Building a Sovereign n8n RAG Agent on OVHcloud Public Cloud: A Practical Reference Architecture (and Why It Matters in 2026)

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Enterprises love the idea of AI agents that can answer questions about internal docs, tickets, policies, and product knowledge. Enterprises also love not being on the front page because their “helpful bot” leaked sensitive data into the wrong jurisdiction. These two loves don’t always get along.

That’s why the OVHcloud blog post “Reference Architecture: build a sovereign n8n RAG workflow for AI agent using OVHcloud Public Cloud solutions” caught my eye. It’s a hands-on blueprint that combines n8n (workflow automation), a classic RAG pipeline (Retrieval-Augmented Generation), and OVHcloud building blocks like Object Storage (S3-compatible), Managed Databases for PostgreSQL + pgvector, and OVHcloud AI Endpoints for embeddings and LLM inference.

The original article was published on January 27, 2026 and written by Eléa Petton. This piece builds on that foundation, adds operational detail, security context, and industry implications—and yes, it includes a few hard-earned reminders about running self-hosted automation servers on the public internet in 2026.

Why “sovereign RAG” is suddenly everyone’s favorite phrase

RAG is the architecture pattern that makes LLMs useful inside organizations without forcing you to fine-tune models on proprietary data. Instead of stuffing everything into the prompt and hoping for the best, RAG typically:

  • Ingests and chunks documents (policies, manuals, runbooks, knowledge base articles)
  • Creates embeddings (vector representations) for each chunk
  • Stores vectors in a vector database (or a DB with vector search)
  • Retrieves top-k relevant chunks at query time
  • Prompts the LLM with retrieved context so answers stay grounded

The “sovereign” part is about control: where the data sits, who can access it, and what legal/regulatory frameworks apply. In Europe, sovereignty conversations are often tied to GDPR, sector regulation, and concerns about extraterritorial access (whether those concerns are legal, political, or simply risk-management optics). OVHcloud explicitly positions AI Endpoints as running on its European infrastructure and emphasizes privacy and non-reuse of customer data. citeturn6search0turn0search1

From a technical perspective, “sovereign RAG” usually means you’re trying to ensure:

  • Document storage stays within a chosen region (and is access-controlled).
  • Embeddings generation doesn’t leak content to third parties.
  • LLM inference happens through an environment you trust with your prompts.
  • Observability and logs don’t become an accidental data exfil channel.

What OVHcloud’s reference architecture actually builds

The OVHcloud reference architecture is refreshingly specific about the moving parts. In short, the system:

  • Pulls Markdown documentation from OVHcloud Object Storage (S3 compatible).
  • Preprocesses and cleans the text inside an n8n workflow (including removing YAML front-matter and odd encoding issues).
  • Creates embeddings using the BGE-M3 embedding model via OVHcloud AI Endpoints, set to 1024 dimensions.
  • Stores vectors and metadata in OVHcloud Managed PostgreSQL using the pgvector extension.
  • Exposes a chat interface in n8n (“Chat Trigger”) and runs a LangChain-capable agent that retrieves relevant chunks and uses an LLM (the article uses GPT-OSS-120B on AI Endpoints) to answer.
  • Optionally adds an LLM guard step deployed on OVHcloud AI Deploy (example: Llama Guard 3 via vLLM OpenAI-compatible server).

Those specifics matter because most RAG write-ups stop at “store vectors somewhere” and “call an LLM somehow.” Here, the components and their responsibilities are spelled out clearly, including the key trick: OVHcloud AI Endpoints provides an OpenAI-compatible API, so tools that expect OpenAI-style endpoints can be pointed at OVHcloud instead. citeturn2view0turn6search4

Meet the conductor: why n8n is the glue in this design

n8n is often described as “Zapier for people who like control,” and that’s not unfair. It’s a workflow automation platform that you can self-host, and it has native nodes for lots of SaaS tools, plus code nodes when you need custom logic. The OVHcloud piece points out its strengths in orchestration and its “sovereign by design” story when self-hosted. citeturn1view0

There’s also a licensing nuance worth knowing: n8n is fair-code, using its Sustainable Use License, which allows use and modification but with limitations (notably around offering it as a service). That’s not a problem for internal deployments, but it is something procurement teams like to discover before launch day. citeturn4search2

What makes n8n interesting for agentic RAG is that you get:

  • Event triggers (cron, webhooks, chat triggers)
  • Connectors to storage, databases, chat systems, ticketing systems
  • Branching logic (if/else, loops)
  • Lightweight code steps to sanitize, transform, and enrich data
  • AI agent nodes that can combine LLM + memory + tools

In the OVHcloud blueprint, n8n isn’t “the AI.” It’s the pipeline engine: it moves documents into a vector store, and it moves questions into a guarded agent flow and back out as answers (or actions).

Step-by-step architecture, expanded with real-world considerations

1) Document source: Object Storage as the knowledge base landing zone

The reference architecture uses Markdown documentation stored in an OVHcloud Object Storage bucket. OVHcloud’s Object Storage is designed to be compatible with the S3 API, making it easier to integrate with S3-friendly tools and libraries. citeturn4search4turn4search0

In practical terms, Object Storage is a good fit for RAG ingestion when:

  • You want a clear separation between raw documents and downstream processing.
  • You need versioning / lifecycle policies (depending on bucket configuration).
  • You’re ingesting from multiple systems and want a single “drop zone.”

Operational advice: treat this bucket like a staging area, not a file cabinet. If your process allows it, enforce a predictable structure (prefix per department, prefix per product, etc.) and ensure you’re attaching metadata early (owner, classification, retention tag). Most painful RAG incidents are not “vector math problems”; they’re “we accidentally indexed the wrong stuff” problems.

2) Preprocessing: cleaning Markdown without destroying meaning

OVHcloud’s workflow shows a JavaScript code node that decodes binary content, cleans non-printable characters, trims whitespace, and truncates long content (example: cut at 14,000 characters). citeturn2view1

That’s a good start, but in production you’ll usually need a bit more nuance:

  • Remove YAML front-matter (as the original article notes) but keep metadata if it’s useful (title, tags, product, last updated).
  • Normalize headings so chunking respects sections.
  • Preserve code blocks carefully. For developer documentation, code blocks are often the most valuable retrieval targets.
  • Deduplicate. Many corp docs contain repeated legal/footer blocks that pollute retrieval.

Chunking tip: if your users ask precise “how do I configure X” questions, smaller chunks help. If they ask “explain the overall process,” larger chunks help. There is no magical chunk size; you tune chunking based on question style, document structure, and model context window.

3) Embeddings: why BGE-M3 is a reasonable default

The OVHcloud reference uses BGE-M3 for embeddings and sets vector dimension to 1024. citeturn2view0

BGE-M3 (M3-Embedding) is described in the research literature as a multi-lingual, multi-functionality, multi-granularity embedding model, supporting over 100 languages and multiple retrieval modes, and handling long inputs (up to 8192 tokens per the paper abstract). citeturn4academia12

Why this matters in “enterprise RAG” reality:

  • Multilinguality: many orgs have English plus “the language your headquarters actually speaks.”
  • Long docs: policies and runbooks are not tweets.
  • Robust retrieval: embeddings quality often matters more than which LLM you use for the final answer.

Cost and scaling angle: embeddings can be your biggest volume driver. If you ingest thousands of documents, chunk them into tens of thousands of chunks, and refresh frequently, you want a predictable embeddings API and a controlled ingestion schedule (batching, rate limits, backoff, and observability).

4) Vector storage: PostgreSQL + pgvector as the “boring is beautiful” option

Instead of adopting a dedicated vector database service, OVHcloud’s reference uses PostgreSQL with the pgvector extension. The workflow creates a table with a vector(1024) column and a JSONB metadata field. citeturn2view0turn3search1

pgvector is a widely used open-source PostgreSQL extension for vector similarity search, supporting multiple distance metrics and indexing approaches. It continues to evolve with performance and feature improvements (including new vector types and indexing enhancements in recent releases). citeturn3search0turn3search3

Why teams like Postgres for RAG:

  • You already operate Postgres, and your SREs already have opinions about it.
  • You get transactions, metadata filtering, and familiar tooling.
  • It’s easier to integrate with existing apps that already depend on SQL.

What to watch for:

  • Index strategy: HNSW/IVFFlat choices affect recall and latency; tune for your query volume and accuracy needs.
  • Metadata filters: you’ll quickly want “only docs from product X” or “only public docs” filters.
  • Re-embedding: model upgrades mean you may need to rebuild embeddings; plan for dual-index or migration windows.

5) Inference: OVHcloud AI Endpoints as OpenAI-compatible “model plumbing”

OVHcloud AI Endpoints is positioned as a serverless inference API for a catalog of open-weight models, with a focus on privacy and straightforward integration (including OpenAI-style APIs). citeturn6search0turn0search1

The reference architecture uses a single OpenAI-compatible base URL for different models (embeddings and chat). citeturn2view0turn6search4

This is a big deal for tooling:

  • Many libraries (LangChain, LlamaIndex, n8n nodes) already know how to talk “OpenAI API.”
  • If you can swap the base URL and keys, you can adopt OVHcloud inference without rewriting half your stack.

OVHcloud also announced AI Endpoints as a product in April 2025, framing it as a way to democratize access to open-source models without managing GPU infrastructure, and emphasizing European hosting and strategic autonomy. citeturn6search0turn6search2

Model choice note: the OVHcloud catalog explicitly lists models like Gpt-oss-120b with pricing per million tokens. citeturn6search1

6) Safety layer: LLM guard via AI Deploy + vLLM

The reference design proposes adding an “LLM guard” before the agent call, and demonstrates deploying a moderation/guard model (example: Llama Guard 3) via OVHcloud AI Deploy using the ovhai CLI, running an OpenAI-compatible server using vLLM. citeturn2view2turn3search4turn3search2

In plain terms, this step is a policy gate:

  • If the input looks unsafe (prompt injection attempts, disallowed content), stop early.
  • If it’s safe, continue and call the main agent/LLM with retrieval.

It’s not a silver bullet (no guard is), but it’s a strong pattern because it reduces your “blast radius” from malicious prompts and helps enforce consistent policy across multiple agents and workflows.

Security reality check: self-hosted automation platforms are high-value targets

If you deploy n8n as the orchestrator for your AI agent, you are effectively deploying a system that:

  • Has access to your documents and databases
  • Stores secrets (API keys, database credentials)
  • Can call internal HTTP endpoints
  • Can trigger actions in third-party systems

That’s a crown-jewel box with a friendly UI.

In late 2025 and early 2026, n8n has also been in the security news because critical vulnerabilities were disclosed, including:

  • GHSA-62r4-hw23-cc8v / CVE-2025-68668: a sandbox bypass in the Pyodide-based Python Code Node enabling arbitrary command execution for authenticated users with workflow-editing permissions; GitHub advisory notes patched in n8n 2.0.0, with a more secure native Python runner introduced optionally in 1.111.0. citeturn5search6turn5search7
  • CVE-2026-21858 (“Ni8mare”): widely reported as a critical issue enabling unauthenticated takeover of vulnerable n8n instances, with public reporting indicating patch availability starting in n8n 1.121.0; third-party coverage also highlights large numbers of exposed instances. citeturn5news10turn5search4

Practical implication for this architecture: if you’re going to expose n8n (or any webhook-driven workflow engine) to the public internet, you should treat patching and hardening as part of the architecture, not as “later.” Especially if you’re building an agent that will accept arbitrary user text.

Recommended baseline controls:

  • Keep n8n updated and monitor security advisories.
  • Restrict network exposure: put n8n behind a reverse proxy/WAF; limit inbound paths; require auth for the editor.
  • Separate roles: don’t give broad workflow-editing permissions to everyone.
  • Secrets hygiene: store credentials in a managed secret store if possible; rotate keys.
  • Outbound allow-listing: limit what the workflow engine can call out to.
  • Audit logging and anomaly detection for workflow changes.

RAG quality: where projects actually succeed or fail

When teams say “RAG didn’t work for us,” the postmortem usually falls into one of these buckets:

1) The documents were wrong, stale, or not meant for humans

If your policy docs are outdated, the agent will be confidently outdated. Consider adding a metadata field like last_updated and bias retrieval or response generation toward newer content. Also, decide whether “outdated but still indexed” should be excluded or explicitly flagged in answers.

2) Chunking and retrieval didn’t match user questions

Chunk size and splitting strategy can be tuned. The OVHcloud workflow shows configurable text splitting options; you should use them. citeturn2view0turn2view1

In production, run a simple evaluation loop:

  • Collect real user questions (or realistic synthetic ones)
  • Log top-k retrieved chunks
  • Have subject matter experts rate retrieval relevance
  • Adjust chunking and metadata filters accordingly

3) The model answered beyond the evidence

RAG only helps if you instruct the model to use retrieved context and to refuse when context is missing. The OVHcloud article explicitly includes a system prompt telling the agent to retrieve and to say it has no information if nothing relevant is found. citeturn2view3

That’s not just “prompting.” It’s a governance decision: you are designing the failure mode to be “safe and honest” instead of “creative and lawsuit-friendly.”

Case study patterns: where this architecture fits best

This particular reference architecture (S3-like storage + n8n orchestration + Postgres/pgvector + OpenAI-compatible inference) shines in a few scenarios:

Internal product expert bots

The OVHcloud example builds an “OVHcloud product expert” bot over documentation. That pattern works for any organization with complex product documentation, internal playbooks, or runbooks.

Support engineering and incident response

RAG agents can speed up incident response by retrieving runbooks, known-issues pages, and postmortems. The key is access control: ensure the agent only retrieves what the user is authorized to see, or you’ve invented a new way to leak incident details.

Compliance and policy assistants

These are great when they answer with citations to internal policy snippets. Use metadata to tag documents by policy domain and update date, and make the agent quote short excerpts (not whole docs) to avoid overexposure.

Cost, performance, and “don’t accidentally DDoS your own embeddings API”

RAG systems have two major cost drivers:

  • Ingestion cost (embeddings generation, chunking, indexing)
  • Query cost (retrieval + LLM inference)

The OVHcloud workflow design includes batching/looping and mentions limits such as “400 requests” before branching logic kicks in, which is a hint at rate-control considerations. citeturn1view0

Practical optimizations:

  • Incremental ingestion: only re-embed documents that changed (hash chunks).
  • Cache retrieval for repeated questions (especially internal FAQs).
  • Use smaller/cheaper models where appropriate (classification/guardrails don’t need 120B parameters).
  • Set token budgets and enforce max context size.

Also remember: vector databases can become the unexpected bottleneck. If you’re using pgvector, monitor query latency, index build times, and disk growth, and plan index maintenance like you would for any other critical database workload. citeturn3search1

What “sovereign” really means operationally (not just in marketing)

It’s tempting to reduce sovereignty to “the provider is European” or “the data center is in Europe.” In practice, a sovereign setup is a chain, and the weakest link is still the link you’ll get audited on.

In this architecture, your sovereignty story is stronger when you can answer questions like:

  • Where is Object Storage physically located, and what are the replication behaviors?
  • Where does the Managed Postgres run, and are backups stored in-region?
  • Where do AI Endpoints requests terminate, and what’s logged?
  • What telemetry leaves the environment (APM, logs, error traces)?
  • What third-party SaaS integrations does n8n call during the workflow?

OVHcloud’s public materials emphasize that AI Endpoints is designed for privacy and that data is not reused to train models, and position the service as part of a sovereignty strategy. citeturn6search0turn0search1

But the rest is on you: you still need IAM, network segmentation, and governance around what gets indexed.

Comparisons: how this stack differs from other RAG approaches

Versus “all-in-one SaaS RAG platforms”

All-in-one platforms can be faster to adopt but often constrain data residency options, model selection, and extensibility. The OVHcloud + n8n approach is more modular: you can swap embedding models, switch retrieval tooling, and keep orchestration under your control. The trade-off is operational responsibility.

Versus “vector DB first” architectures

Many teams start with a dedicated vector DB service. Postgres + pgvector is a pragmatic choice when you want fewer moving parts and can live with Postgres-style scaling. If you need extreme scale, you might evaluate alternatives or extensions, but for many orgs the “boring” choice is the best one.

Versus fine-tuning

Fine-tuning is not a substitute for retrieval. Even when you fine-tune, you often still need RAG for up-to-date or granular knowledge. RAG also provides a clearer governance story because you can show “what context was used to answer.”

Implementation checklist (the part you’ll wish you had on day two)

  • Data scope: define what is allowed to be indexed; create a “do-not-index” list.
  • Classification: store document sensitivity in metadata; enforce filters in retrieval.
  • Evaluation: build a small test set of Q&A pairs and track retrieval hit rate.
  • Prompt discipline: require citations or references to retrieved chunks; refuse when missing.
  • Security posture: patch n8n quickly; restrict access; review webhook exposure.
  • Key management: rotate AI Endpoints keys and database passwords; limit permissions.
  • Observability: log retrieval IDs, not full document content; monitor latency and cost.

Final thoughts: a solid blueprint—just bring your own paranoia

Eléa Petton’s OVHcloud reference architecture is valuable because it is concrete and repeatable: it shows an end-to-end workflow, not a vague concept diagram. It also uses mainstream, understandable primitives—S3-like storage, Postgres, an automation engine, and OpenAI-compatible model endpoints—so the learning curve is manageable. citeturn1view0turn2view0

If you’re building internal AI assistants in 2026, you’re likely balancing three forces: speed to value, governance, and security. This design can hit all three, but only if you treat the workflow engine as production infrastructure and not “that tool we installed on a VPS because it looked fun.” (It is fun. It just also needs patch management.)

Sources

Bas Dorland, Technology Journalist & Founder of dorland.org