Haystack: Production RAG Pipelines from deepset
Haystack (deepset) models LLM applications as typed, inspectable pipelines of components — converters, embedders, retrievers, rankers, generators — running over pluggable document stores. Haystack 2.x rebuilt the framework around explicit, serializable DAGs for production search and RAG.
01.The Problem: A Demo Script Is Not a Search System
Watch how most RAG code gets written. A script: load PDFs, chunk them, embed them, stuff the top 5 into a prompt, print the answer. It runs. It demos. It gets applause.
Then Monday morning arrives:
- Someone "improves" it and the reranker now runs before the retriever. Nothing fails until users notice garbage answers two weeks later.
- The question "what exactly runs when a query comes in?" has no answer anyone trusts. The pipeline is a story, not a diagram.
- A new engineer wants to swap the vector database. They rewrite half the script because every function assumed the old one.
- Compliance asks you to show the system. You point at 600 lines of Python and say... what?
The disease: the steps of the system exist only as execution order in code, not as something you can look at, check, and version.
So the question becomes
Can a retrieval system be built like a factory line — named stations, connectors that only fit correctly, and a blueprint you can file and diff?
Haystack, from the Berlin company deepset (founded 2018 building industrial-grade semantic search), is exactly that.
Haystack 2.x RAG pipeline as a typed component DAG
Haystack 2.x RAG pipeline as a typed component DAG
Every step is a swappable component with declared input/output types; the pipeline connects them into an inspectable, serializable graph that runs over your choice of document store.
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