RAG Frameworks · rag-web-ui

rag-web-ui

RAG Web UI is a TypeScript-based web application for building retrieval-augmented generation (RAG) systems—intelligent Q&A platforms that combine document search with large language models. It provides a frontend UI, backend APIs, and support for multiple LLM providers (OpenAI, DeepSeek, Ollama) with knowledge base management.

Talk to DEV.co Discuss Implementation

Source: GitHub — github.com/rag-web-ui/rag-web-ui

3.1k

GitHub stars

342

Forks

TypeScript

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	rag-web-ui/rag-web-ui
Owner	rag-web-ui
Primary language	TypeScript
License	Apache-2.0 — OSI-approved
Stars	3.1k
Forks	342
Open issues	17
Latest release	v0.8.0 (2026-04-06)
Last updated	2026-04-06
Source	https://github.com/rag-web-ui/rag-web-ui

What rag-web-ui is

Full-stack RAG system built on Next.js 14 (frontend) and Python FastAPI (backend), using ChromaDB/Qdrant for vector storage, MySQL for metadata, MinIO for file storage, and LangChain for LLM orchestration. Supports async document processing, multi-turn dialogue, re-ranking via cross-encoders, and OpenAPI-compatible query endpoints.

Quickstart

Get the rag-web-ui source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/rag-web-ui/rag-web-ui.git2cd rag-web-ui3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Internal Knowledge Base Systems

Enterprises deploying private RAG systems for HR, legal, or product documentation where full data control and privacy are critical. Supports local LLM via Ollama.

AI-Powered Customer Support

SaaS and e-commerce platforms building knowledge-driven chatbots with document upload, reference citations, and multi-turn context—avoiding hallucinations through retrieval.

Content-Heavy Platforms

Publishing, research, or educational platforms needing intelligent search and summarization over large document collections with citation tracking.

Implementation considerations

Requires Python 3.9+ and Node.js 18+ with 8GB+ RAM minimum; Docker Compose orchestration mandatory for standard deployment.
LLM provider choice (OpenAI, DeepSeek, Ollama) must be decided upfront; switching involves configuration changes and potential embedding re-indexing.
Document chunking strategy (segmentation size, overlap) directly impacts retrieval quality and must be tuned per use case.
Vector database selection (ChromaDB vs. Qdrant) impacts scalability; Factory pattern allows switching but migration of indexed vectors requires care.
Embedding service (API or local) choice affects cost, latency, and privacy—local Ollama preferred for sensitive data.

When to avoid it — and what to weigh

Real-Time Data Integration Required — System is document-centric; live data streams or constantly-changing external APIs require additional custom connectors not provided out-of-the-box.
Ultra-Low Latency Constraints (<100ms) — RAG inference involves retrieval, re-ranking, and LLM generation—inherently multi-step. Vector DB and embedding calls add measurable latency.
Minimal Infrastructure Tolerance — Requires Docker, MySQL, MinIO, vector DB, and embedding service. Single-machine deployments are possible but add operational burden.
Heavily Regulated Compliance (HIPAA, FedRAMP) — While Apache 2.0 licensed, no security audit data provided. Deployment in regulated sectors requires independent security review.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license: free use, modification, and redistribution with liability disclaimer and trademark protections. No copyleft obligation; proprietary derivatives allowed.

Apache 2.0 explicitly permits commercial use, modification, and redistribution. No license restrictions on building proprietary systems. However, review Apache 2.0 requirements (attribution, license notice) and verify no dependencies introduce copyleft constraints. No warranty or indemnification provided by licensor.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

No security audit, CVE history, or penetration test data provided. Considerations: (1) API keys for LLM providers and MinIO stored in .env—rotation and secrets management required. (2) JWT + OAuth2 noted for auth but implementation details unknown. (3) Document upload accepts multiple formats—input validation and virus scanning not described. (4) No mention of rate limiting, DDoS mitigation, or encrypted storage. (5) Network exposure of MinIO, MySQL, and embedding service requires careful firewall/VPC configuration. Recommend independent security review before production deployment.

Alternatives to consider

LangChain Chat Playground / LangServe

Lower deployment overhead; uses same LangChain framework. Better for simple prototypes; less opinionated UI and file management than rag-web-ui.

Verba (Weaviate + Generative UI)

Weaviate-native alternative with similar RAG + chat UI. Simpler setup; stronger vector DB integration. Less LLM provider flexibility.

OpenWebUI (Ollama-centric)

Focused on local model deployment; minimal external dependencies. Lightweight but fewer enterprise features (file management, APIs, multi-KB support).

Software development agency

Build on rag-web-ui with DEV.co software developers

Evaluate rag-web-ui with your team. Consider security review, LLM provider selection, and infrastructure requirements before production deployment. Devco can guide architecture and integration planning.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

ragapp RAGHub GraphRAG4OpenWebUI

Related on DEV.co

Explore the category and the services that help you build with it.

RAG Frameworks AI Application Development API Development Cloud Deployment Talk to DEV.co

rag-web-ui FAQ

Can I run this entirely on-premises with no cloud services?

Yes—use Ollama for local LLM inference, on-prem MySQL, ChromaDB or Qdrant locally, and MinIO. Embedding service (e.g., Ollama embeddings) can also be local. Requires infrastructure to host ~6–8 services.

What are the licensing constraints on built systems?

None from rag-web-ui (Apache 2.0 is permissive). However, verify upstream dependencies (LangChain, Ollama, ChromaDB) and your chosen LLM provider's terms—OpenAI, DeepSeek have separate commercial agreements.

How do I migrate between vector databases (ChromaDB → Qdrant)?

Factory pattern allows switching via config. Migrating indexed data requires exporting embeddings from old DB and re-importing to new DB—tooling and procedure not documented in provided excerpt.

Is this production-ready?

Technically viable (active dev, multi-provider LLM support, APIs). Lacks production hardening docs (HA, monitoring, backup, security audit). Recommended for internal tools; requires ops team for critical systems.

Work with a software development agency

From first prototype to production, DEV.co delivers software development services around tools like rag-web-ui. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across rag frameworks and beyond.

Ready to build your RAG system?

Talk to DEV.co Schedule a Consultation