What Is the Open-Source RAG Stack?
14 entriesA production retrieval-augmented generation (RAG) system is easiest to reason about in layers, each solving one problem, from how data is ingested to how users interact with the application. A widely shared LinkedIn post by Muhammad Arslan of Daily Dose of Data Science, dated 31 October 2026, sketches seven open-source layers: frontend, orchestration, models, retrieval and ranking, vector storage, embeddings, and ingestion. This page follows that layering and adds verified release facts and related pages on this site. Descriptions are paraphrased from each project's own pages and are not rankings or endorsements; release facts come from GitHub and PyPI.
Layer 1: Frontend Frameworks
5 entriesThe entry point where users query the system and read answers.
Layer 2: LLM Orchestration Frameworks
6 entriesFrameworks that connect models, tools, retrievers and workflows.
Layer 3: Open Language Models
9 entriesThe reasoning engine of the application; check each model's licence before commercial use.
Layer 4: Retrieval and Ranking
6 entriesGetting the right chunks into the context quickly, then reordering them by relevance.
Layer 5: Vector Databases
6 entriesWhere embeddings are stored for fast similarity search at scale.
Layer 6: Embedding Models
6 entriesModels that turn text into vectors that capture similarity.
Layer 7: Ingestion and Data Processing
6 entriesGetting data into the workflow: parsing, chunking, loading and scheduling.
Beyond the Seven Layers: Evaluation and Observability
4 entriesProduction RAG also needs measurement, which the seven-layer sketch does not cover.
Frequently Asked Questions
What is an open-source RAG stack?
A set of open-source components, arranged in layers, that together retrieve relevant data and pass it to a language model to ground its answers: ingestion, embeddings, vector storage, retrieval and ranking, models, orchestration and a frontend.
Which layers does a production RAG system need?
The seven layers in the post this page follows are frontend, orchestration, models, retrieval and ranking, vector databases, embedding models, and ingestion and data processing. Production systems also need evaluation, observability and guardrails, which are listed separately here.
How do I choose a vector database?
Consider scale, filtering and hybrid search needs, operations effort and whether you already run Postgres. pgvector keeps vectors beside relational data, while Milvus, Weaviate and Qdrant are dedicated engines. See the Vector Databases page for a longer comparison.
Do I need a reranker?
Often yes. A reranker reorders the retrieved chunks by relevance, which can improve answer quality at the cost of some latency. Check model licences, because some popular rerankers are non-commercial. See the Reranker Models page.
Are all the models and tools here truly open source?
No. Many language and embedding models are open-weight with custom or non-commercial licences, and some products listed also offer paid hosted tiers. Read each licence before commercial use.
What is the difference between an orchestration framework and a vector database?
The vector database stores and searches embeddings, while an orchestration framework such as LangChain, LlamaIndex or Haystack connects the retriever, the model, tools and workflows into an application.
How current is this page?
Links and release facts were checked on 1 November 2026 from GitHub and PyPI. The milvus.io site returned redirect loops to automated checks, so the GitHub repository is linked. Chroma and Ragas have had no recent release, as their cards note. LinkedIn may ask visitors to sign in. Descriptions follow each source's own pages and are not rankings.