Retrieval-Augmented Generation (RAG) is the bridge between frozen LLM knowledge and real-time enterprise data. Here is a production-grade architecture for scalable RAG systems.

The Hallucination Problem

Large Language Models are probabilistic engines, not truth engines. Without grounding, they hallucinate. RAG solves this by injecting relevant context into the prompt window before generation.

Architecture Overview

A robust RAG pipeline consists of four distinct stages:

  1. Ingestion & Chunking: Breaking documents into semantic windows (e.g., 512 tokens with 20% overlap).
  2. Embedding: Converting text to dense vectors using models like text-embedding-3-small or bge-m3.
  3. Vector Storage: Storing vectors in Qdrant or pgvector for HNSW indexing.
  4. Generation: Re-ranking retrieved chunks and synthesizing the answer.

Vector Search Implementation

We utilize Rust for the ingestion worker to handle high-throughput PDF parsing.

struct DocumentChunk {
    content: String,
    metadata: HashMap,
    embedding: Vec,
}

async fn index_document(doc: Document, client: &QdrantClient) -> Result<()> {
    let chunks = split_text(&doc.content, 512);
    let embeddings = openai.embed(chunks).await?;
    
    let points: Vec = chunks.iter().zip(embeddings).map(|(c, e)| {
        PointStruct::new(uuid::Uuid::new_v4().to_string(), e, c.payload())
    }).collect();

    client.upsert_points("enterprise_knowledge", points).await?;
    Ok(())
}

Latency Challenges

The bottleneck in RAG is rarely the LLM generation token speed, but the retrieval latency. Using a localized vector cache (like Redis) for frequent queries reduced our P99 latency from 800ms to 150ms.