Who RAGMeUp is for#
ML engineers building custom RAG pipelines
RAGMeUp provides the infrastructure scaffolding, a vectorized Postgres store, an API layer, and a React UI, so engineers can focus on the retrieval logic itself. The modular architecture means you can swap the default vectorstore or chunker without rebuilding the surrounding stack.
Skip if:
You need tight integration with an existing managed cloud service (AWS, Azure, GCP). RAGMeUp is infrastructure-first; teams that prefer fully managed abstractions will find the operational overhead higher than expected.
Developers prototyping LLM applications on private datasets
The Docker Compose setup has the full stack running in minutes, including a query UI. You can ingest a document set and start testing retrieval quality without writing any UI code or database configuration. The Python-based RAG server is straightforward to modify for dataset-specific chunking.
Skip if:
Your dataset is small enough to query directly with a prompt. RAGMeUp adds infrastructure for retrieval at scale; for collections under a few hundred documents, direct LLM querying is simpler.
Teams with data residency requirements
Because every component runs on your own infrastructure, no data is sent to external APIs beyond the LLM provider you configure. The Apache 2.0 license means you can audit and modify every layer of the stack, which is useful for enterprise teams with strict data handling policies.
Skip if:
Your organization needs certified compliance (SOC 2, HIPAA) for the RAG layer. RAGMeUp is community-maintained and does not ship with compliance certifications.
The problem it solves#
Building a RAG system from scratch requires stitching together a vector database, a document chunker, a retrieval layer, and an LLM API, then wiring them into an application with auth, a query interface, and a persistent store. Most teams either build all of this themselves or hand it off to a managed service.
Managed RAG services like Vectara, Ragie AI, Azure AI Search, and AWS Bedrock Knowledge Bases abstract away the complexity but also remove access to the underlying knobs. Chunking strategies, retrieval algorithms, and vectorstore choices are locked to the vendor's defaults or cost extra to tune. When a retrieval pipeline underperforms in production, the debugging surface is limited to what the vendor exposes.
How it solves it#
Modular retrieval pipeline
Every component of the RAG stack is swappable: chunkers, vectorstores, and retrievers can each be replaced with your own implementation without touching the rest of the framework. This lets you tune the retrieval strategy independently from the document ingestion and query layers.
Full Docker Compose deployment
The complete stack, Python RAG server, Node.js API, React client via nginx, and ParadeDB Postgres, starts with a single docker compose command. No per-component manual wiring required. The React UI is available on port 80 after startup.
Hybrid GPU/CPU mode
The full Docker Compose setup runs in CPU-only mode. For production workloads that need GPU-accelerated embeddings and inference, hybrid mode lets the Python RAG server run directly on the host with CUDA access while Postgres, the API server, and the React client remain containerized.
React UI included out of the box
A React-based query interface ships with the framework, so teams do not need to build a front end to start testing retrieval quality. It serves as both a development tool and a lightweight production UI for teams that do not need a custom interface.
Production-tested architecture
RAGMeUp has been used in large-scale production deployments, including SensAI.PT, an AI-powered fitness coaching product. The architecture handles real user workloads rather than demo datasets, giving teams a baseline confidence level before deploying their own applications on top of it.
Strengths and trade-offs#
Strengths
- Apache 2.0 license with full self-hostingApache 2.0 means you can modify and deploy the framework commercially without restriction. Unlike Vectara or Ragie AI, which operate as proprietary API services, you pay once for the server and own every layer of the retrieval pipeline. No data leaves your infrastructure.
- Fast prototyping with minimal boilerplateThe framework handles the scaffolding: vector storage, the query API, and the UI layer. You write the RAG logic specific to your use case rather than rebuilding common infrastructure. Teams new to RAG can reach a working prototype without first becoming infrastructure engineers.
- Active maintenance with zero open issuesThe repository shows zero open issues and received commits as recently as August 2026, which signals responsive maintenance. For teams building production systems, an actively maintained project with a low open issue count is a meaningful signal of project health.
Trade-offs
- -GPU acceleration requires a hybrid deploymentThe standard Docker Compose setup runs the Python server in CPU-only mode. If your embeddings or inference model needs GPU/CUDA, you must run the Python server directly on the host and connect it to the Docker services manually. On Linux, this also requires additional Docker network configuration.
- -Initial setup requires manual environment configurationBefore the stack starts, you must copy docker-compose.env.example and set POSTGRES_PASSWORD and JWT_SECRET by hand. There is no guided setup wizard. Teams unfamiliar with environment-based configuration may encounter startup failures if these variables are missing or malformed.
RAGMeUp vs alternatives#
RAGMeUp vs Vectara
Vectara is a managed RAG API: you send documents and queries to Vectara's endpoints and the service handles chunking, embedding, storage, and retrieval. RAGMeUp inverts that model. You deploy the entire stack on your own infrastructure, choose your own vectorstore, and write your own chunking and retrieval logic.
| Feature | RAGMeUp | Vectara |
|---|---|---|
| License | Apache 2.0 | Proprietary |
| Self-hosting | Yes (Docker Compose) | No |
| Retrieval customization | Full: swappable chunkers and vectorstores | Limited to API parameters |
| Data residency | Your infrastructure | Vectara's cloud |
RAGMeUp is the better choice when you need data to stay on your own servers, when you need to tune the retrieval pipeline at the component level, or when you want to avoid a proprietary API contract. Vectara is worth considering when you need to ship quickly without managing infrastructure: it removes the operational burden of running a vector database and API server yourself.
RAGMeUp vs AWS Bedrock Knowledge Bases
AWS Bedrock Knowledge Bases is a fully managed RAG service inside the AWS ecosystem. It integrates with S3 for document storage and uses Amazon's managed vector stores. RAGMeUp runs anywhere Docker runs and carries no cloud dependency.
| Feature | RAGMeUp | AWS Bedrock Knowledge Bases |
|---|---|---|
| License | Apache 2.0 | Proprietary |
| Self-hosting | Yes | No (AWS-managed) |
| Vectorstore | ParadeDB Postgres (swappable) | AWS-managed OpenSearch or Aurora |
| Cloud dependency | None | Requires AWS account |
Teams already invested in the AWS ecosystem may prefer to keep retrieval inside Bedrock. RAGMeUp is the better pick when you need cloud portability, want to avoid AWS vendor lock-in, or require a retrieval stack that runs identically on local hardware and in production without an AWS account.
Install and self-host#
Self-hosting uses Docker Compose with a Postgres database; configure the docker-compose.env file before starting.
```bash
git clone https://github.com/SensAI-PT/RAGMeUp.git
cd RAGMeUp
cp docker-compose.env.example docker-compose.env
docker compose --env-file docker-compose.env up --build -d
```What it's built on#
- Languages
- JavaScriptPython
- Frameworks
- ExpressReact
- Databases
- PostgreSQL
FAQ#
Is RAGMeUp free to use?
Yes. RAGMeUp is Apache 2.0 licensed, meaning you can use, modify, and deploy it commercially at no cost. Your expenses are limited to the server hosting the Docker stack and any API fees from the LLM provider you connect to the Python RAG server.
Does RAGMeUp support GPU acceleration for embeddings?
Yes, through hybrid mode. The default Docker Compose setup runs the Python RAG server in CPU-only mode. To use GPU/CUDA for embeddings or inference, run the Python server directly on the host machine while the rest of the stack stays containerized. Linux requires additional Docker network configuration for the services to communicate.
How does RAGMeUp compare to managed RAG services like Vectara or Ragie AI?
Managed services like Vectara and Ragie AI handle infrastructure for you but operate as proprietary APIs that limit access to the underlying retrieval mechanics. RAGMeUp is self-hosted and free to run: you own the vectorstore, the retrieval logic, and the data. The tradeoff is that you manage deployment, updates, and infrastructure uptime rather than the vendor.
What components does the RAGMeUp stack consist of?
RAGMeUp runs four components: a Python RAG server (handles chunking, embedding, and retrieval), a Node.js API server, a React client served through nginx, and a ParadeDB Postgres instance for vector storage. All four start together via docker compose up.
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