chore: add merge_models helper

src/llmling/config/utils.py

@@ -3,7 +3,9 @@
 from __future__ import annotations
 
 import os
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, TypeVar
+
+from pydantic import BaseModel
 
 from llmling.config.manager import ConfigManager
 from llmling.config.models import (
@@ -24,6 +26,7 @@
     from llmling.tools.toolsets import ToolSet
 
 logger = get_logger(__name__)
+T = TypeVar("T", bound=BaseModel)
 
 
 def toolset_config_to_toolset(config) -> ToolSet:
@@ -83,3 +86,32 @@ def prepare_runtime(
             raise TypeError(msg)
 
     return runtime_cls.from_config(config)
+
+
+def merge_models(base: T, overlay: T) -> T:
+    """Deep merge two Pydantic models."""
+    if not isinstance(overlay, type(base)):
+        msg = f"Cannot merge different types: {type(base)} and {type(overlay)}"
+        raise TypeError(msg)
+
+    # Start with copy of base
+    merged_data = base.model_dump()
+
+    # Get overlay data (excluding None values)
+    overlay_data = overlay.model_dump(exclude_none=True)
+
+    for field_name, field_value in overlay_data.items():
+        base_value = merged_data.get(field_name)
+
+        match (base_value, field_value):
+            case (list(), list()):
+                merged_data[field_name] = [
+                    *base_value,
+                    *(item for item in field_value if item not in base_value),
+                ]
+            case (dict(), dict()):
+                merged_data[field_name] = base_value | field_value
+            case _:
+                merged_data[field_name] = field_value
+
+    return base.__class__.model_validate(merged_data)