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CAPSTONE
Vector Databases
0 Discussions
vector_pipeline.py
Vector Databases
Embeddings · Similarity Search · Indexing
4:12 / 11:48 CC   ⚙
HNSW Graph Navigation
0 Discussions
ACTIVITY
HNSW Graph Navigation
Watch how Hierarchical Navigable Small World graphs find the nearest neighbor using greedy search — hopping from node to node until reaching the query.
Entry Current Visited Query
Distance
Current Node
Steps Taken0
Neighbors Checked0
Cosine Similarity Search
0 Discussions

Cosine Similarity Search

Rank documents by cosine similarity to a query vector — the core retrieval mechanism inside every vector database.

[ ]
import numpy as np

def cosine_sim(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

store = {
    "cat":  np.array([0.90, 0.10, 0.20]),
    "dog":  np.array([0.80, 0.20, 0.30]),
    "car":  np.array([0.10, 0.90, 0.10]),
}
query = np.array([0.85, 0.15, 0.25])  # "feline pet?"

results = sorted(store.items(),
    key=lambda kv: cosine_sim(query, kv[1]),
    reverse=True)

for rank, (doc, vec) in enumerate(results, 1):
    print(f"{rank}. {doc}: {cosine_sim(query,vec):.3f}")
RankDocumentCosine similarityScore
Vector Databases — Quiz
1 Question
Question 1 of 1
Which data structure does a vector database use to enable fast approximate nearest-neighbor search?
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