Production RAG Architecture

A RAG demo on localhost is not production. Production means handling concurrent users, isolating tenant data, caching for cost and latency, streaming responses, and monitoring everything. This module bridges the gap from "it works" to "it scales."

Scaling RAG Systems

Key bottlenecks: embedding computation (GPU-bound), vector search (memory-bound), LLM API calls (latency-bound, cost-bound). Each needs a different scaling strategy.

Multi-Tenant RAG

Enterprise RAG serves multiple customers. Each tenant's data must be isolated. Options: separate collections per tenant (simplest), metadata filtering with tenant_id (efficient), or separate vector databases (maximum isolation).

Caching Strategies

# Semantic cache: cache answers for similar questions
import redis
import hashlib

cache = redis.Redis()

def cached_rag(question: str) -> str:
    # Check cache first (exact match)
    cache_key = hashlib.sha256(question.encode()).hexdigest()
    cached = cache.get(cache_key)
    if cached:
        return cached.decode()

    # Semantic cache: find similar previously answered questions
    # Embed the question and search the cache index
    answer = rag_pipeline(question)
    cache.setex(cache_key, 3600, answer)  # 1 hour TTL
    return answer

API Design

Production RAG APIs need: streaming responses (token-by-token via SSE), rate limiting per tenant, authentication via API key or JWT, request/response logging, and cost tracking per request.