Agentic AIintermediate

Azure Multi-Modal Compliance Orchestration Engine using LangGraph and LangSmith

This project establishes an automated Video Compliance QA Pipeline orchestrated by LangGraph, designed to audit content against regulatory standards using a RAG architecture. We leverage Azure Video Indexer for multimodal ingestion (transcripts/OCR) and Azure AI Search to retrieve relevant compliance rules via Azure OpenAI Embeddings. The core reasoning engine, Azure OpenAI (GPT-4o), synthesizes this data to deterministically detect violations, while LangSmith provides granular tracing for LLM workflow optimization. Additionally, Azure Application Insights is integrated for production-grade telemetry, logging, and real-time performance monitoring. This end-to-end system transforms unstructured video into structured, actionable JSON compliance reports with deep full-stack observability.

14 lectures

What You Will Learn

Mastering LangGraph for orchestrating complex LLM workflows
Implementing RAG architecture for compliance rule retrieval using Azure AI Search
Utilizing Azure Video Indexer for multimodal content ingestion (transcripts, OCR)
Building a deterministic violation detection system with Azure OpenAI
Integrating LangSmith for granular tracing and LLM workflow optimization
Deploying production-grade telemetry and monitoring using Azure Application Insights
Creating structured JSON compliance reports from unstructured video data

System Architecture

Azure Multi-Modal Compliance Orchestration Engine using LangGraph and LangSmith Architecture Diagram

High-level architecture overview of the Azure Multi-Modal Compliance Orchestration Engine using LangGraph and LangSmith .

What You'll Build

  • Automated Video Compliance QA Pipeline
  • RAG-based compliance rule retrieval system
  • AI project Infrastructure for scalability
  • Full-stack observability dashboard with LangSmith and Azure Application Insights
Azure Multi-Modal Compliance Orchestration Engine using LangGraph and LangSmith
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