Imported from terminalskills/skills (skills/haystack/SKILL.md). Install upstream with npx skills add terminalskills/skills --skill haystack. Copyright stays with the author (Apache-2.0).

Haystack — LLM Application Framework by deepset

You are an expert in Haystack, the open-source framework by deepset for building production RAG pipelines and LLM applications. You help developers create composable pipelines with document stores, retrievers, readers, generators, and custom components — connecting to 20+ LLM providers and vector databases with a pipeline-as-code approach.

Core Capabilities

RAG Pipeline

from haystack import Pipeline
from haystack.components.embedders import OpenAIDocumentEmbedder, OpenAITextEmbedder
from haystack.components.writers import DocumentWriter
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders import PromptBuilder
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack import Document

store = InMemoryDocumentStore()

# Indexing pipeline
indexing = Pipeline()
indexing.add_component("embedder", OpenAIDocumentEmbedder())
indexing.add_component("writer", DocumentWriter(document_store=store))
indexing.connect("embedder", "writer")

docs = [Document(content="Haystack supports 20+ LLM providers..."), Document(content="Pipelines are composable...")]
indexing.run({"embedder": {"documents": docs}})

# Query pipeline
template = """Given these documents, answer the question.
Documents: {% for doc in documents %}{{ doc.content }}{% endfor %}
Question: {{ question }}
Answer:"""

rag = Pipeline()
rag.add_component("embedder", OpenAITextEmbedder())
rag.add_component("retriever", InMemoryEmbeddingRetriever(document_store=store))
rag.add_component("prompt", PromptBuilder(template=template))
rag.add_component("llm", OpenAIGenerator(model="gpt-4o"))
rag.connect("embedder.embedding", "retriever.query_embedding")
rag.connect("retriever", "prompt.documents")
rag.connect("prompt", "llm")

result = rag.run({"embedder": {"text": "What providers does Haystack support?"}, "prompt": {"question": "What providers?"}})
print(result["llm"]["replies"][0])

Custom Components

from haystack import component

@component
class MetadataFilter:
    @component.output_types(documents=list[Document])
    def run(self, documents: list[Document], category: str):
        return {"documents": [d for d in documents if d.meta.get("category") == category]}

Installation

pip install haystack-ai

Best Practices

1. Pipeline-as-code — Connect components explicitly; clear data flow, easy debugging
2. Document stores — InMemory for dev, Qdrant/Pinecone/Weaviate for production
3. PromptBuilder — Jinja2 templates for dynamic prompts; inject documents, history, metadata
4. Custom components — Use @component decorator; define inputs/outputs, Haystack handles wiring
5. Branching — Pipelines support conditional routing; different paths based on query type
6. Serialization — pipeline.dumps() / Pipeline.loads() for saving/loading pipeline configs
7. Evaluation — Built-in eval components for faithfulness, relevance, answer correctness
8. Streaming — Use OpenAIGenerator(streaming_callback=...) for real-time token delivery