> ML_LIBRARY // HAYSTACK_v1.0
Haystack
deepset — An open-source NLP framework for building production-ready LLM pipelines.
nlp-llmv2.5.0Apache-2.0qualified
Model Training
This library is a dedicated runtime engine for inference serving and does not train models.
Model Inference
Inference Accelerators:
CPUCUDAROCMMPS
Deployment Targets:server
What It Does
- +Component-based typed directed acyclic graph (DAG) pipelines for search and RAG
- +First-class integration with OpenSearch, Qdrant, Milvus, and Weaviate
- +Flexible routing and branching logic for hybrid search
What It Does Not Do
- -Train base neural network weights directly
- -Execute low-level CUDA tensor operations natively
- -Run client-side in browsers
>Suitable Work Types
- Enterprise search systems requiring clean software architecture and strong type safety
- Multi-stage RAG pipelines combining sparse BM25 and dense neural vector retrieval
- Document question answering over large on-premises repositories
>Unsuitable Work Types
- Model pre-training or parameter fine-tuning from scratch
- Tabular financial credit scoring
Data Residency Implications
Local host memory and connected enterprise search indices.
Security Considerations
Haystack 2.0 enforces explicit input/output typing, preventing hidden pipeline side effects.
Operational Profile & Known Limitations
Maturity:mature
Learning Curve:moderate
Ops Complexity:low
Cost Tier:free-oss
> Known Limitations:
- Haystack 2.0 rewrite introduced breaking architectural changes compared to legacy 1.x.
- Smaller community integrations catalog compared to LangChain.
Associated Incident Patterns (Incidentpedia)
Enforce safeguards and monitoring to guard against these documented real-world failure modes:
> Primary Evidence & Benchmark Citations
Haystack 2.0 Documentationofficial-docs • >=2.0.0, <=2.5.x
2026-09-25HIGH