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

✅ Where Weaviate Delivers Value — in practice ## 1. Hybrid Search: nearText + where = Fewer False Positives A hands-on review after building an enterprise-grade PoC — not just another “Hello World” As a Technical Lead & AI Architect (Hands-On) with a focus on RAG Systems and experience building solutions for organizations like HSBC, Scotiabank, and CFE, I'm always evaluating cutting-edge technologies. Recently, at AI Research Lab in Mexico City (Feb 2025 – Jun 2025), I spearheaded the architecture for a comprehensive Retrieval Augmented Generation (RAG) solution for an internal Business Intelligence Engine PoC. This was not a client-facing product, but 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: 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, and directing the local LLM integration strategy — leveraging my background as a Google Certified GenAI Leader. 🔗 Full architecture details: ebercruz.com/technical 💻 Code (MIT, non-commercial): github.com/ebercruzf/enterprise-intelligence-engine In real use, users don’t ask clean questions like “summarize Q3 earnings”. They often phrase queries like: “What did the compliance team say about loan approvals last quarter?” Most vector DBs force a choice between semantic or keyword search. Weaviate's ability to combine both significantly reduces false positives: Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: graphql { Get { FinancialDocument( nearText: {concepts: ["loan approval"]} where: { path: ["department"] operator: Equal valueString: "compliance" } ) { title snippet _additional { distance } } } } Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: graphql { Get { FinancialDocument( nearText: {concepts: ["loan approval"]} where: { path: ["department"] operator: Equal valueString: "compliance" } ) { title snippet _additional { distance } } } } CODE_BLOCK: graphql { Get { FinancialDocument( nearText: {concepts: ["loan approval"]} where: { path: ["department"] operator: Equal valueString: "compliance" } ) { title snippet _additional { distance } } } } - Full enterprise patterns (auth, error handling, observability) - Local LLMs (DeepSeek-R1 via Ollama) - 100% data sovereignty - Benchmarks on real hardware (GCP n2-standard-8)