deepcompress 1.4.5
Production-ready document compression library reducing LLM costs by 96% with DeepSeek-OCR integration
Requires Python: >=3.9
Keywords: document-processing, ocr, llm, compression, rag, deepseek, financial-services

# 📦 DeepCompress

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> **Reduce LLM document processing costs by 96%** while improving accuracy and latency.

A Python library that compresses documents from **5,000 tokens/page → 80 tokens/page** using DeepSeek-OCR vision compression and D-TOON optimization. Process 250,000 pages/month for **$4,820** instead of $12,500.

---

## ✨ Key Features

- **96% Token Reduction**: 5,000 → 80 tokens/page (62.5× compression)
- **97% Table Accuracy**: Vision-based extraction preserves table structure
- **Sub-Second Latency**: 0.67s/page (p95) on A100 GPUs
- **200K+ Pages/Day**: Linear scaling with GPU workers
- **82% Cache Hit Rate**: Redis caching eliminates reprocessing
- **PII Scrubbing**: Automatic redaction of sensitive data
- **Multi-LLM Support**: OpenAI, Claude, Llama integration
- **Vector DB Ready**: Pinecone, Weaviate integration
- **Production Grade**: Async I/O, metrics, structured logging

---

## 🚀 Quickstart

### Installation

```bash
# Basic installation
pip install deepcompress

# With GPU support
pip install deepcompress[gpu]

# With all integrations
pip install deepcompress[all]
```

### One-Liner Usage

```python
from deepcompress import compress_and_analyze
import asyncio

async def main():
    result = await compress_and_analyze(
        file="loan_application.pdf",
        query="What is the applicant's total monthly income?",
        llm="openai"
    )
    
    print(f"Answer: {result.answer}")
    print(f"Tokens saved: {result.tokens_saved:,}")
    print(f"Cost saved: ${result.cost_saved_usd:.2f}")
    print(f"Compression ratio: {result.compression_ratio:.1f}x")

asyncio.run(main())
```

**Output:**
```
Answer: The applicant's total monthly income is $20,200 (payroll: $17,000 + freelance: $3,200)
Tokens saved: 244,920
Cost saved: $2.45
Compression ratio: 62.5x
```

---

## 📊 Performance Benchmarks

| Metric | Target | Achieved | Status |
|--------|--------|----------|--------|
| **Throughput** | 200K pages/day | 248K pages/day | ✅ +24% |
| **Latency (p95)** | <1s/page | 0.67s/page | ✅ |
| **Table Accuracy** | >95% | 97.3% | ✅ |
| **Cost Savings** | >60% | 63% | ✅ |
| **Cache Hit Rate** | >70% | 82% | ✅ |
| **Uptime** | >99.5% | 99.8% | ✅ |

### Cost Comparison (250K pages/month)

```
Without DeepCompress: $12,500/month
With DeepCompress:    $4,820/month
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Savings:              $7,680/month (61%)
Annual ROI:           177%
```

---

## 🏗️ Architecture

```mermaid
graph LR
    A[PDF Document] --> B[pdf2image<br/>300 DPI]
    B --> C[DeepSeek-OCR<br/>Vision Encoder]
    C --> D[MoE Decoder<br/>3B params]
    D --> E[JSON Output<br/>200 tokens/page]
    E --> F[D-TOON Optimizer<br/>60% reduction]
    F --> G[Compressed Doc<br/>80 tokens/page]
    G --> H[Vector DB<br/>Pinecone]
    G --> I[LLM Query<br/>OpenAI]
```

### Processing Pipeline

1. **PDF Conversion**: 300 DPI PNG rendering
2. **Vision Encoding**: SAM-base + CLIP-large (16× compression)
3. **OCR Extraction**: DeepSeek-OCR with MoE decoder
4. **D-TOON Optimization**: 60% additional token savings
5. **Cache Storage**: Redis with 24-hour TTL
6. **Vector Indexing**: Pinecone for semantic search
7. **LLM Analysis**: OpenAI/Claude query with compressed context

---

## 📖 Usage Examples

### Basic Compression

```python
from deepcompress import DocumentCompressor, DeepCompressConfig

async def compress_document():
    config = DeepCompressConfig()
    compressor = DocumentCompressor(config)
    
    result = await compressor.compress("financial_report.pdf")
    
    print(f"Original: {result.original_tokens:,} tokens")
    print(f"Compressed: {result.compressed_tokens:,} tokens")
    print(f"Ratio: {result.compression_ratio:.1f}x")
    print(f"D-TOON output:\n{result.optimized_text}")
```

### Batch Processing

```python
from deepcompress import DocumentCompressor, BatchProcessor, DeepCompressConfig
from deepcompress.integrations.cache import CacheManager

async def batch_process():
    config = DeepCompressConfig()
    compressor = DocumentCompressor(config)
    cache = CacheManager(config)
    processor = BatchProcessor(compressor, config, cache)
    
    # Process directory
    async for result in processor.process_directory(
        "s3://my-bucket/loan-applications/",
        batch_size=50
    ):
        print(f"Processed: {result.document_id}")
    
    # Get summary
    summary = processor.get_progress()
    print(f"Total processed: {summary['processed']}")
    print(f"Total savings: ${summary['total_cost_saved_usd']:.2f}")
```

### With Vector Database

```python
from deepcompress import DocumentCompressor, DeepCompressConfig
from deepcompress.integrations.vector_db import VectorDBClient
from deepcompress.integrations.llm import LLMClient

async def index_and_query():
    config = DeepCompressConfig()
    compressor = DocumentCompressor(config)
    vector_db = VectorDBClient(config)
    llm = LLMClient("openai", config)
    
    # Compress and index
    compressed = await compressor.compress("contract.pdf")
    embedding = await llm.embed(compressed.optimized_text)
    
    await vector_db.upsert(
        document_id=compressed.document_id,
        embedding=embedding,
        metadata={
            "compressed_text": compressed.optimized_text,
            "page_count": compressed.extracted.page_count,
        }
    )
    
    # Query similar documents
    query_embedding = await llm.embed("payment terms")
    results = await vector_db.query(query_embedding, top_k=5)
    
    for doc in results:
        print(f"Score: {doc['score']:.3f} - {doc['id']}")
```

### PII Scrubbing

```python
from deepcompress.processing.pii import PIIScrubber

scrubber = PIIScrubber()

text = """
Applicant: John Doe
SSN: 123-45-6789
Email: john@example.com
Phone: (555) 123-4567
"""

scrubbed = scrubber.scrub(text)
print(scrubbed)
# Output:
# Applicant: John Doe
# SSN: [REDACTED_SSN]
# Email: [REDACTED_EMAIL]
# Phone: [REDACTED_PHONE]

# Detect PII
detected = scrubber.detect(text)
print(detected)
# {'ssn': ['123-45-6789'], 'email': ['john@example.com'], 'phone': ['(555) 123-4567']}
```

### Cost Calculator

```python
from deepcompress.utils.cost import calculate_savings

savings = calculate_savings(
    pages_per_month=250000,
    avg_tokens_per_page=5000,
    target_llm="gpt-4o",
    gpu_cost_per_month=4000
)

print(f"Monthly savings: ${savings['monthly_savings']:,.2f}")
print(f"Payback period: {savings['payback_months']:.1f} months")
print(f"3-year ROI: {savings['three_year_roi_percent']:.0f}%")
```

---

## 🔧 Troubleshooting

### ImportError: cannot import name 'LlamaFlashAttention2'

This error indicates an incompatible version of the transformers library. Fix it by upgrading:

```bash
pip install --upgrade transformers>=4.36.0
```

Or reinstall GPU dependencies:

```bash
pip uninstall transformers torch
pip install deepcompress[gpu] --upgrade
```

### GPU Out of Memory

Reduce memory usage by adjusting configuration:

```python
config = DeepCompressConfig(
    ocr_mode="small",  # Use smaller mode (100 tokens vs 400)
    gpu_memory_fraction=0.8,  # Limit GPU memory usage
    ocr_batch_size=4,  # Reduce batch size
)
```

### Flash Attention Not Available

Flash Attention provides 2-3x speedup but is optional. If installation fails:

```bash
# Install manually (requires CUDA and compatible GPU)
pip install flash-attn --no-build-isolation

# Or continue without it - the library will automatically fall back
```

### PDF Processing Errors

Ensure pdf2image dependencies are installed:

```bash
# Ubuntu/Debian
sudo apt-get install poppler-utils

# macOS
brew install poppler

# Windows - download from: https://github.com/oschwartz10612/poppler-windows/releases/
```

---

## ⚙️ Configuration

### Environment Variables

Create a `.env` file:

```bash
# OCR Configuration
OCR_MODEL=deepseek-ai/DeepSeek-OCR
OCR_MODE=small
OCR_DEVICE=cuda:0
OCR_BATCH_SIZE=8

# Cache Configuration
CACHE_URL=redis://localhost:6379
CACHE_TTL=86400
CACHE_ENABLED=True

# Vector Database
VECTOR_DB_PROVIDER=pinecone
VECTOR_DB_API_KEY=your_pinecone_key
VECTOR_DB_INDEX_NAME=deepcompress-documents

# LLM Configuration
LLM_PROVIDER=openai
LLM_API_KEY=your_openai_key
LLM_MODEL=gpt-4o

# Security
PII_SCRUBBING=True
```

### Python Configuration

```python
from deepcompress import DeepCompressConfig

config = DeepCompressConfig(
    ocr_mode="small",  # small (100 tokens), base (200), large (400)
    ocr_device="cuda:0",
    cache_enabled=True,
    pii_scrubbing=True,
    llm_provider="openai",
    vector_db_provider="pinecone",
)
```

---


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