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dimgigov e1bae0c7a0 Add comprehensive documentation with i18n support (EN/BG)

- Add docs/ folder with English (en/) and Bulgarian (bg/) documentation
- Create index.md with language switching and links
- English docs: installation, quickstart, architecture, baraql, storage,
  schema, lsm, btree, vector, graph, fts, columnar, transactions,
  distributed, protocol, udf, api-binary, api-http, api-websocket
- Bulgarian docs: installation, quickstart, architecture, baraql,
  schema, lsm, btree, vector, graph, fts, transactions, distributed
- Update README license to BSD 3-Clause
- Add LICENSE file with BSD 3-Clause text

2026-05-06 16:51:14 +03:00

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Vector Search Engine

Native HNSW and IVF-PQ indexes for similarity search.

Usage

import barabadb/vector/engine

var idx = newHNSWIndex(dimensions = 128)
idx.insert(1, @[1.0'f32, 0.0'f32, ...], {"category": "A"}.toTable)

# Search
let results = idx.search(queryVector, k = 10)

# With metadata filtering
let filtered = idx.searchWithFilter(queryVector, k = 10,
  filter = proc(meta: Table[string, string]): bool =
    return meta.getOrDefault("category") == "A")

Index Types

HNSW

Hierarchical Navigable Small World graph for approximate nearest neighbor search.

var hnsw = newHNSWIndex(
  dimensions = 128,
  m = 16,           # connections per layer
  efConstruction = 200,  # search width during construction
  efSearch = 100    # search width during query
)

IVF-PQ

Inverted File Index with Product Quantization for compression.

var ivfpq = newIVFPQIndex(
  dimensions = 128,
  numCentroids = 256,
  subQuantizers = 8
)

Distance Metrics

Metric	Description
`cosine`	Cosine similarity
`euclidean`	L2 distance
`dotproduct`	Dot product similarity
`manhattan`	L1 distance

Quantization

import barabadb/vector/quant

# Scalar quantization
let scalar = scalarQuantize(data, bits = 8)

# Product quantization
let pq = productQuantize(data, subVectors = 8, bits = 8)

SIMD Acceleration

import barabadb/vector/simd

let dist = simdCosineDistance(vec1, vec2)

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