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.

RagOrchestrator

Universal and extensible RAG module

Universal and extensible RAG module