How To Use Haystack
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What it is
Haystack has been around since before the ChatGPT era, when it focused on neural search. Haystack 2.0 (released 2024) modernised the API around typed components and pipelines, and it’s now one of the most production-focused alternatives to LangChain or LlamaIndex.
Install / sign up
Pipelines are directed graphs of Components (retrievers, generators, rankers, converters) with typed input/output sockets. Document Stores (Elasticsearch, Weaviate, Qdrant, pgvector, OpenSearch, in-memory) hold the indexed content. Haystack ships first-party integrations for every major model provider and vector DB, plus a serverless option via deepset Cloud.
First session
A minimal RAG pipeline has three components: an embedding retriever, a prompt builder, and a generator. Wire them together and call run().
Everyday workflows
Haystack’s strength is that pipelines are serialisable YAML, which makes diffs and CI review easy. Keep prompts in templates, not hard-coded strings, so you can iterate without redeploying. Evaluation components (AnswerExactMatch, SASEvaluator) slot into the same pipeline graph so you can test in CI.
Gotchas and tips
Teams shipping RAG or search-centric LLM products who want a typed, observable, deployable framework rather than a notebook-style toolkit.