Release Notes: Live Video Captioning RAG#

Version 2026.1.0#

June 17, 2026

The Live Video Captioning RAG sample application combines caption ingestion, vector search, and LLM-based response generation into a Retrieval-Augmented Generation workflow. The sample application processes text captions generated from RTSP video streams through the Live Video Captioning application to deliver AI-powered chatbot responses based on text captioning context from video frames. The application leverages the following key features:

  • RAG-based Video Analysis: Generates embeddings from video captions and stores them in a vector database.

  • OpenVINO LLM Integration: Deploys LLM models efficiently using OpenVINO for response generation.

  • Interactive Chatbot Interface: A web-based dashboard for querying video content.

  • Docker Compose Deployment: Simplified deployment with containerized services.

  • REST API: Endpoints for embedding ingestion (/api/embeddings) and chat queries (/api/chat).

  • Multi-device Support: CPU and GPU device options for embedding and LLM inference.

  • Streaming Responses: Real-time chat responses with the retrieved frame references.

New

  • The initial release with core RAG capabilities.

  • Support for embedding and LLM models.

  • Streaming response rendering.

  • Inline frame preview with the caption context.

  • Deployment with the Docker Compose tool for the stack.

Known Issues

  • Limited Standalone Functionality: The sample application works with the Live Video Captioning sample application. Running the sample application standalone provides limited context until embeddings are manually added.
    Workaround: Use the provided demo script (sample/demo_call_embedding.py) to test the standalone functionality.

  • Platform Support: The sample application is not validated either on the Standalone or Developer Node versions of Edge Microvisor Toolkit.

For detailed instructions, see Get Started.

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