This seminar offers an in-depth exploration of semantic search technologies, focusing on advanced tools and techniques that developers can utilize to build sophisticated search systems. Attendees will gain hands-on experience with Retrieval-Augmented Generation (RAG) using pre-trained embeddings from OpenAI, BERT, and other models, integrated with Pinecone and Elasticsearch for building scalable semantic search solutions.

Through practical demonstrations and discussions, the seminar covers the setup, integration, and optimization of semantic search systems, equipping developers with the knowledge to enhance search relevance and performance in their applications.

By the end of the day, participants will be well-versed in the latest advancements in semantic search technology and ready to implement these powerful tools in a variety of projects.

– Python development

– Familiarity with basic ML concepts

– Familiarity with Docker

• Introduction to Semantic Search and Pre-trained Embeddings

– Understanding semantic search and its significance

– Overview of pre-trained embeddings: OpenAI, BERT

– Benefits of using pre-trained models for semantic search

• Deep Dive into Pre-trained Embeddings

– Detailed exploration of OpenAI embeddings (GPT series)

– Introduction to BERT embeddings and their application in search

– Additional recommended embeddings:

1. Sentence-BERT (SBERT): Designed specifically for sentence-level embeddings, enhancing semantic similarity comparison.
2. Universal Sentence Encoder (USE): Offers high-quality sentence embeddings, efficient for semantic similarity tasks.
3. RoBERTa: An optimized version of BERT, known for improved performance on a wide range of NLP tasks.

• Setting Up the Environment for Semantic Search

– Installing necessary libraries and tools

– Accessing and integrating pre-trained embeddings

– Preparing datasets for demonstration

• Implementing Semantic Search with Elasticsearch

– Indexing documents using embeddings for Elasticsearch

– Demonstrating semantic search capabilities with Elasticsearch

– Practical examples and use cases

• Enhancing Semantic Search with Pinecone

– Introduction to Pinecone for scalable vector search

– Indexing and querying with pre-trained embeddings in Pinecone

– Performance comparison and use cases

• Q&A, Wrap-up, and Future Directions

– Open discussion for questions and clarifications

– Summary of key points and takeaways from the seminar

– Resources for continued learning and exploration in semantic search

– Closing remarks and seminar conclusion