rendering signatures and scraping challenges

src/experiments/actor/abstract_renderer.py

@@ -2,7 +2,7 @@
 
 from typetemp.template.render_funcs import render_str
 
-from rdddy.messages import EventStormModel
+from rdddy.messages import EventStormingDomainSpecificationModel
 
 base_class_mapping = {
     "domain_event_classnames": "Event",
@@ -35,7 +35,7 @@ class {{ classname }}({{ base_class_name }}):
 
 
 
-def generate_class_definitions(model: EventStormModel):
+def generate_class_definitions(model: EventStormingDomainSpecificationModel):
     for attr, base_class_name in base_class_mapping.items():
         classnames = getattr(model, attr, [])
         for classname in classnames:
@@ -64,7 +64,7 @@ def main():
         "task_classnames": ["ValidateOrder", "CalculateShippingCosts", "SendOrderConfirmationEmail"],
     }
 
-    event_storm_model = EventStormModel.model_validate(event_storm_model_data)
+    event_storm_model = EventStormingDomainSpecificationModel.model_validate(event_storm_model_data)
 
     generate_class_definitions(event_storm_model)
 

src/experiments/event_storm_renderer.py

@@ -272,7 +272,7 @@ def gold_passages_retrieved(example, pred, trace=None):
 with names that match the description and domain we are modeling. Class names are CamelCase and unique.
 '''
 
-esm = EventStormModel(
+esm = EventStormingDomainSpecificationModel(
     domain_event_classnames=[
         "LanguageModelSetup",
         "RetrievalModelSetup",
@@ -321,7 +321,7 @@ def gold_passages_retrieved(example, pred, trace=None):
 def main():
     import dspy
     from rdddy.messages import (
-        EventStormModel,
+        EventStormingDomainSpecificationModel,
     )
 
     lm = dspy.OpenAI(max_tokens=3000)
@@ -332,11 +332,11 @@ def main():
 
     print("Generating EventStorm")
 
-    event_storm_model = GenPydanticInstance(root_model=EventStormModel)(prompt=prompt)
+    event_storm_model = GenPydanticInstance(root_model=EventStormingDomainSpecificationModel)(prompt=prompt)
 
     print(f"Event Storm Model {event_storm_model}")
 
-    event_storm_model = EventStormModel(
+    event_storm_model = EventStormingDomainSpecificationModel(
         domain_event_classnames=[
             "QuestionAsked",
             "AnswerGenerated",

src/experiments/example/abstract_renderer.py

@@ -3,7 +3,7 @@
 from typetemp.template.render_funcs import render_str
 import dspy
 
-from rdddy.messages import EventStormModel
+from rdddy.messages import EventStormingDomainSpecificationModel
 
 base_class_mapping = {
     "domain_event_classnames": "Event",
@@ -54,7 +54,7 @@ def forward(self, role, classname):
         return prediction.description
 
 
-def generate_verbose_class_definitions(model: EventStormModel, description_generator: VerboseDescriptionGenerator):
+def generate_verbose_class_definitions(model: EventStormingDomainSpecificationModel, description_generator: VerboseDescriptionGenerator):
 
     for attr, base_class_name in base_class_mapping.items():
         role = attr.replace('_classnames', '')  # Simplified role name, adjust as needed
@@ -86,7 +86,7 @@ def main():
         "task_classnames": ["ValidateOrder", "CalculateShippingCosts", "SendOrderConfirmationEmail"],
     }
 
-    event_storm_model = EventStormModel.model_validate(event_storm_model_data)
+    event_storm_model = EventStormingDomainSpecificationModel.model_validate(event_storm_model_data)
 
     # generate_class_definitions(event_storm_model)
 

src/experiments/gen_cli_from_model.py

@@ -19,34 +19,74 @@ class TyperCLI(BaseModel):
 cli_description = """
 
 
-We are building a Typer CLI application named 'DSPyGenerator'. It should include the following commands:
+DSPy Framework CLI v1.0.0
+A Ruby on Rails-style framework CLI for streamlined AI application development.
 
-1. Command Name: generate_module
-   Help: Generates a new DSPy module with a specified name.
+USAGE:
+    dspy [SUBCOMMAND]
 
-2. Command Name: generate_signature
-   Help: Creates a new signature class for defining input-output behavior in DSPy modules.
+SUBCOMMANDS:
+    new             Create a new DSPy project with a default directory structure and configuration.
+    generate        Generate new components within your DSPy project (e.g., modules, datasets, models).
+    server          Start a local server for developing and testing your DSPy application.
+    test            Run tests on your DSPy application, including model evaluations and data validations.
+    deploy          Deploy your DSPy application to various environments (development, testing, production).
+    db              Database operations, such as migrations, seeding, and schema loading.
+    data            Manage your datasets, including import, export, and version control.
+    eval            Evaluate your models or entire pipelines with custom or predefined metrics.
+    help            Displays help information about the available commands.
 
-3. Command Name: generate_chainofthought
-   Help: Generates a ChainOfThought module with a standard question-answering signature.
+EXAMPLES:
+    # Create a new DSPy project named MyAIApp
+    dspy new MyAIApp
 
-4. Command Name: generate_retrieve
-   Help: Generates a Retrieve module for use in information retrieval tasks within DSPy.
+    # Generate a new data model within the project
+    dspy generate model MyNewModel
 
-5. Command Name: generate_teleprompter
-   Help: Creates a teleprompter setup for optimizing DSPy programs.
+    # Run the development server
+    dspy server
+
+    # Run tests
+    dspy test
+
+    # Deploy your application to production
+    dspy deploy --environment=production
+
+    # Perform database migration
+    dspy db migrate
+
+    # Import a dataset from a file
+    dspy data import --source=./path/to/dataset.csv
+
+    # Evaluate a model with a specific metric
+    dspy eval --model=MyModel --metric=f1_score
+
+    # Display help for the 'generate' subcommand
+    dspy help generate
+
+For more information on a specific command, use 'dspy [subcommand] --help'.
+
+GLOBAL OPTIONS:
+    -h, --help       Prints help information
+    -v, --version    Prints version information
+
+ENVIRONMENT VARIABLES:
+    DSPY_ENV         Sets the environment for the DSPy CLI (default: development)
+
+CONFIGURATION:
+    Configuration for the DSPy CLI and projects can be set in `.dspy.yml` within your project directory or global configuration files.
+
+SUPPORT & DOCUMENTATION:
+    For more details, visit the official DSPy documentation at [DSPy Framework Documentation URL].
+    For support, reach out to the community forum or issue tracker on our GitHub repository.
 
-6. Command Name: generate_example
-   Help: Generates an example structure for use in training and testing DSPy modules.
 
-7. Command Name: generate_assertion
-   Help: Generates a template for creating LM Assertions in DSPy programs.
 
 """
 
 
 def main():
-    lm = dspy.OpenAI(max_tokens=3000)
+    lm = dspy.OpenAI(max_tokens=3000, model="gpt-4")
     dspy.settings.configure(lm=lm)
 
     model = GenPydanticInstance(root_model=TyperCLI, child_models=[TyperCommand]).forward(
@@ -94,9 +134,9 @@ def test_{{ command.name }}():
 
     # --- Render Templates ---
     env = Environment(loader=FileSystemLoader("."))
-    env.from_string(cli_template).stream(cli_data=cli_data.model_dump()).dump("generated_cli.py")
+    env.from_string(cli_template).stream(cli_data=cli_data.model_dump()).dump("ror_dspy.py")
     env.from_string(pytest_template).stream(cli_data=cli_data.model_dump()).dump(
-        "test_generated_cli.py"
+        "test_ror_dspy.py"
     )
 
     print("CLI application and tests generated.")