Enterprise RAG-Based Assistant Design (Azure + LLM Stack)
Objective: Design a secure, scalable enterprise assistant that allows employees to query internal documents (PDFs, meeting notes, reports) using natural language. The system returns relevant, grounded responses with references.
📆 High-Level Architecture Overview
Stack: Azure Functions, Azure AI Search (Vector), Azure OpenAI (GPT-4 + Embeddings), Semantic Kernel, Azure AD, RBAC, App Insights
💡 Core Components
1. Document Ingestion & Preprocessing
Trigger: Upload to Azure Blob Storage / SharePoint
Service: Azure Function (Blob Trigger)
Processing Steps:
Extract text using Azure Document Intelligence
Chunk text into semantically meaningful segments
Generate embeddings using
text-embedding-ada-002
2. Indexing
Store vector embeddings + metadata in Azure AI Search
Enable vector search on the content field
Include filters for metadata (e.g., doc type, author, date)
3. Query Workflow
User submits query via UI (e.g., Web App or Teams Bot)
Query is embedded using same embedding model
Vector search on Azure AI Search returns top-N documents
Semantic Kernel handles:
Context assembly (retrieved chunks)
Prompt templating
Call to Azure OpenAI Chat Completion API
Response formatting (with references)
4. Semantic Kernel Role
Provides pluggable architecture to:
Register skills (embedding, search, summarization)
Maintain short/long-term memory
Integrate .NET enterprise apps
Alternative to LangChain, but better aligned with Azure
5. Security & Compliance
Azure AD Authentication (MSAL)
Managed Identity for Azure Functions
RBAC to control access to Search, Blob, OpenAI
Private Endpoints & VNet Integration
6. Monitoring & Governance
Azure Application Insights for telemetry
Azure Monitor for alerting & diagnostics
Cost usage dashboard for OpenAI API
✨ Optional Extensions
Multi-Agent Orchestration: CrewAI or LangGraph to chain agents (e.g., Search Agent → Reviewer Agent)
Feedback Loop: Capture thumbs up/down to improve results
SharePoint/Teams Plugin: Tight M365 integration
Document Enrichment Pipeline using Azure Cognitive Search skillsets
🔹 Summary:
This solution leverages a robust, secure, Azure-native stack to build an enterprise-ready, LLM-powered RAG system. By combining Azure AI Search for retrieval and OpenAI GPT for reasoning, we ensure low-latency and grounded responses. Semantic Kernel enables structured orchestration and clean integration into .NET-based apps and services.
