RAG Architecture: Retrieval-Augmented Generation Technical Guide

Retrieval-Augmented Generation (RAG) is a revolutionary architecture that solves the accuracy and currency issues of large language models (LLMs). In this article, we examine the technical details, implementation strategies, and enterprise applications of RAG architecture.

What is RAG and Why is it Important?

RAG architecture is a hybrid approach that enriches the parametric knowledge of LLMs with external knowledge sources. While traditional LLMs depend on training data, RAG systems provide real-time information access.

Core Components of RAG

1. Retriever: Finds the most relevant documents using vector similarity
2. Generator: Generates responses using the retrieved context
3. Vector Store: Stores embedding vectors and performs searches

Technical Architecture Details

Embedding Pipeline

Document → Chunking → Embedding Model → Vector Database

Chunking Strategies:

• Fixed-size chunking: Fixed character/token count
• Semantic chunking: Splitting based on semantic coherence
• Recursive chunking: Preserving hierarchical structure

Embedding Models Comparison

Vector Database Selection

Popular options:

• Pinecone: Managed service, easy scaling
• Weaviate: Open source, hybrid search
• Qdrant: High performance, filtering
• ChromaDB: Lightweight, ideal for prototyping

Retrieval Strategies

1. Dense Retrieval

Calculating vector similarity using semantic embeddings:

1# Retrieval with cosine similarity
2similarity = dot(query_embedding, doc_embedding) / 
3            (norm(query_embedding) * norm(doc_embedding))

2. Sparse Retrieval (BM25)

Classic search algorithm based on word frequency.

3. Hybrid Retrieval

Combination of dense and sparse methods:

final_score = α × dense_score + (1-α) × sparse_score

Reranking and Sorting

Reranker models are used to improve initial retrieval results:

• Cross-encoder rerankers: High accuracy, slow
• ColBERT: Fast, token-level interaction
• Cohere Rerank: API-based, easy integration

Context Window Optimization

Determining Chunk Size

• Small chunk (256-512 tokens): More specific information, more pieces
• Large chunk (1024-2048 tokens): More context, potential noise

Context Compression

Token savings by compressing large contexts:

Original Context → Summarization → Compressed Context → LLM

Enterprise RAG Implementation

Architecture Example

1┌─────────────┐     ┌─────────────┐     ┌─────────────┐
2│    User     │────▶│   API GW    │────▶│  RAG Service│
3└─────────────┘     └─────────────┘     └──────┬──────┘
4                                                │
5                    ┌─────────────┐     ┌──────▼──────┐
6                    │   LLM API   │◀────│  Retriever  │
7                    └─────────────┘     └──────┬──────┘
8                                                │
9                                        ┌──────▼──────┐
10                                        │ Vector DB   │
11                                        └─────────────┘

Security Considerations

1. Data isolation: Tenant-based namespace separation
2. Access control: Document-level authorization
3. Audit logging: Recording all queries and responses

Performance Metrics

Retrieval Metrics

• Recall@K: Ratio of relevant documents in K results
• Precision@K: Accuracy of relevant documents
• MRR (Mean Reciprocal Rank): Rank of first correct result

End-to-End Metrics

• Faithfulness: Response fidelity to sources
• Relevance: Response relevance to question
• Latency: Total response time

Common Issues and Solutions

1. Low Retrieval Quality

Solution: Embedding model change, hybrid retrieval, reranking

2. Hallucination

Solution: More restrictive prompts, citation requirement

3. High Latency

Solution: Caching, async retrieval, reducing chunk count

Conclusion

RAG architecture is a critical component that increases the reliability of LLMs in enterprise AI applications. The right choice of embedding model, vector database, and retrieval strategy forms the foundation of a successful RAG implementation.

As Veni AI, we offer customized RAG solutions to our enterprise customers. Contact us for your needs.