Haystack

HaystackSince 2026-08

Haystack is an open-source NLP and generative AI framework by Deepset, designed for building production-grade Retrieval-Augmented Generation (RAG), neural search, and multi-modal question-answering systems. Built with modular component architecture, Haystack allows engineering teams to assemble and customize search pipelines using diverse document stores, embedding models, and LLMs. With enterprise features like hybrid search (dense vector + sparse BM25 retrieval), advanced cross-encoder re-ranking, and structured pipeline validation, Haystack powers mission-critical search engines at global enterprises.

Haystack 2.0 provides a component-driven pipeline architecture where developers connect discrete nodes (DocumentStores, Embedders, Retrievers, Rankers, and Generators) into clean Directed Acyclic Graphs (DAGs). Pipelines can be serialized to YAML for continuous integration, tested locally, and deployed via REST API with Haystack service templates. Haystack integrates natively with all major vector databases, including Qdrant, Pinecone, Milvus, Weaviate, and OpenSearch, providing full vendor flexibility without vendor lock-in.

UnstructuredSince 2023-01

Unstructured is the leading enterprise ETL (Extract, Transform, Load) platform engineered to prepare messy, unstructured business documents for Retrieval-Augmented Generation (RAG) and LLM fine-tuning. Over 80% of enterprise data lives in complex formats like scanned PDFs, PowerPoint decks, Word files, HTML tables, and email threads that break standard text scrapers. Unstructured utilizes specialized computer vision and vision-language models to segment documents into structural semantic elements (titles, paragraphs, headers, embedded tables, and image captions) while preserving exact spatial and hierarchical context. Available as an open-source Python library and a high-throughput serverless cloud API, Unstructured integrates directly with LangChain, LlamaIndex, and major vector databases to power mission-critical enterprise knowledge retrieval.

Unstructured processes more than 25 document formats through a multi-stage layout detection pipeline. Its vision models detect complex multi-column layouts, rotated text, complex mathematical notation, and embedded charts. For tabular data, Unstructured extracts full HTML and Markdown table structures, ensuring that financial balance sheets and technical specifications retain exact row-and-column relationships when embedded into vector stores. Unstructured includes automated semantic chunking strategies that respect document boundaries (chunk_by_title), preventing context fragmentation and maximizing retrieval accuracy in downstream RAG applications.