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Squash-merge the verified #412 eval coverage implementation. Required validate and paired evaluation checks passed at exact head b43ac564a5919a0f23fdab49ba052d7c514915cb; droid-review BYOK failure had no findings and is advisory.
Haystack — Production Search & NLP Pipelines (deepset)
An expert-level skill for building production search and NLP pipelines with Haystack. Pipelines are validated DAGs with typed components and explicit connections.
Why Install This Skill
When your agent loads this skill, it becomes a Haystack expert who can:
- Design pipeline DAGs — add_component + connect with typed input/output slots
- Build RAG pipelines — document indexing + query pipelines with embedding retrieval
- Create agentic systems — tool-using agents with ReAct pattern
- Integrate generative AI — PromptBuilder (Jinja2) + LLM generators
- Evaluate pipeline quality — faithfulness, relevancy, and custom metrics
- Deploy with Hayhooks — REST API deployment for production
What You Get
| Directory | Purpose |
|---|---|
SKILL.md |
Core paradigm, where-to-start table, framework comparison |
references/ |
Deep dives into pipeline design, RAG, agents, evaluation, Hayhooks, and framework comparisons |
Framework Comparison
Haystack uses explicit Pipeline DAGs (add_component + connect) — different from LangChain's LCEL pipe operator and LlamaIndex's query engines. Pipelines are validated at declaration time.
Requirements
Python 3.8+ with haystack-ai package.
Quick Start
Start with the setup and first workflow in SKILL.md, then use the linked resources for the specific task you need to complete.
Triggers
Use this skill for the task types and keywords described in its SKILL.md description.