Weaviate for RAG: When It Shines (and When It Doesn’t)

Source: Dev.to

Overview

A hands‑on review after building an enterprise‑grade PoC — not just another “Hello World”.

As a Technical Lead & AI Architect (Hands‑On) focused on Retrieval‑Augmented Generation (RAG) systems, I’ve built solutions for organizations such as HSBC, Scotiabank, and CFE. Recently, at AI Research Lab in Mexico City (Feb 2025 – Jun 2025), I led the architecture for a comprehensive RAG solution for an internal Business Intelligence Engine PoC. This effort was a technical deep‑dive to validate architecture, latency, and security patterns for future enterprise deployment.

The PoC was designed to rigorously test RAG architectures for real‑world readiness, incorporating:

• Full enterprise patterns (auth, error handling, observability)
• Local LLMs (DeepSeek‑R1 via Ollama)
• 100 % data sovereignty
• Benchmarks on real hardware (GCP n2‑standard‑8)

My contributions included:

• Designing a multi‑layered RAG architecture with reactive streaming patterns (Spring WebFlux, Project Reactor)
• Architecting Weaviate v4 integration with optimized Sentence‑BERT embeddings for financial document processing
• Directing the local LLM integration strategy

🔗 Full architecture details:
💻 Code (MIT, non‑commercial):

Where Weaviate Delivers Value — in practice

Hybrid Search: nearText + where = Fewer False Positives

In real use, users don’t ask clean questions like “summarize Q3 earnings”. They often phrase queries such as:

“What did the compliance team say about loan approvals last quarter?”

Most vector databases force a choice between semantic or keyword search. Weaviate’s ability to combine both significantly reduces false positives.

{
  Get {
    FinancialDocument(
      nearText: { concepts: ["loan approval"] }
      where: {
        path: ["department"]
        operator: Equal
        valueString: "compliance"
      }
    ) {
      title
      snippet
      _additional { distance }
    }
  }
}