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Sources and admin layer
The architecture starts with PDFs, images, text files, Markdown, and controlled upload or bucket-sync processes. The key is that sources remain clearly identifiable.
Enterprise RAG
RAG architecture for secure enterprise knowledge systems
This reference architecture shows how Retrieval Augmented Generation can be implemented reliably in an enterprise context: documents are processed deterministically, stored with versions, and exposed to agents only through read-only retrieval APIs and MCP.
What is a RAG architecture?
A RAG architecture describes the technical structure that allows an AI system to answer from verified enterprise sources instead of relying only on model memory.
At its core, Retrieval Augmented Generation combines controlled document ingestion, a centralized knowledge store, a semantic search index, and a retrieval layer for applications, chatbots, or agents. For enterprise use cases, a vector database alone is not enough: versioning, citations, permissions, reindex jobs, and a read-only access path must be designed as architecture decisions.
- Primary search intent
- understand RAG architecture for enterprises
- Target system
- secure AI knowledge systems with sources and permissions
- Core principle
- ingestion writes, retrieval reads, agents do not modify the knowledge base
Components of an enterprise RAG architecture
The diagram condenses the architecture visually. The same structure is also described here as a clear architecture overview for decision makers, domain teams, and technical teams.
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Ingestion worker
The worker extracts text and page images, normalizes content, creates chunks, generates hashes, creates embeddings, and triggers controlled reindex jobs.
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Knowledge store
Object storage and PostgreSQL with pgvector keep original files, previews, metadata, document versions, chunks, embeddings, and access information together.
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Retrieval layer
A RAG API and MCP server provide semantic search, document lookup, chunk context, citations, filters, and permissions.
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Agent clients and model runtime
Clients such as Codex, Claude, or Open Harness use read-only tools only. Models can run self-hosted or as cloud LLMs without receiving direct database access.
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Operations and compliance
Kubernetes, starkAI Cloud, GDPR alignment, EU AI Act readiness, and ISO-27001-oriented controls belong to the operating model, not to a later add-on layer.
Why this structure holds up
The architecture deliberately separates ingestion, storage, retrieval, and model use. Sources stay traceable, permissions remain auditable, and agents stay controlled in production.
Deterministic ingestion
PDFs, images, and Markdown are normalized, hashed, versioned, and reindexed in a repeatable way. Reindex jobs are controlled instead of implicit.
Centralized knowledge store
Original files, previews, metadata, document versions, chunks, and embeddings stay connected in a clear storage and index layer.
Read-only retrieval
Agents access knowledge through the RAG API and MCP tools. Direct database or storage write permissions are not part of the client path.
When is this RAG architecture worth it?
An enterprise RAG architecture is useful when knowledge should not only be searched, but also used in an auditable, repeatable, and accountable way.
Internal knowledge systems
Employees need to find policies, project documents, manuals, or process knowledge through search and chat without losing the source trail.
Document-heavy processes
Contracts, invoices, freight papers, technical specifications, or quote documents need to be ingested in a structured way and made reusable.
Agents with controlled access
AI agents should use knowledge without receiving database or storage write permissions. Retrieval becomes the controlled interface between agent and knowledge.
Regulated organizations
When privacy, auditability, tenancy, citations, and version history matter, the RAG architecture has to support those requirements from the beginning.
Related topics and next depth
A pillar page should not stand alone. These pages deepen the main search intents around enterprise knowledge, document processing, and AI enablement.
AI Knowledge SystemHow the RAG architecture becomes a productive search and chat interface for internal enterprise knowledge.AI Document AnalysisHow unstructured documents are extracted, classified, and validated before indexing.AI AcademyHow teams learn to work reliably with retrieval, citations, prompts, and AI knowledge systems.
Common questions about RAG architecture
These answers cover the questions that usually need to be clarified before building an enterprise RAG system.
What is the difference between RAG and a vector database?
A vector database is only one component. A RAG architecture also includes ingestion, chunking, metadata, permissions, citations, retrieval APIs, MCP tools, model access, and operating processes.
Why is deterministic ingestion important?
Deterministic ingestion makes repeated document processing traceable through versions, hashes, and index runs. This matters for audits, debugging, and reliable updates.
What role does MCP play in a RAG architecture?
MCP exposes agent-ready tools such as semantic search, document lookup, and chunk context. Agent clients can use knowledge without direct access to the database or object storage.
Which data belongs in the knowledge store?
Besides embeddings, the knowledge store should keep original files, page previews, extracted assets, document versions, metadata, access information, and generated chunks.
Can the architecture work with cloud LLMs and self-hosted models?
Yes. The retrieval layer decouples knowledge access from the model runtime. Self-hosted models, cloud LLMs, or hybrid setups can be used without changing the enterprise data access path.
Plan a RAG architecture for your knowledge system
stark AI translates distributed documents, permissions, and operating requirements into a robust RAG target architecture with ingestion, knowledge store, retrieval, and operating model.