02_ARCHITECTURE.md

Architecture

Overview

The Thinking Dataset Project is designed to develop and analyze complex strategic scenarios, ethical dilemmas, and decision-making processes. The architecture includes key components such as data ingestion, preprocessing, enrichment, model training, evaluation, and continuous improvement. The system leverages various serverless endpoints and adapters to ensure flexibility and scalability.

Key Components

The project relies on several core elements: Data Ingestion for collecting raw data; Data Preprocessing for cleaning, transforming, and validating data; Data Enrichment and Case Study Creation for generating scenarios and case studies; Model Training and Evaluation for training and assessing AI models; Inference Endpoint Adapters for integrating various serverless endpoints; and Continuous Improvement for refining the dataset and models based on feedback. Each component plays a vital role in analyzing complex strategic scenarios, ethical dilemmas, and decision-making processes.

1. Data Ingestion

Data ingestion involves collecting raw data from multiple sources, including historical records, literature, user-generated content, and the WikiLeaks Cablegate dataset. This raw data is stored in a structured format using SQLite, ensuring it is readily accessible for further processing.

2. Data Preprocessing

Data preprocessing involves several steps to prepare the raw data for analysis:

• Data Cleaning: Removing duplicates, handling missing values, and normalizing the data.
• Data Transformation: Converting raw data into a standardized format suitable for analysis.
• Data Validation: Ensuring the integrity and quality of the data before it moves to the next phase.

3. Data Enrichment and Case Study Creation

In the data enrichment and case study creation phase:

• Seed Generation: Seed objects are generated using predefined keywords and knowledge from source cables.
• Cable Creation: Seed objects are combined to create detailed scenarios called cables.
• Persona Database: Stakeholder personas are generated and stored using the personas dataset on Hugging Face.
• Synthetic SitRep Generation: Synthetic Situation Reports (SitReps) are generated inspired by random seed objects.
• Case Study Generation: SitReps are distributed to various imaginary stakeholders, who then write detailed case studies maximizing their stake or claim. Case studies are enriched with a chain of thoughts for use in Self-Teaching with Reinforcement (STaR).

4. Model Training and Evaluation

Model training and evaluation begin with:

• Dataset Preparation: Validating data integrity, balancing classes, and splitting the data into training and testing sets.
• Model Training: Training baseline models using the prepared dataset, followed by fine-tuning models for improved performance. Our model is built on the foundation of the Yi model.
• Evaluation System Development: Implementing an evaluation system to score models and compare the performance of fine-tuned models against baseline models. We are developing the STaR (Self-Teaching with Reinforcement) system to augment the capabilities of our thinking model to be on par with the capabilities of GPT-4.

5. Inference Endpoint Adapters

Inference Endpoint Adapters provide flexibility and scalability by integrating with various serverless endpoints:

• Hugging Face: For accessing pretrained NLP models and custom endpoints.
• Ollama: For leveraging proprietary models and services.
• Testcontainers: For local testing and development in isolated environments.
• Runpod: For scalable, serverless computing resources.

6. Continuous Improvement

Continuous improvement involves iteratively refining the datasets and models based on user and system feedback:

• Feedback Loop: Gathering user and system feedback to iteratively refine and update the dataset and models.
• Regular Updates: Regularly updating the project with new data sources, model improvements, and enhanced algorithms to keep it current and relevant.
• Documentation: Maintaining comprehensive documentation of methods, changes, and updates to ensure transparency and reproducibility.
• Community Engagement: Engaging with the community for contributions and collaboration, fostering an environment of continuous enhancement and innovation.

Technology Stack

The Thinking Dataset Project uses technologies chosen for their efficiency, scalability, and ease of integration. Python serves as the core language due to its extensive libraries and community support. SQLite is lightweight and fast for structured data. Libraries like pandas and scikit-learn offer robust data manipulation and machine learning capabilities, while rich enhances console output and error handling. Tools like python-dotenv and SQLAlchemy manage configuration and database interactions. Serverless computing platforms and state machine management enhance flexibility and scalability.

• Python: Core programming language for the project.
• SQLite: Lightweight database for storing structured data.
• pandas: Data manipulation and preprocessing.
• scikit-learn: Machine learning library for model training and evaluation.
• rich: Enhanced console output and error handling.
• python-dotenv: Manage configuration and environment variables.
• Hugging Face Transformers: Library for state-of-the-art NLP models.
• Testcontainers: Library for integration testing with containers.
• Runpod: Serverless computing platform.
• python-statemachine: Library for managing state machines.
• SQLAlchemy: SQL toolkit and Object-Relational Mapping (ORM) library for database interactions.
• DB Browser for SQLite: Open-source tool for database administration.
• datasets: Library for accessing and sharing datasets.
• PyPDF2: PDF toolkit.
• click: Command line interface creation tool.
• requests: Library for making HTTP requests.
• sqlite-utils: Tools for working with SQLite databases.
• pytest: Testing framework.
• pytest-html: HTML report generation for pytest.
• pytest-cov: Coverage reporting for pytest.
• loguru: Logging library.
• numpy: Fundamental package for scientific computing.
• tqdm: Progress bar library.
• pydantic: Data validation and settings management.

By incorporating these technologies, the project achieves a robust, flexible, and scalable architecture capable of handling complex strategic scenarios, ethical dilemmas, and decision-making processes efficiently.

Architectural Diagram

The architectural diagram below provides a high-level overview of the Thinking Dataset Project. It illustrates the flow and interaction between the project's key components, ensuring a robust, scalable, and efficient system. Each component plays a vital role in the seamless operation of the project, from data ingestion to continuous improvement.

The diagram starts with Data Ingestion, where raw data from multiple sources, including historical records, literature, and user-generated content, is collected and stored in a structured format using SQLite. Data Preprocessing follows, involving data cleaning to remove duplicates and handle missing values, data transformation to standardize the format, and data validation to ensure integrity.

Next, the Data Enrichment phase generates seed objects using predefined keywords and cables, combines them to create detailed scenarios (cables), and generates case studies refined into a consistent format for model training. Model Training involves preparing datasets, training both baseline and fine-tuned models, and evaluating their performance.

Inference Endpoint Adapters integrate various serverless endpoints like Hugging Face, Ollama, testcontainers, and Runpod through a unified interface, ensuring consistency and seamless operation. The Continuous Improvement phase gathers user and system feedback to iteratively refine the dataset and models, maintaining comprehensive documentation and fostering community engagement.

At the core lies the Foundation, comprising core technologies and infrastructure that support all components. The feedback loop ensures continuous data ingestion and improvement by feeding insights back into the system, creating a cycle of synthetic SitReps and case study generation.

+------------------------------+-----------------------------+
|       Data Ingestion         |     Data Preprocessing      |
| +-------------------------+  | +-------------------------+ |
| |     Raw Data Sources    |  | |     Data Cleaning       | |
| +-------------------------+  | |     Data Transformation | |
|                              | |     Data Validation     | |
+-------------|----------------+ +------------|--------------+
              |                                 |
              v                                 v
+------------------------------+ +---------------------------+
|       Data Enrichment        |      Model Training         |
| +--------------------------+ | +------------------------+  |
| |     Seed Generation      | | |    Dataset Preparation |  |
| |     Cable Creation       | | |    Model Training      |  |
| |     Case Studies         | | |    Evaluation System   |  |
| +--------------------------+ | +------------------------+  |
+-------------|---------------+ +----------------|-----------+
              |                                 |
              v                                 v
+-----------------------------+ +----------------------------+
|    Inference Endpoint       |    Continuous Improvement    |
|        Adapters and Bridges |                              |
| +-------------------------+ | +--------------------------+ |
| |    Unified Interface    | | |     Feedback Loop        | |
| |    Adapter API          | | |     Documentation        | |
| +-------------------------+ | |     Community Engagement | |
+-------------|---------------+ +------------|---------------+
              |                                 |
              v                                 v
+------------------------------------------------------------+
|                          Foundation                        |
| +--------------------------------------------------------+ |
| |               Core Technologies & Infrastructure       | |
| +--------------------------------------------------------+ |
+-------------|-----------------^----------------------------+
              |                 |                            |
              +-----------------+                            |
                       Feedback Loop                         |
                       (Generation to Input)                 |
+------------------------------------------------------------+
|     Synthetic SitReps and Case Studies Creation Cycle      |
| +--------------------------------------------------------+ |
| |  Synthetic SitReps Creation -> Case Study Generation ->| |
| |  Feeding Back into Data Ingestion for Continuous Loop  | |
| +--------------------------------------------------------+ |
+------------------------------------------------------------+

This diagram visually represents the interactions and flow between the various components, highlighting how each part contributes to the overall project architecture, ensuring a robust and efficient system.

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Conclusion

The Thinking Dataset Project features a robust and scalable architecture designed for analyzing complex strategic scenarios, ethical dilemmas, and decision-making processes. Integrating various serverless endpoints and adapters ensures flexibility and scalability, while a continuous feedback loop facilitates iterative improvement. Built on the foundation model Yi and enhanced with the STaR (Self-Teaching with Reinforcement) system, our thinking model aims to match the capabilities of GPT-4. This design meets current needs and lays the foundation for future enhancements, significantly contributing to AI and cognitive science research by helping businesses and governments reason through complex dilemmas and strategic decisions, ultimately maximizing organizational effectiveness and profitability.