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Microsoft Semantic Kernel

Haystack Agents

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

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What is Haystack?

Haystack is an open-source framework by deepset for building production-ready NLP applications with retrieval-augmented generation (RAG), semantic search, and question answering. It's designed for real-world deployment at scale.

Traditional RAG vs Haystack

Traditional RAG Implementation

  • •Manually connect vector database, embedding model, and LLM
  • •Write custom glue code for document processing and chunking
  • •Handle errors, retries, and edge cases yourself
  • •Difficult to swap components or test variations
⚠️ Requires significant engineering effort - maintenance overhead and hard to scale
🧩

Pipeline Architecture

Build complex workflows by connecting modular components. Each component does one thing well (retrieval, ranking, generation).

🔍

RAG-Native Design

Built specifically for retrieval-augmented generation with optimized document stores, retrievers, and answer generation.

⚡

Production-Grade

Includes monitoring, caching, error handling, and REST API out of the box. Deploy confidently to production.

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Fully Customizable

Create custom components that integrate seamlessly. Use any LLM, vector DB, or processing logic you need.

Why Haystack for Agents?

1. Retrieval-First Philosophy

Unlike generic agent frameworks, Haystack is optimized for RAG. It excels at grounding LLM responses in your proprietary documents and data.

2. Enterprise Deployment Focus

Haystack includes REST APIs, Docker support, monitoring, and production patterns. Deploy to AWS, Azure, or on-premise with confidence.

3. Transparent Pipeline Execution

Every step in the pipeline is inspectable and debuggable. See exactly which documents were retrieved, how they were ranked, and what the LLM received.

💡 When to Choose Haystack

  • ✓RAG is your primary use case - semantic search, QA, document retrieval
  • ✓Need production-ready deployment - REST APIs, Docker, monitoring built-in
  • ✓Working with large document collections - optimized indexing and retrieval
  • ✓Want modular, testable architecture - swap components easily for experimentation
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