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Course Outline
Course Overview:
- Foundations of vectors
- AI-driven vector embeddings
- Leading embedding architectures for government use cases
- Semantic search capabilities
- Vector distance metrics
Survey of vector indexing methodologies:
- IVFFlat indexing strategy
- HNSW indexing strategy
PgVector extension for PostgreSQL deployment:
- System configuration and installation
- Ingestion and querying of high-dimensional vector data
- Application of distance metrics
- Implementation of vector indexes
PgAI extension for PostgreSQL deployment:
- System configuration and installation
- Embedding generation workflows
- Deployment of Retrieval-Augmented Generation (RAG) architectures
- Advanced development patterns for government applications
Survey of Text-to-SQL solutions utilizing the LangChain framework
Learning Objectives: Upon completion, participants will be able to:
- Architect and develop components of AI-enabled database systems using PostgreSQL extensions and associated libraries.
- Acquire hands-on proficiency in integrating large language models (LLMs) and vector search technologies into operational environments, facilitating the creation of semantic search platforms, AI-driven assistants, and natural-language database interfaces for government operations.
Requirements
Participants are required to demonstrate foundational proficiency in SQL and practical experience with PostgreSQL, alongside elementary competency in either Python or JavaScript. This curriculum is designed for government professionals engaged in roles such as database development and systems architecture.
14 Hours
Testimonials (2)
Tuning strategies.
Jeffrey Zieg - Matrix Consulting
Course - PostgreSQL Performance Tuning
Logging behaviour when the instance is under stress, and the hierarchy/nomenclature of instances, databases, files, etc.