Similarity Search Explained
Understand the algorithms and mechanisms behind finding similar items using vector distance metrics.
Similarity Search Explained is a free Vector Databases: Pinecone, Weaviate & pgvector lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Vector Databases: Pinecone, Weaviate & pgvector learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Finding What's Alike
You know vectors and embeddings — now see how vector databases use them to find items that are alike. That process is similarity search.
Vectors: Your Data's Coordinates
An embedding is just coordinates in a space, like [0.1, 0.5, -0.2]. Similarity search compares them: close coordinates mean similar original items.
The Idea of 'Distance'
We measure closeness with distance metrics — formulas that return a number for how far apart two vectors are. Smaller distance, more similar.
Euclidean Distance: Straight Line
Euclidean distance is the most intuitive: the straight-line length between two points. It shines when a vector's magnitude matters for similarity.
Euclidean in Action
Here's how to compute Euclidean distance between two simple 2D vectors in code.
import math
def euclidean_distance(v1, v2):
# Calculates the square root of the sum of squared differences
return math.sqrt(sum((x - y)**2 for x, y in zip(v1, v2)))
# Example vectors
vec_a = [1, 2]
vec_b = [4, 6]
distance = euclidean_distance(vec_a, vec_b)
print(f"Vector A: {vec_a}")
print(f"Vector B: {vec_b}")
print(f"Euclidean Distance: {distance:.2f}")Cosine Similarity: Angle Matters
Cosine similarity measures the angle between vectors — whether they point the same way. Great for text, since it tracks theme over length and ignores magnitude.
Cosine in Action
A cosine similarity near 1 means the vectors are closely aligned, so more similar in direction. See it in action here.
import math
def dot_product(v1, v2):
return sum(x * y for x, y in zip(v1, v2))
def magnitude(v):
return math.sqrt(sum(x**2 for x in v))
def cosine_similarity(v1, v2):
dp = dot_product(v1, v2)
mag1 = magnitude(v1)
mag2 = magnitude(v2)
if mag1 == 0 or mag2 == 0:
return 0 # Handle zero vectors
return dp / (mag1 * mag2)
# Example vectors
vec_x = [1, 1] # Direction 45 degrees
vec_y = [2, 2] # Same direction, larger magnitude
vec_z = [-1, 1] # Different direction
sim_xy = cosine_similarity(vec_x, vec_y)
sim_xz = cosine_similarity(vec_x, vec_z)
print(f"Cosine Similarity (X, Y): {sim_xy:.2f}")
print(f"Cosine Similarity (X, Z): {sim_xz:.2f}")Beyond Euclidean & Cosine
Beyond Euclidean and cosine, other metrics exist: Manhattan distance sums absolute differences, dot product measures projection. Pick what 'similar' means for your data.
How Vector DBs Search
Vector DBs don't compute distances one by one — too slow for millions. They use indexing structures, like a library catalog for vectors, to narrow the search fast.
Approximate Nearest Neighbor (ANN)
Most vector DBs use Approximate Nearest Neighbor (ANN) search: instead of the exact closest vector, they return ones very close — trading a little accuracy for huge speed.
Quick Check: Which Metric?
You're building a system to find articles with similar themes, even if one article is much longer than another. Which similarity metric would generally be most appropriate?
Recap: Your Similarity Search Toolkit
Recap: similarity search compares vectors via distance metrics — Euclidean (magnitude-sensitive) and cosine (angle, robust to length) — sped up by indexes and ANN.
Frequently asked questions
Is the “Similarity Search Explained” lesson free?
Yes — the full text of “Similarity Search Explained” is free to read here on the web, and the Vector Databases: Pinecone, Weaviate & pgvector course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Vector Databases: Pinecone, Weaviate & pgvector course, upgrade to CoddyKit PRO.
What will I learn in “Similarity Search Explained”?
Understand the algorithms and mechanisms behind finding similar items using vector distance metrics. You practise Vector Databases: Pinecone, Weaviate & pgvector with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Vector Databases: Pinecone, Weaviate & pgvector?
No prior experience is required. Vector Databases: Pinecone, Weaviate & pgvector on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Similarity Search Explained” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Vector Databases: Pinecone, Weaviate & pgvector lesson?
Yes. Every Vector Databases: Pinecone, Weaviate & pgvector lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- What are Vector Databases?
- Embeddings: The Core Concept
- Similarity Search Explained
- Distance Metrics and Indexing Basics