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HNSW Index
Builds a navigable graph · jumps node-to-node to find the nearest neighbor fast
HNSW builds a graph where each vector is a node. Search starts at entry node A and greedily hops to whichever neighbor is most similar and stops when no neighbor is better.
Flow:
🚪 Start at A
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🔍 Compare neighbors
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🏃 Move to best
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🏆 Done
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Query Vector
Editable📍
Node Vectors
EditableEdit node values. similarity scores update live. New nodes connect to the last existing node.
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Graph Visualization
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Connections: A↔B, A↔C, B↔D, C↔D, C↔E, D↔F, E↔F
Press "Start Search" to begin the greedy graph traversal.
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FLAT checks
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💡 Key Idea
Follows best-path through a graph. Like GPS navigation, skips dead ends, finds nearest fast.
✅ Best For
High-speed search, large in-memory datasets, low latency.
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Tips
3 tips
Graph traversal, not clustering. HNSW builds layers of navigable graphs and jumps node-to-node toward the nearest neighbor.
It's one of the fastest approximate methods available, widely used in production vector databases.
The trade-off is higher memory use for the graph structure, and index construction can be slower than simpler methods.
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Key Takeaway
HNSW searches by hopping across a navigable graph instead of scanning vectors, delivering very fast approximate search at the cost of extra memory.