Glossary Summary

This glossary provides concise definitions for key terms related to machine learning (ML), programming in Python, database and vector storage technologies, and concepts central to Retrieval-Augmented Generation (RAG) pipelines.

Retrieval-Augmented Generation (RAG) Pipeline Terms

• Adapter Pattern: Enables interaction between incompatible interfaces by data transformation.
• Metadata & Session: Information about data and individual database connections.
• Adapter Context & Chunking: Context for data transformation and dividing data for processing.
• Normalize Embeddings: Process of scaling vectors to unit norm for consistent similarity metrics.

Machine Learning Terms

• Embeddings: Numerical vectors representing data (text, images) to capture similarity.
• Sentence Transformers: Library for dense vector representations of text for semantic search and similarity.

Database and Vector Storage Terms

• PostgreSQL: Open-source relational database for extensible SQL operations.
• pgvector: Extension for storing/searching high-dimensional vectors in PostgreSQL.
• HNSW & IVFFlat: Algorithms for efficient nearest neighbor search in vector data.
• Upsert & Index: Operations and structures for data insertion/update and speedy retrieval.

Programming and Python Concepts

• Generator: Iterable in Python using yield for lazy item generation.
• Context Manager: Manages resources (files, database connections) with with statement.