🚀 Get Started#

The Video Search and Summary (VSS) sample application helps developers create summary of long form video, search for the right video, and combine both search and summary pipelines. This guide will help you set up, run, and modify the sample application on local and Edge AI systems.

This guide shows how to:

Set up the sample application: Use Setup script to quickly deploy the application in your environment.
Run different application stacks: Execute different application stacks available in the application to perform video search and summary.
Modify application parameters: Customize settings like inference models and deployment configurations to adapt the application to your specific requirements.

✅ Prerequisites#

Verify that your system meets the minimum requirements.
Install Docker tool: Installation Guide.
Install Docker Compose tool: Installation Guide.
Install Python* programming language v3.11

📂 Project Structure#

The repository is organized as follows:

sample-applications/video-search-and-summarization/
├── config                     # Configuration files
│   ├── nginx.conf             # Nginx configuration
│   └── rmq.conf               # RabbitMQ configuration
├── docker                     # Docker compose files
│   ├── compose.base.yaml      # Base services configuration
│   ├── compose.summary.yaml   # Compose override file for video summarization services
│   ├── compose.search.yaml    # Compose override file for Video search services 
│   └── compose.gpu_ovms.yaml  # GPU configuration for OVMS
├── docs                       # Documentation
│   └── user-guide             # User guides and tutorials
├── pipeline-manager           # Backend service which orchestrates the Video Summarization and Search
├── search-ms                  # Video Search Microservice
├── ui                         # Video Summary and Search UI code
├── build.sh                   # Script for building application images
├── setup.sh                   # Setup script for environment and deployment
└── README.md                  # Project documentation

⚙️ Setting Required Environment Variables#

Before running the application, you need to set several environment variables:

Registry Configuration: The application uses registry URL and tag to pull the required images.
```
export REGISTRY_URL=intel   
export TAG=1.2.0   
```

Required credentials for some services: Following variables MUST be set on your current shell before running the setup script:

# MinIO credentials (object storage)
export MINIO_ROOT_USER=<your-minio-username>
export MINIO_ROOT_PASSWORD=<your-minio-password>

# PostgreSQL credentials (database)
export POSTGRES_USER=<your-postgres-username>
export POSTGRES_PASSWORD=<your-postgres-password>

# RabbitMQ credentials (message broker)
export RABBITMQ_USER=<your-rabbitmq-username>
export RABBITMQ_PASSWORD=<your-rabbitmq-password>

Setting environment variables for customizing model selection:

These environment variables MUST be set on your current shell. Setting these variables help you customize which models are used for deployment.

# For VLM-based chunk captioning and video summary on CPU
export VLM_MODEL_NAME="Qwen/Qwen2.5-VL-3B-Instruct"  # or any other supported VLM model on CPU

# For VLM-based chunk captioning and video summary on GPU
export VLM_MODEL_NAME="microsoft/Phi-3.5-vision-instruct"  # or any other supported VLM model on GPU

# (Optional) For OVMS-based video summary (when using with ENABLE_OVMS_LLM_SUMMARY=true or ENABLE_OVMS_LLM_SUMMARY_GPU=true)
export OVMS_LLM_MODEL_NAME="Intel/neural-chat-7b-v3-3"  # or any other supported LLM model

# Model used by Audio Analyzer service. Only Whisper models variants are supported.
# Common Supported models: tiny.en, small.en, medium.en, base.en, large-v1, large-v2, large-v3.
# You can provide just one or comma-separated list of models.
export ENABLED_WHISPER_MODELS="tiny.en,small.en,medium.en"

# Object detection model used for Video Ingestion Service. Only Yolo models are supported.
export OD_MODEL_NAME="yolov8l-worldv2"

# Multimodal embedding model. Only openai/clip-vit-base models are supported
export VCLIP_MODEL=openai/clip-vit-base-patch32

Advanced VLM Configuration Options:

The following environment variables provide additional control over VLM inference behavior and logging:
```
# (Optional) OpenVINO Configuration for VLM inference optimization
# Pass OpenVINO configuration parameters as a JSON string to fine-tune inference performance
# Default latency-optimized configuration (equivalent to not setting OV_CONFIG)
# export OV_CONFIG='{"PERFORMANCE_HINT": "LATENCY"}'

# Throughput-optimized configuration
export OV_CONFIG='{"PERFORMANCE_HINT": "THROUGHPUT"}'
```
IMPORTANT: The OV_CONFIG variable is used to pass OpenVINO configuration parameters to the VLM service. It allows you to optimize inference performance based on your hardware and workload. For a complete list of OpenVINO configuration options, refer to the OpenVINO Documentation. Note: If OV_CONFIG is not set, the default configuration {"PERFORMANCE_HINT": "LATENCY"} will be used.

🔐 Working with Gated Models

To run a GATED MODEL like Llama models, the user will need to pass their huggingface token. The user will need to request access to specific model by going to the respective model page on HuggingFace.

Go to https://huggingface.co/settings/tokens to get your token.

export GATED_MODEL=true
export HUGGINGFACE_TOKEN=<your_huggingface_token>

Once exported, run the setup script as mentioned here. Please switch off the GATED_MODEL flag by running export GATED_MODEL=false, once you are no more using gated models. This avoids unnecessary authentication step during setup.

📊 Application Stacks Overview#

The Video Summary application offers multiple stacks and deployment options:

Stack	Description	Flag (used with setup script)
Video Summary	Video frame captioning and Summary	`--summary`
Video Search	Video indexing and semantic search	`--search`
Video Search + Summary *(Under Construction)*	Both summary and search capabilities	`--all`

🧩 Deployment Options for Video Summary#

Option	Chunk-Wise Summary	Final Summary	Environment Variables	Recommended Models
VLM-CPU	vlm-openvino-serving on CPU	vlm-openvino-serving on CPU	Default	VLM: `Qwen/Qwen2.5-VL-3B-Instruct`
VLM-GPU	vlm-openvino-serving	vlm-openvino-serving GPU	`ENABLE_VLM_GPU=true`	VLM: `microsoft/Phi-3.5-vision-instruct`
VLM-OVMS-CPU	vlm-openvino-serving on CPU	OVMS Microservice on CPU	`ENABLE_OVMS_LLM_SUMMARY=true`	VLM: `Qwen/Qwen2.5-VL-3B-Instruct` LLM: `Intel/neural-chat-7b-v3-3`
VLM-CPU-OVMS-GPU	vlm-openvino-serving on CPU	OVMS Microservice on GPU	`ENABLE_OVMS_LLM_SUMMARY_GPU=true`	VLM: `Qwen/Qwen2.5-VL-3B-Instruct` LLM: `Intel/neural-chat-7b-v3-3`

▶️ Running the Application#

ℹ️ Note for EMT (Edge Microvisor Toolkit) Users

If you are running the VSS application on an OS image built with Edge Microvisor Toolkit (EMT)—an Azure Linux-based build pipeline for Intel® platforms—you must install the following package:
sudo dnf install mesa-libGL
# If you are using TDNF, you can use the following command to install:
sudo tdnf search mesa-libGL
sudo tdnf install mesa-libGL
Installing mesa-libGL provides the OpenGL library which is needed by Audio Analyzer service.

Follow these steps to run the application:

Clone the repository and navigate to the project directory:

git clone https://github.com/open-edge-platform/edge-ai-libraries.git -b release-1.2.0
cd edge-ai-libraries/sample-applications/video-search-and-summarization

Set the required environment variables as described above.
Run the setup script with the appropriate flag, depending on your use case.

NOTE: Before switching to a different mode always stop the current application stack by running:
```
source setup.sh --down
```

To run Video Summary only:
```
source setup.sh --summary
```
To run Video Search only:
```
source setup.sh --search
```
To run Video Summary with OVMS Microservice for final summary :
```
ENABLE_OVMS_LLM_SUMMARY=true source setup.sh --summary
```

(Optional) Verify the resolved environment variables and setup configurations.

# To just set environment variables without starting containers
source setup.sh --setenv

# To see resolved configurations for summary services without starting containers
source setup.sh --summary config

# To see resolved configurations for search services without starting containers
source setup.sh --search config

# To see resolved configurations for both search and summary services combined without starting containers
source setup.sh --all config

# To see resolved configurations for summary services with OVMS setup on CPU without starting containers
ENABLE_OVMS_LLM_SUMMARY=true source setup.sh --summary config

⚡ Using GPU Acceleration#

To use GPU acceleration for VLM inference:

NOTE: Before switching to a different mode always stop the current application stack by running:

source setup.sh --down

ENABLE_VLM_GPU=true source setup.sh --summary

To use GPU acceleration for OVMS-based summary:

ENABLE_OVMS_LLM_SUMMARY_GPU=true source setup.sh --summary

To use GPU acceleration for vclip-embedding-ms for search usecase:

ENABLE_EMBEDDING_GPU=true source setup.sh --search

To verify configuration and resolved environment variables without running the application:

# For VLM inference on GPU
ENABLE_VLM_GPU=true source setup.sh --summary config

# For OVMS inference on GPU
ENABLE_OVMS_LLM_SUMMARY_GPU=true source setup.sh --summary config

# For vclip-embedding-ms on GPU
ENABLE_EMBEDDING_GPU=true source setup.sh --search config

NOTE: Please avoid setting ENABLE_VLM_GPU, ENABLE_OVMS_LLM_SUMMARY_GPU, or ENABLE_EMBEDDING_GPU explicitly on shell using export, as you need to switch these flags off as well, to return back to CPU configuration.

🌐 Accessing the Application#

After successfully starting the application, open a browser and go to http://<host-ip>:12345 to access the application dashboard.

☸️ Running in Kubernetes#

Refer to Deploy with Helm for the details. Ensure the prerequisites mentioned on this page are addressed before proceeding to deploy with Helm.

🔍 Advanced Setup Options#

For alternative ways to set up the sample application, see:

How to Build from Source

📚 Supporting Resources#

Docker Compose Documentation

Troubleshooting#

Containers started but Application not working#

You can try resetting the volume storage, by deleting the previously created volumes using following commands:
```
source setup.sh --down
docker volume rm audio_analyzer_data data-prep
```
NOTE : This step does not apply when you are setting up the application for the first time.

VLM Microservice Model Loading Issues#

Problem: VLM microservice fails to load or save models with permission errors, or you see errors related to model access in the logs.

Cause: This issue occurs when the ov-models Docker volume was created with incorrect ownership (root user) in previous versions of the application. The VLM microservice runs as a non-root user and requires proper permissions to read/write models.

Symptoms:

VLM microservice container fails to start or crashes during model loading
Permission denied errors in VLM service logs
Model conversion or caching failures
Error messages mentioning /home/appuser/.cache/huggingface or /app/ov-model access issues

Solution:

Stop the running application:
```
source setup.sh --down
```
Remove the existing ov-models (old volume name) and docker_ov-models (updated volume name) Docker volume:
```
docker volume rm ov-models docker_ov-models
```

Restart the application (the volume will be recreated with correct permissions):

# For Video Summary
source setup.sh --summary

# Or for Video Search
source setup.sh --search

Note: Removing the ov-models/docker_ov-models volume will delete any previously cached/converted models. The VLM service will automatically re-download and convert models on the next startup, which may take additional time depending on your internet connection and the model size.

Prevention: This issue has been fixed in the current version of the VLM microservice Dockerfile. New installations will automatically create the volume with correct permissions.

VLM Final Summary Hallucination Issues#

Problem: The final summary generated by the VLM microservice contains hallucinated or inaccurate information that doesn’t reflect the actual video content.

Cause: This issue can occur when using smaller VLM models that may not have sufficient capacity to accurately process and summarize complex video content, leading to generation of plausible but incorrect information.