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

docs/en/vector.md

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+# Vector Search Engine
+
+Native HNSW and IVF-PQ indexes for similarity search.
+
+## Usage
+
+```nim
+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.
+
+```nim
+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.
+
+```nim
+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
+
+```nim
+import barabadb/vector/quant
+
+# Scalar quantization
+let scalar = scalarQuantize(data, bits = 8)
+
+# Product quantization
+let pq = productQuantize(data, subVectors = 8, bits = 8)
+```
+
+## SIMD Acceleration
+
+```nim
+import barabadb/vector/simd
+
+let dist = simdCosineDistance(vec1, vec2)
+```