Building Visual AI Agents for Video Search and Summarization
Introduction
Traditional video analytics applications and their development workflow are typically built on fixed-function, limited models that are designed to detect and identify only a select set of predefined objects. However, with the advent of generative AI, NVIDIA NIM microservices, and foundation models, you can now build applications with fewer models that have broad perception and rich contextual understanding.
Building a Visual AI Agent for Video Search and Summarization
The NVIDIA AI Blueprint for Video Search and Summarization accelerates the development of visual AI agents by providing a recipe for long-form video understanding using VLMs, LLMs, and the latest RAG techniques. This blueprint is powered by NVIDIA NIM, a set of microservices that includes industry-standard APIs, domain-specific code, optimized inference engines, and enterprise runtime.
Components of the Blueprint
The blueprint consists of the following components:
- Stream handler: Manages the interaction and synchronization with other components, such as NeMo Guardrails, CA-RAG, the VLM pipeline, chunking, and the Milvus Vector DB.
- NeMo Guardrails: Filters out invalid user prompts. It makes use of the REST API of an LLM NIM microservice.
- VLM pipeline: Decodes video chunks generated by the stream handler, generates embeddings for the video chunks using an NVIDIA Tensor RT-based visual encoder model, and then uses a VLM to generate per-chunk responses for the user query. It is based on the NVIDIA DeepStream SDK.
- VectorDB: Stores the intermediate per-chunk VLM response.
- CA-RAG module: Extracts useful information from the per-chunk VLM response and aggregates it to generate a single unified summary. CA-RAG (Context-Aware-Retrieval-Augmented Generation) uses the REST API of an LLM NIM microservice.
- Graph-RAG module: Captures the complex relationships present in the video and stores important information in a graph database as sets of nodes and edges. This is then queried by an LLM for interactive Q&A.
Video Ingestion and Retrieval Pipeline
To summarize a video or perform Q&A, a comprehensive index of the video must be built that captures all the important information. This is done by combining VLMs and LLMs to produce dense captions and metadata to build a knowledge graph of the video. This video ingestion pipeline is GPU-accelerated and scales with more GPUs to lower processing time.
Knowledge Graph and Graph-RAG Module
To capture the complex information produced by the VLM, a knowledge graph is built and stored during video ingestion. Use an LLM to convert the dense captions into a set of nodes, edges, and associated properties. This knowledge graph is stored in a graph database. By using Graph-RAG techniques, an LLM can access this information to extract key insights for summarization, Q&A, and alerts and go beyond what VLMs are capable of on their own.
Video Retrieval
When the video has been ingested, the databases behind the CA-RAG and Graph-RAG modules contain an immense amount of information about the objects, events, and descriptions of what occurred in the video. This information can be queried and consumed by an LLM for several tasks, including summarization, Q&A, and alerts.
Summarization
When a video file has been uploaded to the agent through the APIs, call the summarize endpoint to get a summary of the video. The blueprint takes care of all the heavy lifting while providing a lot of configurable parameters.
Q&A
The knowledge graph built during video ingestion can be queried by an LLM to provide a natural language interface into the video. This enables users to ask open-ended questions over the input video and have a chatbot experience.
Alerts
In addition to video files, the blueprint can also accept a video live stream as input. For live streaming use cases, it is often critical to know when certain events take place in near real-time. To accomplish this, the blueprint enables live streams to be registered and alert rules can be set to monitor the stream. These alert rules are in natural language and are used to trigger notifications when user-defined events occur.
Conclusion
The NVIDIA AI Blueprint for Video Search and Summarization provides a powerful framework for building visual AI agents that can understand long-form videos and provide valuable insights. With its ability to combine VLMs, LLMs, and RAG techniques, this blueprint enables the development of applications that can perform video summarization, Q&A, and alerts over live streams and long videos.
Frequently Asked Questions
Q: What is the NVIDIA AI Blueprint for Video Search and Summarization?
A: The NVIDIA AI Blueprint for Video Search and Summarization is a reference workflow for building visual AI agents that can understand long-form videos and provide valuable insights.
Q: What are the components of the blueprint?
A: The blueprint consists of the following components: stream handler, NeMo Guardrails, VLM pipeline, VectorDB, CA-RAG module, and Graph-RAG module.
Q: How does the blueprint work?
A: The blueprint works by combining VLMs and LLMs to produce dense captions and metadata to build a knowledge graph of the video. This video ingestion pipeline is GPU-accelerated and scales with more GPUs to lower processing time.
Q: What are the benefits of using the NVIDIA AI Blueprint for Video Search and Summarization?
A: The benefits of using the NVIDIA AI Blueprint for Video Search and Summarization include the ability to build powerful VLM-based AI agents, ease of integration with existing customer applications, and the ability to provide valuable insights from long-form videos.
