62 lines
2.1 KiB
Markdown
62 lines
2.1 KiB
Markdown
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---
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title: Choose HNSW vs FLAT Based on Requirements
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impact: HIGH
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impactDescription: HNSW trades accuracy for speed, FLAT provides exact results
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tags: vector, hnsw, flat, algorithm, performance
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description: Choose HNSW vs FLAT Based on Requirements
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alwaysApply: true
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---
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## Choose HNSW vs FLAT Based on Requirements
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Select the right algorithm based on your accuracy requirements and dataset size.
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| Algorithm | Speed | Accuracy | Memory | Best For |
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|-----------|-------|----------|--------|----------|
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| HNSW | Fast (approximate) | ~95%+ recall tunable | Higher | Large datasets (>10k vectors) |
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| FLAT | Slower (exact) | 100% (exact) | Lower | Small datasets, accuracy-critical |
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**Correct:** Use HNSW for large-scale production workloads.
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```python
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from redisvl.schema import IndexSchema
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# HNSW - fast approximate search, tunable accuracy
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schema = IndexSchema.from_dict({
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"index": {"name": "idx:docs", "prefix": "doc:"},
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"fields": [
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{"name": "embedding", "type": "vector", "attrs": {
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"dims": 1536,
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"algorithm": "HNSW",
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"distance_metric": "COSINE",
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"datatype": "FLOAT32",
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"m": 16, # Higher = more accurate, more memory
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"ef_construction": 200 # Higher = better index quality, slower build
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}}
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]
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})
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```
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**Correct:** Use FLAT when exact results are required.
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```python
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# FLAT - exact brute-force search, guaranteed accuracy
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schema = IndexSchema.from_dict({
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"index": {"name": "idx:small", "prefix": "small:"},
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"fields": [
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{"name": "embedding", "type": "vector", "attrs": {
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"dims": 1536,
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"algorithm": "FLAT",
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"distance_metric": "COSINE"
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}}
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]
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})
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```
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**Tuning HNSW accuracy vs speed:**
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- `M`: Connections per node (16-64). Higher = better recall, more memory
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- `EF_CONSTRUCTION`: Build-time parameter (100-500). Higher = better graph quality
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- `EF_RUNTIME`: Query-time parameter. Higher = better recall, slower queries
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Reference: [Redis Vector Search](https://redis.io/docs/latest/develop/ai/search-and-query/vectors/)
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