// Apache solr
End-to-End Apache Solr Neural Search Tutorial
The vector-based approach is already widely used in search and is finally available in Apache Solr from version 9.0.
In this training, you will learn how to leverage this new Apache Solr feature to index and search vectors and perform a full end-to-end neural search with real examples to improve your search experience!
Recorded
£ 200,00
If you are not able to attend public training, this is the best option for you. You will be able to take the course at your own pace and rhythm and learn whenever it fits your schedule and mood.
- Top expert trainers
- Q&A by e-mail
- Certificate of Attendance
Public classroom
£ 250,00
SCHEDULE
8th May 2024
3:00 – 7:00 PM GMT+1
- Live on Zoom
- Top expert trainers
- Certificate of Attendance
Private
ask for quote
If you are looking for intensive sessions tailored to your (or your team’s) experience, then private training is your perfect choice!
- In presence or Online
- Tailored Training
- Top Expert Trainers
- Certificate of Attendance
Recorded
£ 200,00
If you are not able to attend public training, this is the best option for you. You will be able to take the course at your own pace and rhythm and learn whenever it fits your schedule and mood.
- Top expert trainers
- Q&A by e-mail
- Certificate of Attendance
Private
ask for quote
If you are looking for intensive sessions tailored to your (or your team’s) experience, then private training is your perfect choice!
- In presence or Online
- Tailored Training
- Top Expert Trainers
- Certificate of Attendance
Based on experience with leading companies including
Based on experience with leading companies including
Feedback
I highly recommend this course to anyone looking to avail of Solr neural search capabilities to improve search experience.
PREREQUISITES
• Basic understanding of Search Engines
• Basic understanding of Neural Search principles
• Familiarity with Apache Solr
• Attendees need Java installed on their computer
WHAT YOU WILL LEARN
• How Apache Solr implementation works, with the new field type and query parser introduced
• How to generate vector embeddings from text using pre-trained language models
• How to create an index containing vector fields
• How to Index documents using pysolr
• How to Run KNN queries (with and without filters) and use them to rerank a first-stage pass
INTENDED AUDIENCE
• Data scientists
• Software Engineers
• Developers
Your Trainer
Alessandro Benedetti
APACHE LUCENE/SOLR COMMITTER
APACHE SOLR PMC MEMBER
Alessandro has been involved in designing and developing search-relevant solutions from 2010.
Over the years he has worked on various projects, with various open source technologies aiming to build search solutions able to satisfy the user information needs, often integrating such solutions with machine learning and artificial intelligence technologies.
The schedule
The training session is meticulously structured for optimal comprehension and retention.
It commences with a comprehensive 1-hour theory session, followed by a brief 15-minute test to assess understanding. Subsequently, participants engage in a 45-minute Q&A segment, fostering interactive learning and clarification of concepts. The training then resumes with another 1-hour theory module, another 15-minute evaluation test, and concludes with a final 45-minute Q&A session to consolidate knowledge and address any remaining queries.
3:00-4:00 theory session
4:00-4:15 test
4:15-5:00 Q&A segment
5:00-6:00 theory session
6:00-6:15 test
6:15-7:00 Q&A segment
Topics
1. BRIEF OVERVIEW
The training begins by providing participants with a foundational understanding of Apache Solr and Neural Search. This introductory module offers insights into the significance, applications, and fundamental concepts underlying Apache Solr and Neural Search, setting the stage for deeper exploration.
2. DOWNLOAD SOLR
Get started on your Apache Solr journey by downloading the latest version of the Solr search platform. In this section, learn how to access and install Solr on your local environment or server. Follow step-by-step instructions to ensure a seamless setup process, enabling you to dive into neural search implementation with ease.
3. Produce Vectors Externally
Learn how to generate feature vectors externally for use in Apache Solr’s neural search implementation. Explore various techniques and tools for extracting and processing data to produce high-quality vectors. Discover the advantages of external vector production and gain insights into integrating these vectors seamlessly into Solr for enhanced search capabilities.
4. Create an index containing a vector field
In this section, learn how to create an index in Apache Solr that includes a vector field. Explore the steps to define and configure the vector field within your Solr schema. Gain practical insights into optimizing the indexing process to efficiently store and manage vector data. Lay the foundation for leveraging vector fields in neural search applications within Solr.
5. Index documents
Discover how to index documents into Apache Solr’s neural search index. Follow a step-by-step guide to ingest and store documents within the Solr index, ensuring efficient retrieval and search capabilities. Learn best practices for optimizing indexing performance and maintaining data integrity. Gain practical experience in seamlessly integrating document indexing with Solr’s neural search functionality.
6. Search exploiting vector fields (KNN query, KNN query + Filter, Boolean Search, Re-ranking)
Unlock the full potential of Apache Solr’s neural search capabilities in this comprehensive section. Explore advanced search techniques, including KNN query, KNN query + filter, boolean search, and re-ranking, leveraging vector fields. Gain practical insights into optimizing search performance and enhancing search relevance through innovative approaches. Develop proficiency in harnessing Solr’s neural search features to deliver superior search experiences.
7. Future works
Explore potential avenues for future exploration and enhancement of Apache Solr’s neural search capabilities. Dive into emerging trends, research areas, and advancements in the field of neural search. Discuss possibilities for extending and refining Solr’s neural search features to address evolving user needs and technological developments. Gain inspiration for ongoing learning and innovation in the dynamic landscape of neural search.
FAQ
The training session is conducted online via Zoom for live participation. However, for those who may not be able to attend in real-time or prefer to learn at their own pace, we also offer the option to purchase a recorded version. This allows participants to access the content online and review it at their convenience.
Absolutely! Everyone is welcome to participate in this training. Please note that there are specific prerequisites listed nearby, so be sure to check those before inviting others to ensure they meet the requirements.
The ‘End-To-End Apache Solr Neural Search Tutorial’ training session has a duration of 4 hours. This includes a 2-hour theory session, a 30-minute test, followed by a 1.5-hour Q&A segment.
Yes, absolutely! You will have the opportunity to ask any questions you may have at the end of the test module.
Your teachers will be:
– Alessandro Benedetti, Apache Lucene/Solr committer and Apache Solr PMC member.
Yes, indeed! At the conclusion of the training, you will receive a certificate of attendance via email.
If you need it, you can consult our Training’s Terms and Conditions.
Yes, we do offer corporate training sessions. Feel free to reach out to us, and we’ll be happy to discuss the best options tailored for you and your team!