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Vector Search for Practitioners with Elastic

You're reading from   Vector Search for Practitioners with Elastic A toolkit for building NLP solutions for search, observability, and security using vector search

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781805121022
Length 240 pages
Edition 1st Edition
Languages
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Authors (2):
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Jeff Vestal Jeff Vestal
Author Profile Icon Jeff Vestal
Jeff Vestal
Bahaaldine Azarmi Bahaaldine Azarmi
Author Profile Icon Bahaaldine Azarmi
Bahaaldine Azarmi
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Table of Contents (17) Chapters Close

Preface 1. Part 1:Fundamentals of Vector Search
2. Chapter 1: Introduction to Vectors and Embeddings FREE CHAPTER 3. Chapter 2: Getting Started with Vector Search in Elastic 4. Part 2: Advanced Applications and Performance Optimization
5. Chapter 3: Model Management and Vector Considerations in Elastic 6. Chapter 4: Performance Tuning – Working with Data 7. Part 3: Specialized Use Cases
8. Chapter 5: Image Search 9. Chapter 6: Redacting Personal Identifiable Information Using Elasticsearch 10. Chapter 7: Next Generation of Observability Powered by Vectors 11. Chapter 8: The Power of Vectors and Embedding in Bolstering Cybersecurity 12. Part 4: Innovative Integrations and Future Directions
13. Chapter 9: Retrieval Augmented Generation with Elastic 14. Chapter 10: Building an Elastic Plugin for ChatGPT 15. Index 16. Other Books You May Enjoy

Expanding and customizing options for the PII redaction pipeline in Elasticsearch

Ingest processors in Elasticsearch provide a powerful and flexible way to customize data processing and manipulation, which can be tailored to fit a company’s individual PII data redaction needs. In this section, we will discuss several options for expanding and enhancing the default PII redaction pipeline to better serve specific use cases and requirements.

Customizing the default PII example

The default PII redaction pipeline provided in the example can easily be customized to better suit your organization’s data and requirements. Some possible customizations include the following:

  • Replacing the example NER model with any other Elastic-compatible NER model: The default pipeline uses the dslim/bert-base-NER model from Hugging Face, but you can replace it with any other Elastic-compatible NER model that better fits your specific needs.
  • Removing the NER model if this form...
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