Stratvithor

            - An AI-Powered Expanding Knowledge Ecosystem

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                    Project Under Construction

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Stratvithor redefines the way insights are gathered, structured, and continuously updated. It is a multi-dimensional intelligence dashboard that adapts in real time, integrating AI-driven automation with live data pipelines. Users can design, explore, and refine structured knowledge environments, ensuring that critical information remains current, relevant, and actionable.

How Users Experience Stratvithor

Users interact with Stratvithor through a highly intuitive, customizable interface where they define the structure of their insights. Each section of the dashboard is a dynamic knowledge module, continuously updated with fresh data from AI-driven queries, live sources, and structured pipelines.

• Build & Customize: Users start by defining key focus areas, shaping their dashboard to reflect their priorities. Whether tracking financial markets, real-time traffic patterns, legislative changes, or developing weather systems, Stratvithor structures the data according to their specifications.
• Live, Evolving Data Streams: Each module is a living entity, not a static document. Sections update automatically based on real-time inputs, ensuring insights remain fresh and actionable. A dashboard monitoring congressional law proposals will stay updated as new bills are introduced, just as a disaster response analyst can track and adapt to live meteorological changes.
• Flexible Data Transformation: Sections are not only interconnected but also adaptable in form. A module that retrieves raw stock prices can output textual analysis, while another that processes legislative documents can generate numerical impact scores. A section tracking court rulings can analyze the frequency of case outcomes and translate them into predictive legal trends. This seamless transformation of data into meaning allows users to shape their insights in ways that best serve their needs.
• Intelligent Relationships Between Sections: Sections can be linked to inform each other. A traffic congestion monitoring module can adjust its predictions based on weather conditions, just as a public policy tracker can analyze economic impacts based on newly passed laws. This interconnectivity enables a holistic view of complex information landscapes where insights evolve together rather than in isolation.
• Interactive Exploration: Users can expand, refine, or eliminate sections dynamically, controlling the depth and breadth of their insights. A journalist might start with a high-level summary of emerging geopolitical conflicts and then drill down into detailed reports, while an investor could refine a broad market outlook into sector-specific intelligence.
• Seamless Visualization: Insights are not just presented as text but are enriched with charts, time-series data, AI-generated summaries, and interactive visualizations, allowing users to grasp complex information quickly and efficiently. A city planner can visualize urban expansion trends, while a climate researcher can monitor long-term environmental changes.
• AI-Assisted Adaptation: Stratvithor continuously refines itself based on user interactions, learning which insights matter most and intelligently prioritizing relevant data. Whether tracking court rulings, international trade developments, or breaking news, the system ensures that users receive the most meaningful and up-to-date intelligence.

A Continuous Intelligence Hub

By eliminating information overload and enabling real-time knowledge expansion, Stratvithor transforms traditional reporting into an ongoing, interactive discovery process. With its ability to dynamically reshape information, link insights across modules, and refine knowledge structures, Stratvithor ensures that every decision is backed by the most current, relevant, and structured intelligence—empowering users to stay ahead in a world where information never stops evolving.

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Architecture

System Components

• Backend: Handles data querying, transformation, and integration, ensuring real-time updates and intelligent data flow.
• FrontEnd: Provides an interactive multi-dimensional dashboard for exploring, customizing, and refining live knowledge modules.
• DataQuerier: Executes asynchronous HTTP requests to APIs, validates structured responses, and ensures data integrity across interconnected sections.
• Integrator: Implements the generate_knowledge_module method, executing prompts in topological order within the DAG. It processes queries, links interdependent sections, and integrates structured outputs into a dynamic, live-updating knowledge system.
• DataMolder: Transforms raw queried data using an AI-powered Text Processing microservice, allowing seamless adaptation between data formats (e.g., numerical data into textual summaries, text-based insights into quantifiable metrics).
• Knowledge Modules (Formerly "Prompts"): Defines structured, interrelated queries that shape the dashboard layout and guide how information evolves over time.

Workflow

1. Topological Sorting of Knowledge Modules: The DAG structures interdependencies between live data sections.
2. Real-Time Data Retrieval: Each module asynchronously queries external sources, ensuring continuous updates.
3. Adaptive Data Transformation: Retrieved data undergoes context merging, reshaping its form based on output needs (e.g., financial indicators generating textual market summaries or vice versa).
4. Dynamic Knowledge Integration: The system assembles refined sections into a live intelligence dashboard, seamlessly blending data streams with visual and interactive elements.
5. User Interaction & Exploration: The interface enables hands-on navigation, where users can expand, refine, or restructure their dashboard dynamically—adding new insights, filtering irrelevant data, and linking related topics into an evolving knowledge network.

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Key Features

✅ Asynchronous Multi-Source Data Retrieval - Efficient real-time API calls using aiohttp
✅ DAG-Based Dependency Management - Ensures logical execution order and context-aware section updates
✅ Interactive & Configurable Knowledge Modules - Users can shape, modify, and explore interconnected insights dynamically
✅ Live Data Processing & Transformation - Uses Text_Processing for intelligent adaptation between text, numbers, and structured outputs
✅ Scalable & Modular - Designed for adaptability across finance, law, traffic analysis, weather monitoring, and more
✅ AI-Powered Exploration - Navigate the dashboard as an expanding knowledge ecosystem, linking relevant topics and pruning unnecessary details
✅ Multi-Modal Intelligence - Supports text, numerical insights, visualizations, time-series data, and interactive graphs for a rich analytical experience

Stratvithor transforms static reporting into an ever-evolving intelligence network, ensuring users have the most current, relevant, and structured knowledge at their fingertips.

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Deployment

The system is multi-containerized using Docker for easy deployment.

Set-up:

Clone repo

    git clone https://github.com/luislascano01/Stratvithor

Navigate to directory and make credentials file

    cd Stratvithor
    cd Credentials
    touch Credential.yaml

Add valid credentials for OpenAI and Google Search Engine inside Credential.yaml

  API_Keys:
      Google_Cloud: "G-Cloud_Keys"
      OpenAI: "OpenAI Keys"

  Online_Tool_ID:
    Custom_G_Search: 'G_Custom_Search_CSE_ID'

Execution:

No CUDA

    docker compose build
    docker compose up

CUDA

export BACKEND_BASE_IMAGE=pytorch/pytorch:2.1.0-cuda11.8-cudnn8-runtime
docker compose -f docker-compose.yml -f docker-compose.gpu.yml up --build

To access front end:

    localhost:5155

Prompt Settings

You may access the prompts manually under "Prompts folder"

Copyright

Open to collaborate under open source agreement