About This Architecture

Azure RAG pipeline ingesting CSV, PNG, and PDF documents through tiered blob storage into AI Search for vector indexing and semantic retrieval. Raw data flows from hot-tier storage through OCR, document parsing, chunking, and embedding skills managed by AI Search Indexer, producing searchable indexes with 3072-dimensional vectors and filterable metadata. This architecture enables Copilot Studio agents to deliver grounded generative answers by querying indexed content via Azure OpenAI Service while maintaining security through Azure AD and Key Vault. Fork this diagram to customize skill configurations, adjust embedding dimensions, or integrate additional data sources into your knowledge base. Cool-tier archival and Application Insights monitoring ensure cost optimization and observability across the entire pipeline.

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How do I build a retrieval-augmented generation pipeline in Azure that ingests documents, creates vector embeddings, and powers Copilot Studio agents?

This diagram shows a complete Azure RAG pipeline: CSV, PNG, and PDF documents flow into tiered Blob Storage (hot for active, cool for archived), then through Azure AI Search Indexer with OCR, parsing, chunking, and embedding skills to create 3072-dimensional vectors and searchable indexes. Copilot Studio agents query these indexes via Azure OpenAI Service to deliver grounded generative answers, wi