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Each README is written for a human audience, explaining: - What the skill does (not what format it follows) - What benefit the user gets from installing it - Quick setup and usage patterns - When to load/trigger the skill - What scripts, references, and templates it ships data-scientist already had a README — left unchanged. 48 READMEs added across all skill and bundle directories.
30 lines
1.3 KiB
Markdown
30 lines
1.3 KiB
Markdown
# Haystack — Production Search & NLP Pipelines (deepset)
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An expert-level skill for building **production search and NLP pipelines** with Haystack. Pipelines are validated DAGs with typed components and explicit connections.
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## Why Install This Skill
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When your agent loads this skill, it becomes a Haystack expert who can:
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- **Design pipeline DAGs** — add_component + connect with typed input/output slots
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- **Build RAG pipelines** — document indexing + query pipelines with embedding retrieval
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- **Create agentic systems** — tool-using agents with ReAct pattern
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- **Integrate generative AI** — PromptBuilder (Jinja2) + LLM generators
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- **Evaluate pipeline quality** — faithfulness, relevancy, and custom metrics
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- **Deploy with Hayhooks** — REST API deployment for production
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## What You Get
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| Directory | Purpose |
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|-----------|---------|
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| `SKILL.md` | Core paradigm, where-to-start table, framework comparison |
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| `references/` | Deep dives into pipeline design, RAG, agents, evaluation, Hayhooks, and framework comparisons |
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## Framework Comparison
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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.
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## Requirements
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Python 3.8+ with `haystack-ai` package.
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