Haystack

Haystack is an end-to-end NLP framework developed by deepset, specifically designed for building production-ready search systems, question-answering applications, and Retrieval-Augmented Generation (RAG) pipelines. While LangChain focuses on general LLM orchestration, Haystack specializes in the data-intensive components of AI systems—retrieval, indexing, and information extraction.

Haystack's architecture is built around modular, composable pipelines that connect specialized components:

• Document Stores: Flexible backends for storing and retrieving documents (Elasticsearch, OpenSearch, Weaviate, Pinecone, Qdrant, and more). Haystack abstracts the complexity of different vector databases behind a unified interface.
• Retrievers: Components that find relevant documents based on queries. Supports dense retrieval (embedding-based semantic search), sparse retrieval (BM25 keyword search), and hybrid approaches combining both.
• Readers: Extract precise answers from retrieved documents using extractive QA models or generative LLMs.
• Generators: Integration with LLMs (OpenAI, Cohere, Anthropic, Hugging Face) for generative question answering and text synthesis.
• Preprocessors: Clean, split, and prepare documents for indexing with sophisticated text preprocessing and chunking strategies.

Haystack excels in enterprise search and RAG applications where retrieval quality is paramount:

• Semantic Search: Build Google-like search over internal documents, support tickets, or knowledge bases using neural embeddings.
• Question Answering: Create systems that answer questions directly from document collections, citing sources and confidence scores.
• Conversational Search: Enable multi-turn dialogues where the system maintains context across questions and refines searches iteratively.
• Document Analysis: Automatically extract structured information from unstructured text at scale.

Haystack 2.0 (released in late 2023) introduced a completely redesigned architecture with:

• Custom Components: Easy creation of custom pipeline components using simple Python classes and type hints.
• Serializable Pipelines: Export and version pipelines as YAML for reproducibility and deployment.
• Streaming Support: Process large datasets efficiently without loading everything into memory.
• Better Observability: Built-in tracing and logging for debugging and optimization.

The framework is particularly strong for teams prioritizing retrieval quality and evaluation. Haystack includes built-in evaluation metrics for RAG systems, allowing developers to measure and optimize retrieval precision, answer accuracy, and end-to-end performance systematically. Companies building internal search engines, customer support automation, or compliance/legal document systems frequently choose Haystack for its retrieval-first design and production-grade tooling. It integrates seamlessly with popular vector databases and provides a cleaner separation of concerns than general-purpose LLM frameworks.