Comprehensive Analysis of 8 RAG Architectures: A Complete Practical Guide from Basic Implementation to Agent Workflow

This article analyzes the rag-research project open-sourced by henry0hai on GitHub. The project implements 8 mainstream RAG architectures from Naive RAG to Agentic RAG, covering three layers: basic implementation, routing and graph computing, and multimodal and agent systems, providing developers with references for local deployment and architecture selection.

RAG technology is a core solution to address LLM hallucinations, knowledge timeliness, and domain adaptation issues. The rag-research project focuses on local deployment, implements offline inference based on Ollama, deeply integrates the LangChain and LangGraph frameworks, adopts a modular design where each architecture is implemented independently for easy learning and comparison, and includes Mermaid flowcharts to visualize data flow.

• Naive RAG: The simplest paradigm: convert user queries into vectors, search in the Chroma vector database, generate answers by combining context with LLM. Suitable for scenarios with clear structure and explicit intent.
• Hybrid RAG: Fuses dense vector retrieval and sparse BM25 keyword retrieval, re-ranks results via reciprocal rank fusion, improving recall rate for technical documents.
• HyDE: Generates hypothetical answers and embeds them, uses hypothetical documents to retrieve real documents, bridging the semantic gap between user queries and documents.

• Corrective RAG: Introduces quality control: uses LangGraph StateGraph to evaluate the quality of retrieved documents; if not up to standard, automatically supplements information from web searches. Suitable for high-accuracy scenarios.
• Adaptive RAG: Pre-classifies queries (direct LLM answer, vector search, web search), routes intelligently to reduce latency and token consumption.
• Graph RAG: Builds a knowledge graph (using NetworkX) to explicitly model entity relationships, implements multi-hop reasoning via graph traversal. Suitable for relational data analysis.

• Multimodal RAG: Processes images: uses visual LLM to generate image summaries and store them in the vector database; original images are stored in base64 format. Combines images and queries to generate answers during retrieval.
• Agentic RAG: Based on the LangGraph ReAct pattern, equipped with tools like vector search and web search; autonomously makes cyclic decisions to call tools, solving complex multi-step reasoning tasks.

Application Scenario	Recommended Architecture	Reason
Internal knowledge base Q&A	Naive/Hybrid RAG	Simple implementation, fast response
Technical document retrieval	Hybrid RAG	Balances semantic and keyword matching
Domains with unfamiliar user terminology	HyDE	Bridges semantic gap between queries and documents
High accuracy requirements	CRAG	Dynamic quality control and external supplementation
Cost-sensitive production environments	Adaptive RAG	Intelligent routing reduces token consumption
Relational data analysis	Graph RAG	Multi-hop reasoning and explicit relationship modeling
Knowledge bases with charts/images	Multimodal RAG	Unified text and visual processing
Complex multi-step reasoning tasks	Agentic RAG	Autonomous tool calling and decision-making

The rag-research project provides a progressive learning path, helping developers understand the problem-solving approaches and complexity trade-offs of each architecture. It also demonstrates modern LLM application engineering practices: uv dependency management, Makefile standardized commands, modular unit testing, and LLM-as-a-Judge automated evaluation processes—these details are crucial for production deployment.