feat(pineagents): 模型静态展示目录(描述/价格/分类/标签) + ModelInfo 扩展

- ModelInfo 追加可选 description/price/category/tags(默认为空,不破坏现有构造,经
  model_dump + response_model 自动透传前端,无需改序列化)。
- 新增 providers/pineagents_catalog.py:按模型 id 的静态展示目录(覆盖 Qwen/DeepSeek/
  GLM/Kimi/MiniMax/MiMo/ModelScope 等) + enrich_catalog() 在拉取结果上回填;未命中
  优雅降级为仅名+能力标签,不修改调用参数。
- provider_manager.list_provider_info:pineagents 拉取成功后回填目录元数据。
- 新增 tests/test_pineagents_catalog.py(命中/免费/未命中/空/大小写),6 passed。

Co-Authored-By: Claude <noreply@anthropic.com>

src/pineagents/providers/pineagents_catalog.py

@@ -0,0 +1,220 @@
+# -*- coding: utf-8 -*-
+"""PineAgents（算力中心）模型静态展示目录。
+
+PineAgents 的模型列表由 server-core `/v1/models`（compute-engine new-api）动态拉取，
+`fetch_models()` 只保留 `id/name`。这里补一份按模型 id 的**展示元数据**（描述/价格/分类/标签），
+在拉取结果上回填，供桌面端「设置 → 模型 → PineAgents」的市场风卡片渲染。
+
+约定：
+- key 用小写模型 id，匹配时做大小写归一；未命中的 id 由调用方跳过，优雅降级为「仅名 + 能力标签」。
+- 价格 `price` 为展示字符串，形如 `¥x / 1M tokens`；免费模型用 `is_free=True` 打免费徽标。
+- 仅用于展示，不改模型调用行为；后端不参与计费（计费由 compute-engine 负责）。
+"""
+from __future__ import annotations
+
+from typing import List
+
+from .provider import ModelInfo
+
+
+# 每个模型一条展示元数据
+_META = {
+    # ---- Qwen 系列（文本旗舰） ----
+    "qwen3.7-max": {
+        "description": "通义千问系列最强旗舰模型，复杂推理与长程任务首选，思维链推理开箱即用。",
+        "price": "¥28 / 1M tokens",
+        "category": "文本",
+        "tags": ["旗舰", "推理"],
+    },
+    "qwen3.7-plus": {
+        "description": "通义千问高性能均衡款，图像/视频多模态输入，适合生产级智能体场景。",
+        "price": "¥9 / 1M tokens",
+        "category": "文本",
+        "tags": ["多模态", "均衡"],
+    },
+    "qwen3.6-plus": {
+        "description": "通义千问通用款，多模态能力与性价比平衡，日常对话与工具调用主力。",
+        "price": "¥6 / 1M tokens",
+        "category": "文本",
+        "tags": ["多模态"],
+    },
+    "qwen3.6-flash": {
+        "description": "通义千问轻量高速款，低延迟低成本，适合高并发实时场景。",
+        "price": "¥2 / 1M tokens",
+        "category": "文本",
+        "tags": ["轻量", "低延迟"],
+    },
+    "qwen3.5-plus": {
+        "description": "通义千问上一代通用款，成熟稳定，兼容多模态输入。",
+        "price": "¥4 / 1M tokens",
+        "category": "文本",
+        "tags": ["多模态"],
+    },
+    "qwen3-max-2026-01-23": {
+        "description": "通义千问旗舰系列（1 月快照），强推理与海量上下文，适合 Agent 长链调度。",
+        "price": "¥24 / 1M tokens",
+        "category": "文本",
+        "tags": ["旗舰", "长上下文"],
+    },
+    "qwen3-coder-next": {
+        "description": "通义千问代码专项旗舰，面向编程任务的推理与生成优化，支持长代码库理解。",
+        "price": "¥16 / 1M tokens",
+        "category": "文本",
+        "tags": ["代码", "旗舰"],
+    },
+    "qwen3-coder-plus": {
+        "description": "通义千问代码通用款，代码补全与理解的高性价比选择。",
+        "price": "¥6 / 1M tokens",
+        "category": "文本",
+        "tags": ["代码"],
+    },
+    # ---- 视觉/图像 ----
+    "qwen-image-3.0-pro": {
+        "description": "通义千问图像生成旗舰，高质量文生图与图像编辑，多轮文本指令可控。",
+        "price": "¥0.4 / 张",
+        "category": "图片",
+        "tags": ["文生图", "生成"],
+    },
+    # ---- 视频 ----
+    "wan3.0-video-prime": {
+        "description": "万相视频生成旗舰，文本/图像转视频，支持长镜头一致性与运动连贯。",
+        "price": "¥2 / 秒",
+        "category": "视频",
+        "tags": ["文生视频", "生成"],
+    },
+    # ---- DeepSeek ----
+    "deepseek-v4-pro": {
+        "description": "DeepSeek 深度思考旗舰，强推理与代码能力，支持 effort 思考强度调节。",
+        "price": "¥8 / 1M tokens",
+        "category": "文本",
+        "tags": ["推理", "代码"],
+        "is_free": False,
+    },
+    "deepseek-v4-flash": {
+        "description": "DeepSeek 轻量款，推理与回复速度相配，适合高吞吐场景。",
+        "price": "¥2 / 1M tokens",
+        "category": "文本",
+        "tags": ["轻量"],
+    },
+    "deepseek-v3.2": {
+        "description": "DeepSeek 通用款，成熟稳定的对话与能力调用体验。",
+        "price": "¥3 / 1M tokens",
+        "category": "文本",
+        "tags": ["均衡"],
+    },
+    # ---- GLM（智谱） ----
+    "glm-5.2": {
+        "description": "智谱 GLM 旗舰，通用能力与推理全价覆盖，支持 effort 思考强度。",
+        "price": "¥12 / 1M tokens",
+        "category": "文本",
+        "tags": ["旗舰", "推理"],
+    },
+    "glm-5.1": {
+        "description": "智谱 GLM 上一代旗舰，成熟稳定，通用场景可靠。",
+        "price": "¥8 / 1M tokens",
+        "category": "文本",
+        "tags": ["均衡"],
+    },
+    "glm-5": {
+        "description": "智谱 GLM 高性能款，覆盖编码与复杂任务。",
+        "price": "¥5 / 1M tokens",
+        "category": "文本",
+        "tags": ["推理"],
+    },
+    "glm-4.7": {
+        "description": "智谱 GLM 通用款，性价比与能力均衡。",
+        "price": "¥3 / 1M tokens",
+        "category": "文本",
+        "tags": ["均衡"],
+    },
+    "glm-4.7-flash": {
+        "description": "智谱 GLM 轻量高速款，低延迟低成本。",
+        "price": "¥1 / 1M tokens",
+        "category": "文本",
+        "tags": ["轻量", "低延迟"],
+    },
+    # ---- Kimi（月之暗面） ----
+    "kimi-k2.6": {
+        "description": "Kimi 最新旗舰，长上下文与强推理，适合复杂工具链调度。",
+        "price": "¥10 / 1M tokens",
+        "category": "文本",
+        "tags": ["旗舰", "长上下文"],
+    },
+    "kimi-k2.5": {
+        "description": "Kimi 长上下文通用款，读长文档与多项工具调用表现均衡。",
+        "price": "¥6 / 1M tokens",
+        "category": "文本",
+        "tags": ["长上下文"],
+    },
+    # ---- MiniMax ----
+    "MiniMax-M2.5": {
+        "description": "MiniMax 旗舰，语言理解与生成能力强，多语言友好。",
+        "price": "¥4 / 1M tokens",
+        "category": "文本",
+        "tags": ["均衡", "多语言"],
+    },
+    # ---- MiMo（小米） ----
+    "mimo-v2.5-pro": {
+        "description": "MiMo 推理旗舰，面向复杂推理与长任务。",
+        "price": "¥8 / 1M tokens",
+        "category": "文本",
+        "tags": ["推理"],
+    },
+    "mimo-v2.5": {
+        "description": "MiMo 多模态通用款，图像输入与对话体验均衡。",
+        "price": "¥4 / 1M tokens",
+        "category": "文本",
+        "tags": ["多模态"],
+    },
+    # ---- ModelScope（开源/免费） ----
+    "Qwen/Qwen3.5-122B-A10B": {
+        "description": "ModelScope 开源 Qwen3.5 混合专家模型，支持图像/视频输入，免费可用。",
+        "price": "免费",
+        "category": "文本",
+        "tags": ["开源", "多模态"],
+        "is_free": True,
+    },
+    "ZhipuAI/GLM-5": {
+        "description": "ModelScope 开源智谱 GLM-5，纯文本强推理，免费可用。",
+        "price": "免费",
+        "category": "文本",
+        "tags": ["开源", "推理"],
+        "is_free": True,
+    },
+}
+
+# 大小写归一化的查找表（引擎可能返回不同大小写的 id）
+_LOOKUP = {key.strip().lower(): val for key, val in _META.items()}
+
+# 公共别名（供测试/调用方读取目录规模）
+PINEAGENTS_MODEL_CATALOG = _META
+
+
+def enrich_catalog(models: List[ModelInfo]) -> List[ModelInfo]:
+    """按模型 id 从静态目录回填展示元数据；未命中的模型原样返回。
+
+    命中时写入 `description/price/category/tags/is_free`；均覆盖为目录值，
+    但不改变模型的调用参数（max_tokens/thinking 等不受影响）。
+    """
+    if not models:
+        return models
+
+    enriched: List[ModelInfo] = []
+    for model in models:
+        meta = _LOOKUP.get((model.id or "").strip().lower())
+        if meta is None:
+            enriched.append(model)
+            continue
+        enriched.append(
+            ModelInfo(
+                **model.model_dump(
+                    exclude={"description", "price", "category", "tags", "is_free"},
+                ),
+                description=meta.get("description"),
+                price=meta.get("price"),
+                category=meta.get("category"),
+                tags=meta.get("tags", []),
+                is_free=bool(meta.get("is_free", model.is_free)),
+            )
+        )
+    return enriched

src/pineagents/providers/provider.py

@@ -118,6 +118,22 @@ class ModelInfo(BaseModel):
         description="Override provider-level thinking_budget_range [min, max] "
         "for this model.",
     )
+    description: str | None = Field(
+        default=None,
+        description="Human-readable model description (marketplace/display).",
+    )
+    price: str | None = Field(
+        default=None,
+        description="Display price for the model, e.g. '¥2.5 / 1M tokens'.",
+    )
+    category: str | None = Field(
+        default=None,
+        description="Model category, e.g. 文本/图片/视频/音频.",
+    )
+    tags: List[str] = Field(
+        default_factory=list,
+        description="Display tags for the model (e.g. 旗舰/推理/视觉).",
+    )
 
 
 class ExtendedModelInfo(ModelInfo):

src/pineagents/providers/provider_manager.py

@@ -37,6 +37,7 @@ from .openai_provider import (
 )
 from .openai_response_provider import OpenAIResponseProvider
 from .openrouter_provider import OpenRouterProvider
+from .pineagents_catalog import enrich_catalog
 from .provider import ModelInfo, Provider, ProviderInfo
 
 logger = logging.getLogger(__name__)
@@ -1460,7 +1461,8 @@ class ProviderManager:  # pylint: disable=too-many-public-methods
             try:
                 fetched = await pine.fetch_models()
                 if fetched:
-                    pine.models = fetched
+                    # 回填静态展示元数据（描述/价格/分类/标签），未命中的模型保持原样。
+                    pine.models = enrich_catalog(fetched)
             except Exception:  # noqa: BLE001
                 pass
         tasks = [

tests/test_pineagents_catalog.py

@@ -0,0 +1,67 @@
+# -*- coding: utf-8 -*-
+"""PineAgents 模型静态展示目录单测。"""
+import pytest
+
+from pineagents.providers.pineagents_catalog import (
+    PINEAGENTS_MODEL_CATALOG as _catalog_pub,
+    enrich_catalog,
+)
+from pineagents.providers.provider import ModelInfo
+
+
+pytestmark = pytest.mark.unit
+
+
+def test_enrich_catalog_hit_fills_metadata():
+    """命中目录：回填描述/价格/分类/标签/is_free，且不改调用参数。"""
+    src = [
+        ModelInfo(
+            id="qwen3.7-max",
+            name="Qwen3.7 Max",
+            supports_image=False,
+            supports_video=False,
+            thinking_enabled=True,
+        )
+    ]
+    out = enrich_catalog(src)
+    m = out[0]
+    assert m.description
+    assert m.price
+    assert m.category == "文本"
+    assert m.tags
+    # 调用参数保持不变
+    assert m.thinking_enabled is True
+    assert m.supports_multimodal is None
+
+
+def test_enrich_catalog_free_model():
+    """免费模型 is_free 命中为 True。"""
+    out = enrich_catalog([ModelInfo(id="Qwen/Qwen3.5-122B-A10B", name="Q3.5")])
+    assert out[0].is_free is True
+    assert out[0].price == "免费"
+
+
+def test_enrich_catalog_miss_degrades():
+    """未命中目录：原样返回，不填充展示字段。"""
+    out = enrich_catalog([ModelInfo(id="unknown-model-xyz", name="X")])
+    m = out[0]
+    assert m.id == "unknown-model-xyz"
+    assert m.name == "X"
+    assert m.description is None
+    assert m.price is None
+    assert m.tags == []
+
+
+def test_enrich_catalog_empty():
+    assert enrich_catalog([]) == []
+
+
+def test_enrich_catalog_case_insensitive():
+    """id 大小写不同仍能命中（查找表已归一化）。"""
+    out = enrich_catalog([ModelInfo(id="MINIMAX-M2.5", name="MM")])
+    assert out[0].category == "文本"
+
+
+def test_catalog_public_entries():
+    """公共目录表不应为空（供回填使用）。"""
+    assert len(_catalog_pub) > 0