65 lines
1.0 KiB
Markdown
65 lines
1.0 KiB
Markdown
# 向量搜索引擎
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用于相似性搜索的本机 HNSW 和 IVF-PQ 索引。
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## 用法
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```nim
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import barabadb/vector/engine
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var idx = newHNSWIndex(dimensions = 128)
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idx.insert(1, @[1.0'f32, 0.0'f32, ...], {"category": "A"}.toTable)
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let results = idx.search(queryVector, k = 10)
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```
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## 索引类型
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### HNSW
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用于近似最近邻搜索的分层可导航小世界图。
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```nim
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var hnsw = newHNSWIndex(
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dimensions = 128,
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m = 16,
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efConstruction = 200,
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efSearch = 100
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)
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```
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### IVF-PQ
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带乘积量化的倒排文件索引。
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```nim
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var ivfpq = newIVFPQIndex(
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dimensions = 128,
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numCentroids = 256,
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subQuantizers = 8
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)
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```
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## 距离度量
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| 度量 | 描述 |
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|------|------|
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| `cosine` | 余弦相似度 |
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| `euclidean` | L2 距离 |
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| `dotproduct` | 点积相似度 |
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| `manhattan` | L1 距离 |
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## 量化
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```nim
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let scalar = scalarQuantize(data, bits = 8)
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let pq = productQuantize(data, subVectors = 8, bits = 8)
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```
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## SIMD 加速
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```nim
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import barabadb/vector/simd
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let dist = simdCosineDistance(vec1, vec2)
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``` |