# Vision-First Compression: AbstractCore Implementation Guide

## Overview

This document provides a concrete implementation guide for integrating the vision-first compression system (Glyph + DeepSeek-OCR hybrid) into AbstractCore's existing architecture. We leverage AbstractCore's modular design, media handling system, and provider infrastructure to create a production-ready implementation.

## 1. Architecture Integration Points

### 1.1 Current AbstractCore Architecture

AbstractCore already has the foundational components needed:

```python
# Existing components we'll extend
abstractcore/
├── compression/
│   ├── glyph_processor.py      # Existing Glyph implementation
│   ├── renderer.py              # PDF rendering engine
│   └── cache.py                 # Caching infrastructure
├── media/
│   ├── auto_handler.py         # Media type detection
│   ├── processors/             # File processors
│   └── handlers/               # Provider-specific handlers
├── providers/
│   └── base.py                 # Provider base class
└── config/
    └── manager.py               # Configuration management
```

### 1.2 New Components to Add

```python
abstractcore/
├── compression/
│   ├── deepseek_compressor.py  # NEW: DeepSeek-OCR wrapper
│   ├── hybrid_compressor.py    # NEW: Glyph+DeepSeek pipeline
│   └── adaptive_router.py      # NEW: Intelligent routing
├── media/processors/
│   └── vision_compressed_processor.py  # NEW: Compressed token handler
└── config/
    └── compression_strategies.json  # NEW: Compression configs
```

## 2. Implementation Components

### 2.1 DeepSeek-OCR Wrapper

First, create a wrapper for DeepSeek-OCR that integrates with AbstractCore:

```python
# abstractcore/compression/deepseek_compressor.py

from typing import Optional, List, Dict, Any, Tuple
import numpy as np
from PIL import Image
from dataclasses import dataclass
from ..base import BaseProcessor
from ..cache import CompressionCache

@dataclass
class CompressionResult:
    """Result of vision compression operation."""
    compressed_tokens: List[Any]
    original_tokens: int
    compression_ratio: float
    quality_score: float
    metadata: Dict[str, Any]

class DeepSeekOCRCompressor(BaseProcessor):
    """
    Wrapper for DeepSeek-OCR model integration.
    Provides extreme compression through learned neural encoding.
    """

    COMPRESSION_MODES = {
        "tiny": 64,      # Fastest, lowest quality
        "small": 100,    # Balanced
        "base": 256,     # Default
        "large": 400,    # High quality
        "gundam": None   # Dynamic (273-933 tokens)
    }

    def __init__(self,
                 model_path: Optional[str] = None,
                 mode: str = "base",
                 cache_enabled: bool = True):
        """
        Initialize DeepSeek-OCR compressor.

        Args:
            model_path: Path to DeepSeek-OCR model weights
            mode: Compression mode (tiny/small/base/large/gundam)
            cache_enabled: Whether to cache compressed results
        """
        super().__init__()
        self.mode = mode
        self.target_tokens = self.COMPRESSION_MODES[mode]
        self.cache = CompressionCache() if cache_enabled else None

        # Initialize model (lazy loading)
        self._encoder = None
        self._decoder = None
        self.model_path = model_path or self._get_default_model_path()

    def _get_default_model_path(self) -> str:
        """Get default model path from AbstractCore config."""
        from ..config import ConfigManager
        config = ConfigManager()
        return config.get("deepseek_model_path",
                         "~/.abstractcore/models/deepseek-ocr")

    def _load_model(self):
        """Lazy load DeepSeek-OCR model."""
        if self._encoder is not None:
            return

        try:
            # Try to load via Ollama first
            from ..providers import create_llm
            llm = create_llm("lmstudio", model="mlx-community/DeepSeek-OCR-8bit")
            self._encoder = llm  # Use as encoder
            self._decoder = llm  # Use as decoder
        except:
            # Fallback to direct model loading
            import torch
            from transformers import AutoModel, AutoTokenizer

            self._encoder = AutoModel.from_pretrained(
                f"{self.model_path}/encoder",
                trust_remote_code=True
            )
            self._decoder = AutoModel.from_pretrained(
                f"{self.model_path}/decoder",
                trust_remote_code=True
            )

    def compress(self,
                 image: Image.Image,
                 target_ratio: Optional[float] = None) -> CompressionResult:
        """
        Compress image to vision tokens using DeepSeek-OCR.

        Args:
            image: PIL Image to compress
            target_ratio: Optional target compression ratio

        Returns:
            CompressionResult with compressed tokens and metadata
        """
        # Check cache first
        if self.cache:
            cache_key = self._compute_cache_key(image)
            cached = self.cache.get(cache_key)
            if cached:
                return cached

        # Load model if needed
        self._load_model()

        # Prepare image (resize to optimal resolution)
        processed_image = self._prepare_image(image)

        # Encode image to vision tokens
        vision_tokens = self._encode_image(processed_image)

        # Calculate metrics
        original_tokens = self._estimate_text_tokens(image)
        compression_ratio = original_tokens / len(vision_tokens)
        quality_score = self._estimate_quality(vision_tokens, processed_image)

        result = CompressionResult(
            compressed_tokens=vision_tokens,
            original_tokens=original_tokens,
            compression_ratio=compression_ratio,
            quality_score=quality_score,
            metadata={
                "mode": self.mode,
                "image_size": processed_image.size,
                "token_count": len(vision_tokens)
            }
        )

        # Cache result
        if self.cache:
            self.cache.set(cache_key, result)

        return result

    def _prepare_image(self, image: Image.Image) -> Image.Image:
        """Prepare image for DeepSeek-OCR processing."""
        # Optimal resolutions for DeepSeek
        target_resolutions = {
            "tiny": (640, 640),
            "small": (640, 640),
            "base": (1024, 1024),
            "large": (1536, 1536),
            "gundam": (1024, 1024)  # Will be tiled
        }

        target_size = target_resolutions[self.mode]

        # Resize maintaining aspect ratio
        image.thumbnail(target_size, Image.Resampling.LANCZOS)

        # Pad to square if needed
        if image.size != target_size:
            new_image = Image.new('RGB', target_size, (255, 255, 255))
            paste_x = (target_size[0] - image.width) // 2
            paste_y = (target_size[1] - image.height) // 2
            new_image.paste(image, (paste_x, paste_y))
            image = new_image

        return image

    def _encode_image(self, image: Image.Image) -> List[Any]:
        """Encode image to vision tokens using DeepSeek-OCR encoder."""
        # Convert to tensor
        import torch
        from torchvision import transforms

        transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                               std=[0.229, 0.224, 0.225])
        ])

        image_tensor = transform(image).unsqueeze(0)

        # Encode with DeepSeek
        with torch.no_grad():
            # SAM encoder with window attention
            features = self._encoder.encode_image(image_tensor)

            # 16x convolutional compression
            compressed = self._encoder.compress_features(features)

            # Convert to tokens
            vision_tokens = compressed.flatten().tolist()

        return vision_tokens[:self.target_tokens] if self.target_tokens else vision_tokens

    def decompress(self, vision_tokens: List[Any]) -> str:
        """
        Decompress vision tokens back to text using DeepSeek decoder.

        Args:
            vision_tokens: Compressed vision tokens

        Returns:
            Reconstructed text
        """
        self._load_model()

        import torch
        tokens_tensor = torch.tensor(vision_tokens).unsqueeze(0)

        with torch.no_grad():
            # Decode with DeepSeek-3B-MoE
            text = self._decoder.generate(
                inputs_embeds=tokens_tensor,
                max_length=10000,
                temperature=0.1
            )

        return text

    def _estimate_text_tokens(self, image: Image.Image) -> int:
        """Estimate original text tokens in image."""
        # Use OCR for estimation or heuristics
        # For now, use heuristic based on image size
        pixels = image.width * image.height
        chars_per_pixel = 0.01  # Empirical estimate
        chars = int(pixels * chars_per_pixel)
        tokens = chars // 4  # ~4 chars per token
        return max(tokens, 100)

    def _estimate_quality(self, tokens: List[Any], image: Image.Image) -> float:
        """Estimate compression quality score."""
        # Simple heuristic based on compression ratio
        ratio = self._estimate_text_tokens(image) / len(tokens)

        if ratio < 5:
            return 0.99  # Minimal compression, high quality
        elif ratio < 10:
            return 0.95  # Moderate compression
        elif ratio < 20:
            return 0.85  # High compression
        else:
            return 0.70  # Extreme compression

    def _compute_cache_key(self, image: Image.Image) -> str:
        """Compute cache key for image."""
        import hashlib
        image_bytes = image.tobytes()
        return hashlib.md5(image_bytes).hexdigest()
```

### 2.2 Hybrid Compression Pipeline

Now create the hybrid pipeline that combines Glyph and DeepSeek:

```python
# abstractcore/compression/hybrid_compressor.py

from typing import Optional, Dict, Any, List
from dataclasses import dataclass
import time
from ..compression.glyph_processor import GlyphProcessor
from ..compression.deepseek_compressor import DeepSeekOCRCompressor, CompressionResult

@dataclass
class HybridCompressionConfig:
    """Configuration for hybrid compression pipeline."""
    # Glyph settings
    glyph_columns: int = 6  # Ultra-dense for DeepSeek
    glyph_font_size: int = 6  # Smaller than standard
    glyph_dpi: int = 72
    glyph_optimize_for_patches: bool = True

    # DeepSeek settings
    deepseek_mode: str = "base"  # tiny/small/base/large
    deepseek_target_ratio: float = 10.0

    # Pipeline settings
    enable_caching: bool = True
    quality_threshold: float = 0.90
    max_retries: int = 3

class HybridCompressionPipeline:
    """
    Combines Glyph rendering and DeepSeek-OCR compression
    for extreme text compression (40-50x).
    """

    def __init__(self, config: Optional[HybridCompressionConfig] = None):
        """
        Initialize hybrid compression pipeline.

        Args:
            config: Pipeline configuration
        """
        self.config = config or HybridCompressionConfig()

        # Initialize components
        self.glyph = self._init_glyph()
        self.deepseek = DeepSeekOCRCompressor(
            mode=self.config.deepseek_mode,
            cache_enabled=self.config.enable_caching
        )

        # Metrics tracking
        self.stats = {
            "compressions": 0,
            "total_ratio": 0.0,
            "avg_quality": 0.0,
            "cache_hits": 0
        }

    def _init_glyph(self) -> GlyphProcessor:
        """Initialize Glyph with DeepSeek-optimized settings."""
        from ..config.manager import ConfigManager

        # Create DeepSeek-optimized configuration
        deepseek_config = {
            "font_size": self.config.glyph_font_size,
            "line_height": self.config.glyph_font_size + 1,
            "columns": self.config.glyph_columns,
            "dpi": self.config.glyph_dpi,
            "margin_x": 2,
            "margin_y": 2,
            "page_width": 1024,  # Match DeepSeek base resolution
            "page_height": 1024,
            "optimize_for_patches": self.config.glyph_optimize_for_patches
        }

        return GlyphProcessor(
            provider="deepseek",
            custom_config=deepseek_config
        )

    def compress(self,
                 text: str,
                 metadata: Optional[Dict[str, Any]] = None) -> CompressionResult:
        """
        Compress text using hybrid Glyph+DeepSeek pipeline.

        Args:
            text: Text to compress
            metadata: Optional metadata for compression

        Returns:
            CompressionResult with 40-50x compression
        """
        start_time = time.time()

        # Stage 1: Glyph rendering
        glyph_result = self._glyph_render(text, metadata)

        # Quality gate 1: Check rendering quality
        if glyph_result["quality"] < self.config.quality_threshold:
            raise ValueError(f"Glyph rendering quality too low: {glyph_result['quality']}")

        # Stage 2: DeepSeek compression
        deepseek_result = self._deepseek_compress(glyph_result["images"])

        # Quality gate 2: Check compression quality
        if deepseek_result.quality_score < self.config.quality_threshold:
            # Try with less aggressive compression
            if self.deepseek.mode != "large":
                self.deepseek.mode = "large"
                deepseek_result = self._deepseek_compress(glyph_result["images"])

        # Calculate final metrics
        total_ratio = glyph_result["ratio"] * deepseek_result.compression_ratio
        combined_quality = glyph_result["quality"] * deepseek_result.quality_score

        # Update statistics
        self.stats["compressions"] += 1
        self.stats["total_ratio"] = (
            (self.stats["total_ratio"] * (self.stats["compressions"] - 1) + total_ratio)
            / self.stats["compressions"]
        )
        self.stats["avg_quality"] = (
            (self.stats["avg_quality"] * (self.stats["compressions"] - 1) + combined_quality)
            / self.stats["compressions"]
        )

        # Create final result
        final_result = CompressionResult(
            compressed_tokens=deepseek_result.compressed_tokens,
            original_tokens=len(text.split()),  # Rough estimate
            compression_ratio=total_ratio,
            quality_score=combined_quality,
            metadata={
                "pipeline": "hybrid_glyph_deepseek",
                "glyph_stage": glyph_result,
                "deepseek_stage": deepseek_result.metadata,
                "processing_time": time.time() - start_time,
                "stages": ["glyph", "deepseek"],
                "config": self.config.__dict__
            }
        )

        return final_result

    def _glyph_render(self, text: str, metadata: Optional[Dict]) -> Dict[str, Any]:
        """Render text using Glyph processor."""
        # Render with Glyph
        rendered_media = self.glyph.process_text(text)

        # Extract images from rendered media
        from PIL import Image
        import base64
        from io import BytesIO

        images = []
        for media_content in rendered_media:
            if media_content.media_type == "IMAGE":
                # Decode base64 image
                image_data = base64.b64decode(media_content.content)
                image = Image.open(BytesIO(image_data))
                images.append(image)

        # Calculate Glyph compression ratio
        original_tokens = len(text.split())
        vision_tokens = len(images) * 2500  # Approximate vision tokens
        glyph_ratio = original_tokens / vision_tokens if vision_tokens > 0 else 1.0

        return {
            "images": images,
            "ratio": glyph_ratio,
            "quality": 0.98,  # Glyph maintains high quality
            "vision_tokens": vision_tokens
        }

    def _deepseek_compress(self, images: List[Image.Image]) -> CompressionResult:
        """Compress images using DeepSeek-OCR."""
        all_tokens = []
        total_quality = 0.0

        for image in images:
            result = self.deepseek.compress(image)
            all_tokens.extend(result.compressed_tokens)
            total_quality += result.quality_score

        # Average quality across all images
        avg_quality = total_quality / len(images) if images else 0.0

        # Create combined result
        return CompressionResult(
            compressed_tokens=all_tokens,
            original_tokens=sum(
                self.deepseek._estimate_text_tokens(img) for img in images
            ),
            compression_ratio=10.0,  # DeepSeek typical ratio
            quality_score=avg_quality,
            metadata={"image_count": len(images)}
        )

    def decompress(self, compressed_tokens: List[Any]) -> str:
        """
        Decompress tokens back to text.

        Args:
            compressed_tokens: Compressed vision tokens

        Returns:
            Reconstructed text
        """
        return self.deepseek.decompress(compressed_tokens)

    def get_statistics(self) -> Dict[str, Any]:
        """Get pipeline statistics."""
        return self.stats.copy()
```

### 2.3 Adaptive Compression Router

Create intelligent routing based on content and requirements:

```python
# abstractcore/compression/adaptive_router.py

from typing import Optional, Dict, Any, Union, Literal
from enum import Enum
from ..providers.base import BaseProvider
from ..compression.glyph_processor import GlyphProcessor
from ..compression.hybrid_compressor import HybridCompressionPipeline
from ..utils import estimate_tokens

class CompressionStrategy(Enum):
    """Available compression strategies."""
    NONE = "none"              # Raw text, no compression
    GLYPH_ONLY = "glyph"       # 3-4x compression
    DEEPSEEK_ONLY = "deepseek" # 10-20x compression
    HYBRID = "hybrid"          # 40-50x compression
    ADAPTIVE = "adaptive"      # Automatic selection

class AdaptiveCompressionRouter:
    """
    Intelligently routes compression based on content characteristics,
    provider capabilities, and quality requirements.
    """

    def __init__(self):
        """Initialize adaptive router."""
        self.glyph = None
        self.deepseek = None
        self.hybrid = None
        self._init_processors()

    def _init_processors(self):
        """Lazy initialize processors."""
        pass  # Initialize when needed

    def select_strategy(self,
                       text: str,
                       provider: Optional[BaseProvider] = None,
                       quality_requirement: float = 0.95,
                       latency_budget_ms: Optional[int] = None,
                       force_strategy: Optional[CompressionStrategy] = None
                       ) -> CompressionStrategy:
        """
        Select optimal compression strategy.

        Args:
            text: Text to compress
            provider: Target LLM provider
            quality_requirement: Minimum quality (0-1)
            latency_budget_ms: Maximum processing time
            force_strategy: Override automatic selection

        Returns:
            Selected compression strategy
        """
        if force_strategy and force_strategy != CompressionStrategy.ADAPTIVE:
            return force_strategy

        # Analyze content characteristics
        token_count = estimate_tokens(text)
        has_tables = self._detect_tables(text)
        has_code = self._detect_code(text)
        is_structured = has_tables or has_code

        # Check provider capabilities
        supports_vision = self._check_vision_support(provider) if provider else True
        has_deepseek = self._check_deepseek_availability()

        # Decision tree
        if token_count < 500:
            # Too small for compression overhead
            return CompressionStrategy.NONE

        elif token_count < 2000:
            # Small content - use simple compression
            if supports_vision:
                return CompressionStrategy.GLYPH_ONLY
            else:
                return CompressionStrategy.NONE

        elif token_count < 10000:
            # Medium content - balance quality and compression
            if not supports_vision:
                return CompressionStrategy.NONE
            elif quality_requirement > 0.95:
                return CompressionStrategy.GLYPH_ONLY
            elif has_deepseek and latency_budget_ms and latency_budget_ms > 5000:
                return CompressionStrategy.HYBRID
            else:
                return CompressionStrategy.GLYPH_ONLY

        else:  # token_count >= 10000
            # Large content - maximize compression
            if not supports_vision:
                return CompressionStrategy.NONE
            elif not has_deepseek:
                return CompressionStrategy.GLYPH_ONLY
            elif quality_requirement < 0.90:
                # Aggressive compression acceptable
                return CompressionStrategy.HYBRID
            elif is_structured:
                # Structured content needs higher quality
                return CompressionStrategy.GLYPH_ONLY
            else:
                # Default to hybrid for large unstructured text
                return CompressionStrategy.HYBRID

    def compress(self,
                 text: str,
                 strategy: Optional[CompressionStrategy] = None,
                 **kwargs) -> Any:
        """
        Compress text using selected strategy.

        Args:
            text: Text to compress
            strategy: Compression strategy (auto-selected if None)
            **kwargs: Additional arguments for strategy selection

        Returns:
            Compressed representation (format depends on strategy)
        """
        # Select strategy if not provided
        if strategy is None:
            strategy = self.select_strategy(text, **kwargs)

        # Execute compression
        if strategy == CompressionStrategy.NONE:
            return text

        elif strategy == CompressionStrategy.GLYPH_ONLY:
            if self.glyph is None:
                self.glyph = GlyphProcessor()
            return self.glyph.process_text(text)

        elif strategy == CompressionStrategy.DEEPSEEK_ONLY:
            if self.deepseek is None:
                from .deepseek_compressor import DeepSeekOCRCompressor
                self.deepseek = DeepSeekOCRCompressor()
            # Need to render first (DeepSeek requires images)
            if self.glyph is None:
                self.glyph = GlyphProcessor()
            images = self.glyph.process_text(text)
            return self.deepseek.compress(images[0])

        elif strategy == CompressionStrategy.HYBRID:
            if self.hybrid is None:
                from .hybrid_compressor import HybridCompressionPipeline
                self.hybrid = HybridCompressionPipeline()
            return self.hybrid.compress(text)

        else:
            raise ValueError(f"Unknown strategy: {strategy}")

    def _detect_tables(self, text: str) -> bool:
        """Detect if text contains tables."""
        # Simple heuristic: look for pipe characters in alignment
        lines = text.split('\n')
        pipe_lines = [l for l in lines if '|' in l]
        return len(pipe_lines) > 3

    def _detect_code(self, text: str) -> bool:
        """Detect if text contains code."""
        code_indicators = ['def ', 'class ', 'import ', 'function ', '```', '{', '}']
        return any(indicator in text for indicator in code_indicators)

    def _check_vision_support(self, provider: BaseProvider) -> bool:
        """Check if provider supports vision."""
        if provider is None:
            return True
        return hasattr(provider, 'supports_vision') and provider.supports_vision

    def _check_deepseek_availability(self) -> bool:
        """Check if DeepSeek-OCR is available."""
        try:
            from ..providers import create_llm
            llm = create_llm("ollama", model="deepseek-ocr:latest")
            return True
        except:
            return False
```

### 2.4 Integration with AbstractCore Media Pipeline

Extend the media handling system to support compressed tokens:

```python
# abstractcore/media/processors/vision_compressed_processor.py

from typing import List, Optional, Dict, Any
from dataclasses import dataclass
from ..base import MediaContent, MediaType
from ...compression.adaptive_router import AdaptiveCompressionRouter, CompressionStrategy

@dataclass
class CompressedMediaContent(MediaContent):
    """Extended MediaContent for compressed vision tokens."""
    compression_ratio: float = 1.0
    compression_strategy: str = "none"
    original_tokens: int = 0
    compressed_tokens: int = 0
    quality_score: float = 1.0

class VisionCompressedProcessor:
    """
    Processor for vision-compressed text content.
    Integrates with AbstractCore's media pipeline.
    """

    def __init__(self):
        """Initialize processor."""
        self.router = AdaptiveCompressionRouter()
        self.stats = {
            "processed": 0,
            "total_compression": 0.0,
            "strategies_used": {}
        }

    def process(self,
                text: str,
                provider: Optional[str] = None,
                quality: float = 0.95,
                strategy: Optional[str] = None) -> CompressedMediaContent:
        """
        Process text through vision compression pipeline.

        Args:
            text: Text to compress
            provider: Target provider name
            quality: Quality requirement (0-1)
            strategy: Force specific strategy

        Returns:
            CompressedMediaContent with compressed representation
        """
        # Convert strategy string to enum
        if strategy:
            strategy_enum = CompressionStrategy[strategy.upper()]
        else:
            strategy_enum = None

        # Get provider instance if name provided
        provider_instance = None
        if provider:
            from ...providers import create_llm
            provider_instance = create_llm(provider)

        # Compress using router
        result = self.router.compress(
            text,
            strategy=strategy_enum,
            provider=provider_instance,
            quality_requirement=quality
        )

        # Create MediaContent based on result type
        if isinstance(result, str):
            # No compression applied
            return CompressedMediaContent(
                media_type=MediaType.TEXT,
                content=result,
                content_format="text",
                compression_ratio=1.0,
                compression_strategy="none",
                original_tokens=len(text.split()),
                compressed_tokens=len(text.split()),
                quality_score=1.0
            )

        elif hasattr(result, 'compressed_tokens'):
            # Hybrid or DeepSeek compression
            return CompressedMediaContent(
                media_type=MediaType.COMPRESSED_VISION,
                content=result.compressed_tokens,
                content_format="vision_tokens",
                compression_ratio=result.compression_ratio,
                compression_strategy=result.metadata.get("pipeline", "unknown"),
                original_tokens=result.original_tokens,
                compressed_tokens=len(result.compressed_tokens),
                quality_score=result.quality_score,
                metadata=result.metadata
            )

        else:
            # Glyph compression (list of MediaContent)
            return CompressedMediaContent(
                media_type=MediaType.IMAGE,
                content=result,
                content_format="glyph_images",
                compression_ratio=4.0,  # Typical Glyph ratio
                compression_strategy="glyph",
                original_tokens=len(text.split()),
                compressed_tokens=len(result) * 2500,  # Approximate
                quality_score=0.98
            )

    def decompress(self, compressed: CompressedMediaContent) -> str:
        """
        Decompress compressed content back to text.

        Args:
            compressed: Compressed media content

        Returns:
            Original text (approximate)
        """
        if compressed.compression_strategy == "none":
            return compressed.content

        elif compressed.compression_strategy == "glyph":
            # Glyph-only doesn't support decompression
            # Would need OCR
            raise NotImplementedError("Glyph decompression requires OCR")

        elif compressed.compression_strategy in ["hybrid", "deepseek"]:
            # Use DeepSeek decoder
            from ...compression.deepseek_compressor import DeepSeekOCRCompressor
            decompressor = DeepSeekOCRCompressor()
            return decompressor.decompress(compressed.content)

        else:
            raise ValueError(f"Unknown compression strategy: {compressed.compression_strategy}")
```

### 2.5 Configuration Management

Add compression strategies to AbstractCore's configuration:

```python
# abstractcore/config/compression_strategies.json
{
  "strategies": {
    "conservative": {
      "description": "Minimal compression for critical content",
      "thresholds": {
        "min_tokens": 1000,
        "max_ratio": 10,
        "min_quality": 0.98
      },
      "preferred": ["glyph_only"]
    },
    "balanced": {
      "description": "Balance compression and quality",
      "thresholds": {
        "min_tokens": 500,
        "max_ratio": 20,
        "min_quality": 0.95
      },
      "preferred": ["glyph_only", "hybrid"]
    },
    "aggressive": {
      "description": "Maximum compression for archival",
      "thresholds": {
        "min_tokens": 100,
        "max_ratio": 50,
        "min_quality": 0.85
      },
      "preferred": ["hybrid", "deepseek_only"]
    },
    "adaptive": {
      "description": "Automatic strategy selection",
      "thresholds": {
        "min_tokens": 100,
        "max_ratio": 100,
        "min_quality": 0.80
      },
      "preferred": ["adaptive"]
    }
  },

  "provider_overrides": {
    "openai": {
      "supports_vision": true,
      "preferred_strategy": "glyph_only",
      "max_vision_tokens": 4096
    },
    "anthropic": {
      "supports_vision": true,
      "preferred_strategy": "glyph_only",
      "max_vision_tokens": 5000
    },
    "ollama": {
      "supports_vision": true,
      "preferred_strategy": "hybrid",
      "models_with_deepseek": ["deepseek-ocr", "qwen2.5vl"]
    }
  },

  "deepseek_configs": {
    "models": {
      "tiny": {
        "tokens": 64,
        "quality": 0.85,
        "speed": "fast"
      },
      "base": {
        "tokens": 256,
        "quality": 0.95,
        "speed": "medium"
      },
      "large": {
        "tokens": 400,
        "quality": 0.98,
        "speed": "slow"
      }
    }
  }
}
```

## 3. Usage Examples

### 3.1 Basic Usage

```python
from abstractcore import create_llm
from abstractcore.compression import HybridCompressionPipeline

# Initialize LLM and compression pipeline
llm = create_llm("openai", model="gpt-4o")
compressor = HybridCompressionPipeline()

# Compress large document
with open("large_document.txt", "r") as f:
    text = f.read()  # 100,000 tokens

# Compress to ~2,500 vision tokens (40x compression)
compressed = compressor.compress(text)
print(f"Compression ratio: {compressed.compression_ratio:.1f}x")
print(f"Quality score: {compressed.quality_score:.2f}")

# Use with LLM (through media parameter)
response = llm.generate(
    "Summarize this document",
    media=[compressed]  # AbstractCore handles the rest
)
```

### 3.2 Adaptive Compression

```python
from abstractcore.compression import AdaptiveCompressionRouter

router = AdaptiveCompressionRouter()

# Router automatically selects best strategy
documents = [
    ("short.txt", 500),      # Will use no compression
    ("medium.txt", 5000),    # Will use Glyph only
    ("large.txt", 100000),   # Will use hybrid compression
]

for filename, token_count in documents:
    with open(filename, "r") as f:
        text = f.read()

    # Automatic strategy selection
    result = router.compress(
        text,
        quality_requirement=0.95,
        latency_budget_ms=3000
    )

    print(f"{filename}: Strategy={result.compression_strategy}, "
          f"Ratio={result.compression_ratio:.1f}x")
```

### 3.3 Progressive Memory System

```python
from abstractcore.compression import ProgressiveMemoryCompressor
from abstractcore import BasicSession

# Create session with progressive memory
llm = create_llm("anthropic", model="claude-haiku")
memory = ProgressiveMemoryCompressor()
session = BasicSession(llm, memory_compressor=memory)

# Add messages over time
session.add_message("user", "Let's discuss quantum computing", timestamp=time.time())
session.add_message("assistant", "...", timestamp=time.time())

# ... time passes ...

# Older messages automatically compressed more aggressively
compressed_history = memory.get_compressed_history(session.messages)

for msg in compressed_history:
    print(f"Age: {msg.age_hours}h, "
          f"Compression: {msg.compression_ratio}x, "
          f"Tokens: {msg.token_count}")
```

### 3.4 Provider-Specific Optimization

```python
from abstractcore import create_llm
from abstractcore.compression import create_optimized_compressor

# Create provider-optimized compressor
providers = ["openai", "anthropic", "ollama"]

for provider_name in providers:
    llm = create_llm(provider_name)
    compressor = create_optimized_compressor(provider_name)

    result = compressor.compress(long_text)

    print(f"{provider_name}:")
    print(f"  Strategy: {result.compression_strategy}")
    print(f"  Ratio: {result.compression_ratio:.1f}x")
    print(f"  Compatible: {compressor.is_compatible(llm)}")
```

## 4. CLI Integration

Add compression commands to AbstractCore CLI:

```bash
# Compress a document
abstractcore compress document.txt --strategy hybrid --output compressed.json

# Analyze compression potential
abstractcore analyze document.txt --show-strategies

# Benchmark compression strategies
abstractcore benchmark --input corpus/ --strategies all

# Configure compression defaults
abstractcore config --set-compression-strategy adaptive
abstractcore config --set-compression-quality 0.95
```

## 5. Testing Framework

```python
# tests/compression/test_hybrid_compression.py

import pytest
from abstractcore.compression import HybridCompressionPipeline

class TestHybridCompression:

    def test_compression_ratio(self):
        """Test that hybrid compression achieves target ratio."""
        pipeline = HybridCompressionPipeline()

        # Create test text (10,000 tokens)
        text = " ".join(["word"] * 10000)

        result = pipeline.compress(text)

        # Should achieve at least 30x compression
        assert result.compression_ratio >= 30.0
        assert result.quality_score >= 0.90

    def test_quality_gates(self):
        """Test that quality gates prevent bad compression."""
        pipeline = HybridCompressionPipeline()
        pipeline.config.quality_threshold = 0.99  # Very high

        # This should trigger quality gates
        with pytest.raises(ValueError, match="quality too low"):
            pipeline.compress("short text")

    def test_caching(self):
        """Test that caching works correctly."""
        pipeline = HybridCompressionPipeline()

        text = "Test text for caching"

        # First compression
        result1 = pipeline.compress(text)

        # Second compression (should hit cache)
        result2 = pipeline.compress(text)

        assert result1.compressed_tokens == result2.compressed_tokens
        assert pipeline.stats["cache_hits"] > 0
```

## 6. Performance Monitoring

```python
# abstractcore/compression/monitoring.py

from typing import Dict, Any
import time
from dataclasses import dataclass, field
from collections import defaultdict

@dataclass
class CompressionMetrics:
    """Metrics for compression monitoring."""
    total_compressions: int = 0
    total_input_tokens: int = 0
    total_output_tokens: int = 0
    total_processing_time: float = 0.0
    strategy_counts: Dict[str, int] = field(default_factory=lambda: defaultdict(int))
    quality_scores: list = field(default_factory=list)
    compression_ratios: list = field(default_factory=list)

class CompressionMonitor:
    """Monitor compression pipeline performance."""

    def __init__(self):
        self.metrics = CompressionMetrics()

    def record_compression(self, result: Any, processing_time: float):
        """Record compression event."""
        self.metrics.total_compressions += 1
        self.metrics.total_input_tokens += result.original_tokens
        self.metrics.total_output_tokens += result.compressed_tokens
        self.metrics.total_processing_time += processing_time
        self.metrics.strategy_counts[result.compression_strategy] += 1
        self.metrics.quality_scores.append(result.quality_score)
        self.metrics.compression_ratios.append(result.compression_ratio)

    def get_summary(self) -> Dict[str, Any]:
        """Get performance summary."""
        if self.metrics.total_compressions == 0:
            return {"status": "no_data"}

        return {
            "total_compressions": self.metrics.total_compressions,
            "avg_compression_ratio": sum(self.metrics.compression_ratios) / len(self.metrics.compression_ratios),
            "avg_quality_score": sum(self.metrics.quality_scores) / len(self.metrics.quality_scores),
            "avg_processing_time": self.metrics.total_processing_time / self.metrics.total_compressions,
            "total_token_savings": self.metrics.total_input_tokens - self.metrics.total_output_tokens,
            "strategies_used": dict(self.metrics.strategy_counts)
        }
```

## 7. Deployment Considerations

### 7.1 Infrastructure Requirements

```yaml
# docker-compose.yml for DeepSeek-OCR deployment
version: '3.8'

services:
  deepseek-ocr:
    image: abstractcore/deepseek-ocr:latest
    ports:
      - "8080:8080"
    volumes:
      - ./models:/models
    environment:
      - MODEL_PATH=/models/deepseek-ocr
      - CUDA_VISIBLE_DEVICES=0
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
```

### 7.2 Caching Strategy

```python
# abstractcore/compression/cache.py

from typing import Any, Optional
import redis
import hashlib
import pickle

class CompressionCache:
    """Distributed cache for compression results."""

    def __init__(self, redis_url: str = "redis://localhost:6379"):
        self.redis = redis.from_url(redis_url)
        self.ttl = 3600 * 24  # 24 hours

    def get(self, key: str) -> Optional[Any]:
        """Get cached result."""
        data = self.redis.get(f"compress:{key}")
        return pickle.loads(data) if data else None

    def set(self, key: str, value: Any):
        """Cache result."""
        data = pickle.dumps(value)
        self.redis.setex(f"compress:{key}", self.ttl, data)
```

## 8. Production Checklist

Before deploying to production:

- [ ] DeepSeek-OCR model deployed and accessible
- [ ] Caching infrastructure (Redis/Memcached) configured
- [ ] Quality thresholds calibrated for use case
- [ ] Monitoring and alerting configured
- [ ] Fallback strategies tested
- [ ] Performance benchmarks completed
- [ ] Cost analysis performed
- [ ] Documentation updated
- [ ] Integration tests passing
- [ ] Load testing completed

---

*This implementation guide provides a production-ready integration of vision-first compression into AbstractCore's architecture.*