cognitive-twin 3.0.0

User pattern learning with trajectory-aware DPO training

CognitiveTwin

User pattern learning with trajectory-aware DPO (Direct Preference Optimization) training.

Overview

CognitiveTwin is a sophisticated system for learning user communication patterns through:

• Corpus Surgery: Data cleaning, validation, and quality filtering
• WORMS: Trajectory generators for synthetic training data
  • Conversation Worm: Dialogue trajectory generation
  • Repo Worm: Code repository analysis
  • Task Worm: Task execution patterns
  • DPO Generator: Preference pair generation
• Dataset Building: Preference pair labeling and export
• Evaluation Suite: Comprehensive testing framework

Installation

pip install cognitive-twin

# With training dependencies
pip install cognitive-twin[training]

Quick Start

from cognitive_twin.v3 import pipeline, schema
from cognitive_twin.framework import config

# Initialize pipeline
cfg = config.CognitiveTwinConfig(
    model_name="your-base-model",
    output_dir="./output"
)

# Run corpus surgery
pipeline.run_corpus_surgery(cfg)

# Generate training data
pipeline.generate_dpo_pairs(cfg)

# Train
pipeline.train(cfg)

Components

v3/ - Main Implementation

• corpus_surgery/ - Data cleaning and validation
• dataset/ - Dataset generation and labeling
• eval/ - Evaluation framework
• generators/ - Batch and DPO generators
• ingest/ - Data ingestion (Claude, OpenAI, Supabase)
• worms/ - Trajectory generators
• pipeline.py - Main orchestrator
• schema.py - Type definitions

framework/ - Supporting Infrastructure

• config.py - Configuration management
• twin.py - Core twin abstraction
• trainer.py - Training loop

Documentation

See the docs/ directory for detailed documentation:

• 00_OVERVIEW.md - System overview
• 01_CORPUS_SURGERY.md - Data cleaning pipeline
• 02_REPO_WORM.md - Repository analysis
• 03_CONVERSATION_WORM.md - Dialogue generation
• 04_ENHANCER_AGENT.md - Quality enhancement
• 05_DATASET_BUILDER.md - Dataset construction
• 06_TRAINING_PIPELINE.md - Training guide
• 07_EVALUATION_SUITE.md - Evaluation metrics
• 08_API_INTEGRATION.md - API usage

License

MIT