This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
RagOrchestrator
Universal and extensible RAG module
rag-orchestrator — lightweight Python RAG orchestration with SQLite, PGVector & Qdrant Hey everyone I built rag-orchestrator for developers who want a lightweight and modular RAG orchestration layer without huge framework overhead. What it supports SQLite PGVector Qdrant through an abstract storage layer. Fully pluggable You can plug in your own: Embeddings Retrievers Cleaners Processing logic without rewriting the core system. Minimal LLM usage
Most RAG frameworks today assume:
a huge dependency graph
mandatory LLM orchestration
opinionated pipelines
complex configuration
But many real-world systems need something simpler.
Especially when:
you already have an existing pipeline
you want local/offline execution
you need predictable retrieval
you do not want every step delegated to an LLM
So I built rag-orchestrator.
What makes it different?
The project was designed around one key idea:
RAG infrastructure should be modular, lightweight, and database-agnostic.
Works with multiple vector databases
The orchestrator supports:
SQLite
PGVector
Qdrant
through an abstract storage layer.
This means you can switch backends without rebuilding the whole pipeline.
Fully pluggable architecture
The project provides abstraction layers for:
Embeddings
Retrievers
Cleaners
Vector stores
Processing steps
You can easily plug in:
your own embedding provider
your own retriever
custom preprocessing logic
external pipelines
without rewriting internal logic.
Minimal LLM usage
One important design decision:
The orchestrator works without an LLM for almost the entire pipeline.
LLMs are only required at a single step where they actually add value.
This makes the system:
cheaper
faster
more deterministic
easier to debug
Minimal configuration
The module intentionally requires very few input parameters.
The goal was:
fast onboarding
simple integration
production-friendly defaults
Tested and production-oriented
The repository already includes:
integration tests
runnable scripts
usage examples
You can inspect them directly in the scripts/ directory.
Easy integration into existing systems
The project was built to integrate into:
existing RAG pipelines
enterprise systems
AI backends
local AI stacks
internal search systems
instead of forcing users into a completely new ecosystem.
Installation
pip install rag-orchestrator
Why this matters
A lot of modern RAG tooling is becoming increasingly framework-heavy.
But many production systems actually need:
predictability
portability
low overhead
composability
rather than autonomous agent complexity.
This project focuses exactly on that.
About RagOrchestrator on Product Hunt
“Universal and extensible RAG module”
RagOrchestrator was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #159 on the daily leaderboard. rag-orchestrator — lightweight Python RAG orchestration with SQLite, PGVector & Qdrant Hey everyone I built rag-orchestrator for developers who want a lightweight and modular RAG orchestration layer without huge framework overhead. What it supports SQLite PGVector Qdrant through an abstract storage layer. Fully pluggable You can plug in your own: Embeddings Retrievers Cleaners Processing logic without rewriting the core system. Minimal LLM usage
On the analytics side, RagOrchestrator competes within Open Source, Developer Tools and Artificial Intelligence — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how RagOrchestrator performed against the three products that launched closest to it on the same day.
Who hunted RagOrchestrator?
RagOrchestrator was hunted by Alexander. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of RagOrchestrator including community comment highlights and product details, visit the product overview.
Alexander