support filter when printing evaluable models

docs/cli.md

@@ -309,7 +309,6 @@ $ qianfan trainer run [OPTIONS]
 
 * `--train-type TEXT`：训练类型  [required]
 * `--dataset-id INTEGER`：数据集 id  [required]
-* `--list-evaluable-models`: 打印支持进行评估的模型列表
 * `--help`：展示帮助文档
 
 训练相关配置，参数含义与 [训练 API 文档](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/mlmrgo4yx#body%E5%8F%82%E6%95%B0) 中对应参数含义一致：
@@ -368,6 +367,7 @@ $ qianfan evaluation run [OPTIONS] MODELS...
 **Options 选项**:
 
 * `--dataset-id TEXT`: 待评估的数据集 id  [required]
+* `--list-evaluable-models`: 打印支持进行评估的模型列表（已过时，请使用 `qianfan evaluation list-evaluable-models`）
 * `--enable-rule-evaluator / --no-enable-rule-evaluator`: 启用 RuleEvaluator，使用一定的评估规则来对推理结果进行评估，该评估器支持如下参数 [default: no-enable-rule-evaluator]
   * `--using-similarity / --no-using-similarity`: 使用相似度评估结果  [default: no-using-similarity]
   * `--using-accuracy / --no-using-accuracy`: 使用准确率评估结果  [default: no-using-accuracy]
@@ -385,3 +385,19 @@ $ qianfan evaluation run [OPTIONS] MODELS...
 * `--enable-manual-evaluator / --no-enable-manual-evaluator`: 使用手动评估器，完成推理后由用户在平台上手动对推理结果进行打分评估  [default: no-enable-manual-evaluator]
   * `--dimensions TEXT`: 评估的维度，通过 `,` 分隔不同的维度
 * `--help`: 展示帮助信息
+
+#### list-evaluable-models 打印可评估模型
+
+**用法**:
+
+```console
+$ qianfan evaluation list-evaluable-models [OPTIONS]
+```
+
+**Options 选项**:
+
+* `--preset / --no-preset`: 是否仅打印（非）预置模型，不设置则打印所有模型
+* `--train-type`: 仅打印特定训练类型的模型，可以通过 `,` 分隔多个训练类型
+* `--name`: 过滤模型名称，可以通过 `,` 分隔多个名称
+* `--help`: 展示帮助信息
+

python/qianfan/common/client/evaluation.py

@@ -57,39 +57,77 @@
 
 
 @credential_required
+@evaluation_app.command()
 def list_evaluable_models(
-    ctx: typer.Context, param: typer.CallbackParam, value: bool
+    preset: Optional[bool] = typer.Option(
+        None,
+        help="Whether only print (non-)preset models. Not set to print all models.",
+        show_default=False,
+    ),
+    train_type: Optional[str] = typer.Option(
+        None, help="Filter by train type. Use comma(,) to set multiple values."
+    ),
+    name: Optional[str] = typer.Option(
+        None, help="Filter by model name. Use comma(,) to set multiple values."
+    ),
 ) -> None:
     """
-    Print models of ChatCompletion and exit.
+    Print evaluable models.
     """
+    train_type_set = None if train_type is None else set(train_type.split(","))
+    name_set = None if name is None else set(name.split(","))
+    model_list = ModelResource.evaluable_model_list()["result"]
+    console = replace_logger_handler()
+    table = Table(show_lines=True)
+    col_list = ["Model Name", "Platform Preset", "Train Type", "Model Version List"]
+    for col in col_list:
+        table.add_column(col)
+    for model in model_list:
+        if preset is not None:
+            if model["source"] != "PlatformPreset" and preset:
+                continue
+            if model["source"] == "PlatformPreset" and not preset:
+                continue
+        if train_type_set is not None:
+            if model["trainType"] not in train_type_set:
+                continue
+        if name_set is not None:
+            found = False
+            for n in name_set:
+                if n in model["modelName"]:
+                    found = True
+            if not found:
+                continue
+        row_items: List[RenderableType] = []
+        # Model Name
+        row_items.append(f"{model['modelName']}\n[dim]{model['modelIdStr']}[/]")
+        # Platform Preset
+        model_source = model["source"]
+        if model_source == "PlatformPreset":
+            row_items.append("Yes")
+        else:
+            row_items.append(f"No\n[dim]{model_source}[/]")
+        # Train Type
+        row_items.append(model["trainType"])
+        # Model Version List
+        version_list = [
+            f"{version['version']} [dim]({version['modelVersionIdStr']})[/]"
+            for version in model["modelVersionList"]
+        ]
+        row_items.append("\n".join(version_list))
+        table.add_row(*row_items)
+    console.print(table)
+
+
+def list_evaluable_models_callback(
+    ctx: typer.Context, param: typer.CallbackParam, value: bool
+) -> None:
     if value:
-        model_list = ModelResource.evaluable_model_list()["result"]
-        console = replace_logger_handler()
-        table = Table(show_lines=True)
-        col_list = ["Model Name", "Platform Preset", "Train Type", "Model Version List"]
-        for col in col_list:
-            table.add_column(col)
-        for model in model_list:
-            row_items: List[RenderableType] = []
-            # Model Name
-            row_items.append(f"{model['modelName']}\n[dim]{model['modelIdStr']}[/]")
-            # Platform Preset
-            model_source = model["source"]
-            if model_source == "PlatformPreset":
-                row_items.append("Yes")
-            else:
-                row_items.append(f"No\n[dim]{model_source}[/]")
-            # Train Type
-            row_items.append(model["trainType"])
-            # Model Version List
-            version_list = [
-                f"{version['version']} [dim]({version['modelVersionIdStr']})[/]"
-                for version in model["modelVersionList"]
-            ]
-            row_items.append("\n".join(version_list))
-            table.add_row(*row_items)
-        console.print(table)
+        print_warn_msg(
+            "This option is [bold]deprecated[/]. Use `qianfan evaluation"
+            " list-evaluable-models` instead."
+        )
+        list_evaluable_models(None, None, None)
         raise typer.Exit()
 
 
@@ -153,7 +191,7 @@ def run(
     list_evaluable_models: Optional[bool] = typer.Option(
         None,
         "--list-evaluable-models",
-        callback=list_evaluable_models,
+        callback=list_evaluable_models_callback,
         is_eager=True,
         help="Print evaluable models.",
     ),