Imagine you walk into a huge conference hall full of documents. You shout:
“What are the elephants in the room and how do they differ?”
Somewhere in that hall, a few paragraphs describe “unspoken risks,” “hidden problems,” maybe even literally “elephants in the room.”
Vector search is the trick that lets a system:
- Understand what you meant by that question,
- Find the closest-meaning passages (the “elephant” chunks), and
- Hand those back as context for an AI answer.
Under the hood, this is all just vectors and distance. Let’s unpack that – with elephants.
Step 1: Turn Text into Elephant-Sized Chunks
First, we need to chop our documents into manageable pieces.
Instead of storing whole books or giant wiki pages as single blobs, we split them into chunks:
- A few paragraphs each,
- Or a function / method in code,
- Or maybe a section of a spec.
So if you had a project post-mortem, it might get chunked like this:
- Chunk 1: “Timeline of the project and initial goals…”
- Chunk 2: “Major blockers from the data team…”
- Chunk 3: “Unspoken risks – the real elephants in the room were performance and vendor lock-in…”
- Chunk 4: “Follow-up actions and mitigation plans…”
Why chunks?
- They’re small enough to be precise,
- Large enough to be meaningful,
- And they can be mixed and matched as needed.
Each chunk is like one elephant-sized thought, not the whole zoo.
Step 2: Vectors – Putting Elephants into N-Dimensional Space
Now we need a way to represent each chunk so a computer can tell whether two chunks “mean” similar things.
We do that by turning every chunk into a vector.
What’s a vector (intuitively)?
In 2D, a vector is just a point like (x, y) – an arrow from the origin to that point.
In vector search, we do the same thing, but in N dimensions:
- A chunk is now a point like
(v₁, v₂, …, vₙ). - N might be 256, 768, 1024 – whatever the embedding model uses.
You can think of this as:
Each chunk gets coordinates in a giant, invisible space where nearby points mean “similar meaning.”
Chunks that talk about “hidden risks,” “unspoken issues,” and “elephants in the room” end up close together in that space. A chunk about “database indexing tips” will live somewhere else entirely.
Embeddings = Coordinates
How do we get those coordinates? With an embedding model.
An embedding model takes text and returns a vector:
"Unspoken risks – the real elephants in the room were performance and vendor lock-in"
-> [0.13, -0.42, 0.07, ..., 0.55] (N numbers)
That array of N numbers is called an embedding. It is literally:
“The coordinates of this text chunk in N-dimensional meaning-space.”
We do this for every chunk and store those vectors in a vector database.
So the hall of documents turns into:
- Chunk 1 → vector A
- Chunk 2 → vector B
- Chunk 3 → vector C (our elephant chunk)
- …and so on.
Step 3: Measuring Similarity – How Close Are Two Elephants?
Once everything is a vector, “how similar are these two pieces of text?” becomes:
“How close are these two points in N-dimensional space?”
Two common ways to measure this:
-
Cosine similarity – looks at the angle between two vectors.
- 1.0 = pointing in exactly the same direction (very similar)
- 0 = unrelated
- -1 = opposite meanings (rare in embeddings, but that’s the idea)
-
Euclidean distance – straight-line distance between two points.
- Small distance = very similar
- Large distance = very different
Most vector databases use cosine similarity (or a close cousin) for text.
So if:
- Vector
E= embedding of your question about “elephants in the room” - Vector
C= embedding of chunk 3 (the paragraph literally mentioning “elephants in the room”)
Then similarity(E, C) will be higher than similarity(E, random-database-tuning-chunk).
In other words, the elephant chunks are closer to your question in vector space.
Step 4: The Full Loop – From Question to Closest Chunks
Let’s walk through the entire process for this query:
“What are the elephants in the room and how do they differ?”
1. Chunk the documents
Before you ever ask a question, the system:
- Breaks documents into chunks,
- Computes an embedding (vector) for each chunk,
- Stores those vectors in a vector index.
So the index might contain things like:
- Chunk A: “We underestimated scaling costs…” → vector
A - Chunk B: “Security gaps in the auth layer…” → vector
B - Chunk C: “Unspoken risks – the real elephants in the room were performance and vendor lock-in…” → vector
C - Chunk D: “Refactoring plan for next quarter…” → vector
D
2. Turn your question into a vector
When you ask:
“What are the elephants in the room and how do they differ?”
The system:
- Sends that text to the same embedding model,
- Gets back a vector
Q– your query embedding.
Now the query is another point in the same N-dimensional space.
3. Find the nearest neighbors (the closest elephants)
The vector database now searches for vectors closest to Q:
- Compute similarity between
QandA,B,C,D, … - Sort from most similar to least similar.
In our example, chunk C explicitly talks about “elephants in the room,” and maybe chunks A and B hint at big hidden risks.
So the ranking might look like:
- Chunk C – “Unspoken risks – the real elephants in the room were performance and vendor lock-in…” (highest similarity)
- Chunk A – “We underestimated scaling costs…” (also an “elephant,” but not named that way)
- Chunk B – “Security gaps in the auth layer…”
- Chunk D – “Refactoring plan for next quarter…” (mostly unrelated)
4. Return the top chunks as context
The system then returns, say, the top 3 chunks to the AI model as context.
From your perspective, it looks like magic:
- You ask about “elephants in the room,”
- The answer talks about performance bottlenecks, vendor lock-in, and security gaps,
- With quotes or summaries pulled from exactly those chunks.
Under the hood, it’s just:
- Chunk text → vectors
- Query text → vector
- Compare vectors → find nearest neighbors
- Use those chunks to answer your question
Why This Beats Simple Keyword Search
If we only used keywords, we’d match documents that literally contain the words:
- “elephants”
- “room”
- “differ”
But vector search can do much more:
- It can match chunks that talk about “unspoken risks” or “things nobody wants to mention” even if the word “elephant” never appears.
- It can understand that “vendor lock-in” and “performance ceilings” may both be different kinds of “big hidden problems.”
Because all of those ideas land in a similar region of vector space, they’re close to your question vector.
So when you ask for the elephants in the room:
- The system doesn’t just look for the word “elephant”;
- It looks for meaningfully similar chunks.
That’s the real power of vector search.
Recap (No Math, Just Elephants)
Putting it all together:
- Chunking – Big documents are split into smaller text fragments (chunks).
- Embeddings – Each chunk is turned into a high-dimensional vector (its coordinates in meaning-space).
- Query embedding – Your question is also turned into a vector.
- Similarity search – The system finds the chunk-vectors closest to your query vector.
- Answering – Those closest chunks (the ones “about the same thing”) are fed to an AI model to answer your question.
So next time you hear “vector search,” think of a room full of elephants – each one a chunk of text with its own coordinates in a strange, high-dimensional space. Ask, “What are the elephants in the room and how do they differ?” and vector search is the process that finds the right herd.
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