PoornaChandra797/knowledge-encoder

Enhanced Knowledge Encoder v2.0.0

A revolutionary self-learning and continual learning model that completely replaces previous features with advanced capabilities for document understanding and knowledge extraction.

🚀 Enhanced Features (v2.0.0)

🧠 Neural Memory System

• Persistent Knowledge Storage: No external databases required
• Intelligent Memory Management: Automatic memory slot allocation and optimization
• Memory Utilization Tracking: Real-time monitoring of knowledge storage efficiency

📚 Continual Learning

• Document-Based Learning: Model improves with each new document
• Adaptive Learning Rate: Dynamic adjustment based on document quality
• Learning Statistics: Comprehensive tracking of learning progress and metrics

🚀 Self-Improving Inference

• Knowledge Fusion: Intelligent combination of memory and current input
• Advanced Attention Mechanisms: Multi-head attention with memory integration
• Quality-Aware Processing: Document quality assessment and learning

🔍 Advanced Attention Mechanisms

• Memory-Aware Attention: Attention that considers stored knowledge
• Multi-Head Memory Attention: Parallel attention across knowledge dimensions
• Dynamic Attention Weights: Adaptive attention based on input relevance

💡 Intelligent Tokenization

• Subword Tokenization: BPE-like tokenization for better word handling
• Learning Tokenizer: Vocabulary expansion based on document learning
• Quality-Weighted Learning: Token importance based on document quality

📚 Use Cases

• Document Understanding: Comprehensive analysis of complex documents
• Knowledge Extraction: Intelligent extraction of key information
• Continual Learning: Models that improve over time with new data
• Intelligent Q&A Systems: Context-aware document question answering
• Research Automation: Automated research and analysis workflows
• Content Analysis: Deep understanding of text content and structure

🔧 Quick Start

Installation

# Install from Hugging Face
pip install git+https://huggingface.co/PoornaChandra797/knowledge-encoder

# Or install locally
git clone https://huggingface.co/PoornaChandra797/knowledge-encoder
cd knowledge-encoder
pip install -e .

Basic Usage

from knowledge_encoder import EnhancedKnowledgeEncoder, EnhancedTokenizer

# Initialize enhanced model and tokenizer
model = EnhancedKnowledgeEncoder(
    vocab_size=1000,
    hidden_size=256,
    num_attention_heads=8,
    num_hidden_layers=4,
    memory_size=1000,
    learning_rate=1e-4
)

tokenizer = EnhancedTokenizer(
    vocab_size=1000,
    min_frequency=1,
    max_word_length=50
)

# Learn from documents
document_text = "Your document content here..."
document_embeddings = model.encode_text(document_text)

# Continual learning
learning_result = model.learn_from_document(document_embeddings, document_quality=0.9)
tokenizer.learn_from_document(document_text, document_quality=0.9)

# Get intelligent responses
query_text = "What is the main topic?"
query_embeddings = model.encode_text(query_text)
response = model.forward(query_embeddings)

# Retrieve knowledge
retrieved_knowledge, similarities = model.retrieve_knowledge(query_embeddings, top_k=5)

# Get learning statistics
stats = model.get_learning_statistics()
print(f"Learning sessions: {stats['learning_metrics']['learning_sessions']}")
print(f"Memory utilization: {stats['learning_metrics']['memory_utilization']:.2f}")

📊 Advanced Features

Learning from Documents

# Batch learning from multiple documents
documents = [
    ("Document 1 content...", 0.9),
    ("Document 2 content...", 0.8),
    ("Document 3 content...", 0.95)
]

for doc_text, quality in documents:
    # Learn from document
    doc_embeddings = model.encode_text(doc_text)
    learning_result = model.learn_from_document(doc_embeddings, quality)
    
    # Learn tokenization patterns
    tokenizer.learn_from_document(doc_text, quality)
    
    print(f"Learned from document with quality {quality}: {learning_result}")

Knowledge Retrieval

# Retrieve relevant knowledge
query = "What are the key concepts?"
query_embeddings = model.encode_text(query)

# Get top-k most relevant knowledge
knowledge, similarities = model.retrieve_knowledge(query_embeddings, top_k=10)

print(f"Retrieved {len(knowledge)} knowledge items")
for i, (k, s) in enumerate(zip(knowledge, similarities)):
    print(f"Knowledge {i+1}: Similarity {s:.3f}")

Learning Statistics

# Comprehensive learning statistics
stats = model.get_learning_statistics()

print("=== Model Information ===")
print(f"Total parameters: {stats['model_info']['total_parameters']:,}")
print(f"Memory size: {stats['model_info']['memory_size']}")
print(f"Learning rate: {stats['model_info']['learning_rate']}")

print("\n=== Learning Metrics ===")
print(f"Total documents: {stats['learning_metrics']['total_documents']}")
print(f"Learning sessions: {stats['learning_metrics']['learning_sessions']}")
print(f"Memory utilization: {stats['learning_metrics']['memory_utilization']:.2f}")
print(f"Knowledge diversity: {stats['learning_metrics']['knowledge_diversity']:.2f}")

print("\n=== Recent Learning History ===")
for session in stats['learning_history'][-5:]:
    print(f"Session: Loss {session['loss']:.4f}, Quality {session['document_quality']:.2f}")

🏗️ Architecture

Enhanced Model Structure

EnhancedKnowledgeEncoder
├── Token Embeddings
├── Positional Encoding
├── Transformer Encoder Layers
├── Neural Memory System
│   ├── Knowledge Memory
│   ├── Memory Attention
│   └── Memory Gate
├── Knowledge Fusion
├── Learning Mechanisms
│   ├── Optimizer (AdamW)
│   ├── Scheduler (CosineAnnealing)
│   └── Learning Metrics
└── Output Projections

Key Components

• Neural Memory: Persistent storage of learned knowledge
• Memory Attention: Intelligent retrieval of relevant knowledge
• Knowledge Fusion: Combination of memory and current input
• Continual Learning: Ongoing model improvement
• Quality Assessment: Document quality-based learning

📈 Performance

Memory Efficiency

• Dynamic Memory Allocation: Automatic optimization of memory usage
• Memory Utilization Tracking: Real-time monitoring of efficiency
• Adaptive Memory Management: Intelligent memory slot allocation

Learning Efficiency

• Quality-Weighted Learning: Better learning from high-quality documents
• Adaptive Learning Rate: Dynamic adjustment for optimal learning
• Learning Statistics: Comprehensive tracking of learning progress

Inference Performance

• Enhanced Attention: Faster and more accurate attention mechanisms
• Memory Integration: Efficient knowledge retrieval and integration
• Optimized Forward Pass: Streamlined inference pipeline

🔄 Backward Compatibility

All previous imports continue to work seamlessly:

# Old imports still work
from knowledge_encoder import KnowledgeEncoder, SimpleTokenizer
from knowledge_encoder import load_model, save_model, validate_model

# New enhanced imports
from knowledge_encoder import EnhancedKnowledgeEncoder, EnhancedTokenizer
from knowledge_encoder import load_enhanced_model, save_enhanced_model, validate_enhanced_model

🧪 Testing

Model Validation

from knowledge_encoder import validate_enhanced_model

# Validate enhanced model
is_valid = validate_enhanced_model("path/to/model.pth")
print(f"Model validation: {'✅ PASSED' if is_valid else '❌ FAILED'}")

Inference Testing

from knowledge_encoder import test_enhanced_model_inference

# Test model inference
results = test_enhanced_model_inference("path/to/model.pth", "Test document content")
print(f"Test results: {results}")

Performance Benchmarking

from knowledge_encoder import benchmark_enhanced_model

# Benchmark model performance
benchmark_results = benchmark_enhanced_model("path/to/model.pth")
print(f"Benchmark results: {benchmark_results}")

📦 Package Management

Creating Model Packages

from knowledge_encoder import create_enhanced_model_package

# Create distribution package
package_path = create_enhanced_model_package(
    "path/to/model.pth",
    "output/package",
    include_tokenizer=True
)
print(f"Package created at: {package_path}")

Saving Enhanced Models

# Save with all learning state
model.save_pretrained("enhanced_model_v2.pth")

# Save tokenizer with learning state
tokenizer.save_pretrained("enhanced_tokenizer_v2/")

🌟 Key Advantages

1. No External Dependencies: Self-contained neural memory system
2. Continual Improvement: Model gets better with each document
3. Intelligent Learning: Quality-aware document processing
4. Advanced Architecture: State-of-the-art transformer design
5. Easy Integration: Simple API for any application
6. Production Ready: Stable, tested, and optimized
7. Open Source: Free to use and modify
8. Active Development: Ongoing improvements and updates

🤝 Contributing

We welcome contributions! Please see our contributing guidelines for more information.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with PyTorch and Transformers
• Inspired by modern neural network architectures
• Designed for real-world document understanding applications

📞 Support

• Documentation: Hugging Face Hub
• Issues: GitHub Issues
• Email: poornachandrak@ideyalabs.com

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Enhanced Knowledge Encoder v2.0.0 - Revolutionizing document understanding with self-learning and continual learning capabilities.