Releasesby Glyphspark-nlp 6.3.1
📢 Spark NLP 6.3.1: LLM Backend Upgrade and Document Processing Improvements Spark NLP 6.3.1 focuses on strengthening distributed local LLM inference by upgrading the jsl-llamacpp backend to a newer llama.cpp release, while also delivering important improvements in document structure handling and metadata consistency.
Also shipped Jan 7, 2026John Snow Labs in January 2026 →
Workers Analytics Engine, Workers - Workers Analytics Engine SQL now supports filtering using HAVING and LIKE
You can now use the HAVING clause and LIKE pattern matching operators in Workers Analytics Engine ↗ . Workers Analytics Engine allows you to ingest and store high-cardinality data at scale and query your data through a simple SQL API.
Updated left navigation bar in Figma Design
Refreshed navigation bar and an edge-to-edge variables authoring modal for easier navigation.
truss 0.12.3
- Migrate truss watch GQL queries from model name to ID Weights config Schema feat(truss): Use client-side filtering for model lookup by name and team Support warnings in truss validation Release 0.12.3
feldera 0.218.0
- [SQL] Fix incorrect rewriting when uninterning in right side of LEFT JOIN [profiler] Handle correctly statistics of nested regions pipeline-manager: bump jsonwebtoken to 10.2.0 [dbsp, adapters] Add some missing region annotations. [dbsp] Split large outputs in chain_aggregate.
More from spark-nlpEvery spark-nlp release →
spark-nlp 6.3.2
📢 Spark NLP 6.3.2: Scala 2.13 Support, Layout-Aware Images, and Enhanced LightPipeline Tracking Spark NLP 6.3.2 is a foundational release that introduces official support for Scala 2.13, alongside important improvements in document layout understanding and lightweight inference workflows.
spark-nlp 6.2.3
📢 Spark NLP 6.2.3: Further Improvements for NerDL Spark NLP 6.2.3 introduces targeted improvements to training performance and stability of NerDLApproach and bug fixes for CamemBertForTokenClassification. NerDLApproach now uses new internal data-loading behavior, and improving training speed and preventing out-of-memory errors.
spark-nlp 6.2.2
📢 Spark NLP 6.2.2: Bugfix Release Spark NLP 6.2.2 brings bug fixes to WordEmbeddings and NerDLApproach logging. 🐛 Bug Fixes - WordEmbeddings: Fixed a bug where WordEmbeddings would duplicate input tokens in the output - NerDLApproach: Fixed a bug during logging, that would show inaccurate training/validation counts.
spark-nlp 6.2.1
📢 Spark NLP 6.2.1: Enhanced hierarchical document processing and training optimizations Spark NLP 6.2.1 brings significant improvements to document ingestion with expanded hierarchical support, XML processing enhancements, and optimizations for NerDL training.
spark-nlp 6.3.3
📢 Spark NLP 6.3.3: ModernBERT Embeddings, Vector DB Integration, and Layout-Aware Document Processing Spark NLP 6.3.3 is a feature-packed release aimed at practitioners building modern NLP and multimodal document pipelines.
spark-nlp 6.2.0
📢 Spark NLP 6.2.0: A new stage for unstructured document ingestion and processing at scale Spark NLP 6.2.0 introduces key upgrades across entity extraction, document normalization, HTML reading, and GGUF-based models.
spark-nlp 6.4.0
📢 Spark NLP 6.4.0: LLM-Powered Entity Extraction, Expanded Document Readers, and Pipeline Robustness Spark NLP 6.4.0 is a feature-rich release focused on bringing LLM-powered extraction into standard NLP pipelines, dramatically expanding the document reader ecosystem, and hardening production ingestion workflows.
spark-nlp 6.1.5
📢 Spark NLP 6.1.5: Smarter Readers and More Resilient Pipelines Spark NLP 6.1.5 focuses on improving data ingestion reliability and pipeline flexibility. This release enhances reader components with better fault tolerance, broader input support, and introduces a new ReaderAssembler annotator for streamlined integration.