ArgoFloat

ArgoFloat is a modular AI-powered data platform designed to support data ingestion, processing, analytics, workflow orchestration, and Retrieval-Augmented Generation (RAG) workflows. The platform follows a service-oriented architecture where individual services can be developed, deployed, and scaled independently.

Features

• Multi-source data ingestion
• Data validation and transformation pipelines
• Workflow orchestration and task management
• Retrieval-Augmented Generation (RAG) support
• Modular microservice architecture
• REST API integration
• Scalable processing pipelines
• Structured logging and monitoring support
• Extensible service framework

Architecture

ArgoFloat is composed of multiple independent services:

Frontend Service

Provides the user interface for interacting with platform features, dashboards, analytics, and workflow management.

Backend Service

Exposes APIs, manages business logic, authentication, task orchestration, and communication between platform components.

Ingestion Service

Responsible for collecting, validating, transforming, and routing incoming data into processing pipelines.

RAG Service

Handles document ingestion, embedding generation, vector retrieval, context assembly, and LLM interaction workflows.

Supporting Services

Additional subprocesses and utilities that perform specialized processing tasks, automation, and system operations.

High-Level Workflow

1. Data is collected from external sources.
2. The ingestion service validates and transforms incoming data.
3. Processed data is stored and indexed.
4. Backend services expose APIs for platform interaction.
5. RAG services retrieve relevant context and generate responses.
6. Results are presented through the frontend interface.

Technology Stack

• Python
• Django / FastAPI
• React
• SQLite / PostgreSQL
• REST APIs
• Vector Databases
• Docker (optional)
• GitHub

Project Structure

ArgoFloat/
│
├── frontend/
├── backend/
├── ingestion/
├── rag/
├── docs/
├── logs/
└── README.md

Design Principles

• Modular architecture
• Independent service deployment
• Scalability and maintainability
• Clear separation of concerns
• Extensible processing pipelines
• Reusable components

Future Improvements

• Distributed task queues
• Advanced monitoring and observability
• Multi-tenant support
• Real-time processing pipelines
• Enhanced AI workflows
• Cloud-native deployment

Repository Contents

• Source code
• Documentation
• Configuration files
• API specifications
• Logging setup
• Deployment resources

License

This project is intended for educational, research, and platform development purposes.