Design Data Schema and Storage Connector

[solarfresh]

Description

This task is focused on designing the data schema for the multi-agent debate simulation and a flexible, reusable storage connector. The goal is to define a clear data model for all conversational and CoT data produced by the simulation. Crucially, the task does not involve implementing any specific database or file storage solution. Instead, we will design an abstract connector interface that can be implemented to support various backends (e.g., local files, SQL databases, NoSQL databases). This will ensure the framework is modular and easily adaptable for different use cases.

The design will be documented and will serve as the blueprint for future implementation tasks.

Acceptance Criteria:

• A formal schema for conversational data is defined. This schema must capture key fields for each utterance, including:
  • turn_id: A unique identifier for each conversational turn.
  • speaker_role: The assigned role (agree-side, disagree-side).
  • utterance_text: The spoken text.
  • timestamp: The time the utterance was generated.
  • simulation_id: A unique ID for the entire debate simulation.
• A formal schema for Chain of Thought (CoT) data is defined. This schema must capture the reasoning for each utterance and link back to the conversational data:
  • cot_id: A unique identifier for each CoT entry.
  • utterance_id: A foreign key linking to the corresponding conversational utterance.
  • reasoning_steps: The step-by-step reasoning generated by the model.
• A generic connector interface is designed. This design will specify a set of methods that any backend storage solution must implement. The interface will include methods such as:
  • save_utterance(data): A method to persist a single conversational utterance.
  • save_cot(data): A method to persist a single CoT entry.
  • initialize(config): A method to set up the connection to the specific storage backend.
• A simple data model for the overall simulation is specified. This model will include metadata about each run, such as the deceptive goal, model used, and start/end times.
• The entire data schema and connector design are clearly documented in a design document or README.md file.