QPrisma: Technical Architecture Analysis of an Enterprise-Grade Multimodal Video Intelligent Analysis Platform

QPrisma is an open-source enterprise-level multimedia processing platform that integrates computer vision, large language models (LLMs), and Retrieval-Augmented Generation (RAG) technology to transform unstructured video content into a searchable and actionable knowledge base. This article will analyze key aspects such as its technical architecture, core capabilities, and application scenarios to help readers understand the platform's value and implementation methods.

In the digital age, enterprises have accumulated massive amounts of video content (surveillance, meetings, training, marketing, etc.), but these unstructured data are difficult to retrieve quickly and utilize effectively. Traditional video analysis relies on manual annotation and simple keyword matching, which is inefficient and cannot capture deep semantic information. QPrisma emerged to address this challenge through AI technology.

QPrisma is a research prototype project built on Azure AI services, aiming to reduce the time for manual video review from hours to instant responses via natural language queries. Its tech stack includes:

• Frontend: Next.js 16 + React 19 + Tailwind CSS
• Backend: FastAPI + Python
• Agent Runtime: LangGraph-driven video agent
• AI Capabilities: Azure OpenAI multimodal/chat/embedding/transcription models
• Video Processing: PyAV (FFmpeg C-level binding)
• Scene Detection: PySceneDetect (adaptive detector + content detector)
• Speech Recognition: Azure Whisper (default) or faster-whisper (optional, 4x faster)
• Data Storage: PostgreSQL + Neo4j graph database + Redis cache
• Cloud Storage: Azure Blob Storage
• Infrastructure: Azure Bicep + GitHub Actions + Azure Container Apps

QPrisma's core capabilities include:

1. Video Understanding Layer: Perform scene detection using PySceneDetect, generate titles and summaries, and form a video→chapter→scene hierarchy for easy quick browsing.
2. Conversational Retrieval (RAG): Support natural language queries, search for relevant information in videos and return timestamped evidence clips to ensure accurate and traceable answers.
3. Knowledge Graph Enhancement: Build cross-video knowledge graphs, supporting entity normalization (30+ alias mappings), description accumulation (up to 5 clips), relationship storage (evidence count and semantic weight), community detection (Leiden algorithm clustering), and cross-video entity resolution (connected via SAME_ENTITY edges).
4. Temporal Knowledge Chain: Construct time chains through relationships like NEXT_FRAME and NEXT_SEGMENT to support event context tracking.

QPrisma adopts a hierarchical memory architecture and hybrid retrieval process:

• Hierarchical Memory:

1. Production-level Session Management: Azure AI Foundry hosts agents to manage session history and tool calls, with metadata desensitization.
2. Operational State Layer: LangGraph checkpoints maintain state, and tool loads are stored in Redis, Blob, and PostgreSQL.
3. Long-term User Memory: Azure AI Foundry Memory Store stores data by user/Entra ID, supporting timeliness and semantic sorting.

• Hybrid Retrieval: Combines semantic search (vector similarity), lexical search (BM25), graph traversal (Neo4j), and community awareness (topic clustering) to adapt to different query types.

QPrisma supports multi-region deployment (default: West Europe for computing + AI Foundry, North Europe for PostgreSQL). Infrastructure is codified via Azure Bicep definitions. Security measures include:

• Microsoft Entra ID authentication (MSAL v5 pop-up flow)
• Rate limiting (slowapi)
• A2A ownership isolation (hide tasks across users)
• Security header middleware

QPrisma's enterprise-level application scenarios include:

• Compliance Review: Quickly locate sensitive content or violations
• Training Analysis: Extract knowledge points and common questions from training videos
• Meeting Summarization: Automatically generate minutes and track action items
• Content Moderation: Large-scale video quality inspection and classification
• Knowledge Management: Transform scattered video assets into a queryable knowledge base This platform provides an intelligent solution for enterprises dealing with large amounts of video content, with significant reference value.

QPrisma represents the direction of multimedia AI moving from simple content recognition to deep semantic understanding and knowledge construction. With the development of LLMs and multimodal technologies, its accuracy, efficiency, and scalability will be further improved. As an open-source project, QPrisma provides a full-chain reference solution for technical practitioners from video processing to knowledge graph construction.