chore: release 0.29.0

docs/guides/artifact-storage.md

@@ -267,17 +267,9 @@ Update artifact data.
 
 Delete artifact (fails if artifact has children).
 
-### POST /api/v1/artifacts/$tree
+### GET /api/v1/artifacts/{id}/$tree
 
-Get artifact tree structure.
-
-**Request:**
-```json
-{
-  "root_id": "01ARTIFACT456...",
-  "max_depth": 3
-}
-```
+Get artifact tree structure rooted at the given artifact (no request body).
 
 **Response:**
 ```json
@@ -615,9 +607,7 @@ CHILD_ID=$(curl -s -X POST http://localhost:9090/api/v1/artifacts \
   }' | jq -r '.id')
 
 # Get tree
-curl -X POST http://localhost:9090/api/v1/artifacts/\$tree \
-  -H "Content-Type: application/json" \
-  -d '{"root_id": "'$ROOT_ID'"}' | jq
+curl http://localhost:9090/api/v1/artifacts/$ROOT_ID/\$tree | jq
 ```
 
 ---

docs/guides/configuration-management.md

@@ -238,8 +238,8 @@ Enable artifact operations when building the service:
 ```python
 app = (
     ServiceBuilder(info=info)
-    .with_config(MLConfig, enable_artifact_operations=True)
-    .with_artifacts(hierarchy=hierarchy)
+    .with_config(MLConfig)
+    .with_artifacts(hierarchy=hierarchy, enable_config_linking=True)
     .build()
 )
 ```
@@ -486,11 +486,11 @@ class WeatherModelConfig(BaseConfig):
 app = (
     ServiceBuilder(info=ServiceInfo(id="weather-model-service", display_name="Weather Model Service"))
     .with_health()
-    .with_config(WeatherModelConfig, enable_artifact_operations=True)
+    .with_config(WeatherModelConfig)
     .with_artifacts(hierarchy=ArtifactHierarchy(
         name="weather_models",
         level_labels={0: "ml_training_workspace", 1: "ml_prediction"}
-    ))
+    ), enable_config_linking=True)
     .build()
 )
 ```

docs/guides/dataframe.md

@@ -1362,7 +1362,7 @@ high_sales = df.filter(lambda r: r['sales'] > 1000)
 summary = high_sales.groupby('region').count()
 
 # Chain with other operations
-df.groupby('product').sum('sales').sort('sales_sum', reverse=True).head(5)
+df.groupby('product').sum('sales').sort('sales_sum', ascending=False).head(5)
 ```
 
 **Design notes:**

docs/guides/ml-workflows.md

@@ -10,7 +10,7 @@ Chapkit provides a complete ML workflow system for training models and making pr
 from chapkit.artifact import ArtifactHierarchy
 
 from chapkit import BaseConfig
-from chapkit.api import MLServiceBuilder, MLServiceInfo
+from chapkit.api import MLServiceBuilder, MLServiceInfo, AssessedStatus, ModelMetadata, PeriodType
 from chapkit.ml import FunctionalModelRunner
 import pandas as pd
 from sklearn.linear_model import LinearRegression
@@ -34,7 +34,12 @@ runner = FunctionalModelRunner(on_train=on_train, on_predict=on_predict)
 
 app = (
     MLServiceBuilder(
-        info=MLServiceInfo(id="my-ml-service", display_name="My ML Service"),
+        info=MLServiceInfo(
+            id="my-ml-service",
+            display_name="My ML Service",
+            model_metadata=ModelMetadata(author="ML Team", author_assessed_status=AssessedStatus.green),
+            period_type=PeriodType.monthly,
+        ),
         config_schema=ModelConfig,
         hierarchy=ArtifactHierarchy(name="ml", level_labels={0: "ml_training_workspace", 1: "ml_prediction"}),
         runner=runner,
@@ -403,7 +408,6 @@ MLServiceBuilder(
     config_schema=YourConfig,
     hierarchy=hierarchy,
     runner=runner,
-    max_concurrency=5,       # Limit concurrent jobs (default: unlimited)
     database_url="ml.db",    # Persistent storage (default: in-memory)
 )
 ```
@@ -729,7 +733,7 @@ All tabular data uses the `DataFrame` schema:
 
 **Python Usage:**
 ```python
-from servicekit.data import DataFrame
+from chapkit.data import DataFrame
 
 # Create from DataFrame
 df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
@@ -1316,7 +1320,6 @@ MLServiceBuilder(
     config_schema=config_schema,
     hierarchy=hierarchy,
     runner=runner,
-    max_concurrency=3,  # Limit concurrent training jobs
 )
 ```
 
@@ -1708,7 +1711,7 @@ curl http://localhost:9090/api/v1/jobs/$JOB_ID | jq '.error'
 **Solution:**
 ```python
 # Limit concurrent jobs
-MLServiceBuilder(..., max_concurrency=2)
+MLServiceBuilder(...)
 
 # Implement artifact cleanup
 async def cleanup_old_artifacts(app):

docs/guides/mlproject-runner.md

@@ -12,7 +12,7 @@ chapkit mlproject run .            # same
 chapkit mlproject run /path/to/my_mlproject
 ```
 
-This parses the `MLproject` file, translates the entry-point commands to chapkit's workspace conventions, builds a FastAPI service with `/api/v1/ml/$train` and `/$predict` endpoints, and serves on `127.0.0.1:8000` by default. Override host/port with `--host` and `--port`.
+This parses the `MLproject` file, translates the entry-point commands to chapkit's workspace conventions, builds a FastAPI service with `/api/v1/ml/$train` and `/$predict` endpoints, and serves on `127.0.0.1:9090` by default. Override host/port with `--host` and `--port`.
 
 A minimal R MLproject like `dhis2-chap/minimalist_example_r`:
 

docs/index.md

@@ -13,7 +13,7 @@ Build production-ready ML services with train/predict workflows, artifact storag
 from geojson_pydantic import FeatureCollection
 
 from chapkit import BaseConfig
-from chapkit.api import MLServiceBuilder, MLServiceInfo
+from chapkit.api import MLServiceBuilder, MLServiceInfo, AssessedStatus, ModelMetadata, PeriodType
 from chapkit.artifact import ArtifactHierarchy
 from chapkit.data import DataFrame
 from chapkit.ml import FunctionalModelRunner
@@ -55,7 +55,12 @@ async def predict(
 
 app = (
     MLServiceBuilder(
-        info=MLServiceInfo(id="disease-prediction-service", display_name="Disease Prediction Service"),
+        info=MLServiceInfo(
+            id="disease-prediction-service",
+            display_name="Disease Prediction Service",
+            model_metadata=ModelMetadata(author="ML Team", author_assessed_status=AssessedStatus.green),
+            period_type=PeriodType.monthly,
+        ),
         config_schema=MyMLConfig,
         hierarchy=ArtifactHierarchy(
             name="ml",
@@ -69,8 +74,9 @@ app = (
 ```
 
 **What you get:**
-- `POST /api/v1/ml/train` - Train models with versioning
-- `POST /api/v1/ml/predict` - Make predictions
+- `POST /api/v1/ml/$train` - Train models with versioning
+- `POST /api/v1/ml/$predict` - Make predictions
+- `POST /api/v1/ml/$validate` - Validate model configurations
 - `GET /api/v1/configs` - Manage model configurations
 - `GET /api/v1/artifacts` - Browse trained models and predictions
 - `GET /api/v1/jobs` - Monitor training/prediction jobs

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "chapkit"
-version = "0.29.0.dev0"
+version = "0.29.0"
 description = "ML and data service modules built on servicekit - config, artifacts, and ML workflows"
 readme = "README.md"
 authors = [{ name = "Morten Hansen", email = "morten@dhis2.org" }]