Applications
VAST Foundation Stacks
AdvancedNVIDIA AI Blueprints get you a working demo; the gap to production is the integration tax. VAST Foundation Stacks are open-source implementations that close it - running RAG, AI-Q deep research, and Video Search & Summarization as production pipelines on the VAST AI OS.
From Blueprint to production
An NVIDIA AI Blueprint is a reference workflow - a runnable starting point built from NIM microservices. Getting it into reliable, secure, scalable production is the hard part: data pipelines, vector storage, eventing, orchestration, security, ops. VAST Foundation Stacks pre-package that on one platform, so a Blueprint becomes something you git clone and deploy. They are open source at github.com/vast-data/cosmos-labs.
Blueprint → Foundation Stack
An NVIDIA Blueprint gets you a working demo. A Foundation Stack adds the production plumbing - so the gap from pilot to production closes to a git clone.
VAST Foundation Stack
production-ready, on the VAST AI OS
Serverless ingest pipeline
Event-driven DataEngine functions + triggers - data is processed the moment it lands.
One unified store
VastDB holds documents, vectors, metadata, and conversation state - no separate vector DB to wire up.
Governed & secure
Tenant-aware auth, permission-filtered retrieval, lifecycle management built in.
Open-source, deployable
Public on github.com/vast-data/cosmos-labs with deploy scripts - clone and run on cloud or on-prem.
One shape under every stack
However different the jobs look, all three stacks share the same anatomy: a serverless ingest pipeline on DataEngine fills a unified VastDB store, and a Kubernetes app serves users by calling NVIDIA NIMs. Learn the shape once and all three click into place.
Kubernetes app
the serve plane
A web UI plus a REST/agent API (FastAPI), deployed with the included scripts. This is what users actually touch.
DataEngine ingest
serverless, event-driven
Triggers + functions fire the instant data lands in S3 - no always-on service. Chunk, embed, segment, write.
VastDB
one unified store
Vectors, document metadata, and conversation state in a single database - no separate vector DB to operate.
NVIDIA NIM
the AI, on GPUs
Embedding, reranking, LLM, and vision-language microservices - run locally or against the NVIDIA API.
The three pipelines, running
Each launch stack maps to a different NVIDIA Blueprint. Step through the real ordered stages - the function and model names come straight from the VAST blueprint repos.
RAG
The foundational retrieve-augmented-generation pipeline: ingest and embed documents, then answer questions grounded on the most relevant chunks with citations.
Enterprise RAG Blueprint
AI-Q Research Assistant
An agentic deep-research assistant that plans sub-questions, retrieves repeatedly, reflects on gaps, and writes a cited report - RAG wrapped in an agent loop.
dataengine-research-assistant-blueprint
VSS — Video Search & Summarization
Ingests live or archived video, describes each segment with a vision-language model, and makes it searchable and summarizable in natural language.
dataengine-vss-blueprint
The three Foundation Stacks - one production shape
Pick a stack and watch its real pipeline run. Each one is a serverless ingest pipeline that fills a unified VastDB store, plus a serve plane calling NVIDIA NIMs. Same backbone, three very different jobs.
Based on the NVIDIA Enterprise RAG Blueprint.
① Ingest pipeline (serverless)
② Query (read path)
Documents land in an S3 bucket, or are pulled from Confluence / GDrive / SharePoint by the Sync Engine.
One-shot retrieve → generate. The foundation every other stack builds on.
RAG vs AI-Q: one-shot vs a research loop
The clearest way to understand AI-Q is to see it as RAG with an agent wrapped around it. Plain RAG retrieves once and generates one answer. AI-Q plans the question into sub-questions, calls retrieval several times with different angles (each call is a full RAG retrieve-and-rerank), reflects on what is missing, and only then writes a structured, cited report. The same ingest pipeline and the same VastDB store feed both - VastDB also persists the agent's conversation state, so context never goes stale.
RAG — one pass
Embed query → retrieve top-30 → rerank to 10 → generate one grounded answer with citations. Fast, predictable, great for direct questions over your docs.
AI-Q — a research loop
Plan → retrieve repeatedly (multi-angle) → reflect on gaps → synthesize a long-form report. Trades latency for depth - built for “research this for me,” not “answer this.”
Go deeper
Foundation Stacks tie together the building blocks taught across the site - the retrieval mechanics, the agent patterns, the serverless engine, and the real-time RAG platform they run on.