PyroChain 🔥

Intelligent Feature Engineering with AI Agents

PyroChain combines PyTorch's deep learning capabilities with LangChain's agentic AI to automate feature extraction from complex, multimodal data. AI agents collaborate to understand, process, and extract meaningful features from text, images, and structured data.

🎯 What Problem Does PyroChain Solve?

Traditional Feature Engineering is Hard:

• Manual feature extraction is time-consuming and error-prone
• Different data types require different approaches
• Domain expertise is needed to create meaningful features
• Features become outdated as data patterns change

PyroChain Makes It Easy:

• AI agents automatically extract relevant features from any data type
• Collaborative agents validate and refine features using chain-of-thought reasoning
• Learns from your data to improve feature quality over time
• Works seamlessly with existing ML pipelines

🚀 Key Features

• 🤖 AI Agents: Intelligent agents that collaborate to extract, validate, and refine features
• 📊 Multimodal Processing: Handle text, images, and structured data in one pipeline
• ⚡ Lightweight & Fast: Efficient LoRA adapters that train quickly on your data
• 🧠 Memory & Learning: Agents remember past decisions and improve over time
• 🛒 E-commerce Ready: Built-in tools for product recommendations and customer analysis
• 🏗️ Production Ready: Scalable architecture designed for real-world applications

💡 Use Cases

E-commerce & Retail:

• Product recommendation systems
• Customer sentiment analysis
• Inventory optimization
• Price prediction and analysis

Content & Media:

• Text classification and tagging
• Image content analysis
• Content recommendation
• Automated content moderation

Business Intelligence:

• Customer behavior analysis
• Market trend detection
• Risk assessment
• Automated reporting

🛠️ Installation

Quick Install

pip install pyrochain

From Source

git clone https://github.com/irfanalidv/PyroChain.git
cd PyroChain
pip install -e .

Requirements

• Python 3.8+
• PyTorch 2.0+
• LangChain 0.1+
• Transformers 4.20+

🚀 Quick Start

Basic Usage

frompyrochainimportPyroChainfromtransformersimportAutoTokenizer, AutoModelfromtextblobimportTextBlobimporttorchfromdatasetsimportload_dataset# Load real transformer model and tokenizermodel_name="microsoft/DialoGPT-small"tokenizer=AutoTokenizer.from_pretrained(model_name)
model=AutoModel.from_pretrained(model_name)
# Initialize PyroChain with transformer modelpyrochain=PyroChain()
# Load REAL data from IMDB datasetprint("📚 Loading real IMDB dataset...")
dataset=load_dataset("imdb", split="train[:4]") # Load first 4 real reviews# Extract features from REAL dataset with TextBlob sentiment analysisfori, sampleinenumerate(dataset):
text=sample["text"]
label=sample["label"] # 0 = negative, 1 = positive# Use TextBlob for real sentiment analysisblob=TextBlob(text)
sentiment_score= (blob.sentiment.polarity+1) /2# Convert to 0-1 scaledata= {
"text": text,
"title": f"IMDB Review {i+1}",
"rating": 5iflabel==1else1,
"category": "movie_review"
}
features=pyrochain.extract_features(
data,
"Extract features for sentiment analysis using TextBlob and transformer model"
)
print(f"Text: {text[:100]}...")
print(f"Real Label: {label} | TextBlob Sentiment: {sentiment_score:.3f}")
print(f"Features: {len(features['features'])}")
print("---")

Real Data Example

# Run the complete real data examplecd examples
python main_example.py

What you'll see:

🔥 PyroChain Real Data Demo - 100% Real Analysis
============================================================
🚀 Real Data Feature Extraction Example
==================================================
📚 Loading real IMDB dataset using transformer models...
📥 Downloading real IMDB dataset...
✅ Loaded 5 real IMDB samples using transformer model
📝 Processing: IMDB Review 1
Text: I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it w...
Rating: 1/5 (Real IMDB Label: 0 = Negative)
✅ Extracted 2 feature sets
📊 Modalities: ['text']
⏱️ Processing time: 0.025s
📊 Data source: real_imdb_dataset
🔍 sentiment_analysis:
sentiment_score: 0.57
polarity: 0.14
subjectivity: 0.85
positive_words: 16
negative_words: 4
total_sentiment_words: 20
confidence: 0.95
🔍 text_features:
word_count: 288
char_count: 1640
sentence_count: 14
avg_word_length: 4.7
avg_sentence_length: 20.57
readability_score: 0.0
topic_keywords: ['movie', 'review', 'story', 'direction', 'visuals', 'drama']
🛒 Real Data E-commerce Analysis
==================================================
🔍 Analyzing: Wireless Bluetooth Headphones
💰 Price: $199.99
⭐ Rating: 4.5/5 (128 votes)
✅ Recommendation score: 0.91
📊 Features extracted: 2
🏆 Top Recommendations:
1. Wireless Bluetooth Headphones - Score: 0.91
2. Organic Cotton T-Shirt - Score: 0.815

🏗️ How It Works

1. Data Ingestion: Accepts multimodal data (text, images, structured)
2. Agent Processing: AI agents analyze data using chain-of-thought reasoning
3. Feature Extraction: Collaborative agents extract relevant features
4. Validation: Agents validate and refine features through discussion
5. Output: Clean, structured features ready for ML models

⚙️ Configuration

frompyrochainimportPyroChain, PyroChainConfigfromtransformersimportAutoTokenizer, AutoModelimporttorch# Load real transformer model for e-commerce analysismodel_name="microsoft/DialoGPT-small"tokenizer=AutoTokenizer.from_pretrained(model_name)
model=AutoModel.from_pretrained(model_name)
# Real e-commerce product dataproducts= [
{
"id": "prod_001",
"title": "Wireless Bluetooth Headphones",
"description": "High-quality wireless headphones with noise cancellation and 30-hour battery life. Perfect for music lovers and professionals.",
"price": 199.99,
"category": "electronics",
"rating": 4.5
},
{
"id": "prod_002",
"title": "Organic Cotton T-Shirt",
"description": "Comfortable organic cotton t-shirt in various colors and sizes. Made from 100% organic cotton, eco-friendly and sustainable.",
"price": 29.99,
"category": "clothing",
"rating": 4.2
}
]
# Configure for e-commerce with transformer modelconfig=PyroChainConfig(
task_type="ecommerce", # Task type: "general", "ecommerce", "custom"enable_agents=True, # Enable AI agent collaborationenable_training=False, # Enable model trainingmax_length=512, # Maximum input lengthlearning_rate=1e-4, # Learning rate for trainingnum_epochs=3, # Number of training epochsdevice="auto"# Device: "auto", "cpu", "cuda"
)
pyrochain=PyroChain(config=config)
# Process real product data with transformer analysisforproductinproducts:
features=pyrochain.extract_features(
product,
"Extract features for product recommendation using transformer model"
)
print(f"Product: {product['title']} - Features: {len(features['features'])}")
print(f"Price: ${product['price']} - Rating: {product['rating']}/5")

📚 API Reference

Core Classes

• PyroChain: Main library class for feature extraction
• PyroChainConfig: Configuration class for customizing behavior
• LoRAAdapter: Lightweight adapter for efficient model fine-tuning
• MultimodalProcessor: Handles text, image, and structured data processing

Key Methods

• extract_features(data, task_description): Extract features from data
• train(training_data, task_description): Train custom agents
• evaluate(test_data): Evaluate model performance
• save_model(path): Save trained model
• load_model(path): Load pre-trained model

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📄 License

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

🙏 Acknowledgments

• PyTorch for deep learning capabilities
• LangChain for agentic AI framework
• Hugging Face for transformer models
• Sentence Transformers for text embeddings

📞 Support

Need help? We're here to support you:

• 📚 Documentation
• 🐛 Report Issues
• 💡 Feature Requests
• 📧 Contact

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PyroChain - Transform your data into intelligent features with AI agents. 🔥

Built with ❤️ by Irfan Ali