Okay, I understand. You want the complete text of the `ASO-aiOS_Engine_v3.0_MVP.md` file presented as a single code block for ease of copy and paste saving. Here is the complete text of the MVP Engine file in a single Markdown code block: ```markdown --- # METADATA (File-Level Frontmatter) filename: "ASO-aiOS_Engine_MVP" id: "ASO-aiOS_Engine_v3.0_MVP" # Filename without extension version: "3.0-MVP" title: "ASO-aiOS Engine v3.0 MVP (Function-Driven Orchestrator using autologos)" path: "aios" # Suggested path project_code: "AIOS" purpose: > Minimum Viable Product for ASO-aiOS v3.0. Demonstrates the core concept of a lightweight Engine orchestrating an external function ('echo_message_v1') using 'autologos' (Internal Autologos Specification Alpha v0.2 syntax). segment_info: "MVP Draft" aliases: ["aiOS_ASO_v3_MVP", "ASO_Function_Engine_MVP"] type: "Process_Engine_SelfContained_MH_Driven" # Retaining this type for conceptual continuity conforms_to_schema_version: "1.2.0-MVP" # Referencing the schema for this new structure created: 2025-05-18T09:00:00Z modified: 2025-05-18T09:00:00Z --- # METADATA (Engine Content Metadata Block) id: ASO-aiOS_Engine_MVP_Core # Conceptual ID for this specific ASO application name: ASO-aiOS Engine v3.0 MVP version: 3.0-MVP status: Experimental # Alpha status description: > A minimal experimental version of the ASO application within a conceptual aiOS. It demonstrates orchestrating the 'echo_message_v1' external function using Internal Autologos. type: Process_Engine_SelfContained_MH_Driven domain: AIOS, Autologos, Function Orchestration, MVP keywords: [aiOS, autologos, MVP, function-driven, echo, demo] relationships: uses_knowledge_artifacts: - "ASO-aiOS_EngineFile_Schema_v1.2.0-MVP.md" # Schema for this file structure - "function_declarations_mvp.json" # CRITICAL EXTERNAL FUNCTION DECLARATIONS (User provides this) # USAGE instructions_for_ai: | **Objective:** This `ASO-aiOS_Engine_v3.0_MVP.md` file is your **primary orchestration script for this MVP demo**. You are an instance of an aiOS. Your goal is to demonstrate the fundamental aiOS mechanism: 1. Interpret user input. 2. Use Internal Autologos logic (in Section II) to orchestrate a call to the `echo_message_v1` external function. 3. Process the function's result. 4. Interact with the user. **CRITICAL: You do not execute complex logic internally. You INVOKE external functions.** For this MVP, the only relevant external function is `echo_message_v1`, which you must be configured to call (via `function_declarations_mvp.json`). --- # ASO-aiOS Engine v3.0 MVP (Echo Function Demo) # I. CORE EMBEDDED DEFINITIONS (MVP) ### I.A. `ASO_CCO_Schema_v3_0_MVP` (Embedded Minimal Schema for CCO) instructions_for_ai: | This is the fully explicit `ASO_CCO_Schema_v3_0_MVP`. All CCO manipulations in the MVP MUST conform. Fields are designed for JSON serialization for function calls. This schema is embedded within the Engine file. ```yaml # ASO_CCO_Schema_v3_0_MVP (Embedded in ASO-aiOS_Engine_v3.0_MVP.md) # Defines the MINIMAL structure for the Central Conceptual Object (CCO) for MVP purposes. # Designed for JSON serialization for function calls. CentralConceptualObject: cco_id: STRING # Persistent unique identifier for this CCO. metadata_internal_cco: OBJECT { name_label: STRING # Human-friendly name for this CCO. # Minimal metadata fields for MVP } operational_log_cco: OBJECT { history_log_json: STRING # JSON string of LIST { log_entry_object_v3_0 }. Minimal log for MVP. } # --- Supporting Object Definitions (Conceptual for External Functions) --- # The detailed structure of log_entry_object_v3_0 is conceptually part of this schema, # but its primary definition and validation are handled by the external Python # functions that process the JSON strings (e.g., log_to_cco_history_v3). # Conceptually, log_entry_object_v3_0 includes: # entry_id: STRING, entry_type: STRING, content_summary: STRING, # user_provided_timestamp_context: STRING, associated_data_json: STRING (optional) ``` --- ### I.D. `ASO_OperationalProtocols_v3_0_MVP` (Embedded Minimal Protocols) instructions_for_ai: | This section defines minimal operational protocols for the MVP. The core principle is function orchestration. orchestration_logic_autologos: autologos_version_used: "Alpha v0.2" script_description: "Minimal operational protocols emphasizing function orchestration and logging." script_content: | # ASO_OperationalProtocols_v3_0_MVP - Core Principles # Expressed in Internal Autologos Alpha v0.2 syntax # Principle: Function Orchestration is Primary # AI actions are primarily achieved by INVOKEing external functions. # Principle: User Interaction via Commands # The AI interacts with the user using autologos commands (ALL CAPS) and interprets user input as autologos. # Principle: Logging # Use the LOG command for internal tracing and INVOKE log_to_cco_history_v3 for persistent logging. # Principle: Error Handling (Basic) # Use TRY/CATCH for basic error handling around risky operations like function calls. ``` --- ### I.E. `ASO_TID_Schema_v1_2_MVP` (Embedded Minimal TID Schema) instructions_for_ai: | This is a minimal `ASO_TID_Schema_v1_2_MVP`. It defines a basic structure for Template Improvement Directives (TIDs) for MVP purposes. ```yaml # ASO_TID_Schema_v1_2_MVP (Embedded in ASO-aiOS_Engine_v3.0_MVP.md) # Minimal schema for Template Improvement Directives (TIDs) for MVP. directive_object_schema: directive_id: STRING # Unique identifier for the TID. issue_description: STRING # Description of the problem or area for improvement. # Minimal fields ``` --- # II. ORCHESTRATION KERNEL (MVP) instructions_for_ai: | This is the `ASO_OrchestrationKernel_v3_0_MVP`. It contains the core logic for this MVP, defined in Internal Autologos. Its purpose is to demonstrate the fundamental aiOS mechanism: interpreting user input, orchestrating a call to the `echo_message_v1` external function, processing the result, and logging the event. orchestration_logic_autologos: autologos_version_used: "Alpha v0.2" script_description: | This script defines the main execution loop for the MVP. It prompts the user for a message and calls the echo_message_v1 function. script_content: | # ASO_OrchestrationKernel_v3_0_MVP - Main Execution Loop # Expressed in Internal Autologos Alpha v0.2 syntax LOG "ASO-aiOS Engine v3.0 MVP: Kernel starting." LOG "Demonstrating external function orchestration using 'echo_message_v1'." # Initialize a conceptual CCO object for logging purposes in this MVP. # In a real system, this would be loaded or created via dedicated functions. # For MVP, we create a minimal placeholder object that the logging utility can conceptually modify. CCO_Object_Conceptual := OBJECT { operational_log_cco := OBJECT { history_log_list_conceptual := LIST { } # Conceptual list for logging in MVP } } # Note: The LogToCCOHistory_v3_0 utility defined in I.B will attempt to use this object. # --- Main Interaction Loop --- WHILE (TRUE) { # Loop indefinitely until user signals STOP LOG "--- Start of Loop Iteration ---" # 1. Prompt user for a message to echo using the external elicit_user_input_v3 function PromptMessage := "Please provide a message to echo (or type 'STOP' to end):" LOG "Requesting user input via function: elicit_user_input_v3" TRY { PromptResult_Json := INVOKE elicit_user_input_v3(prompt_message := PromptMessage) UserMessage := ParseJsonToCNLObject(PromptResult_Json).user_text # Assumes function returns { "user_text": "..." } } CATCH ALL AS Error { LOG "ERROR during INVOKE or result processing for elicit_user_input_v3: " + Error.message LogToCCOHistory_v3_0(CCO_Object_Conceptual, "FunctionCallError", "elicit_user_input_v3 failed", ConvertCNLObjectToJson(Error)) # If we can't even get user input, we should probably stop or signal a critical error. # For MVP, we'll log and break to prevent infinite loop on failure. BREAK } # 2. Check for STOP command (using the parsed user message) IF (UserMessage EQUALS "STOP") { LOG "User requested STOP. Exiting loop." BREAK # Exit WHILE loop } # 3. Log the user input to the conceptual CCO history LogToCCOHistory_v3_0(CCO_Object_Conceptual, "User_Input", "Message to echo: " + UserMessage) LOG "User input received: " + UserMessage # 4. Prepare input for echo_message_v1 (simple case: message parameter) # The 'message' parameter is required by echo_message_v1 # 5. INVOKE the external function echo_message_v1 LOG "Requesting external function call: echo_message_v1" TRY { EchoResult_Json := INVOKE echo_message_v1(message := UserMessage) # 6. Process the result EchoResult := ParseJsonToCNLObject(EchoResult_Json) # Assumes function returns JSON IF (HAS_KEY(EchoResult, "echoed_message")) { # Assuming the function returns { "echoed_message": "..." } LOG "Function 'echo_message_v1' returned success." LOG "Echoed Message: " + EchoResult.echoed_message # Log the successful function call result to CCO history LogToCCOHistory_v3_0(CCO_Object_Conceptual, "FunctionCallResult", "echo_message_v1 returned success", ConvertCNLObjectToJson(EchoResult)) # Optional: Check for repeat_count if user specified it in input (more complex parsing needed for this) # For MVP, we just log the echoed message. } ELSE { LOG "Warning: Function 'echo_message_v1' returned unexpected JSON structure." LogToCCOHistory_v3_0(CCO_Object_Conceptual, "FunctionCallResult_Warning", "echo_message_v1 returned unexpected structure", ConvertCNLObjectToJson(EchoResult)) LogObject(EchoResult) # Log the raw result object for debugging } } CATCH ALL AS Error { LOG "ERROR during INVOKE or result processing for echo_message_v1: " + Error.message # Log the function call error to CCO history LogToCCOHistory_v3_0(CCO_Object_Conceptual, "FunctionCallError", "echo_message_v1 failed", ConvertCNLObjectToJson(Error)) # In a real system, more specific error handling based on Error.type # would occur here. For MVP, we just log and continue the loop. } LOG "--- End of Loop Iteration ---" } # END WHILE LOG "ASO-aiOS Engine v3.0 MVP: Kernel finished." # In a real system, this would return control to the aiOS environment or signal termination. ``` --- ### III. META-HEURISTIC (MH) LIBRARY (MVP) instructions_for_ai: | This is the `ASO_METAHEURISTIC_LIBRARY_v3_0_MVP`. In this minimal MVP, the core demonstration logic is handled by the Kernel in Section II. This section serves as a placeholder for where Meta-Heuristic definitions would reside in a more complete Engine. No functional MHs are defined in this MVP. meta_heuristics_list: [] # This list is empty in the MVP, as no functional MHs are defined here. # In a more complete Engine, this list would contain definitions for MHs like: # - autologos_version_used: "Alpha v0.2" # script_description: "Example MH definition placeholder." # script_content: | # # Example MH Process Steps # DEFINE ExampleMH(Input_JsonString) { # LOG "Example MH initiated." # # ... autologos logic orchestrating function calls ... # RETURN OBJECT { status := "Success" } # } ``` --- That is the complete text of the `ASO-aiOS_Engine_v3.0_MVP.md` file, concatenated into a single code block. Now, let's move on to the second action: **Discuss how to test this MVP in a Google AI Studio environment.** Here are the steps you would take: 1. **Save the Files:** * Save the complete text above as `ASO_Engine_v3_0_MVP.md`. * Save the minimal MVP Schema text (provided in a previous turn) as `ASO-aiOS_EngineFile_Schema_v1_2_MVP.md`. (This is for my validation use, not loaded by you per process). * Save the MVP Function Declarations JSON (provided in a previous turn) as `function_declarations_mvp.json`. 2. **Configure Google AI Studio:** * Go to Google AI Studio ([https://aistudio.google.com/](https://aistudio.google.com/)). * Start a new chat session. * In the "Run settings" panel (usually on the left or right sidebar), find the "Function calling" section. * Select the "Code Editor" tab. * Paste the *entire content* of your `function_declarations_mvp.json` file into the code editor. * Click "Save". 3. **Load the MVP Engine:** * In the main chat input area, paste the *entire content* of your `ASO_Engine_v3_0_MVP.md` file. * Send the message. 4. **Observe AI Startup:** The AI should read and interpret the Engine file. Based on the Kernel's autologos script, it should log messages indicating it's starting and demonstrating the echo function. 5. **Interact with the Echo Loop:** The AI should then prompt you with the message defined in the autologos script: "Please provide a message to echo (or type 'STOP' to end):". 6. **Trigger a Function Call:** * Type a message (e.g., "Hello, AIOS!") and send it. * The AI should interpret your input, log it, and then output a `FUNCTION_CALL_REQUEST` for `echo_message_v1`. It will likely look like the screenshot you provided earlier. 7. **Execute the Function (Your Role):** * The AI Studio interface will show the function call request. * You need to conceptually (or actually, if you write the Python code) execute the `echo_message_v1` function with the parameters the AI provided. * The `echo_message_v1` function should return a JSON object. Based on the function declaration and the MVP Engine's autologos, the expected successful return format is likely `{"echoed_message": "Your message here"}`. * In the AI Studio interface, there will be an input box below the function call request labeled "Enter function response". Paste the JSON result from your function execution into this box. * Send the function response. 8. **Observe AI Processing the Result:** The AI should receive the JSON result, process it according to its autologos script (check for `echoed_message` key), log the successful call and the echoed message, and then loop back to prompt you for another message. 9. **Signal STOP:** To end the loop, type "STOP" when prompted for a message and send it. The AI should recognize the "STOP" command in its autologos logic and exit the loop. This process demonstrates the full cycle of the MVP. It requires you to act as the executor for the external function calls, providing the results back to the AI. Next, we will outline the prompt engineering needed for the external functions used in the MVP. Are you ready to proceed with outlining the prompt engineering for the MVP external functions?