vlm-engine 0.8.45

Advanced Vision-Language Model Engine for content tagging

VLM Engine

A high-performance Python package for Vision-Language Model (VLM) based content tagging and analysis. This package provides an advanced implementation for automatic content detection and tagging, delivering superior accuracy compared to traditional image classification methods.

Features

• Remote VLM Integration: Connects to any OpenAI-compatible VLM endpoint (no local model loading required)
• Context-Aware Detection: Leverages Vision-Language Models' understanding of visual relationships for accurate content tagging
• Flexible Architecture: Modular pipeline system with configurable models and processing stages
• Asynchronous Processing: Built on asyncio for efficient video and image processing
• Customizable Tag Sets: Easy configuration of detection categories
• Production Ready: Includes retry logic, error handling, and comprehensive logging

Documentation

• USER_GUIDE.md - Comprehensive configuration guide with detailed parameter descriptions, examples, and best practices
• examples/ - Working code examples for various use cases
• MULTIPLEXER_INTEGRATION.md - Detailed multiplexer setup and configuration

Features

• Remote VLM Integration: Connects to any OpenAI-compatible VLM endpoint (no local model loading required)
• Context-Aware Detection: Leverages Vision-Language Models' understanding of visual relationships for accurate content tagging
• Flexible Architecture: Modular pipeline system with configurable models and processing stages
• Asynchronous Processing: Built on asyncio for efficient video and image processing
• Customizable Tag Sets: Easy configuration of detection categories
• Production Ready: Includes retry logic, error handling, and comprehensive logging

Installation

From PyPI (when published)

pip install vlm-engine

From Source

git clone https://github.com/Haven-hvn/haven-vlm-engine-package.git
cd vlm-engine-package
pip install -e .

Requirements

• Python 3.8+
• Sufficient RAM: Video preprocessing loads entire videos into memory (not GPU memory)
• Compatible VLM server endpoint:
  • Remote OpenAI-compatible API (recommended)
  • Local server using LM Studio

Quick Start

import asyncio
from vlm_engine import VLMEngine
from vlm_engine.config_models import EngineConfig, ModelConfig

# Configure the engine
config = EngineConfig(
    active_ai_models=["llm_vlm_model"],
    models={
        "llm_vlm_model": ModelConfig(
            type="vlm_model",
            model_id="HuggingFaceTB/SmolVLM-Instruct",
            api_base_url="http://localhost:7045",
            tag_list=["tag1", "tag2", "tag3"]  # Your custom tags
        )
    }
)

# Initialize and use
async def main():
    engine = VLMEngine(config)
    await engine.initialize()

    results = await engine.process_video(
        "path/to/video.mp4",
        frame_interval=2.0,
        threshold=0.5
    )
    print(f"Detected tags: {results}")

asyncio.run(main())

For more detailed configuration options, parameter descriptions, and best practices, see the USER_GUIDE.md.

Multiplexer Configuration (Load Balancing)

For high-performance deployments, you can configure multiple VLM endpoints with automatic load balancing:

from vlm_engine.config_models import EngineConfig, ModelConfig

config = EngineConfig(
    active_ai_models=["vlm_multiplexer_model"],
    models={
        "vlm_multiplexer_model": ModelConfig(
            type="vlm_model",
            model_id="HuggingFaceTB/SmolVLM-Instruct",
            use_multiplexer=True,  # Enable multiplexer mode
            multiplexer_endpoints=[
                {
                    "base_url": "http://server1:7045/v1",
                    "api_key": "",
                    "name": "primary-server",
                    "weight": 5,  # Higher weight = more requests
                    "is_fallback": False
                },
                {
                    "base_url": "http://server2:7045/v1",
                    "api_key": "",
                    "name": "secondary-server",
                    "weight": 3,
                    "is_fallback": False
                },
                {
                    "base_url": "http://backup:7045/v1",
                    "api_key": "",
                    "name": "backup-server",
                    "weight": 1,
                    "is_fallback": True  # Used only when primaries fail
                }
            ],
            tag_list=["tag1", "tag2", "tag3"]
        )
    }
)

Architecture

Core Components

1. VLMEngine: Main entry point for the package

   • Manages model initialization and pipeline execution
   • Handles asynchronous processing of videos and images

2. VLMClient: OpenAI-compatible API client with multiplexer support

   • Supports any VLM with chat completions endpoint
   • Load balancing across multiple endpoints using multiplexer-llm
   • Automatic failover for high availability
   • Includes retry logic with exponential backoff and jitter
   • Handles image encoding and prompt formatting

3. Pipeline System: Flexible processing pipeline

   • Modular design allows custom processing stages
   • Built-in support for preprocessing, analysis, and postprocessing
   • Configurable through YAML or Python objects

4. Model Management: Dynamic model loading

   • Supports multiple model types (VLM, preprocessors, postprocessors)
   • Lazy loading for efficient resource usage
   • Thread-safe model access

For detailed architecture information and component interactions, see USER_GUIDE.md.

Configuration

The VLM Engine uses four main configuration classes:

1. EngineConfig - Global engine settings and behavior
2. PipelineConfig - Defines processing workflows
3. ModelConfig - Configures individual AI models and processors
4. PipelineModelConfig - Defines how models integrate into pipelines

For detailed parameter descriptions, configuration examples, and best practices, see USER_GUIDE.md.

Basic Configuration

from vlm_engine.config_models import EngineConfig, ModelConfig, PipelineConfig

config = EngineConfig(
    active_ai_models=["my_vlm_model"],
    models={
        "my_vlm_model": ModelConfig(
            type="vlm_model",
            model_id="model-name",
            api_base_url="http://localhost:8000",
            tag_list=["action1", "action2", "action3"],
            max_batch_size=5,
            instance_count=3,
            model_return_confidence=True
        )
    },
    pipelines={
        "video_pipeline": PipelineConfig(
            inputs=["video_path", "frame_interval"],
            output="results",
            version=1.0,
            models=[
                PipelineModelConfig(
                    name="my_vlm_model",
                    inputs=["video_path"],
                    outputs=["results"]
                )
            ]
        )
    }
)

Multiplexer Configuration

For high-performance deployments with load balancing:

from vlm_engine.config_models import ModelConfig

config = EngineConfig(
    active_ai_models=["vlm_multiplexer_model"],
    models={
        "vlm_multiplexer_model": ModelConfig(
            type="vlm_model",
            model_id="model-name",
            use_multiplexer=True,
            multiplexer_endpoints=[
                {
                    "api_base_url": "http://server1:7045/v1",
                    "model_id": "model-name",
                    "weight": 5
                },
                {
                    "api_base_url": "http://server2:7045/v1",
                    "model_id": "model-name",
                    "weight": 3
                }
            ],
            tag_list=["tag1", "tag2", "tag3"]
        )
    }
)

Advanced Configuration

The package supports complex configurations including:

• Multiple models in a pipeline
• Custom preprocessing and postprocessing stages
• Category-specific settings (thresholds, durations, etc.)
• Batch processing configurations
• Category filtering and transformation rules

See the examples directory for detailed configuration examples.

For comprehensive configuration details, parameter descriptions, and best practices, see USER_GUIDE.md.

API Reference

VLMEngine

class VLMEngine:
    def __init__(self, config: EngineConfig)
    async def initialize()
    async def process_video(video_path: str, **kwargs) -> Dict[str, Any]

Processing Parameters

• video_path: Path to the video file
• frame_interval: Seconds between frame samples (default: 0.5)
• threshold: Confidence threshold for tag detection (default: 0.5)
• return_timestamps: Include timestamp information (default: True)
• return_confidence: Include confidence scores (default: True)

For detailed parameter descriptions and configuration options, see USER_GUIDE.md.

Performance Optimization

Memory Requirements

• Important: Video preprocessing loads the entire video into system RAM (not GPU memory)
• Ensure sufficient RAM for your video sizes (e.g., a 1GB video may require 4-8GB of available RAM)
• Consider processing videos in segments for very large files

API Optimization

• Configure retry settings based on your VLM server's capacity
• Adjust max_batch_size to balance throughput vs memory usage
• Use appropriate frame_interval to reduce processing time and API calls

Processing Speed

• Increase frame_interval to sample fewer frames (faster but less accurate)
• Use batch processing when your VLM endpoint supports it
• Consider running multiple VLM instances for parallel processing

For detailed performance tuning guidelines and best practices, see USER_GUIDE.md.

Extending the Package

Custom Models

Create custom model classes by inheriting from the base Model class:

from vlm_engine.models import Model

class CustomModel(Model):
    async def process(self, inputs):
        # Your custom processing logic
        return results

Custom Pipelines

Define custom pipelines for specific use cases:

custom_pipeline = PipelineConfig(
    inputs=["image_path"],
    output="analysis",
    models=[
        {"name": "preprocessor", "inputs": ["image_path"], "outputs": "processed_image"},
        {"name": "analyzer", "inputs": ["processed_image"], "outputs": "analysis"}
    ]
)

For detailed information on model types, pipeline design, and best practices, see USER_GUIDE.md.

Troubleshooting

Common Issues

1. "No valid pipelines loaded" Error

   • Cause: Configuration is missing required pipeline definitions or models
   • Solution: Ensure your EngineConfig includes:
     • At least one pipeline in the pipelines dictionary
     • Models defined in the models dictionary that are referenced by pipelines
     • Valid active_ai_models list pointing to existing model names
   • Best Practice: Use the provided haven_vlm_config.py as a reference configuration

2. "Cannot import EngineConfig" Error

   • Cause: Incorrect import statement
   • Solution: Import from the correct module:

     from vlm_engine import VLMEngine  # Only VLMEngine is exposed
     from vlm_engine.config_models import EngineConfig  # Config classes are in separate module
     

3. Connection Errors

   • Ensure your VLM server is running and accessible
   • Check the api_base_url configuration
   • Verify firewall settings

4. GPU Memory Errors

   • Reduce batch size or frame interval
   • Ensure proper CUDA installation
   • Check GPU memory availability

5. Slow Processing

   • Increase frame interval for faster processing
   • Use GPU acceleration if available
   • Optimize VLM server settings

Package Import Best Practices

What's exposed to consumers:

• Only VLMEngine is exported via from vlm_engine import *
• All configuration classes are in vlm_engine.config_models
• Internal classes (Pipeline, ModelManager, etc.) are not exported

Correct usage pattern:

# ✅ CORRECT - Import what you need
from vlm_engine import VLMEngine
from vlm_engine.config_models import EngineConfig, ModelConfig, PipelineConfig

# ❌ WRONG - Don't try to import internal classes
from vlm_engine import Pipeline  # This will fail
from vlm_engine import ModelManager  # This will fail

Platform-Specific Notes

macOS Users:

• The package uses PyAV for video processing (no decord required)
• Video preprocessing loads entire videos into system RAM (not GPU memory)
• Ensure sufficient RAM for your video sizes (e.g., 1GB video may require 4-8GB RAM)

Linux/Windows Users:

• Optionally install decord for faster video decoding: pip install vlm-engine[decord]
• PyAV is the default and works on all platforms

For detailed troubleshooting steps and validation checks, see USER_GUIDE.md.

Logging

Enable debug logging for troubleshooting:

import logging
logging.basicConfig(level=logging.DEBUG)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Setup

git clone https://github.com/yourusername/vlm-engine.git
cd vlm-engine
pip install -e ".[dev]"

Running Tests

pytest tests/

License

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

Acknowledgments

• Built on top of modern Python async patterns

• Inspired by production ML serving architectures

• Haven's custom VLM models trained using SmolVLM-Finetune - Model Download found on https://havenmodels.orbiter.website/

• Designed for integration with OpenAI-compatible VLM endpoints

Support

For issues and feature requests, please use the GitHub issue tracker.

For questions and discussions, join our community:

• Discord: Link to Discord

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Note: This package requires an OpenAI-compatible VLM endpoint. Options include:

Remote Services

• Any OpenAI-compatible API endpoint
• Akash deployment - https://github.com/Haven-hvn/haven-inference

Local Setup

• LM Studio - Easy local VLM hosting with OpenAI-compatible API

The package does not load VLM models directly - it communicates with external VLM services via API.

Documentation

• USER_GUIDE.md - Comprehensive configuration guide with detailed parameter descriptions, examples, and best practices
• examples/ - Working code examples for various use cases
• MULTIPLEXER_INTEGRATION.md - Detailed multiplexer setup and configuration