Hybrid Local-Cloud Architectures

In many enterprise settings, you want the best of both worlds: the Infinite Storage of the cloud and the Local Security/Speed of a private server.

Design Pattern: "Local Search, Cloud Reason"

1. Ingestion (Local): Documents are stored and embedded locally.
2. Retrieval (Local): The vector search happens on-premise.
3. Redaction (Local): Before sending data to the cloud, sensitive info (SSNs, Names) is masked.
4. Generation (Cloud): The "Cleaned" context is sent to Claude 3.5 Sonnet for a final report.

The Reverse Pattern: "Cloud Search, Local Reason"

• Used when documents are publicly available (e.g., news) but your reasoning logic is proprietary or too sensitive to share with a cloud provider.

Implementation: Redaction Pipelines

import re

def redact_pii(text):
    # Rough example scraping emails
    return re.sub(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', '[REDACTED]', text)

Managing State Across Hybrid Environments

You must ensure that your Local Metadata stays in sync with your Cloud History. Using a shared database (like PostgreSQL with pgvector) in a Virtual Private Cloud (VPC) is a common way to bridge this gap.

Cost/Latency Matrix

Task	Local	Cloud	Recommendation
Embedding	✅ Fast/Free	⚠️ Latency Hit	Local
Re-ranking	✅ Precise	💰 Expensive	Mixed
Generation	⚠️ Limited logic	✅ High logic	Cloud

Exercises

1. Why would you want to "Embed" locally but "Search" on a cloud vector DB?
2. Design a "Privacy-First" workflow for a customer support bot.
3. What is the impact of "Redaction" on the LLM's ability to provide a personalized answer?