What is Haystack?
Haystack is an end-to-end NLP framework for building production-ready search systems and RAG applications. Built by deepset, it has been in production at enterprise scale longer than most LLM frameworks.
Core Concepts
- Pipeline — A directed graph of components. Each component has
run()input/output. - DocumentStore — Where your indexed documents live. Supports Elasticsearch, OpenSearch, Weaviate, Qdrant, Chroma, and more.
- Retriever — Fetches relevant documents from the store (dense, sparse, or hybrid).
- Reader/Generator — Extracts answers or generates responses from retrieved documents.
Example Pipeline
from haystack.pipelines import ExtractiveQAPipeline
pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever)
result = pipeline.run(query="What is the boiling point of water?", params={"Retriever": {"top_k": 5}})
Haystack vs. LangChain
Haystack has a more explicit pipeline API — every component's inputs and outputs are typed and validated. This is more verbose but catches errors at construction time. LangChain is more flexible and has a much larger ecosystem. Haystack is the better choice when correctness and observability matter more than speed of development.
Cloud
ddeepset Cloud offers a managed Haystack deployment with annotation tools and evaluation pipelines.