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RAG & Knowledge Systems

Rag Saas

Rag Saas is a TypeScript application starter for building web-based retrieval-augmented generation and agentic applications, with an administration dashboard for ingestion, RAG configuration, monitoring and observability.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Rag Saas — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a configurable application foundation for RAG chat and can provide the model, database and deployment configuration it requires.

Who it is for

  • Developers building a document-grounded chat application.
  • Teams that want an application foundation for managing ingestion and RAG configuration.
  • Developers prepared to configure model, database, authentication and deployment settings.

Documented uses

  • Ingest data, including PDFs through the documented S3 integration.
  • Offer chat with conversation-history tracking.
  • Switch among multiple RAG configurations, including basic RAG.
  • Monitor and observe a RAG system through the administration dashboard and listed Phoenix integration.
Before you choose

Requirements & limits

  • The documented frontend is Next.js and the backend is FastAPI, with LlamaIndex for RAG orchestration.
  • MongoDB is described for application data and vector storage; Qdrant is also listed for similarity search.
  • Model configuration, OpenAI credentials, MongoDB and Qdrant connection details, and authentication secrets are required in the documented backend setup.
  • S3-backed PDF uploads and Arize Phoenix observability have optional configuration settings.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare the repository and configuration for deployment

Clone the starter and identify the environment settings required to configure its frontend, backend and integrations.

Before you start

  • Git
  • A model provider and model, plus an embedding model and its dimensionality
  • An OpenAI API key
  • MongoDB and Qdrant connection details
  • Administrator email and password, plus JWT signing secrets

Steps

  1. Clone the RAG-SaaS repository and change into its directory.

  2. Set frontend server and chat API URLs in ./frontend/.env.local.

  3. Configure the backend model, API key, prompts, database connections, JWT secrets and administrator credentials in ./backend/.env.

  4. If using S3 PDF uploads, provide the AWS access key, secret, region and bucket name; set the Phoenix endpoint if using Arize Phoenix observability.

Expected outcome

The repository is cloned and the documented configuration values are prepared for the Docker Compose deployment stage.

Guide limitations

  • S3 and Arize Phoenix settings are optional; S3 credentials are needed for the described S3 PDF integration.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Rag SaasIngest data, including PDFs through the documented S3 integration.Docker, Web
Rag Powered AI Assistant Transforming Data Retrieval And Analysis Across The Web And PdfsRetrieve relevant passages from web and PDF content to answer a question.Web
LangChain in Supabase Edge FunctionsInitialize and start a Supabase project locally.Cloud, Server
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
adithya-s-k/RAG-SaaS · Official homepage title · retained in its source language
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