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Data Flow

This page describes the data flow in the Bluefly LLM ecosystem.

Overview

The Bluefly LLM ecosystem processes various types of data, including user inputs, model outputs, training data, and feedback. Understanding these data flows is essential for effective integration and development.

Input Data Flow

  1. User input is received through BFCLI, BFUI, or direct API calls
  2. Input data is validated and preprocessed by BFAPI
  3. Preprocessed data is sent to BFLLM for model inference
  4. Model outputs are processed and returned to the user

Training Data Flow

  1. User feedback and inputs are collected and stored
  2. Data is processed and formatted for training
  3. BFLLM uses the processed data to train or fine-tune models
  4. Updated models are evaluated and deployed

Data Storage

The ecosystem uses several storage mechanisms:

  • Database: For structured data including user preferences and feedback
  • File System: For model artifacts and large datasets
  • Caching: For frequently accessed data and model outputs

Data Security

All data flow in the ecosystem is secured using:

  • Encryption in transit (TLS/SSL)
  • Encryption at rest for sensitive data
  • Access controls based on user roles
  • Data minimization principles

Data Transformations

Data undergoes several transformations as it flows through the system:

  1. Normalization: Standardizing input formats
  2. Tokenization: Converting text to tokens for model processing
  3. Aggregation: Combining data for training and analysis
  4. Formatting: Preparing outputs for different consumption patterns