In this blog post, we will explore how vector search and NLP work to enhance chatbot capabilities and demonstrate how Elasticsearch facilitates the process. Let's begin with a brief overview of vector search.

Vector search

Question-answering

Chatbot data processing: NLP and vector search

Vector processing

Chatbot user input processing

Semantic search and hybrid retrieval

Combining both sparse and dense models often yields the best results

Chatbot optimization

Summary

Enhancing chatbot capabilities with NLP and vector search in Elasticsearch

In this blog post, you will explore various approaches to retrieving information using Elasticsearch, focusing specifically on text: lexical and semantic search.

Lexical and Semantic Search with Elasticsearch

Prerequisites

Lexical Search - Sparse Retrieval

String tokenization for Lexical Search

Lexical Matching

Semantic Search - Dense Retrieval

Semantic search with approximate kNN

Semantic Search - Learned Sparse Retrieval

Sparse vector search with Elastic Learned Sparse Encoder

Hybrid Search: relevant results by combining lexical and semantic search

Elasticsearch: best of both worlds with lexical and semantic search

Conclusion:

Author

Priscilla Parodi

Articles

Enhancing chatbot capabilities with NLP and vector search in Elasticsearch

Lexical and Semantic Search with Elasticsearch