Haystack Agents

Master Haystack for building production-ready RAG agents and NLP pipelines

0 / 5 completed

Key Takeaways: Haystack Mastery

Check off each concept as you understand it. You've learned about Haystack's pipeline architecture, RAG agents, and production deployment!

0% Complete

Haystack is an open-source framework by deepset for building production-ready NLP applications with RAG

Pipeline architecture connects modular components to form processing workflows

Core components: Retrievers (fetch docs), Rankers (re-rank results), Generators (LLM answers)

Document stores: InMemory (dev), Elasticsearch/Pinecone/Weaviate (production)

RAG combines semantic retrieval with LLM generation for accurate, grounded responses

Typical RAG flow: Embed query → Retrieve docs → Rank → Build prompt → Generate answer

Advanced patterns: Conversational RAG, filtered retrieval, hybrid search (BM25 + embeddings)

Custom components extend Haystack with business logic, APIs, or specialized processing

Use @component decorator and define run() method with type hints for custom components

Components are testable in isolation and integrate seamlessly into pipelines

Built-in REST API support makes deployment straightforward (launch_app())

Deploy with Docker, AWS Lambda, Azure Functions, or Google Cloud Run

Haystack excels at retrieval-first use cases: semantic search, QA, document analysis

Production features: Monitoring, caching, error handling, metadata filtering built-in

Best for: Large document collections, enterprise deployment, modular testable architecture

Complete checklist to continue →