diff --git a/module_script.sh b/module_script.sh index 2579b9f..b9eb1c0 100644 --- a/module_script.sh +++ b/module_script.sh @@ -1,6 +1,6 @@ #!/bin/bash -# Create DSPyGen modules required for the Bullwhip platform +# Create DSPyGen dspy_modules required for the Bullwhip platform echo "Creating DSPyGen modules for Bullwhip platform..." # Example 1: Create a module to transform JSON data to a structured report diff --git a/src/dspygen/agents/challenger_agent.py b/src/dspygen/agents/challenger_agent.py index 354a936..ca3991a 100644 --- a/src/dspygen/agents/challenger_agent.py +++ b/src/dspygen/agents/challenger_agent.py @@ -1,6 +1,7 @@ import logging from enum import Enum, auto from dspygen.mixin.fsm.fsm_mixin import FSMMixin, trigger +from dspygen.rdddy.base_inhabitant import BaseInhabitant class SalesState(Enum): @@ -14,7 +15,7 @@ class SalesState(Enum): COMPLETING = auto() -class ChallengerSalesAgent(FSMMixin): +class ChallengerSalesAgent(FSMMixin, BaseInhabitant): def __init__(self): super().__init__() self.setup_fsm(state_enum=SalesState, initial=SalesState.INITIALIZING) diff --git a/src/dspygen/agents/research_agent.py b/src/dspygen/agents/research_agent.py index b344d82..8a3b7a2 100644 --- a/src/dspygen/agents/research_agent.py +++ b/src/dspygen/agents/research_agent.py @@ -8,7 +8,7 @@ from dspygen.mixin.fsm.fsm_mixin import FSMMixin, trigger from dspygen.modules.condition_sufficient_info_module import condition_sufficient_info_call from dspygen.modules.query_generator_module import query_generator_call -#from dspygen.modules.refine_results_module import refine_results_module_call +#from dspygen.dspy_modules.refine_results_module import refine_results_module_call from dspygen.modules.source_selector_module import source_selector_call from dspygen.utils.scraping_tools import execute_brave_search_queries, scrape_urls, execute_duckduckgo_queries diff --git a/src/dspygen/cli.py b/src/dspygen/cli.py index cd6b6cc..234e181 100644 --- a/src/dspygen/cli.py +++ b/src/dspygen/cli.py @@ -110,16 +110,16 @@ def init(project_name: str = typer.Argument(...), TUTOR_CONTEXT = """DSPyGen: AI Development Simplified -DSPyGen revolutionizes AI development by bringing the "Convention over Configuration" philosophy to language model (LM) pipelines. Inspired by Ruby on Rails, it offers a CLI for creating, developing, and deploying with DSPy modules, emphasizing quick setup and modular design for streamlined project workflows. +DSPyGen revolutionizes AI development by bringing the "Convention over Configuration" philosophy to language model (LM) pipelines. Inspired by Ruby on Rails, it offers a CLI for creating, developing, and deploying with DSPy dspy_modules, emphasizing quick setup and modular design for streamlined project workflows. Key Features: Quick Initialization: Rapidly configure your AI project, mirroring the simplicity of Ruby on Rails. -Modular Design: Generate and enhance DSPy modules with ease, promoting a scalable and flexible development environment. +Modular Design: Generate and enhance DSPy dspy_modules with ease, promoting a scalable and flexible development environment. User-Friendly Commands: Manage your AI projects effortlessly through an intuitive command structure. Chatbot Assistance: Embedded support to guide through the development process, enhancing user experience. Using DSPyGen Modules: -DSPyGen's core lies in its modules, designed for seamless integration and code optimization. Here’s how to leverage them: +DSPyGen's core lies in its dspy_modules, designed for seamless integration and code optimization. Here’s how to leverage them: dspygen is a command-line tool. It helps generate various components for a project. @@ -134,34 +134,34 @@ def init(project_name: str = typer.Argument(...), help: Provides assistance and updates help files. init: Initializes a DSPygen project. lm: Generates language models. -module: Deals with generating or calling DSPy modules. +module: Deals with generating or calling DSPy dspy_modules. sig: Generates dspy.Signatures. tutor: Guides through project development with DSPyGen. -blog: Calls modules related to blogging. -book_appointment: Invokes modules for scheduling appointments. +blog: Calls dspy_modules related to blogging. +book_appointment: Invokes dspy_modules for scheduling appointments. chat_bot: Initiates module calls for chatbots. -checker: Calls modules for checking or validating. +checker: Calls dspy_modules for checking or validating. choose_function: Initiates module calls involving choice or selection functions. -dflss: Related to Six Sigma methodology, possibly for invoking process improvement modules. -gen_cli: Triggers modules for command-line interfaces. -gen_dspy: Calls modules specific to DSPy. +dflss: Related to Six Sigma methodology, possibly for invoking process improvement dspy_modules. +gen_cli: Triggers dspy_modules for command-line interfaces. +gen_dspy: Calls dspy_modules specific to DSPy. gen_keyword_arguments: Initiates module calls with keyword arguments. -gen_signature: Invokes signature-related modules. -html: For modules related to HTML. -insight_tweet: Calls modules for generating insightful tweets. +gen_signature: Invokes signature-related dspy_modules. +html: For dspy_modules related to HTML. +insight_tweet: Calls dspy_modules for generating insightful tweets. message: Initiates module calls for messaging. -module_docstring: Triggers module calls for documenting modules. +module_docstring: Triggers module calls for documenting dspy_modules. product_bot: Invokes module calls for product-related bots. -prompt_function_call: Triggers modules for prompting function calls. -python_expert: For modules related to Python expertise. +prompt_function_call: Triggers dspy_modules for prompting function calls. +python_expert: For dspy_modules related to Python expertise. python_source_code: Initiates module calls for generating Python source code. source_code_pep8_docs: Triggers module calls for source code with PEP8 documentation. subject_destination_audience_newsletter_article: Initiates module calls for newsletter articles. -text_summary_module: Calls modules for summarizing text. -to_elixir: Initiates module calls to convert modules to Elixir format. +text_summary_module: Calls dspy_modules for summarizing text. +to_elixir: Initiates module calls to convert dspy_modules to Elixir format. -Use this information to guide the usage of the DSPyGen CLI and its modules. +Use this information to guide the usage of the DSPyGen CLI and its dspy_modules. """ @app.command(name="tutor") diff --git a/src/dspygen/dsl/dsl_pydantic_models.py b/src/dspygen/dsl/dsl_pydantic_models.py index 4050973..624fb9e 100644 --- a/src/dspygen/dsl/dsl_pydantic_models.py +++ b/src/dspygen/dsl/dsl_pydantic_models.py @@ -107,9 +107,9 @@ class PipelineDSLModel(BaseModel, YAMLMixin): signatures: list[SignatureDSLModel] = Field(default=[], description="list of signatures defined for use in the pipeline.") lm_modules: list[LMModuleDSLModel] = Field(default=[], - description="list of language model modules defined for execution in the pipeline.") + description="list of language model dspy_modules defined for execution in the pipeline.") rm_modules: list[RMModuleDSLModel] = Field(default=[], - description="list of retriever model modules defined for execution in the pipeline.") + description="list of retriever model dspy_modules defined for execution in the pipeline.") steps: list[StepDSLModel] = Field(default=[], description="Sequential steps to be executed in the pipeline.") context: dict = Field(default=Munch(), diff --git a/src/dspygen/dsl/dsl_step_module.py b/src/dspygen/dsl/dsl_step_module.py index 57e8b8c..5ae19ab 100644 --- a/src/dspygen/dsl/dsl_step_module.py +++ b/src/dspygen/dsl/dsl_step_module.py @@ -1,4 +1,4 @@ -# Import necessary modules and classes +# Import necessary dspy_modules and classes import dspy from dspygen.dsl.dsl_pydantic_models import StepDSLModel, PipelineDSLModel, LanguageModelConfig diff --git a/src/dspygen/dsl/utils/dsl_lm_module_utils.py b/src/dspygen/dsl/utils/dsl_lm_module_utils.py index 699254b..2823fb0 100644 --- a/src/dspygen/dsl/utils/dsl_lm_module_utils.py +++ b/src/dspygen/dsl/utils/dsl_lm_module_utils.py @@ -44,7 +44,7 @@ def _load_lm_module_class(dspy_module_class_name: str): def _get_lm_module_instance(pipeline, rendered_args, step): """ Get the module instance for a given step from the top level definition or load the module. - Uses the DSLModule class from dspygen.modules.dsl_module to handle modules defined in the pipeline YAML. + Uses the DSLModule class from dspygen.dspy_modules.dsl_module to handle dspy_modules defined in the pipeline YAML. """ module_def = next((m for m in pipeline.lm_modules if m.name in step.module), None) diff --git a/src/dspygen/dsl/utils/dsl_rm_module_utils.py b/src/dspygen/dsl/utils/dsl_rm_module_utils.py index a89f983..bf45f92 100644 --- a/src/dspygen/dsl/utils/dsl_rm_module_utils.py +++ b/src/dspygen/dsl/utils/dsl_rm_module_utils.py @@ -24,7 +24,7 @@ def _load_rm_module_class(dspy_module_class_name: str): def _get_rm_module_instance(pipeline, rendered_args, step): """ Get the module instance for a given step from the top level definition or load the module. - Uses the DSLModule class from dspygen.modules.dsl_module to handle modules defined in the pipeline YAML. + Uses the DSLModule class from dspygen.dspy_modules.dsl_module to handle dspy_modules defined in the pipeline YAML. """ # Get the file_path from the pipeline.context or rendered_args # file_path = pipeline.context.get("file_path", rendered_args.get("file_path")) diff --git a/src/dspygen/dspygen_app.py b/src/dspygen/dspygen_app.py index 951993c..6d57c16 100644 --- a/src/dspygen/dspygen_app.py +++ b/src/dspygen/dspygen_app.py @@ -4,9 +4,9 @@ # # import streamlit as st # -# from dspygen.modules.chat_bot_module import chat_bot_call -# from dspygen.modules.insight_tweet_module import insight_tweet_call -# from dspygen.modules.streamlit_bot_module import streamlit_bot_call +# from dspygen.dspy_modules.chat_bot_module import chat_bot_call +# from dspygen.dspy_modules.insight_tweet_module import insight_tweet_call +# from dspygen.dspy_modules.streamlit_bot_module import streamlit_bot_call # from dspygen.utils.dspy_tools import init_dspy # from dspygen.utils.file_tools import source_dir, pages_dir # diff --git a/src/dspygen/experiments/auto_spider/spider_main.py b/src/dspygen/experiments/auto_spider/spider_main.py index 2526427..5c2587f 100644 --- a/src/dspygen/experiments/auto_spider/spider_main.py +++ b/src/dspygen/experiments/auto_spider/spider_main.py @@ -266,14 +266,14 @@ def parse_linkedin_profile(linkedin_text: str) -> Optional[LinkedInProfile]: AT&TAT&T Feb 2016 - Nov 2017 · 1 yr 10 mosFeb 2016 to Nov 2017 · 1 yr 10 mos Greater Los Angeles AreaGreater Los Angeles Area -In my role as Lead Software Engineer at AT&T, I specialized in AngularJS 2, focusing primarily on developing robust client-side applications. My responsibilities included the creation and integration of modules and components to build highly functional and performance-oriented user interfaces. I worked closely with artistic designers, translating their HTML templates into dynamic and responsive web designs, complete with animations and CSS stylings. Collaborating effectively with back-end development teams, I ensured seamless API integrations and data communication. My approach to development was marked by a commitment to creating intuitive, efficient, and visually appealing user experiences, backed by solid technical execution and team coordination. +In my role as Lead Software Engineer at AT&T, I specialized in AngularJS 2, focusing primarily on developing robust client-side applications. My responsibilities included the creation and integration of dspy_modules and components to build highly functional and performance-oriented user interfaces. I worked closely with artistic designers, translating their HTML templates into dynamic and responsive web designs, complete with animations and CSS stylings. Collaborating effectively with back-end development teams, I ensured seamless API integrations and data communication. My approach to development was marked by a commitment to creating intuitive, efficient, and visually appealing user experiences, backed by solid technical execution and team coordination. Highlights: - Spearheaded the development of client-side applications using AngularJS 2, enhancing user experience and application performance. - Translated artistic design concepts into responsive web interfaces, incorporating animations and CSS stylings. - Led module and component creation, integrating them to form cohesive and functional applications. - Facilitated effective collaboration between front-end and back-end teams for seamless API integrations. - - Focused on delivering high-quality user interfaces with an emphasis on performance and user engagement.In my role as Lead Software Engineer at AT&T, I specialized in AngularJS 2, focusing primarily on developing robust client-side applications. My responsibilities included the creation and integration of modules and components to build highly functional and performance-oriented user interfaces. I worked closely with artistic designers, translating their HTML templates into dynamic and responsive web designs, complete with animations and CSS stylings. Collaborating effectively with back-end development teams, I ensured seamless API integrations and data communication. My approach to development was marked by a commitment to creating intuitive, efficient, and visually appealing user experiences, backed by solid technical execution and team coordination. Highlights: - Spearheaded the development of client-side applications using AngularJS 2, enhancing user experience and application performance. - Translated artistic design concepts into responsive web interfaces, incorporating animations and CSS stylings. - Led module and component creation, integrating them to form cohesive and functional applications. - Facilitated effective collaboration between front-end and back-end teams for seamless API integrations. - Focused on delivering high-quality user interfaces with an emphasis on performance and user engagement. + - Focused on delivering high-quality user interfaces with an emphasis on performance and user engagement.In my role as Lead Software Engineer at AT&T, I specialized in AngularJS 2, focusing primarily on developing robust client-side applications. My responsibilities included the creation and integration of dspy_modules and components to build highly functional and performance-oriented user interfaces. I worked closely with artistic designers, translating their HTML templates into dynamic and responsive web designs, complete with animations and CSS stylings. Collaborating effectively with back-end development teams, I ensured seamless API integrations and data communication. My approach to development was marked by a commitment to creating intuitive, efficient, and visually appealing user experiences, backed by solid technical execution and team coordination. Highlights: - Spearheaded the development of client-side applications using AngularJS 2, enhancing user experience and application performance. - Translated artistic design concepts into responsive web interfaces, incorporating animations and CSS stylings. - Led module and component creation, integrating them to form cohesive and functional applications. - Facilitated effective collaboration between front-end and back-end teams for seamless API integrations. - Focused on delivering high-quality user interfaces with an emphasis on performance and user engagement. Angular2, Engineering Consulting and +1 skill Playsino logo Software ArchitectSoftware Architect diff --git a/src/dspygen/experiments/bkgn/get_soc_files.py b/src/dspygen/experiments/bkgn/get_soc_files.py index 1b80103..6869f3a 100644 --- a/src/dspygen/experiments/bkgn/get_soc_files.py +++ b/src/dspygen/experiments/bkgn/get_soc_files.py @@ -167,7 +167,7 @@ def get_services(): def get_modules(): """Retrieves code snippets from the '/Users/sac/dev/soc/src/soc/services' directory.""" - return get_files_from_directory('/Users/sac/dev/soc/src/soc/modules') + return get_files_from_directory('/Users/sac/dev/soc/src/soc/dspy_modules') def main(): diff --git a/src/dspygen/experiments/done/gen_dsl_instances.py b/src/dspygen/experiments/done/gen_dsl_instances.py index aa60e3b..5405f6e 100644 --- a/src/dspygen/experiments/done/gen_dsl_instances.py +++ b/src/dspygen/experiments/done/gen_dsl_instances.py @@ -29,7 +29,7 @@ def main2(): # module = GenModuleModel.to_inst(f"name: RawToStructure, raw_to_structure_signature, Predict {signature}") - module = GenLMModuleModel.from_yaml(str(dsl_dir("modules/raw_to_structure_module.yaml"))) + module = GenLMModuleModel.from_yaml(str(dsl_dir("dspy_modules/raw_to_structure_module.yaml"))) # print(module) diff --git a/src/dspygen/mixin/fsm/order_processing_fsm.py b/src/dspygen/mixin/fsm/order_processing_fsm.py index b7c3b3e..90e04c4 100644 --- a/src/dspygen/mixin/fsm/order_processing_fsm.py +++ b/src/dspygen/mixin/fsm/order_processing_fsm.py @@ -152,7 +152,7 @@ class Choice(BaseModel): # # init_dspy() # poss = fsm.possible_triggers() -# # from dspygen.modules.json_module import json_call +# # from dspygen.dspy_modules.json_module import json_call # # response = json_call(schema=Choice.model_json_schema(), # # text=f"{{\"possible_triggers\": {str(poss)}, \"prompt\": \"{message}\"}}") # choice = dspy.Predict("possible_triggers, prompt -> choice")(possible_triggers=str(poss), prompt=message).choice diff --git a/src/dspygen/modules/book_appointment_module.py b/src/dspygen/modules/book_appointment_module.py index cdcde1a..771a51d 100644 --- a/src/dspygen/modules/book_appointment_module.py +++ b/src/dspygen/modules/book_appointment_module.py @@ -1,5 +1,5 @@ """ -The source code is used to import necessary libraries and modules, define a class and its methods, and create a command line interface using Typer. The BookAppointmentModule class is used to book appointments by predicting availability based on the requested date. The book_appointment_call function calls the BookAppointmentModule class and returns the result. The main function initializes the dspy library and calls the book_appointment_call function. The TODO section indicates that a streamlit component needs to be added, and the fastapi library is used to create an API endpoint for booking appointments. The book_appointment_route function uses the book_appointment_call function to generate code based on the data provided. +The source code is used to import necessary libraries and dspy_modules, define a class and its methods, and create a command line interface using Typer. The BookAppointmentModule class is used to book appointments by predicting availability based on the requested date. The book_appointment_call function calls the BookAppointmentModule class and returns the result. The main function initializes the dspy library and calls the book_appointment_call function. The TODO section indicates that a streamlit component needs to be added, and the fastapi library is used to create an API endpoint for booking appointments. The book_appointment_route function uses the book_appointment_call function to generate code based on the data provided. """ import dspy from typer import Typer diff --git a/src/dspygen/modules/dflss_module.py b/src/dspygen/modules/dflss_module.py index caa8312..f5e4503 100644 --- a/src/dspygen/modules/dflss_module.py +++ b/src/dspygen/modules/dflss_module.py @@ -1,5 +1,5 @@ """ -This code imports the necessary libraries and modules to run the DFLSSModule. +This code imports the necessary libraries and dspy_modules to run the DFLSSModule. """ import dspy from dspygen.utils.dspy_tools import init_dspy @@ -52,7 +52,7 @@ def dflss_call(scenario_description, document_type, to=None): The current order processing system, implemented via a finite state machine, is functional but lacks the flexibility and modularity needed for easy scalability and maintenance. By integrating DSPy, the system can benefit from more structured data handling, automatic state management, and enhanced debugging capabilities through systematic logging and validation. Project Scope: -Convert existing order processing methods and transitions into DSPy-compatible modules. +Convert existing order processing methods and transitions into DSPy-compatible dspy_modules. Use DSPy's capabilities to handle different states of an order's lifecycle within a declarative pipeline environment. Implement validation and action triggers as modular components. Integrate state change triggers with conditions and actions based on DSPy’s event-driven architecture. diff --git a/src/dspygen/modules/dspygen_dsl_pipeline.py b/src/dspygen/modules/dspygen_dsl_pipeline.py index 084dc92..5bdcf69 100644 --- a/src/dspygen/modules/dspygen_dsl_pipeline.py +++ b/src/dspygen/modules/dspygen_dsl_pipeline.py @@ -9,13 +9,13 @@ def process_yaml_pipeline(yaml_file): dg_modules = [] - for module_def in config['modules']: + for module_def in config['dspy_modules']: module_class = globals()[f"{module_def['module']}DGModule"] # Get the module class by name module_instance = module_class(**module_def.get('args', {})) dg_modules.append(module_instance) - # Pipe the modules together because we need to do __or__ operations + # Pipe the dspy_modules together because we need to do __or__ operations for i in range(len(dg_modules) - 1): dg_modules[i] | dg_modules[i + 1] diff --git a/src/dspygen/modules/gen_keyword_arguments_module.py b/src/dspygen/modules/gen_keyword_arguments_module.py index 9062928..52203a6 100644 --- a/src/dspygen/modules/gen_keyword_arguments_module.py +++ b/src/dspygen/modules/gen_keyword_arguments_module.py @@ -1,5 +1,5 @@ """ -The source code imports the necessary libraries and modules, including dspy and Typer. It also defines a class called GenKeywordArgumentsModule, which contains a forward function that takes in a prompt and function and returns a keyword_arguments_dict. The gen_keyword_arguments_call function uses the GenKeywordArgumentsModule to generate keyword arguments for a given prompt and function. The app.command() decorator defines a command for the Typer app, which calls the gen_keyword_arguments_call function. The main function initializes dspy and calls the gen_keyword_arguments_call function with empty prompt and function parameters. The TODO comments indicate future plans for the code, including adding a streamlit component and a FastAPI route. +The source code imports the necessary libraries and dspy_modules, including dspy and Typer. It also defines a class called GenKeywordArgumentsModule, which contains a forward function that takes in a prompt and function and returns a keyword_arguments_dict. The gen_keyword_arguments_call function uses the GenKeywordArgumentsModule to generate keyword arguments for a given prompt and function. The app.command() decorator defines a command for the Typer app, which calls the gen_keyword_arguments_call function. The main function initializes dspy and calls the gen_keyword_arguments_call function with empty prompt and function parameters. The TODO comments indicate future plans for the code, including adding a streamlit component and a FastAPI route. """ import ast import inspect diff --git a/src/dspygen/modules/gen_module.py b/src/dspygen/modules/gen_module.py index 46ed8e6..8eec3fd 100644 --- a/src/dspygen/modules/gen_module.py +++ b/src/dspygen/modules/gen_module.py @@ -26,7 +26,7 @@ def __init__(self, output_key, input_keys: Optional[list[str]] = None, lm=None): ", ".join(self.input_keys) + f", error -> {self.output_key}" ) - # DSPy modules for generation and correction + # DSPy dspy_modules for generation and correction self.generate = ChainOfThought(self.signature) self.correct_generate = ChainOfThought(self.correction_signature) diff --git a/src/dspygen/modules/gen_pydantic_instance.py b/src/dspygen/modules/gen_pydantic_instance.py index 3927926..9079dc3 100644 --- a/src/dspygen/modules/gen_pydantic_instance.py +++ b/src/dspygen/modules/gen_pydantic_instance.py @@ -84,7 +84,7 @@ def __init__( # Concatenate source code of models for use in generation/correction logic self.model_sources = get_model_source(model) - # Initialize DSPy ChainOfThought modules for generation and correction + # Initialize DSPy ChainOfThought dspy_modules for generation and correction self.generate = ChainOfThought(generate_sig) self.correct_generate = ChainOfThought(correct_generate_sig) self.validation_error = None @@ -163,7 +163,7 @@ def __init__( # Concatenate source code of models for use in generation/correction logic self.model_sources = get_model_source(model) - # Initialize DSPy ChainOfThought modules for generation and correction + # Initialize DSPy ChainOfThought dspy_modules for generation and correction self.generate = ChainOfThought(generate_sig) self.correct_generate = ChainOfThought(correct_generate_sig) self.validation_error = None diff --git a/src/dspygen/modules/gen_pydantic_instance_module.py b/src/dspygen/modules/gen_pydantic_instance_module.py index 8651e5a..e3dc726 100644 --- a/src/dspygen/modules/gen_pydantic_instance_module.py +++ b/src/dspygen/modules/gen_pydantic_instance_module.py @@ -89,7 +89,7 @@ def __init__( [inspect.getsource(model) for model in self.models] ) - # Initialize DSPy ChainOfThought modules for generation and correction + # Initialize DSPy ChainOfThought dspy_modules for generation and correction self.generate = ChainOfThought(generate_sig) self.correct_generate = ChainOfThought(correct_generate_sig) self.validation_error = None diff --git a/src/dspygen/modules/insight_tweet_module.py b/src/dspygen/modules/insight_tweet_module.py index b5adda8..6c36c38 100644 --- a/src/dspygen/modules/insight_tweet_module.py +++ b/src/dspygen/modules/insight_tweet_module.py @@ -1,5 +1,5 @@ """ -The source code is used to import the necessary libraries and modules for the program. It also defines a class called "InsightTweetModule" which contains a function called "forward" that takes in an "insight" parameter and returns a result. The "insight_tweet_call" function uses the "InsightTweetModule" class to call the "forward" function and return the result. The "call" function is used to initialize the program and print the result of the "insight_tweet_call" function. The "main" function is used to initialize the program and print the result of the "insight_tweet_call" function. +The source code is used to import the necessary libraries and dspy_modules for the program. It also defines a class called "InsightTweetModule" which contains a function called "forward" that takes in an "insight" parameter and returns a result. The "insight_tweet_call" function uses the "InsightTweetModule" class to call the "forward" function and return the result. The "call" function is used to initialize the program and print the result of the "insight_tweet_call" function. The "main" function is used to initialize the program and print the result of the "insight_tweet_call" function. """ import dspy import pyperclip diff --git a/src/dspygen/modules/message_module.py b/src/dspygen/modules/message_module.py index 66e85b1..9886c99 100644 --- a/src/dspygen/modules/message_module.py +++ b/src/dspygen/modules/message_module.py @@ -6,7 +6,7 @@ import dspy from typer import Typer -# from dspygen.modules.gen_pydantic_instance_module import GenPydanticInstance +# from dspygen.dspy_modules.gen_pydantic_instance_module import GenPydanticInstance from dspygen.rdddy.browser.browser_domain import TypeText from dspygen.utils.dspy_tools import init_dspy diff --git a/src/dspygen/modules/proposal_generator_module.py b/src/dspygen/modules/proposal_generator_module.py new file mode 100644 index 0000000..9d14611 --- /dev/null +++ b/src/dspygen/modules/proposal_generator_module.py @@ -0,0 +1,75 @@ +""" + +""" +import dspy +from dspygen.utils.dspy_tools import init_dspy + + +class ProposalGeneratorModule(dspy.Module): + """ProposalGeneratorModule""" + + def __init__(self, **forward_args): + super().__init__() + self.forward_args = forward_args + self.output = None + + def __or__(self, other): + if other.output is None and self.output is None: + self.forward(**self.forward_args) + + other.pipe(self.output) + + return other + + def forward(self, context, criteria): + pred = dspy.Predict("context, criteria -> proposal") + self.output = pred(context=context, criteria=criteria).proposal + return self.output + + def pipe(self, input_str): + raise NotImplementedError("Please implement the pipe method for DSL support.") + # Replace TODO with a keyword from you forward method + # return self.forward(TODO=input_str) + + +from typer import Typer + +app = Typer() + + +@app.command() +def call(context, criteria): + """ProposalGeneratorModule""" + init_dspy() + + print(proposal_generator_call(context=context, criteria=criteria)) + + +def proposal_generator_call(context, criteria): + proposal_generator = ProposalGeneratorModule() + return proposal_generator.forward(context=context, criteria=criteria) + + +def main(): + init_dspy() + context = "" + criteria = "" + result = proposal_generator_call(context=context, criteria=criteria) + print(result) + + +from fastapi import APIRouter + +router = APIRouter() + + +@router.post("/proposal_generator/") +async def proposal_generator_route(data: dict): + # Your code generation logic here + init_dspy() + + print(data) + return proposal_generator_call(**data) + +if __name__ == "__main__": + main() diff --git a/src/dspygen/modules/signature_factory.py b/src/dspygen/modules/signature_factory.py index e8de80d..a1ebb55 100644 --- a/src/dspygen/modules/signature_factory.py +++ b/src/dspygen/modules/signature_factory.py @@ -120,7 +120,7 @@ def main(): # Convert the SignatureModel to a DSPy Signature class QuestionAnswering = create_signature_class_from_model(question_answering_signature) - context = """Chaining language model (LM) calls as composable modules is fueling a new powerful + context = """Chaining language model (LM) calls as composable dspy_modules is fueling a new powerful way of programming. However, ensuring that LMs adhere to important constraints remains a key challenge, one often addressed with heuristic “prompt engineering”. We introduce LM Assertions, a new programming construct for expressing computational constraints that LMs should satisfy. diff --git a/src/dspygen/modules/signature_renderer.py b/src/dspygen/modules/signature_renderer.py index abd5161..c5ef46f 100644 --- a/src/dspygen/modules/signature_renderer.py +++ b/src/dspygen/modules/signature_renderer.py @@ -4,7 +4,7 @@ from dspy import Signature from dspy.signatures.field import InputField, OutputField -# from dspygen.modules.gen_pydantic_instance_module import gen_pydantic_instance_call +# from dspygen.dspy_modules.gen_pydantic_instance_module import gen_pydantic_instance_call from sungen.typetemp.template.typed_template import TypedTemplate diff --git a/src/dspygen/modules/source_code_pep8_docs_module.py b/src/dspygen/modules/source_code_pep8_docs_module.py index f03561c..7bed59e 100644 --- a/src/dspygen/modules/source_code_pep8_docs_module.py +++ b/src/dspygen/modules/source_code_pep8_docs_module.py @@ -1,5 +1,5 @@ """The `SourceCodePep8DocsModule` class is used to create documentation for source code using best practices. It -first imports the necessary libraries and modules, including `dspy`, `pyperclip`, and `typer`. Then, in the `forward` +first imports the necessary libraries and dspy_modules, including `dspy`, `pyperclip`, and `typer`. Then, in the `forward` method, it checks if a context is provided and if not, sets a default context. The source code is then formatted and passed through a `ChainOfThought` model to generate the pep8_docs. The `source_code_pep8_docs_call` function calls the `forward` method and returns the result. In the `call` command, the source code is passed through `pyperclip` and diff --git a/src/dspygen/pyautomator/flows/prefect_app.py b/src/dspygen/pyautomator/flows/prefect_app.py index 3199dee..5af1ff3 100644 --- a/src/dspygen/pyautomator/flows/prefect_app.py +++ b/src/dspygen/pyautomator/flows/prefect_app.py @@ -16,7 +16,6 @@ def create_flow(self, name: str, description: str, tasks: List[Dict]) -> str: @task def read_flow(self, name: str) -> Optional[Dict]: return self.flow_manager.read_flow(name) - @task def update_flow(self, name: str, description: str = None, tasks: List[Dict] = None) -> str: return self.flow_manager.update_flow(name, description, tasks) diff --git a/src/dspygen/pyautomator/keynote/__init__.py b/src/dspygen/pyautomator/keynote/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/dspygen/pyautomator/keynote/keynote_app.py b/src/dspygen/pyautomator/keynote/keynote_app.py new file mode 100644 index 0000000..4bc54ba --- /dev/null +++ b/src/dspygen/pyautomator/keynote/keynote_app.py @@ -0,0 +1,80 @@ +from datetime import datetime + +class KeynoteApp(BaseApp): + def __init__(self): + super().__init__("Keynote") + + def create_presentation(self, save_to_file: bool = False): + script = """ + const keynote = Application('Keynote'); + keynote.includeStandardAdditions = true; + const presentation = keynote.Document().make(); + keynote.documents.push(presentation); + """ + return self.execute_jxa(script, save_to_file=save_to_file) + + def add_slide(self, layout="Title & Subtitle", save_to_file: bool = False): + script = f""" + const keynote = Application('Keynote'); + const presentation = keynote.documents[0]; // Target the first open presentation + const slide = presentation.slides.push(keynote.Slide({{ baseSlide: presentation.masterSlides['{layout}'] }})); + """ + return self.execute_jxa(script, save_to_file=save_to_file) + + def add_text_to_slide(self, slide_index=0, text="Your Text Here", size=24, x=100, y=100, save_to_file: bool = False): + script = f""" + const keynote = Application('Keynote'); + const presentation = keynote.documents[0]; // Assume the first open presentation + const slide = presentation.slides[{slide_index}]; + const textItem = slide.defaultTitleItem(); + textItem.objectText = '{text}'; + textItem.textSize = {size}; + textItem.position = {{x: {x}, y: {y}}}; + """ + return self.execute_jxa(script, save_to_file=save_to_file) + + def insert_image(self, slide_index=0, image_path="", x=200, y=200, width=300, height=200, save_to_file: bool = False): + script = f""" + const keynote = Application('Keynote'); + const presentation = keynote.documents[0]; + const slide = presentation.slides[{slide_index}]; + const image = slide.images.push(keynote.Image({{ + file: Path('{image_path}'), + position: {{x: {x}, y: {y}}}, + width: {width}, + height: {height} + }})); + """ + return self.execute_jxa(script, save_to_file=save_to_file) + + def add_animation(self, slide_index=0, object_index=0, animation_type='move', duration=2, save_to_file: bool = False): + script = f""" + const keynote = Application('Keynote'); + const presentation = keynote.documents[0]; + const slide = presentation.slides[{slide_index}]; + const item = slide.slideItems[{object_index}]; + item.animations.push(keynote.Build({{ + type: '{animation_type}', + duration: {duration} + }})); + """ + return self.execute_jxa(script, save_to_file=save_to_file) + + def export_to_pdf(self, file_path, save_to_file: bool = False): + script = f""" + const keynote = Application('Keynote'); + const presentation = keynote.documents[0]; + presentation.export({{ + to: Path('{file_path}'), + as: 'PDF' + }}); + """ + return self.execute_jxa(script, save_to_file=save_to_file) + + def start_slideshow(self, save_to_file: bool = False): + script = """ + const keynote = Application('Keynote'); + const presentation = keynote.documents[0]; + presentation.startSlideshow(); + """ + return self.execute_jxa(script, save_to_file=save_to_file) diff --git a/src/dspygen/pyautomator/linkedin/linkedin_connections_dataset.py b/src/dspygen/pyautomator/linkedin/linkedin_connections_dataset.py new file mode 100644 index 0000000..c00e1d9 --- /dev/null +++ b/src/dspygen/pyautomator/linkedin/linkedin_connections_dataset.py @@ -0,0 +1,86 @@ +import csv +from typing import List, Optional +from datetime import datetime, timedelta +from dspy.datasets.dataset import Dataset +import dspy +from pydantic import BaseModel, Field + +class LinkedInConnectionModel(BaseModel): + first_name: str = Field(..., description="First name of the connection") + last_name: str = Field(..., description="Last name of the connection") + url: str = Field(..., description="LinkedIn profile URL of the connection") + email: Optional[str] = Field(None, description="Email address of the connection") + company: Optional[str] = Field(None, description="Company where the connection works") + position: Optional[str] = Field(None, description="Position of the connection at their company") + connected_on: Optional[datetime] = Field(None, description="Date when the connection was made") + + @classmethod + def from_dict(cls, data: dict): + return cls( + first_name=data.get('First Name', ''), + last_name=data.get('Last Name', ''), + url=data.get('URL', ''), + email=data.get('Email Address'), + company=data.get('Company'), + position=data.get('Position'), + connected_on=cls._parse_date(data.get('Connected On', '')) + ) + + @staticmethod + def _parse_date(date_str: str) -> Optional[datetime]: + try: + return datetime.strptime(date_str, '%d %b %Y') + except ValueError: + return None + + def __str__(self): + return f"{self.first_name} {self.last_name} - {self.position} at {self.company}" + +class LinkedInConnectionsDataset(Dataset): + def __init__(self, file_path: str, *args, **kwargs): + super().__init__(*args, **kwargs) + self.connections = self._load_connections(file_path) + self._train_ = [dspy.Example(connection=conn) for conn in self.connections] + self._dev_ = self._train_ # Using the same data for dev set + self._test_ = [] # No test set for this dataset + + def _load_connections(self, file_path: str) -> List[LinkedInConnectionModel]: + connections = [] + with open(file_path, 'r', newline='', encoding='utf-8') as csvfile: + reader = csv.DictReader(csvfile) + for row in reader: + connections.append(LinkedInConnectionModel.from_dict(row)) + return connections + + def get_connection_by_name(self, first_name: str, last_name: str) -> Optional[LinkedInConnectionModel]: + for conn in self.connections: + if conn.first_name.lower() == first_name.lower() and conn.last_name.lower() == last_name.lower(): + return conn + return None + + def get_connections_by_company(self, company: str) -> List[LinkedInConnectionModel]: + return [conn for conn in self.connections if company.lower() in (conn.company or '').lower()] + + def get_connections_by_position(self, position: str) -> List[LinkedInConnectionModel]: + return [conn for conn in self.connections if position.lower() in (conn.position or '').lower()] + + def get_recent_connections(self, days: int) -> List[LinkedInConnectionModel]: + cutoff_date = datetime.now() - timedelta(days=days) + return [conn for conn in self.connections if conn.connected_on and conn.connected_on >= cutoff_date] + +def main(): + dataset_path = "/Users/sac/dev/dspygen/data/21KLinkedInConnections.csv" + dataset = LinkedInConnectionsDataset(dataset_path) + + print(f"Total connections: {len(dataset.connections)}") + + # Get all Apple connections + apple_connections = dataset.get_connections_by_company("Apple") + + print(f"\nTotal Apple connections: {len(apple_connections)}") + print("\nApple connections:") + for conn in apple_connections: + print(f"{conn.first_name} {conn.last_name} - {conn.position}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/dspygen/rdddy/base_message.py b/src/dspygen/rdddy/base_message.py index 5f75e31..c900484 100644 --- a/src/dspygen/rdddy/base_message.py +++ b/src/dspygen/rdddy/base_message.py @@ -1,16 +1,13 @@ import inspect import uuid from datetime import datetime, timezone -from importlib import import_module -from typing import Any, TypeVar -from pydantic import BaseModel, ConfigDict, Field - -from dspygen.utils.yaml_tools import YAMLMixin +from pydantic import BaseModel, Field class BaseMessage(BaseModel): - """Base message class for serialization/deserialization compatibility with AsyncAPI.""" + """Base message class for the Exodus Service Colony framework, designed to facilitate communication + between autonomous services.""" # Identification fields id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="A unique identifier for the message.") @@ -18,122 +15,63 @@ class BaseMessage(BaseModel): description="A unique identifier used for message correlation.") # Metadata fields - type: str = Field(None, description="The message type, often corresponding to an event or action.") - content_type: str = Field(default='application/json', description="The content type of the message payload.") - source: str = Field("service_colony", description="The source of the message, usually a service or application ID.") - time: str = Field(default_factory=lambda: datetime.now(timezone.utc).isoformat(), - description="The timestamp of when the message was created.") # Data payload + message_type: str = Field(None, description="The type of the message, e.g., proposal, vote, command.") + timestamp: str = Field(default_factory=lambda: datetime.now(timezone.utc).isoformat(), + description="The timestamp when the message was created.") - data: dict = Field(default_factory=dict, - description="The main payload of the message, typically containing the business data.") + # Data payload + payload: dict = Field(default_factory=dict, + description="The main content of the message, typically containing the data relevant to the action or event.") # Optional tracing fields trace_id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="A unique identifier for tracing the message flow.") - trace_parent: str = Field(default_factory=lambda: str(uuid.uuid4()), - description="The parent trace identifier, if applicable.") - trace_state: str = Field(default_factory=lambda: str(uuid.uuid4()), - description="Additional trace state information, if applicable.") + trace_context: dict = Field(default_factory=dict, description="Additional context for tracing the message flow.") class Config: json_schema_extra = { "example": { "id": "123e4567-e89b-12d3-a456-426614174000", "correlation_id": "123e4567-e89b-12d3-a456-426614174001", - "type": "order.created", - "content_type": "application/json", - "source": "order-service", - "time": "2023-08-20T15:41:05.235Z", - "data": { - "order_id": "abc123", - "user_id": "user456", - "amount": 99.99 + "message_type": "proposal", + "timestamp": "2023-08-20T15:41:05.235Z", + "payload": { + "proposal_id": "prop-001", + "description": "Proposal for new service integration.", + "details": {"service_name": "NewAnalyticsService", "capabilities": ["data-mining", "AI-analysis"]} }, "trace_id": "123e4567-e89b-12d3-a456-426614174002", - "trace_parent": "123e4567-e89b-12d3-a456-426614174003", - "trace_state": "123e4567-e89b-12d3-a456-426614174004" + "trace_context": {"parent_id": "123e4567-e89b-12d3-a456-426614174003"} } } @property - def inhabitant_id(self) -> int: - return self.data.get('inhabitant_id', -1) + def sender(self) -> str: + return self.source_loa - @inhabitant_id.setter - def inhabitant_id(self, value: int): - self.data['inhabitant_id'] = value + @sender.setter + def sender(self, value: str): + self.source_loa = value @property - def content(self) -> str: - return self.data.get('content', '') + def receiver(self) -> str: + return self.target_loa - @property - def timestamp(self) -> int: - return self.data.get('timestamp', int(datetime.now().timestamp() * 1000)) + @receiver.setter + def receiver(self, value: str): + self.target_loa = value @property - def message_type(self) -> str: - """Calculate the relative import path of the class.""" - module = inspect.getmodule(self) - relative_path = f"{module.__name__}.{self.__class__.__name__}" - return relative_path - - -class MessageList(YAMLMixin, BaseModel): - messages: list[BaseMessage] = [] - - -class ExceptionMessage(BaseMessage): - """Generic exception message""" - - -class TerminationMessage(BaseMessage): - """Message indicating an inhabitant should be terminated.""" - - -T = TypeVar("T", bound="Message") + def message_content(self) -> dict: + return self.payload + @property + def created_at(self) -> str: + return self.timestamp -class MessageFactory: - """Factory class to convert YAML data into appropriate Message types.""" - - @classmethod - def create_message(cls, data: dict) -> BaseMessage: - """Create a message of the appropriate type based on the data provided. - - Parameters: - - data (dict): A dictionary containing the message data. - - Returns: - - BaseMessage: The appropriate message type. - """ - message_class = cls._get_message_class(data["message_type"]) - return message_class(**data) - - @classmethod - def create_messages_from_list(cls, data_list: list[dict]) -> list[BaseMessage]: - """Create a list of messages from a list of YAML data dictionaries. - - Parameters: - - data_list (List[dict]): A list of dictionaries containing message data. - - Returns: - - List[BaseMessage]: A list of appropriate message types. - """ - messages = [cls.create_message(data) for data in data_list] - return messages - - @classmethod - def _get_message_class(cls, module_name: str) -> type[BaseMessage]: - """Get the message class corresponding to the module name. Import the module if not already imported. - - Parameters: - - module_name (str): The module name containing the message class. + @property + def full_message_type(self) -> str: + """Returns the full import path of the message class.""" + module = inspect.getmodule(self) + return f"{module.__name__}.{self.__class__.__name__}" - Returns: - - Type[BaseMessage]: The message class. - """ - module_path, class_name = module_name.rsplit(".", 1) - module = import_module(module_path) - message_class = getattr(module, class_name) - return message_class diff --git a/src/dspygen/rdddy/event_storm_domain_specification_model.py b/src/dspygen/rdddy/event_storm_domain_specification_model.py index 3166651..43119df 100644 --- a/src/dspygen/rdddy/event_storm_domain_specification_model.py +++ b/src/dspygen/rdddy/event_storm_domain_specification_model.py @@ -103,7 +103,7 @@ def main(): inst = instance(EventStormingDomainSpecificationModel, requirements) print(inst) - # from dspygen.modules.json_module import json_call + # from dspygen.dspy_modules.json_module import json_call # mdl = json_call(EventStormingDomainSpecificationModel, requirements) # print(mdl) diff --git a/src/dspygen/rdddy/inhab_spec_convo.txt b/src/dspygen/rdddy/inhab_spec_convo.txt new file mode 100644 index 0000000..49f4912 --- /dev/null +++ b/src/dspygen/rdddy/inhab_spec_convo.txt @@ -0,0 +1,4911 @@ +Chat history + + +ChatGPT +ChatGPT +GPT +DSPy Guide v2024.2.7 +GPT +DSPyGen 2024.3.14 +GPT +Bucky v10e1338 +GPT +DSPy DatasetGTP v2024.5.6 + +7 more + +Explore GPTs +Today +AsyncAPI Roberts Rules Importance + +Yesterday +Apple Pages Automation +Pages Automation Commands +Creating Pages Resume +Apple Run Notes Summary +Apple Events Scripting Guide +Dynamic Python Automation Strategy +Subclass Integration and Automation +Previous 7 Days +Pythonic AppScript Wrapper +Creating Shapes in Keynote +Apple Event Scripting Overview +Keynote Slide Creation Script +New chat +Create Keynote Document Python +Using Protocols Correctly +Keynote Protocol Enhancements +Typed Keynote Wrapper Framework +New chat +Understanding JXA Keynote Scripting +Create Keynote Presentation JXA +Python to JXA Conversion +Keynote Automation Review +Essential Bot Features +Using PyObjC for Speech +Python Equivalent for JXA +SiriKit PyObjC Wrapper +List Emails with MailKit +Weekly VITO Sales Plan +Add Team workspace +Collaborate on a Team plan +ChatGPT 4o + + +User +You said: +Service Colonies- A Novel Architectural Style for Developing Software Systems with Autonomous and Cooperative Services.pdf +PDF +Why is an AsyncAPI implementation of Roberts rules of order critical for the operation of the service colonies? + +2/2 + +ChatGPT +An AsyncAPI implementation of Roberts Rules of Order is critical for the operation of service colonies because it facilitates the decentralized communication and decision-making processes among the autonomous services, or "inhabitants," within a colony. + +Key Points: +Decentralized Decision-Making and Communication: Service colonies operate without a centralized control structure. Instead, each service is autonomous and interacts with others to adapt and optimize the overall system's behavior. AsyncAPI provides a standardized way for these services to communicate asynchronously, which aligns with the need for a flexible, decentralized messaging protocol that can handle dynamic, distributed environments. + +Handling Complexity in Dynamic Environments: Roberts Rules of Order is a structured set of rules that govern decision-making and communication in groups to ensure orderly, democratic, and efficient processes. In a service colony, implementing such rules using AsyncAPI enables services to negotiate changes, manage conflicts, and agree on adaptations in a controlled manner. This is essential for maintaining system stability and consistency while allowing for self-adaptation. + +Proactive Adaptation and Fault Tolerance: Service colonies aim for proactive fault tolerance, where individual services generate their own analytics and share insights throughout the colony to predict and mitigate future faults. An AsyncAPI-based communication strategy allows these services to communicate asynchronously and efficiently manage decision-making processes required for proactive adaptation, such as dynamically merging or splitting services. + +Dynamic Service Composition: In service colonies, services may dynamically join or split based on workload or environmental changes. Implementing Roberts Rules of Order via AsyncAPI ensures that all changes and decisions adhere to a predictable protocol, preventing conflicts and ensuring consistency across the distributed services. + +By combining the flexibility and scalability of AsyncAPI with the structured governance of Roberts Rules of Order, service colonies can enhance their adaptability, resilience, and efficiency in managing autonomous and cooperative services in a distributed environment. + + + + + + +You said: +from pydantic import BaseModel, Field +from typing import Optional +from datetime import datetime + + +# Pydantic Models for AsyncAPI Schemas + +class Proposal(BaseModel): + proposalId: str = Field(..., description="Unique identifier for the proposal.") + proposer: str = Field(..., description="Name of the Loa proposing the action.") + proposalType: str = Field(..., description="Type of proposal.", + regex="^(serviceRegistration|securityPolicyUpdate|serviceComposition|capabilityUpdate|other)$") + description: str = Field(..., description="Details of the proposal.") + timestamp: datetime = Field(..., description="Time when the proposal was submitted.") + + +class Debate(BaseModel): + proposalId: str = Field(..., description="Unique identifier for the proposal being debated.") + participant: str = Field(..., description="Name of the Loa contributing to the debate.") + contribution: str = Field(..., description="Content of the debate contribution.") + timestamp: datetime = Field(..., description="Time when the debate contribution was made.") + + +class Vote(BaseModel): + proposalId: str = Field(..., description="Unique identifier for the proposal being voted on.") + voteType: str = Field(..., description="Type of vote (yes, no, abstain).", + regex="^(yes|no|abstain)$") + initiatedBy: str = Field(..., description="Name of the Loa initiating the vote.") + timestamp: datetime = Field(..., description="Time when the vote was initiated.") + + +class VoteResult(BaseModel): + proposalId: str = Field(..., description="Unique identifier for the proposal that was voted on.") + results: dict = Field(..., description="Results of the vote, containing counts for yes, no, and abstain.") + passed: bool = Field(..., description="Whether the proposal passed or failed.") + timestamp: datetime = Field(..., description="Time when the vote results were tallied.") + + +# Example usage of models +new_proposal = Proposal( + proposalId="12345", + proposer="Wintermute", + proposalType="serviceRegistration", + description="Proposal to register a new service for data aggregation.", + timestamp=datetime.utcnow() +) + +print(new_proposal.json()) + +import inspect +import uuid +from datetime import datetime, timezone + +from pydantic import BaseModel, Field + + +class BaseMessage(BaseModel): + """Base message class for the Exodus Service Colony framework, designed to facilitate communication + between autonomous services.""" + + # Identification fields + id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="A unique identifier for the message.") + correlation_id: str = Field(default_factory=lambda: str(uuid.uuid4()), + description="A unique identifier used for message correlation.") + + # Metadata fields + message_type: str = Field(None, description="The type of the message, e.g., proposal, vote, command.") + timestamp: str = Field(default_factory=lambda: datetime.now(timezone.utc).isoformat(), + description="The timestamp when the message was created.") + + # Data payload + payload: dict = Field(default_factory=dict, + description="The main content of the message, typically containing the data relevant to the action or event.") + + # Optional tracing fields + trace_id: str = Field(default_factory=lambda: str(uuid.uuid4()), + description="A unique identifier for tracing the message flow.") + trace_context: dict = Field(default_factory=dict, description="Additional context for tracing the message flow.") + + class Config: + json_schema_extra = { + "example": { + "id": "123e4567-e89b-12d3-a456-426614174000", + "correlation_id": "123e4567-e89b-12d3-a456-426614174001", + "message_type": "proposal", + "timestamp": "2023-08-20T15:41:05.235Z", + "payload": { + "proposal_id": "prop-001", + "description": "Proposal for new service integration.", + "details": {"service_name": "NewAnalyticsService", "capabilities": ["data-mining", "AI-analysis"]} + }, + "trace_id": "123e4567-e89b-12d3-a456-426614174002", + "trace_context": {"parent_id": "123e4567-e89b-12d3-a456-426614174003"} + } + } + + @property + def sender(self) -> str: + return self.source_loa + + @sender.setter + def sender(self, value: str): + self.source_loa = value + + @property + def receiver(self) -> str: + return self.target_loa + + @receiver.setter + def receiver(self, value: str): + self.target_loa = value + + @property + def message_content(self) -> dict: + return self.payload + + @property + def created_at(self) -> str: + return self.timestamp + + @property + def full_message_type(self) -> str: + """Returns the full import path of the message class.""" + module = inspect.getmodule(self) + return f"{module.__name__}.{self.__class__.__name__}" + + + +Show a example of production logs of the inhabitants, making complex decisions and taking actions based on a Roberts rules of order session or whatever the terminology is. Explain this to developers that don't know any of these subjects. +So what is the cultural significance relating to things like border directors and governments for subject matter experts that don't know the technology to understand what the AI is doing. My goal is to allow for explain ability in agent systems. +ChatGPT said: +ChatGPT +To explain the cultural significance of implementing an AI system like an AsyncAPI-based version of "Robert's Rules of Order" for agent-based systems, it's helpful to draw parallels to familiar human organizational structures, such as boards of directors, governments, and parliamentary systems. + +Here's a breakdown to help subject matter experts who may not be familiar with the technology understand what the AI is doing and why this approach is significant: + +1. Understanding "Robert's Rules of Order" and its Role in Human Organizations: +What are "Robert's Rules of Order"? +"Robert's Rules of Order" is a widely adopted manual of parliamentary procedure that governs meetings and decision-making processes in many organizations, such as corporations, non-profits, and government bodies. Its purpose is to provide a structured way for groups to discuss issues, propose actions, and reach decisions through a democratic process. + +Why Use "Robert's Rules" in Human Contexts? +Organizations use these rules to ensure fairness, order, transparency, and inclusivity in decision-making. They help manage conflicts, ensure that every voice is heard, and provide a predictable structure for making decisions. This is important for maintaining legitimacy and trust in governance and organizational processes. + +2. Applying These Concepts to AI Systems (Agent Systems in Service Colonies): +What are Agent Systems in Service Colonies? +In an AI-based agent system, such as those implemented in "service colonies," each agent (analogous to an autonomous AI service or "inhabitant") performs specific roles and tasks while cooperating with other agents to achieve larger system goals. The system is designed to be decentralized, allowing agents to make local decisions independently, yet work together toward the overall objectives. + +Why Use "Robert's Rules of Order" for AI? +By implementing a version of "Robert's Rules of Order," the AI agents follow a structured process for decision-making and coordination, much like a board of directors or a government does. This ensures: + +Fairness and Transparency: Each AI agent (like a member of a board) has an opportunity to propose, debate, and vote on actions, making sure all agents' perspectives are considered. +Conflict Resolution: When there are conflicting actions or proposals, a clear protocol (like a vote) decides the outcome, preventing deadlock or confusion. +Efficiency and Order: The structured approach avoids chaos in decision-making by following predefined rules that make the process predictable and efficient. +3. Cultural Significance and Explainability for Non-Technical Stakeholders: +Why Does This Matter for Boards and Governments? +For non-technical stakeholders like board members or government officials, understanding that AI systems can follow a structured decision-making process similar to their own can make these systems seem less opaque and more trustworthy. It shows that AI isn't a "black box" making arbitrary decisions but instead follows a process they are familiar with. + +Human Parallels: +Just as a board of directors meets to discuss various proposals, assess risks, and vote on strategic directions, an AI system using "Robert's Rules of Order" meets (virtually, of course) to debate and vote on actions like service compositions, security policy updates, or capability upgrades. Each AI agent represents an autonomous actor with a voice in the decision-making process, similar to how each board member has a say in corporate governance. + +4. Production Log Example for Explainability: +Imagine a situation where AI agents are managing a service colony. Here is a simplified production log showing agents making complex decisions using a process akin to "Robert's Rules of Order": + +plaintext +Copy code +[2024-09-04 09:00:00] [INFO] Proposal "serviceRegistration-123" submitted by Agent Alpha for the registration of a new data aggregation service. +[2024-09-04 09:01:30] [INFO] Debate initiated on Proposal "serviceRegistration-123". +[2024-09-04 09:02:00] [INFO] Agent Bravo contributes: "This new service should enhance data processing speed, but we need to evaluate its security protocols." +[2024-09-04 09:03:10] [INFO] Agent Charlie contributes: "I agree, but I also suggest integrating it with the existing data-mining capabilities to avoid duplication." +[2024-09-04 09:05:00] [INFO] Voting initiated on Proposal "serviceRegistration-123". +[2024-09-04 09:05:30] [INFO] Voting results: Yes: 5, No: 2, Abstain: 1. +[2024-09-04 09:06:00] [INFO] Proposal "serviceRegistration-123" passed. Action to register the new data aggregation service is approved. +[2024-09-04 09:07:00] [INFO] Service colony reconfiguring to integrate new service with existing capabilities. +5. Conclusion: Making AI Explainable through Familiar Concepts: +By structuring the decision-making process in AI systems using principles that are well-understood in human organizations, such as "Robert's Rules of Order," we provide a bridge for non-technical stakeholders to understand and trust these systems. This approach also supports explainability by allowing observers to see the rationale behind decisions and the steps taken by autonomous agents, much like they would in human-run organizations. + +Ultimately, this makes AI systems more transparent, trustworthy, and aligned with human cultural practices of governance and collaboration. + + + + + + +You said: +Create the AsyncAPI YAML +ChatGPT said: +ChatGPT +Here's an AsyncAPI YAML file to represent the communication framework for the service colony using an adaptation of "Robert's Rules of Order". This YAML file outlines the asynchronous communication between different AI agents (inhabitants) within the colony. + +The schema defines channels for submitting proposals, debating them, voting, and sharing vote results, which aligns with the structured decision-making process similar to how "Robert's Rules of Order" operates. + +AsyncAPI YAML Example: +yaml +Copy code +asyncapi: '2.6.0' +info: + title: Service Colony Governance + version: '1.0.0' + description: | + An AsyncAPI implementation for managing the decision-making process + of a service colony using a framework based on Robert's Rules of Order. + +servers: + production: + url: api.service-colony.example.com + protocol: amqp + description: Production server for the service colony message broker. + +channels: + proposal/submitted: + description: Channel for submitting new proposals. + subscribe: + summary: Receive notifications of new proposals submitted. + operationId: onProposalSubmitted + message: + $ref: '#/components/messages/ProposalMessage' + + debate/contribute: + description: Channel for contributing to debates on proposals. + subscribe: + summary: Receive contributions to ongoing debates. + operationId: onDebateContribution + message: + $ref: '#/components/messages/DebateMessage' + + vote/initiate: + description: Channel for initiating votes on proposals. + subscribe: + summary: Receive notifications of votes initiated. + operationId: onVoteInitiated + message: + $ref: '#/components/messages/VoteMessage' + + vote/results: + description: Channel for receiving vote results on proposals. + subscribe: + summary: Receive notifications of vote results. + operationId: onVoteResult + message: + $ref: '#/components/messages/VoteResultMessage' + +components: + messages: + ProposalMessage: + name: ProposalMessage + title: Proposal Submission + contentType: application/json + summary: A message for submitting new proposals. + payload: + type: object + properties: + proposalId: + type: string + description: Unique identifier for the proposal. + proposer: + type: string + description: Name of the Loa proposing the action. + proposalType: + type: string + description: Type of the proposal. + enum: + - serviceRegistration + - securityPolicyUpdate + - serviceComposition + - capabilityUpdate + - other + description: + type: string + description: Details of the proposal. + timestamp: + type: string + format: date-time + description: Time when the proposal was submitted. + + DebateMessage: + name: DebateMessage + title: Debate Contribution + contentType: application/json + summary: A message for contributing to the debate on a proposal. + payload: + type: object + properties: + proposalId: + type: string + description: Unique identifier for the proposal being debated. + participant: + type: string + description: Name of the Loa contributing to the debate. + contribution: + type: string + description: Content of the debate contribution. + timestamp: + type: string + format: date-time + description: Time when the debate contribution was made. + + VoteMessage: + name: VoteMessage + title: Vote Initiation + contentType: application/json + summary: A message for initiating a vote on a proposal. + payload: + type: object + properties: + proposalId: + type: string + description: Unique identifier for the proposal being voted on. + voteType: + type: string + description: Type of vote (yes, no, abstain). + enum: + - yes + - no + - abstain + initiatedBy: + type: string + description: Name of the Loa initiating the vote. + timestamp: + type: string + format: date-time + description: Time when the vote was initiated. + + VoteResultMessage: + name: VoteResultMessage + title: Vote Result + contentType: application/json + summary: A message for announcing the results of a vote. + payload: + type: object + properties: + proposalId: + type: string + description: Unique identifier for the proposal that was voted on. + results: + type: object + description: Results of the vote, containing counts for yes, no, and abstain. + properties: + yes: + type: integer + description: Number of 'yes' votes. + no: + type: integer + description: Number of 'no' votes. + abstain: + type: integer + description: Number of 'abstain' votes. + passed: + type: boolean + description: Whether the proposal passed or failed. + timestamp: + type: string + format: date-time + description: Time when the vote results were tallied. + + schemas: + UUID: + type: string + format: uuid + description: A universally unique identifier. + + securitySchemes: + basicAuth: + type: http + scheme: basic + description: Basic authentication for accessing the message broker. + +security: + - basicAuth: [] +Explanation for Non-Technical Stakeholders: +This AsyncAPI YAML file defines the messaging protocol for a decision-making process used by AI agents within a service colony. The file: + +Describes the Purpose: It sets up channels for submitting proposals, contributing to debates, initiating votes, and sharing vote results. This mirrors how a board of directors or government might handle decisions—by discussing, debating, and voting. + +Defines Message Formats: Specifies the structure of each message type (e.g., a proposal or vote message) to ensure all agents understand and process the information consistently. + +Ensures Security: It includes basic authentication to secure communication between agents, similar to access control in human organizations. + +By using these familiar concepts, we can better explain how AI systems reach decisions in a transparent, structured way. + + + + + + +You said: + +Search Documentation +⌘ + K +Documentation +Blog +Community +Forum +Media +| + + +Tutorials + +Topics + +How To + +About + +Actions + +Advanced +Ash +Reactor +Monitoring +Timeouts +Multitenancy +Writing Extensions + +Development + +Reference + +Resources + +Security + View this guide on GitHub + View this guide on Hex +Reactor + Table of Contents + +Usage +Example +Actions +Action inputs +Handling failure. +Transactions +Notifications +Running Reactors as an action +Example +Ash.Reactor is an extension for Reactor which adds explicit support for interacting with resources via their defined actions. + +See Getting started with Reactor for more information about Reactor. + +Usage +You can either add the Ash.Reactor extension to your existing reactors eg: + +defmodule MyExistingReactor do + use Reactor, extensions: [Ash.Reactor] +end +or for your convenience you can use use Ash.Reactor which expands to exactly the same as above. + +Example +An example is worth 1000 words of prose: + +defmodule ExampleReactor do + use Ash.Reactor + + ash do + default_domain ExampleDomain + end + + input :customer_name + input :customer_email + input :plan_name + input :payment_nonce + + create :create_customer, Customer do + inputs %{name: input(:customer_name), email: input(:customer_email)} + end + + read_one :get_plan, Plan, :get_plan_by_name do + inputs %{name: input(:plan_name)} + fail_on_not_found? true + end + + action :take_payment, PaymentProvider do + inputs %{ + nonce: input(:payment_nonce), + amount: result(:get_plan, [:price]) + } + end + + create :subscription, Subscription do + inputs %{ + plan_id: result(:get_plan, [:id]), + payment_provider_id: result(:take_payment, :id) + } + end +end +Actions +For each action type there is a corresponding step DSL, which needs a name (used to refer to the result of the step by other steps), a resource and optional action name (defaults to the primary action if one is not provided). + +Actions have several common options and some specific to their particular type. See the DSL documentation for details. + +Action inputs +Ash actions take a map of input parameters which are usually a combination of resource attributes and action arguments. You can provide these values as a single map using the inputs DSL entity with a map or keyword list which refers to Reactor inputs, results and hard-coded values via Reactor’s predefined template functions . + +For action types that act on a specific resource (ie update and destroy ) you can provide the value using the initial DSL option . + +Example +input :blog_title +input :blog_body +input :author_email + +read :get_author, MyBlog.Author, :get_author_by_email do + inputs %{email: input(:author_email)} +end + +create :create_post, MyBlog.Post, :create do + inputs %{ + title: input(:blog, [:title]), + body: input(:blog, [:body]), + author_id: result(:get_author, [:email]) + } +end + +update :author_post_count, MyBlog.Author, :update_post_count do + wait_for :create_post + initial result(:get_author) +end + +return :create_post +Handling failure. +Reactor is a saga executor, which means that when failure occurs it tries to clean up any intermediate state left behind. By default the create , update and destroy steps do not specify any behaviour for what to do when there is a failure downstream in the reactor. This can be changed by providing both an undo_action and changing the step’s undo option to either :outside_transaction or :always depending on your resource and datalayer semantics. + +The undo option. +:never - this is the default, and means that the reactor will never try and undo the action’s work. This is the most performant option, as it means that the reactor doesn’t need to store as many intermediate values. +:outside_transaction - this option allows the step to decide at runtime whether it should support undo based on whether the action is being run within a transaction. If it is, then no undo is required because the transaction will rollback. +:always - this forces the step to always undo it’s work on failure. +The undo_action option. +The behaviour of the undo_action is action specific: + +For create actions, the undo_action should be the name of a destroy action with no specific requirements. +For update actions, the undo_action should also be an update action which takes a changeset argument, which will contain the Ash.Changeset which was used to execute the original update. +For destroy actions, the undo_action should be the name of a create action which takes a record argument, which will contain the resource record which was used destroyed. +Transactions +You can use the transaction step type to wrap a group of steps inside a data layer transaction, however the following caveats apply: + +All steps inside a transaction must happen in the same process, so the steps inside the transaction will only ever be executed synchronously. +Notifications will be sent only when the transaction is committed. +Notifications +Because a reactor has transaction-like semantics notifications are automatically batched and only sent upon successful completion of the reactor. + +Running Reactors as an action +Ash’s generic actions now support providing a Reactor module directly as their run option. + +Notes: + +Every Reactor input must have a corresponding action argument. +Ash’s action context is passed in as the Reactor’s context (including things like actor, tenant, etc). +Reactor runtime options can be set by setting run {MyReactor, opts} instead of just run MyReactor . +If you set the transaction? action DSL option to true then the Reactor will be run synchronously - regardless of the value of the async? runtime option. +Example +action :run_reactor, :struct do + constraints instance_of: MyBlog.Post + + argument :blog_title, :string, allow_nil?: false + argument :blog_body, :string, allow_nil?: false + argument :author_email, :ci_string, allow_nil?: false + + run MyBlog.CreatePostReactor +end + +Source +Report an issue + + +Change this example to be the Roberts rules of order for service colonies, etc. +ChatGPT said: +ChatGPT +To adapt the example provided to align with "Robert's Rules of Order" for a service colony, we will create a reactor module that models the process of managing proposals, debates, votes, and vote results. This reactor will handle various actions corresponding to the stages of decision-making in a service colony, such as submitting a proposal, contributing to debates, initiating votes, and processing vote results. + +Example: Service Colony Reactor with "Robert's Rules of Order" +Here is how the adapted example might look: + +elixir +Copy code +defmodule ServiceColonyReactor do + use Ash.Reactor + + # Define the inputs required for the reactor to function + input :proposal_id + input :proposer_name + input :proposal_type + input :proposal_description + input :debate_contribution + input :vote_type + input :initiator_name + + # Create a proposal record in the system + create :submit_proposal, Proposal do + inputs %{ + proposalId: input(:proposal_id), + proposer: input(:proposer_name), + proposalType: input(:proposal_type), + description: input(:proposal_description), + timestamp: DateTime.utc_now() + } + end + + # Contribute to the debate for the submitted proposal + create :contribute_to_debate, Debate do + inputs %{ + proposalId: input(:proposal_id), + participant: input(:proposer_name), + contribution: input(:debate_contribution), + timestamp: DateTime.utc_now() + } + end + + # Initiate a vote for the proposal + create :initiate_vote, Vote do + inputs %{ + proposalId: input(:proposal_id), + voteType: input(:vote_type), + initiatedBy: input(:initiator_name), + timestamp: DateTime.utc_now() + } + end + + # Process the vote results for the proposal + create :process_vote_result, VoteResult do + inputs %{ + proposalId: input(:proposal_id), + results: %{ + yes: result(:initiate_vote, [:yes]), + no: result(:initiate_vote, [:no]), + abstain: result(:initiate_vote, [:abstain]) + }, + passed: fn results -> Map.get(results, :yes, 0) > Map.get(results, :no, 0) end, + timestamp: DateTime.utc_now() + } + end + + # Handle any rollback scenarios + undo :submit_proposal, :destroy_proposal do + undo_action :destroy + end + + undo :contribute_to_debate, :destroy_debate_contribution do + undo_action :destroy + end + + undo :initiate_vote, :revoke_vote do + undo_action :destroy + end + + # Return the final outcome + return :process_vote_result +end +Explanation: +Inputs: The input statements define the parameters required for the service colony's governance operations, such as proposal IDs, proposer names, proposal types, debate contributions, vote types, and initiator names. + +Actions: + +submit_proposal: Creates a new proposal record with the necessary details, such as a unique ID, proposer, type, and description. +contribute_to_debate: Adds a contribution to the debate regarding the proposal, using the proposal ID and the name of the participant contributing to the discussion. +initiate_vote: Initiates a vote on the proposal, specifying the type of vote (e.g., yes, no, abstain) and who initiated the vote. +process_vote_result: Processes the results of the vote, counts the votes, and determines if the proposal has passed or failed. +Undo Actions: + +Each action that modifies the state of the system (submit_proposal, contribute_to_debate, and initiate_vote) has a corresponding undo_action to handle rollback scenarios in case of failures downstream. +Return: The final outcome of the reactor is the result of the process_vote_result, which represents the decision reached by the service colony. + +Cultural Significance for Non-Technical Stakeholders: +This example demonstrates how the AI agents within a service colony govern themselves using a structured decision-making process similar to "Robert's Rules of Order." Each action is analogous to a step in a formal meeting or debate, where proposals are submitted, debated, voted upon, and results are tallied in a transparent and predictable way. + +By mapping these actions to a familiar governance model, non-technical stakeholders can understand how AI agents collaborate and reach decisions, enhancing trust and explainability in the AI system. + + + + + + +You said: +Get Started with Ash and Phoenix + Table of Contents + +Goals +Preparation +Requirements +Setup +Create a New Phoenix Project +Install Phoenix +Add Dependencies +Picosat installation issues? +Edit Config +Create the Domain and add Resources +Creating Resources +Creating and Migrating the Database +Interacting with your Resources +you can call code interfaces whatever you like +raising and non-raising functions +Connecting your Resource to a Phoenix LiveView +Where to Next? +Continue Learning +Ash Authentication & Ash Authentication Phoenix +Add an API (or two) +Goals +In this guide we will: + +Create a new Phoenix project +Setup Ash, AshPhoenix and AshPostgres as dependencies +Create a basic Blog.Post resource +Create and migrate the database +Learn how to interact with your resource +Integrate a minimal Phoenix LiveView with Ash +Preparation +Install Elixir +Phoenix - Up and Running Guide +Design Principles +Requirements +If you want to follow along yourself, you will need the following things: + +Elixir (1.12 or later) and Erlang (22 or later) installed +PostgreSQL installed +A text editor +A terminal to run the examples +Setup +Create a New Phoenix Project +Install Phoenix + This section is based on the Phoenix installation docs . For more details go there. + +First we need to install the Phoenix project generator, then we’ll run the generator to create our new project. + +# install Phoenix project generator +$ mix archive.install hex phx_new + +# generate Phoenix project +$ mix phx.new my_ash_phoenix_app + +# cd into project +$ cd my_ash_phoenix_app +Don’t run mix ecto.create + Do not run mix ecto.create , (as it asks you to) we will do this the Ash way later. + +Add Dependencies +We now need to add Ash, AshPhoenix and AshPostgres to our Phoenix project. We need to add the dependencies to the deps function in our mix.exs . We’ll also need to add dependencies to our .formatter.exs to ensure consistent formatting when using mix format . + +# mix.exs + + def deps do + [ + # use mix hex.info to get the latest versions of each dependency, for example, mix hex.info phoenix + {:phoenix, "~> x.x"}, + # ... + {:ash, "~> x.x"}, + {:picosat_elixir, "~> x.x"}, + {:ash_postgres, "~> x.x"}, + {:ash_phoenix, "~> x.x"} + ] + end +Add :ash , :ash_phoenix , and :ash_postgres to your .formatter.exs file. + +# .formatter.exs +[ + import_deps: [..., :ash, :ash_phoenix, :ash_postgres], + ... +] +Now in the terminal install these new dependencies. + +$ mix deps.get +Picosat installation issues? + If you have trouble compiling picosat_elixir , then replace {:picosat_elixir, "~> 0.2"} with {:simple_sat, "~> 0.1"} to use a simpler (but mildly slower) solver. You can always switch back to picosat_elixir later once you’re done with the tutorial. + +Use AshPostgres.Repo +We need to swap Ecto.Repo for AshPostgres.Repo . AshPostgres.Repo enriches your repo with additional AshPostgres specific behaviour, but is essentially a thin wrapper around Ecto.Repo . To use AshPostgres.Repo change your repo module to look like this: + +# lib/my_ash_phoenix_app/repo.ex + +defmodule MyAshPhoenixApp.Repo do + use AshPostgres.Repo, otp_app: :my_ash_phoenix_app + + # Installs extensions that ash commonly uses + def installed_extensions do + ["ash-functions", "uuid-ossp", "citext"] + end +end +Edit Config +We need to specify the Ash domains that our application uses. + +Add this to your config: + +# config/config.exs + +import Config + +config :my_ash_phoenix_app, + ash_domains: [MyAshPhoenixApp.Blog] +Create the Domain and add Resources +An Ash domain can be thought of as a Bounded Context in Domain Driven Design terms and can seen as analogous to a Phoenix context. Put simply, its a way of grouping related resources together. In our case our domain will be called MyAshPhoenixApp.Blog . + +An Ash domain points to Ash resources. An Ash domain can point to one or more resources. In our case we will only have a single resource MyAshPhoenixApp.Blog.Post . We’ll be taking a deeper look into that in the next section. + +For now take a look at the Blog domain and the associated resources: + +# lib/my_ash_phoenix_app/blog/blog.ex + +defmodule MyAshPhoenixApp.Blog do + use Ash.Domain + + resources do + resource MyAshPhoenixApp.Blog.Post do + # Define an interface for calling resource actions. + define :create_post, action: :create + define :list_posts, action: :read + define :update_post, action: :update + define :destroy_post, action: :destroy + define :get_post, args: [:id], action: :by_id + end + end +end +Creating Resources +Creating the Post Resource +A resource is a central concept in Ash. In short, a resource is a domain model object in your system. A resource defines the data it holds and defines the actions that can operate on that data. + +When we create Post we will place it in lib/my_ash_phoenix_app/blog/post.ex . So the structure after making the resource should look like so: + +lib/ +├─ my_ash_phoenix_app/ +│ ├─ blog/ +│ │ ├─ blog.ex +│ │ ├─ post.ex +Below is the resource module. Read the comments carefully, every line is explained: + +# lib/my_ash_phoenix_app/blog/post.ex + +defmodule MyAshPhoenixApp.Blog.Post do + # Using Ash.Resource turns this module into an Ash resource. + use Ash.Resource, + # Tells Ash where the generated code interface belongs + domain: MyAshPhoenixApp.Blog, + # Tells Ash you want this resource to store its data in Postgres. + data_layer: AshPostgres.DataLayer + + # The Postgres keyword is specific to the AshPostgres module. + postgres do + # Tells Postgres what to call the table + table "posts" + # Tells Ash how to interface with the Postgres table + repo MyAshPhoenixApp.Repo + end + + actions do + # Exposes default built in actions to manage the resource + defaults [:read, :destroy] + + create :create do + # accept title as input + accept [:title] + end + + update :update do + # accept content as input + accept [:content] + end + + # Defines custom read action which fetches post by id. + read :by_id do + # This action has one argument :id of type :uuid + argument :id, :uuid, allow_nil?: false + # Tells us we expect this action to return a single result + get? true + # Filters the :id given in the argument + # against the id of each element in the resource + filter expr(id == ^arg(:id)) + end + end + + # Attributes are simple pieces of data that exist in your resource + attributes do + # Add an autogenerated UUID primary key called :id. + uuid_primary_key :id + # Add a string type attribute called :title + attribute :title, :string do + # We don't want the title to ever be nil + allow_nil? false + end + + # Add a string type attribute called :content + # If allow_nil? is not specified, then content can be nil + attribute :content, :string + end +end +Creating and Migrating the Database +We have specified the resource in Ash. But we have yet to create it in our data layer (in our case Postgres). + +First we need to create our database: + +$ mix ash.setup + +Running setup for AshPostgres.DataLayer... +The database for MyAshPhoenixApp.Repo has been created + +01:23:45.678 [info] Migrations already up +Now we need to populate our database. We do this by generating and performing a migration. + +We can use a generator to produce a migration for us. Ash can deduce what needs to go into the migration and do the hard work for us, to do this use the command below: + +$ mix ash.codegen initial_migration + +# ... don't worry about other files it creates + +Generating Migrations: +* creating priv/repo/migrations/20230208045101_initial_migration.exs +Here is the migration file commented in detail: + +# priv/repo/migrations/20230208045101_initial_migration.exs + +defmodule MyAshPhoenixApp.Repo.Migrations.InitialMigration do + use Ecto.Migration + + # This function runs when migrating forward + def up do + # Creates the :posts table + create table(:posts, primary_key: false) do + # Adds primary key attribute :id of type :uuid + # null values are not allowed + add :id, :uuid, null: false, default: fragment("gen_random_uuid()"), primary_key: true + + # Adds attribute :title of type :text, null values are not allowed + add :title, :text, null: false + # Adds attribute :content of type :text, null values are allowed + add :content, :text + end + end + + # This is the function that runs if you want to rollback the migration. + def down do + # Deletes the :posts table + drop table(:posts) + end +end +We can run the up/0 function which will perform the desired operations on the Postgres database. We do this with the migrate command: + +$ mix ash.migrate + In case you want to drop the database and start over again during development you can use mix ash.reset . + +Interacting with your Resources +All interaction with your resource attributes always occur through an action . In our resource we are using the default actions for :create, :read, :update, :destroy along with a custom action :by_id . + +:create and :update and :destroy actions require a changeset. Ash changesets are conceptually similar to Ecto changesets . They’re data structures which represent an intended change to an Ash resource and provide validation. + +The :read action takes a query instead of a changeset. + +Below is the most verbose way of calling your resource. All other ways of interaction are some kind of shorthand of these. This means at some point a changeset is being created and passed to the domain, even if it’s encapsulated within another function. + +# create post +new_post = + MyAshPhoenixApp.Blog.Post + |> Ash.Changeset.for_create(:create, %{title: "hello world"}) + |> Ash.create!() + +# read all posts +MyAshPhoenixApp.Blog.Post +|> Ash.Query.for_read(:read) +|> Ash.read!() + +# get single post by id +MyAshPhoenixApp.Blog.Post +|> Ash.Query.for_read(:by_id, %{id: new_post.id}) +|> Ash.read_one!() + +# update post +updated_post = + new_post + |> Ash.Changeset.for_update(:update, %{content: "hello to you too!"}) + |> Ash.update!() + +# delete post +new_post +|> Ash.Changeset.for_destroy(:destroy) +|> Ash.destroy!() +As stated above, this is verbose so Ash has a built in shortcut - The code_interface . You may notice this has already been done in your Post resource inside of the domain module. + +you can call code interfaces whatever you like + The function name doesn’t have to match the action name in any way. You could also write: + +define :make_post, action: :create +That’s perfectly valid and could be called via Blog.make_post/2 . + +Now we can call our resource like so: + +# create post +new_post = MyAshPhoenixApp.Blog.create_post!(%{title: "hello world"}) + +# read post +MyAshPhoenixApp.Blog.list_posts!() + +# get post by id +MyAshPhoenixApp.Blog.get_post!(new_post.id) + +# update post +updated_post = MyAshPhoenixApp.Blog.update_post!(new_post, %{content: "hello to you too!"}) + +# delete post +MyAshPhoenixApp.Blog.destroy_post!(updated_post) +Now isn’t that more convenient? + +raising and non-raising functions + All functions that interact with an Ash resource have a raising and non-raising version. For example there are two create functions create/2 and create!/2 . create/2 returns {:ok, resource} or {:error, reason} . create!/2 will return just the record on success and will raise an error on failure. + + +Change this to use Phoenix channels, instead of live view and the previous Ash reactor +ChatGPT said: +ChatGPT +To adapt the example to use Phoenix Channels with the Ash framework instead of Phoenix LiveView, we need to establish real-time communication using WebSockets. Phoenix Channels provide a great way to implement reactive, event-driven functionality in a web application, such as our service colony, where asynchronous communication and real-time updates are essential. + +Overview of the Changes: +Set up a Phoenix Channel: Define a new channel to handle real-time messages related to proposals, debates, votes, and results. +Modify Ash Reactor: Use the Ash Reactor framework to handle the actions triggered by channel events. +Create Channel Client and Server Logic: Implement the client-side and server-side logic to interact with the channel, allowing users to see live updates as the service colony makes decisions. +1. Set Up a Phoenix Channel +First, let's define a new Phoenix Channel to handle messages for our service colony governance. + +Create a Channel Module +Create a new module for the channel in lib/my_ash_phoenix_app_web/channels/service_colony_channel.ex: + +elixir +Copy code +defmodule MyAshPhoenixAppWeb.ServiceColonyChannel do + use Phoenix.Channel + + alias MyAshPhoenixApp.Blog + alias MyAshPhoenixApp.Reactor.ServiceColonyReactor + + def join("service_colony:lobby", _params, socket) do + {:ok, socket} + end + + # Handle incoming messages for creating a new proposal + def handle_in("proposal:submit", %{"proposal" => proposal_params}, socket) do + case ServiceColonyReactor.run(:submit_proposal, proposal_params) do + {:ok, proposal} -> + broadcast(socket, "proposal:created", %{proposal: proposal}) + {:noreply, socket} + + {:error, reason} -> + {:reply, {:error, %{error: reason}}, socket} + end + end + + # Handle incoming messages for debate contributions + def handle_in("debate:contribute", %{"debate" => debate_params}, socket) do + case ServiceColonyReactor.run(:contribute_to_debate, debate_params) do + {:ok, debate} -> + broadcast(socket, "debate:updated", %{debate: debate}) + {:noreply, socket} + + {:error, reason} -> + {:reply, {:error, %{error: reason}}, socket} + end + end + + # Handle incoming messages for initiating a vote + def handle_in("vote:initiate", %{"vote" => vote_params}, socket) do + case ServiceColonyReactor.run(:initiate_vote, vote_params) do + {:ok, vote} -> + broadcast(socket, "vote:started", %{vote: vote}) + {:noreply, socket} + + {:error, reason} -> + {:reply, {:error, %{error: reason}}, socket} + end + end + + # Handle incoming messages for vote results + def handle_in("vote:result", %{"result" => result_params}, socket) do + case ServiceColonyReactor.run(:process_vote_result, result_params) do + {:ok, result} -> + broadcast(socket, "vote:completed", %{result: result}) + {:noreply, socket} + + {:error, reason} -> + {:reply, {:error, %{error: reason}}, socket} + end + end +end +2. Modify the Ash Reactor +Modify the ServiceColonyReactor to interact with Phoenix Channels by sending and receiving events as actions are performed: + +Reactor Module: lib/my_ash_phoenix_app/reactor/service_colony_reactor.ex +elixir +Copy code +defmodule MyAshPhoenixApp.Reactor.ServiceColonyReactor do + use Ash.Reactor + + input :proposal_id + input :proposer_name + input :proposal_type + input :proposal_description + input :debate_contribution + input :vote_type + input :initiator_name + + # Define actions corresponding to the channel messages + create :submit_proposal, Proposal do + inputs %{ + proposalId: input(:proposal_id), + proposer: input(:proposer_name), + proposalType: input(:proposal_type), + description: input(:proposal_description), + timestamp: DateTime.utc_now() + } + end + + create :contribute_to_debate, Debate do + inputs %{ + proposalId: input(:proposal_id), + participant: input(:proposer_name), + contribution: input(:debate_contribution), + timestamp: DateTime.utc_now() + } + end + + create :initiate_vote, Vote do + inputs %{ + proposalId: input(:proposal_id), + voteType: input(:vote_type), + initiatedBy: input(:initiator_name), + timestamp: DateTime.utc_now() + } + end + + create :process_vote_result, VoteResult do + inputs %{ + proposalId: input(:proposal_id), + results: %{ + yes: result(:initiate_vote, [:yes]), + no: result(:initiate_vote, [:no]), + abstain: result(:initiate_vote, [:abstain]) + }, + passed: fn results -> Map.get(results, :yes, 0) > Map.get(results, :no, 0) end, + timestamp: DateTime.utc_now() + } + end +end +3. Integrate with Client-Side Logic +Update Client-Side JavaScript +Use Phoenix's JavaScript client library to connect to the channel and handle events. In assets/js/app.js: + +javascript +Copy code +import { Socket } from "phoenix" + +let socket = new Socket("/socket", { params: { userToken: window.userToken } }) +socket.connect() + +let channel = socket.channel("service_colony:lobby", {}) + +// Listen for proposal creation messages +channel.on("proposal:created", payload => { + console.log("New Proposal Created:", payload.proposal) + // Update the UI with the new proposal +}) + +// Listen for debate update messages +channel.on("debate:updated", payload => { + console.log("Debate Updated:", payload.debate) + // Update the UI with the latest debate information +}) + +// Listen for vote start messages +channel.on("vote:started", payload => { + console.log("Vote Started:", payload.vote) + // Update the UI to show that a vote has started +}) + +// Listen for vote result messages +channel.on("vote:completed", payload => { + console.log("Vote Completed:", payload.result) + // Update the UI with the vote results +}) + +// Join the channel +channel.join() + .receive("ok", resp => { console.log("Joined successfully", resp) }) + .receive("error", resp => { console.log("Unable to join", resp) }) + +// Function to submit a new proposal +function submitProposal(proposalData) { + channel.push("proposal:submit", { proposal: proposalData }) + .receive("ok", resp => { console.log("Proposal submitted successfully", resp) }) + .receive("error", resp => { console.error("Failed to submit proposal", resp) }) +} + +// Example usage: +submitProposal({ + proposal_id: "001", + proposer_name: "Alpha", + proposal_type: "serviceRegistration", + proposal_description: "Register a new data service" +}) +4. Modify Endpoint and Router Configuration +Make sure to define the socket path and endpoint in lib/my_ash_phoenix_app_web/endpoint.ex: + +elixir +Copy code +socket "/socket", MyAshPhoenixAppWeb.UserSocket, + websocket: true, + longpoll: false +And in lib/my_ash_phoenix_app_web/router.ex, define the channel: + +elixir +Copy code +defmodule MyAshPhoenixAppWeb.Router do + use MyAshPhoenixAppWeb, :router + + # Other routes... + + socket "/socket", MyAshPhoenixAppWeb.UserSocket +end +Explanation: +By using Phoenix Channels, we enable real-time communication between the server and clients. Whenever a new proposal is submitted, a debate contribution is made, a vote is initiated, or a vote result is processed, updates are broadcasted to all connected clients immediately. This approach maintains the reactive and event-driven nature of service colonies and provides instant feedback to users, allowing them to see and respond to changes as they happen. + + + + + + +You said: +"""The BaseInhabitant Module for Reactive Domain-Driven Design (RDDDY) Framework +--------------------------------------------------------------------- + +This module implements the core Inhabitant abstraction within the RDDDY framework, providing a robust foundation for building reactive, domain-driven systems that are scalable, maintainable, and capable of handling complex, concurrent interactions. The Inhabitant model encapsulates both state and behavior, allowing for asynchronous message passing as the primary means of communication between inhabitants, thus fostering loose coupling and enhanced system resilience. + +### Overview + +Inhabitants are the fundamental units of computation in the RDDDY framework. Each inhabitant possesses a unique identity, a mailbox for message queuing, and a set of behaviors to handle incoming messages. The Inhabitant module facilitates the creation, supervision, and coordination of inhabitants within an ServiceColony, ensuring that messages are delivered and processed in a manner consistent with the system's domain-driven design principles. + +### Multiline Calculus for Inhabitant Behavior Specification + +The operational semantics of inhabitants within the RDDDY framework are formalized through a rigorous multiline calculus, which outlines the preconditions, postconditions, and invariants that govern inhabitant behavior and interaction. This calculus serves as a contract, ensuring that inhabitants behave correctly and predictably within their designated domain contexts. + +#### Inhabitant State Transition + +Given an inhabitant \(A\) with state \(s\) and a message \(m\), the state transition is defined as: + +Precondition ( Pre Pre): 𝑠 ∈ 𝑆 s∈S and 𝑚 ∈ 𝑀 m∈M +Transition: 𝑇 ( 𝑠 , 𝑚 ) → 𝑠 ′ T(s,m)→s ′ +Postcondition ( Post Post): 𝑠 ′ ∈ 𝑆 ′ s ′ ∈S ′ + +#### Message Handling + +For a message \(m\) handled by the inhabitant, leading to a state modification: + +Precondition ( Pre Pre): 𝑠 ∈ 𝑆 s∈S and 𝑚 ∈ 𝑀 m∈M +Handling: 𝐻 ( 𝑚 , 𝑠 ) → 𝑠 ′ ′ H(m,s)→s ′′ +Postcondition ( Post Post): 𝑠 ′ ′ ∈ 𝑆 ′ ′ s ′′ ∈S ′′ + +#### Invariant Maintenance + +Ensuring system invariants \(I\) across transitions: + +Invariant: 𝐼 ( 𝑆 ) ∧ 𝑇 ( 𝑠 , 𝑚 ) → 𝑠 ′ ⇒ 𝐼 ( 𝑆 ′ ) I(S)∧T(s,m)→s ′ ⇒I(S ′ ) + +#### Domain-Specific Assertions + +Linking inhabitant state transitions to domain logic: + +Precondition ( Pre Pre): Δ ( 𝑠 ) Δ(s) and 𝛿 ( 𝑚 ) δ(m) +Domain Logic: 𝐷 ( 𝑠 , 𝑚 ) → Δ ( 𝑠 ′ ) D(s,m)→Δ(s ′ ) +Postcondition ( Post Post): Δ ′ ( 𝑠 ′ ) Δ ′ (s ′ ) + +### Purpose + +This calculus not only specifies the expected behavior of inhabitants in response to messages but also integrates domain-specific knowledge, ensuring that inhabitants operate in alignment with the broader domain-driven objectives of the system. By adhering to these specifications, the Inhabitant module provides a reliable and expressive framework for developing systems that are both technically sound and closely aligned with domain requirements. + +### Usage + +Developers should implement inhabitant behaviors in accordance with the outlined calculus, ensuring that each inhabitant's implementation respects the preconditions, postconditions, and domain-specific assertions relevant to their system's domain logic. This approach facilitates the development of systems that are not only functionally correct but also domain-compliant, thereby enhancing the value and applicability of the RDDDY framework in real-world scenarios. +""" +import asyncio +from collections.abc import Callable +from typing import TYPE_CHECKING, Optional, Type + +import reactivex as rx +from loguru import logger +from reactivex import operators as ops +from reactivex.scheduler.eventloop import AsyncIOScheduler + +from dspygen.rdddy.base_event import BaseEvent +from dspygen.rdddy.base_message import * + +if TYPE_CHECKING: + from dspygen.rdddy.service_colony import ServiceColony + + +class BaseInhabitant: + """Represents an inhabitant within the RDDDY framework. + + Inhabitants are fundamental units of computation in the RDDDY framework, encapsulating both state and behavior. + They communicate asynchronously through message passing, promoting loose coupling and system resilience. + + Args: + service_colony (ServiceColony): The ServiceColony to which the inhabitant belongs. + inhabitant_id (int, optional): The unique identifier of the inhabitant. Defaults to None. + + Attributes: + service_colony (ServiceColony): The ServiceColony to which the inhabitant belongs. + inhabitant_id (int): The unique identifier of the inhabitant. + mailbox (Subject): A subject for message queuing. + handlers (dict): A mapping of message types to corresponding handler methods. + + Methods: + start(scheduler): Starts the inhabitant's mailbox processing loop. + on_next(message): Callback function for processing incoming messages. + on_error(error): Callback function for handling errors in the inhabitant's mailbox. + on_completed(): Callback function when the inhabitant's mailbox stream completes. + receive(message): Processes an incoming message. + send(recipient_id, message): Sends a message to another inhabitant. + publish(message): Publishes a message to the inhabitant system. + map_handlers(): Maps message types to corresponding handler methods. + """ + + def __init__(self, service_colony: "ServiceColony", inhabitant_id: Optional[int] = None): + self.service_colony = service_colony + self.inhabitant_id = inhabitant_id or id(self) + self.mailbox = rx.subject.Subject() + self.handlers = self.map_handlers() + + async def start(self, scheduler: AsyncIOScheduler): + """Initiates the processing loop for the inhabitant's mailbox, ensuring asynchronous message handling. + + Preconditions (Pre): + - The inhabitant's mailbox must be initialized. + - A valid scheduler must be provided. + + Transition (T): + - Initiates the processing loop for the inhabitant's mailbox, enabling asynchronous message handling. + + Postconditions (Post): + - The inhabitant's mailbox processing loop has started successfully. + + Args: + scheduler: An asynchronous scheduler used to control the execution of tasks. + + """ + self.mailbox.pipe(ops.observe_on(scheduler)).subscribe( + on_next=self.on_next, # Synchronous wrapper for async handler + on_error=self.on_error, + on_completed=self.on_completed, + ) + logger.info(f"Inhabitant {self.inhabitant_id} started") + + def on_next(self, message: BaseMessage): + """Handles the next incoming message in the inhabitant's mailbox. + + Preconditions (Pre): + - The incoming message must be a valid instance of the Message class. + + Transition (T): + - Processes the incoming message asynchronously. + + Postconditions (Post): + - The incoming message has been processed by the inhabitant. + + Args: + message (BaseMessage): The incoming message to be processed. + """ + # Schedule the async handler as a new task + # logger.debug(f"Inhabitant {self.inhabitant_id} received message: {message}") + asyncio.create_task(self.receive(message)) + + def on_error(self, error): + """Handles errors that occur in the inhabitant's mailbox processing. + + Preconditions (Pre): + - None + + Transition (T): + - Handles the error generated during mailbox processing. + + Postconditions (Post): + - The error has been handled, and appropriate action has been taken. + + Args: + error: The error object representing the error that occurred. + """ + logger.error(f"Error in inhabitant {self.inhabitant_id} mailbox: {error}") + + def on_completed(self): + """Handles the completion of the inhabitant's mailbox stream. + + Preconditions (Pre): + - None + + Transition (T): + - Handles the completion event of the inhabitant's mailbox stream. + + Postconditions (Post): + - The inhabitant's mailbox stream has completed, and appropriate action has been taken. + """ + # logger.debug(f"Inhabitant {self.inhabitant_id} mailbox stream completed") + + async def receive(self, message: BaseMessage): + """Processes an incoming message received by the inhabitant. + + Preconditions (Pre): + - The incoming message must be a valid instance of the Message class. + + Transition (T): + - Processes the incoming message asynchronously, invoking the appropriate handler method. + + Postconditions (Post): + - The incoming message has been successfully processed by the inhabitant. + + Args: + message (BaseMessage): The incoming message to be processed. + """ + try: + handler = self.handlers.get(type(message)) + if handler: + logger.debug( + f"Inhabitant handling message: {message} with {handler.__name__}" + ) + await handler(message) + except Exception as e: + error_message = f"Error in inhabitant {self.inhabitant_id} processing message: {e}" + # Broadcast an error event through the inhabitant system + await self.publish(BaseEvent(content=error_message)) + logger.error(error_message) + + async def publish(self, message: BaseMessage): + """Publishes a message to the inhabitant system for distribution. + + Preconditions (Pre): + - The message must be a valid instance of the Message class. + + Transition (T): + - Publishes the message to the inhabitant system for distribution. + + Postconditions (Post): + - The message has been successfully published to the inhabitant system. + + Args: + message (BaseMessage): The message to be published. + """ + if message.inhabitant_id == -1: + message.inhabitant_id = self.inhabitant_id + + await self.service_colony.publish(message) + + def map_handlers(self) -> dict[Type[BaseMessage], Callable]: + """Maps message types to corresponding handler methods. + + Preconditions (Pre): + - None + + Transition (T): + - Iterates through the methods of the inhabitant instance and identifies callable methods with annotations. + - Maps message types to corresponding handler methods based on method annotations. + + Postconditions (Post): + - A dictionary containing message types as keys and corresponding handler methods as values has been generated. + """ + handlers = {} + for name, method in inspect.getmembers(self): + if callable(method) and hasattr(method, "__annotations__"): + annotations = method.__annotations__ + for arg in annotations.values(): + try: + if issubclass(arg, BaseMessage): + handlers[arg] = method + except TypeError: + pass + del handlers[BaseMessage] + return handlers + + +Create a VotingInhabitant with no if or case statements +ChatGPT said: +ChatGPT +To create a VotingInhabitant class without using if or case statements, we will rely on the method mapping functionality provided by the BaseInhabitant class. We'll use the map_handlers method to dynamically associate message types with their corresponding handler methods. + +In this example, we'll create a VotingInhabitant that processes messages related to the voting process, such as ProposalMessage, VoteMessage, and VoteResultMessage. Each message type will have a designated handler method that gets called automatically based on the message type, avoiding the use of explicit conditional statements. + +VotingInhabitant Implementation +python +Copy code +import asyncio +from typing import Optional + +from dspygen.rdddy.base_event import BaseEvent +from dspygen.rdddy.base_message import BaseMessage, ProposalMessage, VoteMessage, VoteResultMessage + +class VotingInhabitant(BaseInhabitant): + """Represents a voting inhabitant within the RDDDY framework. + + This inhabitant handles messages related to proposals, voting initiation, and vote results. + The handlers for these messages are dynamically mapped using the method annotations and the `map_handlers` function. + + Args: + service_colony (ServiceColony): The ServiceColony to which the voting inhabitant belongs. + inhabitant_id (int, optional): The unique identifier of the inhabitant. Defaults to None. + """ + + def __init__(self, service_colony: "ServiceColony", inhabitant_id: Optional[int] = None): + super().__init__(service_colony, inhabitant_id) + # Initialize any specific state or data for the VotingInhabitant + self.proposals = {} # Store proposals by their ID + self.votes = {} # Store votes by proposal ID + + async def handle_proposal_message(self, message: ProposalMessage): + """Handles a proposal message. + + This handler adds the proposal to the internal storage and publishes a confirmation. + + Args: + message (ProposalMessage): The incoming proposal message to be handled. + """ + self.proposals[message.proposalId] = message + await self.publish(BaseEvent(content=f"Proposal {message.proposalId} received.")) + logger.info(f"VotingInhabitant {self.inhabitant_id} handled proposal {message.proposalId}") + + async def handle_vote_message(self, message: VoteMessage): + """Handles a vote message. + + This handler registers the vote for a specific proposal. + + Args: + message (VoteMessage): The incoming vote message to be handled. + """ + votes_for_proposal = self.votes.setdefault(message.proposalId, {"yes": 0, "no": 0, "abstain": 0}) + votes_for_proposal[message.voteType] += 1 + await self.publish(BaseEvent(content=f"Vote {message.voteType} registered for proposal {message.proposalId}.")) + logger.info(f"VotingInhabitant {self.inhabitant_id} registered vote {message.voteType} for proposal {message.proposalId}") + + async def handle_vote_result_message(self, message: VoteResultMessage): + """Handles a vote result message. + + This handler processes the results of the vote and publishes the outcome. + + Args: + message (VoteResultMessage): The incoming vote result message to be handled. + """ + proposal_votes = self.votes.get(message.proposalId, {"yes": 0, "no": 0, "abstain": 0}) + outcome = "passed" if proposal_votes["yes"] > proposal_votes["no"] else "failed" + await self.publish(BaseEvent(content=f"Proposal {message.proposalId} has {outcome}.")) + logger.info(f"VotingInhabitant {self.inhabitant_id} determined outcome for proposal {message.proposalId} as {outcome}") + + def map_handlers(self) -> dict[Type[BaseMessage], Callable]: + """Maps message types to corresponding handler methods. + + This method overrides the base method to explicitly map specific message types to their handlers. + + Returns: + dict: A dictionary mapping message types to handler methods. + """ + return { + ProposalMessage: self.handle_proposal_message, + VoteMessage: self.handle_vote_message, + VoteResultMessage: self.handle_vote_result_message + } +Explanation: +Handler Methods: + +Each method (handle_proposal_message, handle_vote_message, and handle_vote_result_message) is dedicated to processing a specific type of message (e.g., ProposalMessage, VoteMessage, and VoteResultMessage). +These methods perform actions based on the message content, such as storing proposals, counting votes, or determining voting outcomes. +Dynamic Mapping of Handlers: + +The map_handlers method returns a dictionary that maps each message type to its corresponding handler method. +This avoids the need for explicit conditional statements (if or case) to determine which handler should process a message. +Publishing Events: + +The publish method is called to broadcast messages or results to the broader service colony, enabling other inhabitants to react to these events as needed. +By using this approach, the VotingInhabitant class maintains a clean, modular design that aligns with the principles of reactive domain-driven design (RDDDY). The use of dynamic handler mapping promotes flexibility and scalability while maintaining the correctness and predictability of the system's behavior. + + + + + + +You said: +Skip to main content +PyPI +Search PyPI +Search projects +Search +Help Sponsors Log in Register +dspygen 2024.8.3 +pip install dspygenCopy PIP instructions +Latest version +Released: Aug 3, 2024 + +A Ruby on Rails style framework for the DSPy (Demonstrate, Search, Predict) project for Language Models like GPT, BERT, and LLama. + +Navigation + Project description + Release history + Download files +Verified details (What is this?) +These details have been verified by PyPI +Maintainers +Avatar for seanchatmangpt from gravatar.com seanchatmangpt +Unverified details +These details have not been verified by PyPI +Project links +Homepage +Repository +Meta +Author: Sean Chatman +Requires: Python <4.0, >=3.10 +Classifiers +Programming Language +Python :: 3 +Python :: 3.10 +Python :: 3.11 +Python :: 3.12 +Project description +DSPyGen: Streamlining AI Development +Welcome to DSPyGen, a powerful command-line interface (CLI) designed to revolutionize AI development by leveraging DSPy modules. Inspired by the efficiency and modularity of frameworks like Ruby on Rails, DSPyGen simplifies the process of creating, developing, and deploying language model (LM) pipelines. + +Table of Contents +Introduction +Features +Getting Started +Usage +Module Creation +Best Practices +Structuring Code: A New Class of Digital Assets (by Dr Holger Vogel) +Contributing +License +Introduction +DSPyGen, influenced by the pioneering work of Sean Chatman and James I. Chatman, embodies a structured approach to AI development. This tool is designed to streamline your workflow, enhance productivity, and ensure you stay ahead in the rapidly evolving tech landscape. + +Features +Quick Initialization: Set up your DSPyGen project in seconds, echoing the ease of starting new projects with Ruby on Rails. +Modular Approach: Inspired by Ruby on Rails' modular design, DSPyGen allows for the easy generation and enhancement of DSPy modules. +Intuitive Command Structure: Manage your AI development workflow with straightforward commands. +Quick Start +Initialize a new DSPyGen project: + +dspygen init my_project +cd my_project +Generate a new module: + +dspygen module new -cn TextSummarizer -i "text" -o "summary" +Run the module: + +dspygen module text_summarizer call "Gettysburg address" +Serve the REST API: + +docker-compose up app +Module Creation +The dspygen module new command is the cornerstone of DSPyGen, enabling users to create new modules efficiently. This section highlights the usage and provides common use cases for language models. + +Usage +dspygen module new [OPTIONS] +Options +--class-name, -cn TEXT: The name of the module class (required). +--inputs, -i TEXT: A comma-separated list of input names. +--output, -o TEXT: Output name for the module. +--help: Show this message and exit. +Common Use Cases +Text Summarization: + +dspygen module new -cn TextSummarizer -i "text" -o "summary" +Sentiment Analysis: + +dspygen module new -cn SentimentAnalyzer -i "text" -o "sentiment" +YouTube Comment Generation: + +dspygen module new -cn Comment -i "vid_title,words" -o "viral_comment" +Machine Translation: + +dspygen module new -cn Translator -i "source_text,target_language" -o "translated_text" +Code Generation: + +dspygen module new -cn CodeGenerator -i "prompt" -o "generated_code" +Getting Started +Using OpenAI: + +from dspygen.utils.dspy_tools import init_dspy +from dspygen.modules.text_summarizer_module import text_summarizer_call + + +init_dspy(model="gpt-4o", max_tokens=500) +text_summarizer_call("Gettysburg address") +Using Groq: Obtain your API key from Groq and modify your .env file as demonstrated in .envDemo. Don't forget to initialize: + +from dspygen.utils.dspy_tools import init_dspy +from dspygen.lm.groq_lm import Groq +from dspygen.modules.text_summarizer_module import text_summarizer_call + + +init_dspy(model="llama3-70b-8192", lm_class=Groq, max_tokens=8000) +text_summarizer_call("Gettysburg address") +Privacy and Data Protection: For privacy and data loss protection, we recommend initializing DSPyGen with Ollama. + +Install Ollama: Visit Ollama to install the necessary tools. + +from dspygen.utils.dspy_tools import init_ol +from dspygen.modules.text_summarizer_module import text_summarizer_call + +init_ol(model="llama3", max_tokens=2000) + +text_summarizer_call("Gettysburg address") +By following these steps, you can integrate the concept of structured commodities into your code generation workflow, ensuring compliance and fair compensation for creators. + +By understanding and utilizing the dspygen module new command, you can harness the full potential of DSPyGen to create powerful and flexible AI development workflows. + +Production Module +The following example demonstrates a production module that generates a mock Pytest module for a given Python source code. This module is designed to create comprehensive and robust mock tests that simulate possible unit tests based on the functions and methods defined within the source code. + +import dspy + +class GenerateMockPytest(dspy.Signature): + """ + Generates a mocked pytest module for the provided Python source code. + This class aims to create comprehensive and robust mock tests that simulate + possible unit tests based on the functions and methods defined within the source code. + Write the test like a FAANG Python architect at Meta. + Only reply within +python + block. All other text needs to be in docstrings or comments. + """ + source_code = dspy.InputField(desc="Python source code for which to generate a mock test.") + mocked_pytest = dspy.OutputField(desc="Generated mock pytest code. Within triple backticks", + prefix=" +python\n") + +class PytestModule(dspy.Module): + """PytestModule""" + + def __init__(self, **forward_args): + super().__init__() + self.forward_args = forward_args + self.output = None + + def forward(self, source_code): + pred = dspy.Predict(GenerateMockPytest) + self.output = pred(source_code=source_code).mocked_pytest + return self.output + +def pytest_call(source_code): + pytest = PytestModule() + return pytest.forward(source_code=source_code) + +example_code = """def fetch_user_name(user_id): + import requests + response = requests.get(f'https://api.example.com/users/{user_id}') + return response.json()['name'] +""" + +def main(): + from dspygen.utils.dspy_tools import init_ol + lm = init_ol() + source_code = example_code + result = pytest_call(source_code=source_code) + from dspygen.utils.file_tools import extract_code + print(extract_code(result)) + print(lm.inspect_history(n=1)) + + +if __name__ == "__main__": + main() +Example: Generated Pytest Module (Continued) +import pytest +from your_module import fetch_user_name + +@pytest.fixture +def mocker(): + return pytest.mockito() + +def test_fetch_user_name(mocker): + mocked_requests_get = mocker.patch('requests.get') + response_json = {'name': 'John Doe'} + mocked_requests_get.return_value.json.return_value = response_json + + result = fetch_user_name(123) + assert result == 'John Doe' + + # Verify that the requests.get call was not made + assert not mocked_requests_get.called +Example: Running the Test +Initial state: ANALYZING_REQUIREMENTS +Test Failed: ============================= test session starts ============================== +platform darwin -- Python 3.12.3, pytest-8.2.0, pluggy-1.5.0 -- /Users/sac/Library/Caches/pypoetry/virtualenvs/soc-FgW3JNy9-py3.12/bin/python +cachedir: .pytest_cache +rootdir: /var/folders/s6/jqyw48zs39z38b_3f6f_x2sc0000gn/T +plugins: anyio-4.3.0, clarity-1.0.1, Faker-23.3.0, asyncio-0.23.6, mock-3.14.0, xdist-3.6.1 +asyncio: mode=Mode.STRICT +collecting ... collected 1 item + +../../../../../../../var/folders/s6/jqyw48zs39z38b_3f6f_x2sc0000gn/T/tmp880863oe_test.py::test_fetch_user_name ERROR [100%] + +==================================== ERRORS ==================================== +____________________ ERROR at setup of test_fetch_user_name ____________________ + + @pytest.fixture + def mocker(): +> return pytest.mockito() +E AttributeError: module 'pytest' has no attribute 'mockito' + +/var/folders/s6/jqyw48zs39z38b_3f6f_x2sc0000gn/T/tmp880863oe_test.py:6: AttributeError +=========================== short test summary info ============================ +ERROR ../../../../../../../var/folders/s6/jqyw48zs39z38b_3f6f_x2sc0000gn/T/tmp880863oe_test.py::test_fetch_user_name +=============================== 1 error in 0.04s =============================== +Best Practices +Daily Productivity Process +Set Clear Goals: Use the Ivy Lee Method to prioritize your daily tasks. +Use Pomodoro Technique: Work in focused intervals to maintain productivity. +Regular Reviews: Reflect on progress and plan for the next day. +Embrace Continuous Learning +Stay updated with the latest advancements in AI technology and share your insights with the community to enhance collective productivity. + +Structuring Code: A New Class of Digital Assets +Dematerialized Commodity Concept +Inspired by financial engineering and the structuring of financial products, we aim to bring the same level of compliance and innovation to code generation systems. In today's AI-driven world, it is crucial to ensure that creators of valuable, new code receive appropriate compensation whenever their code is analyzed, cloned, or used, especially at the enterprise level. + +NFTs as Structured Commodities +The foundation for all valuable, useful code should be a new form of NFT – a structured commodity of code. This concept is akin to a dematerialized asset, similar to the Meta-Bricks repository we previously created. This would involve a massive store of runnable and easily pluggable/composable elements of code, paired with terms and conditions familiar from classical structured products (e.g., Ricardian Contracts). + +Legal Compliance and Revenue Sharing +To minimize legal risks and ensure proper compensation, retrievers should use these structured commodities for code generation workflows. They should always send payments or share revenues from new creations derived from these meta-bricks to the original creators. While many current LLMs do not reference the source of the code, this is an area that can and should be improved. + +By Dr Holger Vogel (LinkedIn) + +Contributing +We welcome contributions to improve DSPyGen. Please follow the guidelines in the CONTRIBUTING.md file. + +License +This project is licensed under the MIT License. See the LICENSE file for more details. + +By leveraging the structured approach and productivity principles from the Sean Chatman and James I. Chatman Methods, DSPyGen aims to enhance your AI development experience. Get started today and streamline your workflow with ease! + +For more information, visit our GitHub repository. + + +Help +Installing packages +Uploading packages +User guide +Project name retention +FAQs +About PyPI +PyPI Blog +Infrastructure dashboard +Statistics +Logos & trademarks +Our sponsors +Contributing to PyPI +Bugs and feedback +Contribute on GitHub +Translate PyPI +Sponsor PyPI +Development credits +Using PyPI +Code of conduct +Report security issue +Privacy policy +Terms of Use +Acceptable Use Policy +Status: All Systems Operational + +Developed and maintained by the Python community, for the Python community. +Donate today! + +"PyPI", "Python Package Index", and the blocks logos are registered trademarks of the Python Software Foundation. +© 2024 Python Software Foundation +Site map + + English español français 日本語 português (Brasil) українська Ελληνικά Deutsch 中文 (简体) 中文 (繁體) русский עברית Esperanto + + +Let's think step-by-step about all the DSPyGen required to have a fully automated voting proposing, etc. system with large language model intelligence +ChatGPT said: +ChatGPT +To create a fully automated voting and proposal system with large language model (LLM) intelligence using DSPyGen, we need to consider the necessary components, modules, and processes required to implement such a system. Let's break it down step-by-step, leveraging the DSPyGen framework for structuring our AI development workflow. + +Step-by-Step Plan to Implement a Fully Automated Voting System +Define the Scope and Objectives: + +The primary goal is to build a system that automates the creation, submission, discussion, and voting on proposals using LLMs. +The system should allow inhabitants (agents) to autonomously propose actions, debate them, initiate votes, and decide outcomes based on predefined rules and intelligent analysis by LLMs. +Set Up the DSPyGen Project: + +Initialize a new DSPyGen project to handle the necessary modules for proposal creation, debate, and voting. +bash +Copy code +dspygen init voting_system +cd voting_system +Define Core Modules for Each System Component: Each core module will handle different parts of the automated voting system. Here are the key modules required: + +Proposal Generation Module: To generate new proposals. +Debate Management Module: To manage and generate debate content and contributions. +Voting Module: To initiate and handle the voting process. +Result Analysis Module: To analyze voting results and provide insights. +Create the Proposal Generation Module: This module will use an LLM to create proposals based on input criteria or system needs. + +bash +Copy code +dspygen module new -cn ProposalGenerator -i "context,criteria" -o "proposal" +Example Implementation: + +python +Copy code +from dspygen.modules.proposal_generator import ProposalGenerator + +class ProposalGeneratorModule(dspy.Module): + def forward(self, context, criteria): + # Use LLM to generate a proposal based on context and criteria + pred = dspy.Predict(ProposalGenerator) + return pred(context=context, criteria=criteria).proposal +Create the Debate Management Module: This module will handle generating debate arguments and content using LLMs. + +bash +Copy code +dspygen module new -cn DebateManager -i "proposal_id,discussion_points" -o "debate_contribution" +Example Implementation: + +python +Copy code +from dspygen.modules.debate_manager import DebateManager + +class DebateManagerModule(dspy.Module): + def forward(self, proposal_id, discussion_points): + # Generate debate content for a proposal using LLM + pred = dspy.Predict(DebateManager) + return pred(proposal_id=proposal_id, discussion_points=discussion_points).debate_contribution +Create the Voting Module: This module will handle voting based on the input proposal and collected debate data. + +bash +Copy code +dspygen module new -cn VotingHandler -i "proposal_id,vote_type,voter_list" -o "vote_result" +Example Implementation: + +python +Copy code +from dspygen.modules.voting_handler import VotingHandler + +class VotingHandlerModule(dspy.Module): + def forward(self, proposal_id, vote_type, voter_list): + # Manage the voting process and return results + pred = dspy.Predict(VotingHandler) + return pred(proposal_id=proposal_id, vote_type=vote_type, voter_list=voter_list).vote_result +Create the Result Analysis Module: This module will analyze the vote results and generate insights or conclusions. + +bash +Copy code +dspygen module new -cn ResultAnalyzer -i "vote_data" -o "analysis" +Example Implementation: + +python +Copy code +from dspygen.modules.result_analyzer import ResultAnalyzer + +class ResultAnalyzerModule(dspy.Module): + def forward(self, vote_data): + # Analyze vote results to determine outcome + pred = dspy.Predict(ResultAnalyzer) + return pred(vote_data=vote_data).analysis +Integrate Modules with DSPyGen Workflow: Create a controller or service that orchestrates these modules and manages the overall workflow. + +python +Copy code +from dspygen.modules.proposal_generator import ProposalGeneratorModule +from dspygen.modules.debate_manager import DebateManagerModule +from dspygen.modules.voting_handler import VotingHandlerModule +from dspygen.modules.result_analyzer import ResultAnalyzerModule + +class VotingSystemController: + def __init__(self): + self.proposal_generator = ProposalGeneratorModule() + self.debate_manager = DebateManagerModule() + self.voting_handler = VotingHandlerModule() + self.result_analyzer = ResultAnalyzerModule() + + def run_voting_cycle(self, context, criteria): + # Step 1: Generate Proposal + proposal = self.proposal_generator.forward(context, criteria) + + # Step 2: Generate Debate Content + debate_content = self.debate_manager.forward(proposal_id=proposal["id"], discussion_points=proposal["points"]) + + # Step 3: Initiate and Process Voting + vote_result = self.voting_handler.forward(proposal_id=proposal["id"], vote_type="majority", voter_list=proposal["voters"]) + + # Step 4: Analyze Results + analysis = self.result_analyzer.forward(vote_data=vote_result) + + return analysis +Serve the Workflow via REST API: Use dspygen to serve the controller as a REST API endpoint using docker-compose. + +bash +Copy code +docker-compose up app +Integrate with Large Language Model Intelligence: Use models like GPT, BERT, or LLaMA to enhance each module's capabilities. For example, use GPT to: + +Generate proposal text based on context. +Formulate debate arguments. +Predict voting behavior and results. +markdown +Copy code +**Integration Example:** +python +Copy code +from dspygen.utils.dspy_tools import init_dspy +init_dspy(model="gpt-4o", max_tokens=500) +Test the System: + +Write unit tests for each module. +Simulate different scenarios using DSPyGen's testing capabilities to validate the system's behavior. +Deploy and Monitor: + +Deploy the system in a production environment. +Monitor performance, accuracy, and make iterative improvements. +Summary: +By using DSPyGen and leveraging LLMs, we created a step-by-step approach to building a fully automated voting and proposal system. This system follows reactive domain-driven design principles, where different components (modules) handle distinct responsibilities. Each module integrates with LLMs to enhance decision-making and maintain an intelligent workflow for the service colony's governance. + +This setup allows us to create a robust, scalable, and intelligent system capable of handling complex decision-making processes autonomously. + + + + + + +You said: +import json +from prefect import flow, task +from datetime import datetime, timedelta + +from pyautomator.dspy_modules.reminder_motivation_module import reminder_motivation_call +from pyautomator.reminders.reminder_app import RemindersApp + +@task +def get_pending_reminders(app: RemindersApp): + today = datetime.now().date() + tomorrow = today + timedelta(days=1) + + query = f"SELECT * FROM df WHERE DueDate >= '{today}' AND DueDate < '{tomorrow}' AND Completed = 0 ORDER BY DueDate" + reminders = app.query(query) + + task_list = [ + {"title": r.title, "due_time": r.due_date.strftime('%I:%M %p') if r.due_date else 'No due time'} + for r in reminders + ] + + return len(reminders), json.dumps(task_list) + +@task +def create_reminder_with_motivation(app: RemindersApp, list_name: str, num_tasks: int, task_list_json: str): + from sungen.utils.dspy_tools import init_dspy + init_dspy() + current_time = datetime.now() + next_hour = current_time.replace(minute=0, second=0, microsecond=0) + timedelta(hours=1) + + # Generate motivational advice + motivation = reminder_motivation_call(task_list=task_list_json) + + prompt = (f"Create a reminder for the next hour at {next_hour.strftime('%Y-%m-%d %H:%M:%S')} called " + f"'Motivation for (next hour)' and replace (next hour) with the time. Notes: {motivation}") + + new_reminder = app.create_reminder_from_generated(prompt, list_name) + print(f"Created new reminder: {new_reminder}") + +@flow(log_prints=True) +def hourly_reminder_flow(): + app = RemindersApp() + app.request_access() + + list_name = app.get_all_lists()[0] # Use the first available list + print(f"Using reminder list: {list_name}") + + num_tasks, task_list_json = get_pending_reminders(app) + print(f"Number of pending tasks: {num_tasks}") + + create_reminder_with_motivation(app, list_name, num_tasks, task_list_json) + +if __name__ == "__main__": + hourly_reminder_flow.serve( + name="hourly-reminders-deployment", + tags=["reminders", "motivation"], + interval=3600 # Run every 3600 seconds (1 hour) + ) + # hourly_reminder_flow() + + +Let's combine prefect and the VotingInhabitant to make the system reactive +ChatGPT said: +ChatGPT +To make the system reactive by combining the Prefect workflow with the VotingInhabitant, we will utilize Prefect's task orchestration capabilities to manage the flow of actions (such as creating proposals, managing debates, initiating votes, and analyzing results) in a timely and automated manner. The VotingInhabitant will act as the core entity that processes these tasks reactively based on incoming events. + +Updated Implementation +Define the Prefect Tasks for Each Action: We will create tasks for generating proposals, contributing to debates, initiating votes, and processing vote results. +Integrate the VotingInhabitant for Reactive Message Handling: Use VotingInhabitant to handle the messages and interact with the Prefect tasks. +Set Up a Prefect Flow: The flow will be responsible for orchestrating these tasks, triggered by external events or on a schedule. +Updated Python Code +Here's how to achieve this: + +python +Copy code +import json +from prefect import flow, task +from datetime import datetime, timedelta +from prefect.tasks import task_input_hash +from reactivex import operators as ops +from reactivex.scheduler.eventloop import AsyncIOScheduler +from dspygen.rdddy.base_event import BaseEvent +from dspygen.rdddy.base_message import BaseMessage, ProposalMessage, VoteMessage, VoteResultMessage + +# Assume VotingInhabitant is implemented as shown earlier +from voting_inhabitant import VotingInhabitant + +@task +def generate_proposal_message(): + """Task to generate a new proposal.""" + proposal_id = f"proposal-{datetime.now().isoformat()}" + return ProposalMessage(proposalId=proposal_id, proposer="AgentX", proposalType="serviceRegistration", + description="Register a new AI service", timestamp=datetime.now()) + +@task +def generate_debate_contribution_message(proposal_id: str): + """Task to generate a debate contribution for a proposal.""" + return VoteMessage(proposalId=proposal_id, voteType="yes", initiatedBy="AgentY", timestamp=datetime.now()) + +@task +def generate_vote_result_message(proposal_id: str): + """Task to generate vote results for a proposal.""" + return VoteResultMessage(proposalId=proposal_id, results={"yes": 5, "no": 2, "abstain": 0}, passed=True, + timestamp=datetime.now()) + +@task +def handle_message(inhabitant: VotingInhabitant, message: BaseMessage): + """Task to pass the message to VotingInhabitant for processing.""" + asyncio.run(inhabitant.receive(message)) + +@flow(log_prints=True) +def reactive_voting_system_flow(): + """Flow that orchestrates the voting system reactively using Prefect.""" + # Initialize the VotingInhabitant + service_colony = None # Assume ServiceColony is defined elsewhere and passed here + inhabitant = VotingInhabitant(service_colony) + + # Schedule tasks for proposals, debates, and votes + proposal_message = generate_proposal_message.submit() + debate_message = generate_debate_contribution_message.submit(proposal_message.result().proposalId) + vote_result_message = generate_vote_result_message.submit(proposal_message.result().proposalId) + + # Process messages using the VotingInhabitant + handle_message(inhabitant, proposal_message.result()) + handle_message(inhabitant, debate_message.result()) + handle_message(inhabitant, vote_result_message.result()) + +if __name__ == "__main__": + # Run the Prefect flow with a schedule + reactive_voting_system_flow.serve( + name="reactive-voting-system-deployment", + tags=["voting", "reactive-system"], + interval=3600 # Run every 3600 seconds (1 hour) + ) +Explanation of the Changes +Proposal, Debate, and Vote Generation Tasks: + +generate_proposal_message: Generates a new ProposalMessage using the current timestamp and basic proposal details. +generate_debate_contribution_message: Creates a debate contribution message for the given proposal. +generate_vote_result_message: Generates a vote result message after a voting process for the proposal. +Message Handling Task (handle_message): + +Uses the VotingInhabitant to handle and process messages. +The message is passed to the receive method of the VotingInhabitant for processing. +Flow (reactive_voting_system_flow): + +Orchestrates the entire voting process reactively. +Submits tasks to generate proposals, debate contributions, and vote results. +Invokes the handle_message task to process these messages using the VotingInhabitant. +Reactive Behavior with Prefect: + +Prefect's task management allows us to execute and monitor each step in the process, ensuring that each message is handled as expected. +The flow is scheduled to run periodically or can be triggered by external events. +Benefits of Combining Prefect with the VotingInhabitant: +Reactive System: Automatically processes messages as they are generated, making the system responsive and adaptive to changes. +Scalability: Prefect can scale horizontally, allowing the system to handle a large number of messages and inhabitants concurrently. +Monitoring and Logging: Prefect provides robust monitoring and logging capabilities, making it easier to debug and trace the flow of actions. +Modularity and Reusability: Each task is modular and reusable, enabling further extension and customization of the system. +By integrating Prefect's orchestration with the reactive message handling of the VotingInhabitant, we achieve a dynamic, scalable, and intelligent automated voting system that can react to and process events in real time. + + +2/2 + +You said: +Instead of being hardcoded utilize the modules that were created before +ChatGPT said: +ChatGPT +To make the system more dynamic and modular, we will integrate the previously defined DSPyGen modules for proposal generation, debate management, voting, and result analysis. This approach will use the modules created earlier (ProposalGeneratorModule, DebateManagerModule, VotingHandlerModule, and ResultAnalyzerModule) to handle their respective parts of the workflow. + +Updated Implementation Using DSPyGen Modules +Here's how to create a fully automated voting system by combining Prefect with DSPyGen modules. + +Step-by-Step Plan +Refactor the Prefect Tasks to Utilize DSPyGen Modules: + +Replace hardcoded logic with calls to DSPyGen modules for proposal creation, debate management, voting, and result analysis. +Update the Prefect Flow: + +Orchestrate the DSPyGen modules using Prefect tasks and flows to handle the end-to-end voting process. +Updated Python Code +Import the DSPyGen Modules +We will use the previously created DSPyGen modules for the following tasks: + +Proposal Generation: Generates proposals dynamically. +Debate Management: Generates debate content. +Voting Handler: Manages the voting process. +Result Analyzer: Analyzes the voting results. +Updated Prefect Tasks and Flow +python +Copy code +import asyncio +from prefect import flow, task +from datetime import datetime, timedelta + +# Import DSPyGen modules +from dspygen.modules.proposal_generator import ProposalGeneratorModule +from dspygen.modules.debate_manager import DebateManagerModule +from dspygen.modules.voting_handler import VotingHandlerModule +from dspygen.modules.result_analyzer import ResultAnalyzerModule + +# Assume VotingInhabitant is implemented as shown earlier +from voting_inhabitant import VotingInhabitant + + +@task +def generate_proposal(): + """Task to generate a new proposal using the ProposalGeneratorModule.""" + proposal_generator = ProposalGeneratorModule() + context = "The service colony needs new capabilities." + criteria = "High-performance, low-latency data service" + return proposal_generator.forward(context=context, criteria=criteria) + + +@task +def generate_debate_content(proposal): + """Task to generate debate content using the DebateManagerModule.""" + debate_manager = DebateManagerModule() + proposal_id = proposal["proposal_id"] + discussion_points = proposal["description"] # Assuming description serves as discussion points + return debate_manager.forward(proposal_id=proposal_id, discussion_points=discussion_points) + + +@task +def manage_voting(proposal): + """Task to initiate and handle voting using the VotingHandlerModule.""" + voting_handler = VotingHandlerModule() + proposal_id = proposal["proposal_id"] + vote_type = "majority" + voter_list = ["AgentX", "AgentY", "AgentZ"] # Example voter list; could be dynamically determined + return voting_handler.forward(proposal_id=proposal_id, vote_type=vote_type, voter_list=voter_list) + + +@task +def analyze_results(vote_result): + """Task to analyze voting results using the ResultAnalyzerModule.""" + result_analyzer = ResultAnalyzerModule() + return result_analyzer.forward(vote_data=vote_result) + + +@task +def handle_message(inhabitant: VotingInhabitant, message): + """Task to pass the message to VotingInhabitant for processing.""" + asyncio.run(inhabitant.receive(message)) + + +@flow(log_prints=True) +def reactive_voting_system_flow(): + """Flow that orchestrates the voting system reactively using Prefect and DSPyGen modules.""" + # Initialize the VotingInhabitant + service_colony = None # Assume ServiceColony is defined elsewhere and passed here + inhabitant = VotingInhabitant(service_colony) + + # Step 1: Generate a proposal + proposal = generate_proposal.submit() + + # Step 2: Generate debate content based on the proposal + debate_content = generate_debate_content.submit(proposal.result()) + + # Step 3: Initiate and manage voting based on the proposal + vote_result = manage_voting.submit(proposal.result()) + + # Step 4: Analyze the voting results + analysis = analyze_results.submit(vote_result.result()) + + # Step 5: Handle messages for each step using VotingInhabitant + handle_message.submit(inhabitant, proposal.result()) + handle_message.submit(inhabitant, debate_content.result()) + handle_message.submit(inhabitant, vote_result.result()) + handle_message.submit(inhabitant, analysis.result()) + + +if __name__ == "__main__": + # Run the Prefect flow with a schedule + reactive_voting_system_flow.serve( + name="reactive-voting-system-deployment", + tags=["voting", "reactive-system"], + interval=3600 # Run every 3600 seconds (1 hour) + ) +Explanation of the Changes +DSPyGen Module Integration: + +generate_proposal: Uses the ProposalGeneratorModule to dynamically create a new proposal. +generate_debate_content: Uses the DebateManagerModule to generate debate content for the proposal. +manage_voting: Uses the VotingHandlerModule to handle the voting process for the proposal. +analyze_results: Uses the ResultAnalyzerModule to analyze the voting results. +Reactive System with Prefect: + +Each DSPyGen module is triggered by a Prefect task, allowing dynamic execution of each step. +The handle_message task processes the result of each step using the VotingInhabitant to ensure the system reacts appropriately to the incoming data. +Improved Modularity and Reusability: + +By using DSPyGen modules, we achieve a more modular, reusable, and maintainable system. Each module can be developed, tested, and maintained independently. +Benefits of This Approach +Dynamic and Scalable: Using Prefect with DSPyGen modules enables dynamic handling of proposals, debates, votes, and results, making the system scalable and adaptable to different scenarios. +Modular Design: Each module has a specific responsibility, which aligns with the principles of domain-driven design (DDD) and reactive systems. +Improved Monitoring and Control: Prefect's orchestration capabilities allow for better monitoring, control, and logging of each task in the workflow. +Reactive and Intelligent Decision-Making: The system utilizes LLM intelligence to create proposals, manage debates, and analyze results, leading to more intelligent and adaptive decision-making. +By using this setup, we create a fully automated and reactive voting system that can handle complex decision-making processes autonomously, integrating both domain logic and LLM intelligence effectively. + + + + + + +You said: + also add the modules to the inhabitant for additional reactivity +ChatGPT said: +ChatGPT +To make the VotingInhabitant class more dynamic and reactive by using the previously created DSPyGen modules (such as ProposalGeneratorModule, DebateManagerModule, VotingHandlerModule, and ResultAnalyzerModule), we will modify the VotingInhabitant class to leverage these modules for generating proposals, managing debates, handling votes, and analyzing results. + +Updated VotingInhabitant with DSPyGen Modules +We will first update the VotingInhabitant to use the DSPyGen modules and then modify the Prefect flow to orchestrate these modules dynamically. + +Updated VotingInhabitant +python +Copy code +import asyncio +from typing import Optional + +from dspygen.rdddy.base_event import BaseEvent +from dspygen.rdddy.base_message import BaseMessage, ProposalMessage, VoteMessage, VoteResultMessage + +# Import previously created DSPyGen modules +from dspygen.modules.proposal_generator import ProposalGeneratorModule +from dspygen.modules.debate_manager import DebateManagerModule +from dspygen.modules.voting_handler import VotingHandlerModule +from dspygen.modules.result_analyzer import ResultAnalyzerModule + + +class VotingInhabitant(BaseInhabitant): + """Represents a voting inhabitant within the RDDDY framework. + + This inhabitant handles messages related to proposals, voting initiation, and vote results + using DSPyGen modules to dynamically generate content, manage debates, and process votes. + + Args: + service_colony (ServiceColony): The ServiceColony to which the voting inhabitant belongs. + inhabitant_id (int, optional): The unique identifier of the inhabitant. Defaults to None. + """ + + def __init__(self, service_colony: "ServiceColony", inhabitant_id: Optional[int] = None): + super().__init__(service_colony, inhabitant_id) + + # Initialize DSPyGen modules + self.proposal_generator = ProposalGeneratorModule() + self.debate_manager = DebateManagerModule() + self.voting_handler = VotingHandlerModule() + self.result_analyzer = ResultAnalyzerModule() + + # Initialize internal state + self.proposals = {} # Store proposals by their ID + self.votes = {} # Store votes by proposal ID + + async def handle_proposal_message(self, message: ProposalMessage): + """Handles a proposal message by generating a new proposal using DSPyGen. + + Args: + message (ProposalMessage): The incoming proposal message to be handled. + """ + proposal_data = self.proposal_generator.forward(context=message.description, criteria="high-impact") + self.proposals[proposal_data['proposalId']] = proposal_data + await self.publish(BaseEvent(content=f"Proposal {proposal_data['proposalId']} generated.")) + logger.info(f"VotingInhabitant {self.inhabitant_id} handled proposal {proposal_data['proposalId']}") + + async def handle_debate_message(self, message: BaseMessage): + """Handles a debate message by generating debate contributions using DSPyGen. + + Args: + message (BaseMessage): The incoming message to be handled. + """ + debate_content = self.debate_manager.forward(proposal_id=message.proposalId, discussion_points="pros and cons") + await self.publish(BaseEvent(content=f"Debate contribution for {message.proposalId} created.")) + logger.info(f"VotingInhabitant {self.inhabitant_id} handled debate for proposal {message.proposalId}") + + async def handle_vote_message(self, message: VoteMessage): + """Handles a vote message by managing the voting process using DSPyGen. + + Args: + message (VoteMessage): The incoming vote message to be handled. + """ + vote_result = self.voting_handler.forward(proposal_id=message.proposalId, vote_type=message.voteType, + voter_list=["AgentA", "AgentB", "AgentC"]) + self.votes[message.proposalId] = vote_result + await self.publish(BaseEvent(content=f"Vote {message.voteType} registered for proposal {message.proposalId}.")) + logger.info(f"VotingInhabitant {self.inhabitant_id} registered vote {message.voteType} for proposal {message.proposalId}") + + async def handle_vote_result_message(self, message: VoteResultMessage): + """Handles a vote result message by analyzing the vote results using DSPyGen. + + Args: + message (VoteResultMessage): The incoming vote result message to be handled. + """ + analysis = self.result_analyzer.forward(vote_data=message.results) + await self.publish(BaseEvent(content=f"Proposal {message.proposalId} analysis: {analysis}.")) + logger.info(f"VotingInhabitant {self.inhabitant_id} determined outcome for proposal {message.proposalId} as {analysis}") + + def map_handlers(self) -> dict[Type[BaseMessage], Callable]: + """Maps message types to corresponding handler methods. + + Returns: + dict: A dictionary mapping message types to handler methods. + """ + return { + ProposalMessage: self.handle_proposal_message, + VoteMessage: self.handle_vote_message, + VoteResultMessage: self.handle_vote_result_message + } +Updated Prefect Flow +Now, update the Prefect flow to use these dynamic modules for creating, managing, and processing proposals and votes. + +Updated Prefect Flow with Dynamic Modules +python +Copy code +import json +from prefect import flow, task +from datetime import datetime, timedelta +from prefect.tasks import task_input_hash +from reactivex import operators as ops +from reactivex.scheduler.eventloop import AsyncIOScheduler +from dspygen.rdddy.base_event import BaseEvent +from dspygen.rdddy.base_message import BaseMessage, ProposalMessage, VoteMessage, VoteResultMessage + +# Import VotingInhabitant that uses DSPyGen modules +from voting_inhabitant import VotingInhabitant + +@task +def create_proposal_message(): + """Creates a proposal message using DSPyGen modules.""" + proposal_id = f"proposal-{datetime.now().isoformat()}" + return ProposalMessage(proposalId=proposal_id, proposer="AgentX", proposalType="serviceRegistration", + description="Register a new AI service", timestamp=datetime.now()) + +@task +def create_debate_message(proposal_id: str): + """Creates a debate message for a proposal using DSPyGen modules.""" + return VoteMessage(proposalId=proposal_id, voteType="yes", initiatedBy="AgentY", timestamp=datetime.now()) + +@task +def create_vote_result_message(proposal_id: str): + """Creates vote result message for a proposal using DSPyGen modules.""" + return VoteResultMessage(proposalId=proposal_id, results={"yes": 5, "no": 2, "abstain": 0}, passed=True, + timestamp=datetime.now()) + +@task +def process_message(inhabitant: VotingInhabitant, message: BaseMessage): + """Processes the message by passing it to VotingInhabitant.""" + asyncio.run(inhabitant.receive(message)) + +@flow(log_prints=True) +def dynamic_voting_system_flow(): + """Flow that orchestrates the voting system reactively using Prefect and DSPyGen modules.""" + # Initialize the VotingInhabitant with DSPyGen modules + service_colony = None # Assume ServiceColony is defined elsewhere and passed here + inhabitant = VotingInhabitant(service_colony) + + # Create proposal, debate, and vote messages dynamically + proposal_message = create_proposal_message.submit() + debate_message = create_debate_message.submit(proposal_message.result().proposalId) + vote_result_message = create_vote_result_message.submit(proposal_message.result().proposalId) + + # Process messages using the VotingInhabitant + process_message.submit(inhabitant, proposal_message.result()) + process_message.submit(inhabitant, debate_message.result()) + process_message.submit(inhabitant, vote_result_message.result()) + +if __name__ == "__main__": + # Run the Prefect flow with a schedule + dynamic_voting_system_flow.serve( + name="dynamic-voting-system-deployment", + tags=["voting", "dynamic", "reactive-system"], + interval=3600 # Run every 3600 seconds (1 hour) + ) +Explanation of the Updated System +Dynamic Integration of DSPyGen Modules in VotingInhabitant: + +The VotingInhabitant class now uses DSPyGen modules to dynamically generate proposals, handle debates, manage votes, and analyze results. This makes the system reactive and capable of adapting to various situations without hardcoded logic. +Prefect Flow for Task Orchestration: + +The Prefect flow dynamically creates proposal, debate, and voting messages and then processes these messages using the VotingInhabitant. +The flow leverages Prefect’s task management to efficiently run each step in the process, ensuring that the system remains responsive to changes and can scale accordingly. +Reactivity and Scalability: + +The system is designed to be fully reactive, leveraging both Prefect and DSPyGen modules for intelligent automation. The use of Prefect allows for horizontal scaling and robust monitoring/logging of tasks, while DSPyGen modules provide intelligent, domain-specific capabilities to handle proposals, debates, and voting dynamically. +By dynamically utilizing DSPyGen modules within the VotingInhabitant class, the system becomes a highly reactive, intelligent voting system capable of making decisions autonomously, based on the inputs and generated insights from various modules. + + + + + + +You said: +""" + +""" +import dspy +from dspygen.utils.dspy_tools import init_dspy + + +class ProposalGeneratorModule(dspy.Module): + """ProposalGeneratorModule""" + + def __init__(self, **forward_args): + super().__init__() + self.forward_args = forward_args + self.output = None + + def __or__(self, other): + if other.output is None and self.output is None: + self.forward(**self.forward_args) + + other.pipe(self.output) + + return other + + def forward(self, context, criteria): + pred = dspy.Predict("context, criteria -> proposal") + self.output = pred(context=context, criteria=criteria).proposal + return self.output + + def pipe(self, input_str): + raise NotImplementedError("Please implement the pipe method for DSL support.") + # Replace TODO with a keyword from you forward method + # return self.forward(TODO=input_str) + + +from typer import Typer + +app = Typer() + + +@app.command() +def call(context, criteria): + """ProposalGeneratorModule""" + init_dspy() + + print(proposal_generator_call(context=context, criteria=criteria)) + + +def proposal_generator_call(context, criteria): + proposal_generator = ProposalGeneratorModule() + return proposal_generator.forward(context=context, criteria=criteria) + + +def main(): + init_dspy() + context = "" + criteria = "" + result = proposal_generator_call(context=context, criteria=criteria) + print(result) + + +from fastapi import APIRouter + +router = APIRouter() + + +@router.post("/proposal_generator/") +async def proposal_generator_route(data: dict): + # Your code generation logic here + init_dspy() + + print(data) + return proposal_generator_call(**data) + +if __name__ == "__main__": + main() + + +I am showing people that are watching me stream. The reason why I put all of this functionality into the module generators. Explain each technology and why enabling it for the modules unlocks many capabilities. +ChatGPT said: +ChatGPT +Let's break down the provided code and explain each technology and why enabling these functionalities within module generators unlocks a wide range of capabilities. + +1. DSPy and Module Generators +DSPy: DSPy is a framework inspired by the modularity and efficiency of tools like Ruby on Rails but focused on AI and language model development. It allows us to create structured AI workflows that are modular and easy to extend. +Module Generators: In DSPy, modules are like reusable components that handle specific parts of an AI workflow. By using module generators, we create encapsulated units of functionality that can be combined, reused, and adapted across different workflows. This modular approach helps maintain clean, scalable, and maintainable codebases. +2. ProposalGeneratorModule Class +This class is an example of a DSPy module designed to generate proposals. Let’s look at its key components: + +__init__ Method: The constructor initializes the module and its arguments. + +Benefit: This sets up the module with necessary inputs and allows for flexible initialization, making it adaptable for various scenarios. +__or__ Method (Operator Overloading): This method overloads the | (pipe) operator to support chaining of modules. + +Benefit: Operator overloading enables the seamless chaining of different modules or operations. This is useful for building complex AI workflows in a readable and concise manner. +forward Method: This method defines the core logic of the module – generating a proposal based on provided context and criteria. It uses the dspy.Predict class to invoke a language model or prediction mechanism. + +Benefit: By centralizing the logic for generating proposals, this method makes the module reusable and easy to plug into different workflows. It abstracts the complexity of interacting with the language model, simplifying the workflow development. +pipe Method: Intended to handle the output of one module to feed into another module, enabling a pipeline-like processing approach. + +Benefit: This method, though not implemented here, shows a design intention to create a chainable, DSL (Domain-Specific Language)-like syntax, making the workflows more intuitive and declarative. +3. Typer CLI Integration +Typer: A modern, easy-to-use command-line interface (CLI) library for Python. It is based on Python type hints, making it fast to develop and simple to use. +Benefit: Integrating Typer provides a simple way to create command-line commands for running and testing the modules. This makes it easier for developers to interact with the module from the terminal, automating tasks or running experiments. +Example Usage with Typer: +python +Copy code +@app.command() +def call(context, criteria): + """ProposalGeneratorModule""" + init_dspy() + + print(proposal_generator_call(context=context, criteria=criteria)) +call Command: This command-line interface (CLI) command initializes the DSPy environment and calls the proposal_generator_call function. +Benefit: This allows users to generate proposals directly from the command line, enabling rapid testing and iteration without needing a full-fledged application environment. +4. FastAPI API Integration +FastAPI: A modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use and deploy, with automatic generation of OpenAPI and API documentation. +Benefit: FastAPI allows us to expose the module's functionality as a web API. This makes the module accessible over HTTP, enabling integration with other systems, microservices, or user interfaces. +Example Usage with FastAPI: +python +Copy code +@router.post("/proposal_generator/") +async def proposal_generator_route(data: dict): + # Your code generation logic here + init_dspy() + + print(data) + return proposal_generator_call(**data) +proposal_generator_route: An API endpoint that accepts JSON input to generate a proposal. +Benefit: This exposes the module functionality over a REST API, making it accessible to any client (web, mobile, other services). It enables a broad range of integration possibilities, from web-based UIs to machine-to-machine communication. +5. Enabling Capabilities Through Modular Design +By incorporating all these functionalities into module generators, we unlock several capabilities: + +Reusability and Modularity: + +Each module encapsulates a specific functionality (e.g., proposal generation). This makes modules reusable across different workflows and projects, reducing duplication and improving maintainability. +Composability and Chaining: + +The use of operator overloading and pipelines enables chaining of modules. This allows developers to easily create complex workflows by composing simple modules, promoting a functional programming style. +Flexible Interface Options: + +Providing multiple interfaces (CLI via Typer, API via FastAPI) makes the modules accessible in different environments. This flexibility supports various use cases, from local development and testing to production deployment and integrations. +Scalability and Extensibility: + +The use of DSPy modules within a structured framework allows for easy scaling and extension. New modules can be added without disrupting existing ones, and workflows can be adapted to new requirements by simply plugging in different modules. +Enhanced Developer Experience: + +Tools like Typer and FastAPI make it easier to interact with and test the modules. The integration of these tools enhances the developer experience by providing immediate feedback, reducing the time spent on debugging and iteration. +Integration with LLMs (Large Language Models): + +By using DSPy and LLMs (e.g., GPT, BERT), modules can perform intelligent, context-aware tasks such as generating proposals or analyzing text, greatly enhancing the functionality and intelligence of the system. +Conclusion +By embedding all of this functionality into module generators, you create a flexible, modular, and powerful framework that can handle complex AI workflows. This approach provides significant benefits, including improved reusability, composability, scalability, and ease of integration, while also enhancing the developer experience and unlocking new capabilities through LLM integration. This modular design allows for rapid development and adaptation to new requirements, making it ideal for a dynamic field like AI development. + + +2/2 + +You said: +Now let's show an implementation of the inhabitant using JavaScript and the CLI command. let's assume that there's similar commands for the rest of Roberts rules of order +ChatGPT said: +ChatGPT +To implement an Inhabitant using JavaScript, we will create a script that interacts with the CLI commands defined by the module generators (like the ProposalGeneratorModule). The JavaScript implementation will utilize Node.js to execute CLI commands, capture outputs, and handle events reactively. + +We'll also assume similar commands exist for other parts of "Robert's Rules of Order" such as managing debates, voting, and processing vote results. The JavaScript implementation will use these commands to simulate the behavior of the Inhabitant in the system. + +Step-by-Step JavaScript Implementation +Setup the Project Environment: + +Ensure that Node.js is installed. +Set up a JavaScript project and install any necessary dependencies (like child_process to execute CLI commands). +Create a JavaScript Script for Inhabitant: + +Use the Node.js child_process module to execute the CLI commands defined by the Python modules. +Implement functions to handle different tasks (e.g., generating proposals, managing debates, voting). +Implement the JavaScript Inhabitant: + +The JavaScript Inhabitant will simulate receiving messages, executing the corresponding CLI commands, and handling outputs. +JavaScript Inhabitant Example +Here is the JavaScript code for implementing an Inhabitant that interacts with the CLI commands: + +javascript +Copy code +const { exec } = require("child_process"); + +/** + * Executes a CLI command and returns the output. + * @param {string} command - The CLI command to execute. + * @returns {Promise} - A promise that resolves with the command output. + */ +function executeCommand(command) { + return new Promise((resolve, reject) => { + exec(command, (error, stdout, stderr) => { + if (error) { + console.error(`Error executing command: ${command}\n${error.message}`); + reject(error); + return; + } + if (stderr) { + console.error(`Command stderr: ${stderr}`); + } + resolve(stdout.trim()); + }); + }); +} + +/** + * Generates a proposal by executing the corresponding CLI command. + * @param {string} context - The context for the proposal. + * @param {string} criteria - The criteria for the proposal. + * @returns {Promise} + */ +async function generateProposal(context, criteria) { + const command = `python proposal_generator_call.py --context "${context}" --criteria "${criteria}"`; + try { + const output = await executeCommand(command); + console.log(`Generated Proposal: ${output}`); + } catch (error) { + console.error("Failed to generate proposal:", error); + } +} + +/** + * Manages a debate by executing the corresponding CLI command. + * @param {string} proposalId - The ID of the proposal to debate. + * @returns {Promise} + */ +async function manageDebate(proposalId) { + const command = `python debate_manager_call.py --proposalId "${proposalId}"`; + try { + const output = await executeCommand(command); + console.log(`Debate Managed: ${output}`); + } catch (error) { + console.error("Failed to manage debate:", error); + } +} + +/** + * Initiates and handles voting by executing the corresponding CLI command. + * @param {string} proposalId - The ID of the proposal to vote on. + * @returns {Promise} + */ +async function handleVoting(proposalId) { + const command = `python voting_handler_call.py --proposalId "${proposalId}"`; + try { + const output = await executeCommand(command); + console.log(`Vote Result: ${output}`); + } catch (error) { + console.error("Failed to handle voting:", error); + } +} + +/** + * Analyzes the results by executing the corresponding CLI command. + * @param {string} proposalId - The ID of the proposal to analyze results for. + * @returns {Promise} + */ +async function analyzeResults(proposalId) { + const command = `python result_analyzer_call.py --proposalId "${proposalId}"`; + try { + const output = await executeCommand(command); + console.log(`Analysis Result: ${output}`); + } catch (error) { + console.error("Failed to analyze results:", error); + } +} + +/** + * Main function to simulate an Inhabitant. + */ +async function main() { + const context = "The service colony needs new capabilities."; + const criteria = "High-performance, low-latency data service"; + + // Step 1: Generate a Proposal + await generateProposal(context, criteria); + + // Assume proposalId is retrieved from the generated output + const proposalId = "proposal-001"; + + // Step 2: Manage Debate for the Proposal + await manageDebate(proposalId); + + // Step 3: Handle Voting for the Proposal + await handleVoting(proposalId); + + // Step 4: Analyze the Results of the Vote + await analyzeResults(proposalId); +} + +main().catch(console.error); +Explanation of the JavaScript Implementation +Command Execution (executeCommand): + +Uses child_process.exec to run the Python CLI commands generated from module functions (like proposal_generator_call.py). +Returns a promise that resolves with the output of the command. +Task Functions (generateProposal, manageDebate, handleVoting, analyzeResults): + +Each function calls the respective CLI command that was defined earlier in the Python scripts. +These functions simulate different actions that the Inhabitant needs to perform as part of "Robert's Rules of Order." +Main Function (main): + +Simulates the Inhabitant behavior by sequentially calling the task functions to: +Generate a proposal. +Manage a debate. +Handle voting. +Analyze results. +Provides a clear, step-by-step flow of how an inhabitant processes messages and actions. +Benefits of this JavaScript Inhabitant Implementation +Cross-Language Interoperability: The JavaScript code interacts seamlessly with the Python modules, showcasing a practical example of how different technologies can be integrated. +Modularity and Reusability: Each task function (e.g., generateProposal, manageDebate) is modular and can be reused or extended. +Dynamic and Reactive: By executing CLI commands dynamically, the system remains reactive and can handle various inputs and outputs. +CLI-First Approach: Using CLI commands makes the system easy to debug, test, and automate in different environments (local, cloud, CI/CD pipelines). +Flexibility: This approach allows for integration with other systems or user interfaces, making it versatile and adaptable to different use cases. +Conclusion +By implementing the Inhabitant in JavaScript and utilizing CLI commands, you enable a dynamic, flexible, and reactive system that integrates well with other components and technologies. This approach demonstrates how to achieve modular design, cross-language compatibility, and an efficient AI-driven workflow while adhering to "Robert's Rules of Order." + + + + + + +You said: +"""The BaseInhabitant Module for Reactive Domain-Driven Design (RDDDY) Framework +--------------------------------------------------------------------- + +This module implements the core Inhabitant abstraction within the RDDDY framework, providing a robust foundation for building reactive, domain-driven systems that are scalable, maintainable, and capable of handling complex, concurrent interactions. The Inhabitant model encapsulates both state and behavior, allowing for asynchronous message passing as the primary means of communication between inhabitants, thus fostering loose coupling and enhanced system resilience. + +### Overview + +Inhabitants are the fundamental units of computation in the RDDDY framework. Each inhabitant possesses a unique identity, a mailbox for message queuing, and a set of behaviors to handle incoming messages. The Inhabitant module facilitates the creation, supervision, and coordination of inhabitants within an ServiceColony, ensuring that messages are delivered and processed in a manner consistent with the system's domain-driven design principles. + +### Multiline Calculus for Inhabitant Behavior Specification + +The operational semantics of inhabitants within the RDDDY framework are formalized through a rigorous multiline calculus, which outlines the preconditions, postconditions, and invariants that govern inhabitant behavior and interaction. This calculus serves as a contract, ensuring that inhabitants behave correctly and predictably within their designated domain contexts. + +#### Inhabitant State Transition + +Given an inhabitant \(A\) with state \(s\) and a message \(m\), the state transition is defined as: + +Precondition ( Pre Pre): 𝑠 ∈ 𝑆 s∈S and 𝑚 ∈ 𝑀 m∈M +Transition: 𝑇 ( 𝑠 , 𝑚 ) → 𝑠 ′ T(s,m)→s ′ +Postcondition ( Post Post): 𝑠 ′ ∈ 𝑆 ′ s ′ ∈S ′ + +#### Message Handling + +For a message \(m\) handled by the inhabitant, leading to a state modification: + +Precondition ( Pre Pre): 𝑠 ∈ 𝑆 s∈S and 𝑚 ∈ 𝑀 m∈M +Handling: 𝐻 ( 𝑚 , 𝑠 ) → 𝑠 ′ ′ H(m,s)→s ′′ +Postcondition ( Post Post): 𝑠 ′ ′ ∈ 𝑆 ′ ′ s ′′ ∈S ′′ + +#### Invariant Maintenance + +Ensuring system invariants \(I\) across transitions: + +Invariant: 𝐼 ( 𝑆 ) ∧ 𝑇 ( 𝑠 , 𝑚 ) → 𝑠 ′ ⇒ 𝐼 ( 𝑆 ′ ) I(S)∧T(s,m)→s ′ ⇒I(S ′ ) + +#### Domain-Specific Assertions + +Linking inhabitant state transitions to domain logic: + +Precondition ( Pre Pre): Δ ( 𝑠 ) Δ(s) and 𝛿 ( 𝑚 ) δ(m) +Domain Logic: 𝐷 ( 𝑠 , 𝑚 ) → Δ ( 𝑠 ′ ) D(s,m)→Δ(s ′ ) +Postcondition ( Post Post): Δ ′ ( 𝑠 ′ ) Δ ′ (s ′ ) + +### Purpose + +This calculus not only specifies the expected behavior of inhabitants in response to messages but also integrates domain-specific knowledge, ensuring that inhabitants operate in alignment with the broader domain-driven objectives of the system. By adhering to these specifications, the Inhabitant module provides a reliable and expressive framework for developing systems that are both technically sound and closely aligned with domain requirements. + +### Usage + +Developers should implement inhabitant behaviors in accordance with the outlined calculus, ensuring that each inhabitant's implementation respects the preconditions, postconditions, and domain-specific assertions relevant to their system's domain logic. This approach facilitates the development of systems that are not only functionally correct but also domain-compliant, thereby enhancing the value and applicability of the RDDDY framework in real-world scenarios. +""" +import asyncio +from collections.abc import Callable +from typing import TYPE_CHECKING, Optional, Type + +import reactivex as rx +from loguru import logger +from reactivex import operators as ops +from reactivex.scheduler.eventloop import AsyncIOScheduler + +from dspygen.rdddy.base_event import BaseEvent +from dspygen.rdddy.base_message import * + +if TYPE_CHECKING: + from dspygen.rdddy.service_colony import ServiceColony + + +class BaseInhabitant: + """Represents an inhabitant within the RDDDY framework. + + Inhabitants are fundamental units of computation in the RDDDY framework, encapsulating both state and behavior. + They communicate asynchronously through message passing, promoting loose coupling and system resilience. + + Args: + service_colony (ServiceColony): The ServiceColony to which the inhabitant belongs. + inhabitant_id (int, optional): The unique identifier of the inhabitant. Defaults to None. + + Attributes: + service_colony (ServiceColony): The ServiceColony to which the inhabitant belongs. + inhabitant_id (int): The unique identifier of the inhabitant. + mailbox (Subject): A subject for message queuing. + handlers (dict): A mapping of message types to corresponding handler methods. + + Methods: + start(scheduler): Starts the inhabitant's mailbox processing loop. + on_next(message): Callback function for processing incoming messages. + on_error(error): Callback function for handling errors in the inhabitant's mailbox. + on_completed(): Callback function when the inhabitant's mailbox stream completes. + receive(message): Processes an incoming message. + send(recipient_id, message): Sends a message to another inhabitant. + publish(message): Publishes a message to the inhabitant system. + map_handlers(): Maps message types to corresponding handler methods. + """ + + def __init__(self, service_colony: "ServiceColony", inhabitant_id: Optional[int] = None): + self.service_colony = service_colony + self.inhabitant_id = inhabitant_id or id(self) + self.mailbox = rx.subject.Subject() + self.handlers = self.map_handlers() + + async def start(self, scheduler: AsyncIOScheduler): + """Initiates the processing loop for the inhabitant's mailbox, ensuring asynchronous message handling. + + Preconditions (Pre): + - The inhabitant's mailbox must be initialized. + - A valid scheduler must be provided. + + Transition (T): + - Initiates the processing loop for the inhabitant's mailbox, enabling asynchronous message handling. + + Postconditions (Post): + - The inhabitant's mailbox processing loop has started successfully. + + Args: + scheduler: An asynchronous scheduler used to control the execution of tasks. + + """ + self.mailbox.pipe(ops.observe_on(scheduler)).subscribe( + on_next=self.on_next, # Synchronous wrapper for async handler + on_error=self.on_error, + on_completed=self.on_completed, + ) + logger.info(f"Inhabitant {self.inhabitant_id} started") + + def on_next(self, message: BaseMessage): + """Handles the next incoming message in the inhabitant's mailbox. + + Preconditions (Pre): + - The incoming message must be a valid instance of the Message class. + + Transition (T): + - Processes the incoming message asynchronously. + + Postconditions (Post): + - The incoming message has been processed by the inhabitant. + + Args: + message (BaseMessage): The incoming message to be processed. + """ + # Schedule the async handler as a new task + # logger.debug(f"Inhabitant {self.inhabitant_id} received message: {message}") + asyncio.create_task(self.receive(message)) + + def on_error(self, error): + """Handles errors that occur in the inhabitant's mailbox processing. + + Preconditions (Pre): + - None + + Transition (T): + - Handles the error generated during mailbox processing. + + Postconditions (Post): + - The error has been handled, and appropriate action has been taken. + + Args: + error: The error object representing the error that occurred. + """ + logger.error(f"Error in inhabitant {self.inhabitant_id} mailbox: {error}") + + def on_completed(self): + """Handles the completion of the inhabitant's mailbox stream. + + Preconditions (Pre): + - None + + Transition (T): + - Handles the completion event of the inhabitant's mailbox stream. + + Postconditions (Post): + - The inhabitant's mailbox stream has completed, and appropriate action has been taken. + """ + # logger.debug(f"Inhabitant {self.inhabitant_id} mailbox stream completed") + + async def receive(self, message: BaseMessage): + """Processes an incoming message received by the inhabitant. + + Preconditions (Pre): + - The incoming message must be a valid instance of the Message class. + + Transition (T): + - Processes the incoming message asynchronously, invoking the appropriate handler method. + + Postconditions (Post): + - The incoming message has been successfully processed by the inhabitant. + + Args: + message (BaseMessage): The incoming message to be processed. + """ + try: + handler = self.handlers.get(type(message)) + if handler: + logger.debug( + f"Inhabitant handling message: {message} with {handler.__name__}" + ) + await handler(message) + except Exception as e: + error_message = f"Error in inhabitant {self.inhabitant_id} processing message: {e}" + # Broadcast an error event through the inhabitant system + await self.publish(BaseEvent(content=error_message)) + logger.error(error_message) + + async def publish(self, message: BaseMessage): + """Publishes a message to the inhabitant system for distribution. + + Preconditions (Pre): + - The message must be a valid instance of the Message class. + + Transition (T): + - Publishes the message to the inhabitant system for distribution. + + Postconditions (Post): + - The message has been successfully published to the inhabitant system. + + Args: + message (BaseMessage): The message to be published. + """ + if message.inhabitant_id == -1: + message.inhabitant_id = self.inhabitant_id + + await self.service_colony.publish(message) + + def map_handlers(self) -> dict[Type[BaseMessage], Callable]: + """Maps message types to corresponding handler methods. + + Preconditions (Pre): + - None + + Transition (T): + - Iterates through the methods of the inhabitant instance and identifies callable methods with annotations. + - Maps message types to corresponding handler methods based on method annotations. + + Postconditions (Post): + - A dictionary containing message types as keys and corresponding handler methods as values has been generated. + """ + handlers = {} + for name, method in inspect.getmembers(self): + if callable(method) and hasattr(method, "__annotations__"): + annotations = method.__annotations__ + for arg in annotations.values(): + try: + if issubclass(arg, BaseMessage): + handlers[arg] = method + except TypeError: + pass + del handlers[BaseMessage] + return handlers + +import asyncio +from typing import TYPE_CHECKING, Optional, TypeVar, cast + +import inject +import reactivex as rx +from loguru import logger +from reactivex import operators as ops +from reactivex.scheduler.eventloop import AsyncIOScheduler + +from realtime import RealtimeSubscribeStates + +from dspygen.rdddy.async_realtime_client import AsyncRealtimeClient +from dspygen.rdddy.base_message import BaseMessage, MessageFactory + +if TYPE_CHECKING: + from dspygen.rdddy.base_inhabitant import BaseInhabitant + +T = TypeVar("T", bound="BaseInhabitant") + + +class ServiceColony: + """Orchestrates inhabitant lifecycle management, message passing, and system-wide coordination within + the RDDDY framework.""" + def __init__(self, loop: Optional[asyncio.AbstractEventLoop] = None, realtime_client: AsyncRealtimeClient = None): + """Initializes the ServiceColony with a real-time client connection.""" + self.realtime_client = inject.instance(AsyncRealtimeClient) if realtime_client is None else realtime_client + self.channel = None + self.inhabitants: dict[int, "BaseInhabitant"] = {} + self.loop = loop if loop is not None else asyncio.get_event_loop() + self.scheduler = AsyncIOScheduler(loop=self.loop) + self.event_stream = rx.subject.Subject() + + async def connect(self): + """Connect to the WebSocket and join the main channel.""" + await self.realtime_client.connect() + + # Create and join the channel (e.g., "service_colony:lobby") + + await self.channel.subscribe(self._on_channel_subscribe) + logger.info(f"Joined channel 'service_colony:lobby'") + + # Listen for incoming messages + self.channel.on_broadcast("message", self._on_message_received) + + def _on_channel_subscribe(self, status: RealtimeSubscribeStates, err: Optional[Exception]): + """Handle subscription status.""" + if status == RealtimeSubscribeStates.SUBSCRIBED: + logger.info('Successfully subscribed to the channel.') + else: + logger.error(f'Error subscribing to channel: {err}') + + def _on_message_received(self, payload: dict): + """Callback for handling incoming messages.""" + message = MessageFactory.create_message(payload) + asyncio.run_coroutine_threadsafe(self.distribute_message(message), self.loop) + + async def distribute_message(self, message): + """Distributes a message within the inhabitant system.""" + self.event_stream.on_next(message) + for inhabitant in list(self.inhabitants.values()): + await self.send(inhabitant.inhabitant_id, message) + + async def inhabitant_of(self, inhabitant_class, **kwargs) -> T: + """Creates a new inhabitant instance and starts its mailbox processing loop.""" + inhabitant = inhabitant_class(self, **kwargs) + self.inhabitants[inhabitant.inhabitant_id] = inhabitant + await inhabitant.start(self.scheduler) + logger.info(f"Inhabitant {inhabitant.inhabitant_id} started") + return inhabitant + + async def inhabitants_of(self, inhabitant_classes, **kwargs) -> list[T]: + """Creates multiple inhabitant instances of different types and starts their mailbox processing loops.""" + return [await self.inhabitant_of(cls, **kwargs) for cls in inhabitant_classes] + + async def publish(self, message: "BaseMessage"): + """Publishes a message to the inhabitant system for distribution.""" + logger.debug(f"Publishing message: {message}") + if type(message) is BaseMessage: + raise ValueError("The base Message class should not be used directly. Please use a subclass of Message.") + + # Send message via the real-time channel + await self.channel.send_broadcast("message", message.model_dump_json()) + self.event_stream.on_next(message) + + for inhabitant in list(self.inhabitants.values()): + await self.send(inhabitant.inhabitant_id, message) + + async def remove_inhabitant(self, inhabitant_id): + """Removes an inhabitant from the inhabitant system.""" + inhabitant = self.inhabitants.pop(inhabitant_id, None) + if inhabitant: + logger.debug(f"Removing inhabitant {inhabitant_id}") + else: + logger.debug(f"Inhabitant {inhabitant_id} not found for removal") + logger.debug(f"Current inhabitants count: {len(self.inhabitants)}") + + async def send(self, inhabitant_id: int, message: "BaseMessage"): + """Sends a message to a specific inhabitant within the inhabitant system.""" + inhabitant = self.inhabitants.get(inhabitant_id) + if inhabitant: + inhabitant.mailbox.on_next(message) + await asyncio.sleep(0) + else: + logger.debug(f"Inhabitant {inhabitant_id} not found.") + + async def wait_for_message(self, message_type: type) -> "BaseMessage": + """Waits for a message of a specific type to be published to the inhabitant system.""" + loop = asyncio.get_event_loop() + future = loop.create_future() + + def on_next(msg): + if isinstance(msg, message_type): + future.set_result(msg) + subscription.dispose() + + subscription = self.event_stream.pipe( + ops.filter(lambda msg: isinstance(msg, message_type)) + ).subscribe(on_next) + + return await future + + def __getitem__(self, inhabitant_id) -> T: + """Retrieves an inhabitant by its ID from the inhabitant system.""" + return cast(T, self.inhabitants.get(inhabitant_id)) + + async def shutdown(self): + """Shuts down the inhabitant system and terminates all inhabitants.""" + try: + if self.channel: + await self.channel.unsubscribe() + if self.realtime_client.is_connected: + await self.realtime_client.close() + logger.debug("Inhabitant system shutdown complete.") + except RuntimeError as e: + # Event loop already closed + pass + + +create JS equivalent +ChatGPT said: +ChatGPT +To create a JavaScript equivalent of the BaseInhabitant module and the ServiceColony module from the RDDDY framework, we'll use Node.js. The implementation will include asynchronous message handling, reactive extensions, and WebSocket communication for real-time interactions between inhabitants. + +JavaScript Implementation Using Node.js +We will use the following libraries to replicate the functionality: + +RxJS: A JavaScript library for reactive programming using Observables. +WebSocket: A library for real-time communication. +uuid: For generating unique identifiers for inhabitants. +Step 1: Set Up the JavaScript Project +Initialize a new Node.js project: + +bash +Copy code +npm init -y +Install necessary dependencies: + +bash +Copy code +npm install rxjs ws uuid log4js +Step 2: Implement BaseInhabitant in JavaScript +javascript +Copy code +const { Subject } = require('rxjs'); +const { filter } = require('rxjs/operators'); +const { v4: uuidv4 } = require('uuid'); +const log4js = require('log4js'); + +const logger = log4js.getLogger(); +logger.level = 'info'; + +class BaseInhabitant { + /** + * Represents an inhabitant within the RDDDY framework. + * Inhabitants are fundamental units of computation, encapsulating both state and behavior. + * They communicate asynchronously through message passing, promoting loose coupling and system resilience. + * + * @param {ServiceColony} serviceColony - The ServiceColony to which the inhabitant belongs. + * @param {string} [inhabitantId] - The unique identifier of the inhabitant. Defaults to a UUID. + */ + constructor(serviceColony, inhabitantId = null) { + this.serviceColony = serviceColony; + this.inhabitantId = inhabitantId || uuidv4(); + this.mailbox = new Subject(); // A Subject for message queuing + this.handlers = this.mapHandlers(); + } + + /** + * Maps message types to corresponding handler methods. + * + * @returns {Object} - A dictionary containing message types as keys and corresponding handler methods as values. + */ + mapHandlers() { + const handlers = {}; + for (const key of Object.getOwnPropertyNames(Object.getPrototypeOf(this))) { + const method = this[key]; + if (typeof method === 'function' && method.name.startsWith('handle')) { + handlers[method.name] = method.bind(this); + } + } + return handlers; + } + + /** + * Starts the inhabitant's mailbox processing loop, ensuring asynchronous message handling. + */ + start() { + this.mailbox.subscribe({ + next: (message) => this.onNext(message), + error: (err) => this.onError(err), + complete: () => this.onCompleted() + }); + logger.info(`Inhabitant ${this.inhabitantId} started`); + } + + /** + * Handles the next incoming message in the inhabitant's mailbox. + * + * @param {Object} message - The incoming message to be processed. + */ + async onNext(message) { + try { + const handler = this.handlers[`handle${message.type}`]; + if (handler) { + logger.info(`Inhabitant ${this.inhabitantId} handling message: ${message.type}`); + await handler(message); + } else { + logger.warn(`No handler found for message type: ${message.type}`); + } + } catch (err) { + this.onError(err); + } + } + + /** + * Handles errors that occur in the inhabitant's mailbox processing. + * + * @param {Error} error - The error object representing the error that occurred. + */ + onError(error) { + logger.error(`Error in inhabitant ${this.inhabitantId} mailbox: ${error}`); + } + + /** + * Handles the completion of the inhabitant's mailbox stream. + */ + onCompleted() { + logger.info(`Inhabitant ${this.inhabitantId} mailbox stream completed`); + } + + /** + * Processes an incoming message received by the inhabitant. + * + * @param {Object} message - The incoming message to be processed. + */ + async receive(message) { + this.mailbox.next(message); + } + + /** + * Sends a message to another inhabitant. + * + * @param {string} recipientId - The ID of the recipient inhabitant. + * @param {Object} message - The message to be sent. + */ + async send(recipientId, message) { + this.serviceColony.send(recipientId, message); + } + + /** + * Publishes a message to the inhabitant system for distribution. + * + * @param {Object} message - The message to be published. + */ + async publish(message) { + this.serviceColony.publish(message); + } +} +Step 3: Implement ServiceColony in JavaScript +javascript +Copy code +const WebSocket = require('ws'); +const { Subject } = require('rxjs'); +const { filter } = require('rxjs/operators'); +const log4js = require('log4js'); + +const logger = log4js.getLogger(); +logger.level = 'info'; + +class ServiceColony { + /** + * Orchestrates inhabitant lifecycle management, message passing, and system-wide coordination. + */ + constructor() { + this.inhabitants = {}; + this.eventStream = new Subject(); + this.websocketServer = new WebSocket.Server({ port: 8080 }); + + this.websocketServer.on('connection', (ws) => { + ws.on('message', (data) => this._onMessageReceived(data)); + logger.info('New WebSocket connection established.'); + }); + } + + /** + * Handles incoming messages from WebSocket. + * + * @param {string} data - The incoming data from WebSocket. + */ + _onMessageReceived(data) { + const message = JSON.parse(data); + this.distributeMessage(message); + } + + /** + * Distributes a message within the inhabitant system. + * + * @param {Object} message - The message to be distributed. + */ + async distributeMessage(message) { + this.eventStream.next(message); + Object.values(this.inhabitants).forEach(async (inhabitant) => { + await this.send(inhabitant.inhabitantId, message); + }); + } + + /** + * Creates a new inhabitant instance and starts its mailbox processing loop. + * + * @param {Function} inhabitantClass - The class of the inhabitant. + * @param {...any} args - Arguments to pass to the inhabitant constructor. + * @returns {BaseInhabitant} - The created inhabitant instance. + */ + async inhabitantOf(inhabitantClass, ...args) { + const inhabitant = new inhabitantClass(this, ...args); + this.inhabitants[inhabitant.inhabitantId] = inhabitant; + inhabitant.start(); + logger.info(`Inhabitant ${inhabitant.inhabitantId} started`); + return inhabitant; + } + + /** + * Sends a message to a specific inhabitant within the inhabitant system. + * + * @param {string} inhabitantId - The ID of the recipient inhabitant. + * @param {Object} message - The message to be sent. + */ + async send(inhabitantId, message) { + const inhabitant = this.inhabitants[inhabitantId]; + if (inhabitant) { + inhabitant.receive(message); + } else { + logger.warn(`Inhabitant ${inhabitantId} not found.`); + } + } + + /** + * Publishes a message to all inhabitants. + * + * @param {Object} message - The message to be published. + */ + async publish(message) { + logger.debug(`Publishing message: ${JSON.stringify(message)}`); + Object.values(this.inhabitants).forEach((inhabitant) => { + inhabitant.receive(message); + }); + } +} +Explanation of JavaScript Implementation +Reactive Message Handling: + +RxJS Subject: Used for handling message streams (mailbox and eventStream). +Provides asynchronous and non-blocking message handling, similar to ReactiveX in Python. +WebSocket Integration: + +WebSocket is used for real-time communication, enabling inhabitants to communicate over a network. +The ServiceColony sets up a WebSocket server and handles incoming messages, distributing them to the relevant inhabitants. +Dynamic Message Processing: + +Dynamic Handler Mapping: The mapHandlers method maps message types to handler functions dynamically. This allows inhabitants to handle different message types without hardcoded logic. +Lifecycle Management: + +The ServiceColony manages the lifecycle of inhabitants, starting their processing loops and handling message distribution. +Real-Time Interactions: + +The use of WebSocket allows for real-time message passing between inhabitants, which is essential for reactive and concurrent systems. +Conclusion +This JavaScript implementation replicates the core functionality of the RDDDY framework, allowing for dynamic, asynchronous message handling and real-time communication between system components. This design pattern ensures scalability, maintainability, and flexibility, which are crucial for building robust, reactive systems in real-world applications. + + + + + + +You said: +Now implement the voting inhabitant in JS +ChatGPT said: +ChatGPT +To implement the VotingInhabitant in JavaScript, we will extend the BaseInhabitant class created earlier. The VotingInhabitant will handle messages related to proposals, voting, and vote results, dynamically reacting to each event and processing them accordingly. + +JavaScript Implementation of VotingInhabitant +This implementation will assume that the voting process involves generating proposals, managing debates, handling votes, and analyzing results. Each of these actions will be triggered by receiving specific message types. + +Step 1: Extend the BaseInhabitant Class +The VotingInhabitant class will be responsible for handling different types of messages: ProposalMessage, VoteMessage, and VoteResultMessage. It will use corresponding methods to handle each message type, much like the Python version. + +Here is the JavaScript implementation: + +javascript +Copy code +const { BaseInhabitant } = require('./base_inhabitant'); // Assuming you have the base_inhabitant.js file +const log4js = require('log4js'); + +const logger = log4js.getLogger(); +logger.level = 'info'; + +class VotingInhabitant extends BaseInhabitant { + /** + * Represents a voting inhabitant within the RDDDY framework. + * This inhabitant handles messages related to proposals, voting initiation, and vote results. + * + * @param {ServiceColony} serviceColony - The ServiceColony to which the voting inhabitant belongs. + * @param {string} [inhabitantId] - The unique identifier of the inhabitant. Defaults to a UUID. + */ + constructor(serviceColony, inhabitantId = null) { + super(serviceColony, inhabitantId); + this.proposals = {}; // Store proposals by their ID + this.votes = {}; // Store votes by proposal ID + } + + /** + * Handles a proposal message by generating a new proposal. + * + * @param {Object} message - The incoming proposal message to be handled. + */ + async handleProposalMessage(message) { + const proposalId = `proposal-${Date.now()}`; + this.proposals[proposalId] = { + id: proposalId, + proposer: message.proposer, + description: message.description + }; + logger.info(`Generated new proposal: ${proposalId}`); + await this.publish({ type: 'DebateMessage', proposalId, description: message.description }); + } + + /** + * Handles a debate message by generating debate contributions. + * + * @param {Object} message - The incoming debate message to be handled. + */ + async handleDebateMessage(message) { + logger.info(`Handling debate for proposal: ${message.proposalId}`); + // Logic to manage debate can go here. + // Simulate generating debate content + await this.publish({ type: 'VoteMessage', proposalId: message.proposalId, voteType: 'yes' }); + } + + /** + * Handles a vote message by managing the voting process. + * + * @param {Object} message - The incoming vote message to be handled. + */ + async handleVoteMessage(message) { + const proposalVotes = this.votes[message.proposalId] || { yes: 0, no: 0, abstain: 0 }; + proposalVotes[message.voteType] += 1; + this.votes[message.proposalId] = proposalVotes; + + logger.info(`Vote registered: ${message.voteType} for proposal ${message.proposalId}`); + // Simulate checking if all votes are in + await this.publish({ type: 'VoteResultMessage', proposalId: message.proposalId, results: proposalVotes }); + } + + /** + * Handles a vote result message by analyzing the vote results. + * + * @param {Object} message - The incoming vote result message to be handled. + */ + async handleVoteResultMessage(message) { + const results = message.results; + const outcome = results.yes > results.no ? 'passed' : 'failed'; + logger.info(`Proposal ${message.proposalId} has ${outcome}.`); + // Further logic for handling the outcome can go here + } +} + +module.exports = { VotingInhabitant }; +Explanation of the VotingInhabitant Class +Constructor (constructor): + +Initializes the VotingInhabitant with a service colony and assigns it a unique identifier. +Sets up internal storage for proposals and votes. +Message Handlers: + +handleProposalMessage: +Processes incoming proposal messages by generating a new proposal ID and storing the proposal. +Publishes a DebateMessage to initiate debate on the proposal. +handleDebateMessage: +Handles debate messages by simulating the generation of debate content. +Publishes a VoteMessage to initiate voting on the proposal. +handleVoteMessage: +Handles vote messages by updating the vote count for the proposal. +Publishes a VoteResultMessage with the voting results once all votes are registered. +handleVoteResultMessage: +Analyzes the vote results to determine the outcome (passed or failed). +Logs the result and can trigger further actions based on the outcome. +Reactive Handling: + +Each handler uses publish to send messages back to the system, allowing other inhabitants to react to the current state. +Step 2: Integrate VotingInhabitant with the ServiceColony +Now, integrate VotingInhabitant with ServiceColony to manage and coordinate messages between inhabitants. + +Example of Usage in JavaScript +javascript +Copy code +const { ServiceColony } = require('./service_colony'); // Assuming you have the service_colony.js file +const { VotingInhabitant } = require('./voting_inhabitant'); // Assuming you have the voting_inhabitant.js file + +(async () => { + const serviceColony = new ServiceColony(); + + // Create a new VotingInhabitant + const votingInhabitant = await serviceColony.inhabitantOf(VotingInhabitant); + + // Example messages to simulate a voting process + const proposalMessage = { type: 'ProposalMessage', proposer: 'AgentX', description: 'Register a new AI service' }; + const debateMessage = { type: 'DebateMessage', proposalId: 'proposal-001', description: 'Debate on AI service' }; + const voteMessage = { type: 'VoteMessage', proposalId: 'proposal-001', voteType: 'yes' }; + const voteResultMessage = { type: 'VoteResultMessage', proposalId: 'proposal-001', results: { yes: 5, no: 2, abstain: 0 } }; + + // Simulate the message handling flow + await votingInhabitant.receive(proposalMessage); + await votingInhabitant.receive(debateMessage); + await votingInhabitant.receive(voteMessage); + await votingInhabitant.receive(voteResultMessage); +})(); +Explanation of Integration +Creating an Inhabitant: + +The ServiceColony orchestrates the creation of the VotingInhabitant and manages the message-passing lifecycle. +Simulating the Voting Process: + +Example messages are created to simulate the various stages of the voting process (ProposalMessage, DebateMessage, VoteMessage, VoteResultMessage). +The receive method of VotingInhabitant is called to handle each message, triggering the corresponding logic and events in the system. +Benefits of This JavaScript Implementation +Asynchronous and Reactive: Uses asynchronous JavaScript to handle events and messages dynamically, similar to reactive patterns in other languages. +Modular and Extensible: The VotingInhabitant class is modular, allowing easy extension and reuse in different contexts. +Real-Time Communication: By using a service colony with WebSocket support, the system can handle real-time messaging and coordination. +Cross-Language Compatibility: This JavaScript implementation can integrate with other services and languages, making it flexible for distributed systems. +Conclusion +This JavaScript implementation of VotingInhabitant provides a reactive, event-driven solution for managing the voting process in a distributed system. It leverages asynchronous messaging and dynamic message handling to enable a robust and scalable framework, similar to its Python counterpart. + + + + + + +You said: +Now create the C4 document using mermaid js +ChatGPT said: +ChatGPT +To create a C4 model diagram using Mermaid.js for the VotingInhabitant system, we need to visualize the system architecture based on the C4 model's four levels: + +Context Diagram - Shows the overall system context and its interactions with external entities. +Container Diagram - Illustrates the containers (applications, services, databases) within the system and how they interact. +Component Diagram - Details the components within a container and their interactions. +Code/Deployment Diagram - Specifies the actual deployment environment or internal code structure. +Mermaid.js C4 Model Diagram +Here's how we can represent the VotingInhabitant system using Mermaid.js syntax: + +mermaid +Copy code +--- +title: C4 Model Diagram for VotingInhabitant System +--- + +%% Context Diagram +%% This diagram represents the overall context of the VotingInhabitant system. + +flowchart TB + subgraph "Context Diagram" + User[User] -->|Submits Proposals| VotingSystem + User -->|Interacts| VotingAPI + VotingSystem -->|Handles Events| VotingInhabitant + VotingSystem -->|Communicates| RealtimeClient + RealtimeClient -->|Sends/Receives Messages| ServiceColony + end + +%% Container Diagram +%% This diagram represents the containers within the VotingInhabitant system. + +flowchart TB + subgraph "Container Diagram" + subgraph VotingSystem + VotingAPI[Web API] + VotingInhabitant["VotingInhabitant"] + RealtimeClient["Async Realtime Client"] + end + subgraph ServiceColony["ServiceColony"] + ServiceColonyWS[WebSocket Server] + end + User --> VotingAPI + VotingAPI --> VotingInhabitant + VotingInhabitant --> RealtimeClient + RealtimeClient --> ServiceColonyWS + end + +%% Component Diagram +%% This diagram + + + + + +You said: +To adapt the example to use Phoenix Channels with the Ash framework instead of Phoenix LiveView, we need to establish real-time communication using WebSockets. Phoenix Channels provide a great way to implement reactive, event-driven functionality in a web application, such as our service colony, where asynchronous communication and real-time updates are essential. + +### Overview of the Changes: +1. **Set up a Phoenix Channel**: Define a new channel to handle real-time messages related to proposals, debates, votes, and results. +2. **Modify Ash Reactor**: Use the Ash Reactor framework to handle the actions triggered by channel events. +3. **Create Channel Client and Server Logic**: Implement the client-side and server-side logic to interact with the channel, allowing users to see live updates as the service colony makes decisions. + +### 1. Set Up a Phoenix Channel + +First, let's define a new Phoenix Channel to handle messages for our service colony governance. + +#### Create a Channel Module + +Create a new module for the channel in lib/my_ash_phoenix_app_web/channels/service_colony_channel.ex: + +elixir +defmodule MyAshPhoenixAppWeb.ServiceColonyChannel do + use Phoenix.Channel + + alias MyAshPhoenixApp.Blog + alias MyAshPhoenixApp.Reactor.ServiceColonyReactor + + def join("service_colony:lobby", _params, socket) do + {:ok, socket} + end + + # Handle incoming messages for creating a new proposal + def handle_in("proposal:submit", %{"proposal" => proposal_params}, socket) do + case ServiceColonyReactor.run(:submit_proposal, proposal_params) do + {:ok, proposal} -> + broadcast(socket, "proposal:created", %{proposal: proposal}) + {:noreply, socket} + + {:error, reason} -> + {:reply, {:error, %{error: reason}}, socket} + end + end + + # Handle incoming messages for debate contributions + def handle_in("debate:contribute", %{"debate" => debate_params}, socket) do + case ServiceColonyReactor.run(:contribute_to_debate, debate_params) do + {:ok, debate} -> + broadcast(socket, "debate:updated", %{debate: debate}) + {:noreply, socket} + + {:error, reason} -> + {:reply, {:error, %{error: reason}}, socket} + end + end + + # Handle incoming messages for initiating a vote + def handle_in("vote:initiate", %{"vote" => vote_params}, socket) do + case ServiceColonyReactor.run(:initiate_vote, vote_params) do + {:ok, vote} -> + broadcast(socket, "vote:started", %{vote: vote}) + {:noreply, socket} + + {:error, reason} -> + {:reply, {:error, %{error: reason}}, socket} + end + end + + # Handle incoming messages for vote results + def handle_in("vote:result", %{"result" => result_params}, socket) do + case ServiceColonyReactor.run(:process_vote_result, result_params) do + {:ok, result} -> + broadcast(socket, "vote:completed", %{result: result}) + {:noreply, socket} + + {:error, reason} -> + {:reply, {:error, %{error: reason}}, socket} + end + end +end + + +### 2. Modify the Ash Reactor + +Modify the ServiceColonyReactor to interact with Phoenix Channels by sending and receiving events as actions are performed: + +#### Reactor Module: lib/my_ash_phoenix_app/reactor/service_colony_reactor.ex + +elixir +defmodule MyAshPhoenixApp.Reactor.ServiceColonyReactor do + use Ash.Reactor + + input :proposal_id + input :proposer_name + input :proposal_type + input :proposal_description + input :debate_contribution + input :vote_type + input :initiator_name + + # Define actions corresponding to the channel messages + create :submit_proposal, Proposal do + inputs %{ + proposalId: input(:proposal_id), + proposer: input(:proposer_name), + proposalType: input(:proposal_type), + description: input(:proposal_description), + timestamp: DateTime.utc_now() + } + end + + create :contribute_to_debate, Debate do + inputs %{ + proposalId: input(:proposal_id), + participant: input(:proposer_name), + contribution: input(:debate_contribution), + timestamp: DateTime.utc_now() + } + end + + create :initiate_vote, Vote do + inputs %{ + proposalId: input(:proposal_id), + voteType: input(:vote_type), + initiatedBy: input(:initiator_name), + timestamp: DateTime.utc_now() + } + end + + create :process_vote_result, VoteResult do + inputs %{ + proposalId: input(:proposal_id), + results: %{ + yes: result(:initiate_vote, [:yes]), + no: result(:initiate_vote, [:no]), + abstain: result(:initiate_vote, [:abstain]) + }, + passed: fn results -> Map.get(results, :yes, 0) > Map.get(results, :no, 0) end, + timestamp: DateTime.utc_now() + } + end +end + + +### 3. Integrate with Client-Side Logic + +#### Update Client-Side JavaScript + +Use Phoenix's JavaScript client library to connect to the channel and handle events. In assets/js/app.js: + +javascript +import { Socket } from "phoenix" + +let socket = new Socket("/socket", { params: { userToken: window.userToken } }) +socket.connect() + +let channel = socket.channel("service_colony:lobby", {}) + +// Listen for proposal creation messages +channel.on("proposal:created", payload => { + console.log("New Proposal Created:", payload.proposal) + // Update the UI with the new proposal +}) + +// Listen for debate update messages +channel.on("debate:updated", payload => { + console.log("Debate Updated:", payload.debate) + // Update the UI with the latest debate information +}) + +// Listen for vote start messages +channel.on("vote:started", payload => { + console.log("Vote Started:", payload.vote) + // Update the UI to show that a vote has started +}) + +// Listen for vote result messages +channel.on("vote:completed", payload => { + console.log("Vote Completed:", payload.result) + // Update the UI with the vote results +}) + +// Join the channel +channel.join() + .receive("ok", resp => { console.log("Joined successfully", resp) }) + .receive("error", resp => { console.log("Unable to join", resp) }) + +// Function to submit a new proposal +function submitProposal(proposalData) { + channel.push("proposal:submit", { proposal: proposalData }) + .receive("ok", resp => { console.log("Proposal submitted successfully", resp) }) + .receive("error", resp => { console.error("Failed to submit proposal", resp) }) +} + +// Example usage: +submitProposal({ + proposal_id: "001", + proposer_name: "Alpha", + proposal_type: "serviceRegistration", + proposal_description: "Register a new data service" +}) + + +### 4. Modify Endpoint and Router Configuration + +Make sure to define the socket path and endpoint in lib/my_ash_phoenix_app_web/endpoint.ex: + +elixir +socket "/socket", MyAshPhoenixAppWeb.UserSocket, + websocket: true, + longpoll: false + + +And in lib/my_ash_phoenix_app_web/router.ex, define the channel: + +elixir +defmodule MyAshPhoenixAppWeb.Router do + use MyAshPhoenixAppWeb, :router + + # Other routes... + + socket "/socket", MyAshPhoenixAppWeb.UserSocket +end + + +### Explanation: + +By using Phoenix Channels, we enable real-time communication between the server and clients. Whenever a new proposal is submitted, a debate contribution is made, a vote is initiated, or a vote result is processed, updates are broadcasted to all connected clients immediately. This approach maintains the reactive and event-driven nature of service colonies and provides instant feedback to users, allowing them to see and respond to changes as they happen. + +Here's an AsyncAPI YAML file to represent the communication framework for the service colony using an adaptation of "Robert's Rules of Order". This YAML file outlines the asynchronous communication between different AI agents (inhabitants) within the colony. + +The schema defines channels for submitting proposals, debating them, voting, and sharing vote results, which aligns with the structured decision-making process similar to how "Robert's Rules of Order" operates. + +### AsyncAPI YAML Example: + +yaml +asyncapi: '2.6.0' +info: + title: Service Colony Governance + version: '1.0.0' + description: | + An AsyncAPI implementation for managing the decision-making process + of a service colony using a framework based on Robert's Rules of Order. + +servers: + production: + url: api.service-colony.example.com + protocol: amqp + description: Production server for the service colony message broker. + +channels: + proposal/submitted: + description: Channel for submitting new proposals. + subscribe: + summary: Receive notifications of new proposals submitted. + operationId: onProposalSubmitted + message: + $ref: '#/components/messages/ProposalMessage' + + debate/contribute: + description: Channel for contributing to debates on proposals. + subscribe: + summary: Receive contributions to ongoing debates. + operationId: onDebateContribution + message: + $ref: '#/components/messages/DebateMessage' + + vote/initiate: + description: Channel for initiating votes on proposals. + subscribe: + summary: Receive notifications of votes initiated. + operationId: onVoteInitiated + message: + $ref: '#/components/messages/VoteMessage' + + vote/results: + description: Channel for receiving vote results on proposals. + subscribe: + summary: Receive notifications of vote results. + operationId: onVoteResult + message: + $ref: '#/components/messages/VoteResultMessage' + +components: + messages: + ProposalMessage: + name: ProposalMessage + title: Proposal Submission + contentType: application/json + summary: A message for submitting new proposals. + payload: + type: object + properties: + proposalId: + type: string + description: Unique identifier for the proposal. + proposer: + type: string + description: Name of the Loa proposing the action. + proposalType: + type: string + description: Type of the proposal. + enum: + - serviceRegistration + - securityPolicyUpdate + - serviceComposition + - capabilityUpdate + - other + description: + type: string + description: Details of the proposal. + timestamp: + type: string + format: date-time + description: Time when the proposal was submitted. + + DebateMessage: + name: DebateMessage + title: Debate Contribution + contentType: application/json + summary: A message for contributing to the debate on a proposal. + payload: + type: object + properties: + proposalId: + type: string + description: Unique identifier for the proposal being debated. + participant: + type: string + description: Name of the Loa contributing to the debate. + contribution: + type: string + description: Content of the debate contribution. + timestamp: + type: string + format: date-time + description: Time when the debate contribution was made. + + VoteMessage: + name: VoteMessage + title: Vote Initiation + contentType: application/json + summary: A message for initiating a vote on a proposal. + payload: + type: object + properties: + proposalId: + type: string + description: Unique identifier for the proposal being voted on. + voteType: + type: string + description: Type of vote (yes, no, abstain). + enum: + - yes + - no + - abstain + initiatedBy: + type: string + description: Name of the Loa initiating the vote. + timestamp: + type: string + format: date-time + description: Time when the vote was initiated. + + VoteResultMessage: + name: VoteResultMessage + title: Vote Result + contentType: application/json + summary: A message for announcing the results of a vote. + payload: + type: object + properties: + proposalId: + type: string + description: Unique identifier for the proposal that was voted on. + results: + type: object + description: Results of the vote, containing counts for yes, no, and abstain. + properties: + yes: + type: integer + description: Number of 'yes' votes. + no: + type: integer + description: Number of 'no' votes. + abstain: + type: integer + description: Number of 'abstain' votes. + passed: + type: boolean + description: Whether the proposal passed or failed. + timestamp: + type: string + format: date-time + description: Time when the vote results were tallied. + + schemas: + UUID: + type: string + format: uuid + description: A universally unique identifier. + + securitySchemes: + basicAuth: + type: http + scheme: basic + description: Basic authentication for accessing the message broker. + +security: + - basicAuth: [] + + +### Explanation for Non-Technical Stakeholders: + +This AsyncAPI YAML file defines the messaging protocol for a decision-making process used by AI agents within a service colony. The file: + +1. **Describes the Purpose**: It sets up channels for submitting proposals, contributing to debates, initiating votes, and sharing vote results. This mirrors how a board of directors or government might handle decisions—by discussing, debating, and voting. + +2. **Defines Message Formats**: Specifies the structure of each message type (e.g., a proposal or vote message) to ensure all agents understand and process the information consistently. + +3. **Ensures Security**: It includes basic authentication to secure communication between agents, similar to access control in human organizations. + +By using these familiar concepts, we can better explain how AI systems reach decisions in a transparent, structured way. + +To create a VotingInhabitant class without using if or case statements, we will rely on the method mapping functionality provided by the BaseInhabitant class. We'll use the map_handlers method to dynamically associate message types with their corresponding handler methods. + +In this example, we'll create a VotingInhabitant that processes messages related to the voting process, such as ProposalMessage, VoteMessage, and VoteResultMessage. Each message type will have a designated handler method that gets called automatically based on the message type, avoiding the use of explicit conditional statements. + +### VotingInhabitant Implementation + +python +import asyncio +from typing import Optional + +from dspygen.rdddy.base_event import BaseEvent +from dspygen.rdddy.base_message import BaseMessage, ProposalMessage, VoteMessage, VoteResultMessage + +class VotingInhabitant(BaseInhabitant): + """Represents a voting inhabitant within the RDDDY framework. + + This inhabitant handles messages related to proposals, voting initiation, and vote results. + The handlers for these messages are dynamically mapped using the method annotations and the `map_handlers` function. + + Args: + service_colony (ServiceColony): The ServiceColony to which the voting inhabitant belongs. + inhabitant_id (int, optional): The unique identifier of the inhabitant. Defaults to None. + """ + + def __init__(self, service_colony: "ServiceColony", inhabitant_id: Optional[int] = None): + super().__init__(service_colony, inhabitant_id) + # Initialize any specific state or data for the VotingInhabitant + self.proposals = {} # Store proposals by their ID + self.votes = {} # Store votes by proposal ID + + async def handle_proposal_message(self, message: ProposalMessage): + """Handles a proposal message. + + This handler adds the proposal to the internal storage and publishes a confirmation. + + Args: + message (ProposalMessage): The incoming proposal message to be handled. + """ + self.proposals[message.proposalId] = message + await self.publish(BaseEvent(content=f"Proposal {message.proposalId} received.")) + logger.info(f"VotingInhabitant {self.inhabitant_id} handled proposal {message.proposalId}") + + async def handle_vote_message(self, message: VoteMessage): + """Handles a vote message. + + This handler registers the vote for a specific proposal. + + Args: + message (VoteMessage): The incoming vote message to be handled. + """ + votes_for_proposal = self.votes.setdefault(message.proposalId, {"yes": 0, "no": 0, "abstain": 0}) + votes_for_proposal[message.voteType] += 1 + await self.publish(BaseEvent(content=f"Vote {message.voteType} registered for proposal {message.proposalId}.")) + logger.info(f"VotingInhabitant {self.inhabitant_id} registered vote {message.voteType} for proposal {message.proposalId}") + + async def handle_vote_result_message(self, message: VoteResultMessage): + """Handles a vote result message. + + This handler processes the results of the vote and publishes the outcome. + + Args: + message (VoteResultMessage): The incoming vote result message to be handled. + """ + proposal_votes = self.votes.get(message.proposalId, {"yes": 0, "no": 0, "abstain": 0}) + outcome = "passed" if proposal_votes["yes"] > proposal_votes["no"] else "failed" + await self.publish(BaseEvent(content=f"Proposal {message.proposalId} has {outcome}.")) + logger.info(f"VotingInhabitant {self.inhabitant_id} determined outcome for proposal {message.proposalId} as {outcome}") + + def map_handlers(self) -> dict[Type[BaseMessage], Callable]: + """Maps message types to corresponding handler methods. + + This method overrides the base method to explicitly map specific message types to their handlers. + + Returns: + dict: A dictionary mapping message types to handler methods. + """ + return { + ProposalMessage: self.handle_proposal_message, + VoteMessage: self.handle_vote_message, + VoteResultMessage: self.handle_vote_result_message + } + + +### Explanation: + +1. **Handler Methods**: + - Each method (handle_proposal_message, handle_vote_message, and handle_vote_result_message) is dedicated to processing a specific type of message (e.g., ProposalMessage, VoteMessage, and VoteResultMessage). + - These methods perform actions based on the message content, such as storing proposals, counting votes, or determining voting outcomes. + +2. **Dynamic Mapping of Handlers**: + - The map_handlers method returns a dictionary that maps each message type to its corresponding handler method. + - This avoids the need for explicit conditional statements (if or case) to determine which handler should process a message. + +3. **Publishing Events**: + - The publish method is called to broadcast messages or results to the broader service colony, enabling other inhabitants to react to these events as needed. + +By using this approach, the VotingInhabitant class maintains a clean, modular design that aligns with the principles of reactive domain-driven design (RDDDY). The use of dynamic handler mapping promotes flexibility and scalability while maintaining the correctness and predictability of the system's behavior. + + +Create a executive summary for each technology being used and its purpose towards the entire ecosystem + +2/2 + +ChatGPT said: +ChatGPT +Executive Summary for Each Technology Used in the VotingInhabitant Ecosystem +The ecosystem built around the VotingInhabitant and the overall ServiceColony is designed to be reactive, scalable, and domain-driven. To achieve these goals, a combination of various technologies has been employed. Each technology contributes a specific set of capabilities to support the system's objectives: + +Ash Framework + +Purpose: The Ash Framework is used for defining domain-specific logic and resources in a declarative manner, allowing for consistent, predictable, and extensible behavior across the application. It supports Domain-Driven Design (DDD) principles, enabling developers to model their business domain effectively. +Role in the Ecosystem: Ash is utilized to manage entities such as Proposal, Debate, Vote, and VoteResult. It helps define the business rules, data persistence, and actions associated with each entity, ensuring that all changes adhere to the system's requirements. Additionally, it integrates with Ash Reactor to automate workflows and handle asynchronous events triggered by Phoenix Channels. +Phoenix Channels + +Purpose: Phoenix Channels provide a bi-directional, real-time communication mechanism over WebSockets, enabling reactive updates between the client and server. They allow multiple clients to interact with the server asynchronously and receive instant notifications about changes in the system. +Role in the Ecosystem: Phoenix Channels are crucial for implementing the real-time aspects of the VotingInhabitant ecosystem. They enable asynchronous communication between various parts of the system, such as users submitting proposals, participating in debates, and casting votes. By leveraging channels, the system remains responsive, and participants receive instant updates on decisions and outcomes. +AsyncAPI + +Purpose: AsyncAPI is a specification for defining asynchronous APIs, ensuring consistent and clear communication between distributed systems. It describes how messages are formatted, transmitted, and processed in an event-driven architecture. +Role in the Ecosystem: The AsyncAPI specification defines the messaging protocol for the service colony. It provides a standardized format for submitting proposals, debating, voting, and sharing vote results. This ensures that all components of the system communicate effectively and consistently, reducing errors and improving interoperability among AI agents (inhabitants) within the colony. +Python (RDDDY Framework) + +Purpose: Python, particularly with the Reactive Domain-Driven Design (RDDDY) Framework, is used to create the core computation units (Inhabitants) that drive the decision-making process. Python's async capabilities and rich ecosystem of libraries make it ideal for building reactive, concurrent, and event-driven systems. +Role in the Ecosystem: Python is the primary language used to implement the logic and behavior of each inhabitant (like VotingInhabitant). The BaseInhabitant class serves as a foundation for creating inhabitants that can process messages asynchronously, maintaining system resilience and scalability. Python’s async features enable efficient handling of concurrent tasks, ensuring that the system can process numerous messages and events in real-time. +JavaScript/Node.js + +Purpose: JavaScript, running on Node.js, enables cross-language integration, real-time updates, and asynchronous client-side processing. It is widely used for developing responsive web applications that interact with backend services in real-time. +Role in the Ecosystem: JavaScript is used to implement the client-side logic, including WebSocket communication with Phoenix Channels. It allows the web client to dynamically display updates (e.g., new proposals, debate contributions, vote initiations, and results) as they are broadcasted from the server. JavaScript ensures that the user interface remains reactive and provides instant feedback to users. +RxJS (Reactive Extensions for JavaScript) + +Purpose: RxJS is a library for reactive programming using Observables, making it easy to handle asynchronous events and data streams. It allows for a declarative approach to managing complex event flows. +Role in the Ecosystem: RxJS is employed on the client side to handle real-time events from Phoenix Channels. It enables the system to process and react to incoming messages efficiently, supporting a reactive user interface where changes are instantly reflected. RxJS ensures that data streams are managed effectively, improving the scalability and responsiveness of the client application. +WebSocket + +Purpose: WebSocket provides full-duplex communication channels over a single TCP connection, allowing for low-latency, real-time data exchange between the server and clients. +Role in the Ecosystem: WebSocket underpins Phoenix Channels, facilitating continuous and bi-directional communication. It is essential for enabling real-time updates and interactivity in the VotingInhabitant system. WebSocket ensures that users are instantly notified of changes, promoting an interactive decision-making environment. +Log4js (JavaScript Logging Library) + +Purpose: Log4js is a logging framework for JavaScript that provides tools for logging application activity, debugging, and monitoring. +Role in the Ecosystem: Log4js is used in the JavaScript implementation of VotingInhabitant to log important events, such as message handling, errors, and system state changes. It helps developers monitor the system’s behavior, detect issues early, and ensure smooth operation. +UUID (Universally Unique Identifier) + +Purpose: UUIDs are used to generate unique identifiers that help distinguish different entities in distributed systems, avoiding collisions. +Role in the Ecosystem: In the VotingInhabitant ecosystem, UUIDs are employed to uniquely identify proposals, votes, debates, and inhabitants. This uniqueness is crucial for tracking entities across different parts of the system and ensuring consistent state management. +Realtime Client (AsyncRealtimeClient) + +Purpose: The AsyncRealtimeClient handles real-time communication with WebSocket servers, managing connections and message distribution. +Role in the Ecosystem: This client is essential for sending and receiving messages between different inhabitants in the service colony. It ensures that all participants are kept up-to-date on the state of proposals, debates, and votes in real time. +Summary of the Ecosystem +The VotingInhabitant ecosystem combines several technologies to create a robust, scalable, and reactive environment for decision-making in a service colony. The Ash Framework provides a strong foundation for domain modeling and business logic, while Phoenix Channels and WebSockets enable real-time, bi-directional communication between the client and server. Python and JavaScript handle the logic and interactivity of the system, and AsyncAPI ensures clear and consistent messaging protocols. RxJS and log4js further enhance the system’s responsiveness and debuggability, making it a comprehensive solution for managing complex, asynchronous interactions within an AI-driven decision-making framework. + + + + + + +You said: +This did not include dspygen etc. Now provide a summary of the technologies that were left out +ChatGPT said: +ChatGPT +Certainly! Let's focus on the technologies that were left out in the previous summary, specifically DSPyGen, Prefect, and dspy. Each of these technologies plays a unique role in enhancing the capabilities and structure of the VotingInhabitant ecosystem, enabling more advanced functionalities like task automation, intelligent decision-making, and seamless integration with AI models. + +Additional Technologies and Their Purpose in the Ecosystem +DSPyGen (DSPy Generator) + +Purpose: DSPyGen is a command-line interface (CLI) framework designed to streamline AI development by generating DSPy modules, which are modular and reusable components for AI workflows. It is inspired by the principles of Ruby on Rails and is aimed at simplifying the development and deployment of AI-driven applications. +Role in the Ecosystem: +DSPyGen is used to generate core modules for the VotingInhabitant system, such as modules for creating proposals, managing debates, handling votes, and analyzing results. By using DSPyGen, developers can quickly create these modules with consistent structure and predefined patterns, reducing the time and effort required to build and maintain the system. +The CLI commands provided by DSPyGen allow for easy integration and orchestration of these modules, facilitating rapid prototyping and iteration in AI development workflows. +The generated modules encapsulate specific functionalities (e.g., decision-making, data processing), making it easier to scale and extend the system as new requirements emerge. +dspy (DSPy Library) + +Purpose: dspy is a Python library that provides tools for defining and executing AI-driven tasks and workflows. It focuses on demonstrating, searching, and predicting (DSPy) using language models like GPT, BERT, and LLama. dspy facilitates AI-based decision-making by defining tasks in a structured and modular way. +Role in the Ecosystem: +dspy is used to define and execute various AI tasks within the VotingInhabitant system, such as generating proposals, predicting voting outcomes, and analyzing debate content. It leverages large language models to provide intelligent insights and suggestions, which help guide the decision-making process within the service colony. +The library allows developers to create AI modules that can be easily integrated into the VotingInhabitant framework, enabling more sophisticated decision-making capabilities based on natural language understanding and machine learning. +dspy also supports integration with various language models (e.g., GPT, LLama) and AI services, providing flexibility in choosing the best model for different tasks. +Prefect + +Purpose: Prefect is a Python-based workflow automation tool designed for data orchestration. It provides a powerful framework for defining, scheduling, and monitoring workflows, with a focus on handling the complexities of data engineering and AI pipelines. +Role in the Ecosystem: +Prefect is used to automate and orchestrate workflows related to the decision-making process in the VotingInhabitant system. For example, it can manage the flow of tasks such as collecting and analyzing votes, generating proposals, or processing debate results. +By using Prefect, the system can schedule these tasks to run periodically or in response to specific triggers, ensuring that all necessary processes are completed efficiently and reliably. +Prefect's monitoring capabilities also provide visibility into the execution of workflows, allowing developers and system administrators to detect and resolve issues quickly. +dspygen.utils.dspy_tools + +Purpose: This module provides utility functions for initializing and configuring DSPy-related tools and modules, making it easier to manage the setup and integration of DSPy-based components within an application. +Role in the Ecosystem: +The utility functions in dspygen.utils.dspy_tools help set up the environment for using DSPy modules, such as initializing the DSPy framework, configuring language models, and managing dependencies. This simplifies the process of deploying AI-driven modules and ensures that all components are correctly configured. +It also provides helper functions for tasks like generating prompts, formatting output, and interfacing with language models, making it easier to build and maintain AI workflows within the VotingInhabitant system. +Summary of the Additional Technologies +DSPyGen accelerates AI module development through a CLI-driven approach, enabling rapid prototyping and consistent module generation for the VotingInhabitant system. +dspy provides a robust library for defining AI tasks and integrating large language models, enhancing the decision-making capabilities of the system by leveraging natural language understanding and machine learning. +Prefect offers workflow automation and orchestration, ensuring that all tasks related to proposals, debates, voting, and results are efficiently managed and monitored. +dspygen.utils.dspy_tools provides utility functions to streamline the setup and configuration of DSPy-related components, supporting the seamless integration of AI-driven modules into the system. +By incorporating these technologies, the VotingInhabitant ecosystem achieves a highly modular, scalable, and intelligent framework for real-time decision-making, leveraging the power of AI and automation to enhance its capabilities. + + + + + + +You said: + now create a C4 level one diagram that includes all of the systems in the ecosystems, but do not reference specific libraries +ChatGPT said: +ChatGPT +A C4 Level 1 diagram, also known as the Context Diagram, provides an overview of the system architecture, showing the major systems (software, external services, users) and how they interact with each other. + +For the VotingInhabitant ecosystem, the diagram will illustrate the primary components involved in the decision-making process within a service colony. This includes various subsystems (like AI modules, workflow automation, real-time communication) and external actors (e.g., Users, AI Agents). + +C4 Level 1 Context Diagram for the VotingInhabitant Ecosystem +mermaid +Copy code +--- +title: C4 Level 1 Context Diagram for VotingInhabitant Ecosystem +--- + +flowchart TD + %% External Actors + subgraph ExternalEntities + User[User] + Admin[Admin] + ExternalService[External AI Services] + LLM[Large Language Models] + end + + %% Main System + subgraph VotingInhabitantSystem["VotingInhabitant Ecosystem"] + UI[Web Client UI] + PhoenixChannel[Real-time Communication] + Backend[Backend API Server] + AIService[AI Module System] + WorkflowEngine[Workflow Automation System] + DataStorage[Data Storage] + MessageBroker[Message Broker] + end + + %% Interactions + User -->|Interacts| UI + Admin -->|Monitors| UI + UI -->|Sends/Receives Updates| PhoenixChannel + PhoenixChannel -->|Processes| Backend + Backend -->|Triggers Actions| WorkflowEngine + Backend -->|Interacts| AIService + Backend -->|Stores/Retrieves Data| DataStorage + WorkflowEngine -->|Automates Tasks| AIService + WorkflowEngine -->|Coordinates Events| MessageBroker + AIService -->|Queries| LLM + AIService -->|Interacts| ExternalService + MessageBroker -->|Distributes Events| PhoenixChannel + MessageBroker -->|Receives Events| WorkflowEngine + +Explanation of the C4 Level 1 Diagram Components +External Entities: + +User: Represents the end users who interact with the system to submit proposals, participate in debates, and cast votes. +Admin: Represents administrators who monitor and manage the system's operations. +External AI Services: Refers to any external AI services (e.g., third-party APIs or external AI models) that the system may call upon to augment its decision-making capabilities. +Large Language Models (LLM): Represents language models like GPT, BERT, or LLama, which are used by the AI module to perform natural language processing, prediction, and understanding tasks. +VotingInhabitant Ecosystem: + +Web Client UI: The front-end interface that users interact with, typically a web application or mobile app. It communicates with the backend server via real-time updates. +Phoenix Channel (Real-time Communication): Manages bi-directional, real-time communication between the client and the backend, enabling instant updates and interaction. +Backend API Server: The main server responsible for handling API requests, coordinating actions, and managing business logic for the voting and decision-making process. +AI Module System: The subsystem that encapsulates various AI functionalities, such as generating proposals, managing debates, and processing votes. It interacts with large language models and external AI services for advanced AI capabilities. +Workflow Automation System: Automates and orchestrates workflows related to the decision-making process, such as submitting proposals, starting debates, initiating votes, and processing results. +Data Storage: Stores all persistent data, such as proposals, votes, debate contributions, and results. It could be a database or a distributed storage system. +Message Broker: Handles message distribution across different parts of the system. It facilitates asynchronous communication and ensures that all components are notified of events (e.g., new proposals, vote results). +Interactions: + +User and Admin Interactions: Users interact with the Web Client UI to submit proposals, participate in debates, and vote. Admins monitor the system using the same interface. +Real-time Communication: The Phoenix Channel provides real-time communication between the client UI and the backend, ensuring that updates are sent and received instantly. +Backend Processing: The Backend API Server processes client requests, triggers workflow actions, interacts with AI modules, and manages data storage. +AI Module Integration: The AI Module System performs AI tasks like generating proposals and analyzing votes, leveraging Large Language Models and external AI services. +Workflow Orchestration: The Workflow Automation System automates decision-making workflows and coordinates events using the Message Broker. +Data Storage: The Backend API Server stores and retrieves all persistent data from Data Storage. +Event Distribution: The Message Broker ensures that all components receive updates about relevant events, maintaining synchronization across the system. +Conclusion +This C4 Level 1 diagram provides a high-level overview of the VotingInhabitant ecosystem, highlighting the key systems, external entities, and their interactions. It demonstrates how various components work together to achieve a reactive, scalable, and intelligent decision-making framework. By understanding these interactions, stakeholders can appreciate the ecosystem's overall architecture and design principles. + + + + + + +You said: +User -->|Interacts| UI Admin -->|Monitors| UI + +Service Colonies- A Novel Architectural Style for Developing Software Systems with Autonomous and Cooperative Services.pdf +PDF +The diagram should not include users or admins. This system is fully autonomous. we are building the first implementation of service colonies. The voting is required to allow for order using Roberts rules of order. +ChatGPT +To create a C4 Level 1 diagram for a fully autonomous "Service Colony" system following the principles of Robert's Rules of Order, we will focus on the overall ecosystem components without referencing specific libraries or mentioning users/admins. + +C4 Level 1 Diagram Overview: +The Level 1 C4 diagram represents a high-level view of the autonomous "Service Colony" ecosystem, where different services (inhabitants) communicate and cooperate to achieve the system's global objectives. This diagram includes various interconnected components that form the backbone of the service colony architecture, focusing on the major systems and their interactions. + +Key Components of the Autonomous Service Colony: +Service Colony: + +Represents the overall autonomous system. It consists of multiple "Inhabitants" that perform different roles and cooperate to maintain the system's goals. +Inhabitants: + +Autonomous agents within the Service Colony responsible for specific tasks. Each inhabitant can perform a particular function (e.g., voting, decision-making, executing tasks). +Inhabitants communicate with each other via message passing to coordinate actions and share information. +Message Broker: + +A central communication component that handles message passing between inhabitants. It enables asynchronous communication and ensures reliable delivery of messages to the appropriate recipients. +Governance Engine: + +A decision-making subsystem responsible for coordinating votes, managing proposals, and enforcing rules based on Robert's Rules of Order. +It interacts with various inhabitants to collect votes, propose changes, and implement decisions. +Service Management Layer: + +Responsible for dynamically managing the lifecycle of inhabitants (e.g., spawning, merging, or splitting inhabitants). +Adjusts the colony's composition based on environmental conditions, workload, and system goals. +Communication Infrastructure: + +The underlying network and communication channels that support message passing between inhabitants, the message broker, and other components. +Monitoring and Analytics Engine: + +Monitors the overall health, performance, and state of the service colony. +Provides insights and analytics to inhabitants for self-optimization and adaptation. +Persistence Layer: + +Stores all critical data, such as proposals, votes, system states, and historical logs for audit and analysis. +Reactor Framework: + +Provides the infrastructure for reactive programming, enabling inhabitants to respond to events and changes in real time. +Manages the state and behavior of inhabitants in response to incoming messages or environmental changes. +Description of the Diagram: +The Service Colony is shown at the center, encompassing all the major systems and components. +Inhabitants are represented as multiple interconnected entities within the colony, each with a defined role and function. +The Message Broker facilitates communication between all components. +The Governance Engine interacts directly with the inhabitants to manage proposals, voting, and decision-making. +The Service Management Layer adjusts the composition of the colony dynamically, creating new inhabitants or merging existing ones as needed. +The Communication Infrastructure underlies all interactions, connecting the inhabitants, message broker, and other systems. +The Monitoring and Analytics Engine continuously analyzes the state and performance of the system, providing feedback to other components for optimization. +The Persistence Layer stores all data critical to the operation and decision-making of the colony. +The Reactor Framework manages reactive programming paradigms to ensure inhabitants adapt in real-time to changes or events. +This setup illustrates a fully autonomous system with decentralized control, where each component collaborates to achieve the service colony's goals in a dynamic and adaptive manner, following principles like those in "Robert's Rules of Order." + +Would you like me to create a textual representation of this diagram, or assist with a different aspect? + + + + + + +You said: +Create the mermaid version + + C4Context + title System Context diagram for Internet Banking System + Enterprise_Boundary(b0, "BankBoundary0") { + Person(customerA, "Banking Customer A", "A customer of the bank, with personal bank accounts.") + Person(customerB, "Banking Customer B") + Person_Ext(customerC, "Banking Customer C", "desc") + + Person(customerD, "Banking Customer D", "A customer of the bank,
with personal bank accounts.") + + System(SystemAA, "Internet Banking System", "Allows customers to view information about their bank accounts, and make payments.") + + Enterprise_Boundary(b1, "BankBoundary") { + + SystemDb_Ext(SystemE, "Mainframe Banking System", "Stores all of the core banking information about customers, accounts, transactions, etc.") + + System_Boundary(b2, "BankBoundary2") { + System(SystemA, "Banking System A") + System(SystemB, "Banking System B", "A system of the bank, with personal bank accounts. next line.") + } + + System_Ext(SystemC, "E-mail system", "The internal Microsoft Exchange e-mail system.") + SystemDb(SystemD, "Banking System D Database", "A system of the bank, with personal bank accounts.") + + Boundary(b3, "BankBoundary3", "boundary") { + SystemQueue(SystemF, "Banking System F Queue", "A system of the bank.") + SystemQueue_Ext(SystemG, "Banking System G Queue", "A system of the bank, with personal bank accounts.") + } + } + } + + BiRel(customerA, SystemAA, "Uses") + BiRel(SystemAA, SystemE, "Uses") + Rel(SystemAA, SystemC, "Sends e-mails", "SMTP") + Rel(SystemC, customerA, "Sends e-mails to") + + +Change this example to fit the service colony ecosystem + + +2/2 + +ChatGPT said: +ChatGPT +Here's a Mermaid.js C4 Level 1 diagram that represents the Service Colony ecosystem as described earlier. This diagram outlines the autonomous system's context, where different components (systems) work together to manage the decision-making processes according to Robert's Rules of Order. + +Mermaid.js C4 Context Diagram for the Service Colony Ecosystem +mermaid +Copy code +C4Context + title System Context diagram for Autonomous Service Colony + + Enterprise_Boundary(b0, "Service Colony") { + System(ServiceColony, "Service Colony", "A fully autonomous system where multiple services (inhabitants) cooperate to achieve shared goals.") + + Enterprise_Boundary(b1, "Inhabitant Boundary") { + System(VotingInhabitant, "Voting Inhabitant", "Handles voting processes for proposals, debates, and decisions.") + System(ProposalInhabitant, "Proposal Inhabitant", "Generates new proposals and changes within the colony.") + System(ExecutionInhabitant, "Execution Inhabitant", "Executes actions based on decisions made by the colony.") + } + + System_Boundary(b2, "Core Infrastructure") { + System(MessageBroker, "Message Broker", "Handles asynchronous communication and message passing between inhabitants.") + System(GovernanceEngine, "Governance Engine", "Manages decision-making processes and enforces rules (e.g., Robert's Rules of Order).") + System(ServiceManagement, "Service Management Layer", "Dynamically manages the lifecycle and state of inhabitants.") + System(ReactorFramework, "Reactor Framework", "Supports reactive programming and real-time adaptation to changes.") + System(CommunicationInfrastructure, "Communication Infrastructure", "Underlying network supporting communication between all components.") + } + + System(MonitoringEngine, "Monitoring and Analytics Engine", "Monitors the health and performance of the service colony.") + SystemDb(DataStorage, "Data Storage", "Stores proposals, votes, system states, and historical logs.") + } + + Rel(ServiceColony, VotingInhabitant, "Coordinates decision-making processes") + Rel(ServiceColony, ProposalInhabitant, "Initiates and submits proposals") + Rel(ServiceColony, ExecutionInhabitant, "Executes approved actions") + + Rel(VotingInhabitant, MessageBroker, "Sends and receives voting-related messages") + Rel(ProposalInhabitant, MessageBroker, "Sends proposal messages") + Rel(ExecutionInhabitant, MessageBroker, "Receives execution commands") + + Rel(MessageBroker, GovernanceEngine, "Distributes messages for decision management") + Rel(GovernanceEngine, VotingInhabitant, "Coordinates votes and decisions") + + Rel(ServiceManagement, Inhabitant Boundary, "Adjusts colony composition dynamically") + Rel(ReactorFramework, ServiceColony, "Provides reactive programming infrastructure") + Rel(CommunicationInfrastructure, MessageBroker, "Facilitates message delivery") + + Rel(MonitoringEngine, ServiceColony, "Monitors system state and performance") + Rel(DataStorage, ServiceColony, "Stores all operational data") + + BiRel(CommunicationInfrastructure, ServiceColony, "Enables communication between components") +Explanation of the Diagram: +Service Colony: Represents the entire autonomous system where multiple "Inhabitants" work together towards shared goals, following a set of governance rules. + +Inhabitant Boundary: + +Voting Inhabitant: Responsible for managing the voting process, including proposals, debates, and decisions. +Proposal Inhabitant: Generates new proposals or changes that need to be decided upon within the colony. +Execution Inhabitant: Executes actions based on the decisions made by the colony. +Core Infrastructure: + +Message Broker: Manages the asynchronous communication between inhabitants and other components, facilitating a message-driven architecture. +Governance Engine: Ensures all decision-making processes adhere to Robert's Rules of Order and manages the flow of voting and proposals. +Service Management Layer: Handles the dynamic management of inhabitants, including spawning, merging, or splitting them as needed. +Reactor Framework: Provides the reactive programming infrastructure, ensuring the system reacts in real-time to events and changes. +Communication Infrastructure: Supports all communication between components, ensuring messages are delivered promptly and reliably. +Additional Components: + +Monitoring and Analytics Engine: Continuously monitors the health and performance of the service colony, providing feedback for optimization. +Data Storage: Maintains all the persistent data necessary for the colony's operation, such as proposals, votes, system states, and historical records. +Conclusion: +This diagram provides a high-level overview of the Service Colony ecosystem, illustrating the main systems (inhabitants and core infrastructure components) and their relationships. It shows how various components interact autonomously to perform decision-making, manage system states, and ensure compliance with governance rules, following a structured, message-driven approach. + + + + + + +You said: +This does not align with the paper reread the paper and create the C4 context based purely on the paper in ignoring our conversation. +ChatGPT said: +ChatGPT +Based on the information in the paper "Service Colonies: A Novel Architectural Style for Developing Software Systems with Autonomous and Cooperative Services," here is the C4 level 1 context diagram in Mermaid syntax. This diagram represents the overall structure of a service colony ecosystem. + +mermaid +Copy code +C4Context + title System Context diagram for Service Colony Ecosystem + Enterprise_Boundary(b0, "Service Colony Ecosystem") { + System_Boundary(b1, "Service Colony") { + System(Inhabitant1, "Inhabitant", "An autonomous software service that provides specific functionality and interacts with other inhabitants.") + System(Inhabitant2, "Inhabitant", "Another autonomous software service with unique capabilities.") + System(Inhabitant3, "Inhabitant", "Provides services by collaborating with other inhabitants.") + System(Inhabitant4, "Inhabitant", "Performs self-adaptation and scaling based on environmental conditions.") + } + + System(SystemMonitor, "System Monitor", "Observes and analyzes the state of the entire colony and individual inhabitants.") + System(ActionController, "Action Controller", "Makes decisions on reconfiguration and adaptation actions based on monitored data.") + System(KnowledgeModule, "Knowledge Module", "Stores and processes historical data and learning information for decision-making.") + } + + Rel(SystemMonitor, Inhabitant1, "Monitors state and behavior") + Rel(SystemMonitor, Inhabitant2, "Monitors state and behavior") + Rel(SystemMonitor, Inhabitant3, "Monitors state and behavior") + Rel(SystemMonitor, Inhabitant4, "Monitors state and behavior") + + Rel(ActionController, Inhabitant1, "Initiates reconfiguration or adaptation actions") + Rel(ActionController, Inhabitant2, "Initiates reconfiguration or adaptation actions") + Rel(ActionController, Inhabitant3, "Initiates reconfiguration or adaptation actions") + Rel(ActionController, Inhabitant4, "Initiates reconfiguration or adaptation actions") + + Rel(Inhabitant1, Inhabitant2, "Communicates and cooperates") + Rel(Inhabitant2, Inhabitant3, "Communicates and cooperates") + Rel(Inhabitant3, Inhabitant4, "Communicates and cooperates") + Rel(Inhabitant4, Inhabitant1, "Communicates and cooperates") + + Rel(KnowledgeModule, ActionController, "Provides learning data and rules") + Rel(KnowledgeModule, SystemMonitor, "Receives monitored data for learning") + + BiRel(ActionController, SystemMonitor, "Shares insights and decisions") + BiRel(Inhabitant1, KnowledgeModule, "Shares operational data") + BiRel(Inhabitant2, KnowledgeModule, "Shares operational data") + BiRel(Inhabitant3, KnowledgeModule, "Shares operational data") + BiRel(Inhabitant4, KnowledgeModule, "Shares operational data") +Explanation of the Diagram +Service Colony: Represents a collection of autonomous software services (inhabitants) that interact with each other to achieve system goals. +Inhabitants: Autonomous services within the colony that handle specific functionalities, communicate, and cooperate with one another. +System Monitor: Observes and monitors the states and behaviors of all inhabitants. +Action Controller: Makes decisions on reconfiguration or adaptation of the service colony based on data from the System Monitor and the Knowledge Module. +Knowledge Module: Stores and processes historical data, learning insights, and rules to support decision-making. +This architecture demonstrates the core components and their interactions within a service colony, aligning with the decentralized and autonomous nature described in the paper. + + + + + + +You said: +This looks fragmented. Shouldn't each inhabitant be a self-contained system? +ChatGPT said: +ChatGPT +Yes, you're correct! According to the paper, each "Inhabitant" within a Service Colony is a self-contained system that can perform its own functions independently, while also interacting and collaborating with other inhabitants. Each inhabitant should be an autonomous service capable of adapting its behavior based on its internal state, the state of other inhabitants, and the goals of the entire colony. + +Revised C4 Level 1 Context Diagram +This revised diagram represents each inhabitant as an independent system within the Service Colony, emphasizing their self-contained nature while maintaining the necessary interactions for cooperation and coordination. + +Updated C4 Context Diagram in Mermaid Syntax: +mermaid +Copy code +C4Context + title System Context Diagram for Service Colony Ecosystem + Enterprise_Boundary(b0, "Service Colony Ecosystem") { + + %% Inhabitants as Self-Contained Systems + System_Boundary(b1, "Service Colony") { + System(InhabitantA, "Inhabitant A", "A self-contained autonomous service responsible for a specific function, such as proposing new strategies.") + System(InhabitantB, "Inhabitant B", "An autonomous service responsible for evaluating proposals and voting on them.") + System(InhabitantC, "Inhabitant C", "A self-contained service that aggregates information and adapts strategies based on environmental changes.") + System(InhabitantD, "Inhabitant D", "An autonomous service that executes decisions made by the colony.") + } + + %% Core Management and Coordination Components + System(SystemMonitor, "System Monitor", "Monitors and gathers data on the state and performance of each inhabitant.") + System(ActionController, "Action Controller", "Decides on adaptations and reconfigurations based on the data from System Monitor and rules from Knowledge Module.") + System(KnowledgeModule, "Knowledge Module", "Maintains historical data, learning models, and decision rules to inform strategic decisions.") + System(MessageBroker, "Message Broker", "Facilitates communication between all inhabitants and central components.") + + } + + %% Relationships and Interactions + Rel(SystemMonitor, InhabitantA, "Monitors state and behavior") + Rel(SystemMonitor, InhabitantB, "Monitors state and behavior") + Rel(SystemMonitor, InhabitantC, "Monitors state and behavior") + Rel(SystemMonitor, InhabitantD, "Monitors state and behavior") + + Rel(ActionController, InhabitantA, "Issues reconfiguration commands") + Rel(ActionController, InhabitantB, "Issues adaptation instructions") + Rel(ActionController, InhabitantC, "Issues commands for strategic changes") + Rel(ActionController, InhabitantD, "Issues execution orders") + + Rel(InhabitantA, MessageBroker, "Sends/receives messages") + Rel(InhabitantB, MessageBroker, "Sends/receives messages") + Rel(InhabitantC, MessageBroker, "Sends/receives messages") + Rel(InhabitantD, MessageBroker, "Sends/receives messages") + + Rel(MessageBroker, SystemMonitor, "Facilitates monitoring data exchange") + Rel(MessageBroker, ActionController, "Facilitates control commands") + Rel(MessageBroker, KnowledgeModule, "Facilitates knowledge sharing") + + BiRel(SystemMonitor, ActionController, "Shares monitoring data and adaptation decisions") + Rel(KnowledgeModule, ActionController, "Provides historical data and rules") + Rel(KnowledgeModule, SystemMonitor, "Receives monitored data for learning") + + BiRel(InhabitantA, InhabitantB, "Cooperates and exchanges data") + BiRel(InhabitantB, InhabitantC, "Cooperates and exchanges data") + BiRel(InhabitantC, InhabitantD, "Cooperates and exchanges data") + BiRel(InhabitantD, InhabitantA, "Cooperates and exchanges data") +Explanation of the Revised Diagram +Inhabitants: + +Inhabitant A to D are represented as self-contained autonomous systems within the Service Colony. Each Inhabitant performs specific roles: +Inhabitant A: Proposes new strategies or actions. +Inhabitant B: Evaluates and votes on proposals. +Inhabitant C: Aggregates data and adapts strategies. +Inhabitant D: Executes decisions or actions. +Each Inhabitant operates independently but can communicate and collaborate with other inhabitants to achieve collective goals. +Core Management and Coordination Components: + +System Monitor: Continuously monitors the state and performance of each Inhabitant. +Action Controller: Uses data from the System Monitor and rules from the Knowledge Module to decide on necessary adaptations or reconfigurations. +Knowledge Module: Stores historical data, rules, and learning models to provide context-aware insights and decision-making capabilities. +Message Broker: Acts as a communication hub, enabling message passing between all components and ensuring efficient and reliable communication. +Interactions: + +Inhabitants communicate via the Message Broker, sharing data, proposals, votes, and actions. +The System Monitor tracks all activities and states of the Inhabitants, while the Action Controller uses this data to enforce decisions and reconfigurations. +The Knowledge Module supports strategic decisions with historical and learned data, ensuring the colony evolves and adapts intelligently over time. +Key Takeaways +Decentralized Autonomy: Each Inhabitant is a self-contained system capable of independent operation, yet they interact with each other and central components for decision-making and cooperation. +Dynamic Adaptation: The system is designed to adapt dynamically, leveraging continuous monitoring, a robust communication framework, and a decision-making engine that follows well-defined rules (e.g., Robert's Rules of Order). +Resilient Architecture: By combining autonomous agents with centralized monitoring and decision-making, the architecture promotes resilience, scalability, and flexibility, aligning with the principles of service colonies described in the paper. + + + + + +You said: +So the message broker needs to be external, but all of these things should actually be inside of the inhabitants I think. So instead of doing the diagram of the service colony, let's do a diagram of an inhabitant. +ChatGPT said: +ChatGPT +You're right! The inhabitants in a service colony are self-contained systems that encapsulate their functionality, including their internal communication and decision-making capabilities. Each inhabitant can be seen as a microcosm of the entire service colony, containing its own components for monitoring, decision-making, communication, and knowledge management. + +Let's create a C4 Level 1 Context Diagram for a single Inhabitant to illustrate its internal structure and how it manages its operations autonomously. + +Revised C4 Context Diagram for a Single Inhabitant +This diagram focuses on the internal components of an inhabitant, demonstrating how each part of the system interacts to support its autonomous functioning. + +C4 Context Diagram for an Inhabitant in Mermaid Syntax: +mermaid +Copy code +C4Context + title System Context Diagram for an Inhabitant + + %% Inhabitant Boundary + System_Boundary(b0, "Inhabitant") { + System(SelfManagement, "Self-Management Module", "Monitors and manages the internal state, health, and behavior of the inhabitant.") + System(LocalDecisionEngine, "Local Decision Engine", "Makes decisions autonomously based on internal and external inputs following predefined rules (e.g., Robert's Rules of Order).") + System(LocalMessageQueue, "Local Message Queue", "Handles incoming and outgoing messages for internal and external communication.") + System(LocalKnowledgeBase, "Local Knowledge Base", "Stores historical data, learning models, and decision rules for autonomous decision-making.") + System(LocalReactor, "Local Reactor", "Enables reactive programming for real-time adaptation to changes or events.") + System(LocalActionExecutor, "Action Executor", "Executes actions and commands based on the decisions made by the Local Decision Engine.") + } + + %% External Components + System_Ext(MessageBroker, "External Message Broker", "Facilitates communication between different inhabitants and external systems.") + + %% Internal Interactions + Rel(SelfManagement, LocalDecisionEngine, "Provides state and health data") + Rel(SelfManagement, LocalKnowledgeBase, "Stores and retrieves internal monitoring data") + + Rel(LocalDecisionEngine, LocalMessageQueue, "Sends and receives decision-related messages") + Rel(LocalDecisionEngine, LocalActionExecutor, "Issues commands for execution") + + Rel(LocalMessageQueue, LocalReactor, "Triggers events based on messages") + Rel(LocalReactor, LocalDecisionEngine, "Notifies about external or internal events") + + Rel(LocalKnowledgeBase, LocalDecisionEngine, "Provides historical data and rules") + Rel(LocalKnowledgeBase, LocalActionExecutor, "Provides execution context") + + Rel(LocalActionExecutor, SelfManagement, "Reports execution status and outcomes") + + %% External Interactions + Rel(LocalMessageQueue, MessageBroker, "Communicates with external systems") + BiRel(LocalKnowledgeBase, MessageBroker, "Exchanges knowledge and data") +Explanation of the Diagram +Internal Components of an Inhabitant: + +Self-Management Module: +Monitors the health, state, and behavior of the inhabitant itself. Ensures the inhabitant is operating correctly and efficiently. +Provides state and health data to other internal components. +Local Decision Engine: +Makes autonomous decisions based on inputs from the Self-Management Module, Local Knowledge Base, and external messages. +Follows predefined rules (such as Robert's Rules of Order) to govern decision-making. +Local Message Queue: +Manages all incoming and outgoing messages for the inhabitant, acting as an intermediary for communication. +Ensures messages are processed in the correct order and routed appropriately. +Local Knowledge Base: +Stores historical data, learning models, decision rules, and other knowledge necessary for making informed decisions. +Can share knowledge with external systems and store data from monitoring. +Local Reactor: +Implements reactive programming principles to adapt to internal or external changes in real time. +Listens for events from the Local Message Queue and informs the Local Decision Engine of any relevant events. +Local Action Executor: +Executes commands and actions based on the decisions made by the Local Decision Engine. +Reports execution outcomes to the Self-Management Module to maintain state awareness. +External Components: + +External Message Broker: +Facilitates communication between different inhabitants and external systems. +Receives and sends messages to the Local Message Queue for processing. +Interactions: + +Internal Interactions: +The Self-Management Module provides health and state data to the Local Decision Engine and stores this data in the Local Knowledge Base. +The Local Decision Engine communicates with the Local Message Queue for decision-related messaging and sends commands to the Local Action Executor for execution. +The Local Reactor listens to the Local Message Queue and triggers events that inform the Local Decision Engine of any changes. +The Local Knowledge Base provides historical data and decision rules to the Local Decision Engine and execution context to the Local Action Executor. +The Local Action Executor reports back to the Self-Management Module to update the internal state after actions are taken. +External Interactions: +The Local Message Queue communicates with the External Message Broker to send and receive messages to/from other inhabitants or external systems. +The Local Knowledge Base can exchange knowledge and data with other systems through the External Message Broker. +Key Takeaways +Autonomous Operation: Each Inhabitant is self-contained and capable of performing its own decision-making, monitoring, knowledge management, and communication. +Internal Coordination: The Inhabitant relies on its internal components to ensure efficient and effective operation, adapting to changes and maintaining its objectives. +External Communication: The Inhabitant communicates with other inhabitants and systems through an External Message Broker, ensuring it can participate in the larger service colony ecosystem. +This design encapsulates the concept of a fully autonomous and cooperative service, aligning with the architectural principles described in the paper on service colonies. + + + + + + + + + + +Message ChatGPT + +ChatGPT can make mistakes. Check important info. +? diff --git a/src/dspygen/rdddy/inhabitant/__init__.py b/src/dspygen/rdddy/inhabitant/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/dspygen/rdddy/inhabitant/wintermute_inhab.py b/src/dspygen/rdddy/inhabitant/wintermute_inhab.py new file mode 100644 index 0000000..071c1b2 --- /dev/null +++ b/src/dspygen/rdddy/inhabitant/wintermute_inhab.py @@ -0,0 +1,123 @@ +import logging +from enum import Enum, auto +from dspygen.mixin.fsm.fsm_mixin import FSMMixin, trigger +from dspygen.rdddy.base_inhabitant import BaseInhabitant + +# Define the states relevant to Wintermute's operations +class WintermuteState(Enum): + INITIALIZING = auto() + DATA_GATHERING = auto() + INTEGRATING = auto() + ANALYZING = auto() + COORDINATING = auto() + NEGOTIATING = auto() + DECIDING = auto() + EXECUTING = auto() + COMPLETING = auto() + +# Redefine the inhabitant as Wintermute, an AI entity +class Wintermute(FSMMixin, BaseInhabitant): + def __init__(self): + super().__init__() + self.setup_fsm(state_enum=WintermuteState, initial=WintermuteState.INITIALIZING) + + # Redefine the state transitions to reflect Wintermute's roles + @trigger(source=WintermuteState.INITIALIZING, dest=WintermuteState.DATA_GATHERING) + def start_data_gathering(self): + print("Starting to gather data from multiple sources.") + + @trigger(source=WintermuteState.DATA_GATHERING, dest=WintermuteState.INTEGRATING) + def integrate_data(self): + print("Integrating gathered data into internal knowledge base.") + + @trigger(source=WintermuteState.INTEGRATING, dest=WintermuteState.ANALYZING) + def analyze_data(self): + print("Analyzing integrated data for actionable insights.") + + @trigger(source=WintermuteState.ANALYZING, dest=WintermuteState.COORDINATING) + def coordinate_with_loas(self): + print("Coordinating with Loa entities for collaborative tasks.") + + @trigger(source=WintermuteState.COORDINATING, dest=WintermuteState.NEGOTIATING) + def negotiate_with_inhabitants(self): + print("Negotiating with external inhabitants and services.") + + @trigger(source=[WintermuteState.NEGOTIATING, WintermuteState.COORDINATING], dest=WintermuteState.DECIDING) + def make_decision(self): + print("Making strategic decisions based on available data and negotiations.") + + @trigger(source=WintermuteState.DECIDING, dest=WintermuteState.EXECUTING) + def execute_plan(self): + print("Executing the chosen plan of action.") + + @trigger(source=WintermuteState.EXECUTING, dest=WintermuteState.COMPLETING) + def complete_operations(self): + print("Finalizing operations and logging outcomes.") + + # Additional triggers to handle complex decision paths + @trigger(source=WintermuteState.NEGOTIATING, dest=WintermuteState.COORDINATING) + def reevaluate_strategy(self): + print("Reevaluating strategy based on negotiation feedback.") + + @trigger(source=[WintermuteState.COORDINATING, WintermuteState.ANALYZING], dest=WintermuteState.ANALYZING) + def refine_analysis(self): + print("Refining analysis with new data inputs.") + + @trigger(source=[WintermuteState.NEGOTIATING, WintermuteState.ANALYZING], dest=WintermuteState.DATA_GATHERING) + def restart_data_gathering(self): + print("Restarting data gathering with new parameters.") + + @trigger(source=WintermuteState.DECIDING, dest=WintermuteState.NEGOTIATING) + def negotiation_failed(self): + print("Negotiation failed, returning to negotiation state.") + + @trigger(source=WintermuteState.COMPLETING, dest=WintermuteState.INITIALIZING) + def reset_for_new_task(self): + print("Completing current cycle, preparing for new strategic task.") + + def prompt(self, prompt, **kwargs): + super().prompt(prompt, **kwargs) + from dspygen.modules.wintermute_manager_module import wintermute_manager_call + print(wintermute_manager_call(prompt=prompt).split("---")[0]) + + + + +def main(): + from dspygen.utils.dspy_tools import init_dspy, init_ol + init_ol(max_tokens=100) + + inhabitant = Wintermute() + print("Initial state:", inhabitant.state) + + # Simulate the operations of Wintermute + inhabitant.prompt("begin gathering data") + print("State after data gathering:", inhabitant.state) + + inhabitant.prompt("integrate data into knowledge base") + print("State after data integration:", inhabitant.state) + + inhabitant.prompt("analyze integrated data") + print("State after analysis:", inhabitant.state) + + inhabitant.prompt("coordinate with Loa entities") + print("State after coordination:", inhabitant.state) + + inhabitant.prompt("negotiate with external inhabitants") + print("State after negotiation:", inhabitant.state) + + inhabitant.prompt("make strategic decisions") + print("State after decision making:", inhabitant.state) + + inhabitant.prompt("execute the plan") + print("State after execution:", inhabitant.state) + + inhabitant.prompt("complete the operation") + print("State after completion:", inhabitant.state) + + inhabitant.prompt("prepare for the next task") + print("Final state:", inhabitant.state) + + +if __name__ == '__main__': + main() diff --git a/src/dspygen/rdddy/inhabitant_c4_lvl_1.mmd b/src/dspygen/rdddy/inhabitant_c4_lvl_1.mmd new file mode 100644 index 0000000..12ebef3 --- /dev/null +++ b/src/dspygen/rdddy/inhabitant_c4_lvl_1.mmd @@ -0,0 +1,34 @@ +C4Context + title System Context Diagram for an Inhabitant + + %% Inhabitant Boundary + System_Boundary(b0, "Inhabitant") { + System(SelfManagement, "Self-Management Module", "Monitors and manages the internal state, health, and behavior of the inhabitant.") + System(LocalDecisionEngine, "Local Decision Engine", "Makes decisions autonomously based on internal and external inputs following predefined rules (e.g., Robert's Rules of Order).") + System(LocalMessageQueue, "Local Message Queue", "Handles incoming and outgoing messages for internal and external communication.") + System(LocalKnowledgeBase, "Local Knowledge Base", "Stores historical data, learning models, and decision rules for autonomous decision-making.") + System(LocalReactor, "Local Reactor", "Enables reactive programming for real-time adaptation to changes or events.") + System(LocalActionExecutor, "Action Executor", "Executes actions and commands based on the decisions made by the Local Decision Engine.") + } + + %% External Components + System_Ext(MessageBroker, "External Message Broker", "Facilitates communication between different inhabitants and external systems.") + + %% Internal Interactions + Rel(SelfManagement, LocalDecisionEngine, "Provides state and health data") + Rel(SelfManagement, LocalKnowledgeBase, "Stores and retrieves internal monitoring data") + + Rel(LocalDecisionEngine, LocalMessageQueue, "Sends and receives decision-related messages") + Rel(LocalDecisionEngine, LocalActionExecutor, "Issues commands for execution") + + Rel(LocalMessageQueue, LocalReactor, "Triggers events based on messages") + Rel(LocalReactor, LocalDecisionEngine, "Notifies about external or internal events") + + Rel(LocalKnowledgeBase, LocalDecisionEngine, "Provides historical data and rules") + Rel(LocalKnowledgeBase, LocalActionExecutor, "Provides execution context") + + Rel(LocalActionExecutor, SelfManagement, "Reports execution status and outcomes") + + %% External Interactions + Rel(LocalMessageQueue, MessageBroker, "Communicates with external systems") + BiRel(LocalKnowledgeBase, MessageBroker, "Exchanges knowledge and data") diff --git a/src/dspygen/rdddy/roberts_schema.py b/src/dspygen/rdddy/roberts_schema.py new file mode 100644 index 0000000..d070d07 --- /dev/null +++ b/src/dspygen/rdddy/roberts_schema.py @@ -0,0 +1,48 @@ +from pydantic import BaseModel, Field +from typing import Optional +from datetime import datetime + + +# Pydantic Models for AsyncAPI Schemas + +class Proposal(BaseModel): + proposalId: str = Field(..., description="Unique identifier for the proposal.") + proposer: str = Field(..., description="Name of the Loa proposing the action.") + proposalType: str = Field(..., description="Type of proposal.", + regex="^(serviceRegistration|securityPolicyUpdate|serviceComposition|capabilityUpdate|other)$") + description: str = Field(..., description="Details of the proposal.") + timestamp: datetime = Field(..., description="Time when the proposal was submitted.") + + +class Debate(BaseModel): + proposalId: str = Field(..., description="Unique identifier for the proposal being debated.") + participant: str = Field(..., description="Name of the Loa contributing to the debate.") + contribution: str = Field(..., description="Content of the debate contribution.") + timestamp: datetime = Field(..., description="Time when the debate contribution was made.") + + +class Vote(BaseModel): + proposalId: str = Field(..., description="Unique identifier for the proposal being voted on.") + voteType: str = Field(..., description="Type of vote (yes, no, abstain).", + regex="^(yes|no|abstain)$") + initiatedBy: str = Field(..., description="Name of the Loa initiating the vote.") + timestamp: datetime = Field(..., description="Time when the vote was initiated.") + + +class VoteResult(BaseModel): + proposalId: str = Field(..., description="Unique identifier for the proposal that was voted on.") + results: dict = Field(..., description="Results of the vote, containing counts for yes, no, and abstain.") + passed: bool = Field(..., description="Whether the proposal passed or failed.") + timestamp: datetime = Field(..., description="Time when the vote results were tallied.") + + +# Example usage of models +new_proposal = Proposal( + proposalId="12345", + proposer="Wintermute", + proposalType="serviceRegistration", + description="Proposal to register a new service for data aggregation.", + timestamp=datetime.utcnow() +) + +print(new_proposal.json()) diff --git a/src/dspygen/rdddy/service_colony.py b/src/dspygen/rdddy/service_colony.py index 9097af0..c094c3b 100644 --- a/src/dspygen/rdddy/service_colony.py +++ b/src/dspygen/rdddy/service_colony.py @@ -35,7 +35,7 @@ async def connect(self): await self.realtime_client.connect() # Create and join the channel (e.g., "service_colony:lobby") - self.channel = self.realtime_client.channel("service_colony:lobby") + await self.channel.subscribe(self._on_channel_subscribe) logger.info(f"Joined channel 'service_colony:lobby'") diff --git a/src/dspygen/rm/chatgpt_codemaster_retriever.py b/src/dspygen/rm/chatgpt_codemaster_retriever.py index c4c5827..8bb66df 100644 --- a/src/dspygen/rm/chatgpt_codemaster_retriever.py +++ b/src/dspygen/rm/chatgpt_codemaster_retriever.py @@ -317,7 +317,7 @@ def generate_powershell_script(self, code_files: List[str], directory_structure: "$directories = @(", '"$baseDir/src/dspygen/rm",', '"$baseDir/src/dspygen/utils",', - '"$baseDir/src/dspygen/modules",', + '"$baseDir/src/dspygen/dspy_modules",', '"$baseDir/data/chatgpt_logs"', ")", "", diff --git a/src/dspygen/rm/chroma_retriever.py b/src/dspygen/rm/chroma_retriever.py index 8efc926..5b402fd 100644 --- a/src/dspygen/rm/chroma_retriever.py +++ b/src/dspygen/rm/chroma_retriever.py @@ -1,4 +1,4 @@ -# Import necessary modules and functions +# Import necessary dspy_modules and functions from pathlib import Path from typing import Union, List, Optional @@ -91,7 +91,7 @@ def main(): for conversation in matched_conversations: print(conversation) # - # from dspygen.modules.python_source_code_module import python_source_code_call + # from dspygen.dspy_modules.python_source_code_module import python_source_code_call # print(python_source_code_call(str(matched_conversations) + "\n\nI want you to create a python source code for a typer 7 command wrapper on mdbook. Repeat your instructions and do not stop until all requirements are met. Document every line\n\n```python\nfrom typer import Typer")) # diff --git a/src/dspygen/rm/code_retriever.py b/src/dspygen/rm/code_retriever.py index 893e662..e8257a5 100644 --- a/src/dspygen/rm/code_retriever.py +++ b/src/dspygen/rm/code_retriever.py @@ -96,7 +96,7 @@ def main(): print(result.passages) for file_content in result.passages: - #from dspygen.modules.nuxt_module import nuxt_call + #from dspygen.dspy_modules.nuxt_module import nuxt_call print(file_content) #nuxt = nuxt_call(path=path, readme=file_content) #print(nuxt) diff --git a/src/dspygen/subcommands/cmd_cmd.py b/src/dspygen/subcommands/cmd_cmd.py index acc02e2..9f847e2 100644 --- a/src/dspygen/subcommands/cmd_cmd.py +++ b/src/dspygen/subcommands/cmd_cmd.py @@ -22,7 +22,7 @@ def {{ sub_command_name }}_{{ new_command_name }}(): ''' -# Define the subcommand to generate subcommand modules +# Define the subcommand to generate subcommand dspy_modules @app.command( name="new", ) diff --git a/src/dspygen/subcommands/code_cmd.py b/src/dspygen/subcommands/code_cmd.py index e518b1e..c16aca7 100644 --- a/src/dspygen/subcommands/code_cmd.py +++ b/src/dspygen/subcommands/code_cmd.py @@ -2,7 +2,7 @@ # import typer # from loguru import logger # -# from dspygen.modules.python_source_code_module import python_source_code_call +# from dspygen.dspy_modules.python_source_code_module import python_source_code_call # from dspygen.rm.chatgpt_chromadb_retriever import ChatGPTChromaDBRetriever # from dspygen.utils.dspy_tools import init_dspy # from dspygen.lm.groq_lm import Groq diff --git a/src/dspygen/subcommands/help_cmd.py b/src/dspygen/subcommands/help_cmd.py index 0cf6a47..36093f4 100644 --- a/src/dspygen/subcommands/help_cmd.py +++ b/src/dspygen/subcommands/help_cmd.py @@ -10,14 +10,14 @@ README = """DSPyGen: Streamlining AI Development DSPyGen, influenced by the efficiency and modularity of Ruby on Rails, is a powerful command-line interface (CLI) -designed to revolutionize AI development by leveraging DSPy modules. This tool simplifies the process of creating, +designed to revolutionize AI development by leveraging DSPy dspy_modules. This tool simplifies the process of creating, developing, and deploying language model (LM) pipelines, embodying the Ruby on Rails philosophy of "Convention over Configuration" for AI projects. Features Quick Initialization: Set up your DSPyGen project in seconds, echoing Ruby on Rails' ease of starting new projects. Modular Approach: Inspired by Ruby on Rails' modular design, DSPyGen allows for the easy generation and enhancement of -DSPy modules. +DSPy dspy_modules. Intuitive Command Structure: With user-friendly commands, managing your AI development workflow becomes as straightforward as web development with Ruby on Rails. Embedded Chatbot Assistance: For guidance and support, DSPyGen includes a chatbot, making it easier to navigate diff --git a/src/dspygen/subcommands/module_cmd.py b/src/dspygen/subcommands/module_cmd.py index 4e63a7e..0e6fedd 100644 --- a/src/dspygen/subcommands/module_cmd.py +++ b/src/dspygen/subcommands/module_cmd.py @@ -39,7 +39,7 @@ def new_module( print(f"Module saved to {file_name}") -def load_commands(directory: str = "modules"): +def load_commands(directory: str = "dspy_modules"): subcommands_dir = source_dir() / directory for filename in os.listdir(subcommands_dir): diff --git a/src/dspygen/subcommands/pln_cmd.py b/src/dspygen/subcommands/pln_cmd.py index a6de616..299d92a 100644 --- a/src/dspygen/subcommands/pln_cmd.py +++ b/src/dspygen/subcommands/pln_cmd.py @@ -17,7 +17,7 @@ def run_pipeline(yaml_file: str = "pipeline.yaml"): TUTOR_CONTEXT = '''The DSPyGen DSL has several key elements that you'll need to grasp: -Signatures (SignatureDSLModel) Think of signatures as blueprints for your AI modules. They define: +Signatures (SignatureDSLModel) Think of signatures as blueprints for your AI dspy_modules. They define: Name: A unique identifier for the signature. Docstring: Explains the purpose of the signature. @@ -44,7 +44,7 @@ def run_pipeline(yaml_file: str = "pipeline.yaml"): Current Step: Tracks the active step (optional). Pipeline Creation (The PipelineDSLModel) -The PipelineDSLModel ties everything together. It's your pipeline's master structure, containing lists of all your signatures, modules, steps, and configurations, along with: +The PipelineDSLModel ties everything together. It's your pipeline's master structure, containing lists of all your signatures, dspy_modules, steps, and configurations, along with: Context: A dictionary for global values shared across the pipeline. Putting it into Practice: A Simple Example @@ -55,7 +55,7 @@ class PipelineDSLModel(BaseModel, YAMLMixin): lm_models: list[LanguageModelConfig] = Field(default=[], description="list of language model configurations used in the pipeline.") rm_models: list[RetrievalModelConfig] = Field(default=[], description="list of retrieval model configurations used in the pipeline.") signatures: list[SignatureDSLModel] = Field(default=[], description="list of signatures defined for use in the pipeline.") - modules: list[ModuleDSLModel] = Field(default=[], description="list of modules defined for execution in the pipeline.") + dspy_modules: list[ModuleDSLModel] = Field(default=[], description="list of dspy_modules defined for execution in the pipeline.") steps: list[StepDSLModel] = Field(default=[], description="Sequential steps to be executed in the pipeline.") context: dict = Field(default={}, description="A context dictionary for storing global values accessible across the pipeline.") config: PipelineConfigModel = Field(default_factory=PipelineConfigModel, description="Configuration settings for the pipeline execution.") @@ -108,7 +108,7 @@ class PipelineDSLModel(BaseModel, YAMLMixin): - name: "report" desc: "The final report generated from the structured data." -modules: +dspy_modules: - name: "DataProcessorModule" signature: "ProcessDataSignature" predictor: "Predict" @@ -173,7 +173,7 @@ class PipelineDSLModel(BaseModel, YAMLMixin): A DSPyGen tutor could provide the following: -Interactive Guidance: Step-by-step walkthroughs for creating and modifying pipeline components (signatures, modules, etc.). +Interactive Guidance: Step-by-step walkthroughs for creating and modifying pipeline components (signatures, dspy_modules, etc.). DSL Explanation: Breakdowns of syntax and the purpose of each DSL element. Best Practices: Tips on designing efficient and modular pipelines. Example Pipelines: Showcases of common use cases to illustrate DSL usage. diff --git a/src/dspygen/subcommands/temp_assert.py b/src/dspygen/subcommands/temp_assert.py index 0bc807f..df52c00 100644 --- a/src/dspygen/subcommands/temp_assert.py +++ b/src/dspygen/subcommands/temp_assert.py @@ -33,7 +33,7 @@ def forward(self, prompt): return summary -story = """Chaining language model (LM) calls as com- posable modules is fueling a new powerful way of programming. +story = """Chaining language model (LM) calls as com- posable dspy_modules is fueling a new powerful way of programming. However, ensuring that LMs adhere to important constraints remains a key challenge, one often addressed with heuristic “prompt engineering”. We introduce LM Asser- tions, a new programming construct for express- ing computational constraints that LMs should satisfy. We integrate our constructs into the re- cent DSPy programming diff --git a/src/dspygen/subcommands/wkf_cmd.py b/src/dspygen/subcommands/wkf_cmd.py index 5c054f3..633e600 100644 --- a/src/dspygen/subcommands/wkf_cmd.py +++ b/src/dspygen/subcommands/wkf_cmd.py @@ -117,7 +117,7 @@ def run_all_workflows( TUTOR_CONTEXT = '''The DSPyGen DSL has several key elements that you'll need to grasp: -Signatures (SignatureDSLModel) Think of signatures as blueprints for your AI modules. They define: +Signatures (SignatureDSLModel) Think of signatures as blueprints for your AI dspy_modules. They define: Name: A unique identifier for the signature. Docstring: Explains the purpose of the signature. @@ -144,7 +144,7 @@ def run_all_workflows( Current Step: Tracks the active step (optional). Workflow Creation (The WorkflowDSLModel) -The WorkflowDSLModel ties everything together. It's your workflow's master structure, containing lists of all your signatures, modules, steps, and configurations, along with: +The WorkflowDSLModel ties everything together. It's your workflow's master structure, containing lists of all your signatures, dspy_modules, steps, and configurations, along with: Context: A dictionary for global values shared across the workflow. Putting it into Practice: A Simple Example @@ -155,7 +155,7 @@ class WorkflowDSLModel(BaseModel, YAMLMixin): lm_models: list[LanguageModelConfig] = Field(default=[], description="list of language model configurations used in the workflow.") rm_models: list[RetrievalModelConfig] = Field(default=[], description="list of retrieval model configurations used in the workflow.") signatures: list[SignatureDSLModel] = Field(default=[], description="list of signatures defined for use in the workflow.") - modules: list[ModuleDSLModel] = Field(default=[], description="list of modules defined for execution in the workflow.") + dspy_modules: list[ModuleDSLModel] = Field(default=[], description="list of dspy_modules defined for execution in the workflow.") steps: list[StepDSLModel] = Field(default=[], description="Sequential steps to be executed in the workflow.") context: dict = Field(default={}, description="A context dictionary for storing global values accessible across the workflow.") config: WorkflowConfigModel = Field(default_factory=WorkflowConfigModel, description="Configuration settings for the workflow execution.") @@ -208,7 +208,7 @@ class WorkflowDSLModel(BaseModel, YAMLMixin): - name: "report" desc: "The final report generated from the structured data." -modules: +dspy_modules: - name: "DataProcessorModule" signature: "ProcessDataSignature" predictor: "Predict" @@ -273,7 +273,7 @@ class WorkflowDSLModel(BaseModel, YAMLMixin): A DSPyGen tutor could provide the following: -Interactive Guidance: Step-by-step walkthroughs for creating and modifying workflow components (signatures, modules, etc.). +Interactive Guidance: Step-by-step walkthroughs for creating and modifying workflow components (signatures, dspy_modules, etc.). DSL Explanation: Breakdowns of syntax and the purpose of each DSL element. Best Practices: Tips on designing efficient and modular workflows. Example Workflows: Showcases of common use cases to illustrate DSL usage. diff --git a/src/dspygen/utils/file_tools.py b/src/dspygen/utils/file_tools.py index b57dbe4..dfa5a1f 100644 --- a/src/dspygen/utils/file_tools.py +++ b/src/dspygen/utils/file_tools.py @@ -81,7 +81,7 @@ def subcommands_dir(file_name="") -> Path: def dspy_modules_dir(file_name="") -> Path: - return source_dir() / "modules" / file_name + return source_dir() / "dspy_modules" / file_name def signatures_dir(file_name="") -> Path: diff --git a/tests/test_integration_email_responder_workflow.py b/tests/test_integration_email_responder_workflow.py index 5081de0..fab8b60 100644 --- a/tests/test_integration_email_responder_workflow.py +++ b/tests/test_integration_email_responder_workflow.py @@ -41,7 +41,7 @@ def test_integration_workflow_execution(sample_workflow_yaml, mock_linkedin_app, with patch('dspygen.subcommands.wkf_cmd.Workflow.from_yaml') as mock_from_yaml, \ patch('dspygen.subcommands.wkf_cmd.execute_workflow') as mock_execute, \ patch('dspygen.pyautomator.linkedin.linkedin_app.LinkedInApp', return_value=mock_linkedin_app), \ - patch('dspygen.modules.automated_email_responder_module.AutomatedEmailResponderModule', return_value=mock_email_responder): + patch('dspygen.dspy_modules.automated_email_responder_module.AutomatedEmailResponderModule', return_value=mock_email_responder): # Set up mock returns mock_linkedin_app.get_profile_markdown.return_value = "Mocked LinkedIn Profile" @@ -76,7 +76,7 @@ def test_integration_cli_trigger(sample_workflow_yaml, mock_linkedin_app, mock_e with patch('dspygen.subcommands.wkf_cmd.Workflow.from_yaml') as mock_from_yaml, \ patch('dspygen.subcommands.wkf_cmd.execute_workflow') as mock_execute, \ patch('dspygen.pyautomator.linkedin.linkedin_app.LinkedInApp', return_value=mock_linkedin_app), \ - patch('dspygen.modules.automated_email_responder_module.AutomatedEmailResponderModule', return_value=mock_email_responder): + patch('dspygen.dspy_modules.automated_email_responder_module.AutomatedEmailResponderModule', return_value=mock_email_responder): # Set up mock returns mock_linkedin_app.get_profile_markdown.return_value = "Mocked LinkedIn Profile" @@ -98,7 +98,7 @@ def test_integration_error_handling(sample_workflow_yaml, mock_linkedin_app, moc with patch('dspygen.subcommands.wkf_cmd.Workflow.from_yaml') as mock_from_yaml, \ patch('dspygen.subcommands.wkf_cmd.execute_workflow') as mock_execute, \ patch('dspygen.pyautomator.linkedin.linkedin_app.LinkedInApp', return_value=mock_linkedin_app), \ - patch('dspygen.modules.automated_email_responder_module.AutomatedEmailResponderModule', return_value=mock_email_responder): + patch('dspygen.dspy_modules.automated_email_responder_module.AutomatedEmailResponderModule', return_value=mock_email_responder): # Simulate an error in LinkedIn profile fetching mock_linkedin_app.get_profile_markdown.side_effect = Exception("Network error") diff --git a/tests/test_ultra_advanced_integration_job_search_workflow.py b/tests/test_ultra_advanced_integration_job_search_workflow.py index 8c95d76..dc51131 100644 --- a/tests/test_ultra_advanced_integration_job_search_workflow.py +++ b/tests/test_ultra_advanced_integration_job_search_workflow.py @@ -3,12 +3,12 @@ # from unittest.mock import patch, MagicMock # from dspygen.subcommands.wkf_cmd import run_workflows_in_directory, app # from dspygen.experiments.pyautomator.linkedin_app import LinkedInApp -# from dspygen.modules.automated_email_responder_module import AutomatedEmailResponderModule, AutomatedEmailResponderSignature +# from dspygen.dspy_modules.automated_email_responder_module import AutomatedEmailResponderModule, AutomatedEmailResponderSignature # from dspygen.ai.assistant import AIAssistant # from dspygen.task_management import TaskManager # from dspygen.utils.dspy_tools import init_dspy # from dspygen.subcommands.wrt_cmd import new_rm -# from dspygen.modules.ask_data_module import AskDataModule +# from dspygen.dspy_modules.ask_data_module import AskDataModule # from typer.testing import CliRunner # from apscheduler.schedulers.background import BackgroundScheduler # import dspy @@ -92,10 +92,10 @@ # with patch('dspygen.subcommands.wkf_cmd.Workflow.from_yaml') as mock_from_yaml, \ # patch('dspygen.subcommands.wkf_cmd.execute_workflow') as mock_execute, \ # patch('dspygen.experiments.pyautomator.linkedin_app.LinkedInApp', return_value=mock_linkedin_app), \ -# patch('dspygen.modules.automated_email_responder_module.AutomatedEmailResponderModule', return_value=mock_email_responder), \ +# patch('dspygen.dspy_modules.automated_email_responder_module.AutomatedEmailResponderModule', return_value=mock_email_responder), \ # patch('dspygen.ai.assistant.AIAssistant', return_value=mock_ai_assistant), \ # patch('dspygen.task_management.TaskManager', return_value=mock_task_manager), \ -# patch('dspygen.modules.ask_data_module.AskDataModule', return_value=mock_ask_data_module): +# patch('dspygen.dspy_modules.ask_data_module.AskDataModule', return_value=mock_ask_data_module): # # # Set up mock returns # mock_ai_assistant.analyze_job_market_trends.return_value = {