feat: Pydantic config validation with clear CLI error messages

Convert OptimizationConfig from dataclass to Pydantic BaseModel with
field validators for ranges, types, and enum values. Missing/invalid
fields now produce actionable CLI errors instead of cryptic KeyErrors.

- Range validators: max_iterations>=1, minibatch_size>=1, seed>=0, etc.
- Enum validator: error_strategy must be skip|retry|abort
- Config migration hook via config_version field
- CLI catches ValidationError and prints per-field error messages
- Remove unused AppSettings class (Bug #7)
- 30 unit tests covering all validation edge cases

Co-Authored-By: Paperclip <noreply@paperclip.ing>

src/prometheus/cli/app.py

@@ -12,6 +12,7 @@ from dataclasses import asdict
 
 import dspy
 import typer
+from pydantic import ValidationError
 from rich.console import Console
 from rich.panel import Panel
 from rich.table import Table
@@ -102,75 +103,58 @@ async def _async_optimize(
         )
     )
 
-    # 1. Load config
+    # 1. Load & validate config
     persistence = YamlPersistence()
     raw_config = persistence.read_config(input)
 
+    # CLI flags override config file values
+    raw_config.setdefault("max_retries", max_retries)
+    raw_config.setdefault("error_strategy", error_strategy)
+    raw_config.setdefault("max_concurrency", max_concurrency)
+    raw_config["output_path"] = output
+    raw_config["verbose"] = verbose
+
+    try:
+        config = OptimizationConfig.model_validate(raw_config)
+    except ValidationError as exc:
+        console.print("[bold red]Configuration error:[/bold red]\n")
+        for err in exc.errors():
+            loc = " → ".join(str(l) for l in err["loc"])
+            console.print(f"  [red]• {loc}: {err['msg']}[/red]")
+        raise typer.Exit(code=1) from exc
+    console.print(f"[dim]Task: {config.task_description[:80]}...[/dim]")
+    console.print(f"[dim]Seed prompt: {config.seed_prompt[:80]}...[/dim]")
+
+    # 2. Create per-model DSPy LM instances
     def _model_lm_kwargs(
         model_api_base: str | None,
         model_api_key_env: str | None,
-        global_api_base: str | None,
-        global_api_key_env: str | None,
     ) -> dict:
         """Build kwargs for dspy.LM, using per-model overrides with global fallback."""
         kwargs: dict = {}
-        api_base = model_api_base or global_api_base
-        api_key_env = model_api_key_env or global_api_key_env
+        api_base = model_api_base or config.api_base
+        api_key_env = model_api_key_env or config.api_key_env
         if api_base:
             kwargs["api_base"] = api_base
         if api_key_env:
             kwargs["api_key"] = os.environ.get(api_key_env, "")
         return kwargs
 
-    global_api_base = raw_config.get("api_base")
-    global_api_key_env = raw_config.get("api_key_env")
-
-    config = OptimizationConfig(
-        seed_prompt=raw_config["seed_prompt"],
-        task_description=raw_config["task_description"],
-        task_model=raw_config.get("task_model", "openai/gpt-4o-mini"),
-        judge_model=raw_config.get("judge_model", "openai/gpt-4o"),
-        proposer_model=raw_config.get("proposer_model", "openai/gpt-4o"),
-        synth_model=raw_config.get("synth_model", "openai/gpt-4o"),
-        task_api_base=raw_config.get("task_api_base"),
-        task_api_key_env=raw_config.get("task_api_key_env"),
-        judge_api_base=raw_config.get("judge_api_base"),
-        judge_api_key_env=raw_config.get("judge_api_key_env"),
-        proposer_api_base=raw_config.get("proposer_api_base"),
-        proposer_api_key_env=raw_config.get("proposer_api_key_env"),
-        synth_api_base=raw_config.get("synth_api_base"),
-        synth_api_key_env=raw_config.get("synth_api_key_env"),
-        max_iterations=raw_config.get("max_iterations", 30),
-        n_synthetic_inputs=raw_config.get("n_synthetic_inputs", 20),
-        minibatch_size=raw_config.get("minibatch_size", 5),
-        seed=raw_config.get("seed", 42),
-        max_retries=raw_config.get("max_retries", max_retries),
-        retry_delay_base=raw_config.get("retry_delay_base", 1.0),
-        circuit_breaker_threshold=raw_config.get("circuit_breaker_threshold", 5),
-        error_strategy=raw_config.get("error_strategy", error_strategy),
-        max_concurrency=raw_config.get("max_concurrency", max_concurrency),
-        output_path=output,
-        verbose=verbose,
-    )
-    console.print(f"[dim]Task: {config.task_description[:80]}...[/dim]")
-    console.print(f"[dim]Seed prompt: {config.seed_prompt[:80]}...[/dim]")
-
-    # 2. Create per-model DSPy LM instances
     task_lm = dspy.LM(
         config.task_model,
-        **_model_lm_kwargs(config.task_api_base, config.task_api_key_env, global_api_base, global_api_key_env),
+        **_model_lm_kwargs(config.task_api_base, config.task_api_key_env),
     )
     judge_lm = dspy.LM(
         config.judge_model,
-        **_model_lm_kwargs(config.judge_api_base, config.judge_api_key_env, global_api_base, global_api_key_env),
+        **_model_lm_kwargs(config.judge_api_base, config.judge_api_key_env),
     )
     proposer_lm = dspy.LM(
         config.proposer_model,
-        **_model_lm_kwargs(config.proposer_api_base, config.proposer_api_key_env, global_api_base, global_api_key_env),
+        **_model_lm_kwargs(config.proposer_api_base, config.proposer_api_key_env),
     )
     synth_lm = dspy.LM(
         config.synth_model,
-        **_model_lm_kwargs(config.synth_api_base, config.synth_api_key_env, global_api_base, global_api_key_env),
+        **_model_lm_kwargs(config.synth_api_base, config.synth_api_key_env),
     )
 
     # 3. Build adapters (Dependency Injection — each gets its own LM + retry config)