Scaling RAG Applications with Weaviate
In my ongoing exploration of Retrieval-Augmented Generation (RAG), I recently took a deep dive into Weaviate — a Dutch company making waves with its open-source vector database.
If you're working on AI agents, knowledge bots, or anything that relies on semantically rich information retrieval, understanding how a vector database like Weaviate fits into the architecture is not optional. It's essential.
Why Weaviate caught my attention
Several of my recent projects, especially in healthcare and multilingual knowledge sharing, have reached the limits of what can be done with off-the-shelf tools. I needed something that could scale.
A friend with a deep tech background pointed me to Weaviate—a vector-first database designed for AI-native applications. After attending two demos and speaking directly with one of their experts, it started to make sense.
What makes a vector database different?
Traditional databases store and retrieve exact matches: strings, numbers, Booleans.
Vector databases operate in an entirely different paradigm. They store the "meaning" of content by translating text, images, and other data into vectors — long lists of numbers that live in a multi-dimensional space.
When a user sends a query, it too gets turned into a vector. The database then finds the closest match by meaning, not by keywords.
This approach powers fast, flexible semantic search and is ideal for RAG setups, where you combine a vector search with a large language model to generate rich, context-aware answers.
Live demos that brought it to life
The demo I attended was hosted by JP Hwang, and it stood out immediately. He managed to make the subject matter both light and insightful — no small feat when dealing with something as abstract as vector embeddings and semantic search. His hands-on approach made the technology tangible in minutes.
One of the demos used a dataset of 1,000 movies, showcasing how Weaviate ingested title, overview, year, and popularity fields, vectorised them using models like Cohere or OpenAI, and enabled search queries like:
- "Movies about history"
- "Green action hero"
- or even questions in Dutch or Korean, against an English dataset
What impressed me most was how fast and flexible it was. You can:
- filter like in SQL (e.g. release year between 2000 and 2010)
- perform fuzzy or semantic searches ("history" finds Back to the Future)
- rank results based on distance in vector space
- combine this with generative AI to summarise or extract meaning
Scaling up: the real differentiator
Here's where vector databases shine.
Traditional brute-force search across millions of objects breaks down fast. Vector databases like Weaviate use approximate nearest neighbour (ANN) search, which keeps retrieval fast even as your dataset grows exponentially.
This makes it feasible to build enterprise-grade search and assistant experiences across billions of records.
For example, if you're building a chatbot that answers questions from PDF manuals, medical journals, or user feedback, you can:
- convert your content into chunks
- embed them into vector space
- store them in Weaviate
- query them live via API or LLM
All without retraining your model every time you add new content.
Open-source roots, enterprise readiness
Weaviate is open source, so you can run it locally or on your own infrastructure.
For hosted services, they offer a usage-based cloud model starting at around $25/month, with the option to scale to enterprise support.
Final thoughts
If you're serious about building AI systems that need high-quality, flexible information retrieval at scale, vector databases aren't just nice-to-have. They're foundational.
Weaviate, with its thoughtful integrations, support for multiple modalities and languages, and strong developer experience, is one of the most compelling options I've seen.
I'll be keeping an eye on their roadmap, and possibly doing a follow-up on how it performs in production settings.
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Interested in this space? Let me know if you want to swap notes or brainstorm use cases.
