From 6df2b8ba939c4e0a7d50c05981f51faf1b3d0b2f Mon Sep 17 00:00:00 2001
From: Bobby Noelte <b0661n0e17e@gmail.com>
Date: Thu, 30 Oct 2025 10:54:36 +0100
Subject: [PATCH] fix: generate config markdown

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
---
 openapi.json                  | 11 ++++++++---
 scripts/generate_config_md.py |  9 ++++++---
 2 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/openapi.json b/openapi.json
index b0a7516..98cbb79 100644
--- a/openapi.json
+++ b/openapi.json
@@ -5803,9 +5803,13 @@
             "title": "Total Costs Amt",
             "description": "The total costs [money amount]."
           },
-          "data": {
+          "prediction": {
             "$ref": "#/components/schemas/PydanticDateTimeDataFrame",
-            "description": "Datetime data frame with time series optimization data per optimization interval:- load_energy_wh: Load of all energy consumers in wh- grid_energy_wh: Grid energy feed in (negative) or consumption (positive) in wh- pv_prediction_energy_wh: PV energy prediction (positive) in wh- elec_price_prediction_amt_kwh: Electricity price prediction in money per kwh- costs_amt: Costs in money amount- revenue_amt: Revenue in money amount- losses_energy_wh: Energy losses in wh- <device-id>_operation_mode_id: Operation mode id of the device.- <device-id>_operation_mode_factor: Operation mode factor of the device.- <device-id>_soc_factor: State of charge of a battery/ electric vehicle device as factor of total capacity.- <device-id>_energy_wh: Energy consumption (positive) of a device in wh."
+            "description": "Datetime data frame with time series prediction data per optimization interval:- pv_energy_wh: PV energy prediction (positive) in wh- elec_price_amt_kwh: Electricity price prediction in money per kwh- feed_in_tariff_amt_kwh: Feed in tariff prediction in money per kwh- weather_temp_air_celcius: Temperature in \u00b0C- load_mean_energy_wh: Load mean energy prediction in wh- load_std_energy_wh: Load energy standard deviation prediction in wh- load_mean_adjusted_energy_w: Adjusted load mean energy prediction in wh"
+          },
+          "solution": {
+            "$ref": "#/components/schemas/PydanticDateTimeDataFrame",
+            "description": "Datetime data frame with time series solution data per optimization interval:- load_energy_wh: Load of all energy consumers in wh- grid_energy_wh: Grid energy feed in (negative) or consumption (positive) in wh- costs_amt: Costs in money amount- revenue_amt: Revenue in money amount- losses_energy_wh: Energy losses in wh- <device-id>_operation_mode_id: Operation mode id of the device.- <device-id>_operation_mode_factor: Operation mode factor of the device.- <device-id>_soc_factor: State of charge of a battery/ electric vehicle device as factor of total capacity.- <device-id>_energy_wh: Energy consumption (positive) of a device in wh."
           }
         },
         "type": "object",
@@ -5815,7 +5819,8 @@
           "total_losses_energy_wh",
           "total_revenues_amt",
           "total_costs_amt",
-          "data"
+          "prediction",
+          "solution"
         ],
         "title": "OptimizationSolution",
         "description": "General Optimization Solution."
diff --git a/scripts/generate_config_md.py b/scripts/generate_config_md.py
index 8804f26..12ba815 100755
--- a/scripts/generate_config_md.py
+++ b/scripts/generate_config_md.py
@@ -8,7 +8,7 @@ import re
 import sys
 import textwrap
 from pathlib import Path
-from typing import Any, Union
+from typing import Any, Type, Union
 
 from loguru import logger
 from pydantic.fields import ComputedFieldInfo, FieldInfo
@@ -199,7 +199,8 @@ def generate_config_table_md(
                 description = deprecated
         table += f"| {field_name} {env_entry}| `{type_name}` | `{read_only}` | `{default_value}` | {description} |\n"
 
-        inner_types: dict[PydanticBaseModel, tuple[str, list[str]]] = dict()
+        # inner_types: dict[type[PydanticBaseModel], tuple[str, list[str]]] = dict()
+        inner_types: dict[Any, tuple[str, list[str]]] = dict()
 
         def extract_nested_models(subtype: Any, subprefix: str, parent_types: list[str]):
             """Extract nested models."""
@@ -207,7 +208,9 @@ def generate_config_table_md(
                 return
             nested_types = resolve_nested_types(subtype, [])
             for nested_type, nested_parent_types in nested_types:
-                if issubclass(nested_type, PydanticBaseModel):
+                # Nested type may be of type class, enum, typing.Any
+                if isinstance(nested_type, type) and issubclass(nested_type, PydanticBaseModel):
+                    # Nested type is a subclass of PydanticBaseModel
                     new_parent_types = parent_types + nested_parent_types
                     if "list" in parent_types:
                         new_prefix = ""