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Haystack
End-to-end LLM framework for building production-ready RAG applications
Haystack orchestrates LLMs, embedding models, and vector databases into customizable pipelines for retrieval-augmented generation, semantic search, and question answering applications.
Overview
Build Production-Ready LLM Applications
Haystack is an end-to-end framework for building applications powered by large language models, Transformer models, and vector search. Designed for developers who need flexibility and control, Haystack orchestrates state-of-the-art components into pipelines that solve real-world NLP challenges—from retrieval-augmented generation (RAG) to document search and conversational agents.
Technology-Agnostic Architecture
The framework provides a vendor-neutral approach, letting you integrate models from OpenAI, Cohere, Hugging Face, or your own local deployments. Switch between Azure, Bedrock, SageMaker, or self-hosted infrastructure without rewriting your application. Haystack's explicit component design makes it transparent how data flows through your pipeline, simplifying debugging and optimization.
Complete Tooling for NLP Workflows
Haystack includes everything needed for production deployments: database connectors, file conversion, text preprocessing, model training, evaluation, and inference. Scale to millions of documents with production-grade retrievers, customize behavior with your own components, and leverage user feedback loops for continuous improvement. Deploy pipelines as REST APIs using Hayhooks, or develop visually with deepset Studio.
Highlights
Vendor-agnostic component system supporting OpenAI, Cohere, Hugging Face, Azure, Bedrock, and custom models
Production-ready pipeline orchestration for RAG, semantic search, and question answering
Extensible architecture with custom component support and third-party integrations
Complete NLP toolkit including vector databases, file converters, retrievers, and evaluation tools
Pros
- Flexible architecture allows easy switching between LLM providers and infrastructure
- Comprehensive tooling covers entire pipeline from data ingestion to inference
- Active community with enterprise support options and visual development tools available
- Transparent component design simplifies debugging and customization
Considerations
- Learning curve for understanding pipeline architecture and component interactions
- Requires integration setup for specific vector databases and model providers
- Anonymous telemetry enabled by default (opt-out available)
- Advanced use cases may require building custom components
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Fit guide
Great for
- Teams building retrieval-augmented generation systems with custom data sources
- Organizations requiring vendor flexibility and avoiding LLM provider lock-in
- Developers scaling semantic search or question answering to millions of documents
- Projects needing production-grade NLP pipelines with evaluation and monitoring
Not ideal when
- Simple chatbot projects requiring minimal customization or single-provider solutions
- Teams without Python expertise or NLP pipeline experience
- Prototypes needing immediate results without pipeline configuration
- Applications requiring real-time streaming responses under 100ms latency
How teams use it
Enterprise Knowledge Base RAG
Search across millions of internal documents with context-aware answers combining vector retrieval and LLM generation, deployed on-premises for data security
Multi-Source Customer Support
Resolve complex queries by orchestrating searches across documentation, tickets, and knowledge bases with decision-making logic and provider-agnostic model selection
Semantic Document Discovery
Enable meaning-based search across legal, medical, or research archives using custom embedding models and production-scale vector databases
Conversational Agent with Feedback Loop
Build chatbots that improve over time by collecting user feedback, benchmarking responses, and fine-tuning models on domain-specific data
Tech snapshot
MDX57%
Python41%
HTML1%
JavaScript1%
CSS1%
Jinja1%
Frequently asked questions
What LLM providers does Haystack support?
Haystack supports OpenAI, Cohere, Hugging Face models, Azure OpenAI, AWS Bedrock, AWS SageMaker, and custom local or self-hosted models. The framework is designed to be vendor-agnostic, making it easy to switch providers.
Can I deploy Haystack pipelines as REST APIs?
Yes, Hayhooks provides a simple way to wrap pipelines with custom logic and expose them via HTTP endpoints, including OpenAI-compatible chat completion endpoints compatible with interfaces like open-webui.
How does Haystack handle vector databases?
Haystack provides connectors for multiple vector databases and includes retrievers that scale to millions of documents. You can choose the vector database that fits your infrastructure and switch between them as needed.
Is there a visual development environment for Haystack?
Yes, deepset Studio provides a visual interface to create, deploy, and test Haystack pipelines. For fully managed solutions, deepset AI Platform offers end-to-end LLM integration using Haystack architecture.
What is the difference between Haystack and Haystack Enterprise?
Haystack Enterprise provides expert support from the core team, enterprise-grade templates, deployment guides for cloud and on-premises environments, and best practices for scaling production systems.
Project at a glance
Active- Stars
- 23,938
- Watchers
- 23,938
- Forks
- 2,560
LicenseApache-2.0
Repo age6 years old
Last commit3 hours ago
Self-hostingSupported
Primary languageMDX
Last synced 2 hours ago