Introduction to NLP models and vector search: Part I

Introducing modern NLP and native vector search in Elasticsearch. Leverage new ML models to understand context, increase speed and improve results. Unlock advanced text analytics like named entity recognition (NER), semantic text embedding, emotion and sentiment analysis, or text classification with significantly less effort and time. Start with pre-built models or scale your own.

Highlights

• How to leverage Lucene 9 and dense vector fields
• NLP examples for named entity recognition, text classification, and text embedding
• Working with NLP, HuggingFace, and PyTorch models
• Using vectors and NLP to create modern semantic search applications

Additional resources

• Get your search tool kit for the AI era — the Elasticsearch Relevance Engine™ (ESRE)
• Introduction to NLP models and vector search: Part II
• Documentation: NLP
• Documentation: Dense Vector Field Types
• What is vector search?
• Want to try it for yourself? Learn more about Elastic Cloud or, if you're ready to get started, spin up a free 14-day trial