Prototype

RAG System Design in Go - High-Level Architecture Guide

Chunking - Research

RAG - Approach

Chunking - Approach / design

System Architecture Overview

User Query → Query Processor → Retrieval Engine → Response Generator → Final Answer
             ↓                 ↓                   ↑
             Embedding Gen     Vector Store        Context Manager
                              ↑
                              Document Pipeline

1. Document Processing Pipeline

Purpose

Transform raw documents into searchable, vector-indexed chunks that preserve semantic meaning.

Core Components

• Document Loader: Handles multiple file formats (PDF, DOCX, HTML, markdown, plain text)
• Text Extractor: Extracts clean text while preserving structure and metadata
• Text Chunker: Splits documents into optimal-sized pieces for embedding and retrieval
• Metadata Manager: Preserves document context, source information, and custom attributes

Design Approach

// High-level interface design
type DocumentProcessor interface {
    LoadDocument(filePath string) (*Document, error)
    ChunkDocument(doc *Document) ([]Chunk, error)
    ExtractMetadata(doc *Document) (map[string]interface{}, error)
}