shorter param names

ragulate/cli_commands/query.py

@@ -17,14 +17,14 @@ def setup_query(subparsers):
     )
     query_parser.add_argument(
         "-s",
-        "--script_path",
+        "--script",
         type=str,
         help="The path to the python script that contains the query method",
         required=True,
     )
     query_parser.add_argument(
         "-m",
-        "--method-name",
+        "--method",
         type=str,
         help="The name of the method in the script to run query",
         required=True,
@@ -91,7 +91,7 @@ def setup_query(subparsers):
         action="store_true",
     )
     query_parser.add_argument(
-        "--llm_provider",
+        "--provider",
         type=str,
         help=("The name of the LLM Provider to use for Evaluation."),
         choices=[
@@ -102,27 +102,31 @@ def setup_query(subparsers):
             "Langchain",
             "Huggingface",
         ],
+        default="OpenAI",
     )
     query_parser.add_argument(
-        "--model_name",
+        "--model",
         type=str,
-        help=("The name or id of the LLM model or deployment to use for Evaluation."),
+        help=(
+            "The name or id of the LLM model or deployment to use for Evaluation.",
+            "Generally used in combination with the --provider param.",
+        ),
     )
     query_parser.set_defaults(func=lambda args: call_query(**vars(args)))
 
     def call_query(
         name: str,
-        script_path: str,
-        method_name: str,
+        script: str,
+        method: str,
         var_name: List[str],
         var_value: List[str],
         dataset: List[str],
         subset: List[str],
         sample: float,
         seed: int,
         restart: bool,
-        llm_provider: str,
-        model_name: str,
+        provider: str,
+        model: str,
         **kwargs,
     ):
         if sample <= 0.0 or sample > 1.0:
@@ -144,14 +148,14 @@ def call_query(
 
         query_pipeline = QueryPipeline(
             recipe_name=name,
-            script_path=script_path,
-            method_name=method_name,
+            script_path=script,
+            method_name=method,
             ingredients=ingredients,
             datasets=datasets,
             sample_percent=sample,
             random_seed=seed,
             restart_pipeline=restart,
-            llm_provider=llm_provider,
-            model_name=model_name,
+            llm_provider=provider,
+            model_name=model,
         )
         query_pipeline.query()