Understanding Meaning in Motion
How Vector Search Helps You Explore What You Didn’t Know You Were Looking For (Part 3)
Hi, I’m Florian, Fractional CTO and builder of AI-powered tools designed to solve real, everyday problems.
In Part 2, I explained why we store incoming content in two places: a structured database for clear filtering, and a vector database for deep search by meaning. But I didn’t explain how vector search actually works or why it’s more than just a fancy search bar.
Let’s unpack it.
A Different Kind of Search: One That Understands Meaning
When you use a traditional search (like Ctrl+F in a PDF or a keyword filter in a dashboard), it only finds exact terms. That’s helpful if you know precisely what you’re looking for.
But what if the terms are phrased differently? What if you want to explore concepts, not just exact matches?
That’s where vector search comes in. And behind it, something magical: embeddings.
Embeddings: Turning Text into Geometry
Every article, paragraph, or chunk of text in our system is run through an AI model that transforms it into a vector – a list of numbers.
Each of these vectors represents meaning in a multi-dimensional space. The result? Your content is no longer just words – it’s coordinates in a semantic landscape.
- Texts that mean similar things are close together.
- Texts with different meanings are far apart.
- Every new chunk becomes a point you can measure distances to.
This is how our assistant can:
- Suggest similar content even if it uses completely different language
- Link regulations with workflows or strategies they might affect
- Map large volumes of content to discover unexpected relationships
In short: it sees connections the human reader might miss or wouldn’t have time to find.
A Real Example from Our Project
Suppose we ingest 500 documents per day. These include:
- Legal updates on AI regulation
- Technical papers on privacy-preserving models
- Blog posts on recruiting automation
With a structured database, we can search: “All legal articles since April 2024 tagged ‘AI’.”
With a vector database, we can go deeper:
- “Which articles discuss accountability or fairness, even if they don’t mention AI directly?”
- “Find me everything semantically related to this report on GDPR and hiring.”
That second kind of question is where vector search shines. It opens the door to discovery, not just lookup.
Metadata Still Matters
Of course, meaning is nothing without context. That’s why we still use metadata to:
- Sort by source or author
- Prioritize recent updates
- Filter by document type or topic
Combining vector search with structured filtering gives us the best of both worlds:
- Explore the unknown
- Validate what you find
- Personalize for each user’s needs
It also sets the stage for richer insights. Because as we scale, we’ll want to:
- Find patterns in user behavior
- Recommend clusters of related content
- Support future research questions we haven’t thought of yet
What’s next in the build?
In Part 4, I’ll share how we prepare incoming documents before they even reach the database:
- How we clean and normalize raw content
- How we split it into smart chunks
- How we generate meaningful embeddings without breaking the bank
All openly shared, same goal as always: build tools that bring clarity – not complexity.