RAG Architecture

Understanding Retrieval-Augmented Generation and how ragistry implements it

What is RAG?

Retrieval-Augmented Generation (RAG) is an AI architecture that combines the power of large language models with external knowledge retrieval. Instead of relying solely on the model's training data, RAG systems fetch relevant information from your documents and data sources before generating responses.

This approach provides several key advantages: responses grounded in your actual data, reduced hallucinations, ability to reference specific sources, and continuously updated knowledge without retraining the model.

ragistry's RAG Pipeline

1

Data Ingestion

Content is extracted from various sources (websites, databases, cloud storage) and processed into structured chunks optimized for retrieval.

2

Embedding Generation

Each content chunk is converted into a high-dimensional vector embedding that captures semantic meaning, enabling similarity-based search.

3

Vector Storage

Embeddings are stored in a vector database with metadata, enabling fast similarity search across millions of documents.

4

Query Processing

User questions are embedded using the same model, then used to search for the most relevant content chunks via vector similarity.

5

Context Assembly

Retrieved chunks are ranked, filtered, and assembled into a context prompt that provides the LLM with relevant information.

6

Response Generation

The LLM generates a response using both the retrieved context and its general knowledge, formatted as rich HTML when appropriate.

Technical Stack

ragistry's backend is built with FastAPI and PostgreSQL with pgvector extension for vector operations. We use Supabase for authentication and database management, ensuring enterprise-grade security and scalability.

Backend Framework

FastAPI with Python for high-performance async operations

Vector Database

PostgreSQL with pgvector for efficient similarity search

Embedding Models

State-of-the-art models for semantic understanding

LLM Integration

Support for OpenAI, Anthropic, and other providers

Performance Optimization

ragistry implements caching strategies, connection pooling, and circuit breakers to ensure fast response times even under high load. Learn more in the Search & Retrieval section.