Core Concepts#

Understanding the key concepts behind embapi helps you make the most of its features. This section explains the fundamental building blocks and how they work together.

Overview#

embapi is a vector database designed for Retrieval Augmented Generation (RAG) workflows. It stores embeddings with metadata and provides fast similarity search capabilities.

Key Components#

• Users - Individual accounts with authentication
• Projects - Containers for embeddings with access control
• Embeddings - Vector representations of text with metadata
• LLM Services - Configurations for embedding models
• Similarity Search - Find similar documents using vector distance
• Metadata - Structured data with validation and filtering
• Architecture - Technical architecture and design

Architecture#

embapi uses PostgreSQL with the pgvector extension for vector operations. It provides a RESTful API with token-based authentication and supports multi-user environments with project sharing.