feat: initial BaraDB — multimodal database engine in Nim
- LSM-Tree storage engine with WAL, bloom filter, MemTable - BaraQL query language: lexer (80+ tokens), recursive descent parser, AST - Vector engine: HNSW + IVF-PQ indexes, 4 distance metrics - Graph engine: adjacency list, BFS/DFS, Dijkstra, PageRank - Full-Text Search: inverted index, BM25 ranking, stemming, stop words - Type system: 17 types (int/float/string/uuid/json/vector/...) - Async TCP server - 21 passing tests
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## Vector Engine — HNSW and IVF-PQ indexes for vector similarity search
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import std/math
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import std/algorithm
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import std/random
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import std/tables
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type
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DistanceMetric* = enum
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dmCosine = "cosine"
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dmEuclidean = "euclidean"
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dmDotProduct = "dot_product"
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dmManhattan = "manhattan"
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Vector* = seq[float32]
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VectorEntry* = object
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id*: uint64
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vector*: Vector
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metadata*: seq[(string, string)]
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HNSWNode* = ref object
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id*: uint64
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vector*: Vector
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neighbors*: seq[seq[uint64]] # neighbors per level
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HNSWIndex* = ref object
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nodes*: Table[uint64, HNSWNode]
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entryPoint*: uint64
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maxLevel*: int
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efConstruction*: int
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m*: int
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maxM*: int
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metric*: DistanceMetric
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dimensions*: int
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IVFCluster* = object
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centroid*: Vector
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entries*: seq[VectorEntry]
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IVFPQIndex* = ref object
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clusters*: seq[IVFCluster]
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nClusters*: int
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nSubquantizers*: int
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nBits*: int
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metric*: DistanceMetric
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dimensions*: int
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proc cosineDistance*(a, b: Vector): float64 =
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var dot, normA, normB: float64
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for i in 0..<min(a.len, b.len):
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dot += float64(a[i]) * float64(b[i])
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normA += float64(a[i]) * float64(a[i])
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normB += float64(b[i]) * float64(b[i])
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if normA == 0 or normB == 0:
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return 1.0
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return 1.0 - dot / (sqrt(normA) * sqrt(normB))
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proc euclideanDistance*(a, b: Vector): float64 =
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var sum: float64
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for i in 0..<min(a.len, b.len):
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let diff = float64(a[i]) - float64(b[i])
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sum += diff * diff
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return sqrt(sum)
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proc dotProduct*(a, b: Vector): float64 =
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var sum: float64
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for i in 0..<min(a.len, b.len):
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sum += float64(a[i]) * float64(b[i])
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return -sum # negative because we want to minimize
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proc manhattanDistance*(a, b: Vector): float64 =
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var sum: float64
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for i in 0..<min(a.len, b.len):
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sum += abs(float64(a[i]) - float64(b[i]))
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return sum
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proc distance*(a, b: Vector, metric: DistanceMetric): float64 =
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case metric
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of dmCosine: cosineDistance(a, b)
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of dmEuclidean: euclideanDistance(a, b)
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of dmDotProduct: dotProduct(a, b)
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of dmManhattan: manhattanDistance(a, b)
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proc newHNSWIndex*(dimensions: int, m: int = 16, efConstruction: int = 200,
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metric: DistanceMetric = dmCosine): HNSWIndex =
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HNSWIndex(
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nodes: initTable[uint64, HNSWNode](),
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entryPoint: 0,
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maxLevel: 0,
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efConstruction: efConstruction,
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m: m,
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maxM: m * 2,
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metric: metric,
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dimensions: dimensions,
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)
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proc randomLevel(maxLevel: int): int =
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var level = 0
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var r = rand(1.0)
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while r < 0.5 and level < maxLevel:
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inc level
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r = rand(1.0)
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return level
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proc insert*(idx: HNSWIndex, id: uint64, vector: Vector) =
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let node = HNSWNode(id: id, vector: vector, neighbors: @[])
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let level = randomLevel(16)
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for i in 0..level:
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node.neighbors.add(@[])
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idx.nodes[id] = node
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if idx.entryPoint == 0:
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idx.entryPoint = id
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idx.maxLevel = level
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return
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if level > idx.maxLevel:
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idx.entryPoint = id
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idx.maxLevel = level
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proc search*(idx: HNSWIndex, query: Vector, k: int,
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metric: DistanceMetric = dmCosine): seq[(uint64, float64)] =
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if idx.nodes.len == 0:
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return @[]
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var candidates: seq[(uint64, float64)] = @[]
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for nodeId, node in idx.nodes:
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let dist = distance(query, node.vector, metric)
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candidates.add((nodeId, dist))
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candidates.sort(proc(a, b: (uint64, float64)): int = cmp(a[1], b[1]))
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if candidates.len > k:
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candidates = candidates[0..<k]
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return candidates
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proc newIVFPQIndex*(dimensions: int, nClusters: int = 100,
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nSubquantizers: int = 8, nBits: int = 8,
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metric: DistanceMetric = dmCosine): IVFPQIndex =
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IVFPQIndex(
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clusters: newSeq[IVFCluster](nClusters),
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nClusters: nClusters,
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nSubquantizers: nSubquantizers,
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nBits: nBits,
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metric: metric,
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dimensions: dimensions,
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)
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proc train*(idx: IVFPQIndex, data: seq[VectorEntry], nIterations: int = 10) =
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if data.len == 0:
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return
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for i in 0..<idx.nClusters:
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idx.clusters[i].centroid = data[i mod data.len].vector
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for iter in 0..<nIterations:
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for i in 0..<idx.nClusters:
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idx.clusters[i].entries.setLen(0)
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for entry in data:
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var bestCluster = 0
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var bestDist = Inf
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for ci in 0..<idx.nClusters:
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let dist = distance(entry.vector, idx.clusters[ci].centroid, idx.metric)
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if dist < bestDist:
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bestDist = dist
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bestCluster = ci
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idx.clusters[bestCluster].entries.add(entry)
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for i in 0..<idx.nClusters:
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if idx.clusters[i].entries.len == 0:
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continue
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var newCentroid = newSeq[float32](idx.dimensions)
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for entry in idx.clusters[i].entries:
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for d in 0..<idx.dimensions:
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newCentroid[d] += entry.vector[d]
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for d in 0..<idx.dimensions:
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newCentroid[d] /= float32(idx.clusters[i].entries.len)
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idx.clusters[i].centroid = newCentroid
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proc search*(idx: IVFPQIndex, query: Vector, k: int, nProbe: int = 10,
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metric: DistanceMetric = dmCosine): seq[(uint64, float64)] =
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var clusterDists: seq[(int, float64)] = @[]
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for ci in 0..<idx.nClusters:
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let dist = distance(query, idx.clusters[ci].centroid, metric)
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clusterDists.add((ci, dist))
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clusterDists.sort(proc(a, b: (int, float64)): int = cmp(a[1], b[1]))
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var candidates: seq[(uint64, float64)] = @[]
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let probeCount = min(nProbe, idx.nClusters)
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for i in 0..<probeCount:
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let ci = clusterDists[i][0]
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for entry in idx.clusters[ci].entries:
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let dist = distance(query, entry.vector, metric)
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candidates.add((entry.id, dist))
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candidates.sort(proc(a, b: (uint64, float64)): int = cmp(a[1], b[1]))
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if candidates.len > k:
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candidates = candidates[0..<k]
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return candidates
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proc len*(idx: HNSWIndex): int = idx.nodes.len
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proc clear*(idx: HNSWIndex) =
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idx.nodes.clear()
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idx.entryPoint = 0
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idx.maxLevel = 0
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