Mastering RAG: Vector Search, Embeddings, and LLM Integration
What you will learn
Implement Retrieval Augmented Generation (RAG) using KDB AI and OpenAI, including setting up a complete RAG pipeline.
Gain hands-on experience in data preparation, embedding generation, vector database operations, and integration with language models.
Master vector search techniques, advanced vector operations, and querying methods for efficient information retrieval.
Explore practical applications of RAG in AI-powered systems and NLP projects.
Why take this course?
🌟 Mastering RAG: Vector Search, Embeddings, and LLM Integration 🌟
Course Instructor: Michael Ryaboy
Course Description:
Embark on a journey to master the realm of vector databases with our specialized KDB AI course titled “Vector Database Fundamentals.” This course is meticulously designed for individuals eager to understand and implement efficient vector search, embeddings, and large language model (LLM) integration. Through this engaging online curriculum, you will learn how to manage and analyze high-dimensional data using advanced vector database techniques.
Key Topics Covered:
Vector Search Fundamentals & Applications
Understand the core concepts and real-world applications of vector search within vector databases.
- Advanced Metadata Filtering
Gain expertise in applying sophisticated filters to enrich your data retrieval process. - RAG Pipeline Implementation
Learn to implement a Retrieval Augmented Generation (RAG) pipeline from the ground up, enhancing AI applications with semantic search capabilities. - Embedding Model Selection & Optimization
Discover how to select the most appropriate embedding models for your dataset and optimize them for peak performance. - Mastering Similarity Metrics
Dive deep into the world of similarity metrics, including Euclidean distance, cosine similarity, and dot product, and understand their applications in vector databases. - High-Performance Indexes
Learn to leverage advanced indexing techniques like HNSW (Hierarchical Navigable Small World) and IVF-PQ (Inverted File – Postings Quad-tree) for optimal query performance. - Complex Query Systems
Build sophisticated query systems with metadata filtering, enabling complex queries with groupings and aggregations.
Practical Demonstrations:
- Creating & Managing Tables
Get hands-on experience in creating and managing vector database tables for various datasets. - Implementing a RAG Pipeline from Scratch
Walk through the process of implementing a complete RAG pipeline, tailored to your specific AI application. - Using Metadata Filters
Learn how to effectively use metadata filters to construct complex queries and extract meaningful insights from your data.
Questions You’ll Be Able to Answer:
- Choosing an Index
Understand the factors that influence the choice of an index and learn to apply the right algorithm parameters for different datasets. - Choosing an Embedding Model
Identify the most suitable embedding model for your needs and how to fine-tune it for better results. - Optimizing RAG Performance
Discover strategies to optimize the performance of your RAG pipeline for efficient and effective search queries. - Using Vector Databases for Insights
Learn how vector databases can be utilized to gain actionable insights from unstructured data, transforming it into valuable information for decision-making processes.
Who Should Take This Course:
This comprehensive course is ideal for:
- Data Scientists
Elevate your skills in handling high-dimensional datasets and build intelligent systems with semantic search capabilities. - ML Engineers
Deepen your understanding of how to implement efficient embeddings and leverage vector databases to enhance machine learning models. - AI Enthusiasts
Join the ranks of AI experts who understand the intricacies of vector embeddings and can build scalable, efficient AI systems.
Course Benefits:
- Hands-On Experience with KDB AI Cloud Instances
Get practical experience working with KDB AI Cloud to manage and analyze large datasets using vector databases. - Mastery of Vector Embeddings
Develop a strong grasp of vector embeddings and their applications in real-world scenarios. - Build Scalable, Efficient Systems
Learn to create AI systems that are not only powerful but also optimized for performance and scalability.
Join us on this transformative learning journey with KDB AI Vector Database and unlock the full potential of semantic search and RAG! Whether you’re a data scientist, ML engineer, or an AI enthusiast, this course will provide you with the tools and knowledge to create impactful AI-driven applications across various industries. 🚀
Enroll now and step into the future of intelligent search and generation! 🎓