magnus919_agent-skills/haystack/SKILL.md

name, description, license, metadata

name	description	license	metadata
haystack	Expert skill for production search and NLP pipelines with Haystack (deepset). Pipeline DAG composition, document stores, retrievers, PromptBuilder (Jinja2), generators, evaluation, Hayhooks deployment. Use when building search pipelines or comparing NLP application frameworks.	MIT	author version source Magnus Hedemark 1.1.0 https://docs.haystack.deepset.ai

Haystack Expert Skill

Haystack (by deepset) is a production-oriented framework for building search and NLP pipelines. Its core abstraction is the Pipeline — a directed acyclic graph of typed components with explicit connections. Unlike LangChain's LCEL (pipe operator) or LlamaIndex's query engines, Haystack pipelines are declared upfront with add_component and connect, giving validated, debuggable DAGs.

Core Paradigm

from haystack import Pipeline
from haystack.components.embedders import SentenceTransformersTextEmbedder
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator
from haystack.document_stores.in_memory import InMemoryDocumentStore

# Build a pipeline
document_store = InMemoryDocumentStore()
pipeline = Pipeline()
pipeline.add_component("embedder", SentenceTransformersTextEmbedder())
pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
pipeline.add_component("prompt_builder", PromptBuilder(template="Answer using: {{documents}}\n\nQuestion: {{question}}"))
pipeline.add_component("generator", OpenAIGenerator())

# Connect components
pipeline.connect("embedder.embedding", "retriever.query_embedding")
pipeline.connect("retriever.documents", "prompt_builder.documents")
pipeline.connect("prompt_builder", "generator")

# Run
result = pipeline.run({"embedder": {"text": "What is Haystack?"}, "prompt_builder": {"question": "What is Haystack?"}})

Core Principles

1. Pipelines are validated DAGs. add_component + connect. Pipeline validation catches errors BEFORE execution — leverage this during development.
2. Components are typed. Each component has input/output slots. Connections must match types. This prevents runtime errors.
3. PromptBuilder uses Jinja2. Templates are Jinja2 strings, not f-strings. {{documents}}, {{query}}, {{question}} are variable placeholders.
4. Indexing and query are separate pipelines. One pipeline loads/cleans/embeds/writes documents. Another retrieves/generates answers. They share the DocumentStore.
5. Evaluation is a pipeline too. Add evaluator components to measure faithfulness, relevancy, or custom metrics.

Where to Start

You already have...	Start here
Nothing — exploring Haystack	Build a basic indexing + query pipeline
Documents to index	Build an indexing pipeline (converters, splitter, embedder, writer)
A search use case	Build a query pipeline (embedder, retriever, prompt, generator)
A production deployment	Add Hayhooks + evaluation pipeline

Quick Reference

Task	Approach	Reference
Build indexing pipeline	add_component -> connect -> run	references/pipeline-design.md
Build query pipeline	retriever -> prompt_builder -> generator	references/pipeline-design.md
Choose document store	InMemory (dev), Elasticsearch/Pinecone (prod)	references/document-stores.md
Embedding retrieval	SentenceTransformersTextEmbedder + EmbeddingRetriever	references/retrievers.md
Hybrid retrieval	BM25 + Embedding in parallel, DocumentJoiner	references/retrievers.md
Prompt templates	Jinja2 in PromptBuilder	references/pipeline-design.md
Evaluation	DeepEvalEvaluator, SASEvaluator	references/evaluation.md
Deploy	Hayhooks REST API	references/deployment.md

Framework Routing Guide

Scenario	Reach for	Why
Search / NLP pipelines	Haystack	Pipeline DAG model is most mature for retrieval-heavy workloads
Documents to query / RAG	LlamaIndex	Data ingestion is the primary primitive
Chain/agent composition	LangChain	LCEL pipe operator for general chain building
Compiled prompt programs	DSPy	Auto-optimizes prompts against a metric
Role-based multi-agent	CrewAI	Higher-level agent abstraction

Reference Files

Reference	Load when	File
Pipeline Design	Building indexing and query pipelines	references/pipeline-design.md
Document Stores	Store selection and configuration	references/document-stores.md
Retrievers	Embedding, BM25, hybrid retrieval	references/retrievers.md
Validation Audit	Research validation of all API claims	references/validation-audit.md
File Converters	Multi-format indexing, YAML serialization, component types	references/file-converters.md
Evaluation	Metrics, evaluators, pipeline evaluation	references/evaluation.md
Deployment	Hayhooks, containerization, production	references/deployment.md
FAQ & Troubleshooting	Common errors and fixes	references/faq-and-troubleshooting.md

Templates

Template	When to use	File
Indexing Pipeline	Load, split, embed, write to store	templates/indexing-pipeline.py
Query Pipeline	Retrieve, prompt, generate answer	templates/query-pipeline.py
Hybrid RAG	BM25 + embedding in parallel	templates/hybrid-rag.py

Troubleshooting

Symptom	Likely cause	Fix	Reference
Pipeline run errors	Component connection mismatch	Check component input/output slot types	references/pipeline-design.md
No documents retrieved	Empty document store	Run indexing pipeline first	references/pipeline-design.md
Prompt not rendering	Wrong variable name in Jinja2 template	Check {{variables}} match pipeline input	references/pipeline-design.md
Slow retrieval	Full scan instead of ANN	Configure approximate nearest neighbor index	references/retrievers.md
Embedding mismatch	Different models for indexing vs query	Use same model in both pipelines	references/retrievers.md
Hayhooks not starting	Port conflict or missing config	Check port, run with --help for options	references/deployment.md