Merge pull request #16 from Neverbolt/parameter-descriptions

Adds the possibility to define help text for parameters

Author: andreashappe

usecases/usecase/usecase.py

@@ -3,7 +3,7 @@
 from dataclasses import dataclass
 from typing import Dict, Type
 
-from utils.configurable import get_parameters, ParameterDefinitions, build_parser, get_arguments
+from utils.configurable import ParameterDefinitions, build_parser, get_arguments, get_class_parameters
 
 
 class UseCase(abc.ABC):
@@ -66,7 +66,7 @@ def use_case(name: str, desc: str):
     def inner(cls: Type[UseCase]):
         if name in use_cases:
             raise IndexError(f"Use case with name {name} already exists")
-        use_cases[name] = _WrappedUseCase(name, desc, cls, get_parameters(cls.__init__, name))
+        use_cases[name] = _WrappedUseCase(name, desc, cls, get_class_parameters(cls, name))
 
         return cls
 

utils/configurable.py

@@ -1,4 +1,5 @@
 import argparse
+import dataclasses
 import inspect
 import os
 from dataclasses import dataclass
@@ -12,6 +13,16 @@
 load_dotenv()
 
 
+def parameter(*, desc: str, default=dataclasses.MISSING, init: bool = True, repr: bool = True, hash=None,
+              compare: bool = True, metadata: Dict = None, kw_only: bool = dataclasses.MISSING) -> dataclasses.Field:
+    if metadata is None:
+        metadata = dict()
+    metadata["desc"] = desc
+
+    return dataclasses.field(default=default, default_factory=dataclasses.MISSING, init=init, repr=repr, hash=hash,
+                             compare=compare, metadata=metadata, kw_only=kw_only)
+
+
 def get_default(key, default):
     return os.getenv(key, os.getenv(key.upper(), os.getenv(key.replace(".", "_"), os.getenv(key.replace(".", "_").upper(), default))))
 
@@ -24,12 +35,14 @@ class ParameterDefinition:
     name: str
     type: Type
     default: Any
+    description: str
 
     def parser(self, basename: str, parser: argparse.ArgumentParser):
         name = f"{basename}{self.name}"
         default = get_default(name, self.default)
 
-        parser.add_argument(f"--{name}", type=self.type, default=default, required=default is None)
+        parser.add_argument(f"--{name}", type=self.type, default=default, required=default is None,
+                            help=self.description)
 
     def get(self, basename: str, args: argparse.Namespace):
         return getattr(args, f"{basename}{self.name}")
@@ -62,7 +75,18 @@ def get(self, basename: str, args: argparse.Namespace):
         return parameter
 
 
-def get_parameters(fun, basename: str) -> ParameterDefinitions:
+def get_class_parameters(cls, name: str = None, fields: Dict[str, dataclasses.Field] = None) -> ParameterDefinitions:
+    if name is None:
+        name = cls.__name__
+    if fields is None and hasattr(cls, "__dataclass_fields__"):
+        fields = cls.__dataclass_fields__
+    return get_parameters(cls.__init__, name, fields)
+
+
+def get_parameters(fun, basename: str, fields: Dict[str, dataclasses.Field] = None) -> ParameterDefinitions:
+    if fields is None:
+        fields = dict()
+
     sig = inspect.signature(fun)
     params: ParameterDefinitions = {}
     for name, param in sig.parameters.items():
@@ -73,13 +97,27 @@ def get_parameters(fun, basename: str) -> ParameterDefinitions:
             raise ValueError(f"Parameter {name} of {basename}.{fun.__name__} must have a type annotation")
 
         default = param.default if param.default != inspect.Parameter.empty else None
-
-        if hasattr(param.annotation, "__parameters__"):
-            params[name] = ComplexParameterDefinition(name, param.annotation, default, get_parameters(param.annotation, f"{basename}.{fun.__name__}"))
-        elif param.annotation in (str, int, bool):
-            params[name] = ParameterDefinition(name, param.annotation, default)
+        description = None
+        type = param.annotation
+
+        field = None
+        if isinstance(default, dataclasses.Field):
+            field = default
+            default = field.default
+        elif name in fields:
+            field = fields[name]
+
+        if field is not None:
+            description = field.metadata.get("desc", None)
+            if field.type is not None:
+                type = field.type
+
+        if hasattr(type, "__parameters__"):
+            params[name] = ComplexParameterDefinition(name, type, default, description, get_class_parameters(type, f"{basename}.{fun.__name__}"))
+        elif type in (str, int, bool):
+            params[name] = ParameterDefinition(name, type, default, description)
         else:
-            raise ValueError(f"Parameter {name} of {basename}.{fun.__name__} must have str, int, bool, or a __parameters__ class as type, not {param.annotation}")
+            raise ValueError(f"Parameter {name} of {basename}.{fun.__name__} must have str, int, bool, or a __parameters__ class as type, not {type}")
 
     return params
 
@@ -106,7 +144,7 @@ def inner(cls) -> Configurable:
         cls.name = service_name
         cls.description = service_desc
         cls.__service__ = True
-        cls.__parameters__ = get_parameters(cls.__init__, cls.__name__)
+        cls.__parameters__ = get_class_parameters(cls)
 
         return cls
 

utils/db_storage/db_storage.py

@@ -1,11 +1,11 @@
 import sqlite3
 
-from utils.configurable import configurable
+from utils.configurable import configurable, parameter
 
 
 @configurable("db_storage", "Stores the results of the experiments in a SQLite database")
 class DbStorage:
-    def __init__(self, connection_string: str = ":memory:"):
+    def __init__(self, connection_string: str = parameter(desc="sqlite3 database connection string for logs", default=":memory:")):
         self.connection_string = connection_string
 
     def init(self):

utils/openai/openai_llm.py

@@ -6,7 +6,7 @@
 
 import tiktoken
 
-from utils.configurable import configurable
+from utils.configurable import configurable, parameter
 from utils.llm_util import LLMResult, LLM
 
 
@@ -20,13 +20,13 @@ class OpenAIConnection(LLM):
     If you really must use it, you can import it directly from the utils.openai.openai_llm module, which will later on
     show you, that you did not specialize yet.
     """
-    api_key: str
-    model: str
-    context_size: int
-    api_url: str = "https://api.openai.com"
-    api_timeout: int = 240
-    api_backoff: int = 60
-    api_retries: int = 3
+    api_key: str = parameter(desc="OpenAI API Key")
+    model: str = parameter(desc="OpenAI model name")
+    context_size: int = parameter(desc="Maximum context size for the model, only used internally for things like trimming to the context size")
+    api_url: str = parameter(desc="URL of the OpenAI API", default="https://api.openai.com")
+    api_timeout: int = parameter(desc="Timeout for the API request", default=240)
+    api_backoff: int = parameter(desc="Backoff time in seconds when running into rate-limits", default=60)
+    api_retries: int = parameter(desc="Number of retries when running into rate-limits", default=3)
 
     def get_response(self, prompt, *, retry: int = 0, **kwargs) -> LLMResult:
         if retry >= self.api_retries:

wintermute.py

@@ -10,7 +10,7 @@ def main():
     for name, use_case in use_cases.items():
         use_case.build_parser(subparser.add_parser(
             name=use_case.name,
-            description=use_case.description
+            help=use_case.description
         ))
 
     parsed = parser.parse_args(sys.argv[1:])