Deploy a RAG Stack in Minutes, Not Weeks

What you get

One deploy gives you a working “chat with your documents” service that runs in your cloud account. Your documents never leave it.

• A vector database (Qdrant) on its own encrypted disk, holding the index of your documents.
• The RAG API — answers questions from your documents with numbered citations, through a simple REST API and an OpenAI-compatible /v1/chat/completions endpoint.
• A private web UI — chat with citations, drag-and-drop upload, a model picker, protected by an access key.
• Sync jobs that keep the index up to date with your S3 buckets — on a schedule, within seconds of a change, or both.
• HTTPS URLs for the API and the UI, with certificates issued automatically.

Why this usually takes weeks

A production RAG service is much more than a model call. Doing it by hand means building and wiring every row below yourself. With SeaGit each one is part of the stack template.

Before you start

• An AWS account connected as a provider.
• A started cluster in an environment. The Getting Started guide walks through both.
• At least one approved chat model. An account admin chooses which Bedrock models the account allows, and each organization can narrow that list further (Approved models in the sidebar). See Approved models.
• For Anthropic, Meta and Mistral models: model access enabled for the AWS account in the Amazon Bedrock console. Amazon Nova models work without extra steps.

Step 1 — Open the RAG Stack template

In your organization, go to Applications → Templates and choose RAG Stack, then Deploy. The deploy wizard has four short steps; every field has a sensible default.

Step 2 — Where it runs

• Environment and cluster — the cluster list follows the environment you pick.
• Ingestion endpoints — single runs one stack on one cluster. Multi-cluster runs a complete stack on every started cluster of the environment, each building its own index from the same sources, for resilience and lower latency.

Step 3 — Your documents

Data sources are S3 buckets the stack reads. For each one choose a folder, the file types to index, whether to import the files already there, and how to keep it in sync.

Uploads are files people add through the web UI or the API. Choose where they are kept and how they become searchable:

• Where uploaded files are kept — a new S3 bucket created for the stack, or your existing bucket (in this or another AWS account). For an existing bucket you also choose the folder and whether to import existing files now.
• Keep uploads in sync — On change (S3 events) makes a new file searchable within seconds; On a schedule picks files up on each run; On change + safety sweep (the default) does both; Off turns uploads off for the stack.

The details — access options, cross-account buckets, what each sync mode deploys — are in RAG Stacks: documents and sync.

Step 4 — Pipeline and answering

• Chunk size and overlap — smaller chunks give more precise citations; larger ones give the model more context.
• Embedding model — Amazon Titan Text Embeddings v2. Questions and documents use the same model.
• Vector index disk size — the Qdrant volume; grow it later if needed.
• Chat models — the models people can pick in the web UI and the API. Only models your organization approves are listed. Pick a default model for questions that don’t name one.
• Passages per answer (top-k) — how many matching chunks the model sees; 5 suits most documents.
• Include web UI — adds the chat and upload UI.

Step 5 — Deploy and save your keys

Select Deploy. SeaGit shows two keys once — copy them before closing the dialog:

• API key — for applications calling the RAG API.
• Web UI access key — for people signing in to the web UI.

The stack’s Stack tab shows each component as it starts, the URLs of the API and the UI, and the models the stack uses. Components are usually running within a few minutes; the HTTPS certificates follow shortly after.

Step 6 — Ask your first question

Open the web UI URL, sign in with the web UI access key, drop a document into the upload box and ask about it. Answers cite the passages they came from.

From code, call the API with the API key:

curl -s https://<api-host>/v1/query \
  -H "Authorization: Bearer $RAG_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{"question": "What is our refund policy?"}'

Upload from code with POST /v1/ingest (multipart file). The response returns stored once the file is saved; with an on-change sync mode it is searchable a few seconds later.

Next steps

• RAG Stacks reference — architecture, sync modes, approved models, access keys and the full API.
• Troubleshooting — when a component doesn’t start.

Frequently asked questions