https://github.com/Jij-Inc/ommx/pull/891), [#897](https://github.com/Jij-Inc/ommx/pull/897))

{meth}`~ommx.v1.Instance.substitute` and {meth}`~ommx.v1.ParametricInstance.substitute` are now available from Python. Pass a dictionary from decision-variable IDs to replacement {class}`~ommx.v1.Function` expressions; OMMX rewrites those variables in the objective and active constraints in-place. This exposes the general substitution mechanism behind `log_encode`, so users can implement custom variable transformations such as unary or one-hot encodings.

```python
from ommx.v1 import DecisionVariable, Instance

x = DecisionVariable.integer(0, lower=0, upper=3)
b = [DecisionVariable.binary(i) for i in (1, 2)]
instance = Instance.from_components(
    decision_variables=[x, *b],
    objective=x,
    constraints={},
    sense=Instance.MAXIMIZE,
)

instance.substitute({0: b[0] + 2 * b[1]})
assert str(instance.objective) == "Function(x1 + 2*x2)"
```

This API is an algebraic rewrite. It does not translate the substituted variable's `kind` / `lower` / `upper` into constraints on the replacement expression. To preserve the optimization problem, use a domain-preserving encoding or add the required linking / bound constraints yourself. `ParametricInstance.substitute` may leave parameters in replacement expressions, so symbolic variable transformations can be applied before concrete values are supplied with `with_parameters`.

## 3.0.0 Alpha 3

[![Static Badge](https://img.shields.io/badge/GitHub_Release-Python_SDK_3.0.0a3-orange?logo=github)](https://github.com/Jij-Inc/ommx/releases/tag/python-3.0.0a3)

See the GitHub Release above for full details. The following summarizes the main changes. This is a pre-release version. APIs may change before the final release.

### ⚠ `*_df` accessors are methods + `include=` filter + sidecar DataFrames ([#846](https://github.com/Jij-Inc/ommx/pull/846))

Every `*_df` accessor on `Instance` / `ParametricInstance` / `Solution` / `SampleSet` is now a regular method instead of a `#[getter]` property. Existing call sites need parentheses:

```python
# Before
df = solution.constraints_df

# After
df = solution.constraints_df()
```

The wide `*_df` methods take an `include` argument that gates the metadata / parameters column families. The default `include=("metadata", "parameters")` preserves the v2-equivalent wide shape:

```python
solution.decision_variables_df()                       # core + metadata + parameters
solution.decision_variables_df(include=[])             # core only
solution.decision_variables_df(include=["metadata"])   # core + metadata
solution.decision_variables_df(include=["parameters"]) # core + parameters
```

Six new long-format / id-indexed sidecar accessors read directly from the SoA metadata stores. `kind=` selects the constraint family (`"regular"` / `"indicator"` / `"one_hot"` / `"sos1"`, default `"regular"`):

- `constraint_metadata_df(kind=...)` — id-indexed (`name` / `subscripts` / `description`)
- `constraint_parameters_df(kind=...)` — long format (`{kind}_constraint_id` / `key` / `value`)
- `constraint_provenance_df(kind=...)` — long format (`{kind}_constraint_id` / `step` / `source_kind` / `source_id`)
- `constraint_removed_reasons_df(kind=...)` — long format (`{kind}_constraint_id` / `reason` / `key` / `value`)
- `variable_metadata_df()` — id-indexed
- `variable_parameters_df()` — long format

Sidecar index names are kind-qualified (`regular_constraint_id` / `indicator_constraint_id` / `one_hot_constraint_id` / `sos1_constraint_id` / `variable_id`) so accidental cross-id-space `df.join()` mistakes surface in `df.head()` and friends. Long-format `*_parameters_df` / `*_removed_reasons_df` rows are sorted by `(id, key)`, and empty long-format DataFrames keep their column schema instead of returning a column-less frame.

### ⚠ `removed_reason` column gated by `include=` ([#796](https://github.com/Jij-Inc/ommx/pull/796), [#847](https://github.com/Jij-Inc/ommx/pull/847))

In v2.5.1 {meth}`Solution.constraints_df <ommx.v1.Solution.constraints_df>` carried a `removed_reason` column unconditionally. The initial `include=` gate of that column landed in 3.0.0a2 (#796), and 3.0.0a3 finalizes it into the `kind=` / `include=` / `removed=` dispatch shape documented above (#847): the column is opted in by `"removed_reason"` in `include=` (a unit flag that controls both the reason name and `removed_reason.{key}` parameter columns). Rows whose constraint was not removed before evaluation get NA in those columns.

```python
# Before (2.5.1)
df = solution.constraints_df  # contains a 'removed_reason' column

# After (3.0.0a3 — `*_df` are now methods)
df = solution.constraints_df()  # no removed_reason column
df = solution.constraints_df(include=("metadata", "parameters", "removed_reason"))
# ↳ adds removed_reason / removed_reason.{key} (NA for active rows)
```

The same `kind=` / `include=` shape applies on {class}`~ommx.v1.SampleSet`. On {class}`~ommx.v1.Instance` and {class}`~ommx.v1.ParametricInstance`, `removed=True` returns active + removed rows in one DataFrame and auto-sets `"removed_reason"` so removed rows are distinguishable.

### ⚠ `to_bytes` / `from_bytes` removed from non-top-level types ([#845](https://github.com/Jij-Inc/ommx/pull/845))

Bytes serialization is removed from the following component-level types:

- {class}`~ommx.v1.Function`, {class}`~ommx.v1.Linear`, {class}`~ommx.v1.Quadratic`, {class}`~ommx.v1.Polynomial`
- {class}`~ommx.v1.Parameter`
- {class}`~ommx.v1.NamedFunction`, {class}`~ommx.v1.EvaluatedNamedFunction`, {class}`~ommx.v1.SampledNamedFunction`
- {class}`~ommx.v1.DecisionVariable`, {class}`~ommx.v1.EvaluatedDecisionVariable`, {class}`~ommx.v1.SampledDecisionVariable`

These methods originally existed to ferry values across the Python ↔ Rust boundary back when the Python SDK had its own protobuf-based wrapper layer and had to serialize on every hop. With the v3 transition to direct PyO3 re-exports the boundary disappears, so element-level bytes round-trips no longer serve a purpose, and keeping them aligned with the upcoming metadata-storage redesign would only add maintenance cost. `to_bytes` / `from_bytes` remain available on the container types ({class}`~ommx.v1.Instance`, {class}`~ommx.v1.ParametricInstance`, {class}`~ommx.v1.Solution`, {class}`~ommx.v1.SampleSet`) and on the cross-evaluate DTOs ({class}`~ommx.v1.State`, {class}`~ommx.v1.Samples`, {class}`~ommx.v1.Parameters`) — use those when you need to persist or exchange data on disk or over the wire.

### 🆕 Write-through metadata wrappers: `AttachedConstraint` / `AttachedDecisionVariable` ([#849](https://github.com/Jij-Inc/ommx/pull/849), [#850](https://github.com/Jij-Inc/ommx/pull/850), [#852](https://github.com/Jij-Inc/ommx/pull/852))

`Instance.add_constraint` / `instance.constraints[id]` and the matching accessors on `ParametricInstance` now return write-through handles bound to the parent host instead of snapshot copies. Reads pull live data from the host and metadata setters write straight to its SoA metadata store, so two handles pointing at the same id observe the same state.

```python
c = instance.add_constraint(x + y == 0)         # AttachedConstraint
c.set_name("budget")                             # writes through to instance
assert instance.constraints[c.constraint_id].name == "budget"
```

Five write-through types ship: {class}`~ommx.v1.AttachedConstraint`, {class}`~ommx.v1.AttachedIndicatorConstraint`, {class}`~ommx.v1.AttachedOneHotConstraint`, {class}`~ommx.v1.AttachedSos1Constraint`, and {class}`~ommx.v1.AttachedDecisionVariable`. {class}`~ommx.v1.Constraint` and {class}`~ommx.v1.DecisionVariable` are unchanged in shape — they remain the snapshot wrappers used for modeling input (operator overloading, `Instance.from_components`). Each `AttachedX` exposes `.detach()` to obtain an equivalent snapshot when you need to break the back-reference to the host.

As part of the same change, `instance.decision_variables` now returns `list[AttachedDecisionVariable]` (previously `list[DecisionVariable]` snapshots), aligning with `instance.constraints` and the special-constraint accessors.

### 🆕 OpenTelemetry-based tracing and profiling ([#816](https://github.com/Jij-Inc/ommx/pull/816), [#823](https://github.com/Jij-Inc/ommx/pull/823), [#826](https://github.com/Jij-Inc/ommx/pull/826), [#828](https://github.com/Jij-Inc/ommx/pull/828), [#829](https://github.com/Jij-Inc/ommx/pull/829))

The legacy `log` + `pyo3-log` → Python `logging` bridge is replaced by a `tracing` + `pyo3-tracing-opentelemetry` pipeline, so the Rust core's spans can now be consumed through the Python OTel SDK.

Two entry points ship under `ommx.tracing`:

- **`%%ommx_trace`** — a Jupyter cell magic that renders a per-cell span tree and a Chrome Trace JSON download link
- **`capture_trace` / `@traced`** — a context manager and decorator for the same workflow from regular Python scripts, tests, and CI

See [Tracing and Profiling](../user_guide/tracing.ipynb) for the full walkthrough, configuring your own `TracerProvider`, and troubleshooting.

### 🆕 Tracing spans in solver/sampler adapters ([#833](https://github.com/Jij-Inc/ommx/pull/833))

Every OMMX adapter now emits three OpenTelemetry spans per solve/sample call, so the OTel tracing pipeline above can attribute wall-clock time to the three phases an adapter actually spends time in:

- **`convert`** — OMMX `Instance` → solver-native problem translation
- **`solve`** / **`sample`** — the call into the underlying solver / sampler itself
- **`decode`** — decoding the solver's response back to `Solution` / `SampleSet` (Rust-side `evaluate` spans nest underneath)

Each adapter uses its own tracer name, so runs from different solvers are easy to distinguish in the tree view:

| Adapter | Tracer | Spans |
|---|---|---|
| `ommx-pyscipopt-adapter` | `ommx.adapter.pyscipopt` | `convert` / `solve` / `decode` |
| `ommx-highs-adapter` | `ommx.adapter.highs` | `convert` / `solve` / `decode` |
| `ommx-python-mip-adapter` | `ommx.adapter.python_mip` | `convert` / `solve` / `decode` |
| `ommx-openjij-adapter` | `ommx.adapter.openjij` | `convert` / `sample` / `decode` |

```python
from ommx.tracing import capture_trace, render_text_tree
from ommx_pyscipopt_adapter import OMMXPySCIPOptAdapter

with capture_trace() as trace:
    solution = OMMXPySCIPOptAdapter.solve(instance)

print(render_text_tree(trace))  # shows convert / solve / decode with durations
```

Spans are emitted through the standard OpenTelemetry API, so they are a no-op when no `TracerProvider` is installed — there is no runtime cost for users who do not opt in.

### 🆕 `Function.evaluate_bound` is now available from Python ([#831](https://github.com/Jij-Inc/ommx/pull/831))

{meth}`Function.evaluate_bound <ommx.v1.Function.evaluate_bound>` is now exposed on {class}`~ommx.v1.Function`. Given per-variable bounds, it returns a {class}`~ommx.v1.Bound` that contains the range of the function value — useful when deriving feasibility bounds or doing simple presolve on the Python side.

```python
from ommx.v1 import Function, Linear, Bound

f = Function(Linear(terms={1: 2}, constant=3))  # 2*x1 + 3
b = f.evaluate_bound({1: Bound(0.0, 2.0)})
# b.lower == 3.0, b.upper == 7.0
```

The bound is computed monomial-wise and summed, so it is a sound over-approximation of the true range but is **not guaranteed to be tight** when multiple terms share variables (the classic dependency problem in interval arithmetic). Variable IDs missing from `bounds` are treated as unbounded.

## 3.0.0 Alpha 2

[![Static Badge](https://img.shields.io/badge/GitHub_Release-Python_SDK_3.0.0a2-orange?logo=github)](https://github.com/Jij-Inc/ommx/releases/tag/python-3.0.0a2)

See the GitHub Release above for full details. The following summarizes the main changes. This is a pre-release version. APIs may change before the final release.

### ⚠ Removal of the `Constraint.id` field ([#806](https://github.com/Jij-Inc/ommx/pull/806))

The `id` field (along with the `.id` getter, `set_id()`, and `id=` constructor argument) is removed from {class}`~ommx.v1.Constraint` and its variants ({class}`~ommx.v1.IndicatorConstraint` / {class}`~ommx.v1.OneHotConstraint` / {class}`~ommx.v1.Sos1Constraint` / {class}`~ommx.v1.EvaluatedConstraint` / {class}`~ommx.v1.SampledConstraint` / {class}`~ommx.v1.RemovedConstraint`). A constraint's ID now exists only as the key of the `dict[int, Constraint]` passed to {meth}`Instance.from_components <ommx.v1.Instance.from_components>`.

```python
# Before (2.5.1)
c = Constraint(function=x + y, equality=Constraint.EQUAL_TO_ZERO, id=5)
Instance.from_components(..., constraints=[c], ...)

# After (3.0.0a2)
c = Constraint(function=x + y, equality=Constraint.EQUAL_TO_ZERO)
Instance.from_components(..., constraints={5: c}, ...)
```

Global ID counters (`next_constraint_id` and friends) and per-constraint `to_bytes` / `from_bytes` are also removed. For full details and migration steps, see the [Python SDK v2 to v3 Migration Guide](https://github.com/Jij-Inc/ommx/blob/main/PYTHON_SDK_MIGRATION_GUIDE.md).

### 🆕 First-class special constraint types ([#789](https://github.com/Jij-Inc/ommx/pull/789), [#790](https://github.com/Jij-Inc/ommx/pull/790), [#795](https://github.com/Jij-Inc/ommx/pull/795), [#796](https://github.com/Jij-Inc/ommx/pull/796), [#798](https://github.com/Jij-Inc/ommx/pull/798))

In addition to regular constraints, the following three special constraint types are now first-class citizens — they can be passed to `Instance.from_components` via `indicator_constraints=` / `one_hot_constraints=` / `sos1_constraints=`, and read back through {meth}`~ommx.v1.Solution.constraints_df` / {meth}`~ommx.v1.SampleSet.constraints_df` with `kind=` selecting the family.

- {class}`~ommx.v1.IndicatorConstraint` — conditional constraint on a binary variable (new)
- {class}`~ommx.v1.OneHotConstraint` — replaces the previous `ConstraintHints.OneHot` metadata
- {class}`~ommx.v1.Sos1Constraint` — replaces the previous `ConstraintHints.Sos1` metadata

For concrete usage, evaluation-result access, and the Indicator relax / restore workflow, see [Special Constraints](../user_guide/special_constraints.md).

Accordingly, the legacy `ConstraintHints` / `OneHot` / `Sos1` classes, the `Instance.constraint_hints` property, and the PySCIPOpt Adapter's `use_sos1` flag are removed.

### 🆕 Adapter Capability Model ([#790](https://github.com/Jij-Inc/ommx/pull/790), [#805](https://github.com/Jij-Inc/ommx/pull/805), [#810](https://github.com/Jij-Inc/ommx/pull/810), [#811](https://github.com/Jij-Inc/ommx/pull/811), [#814](https://github.com/Jij-Inc/ommx/pull/814))

Alongside the special constraint types, adapters now declare their own supported capabilities via an `ADDITIONAL_CAPABILITIES` class attribute. When `super().__init__(instance)` is called, any undeclared special constraint is automatically converted to regular constraints (Big-M for Indicator / SOS1, linear equality for OneHot) before the instance reaches the solver.

**Existing OMMX Adapters must be updated for Python SDK 3.0.0 to call `super().__init__(instance)`.** Currently the PySCIPOpt Adapter declares support for Indicator and SOS1.

For details and the manual conversion APIs, see [Adapter Capability Model and Conversions](../user_guide/capability_model.md).

### 🔄 numpy scalar support ([#794](https://github.com/Jij-Inc/ommx/pull/794))

The {class}`~ommx.v1.Function` constructor now accepts `numpy.integer` and `numpy.floating` values. In v2.5.1, `Function(numpy.int64(3))` raised `TypeError`.

## 3.0.0 Alpha 1

[![Static Badge](https://img.shields.io/badge/GitHub_Release-Python_SDK_3.0.0a1-orange?logo=github)](https://github.com/Jij-Inc/ommx/releases/tag/python-3.0.0a1)

See the GitHub Release above for full details. The following summarizes the main changes. This is a pre-release version. APIs may change before the final release.

### Complete Rust re-export of `ommx.v1` and `ommx.artifact` types ([#770](https://github.com/Jij-Inc/ommx/pull/770), [#771](https://github.com/Jij-Inc/ommx/pull/771), [#774](https://github.com/Jij-Inc/ommx/pull/774), [#775](https://github.com/Jij-Inc/ommx/pull/775), [#782](https://github.com/Jij-Inc/ommx/pull/782))

Python SDK 3.0.0 is fully based on Rust/PyO3.
In 2.0.0, the core implementation was rewritten in Rust while Python wrapper classes remained for compatibility. In 3.0.0, those Python wrappers are removed entirely — all types in `ommx.v1` and `ommx.artifact` are now direct re-exports from Rust, and the `protobuf` Python runtime dependency is eliminated. The `.raw` attribute that previously provided access to the underlying PyO3 implementation has also been removed.

### Migration to Sphinx and ReadTheDocs hosting ([#780](https://github.com/Jij-Inc/ommx/pull/780), [#785](https://github.com/Jij-Inc/ommx/pull/785))

In v2, the Sphinx-based API Reference and Jupyter Book-based documentation were each hosted on [GitHub Pages](https://jij-inc.github.io/ommx/en/introduction.html). In v3, documentation has been fully migrated to Sphinx and is now hosted on [ReadTheDocs](https://jij-inc-ommx.readthedocs-hosted.com/en/latest/). GitHub Pages will continue to host the documentation as of v2.5.1, but all future updates will be on ReadTheDocs only.