refactor: use a single agent model path

agent_platform/models.py

@@ -3,8 +3,6 @@ from __future__ import annotations
 from dataclasses import dataclass
 from typing import Literal
 
-Mode = Literal["chat", "work"]
-Strength = Literal["light", "medium", "high", "extreme"]
 Provider = Literal["k1412", "deepseek"]
 
 
@@ -12,49 +10,70 @@ Provider = Literal["k1412", "deepseek"]
 class ModelSpec:
     public_id: str
     display_name: str
-    mode: Mode
-    strength: Strength
     provider: Provider
     provider_model: str
     thinking_enabled: bool
+    thinking_label: str
+    thinking_adjustable: bool
     reasoning_effort: str | None
     max_output_tokens: int
     max_iterations: int
     context_char_budget: int
 
 
-_TIERS = {
-    "light": ("轻度", "k1412", "ChatGPT-5.6:Luna", False, None, 4_096, 8, 120_000),
-    "medium": ("中", "k1412", "ChatGPT-5.6:Terra", False, None, 4_096, 16, 240_000),
-    "high": ("高", "k1412", "ChatGPT-5.6:Sol", False, None, 4_096, 24, 400_000),
-    "extreme": ("极高", "deepseek", "deepseek-v4-pro", True, "max", 16_384, 32, 800_000),
-}
-
 MODEL_SPECS: dict[str, ModelSpec] = {
-    f"{mode}-{strength}": ModelSpec(
-        public_id=f"{mode}-{strength}",
-        display_name=f"{'Chat' if mode == 'chat' else 'Work'} · {label}",
-        mode=mode,
-        strength=strength,
-        provider=provider,
-        provider_model=provider_model,
-        thinking_enabled=thinking_enabled,
-        reasoning_effort=reasoning_effort,
-        max_output_tokens=max_output_tokens,
-        max_iterations=max_iterations,
-        context_char_budget=context_budget,
-    )
-    for mode in ("chat", "work")
-    for strength, (
-        label,
-        provider,
-        provider_model,
-        thinking_enabled,
-        reasoning_effort,
-        max_output_tokens,
-        max_iterations,
-        context_budget,
-    ) in _TIERS.items()
+    "luna": ModelSpec(
+        public_id="luna",
+        display_name="Luna",
+        provider="k1412",
+        provider_model="ChatGPT-5.6:Luna",
+        thinking_enabled=True,
+        thinking_label="开启（不分档）",
+        thinking_adjustable=False,
+        reasoning_effort=None,
+        max_output_tokens=4_096,
+        max_iterations=8,
+        context_char_budget=120_000,
+    ),
+    "terra": ModelSpec(
+        public_id="terra",
+        display_name="Terra",
+        provider="k1412",
+        provider_model="ChatGPT-5.6:Terra",
+        thinking_enabled=True,
+        thinking_label="开启（不分档）",
+        thinking_adjustable=False,
+        reasoning_effort=None,
+        max_output_tokens=4_096,
+        max_iterations=16,
+        context_char_budget=240_000,
+    ),
+    "sol": ModelSpec(
+        public_id="sol",
+        display_name="Sol",
+        provider="k1412",
+        provider_model="ChatGPT-5.6:Sol",
+        thinking_enabled=True,
+        thinking_label="开启（不分档）",
+        thinking_adjustable=False,
+        reasoning_effort=None,
+        max_output_tokens=4_096,
+        max_iterations=24,
+        context_char_budget=400_000,
+    ),
+    "deepseek-v4-pro": ModelSpec(
+        public_id="deepseek-v4-pro",
+        display_name="DeepSeek V4 Pro",
+        provider="deepseek",
+        provider_model="deepseek-v4-pro",
+        thinking_enabled=True,
+        thinking_label="极高",
+        thinking_adjustable=False,
+        reasoning_effort="max",
+        max_output_tokens=16_384,
+        max_iterations=32,
+        context_char_budget=800_000,
+    ),
 }
 
 
@@ -74,6 +93,11 @@ def openai_model_list() -> dict:
                 "object": "model",
                 "owned_by": "k1412-agent",
                 "name": spec.display_name,
+                "k1412": {
+                    "thinking_enabled": spec.thinking_enabled,
+                    "thinking_label": spec.thinking_label,
+                    "thinking_adjustable": spec.thinking_adjustable,
+                },
             }
             for spec in MODEL_SPECS.values()
         ],