Elasticsearch and AI for Effective Search

Join our course and discover how to effectively utilize Elasticsearch to enhance search and integrate artificial intelligence. Learn to improve the relevance of results, implement semantic search using the latest AI models, and expand search capabilities through image search and RAG Search. Ideal for professionals and enthusiasts looking to refine their skills in modern search technologies.

Outline:

1. Introduction to Elasticsearch and Basics of Search

• Overview of Elasticsearch and its use in the context of search
• Fundamental concepts of Elasticsearch, Apache Lucene, and their roles
• Practical examples in Elasticsearch

2. Advanced Elasticsearch Search

• Deepening knowledge of full-text search: match, multimatch, combined fields, and more
• Utilizing compound queries and filters for advanced scenarios
• Practical examples using features like sparse vectors

3. Relevance

• Elasticsearch similarity, sparse vectors
• Explanation of similarity algorithms in Elasticsearch (TF/IDF, BM25)
• Custom similarity, rescoring
• Practical examples of query optimization for better relevance

4. Semantic Search: Principles

• Basics of Semantic Search and the problems it addresses
• Implementation of semantic search in Elasticsearch

5. Integration of models such as BERT, SentenceBERT, E5, OpenAI ADA

• HuggingFace and Elastic eland for model import
• KNN and ANN search: theory and applications in Elasticsearch

6. AI for Additional Search Features

• Image search in Elasticsearch: principles and models (e.g., OpenAI CLIP)
• AI for improving relevance and contextual search

7. RAG Search in Elasticsearch

• Introduction to RAG Search and its role in Elasticsearch
• Integration and application of RAG Search
• Examples of integration with cloud services (OpenAI, Azure OpenAI)
• Elasticsearch and LangChain

8. Conclusion and Discussion

• Summary of key points from the course
• Discussion, questions, and answers
• Recommendations for further study and resources