docs(en): Update English docs for Vector SQL Integration

- docs/en/vector.md — add SQL usage section (CREATE TABLE VECTOR,
  distance functions, <-> operator, CREATE INDEX USING hnsw)
- docs/en/baraql.md — update vector search section with real SQL syntax,
  add VECTOR(n) to data types, update keyword table
- docs/en/changelog.md — add Vector SQL Integration and bugfixes to [Unreleased]
- docs/ARCHITECTURE.md — add SQL Integration bullet to Vector Engine
- README.md — update vector engine section with SQL examples,
  add Vector SQL to roadmap, bump test count to 340+

docs/en/vector.md

@@ -1,8 +1,89 @@
 # Vector Search Engine
 
-Native HNSW and IVF-PQ indexes for similarity search.
+Native HNSW and IVF-PQ indexes for similarity search with full SQL integration.
 
-## Usage
+## SQL Usage
+
+### Creating Vector Columns
+
+```sql
+CREATE TABLE items (
+  id INT PRIMARY KEY,
+  embedding VECTOR(768)
+);
+```
+
+The `VECTOR(n)` type stores float32 arrays of fixed dimension `n`.
+
+### Inserting Vectors
+
+```sql
+INSERT INTO items (id, embedding) VALUES (1, '[0.1, 0.2, 0.3, ...]');
+```
+
+### Vector Distance Functions
+
+```sql
+-- Cosine distance (0 = identical, 1 = orthogonal)
+SELECT id, cosine_distance(embedding, '[0.1, 0.2, 0.3]') AS dist
+FROM items;
+
+-- Euclidean / L2 distance
+SELECT id, euclidean_distance(embedding, '[0.1, 0.2, 0.3]') AS dist
+FROM items;
+
+-- L2 distance with <-> operator
+SELECT id, embedding <-> '[0.1, 0.2, 0.3]' AS dist
+FROM items;
+
+-- Inner product (negative dot product for minimization)
+SELECT id, inner_product(embedding, '[0.1, 0.2, 0.3]') AS dist
+FROM items;
+
+-- Manhattan / L1 distance
+SELECT id, l1_distance(embedding, '[0.1, 0.2, 0.3]') AS dist
+FROM items;
+```
+
+### Nearest Neighbor Search
+
+```sql
+-- Top-10 nearest neighbors by cosine distance
+SELECT id FROM items
+ORDER BY cosine_distance(embedding, '[0.1, 0.2, 0.3]') ASC
+LIMIT 10;
+
+-- Top-5 nearest neighbors by Euclidean distance
+SELECT id FROM items
+ORDER BY embedding <-> '[0.1, 0.2, 0.3]'
+LIMIT 5;
+```
+
+### Vector Indexes
+
+```sql
+-- Create HNSW index for approximate nearest neighbor search
+CREATE INDEX idx_items_vec ON items(embedding) USING hnsw;
+
+-- The index is automatically maintained on INSERT and UPDATE
+```
+
+Supported index methods:
+- `USING hnsw` — Hierarchical Navigable Small World (default: cosine metric)
+- `USING ivfpq` — Inverted File with Product Quantization
+
+### Dimension Validation
+
+BaraDB validates vector dimensions at insert time:
+
+```sql
+-- This will fail: expected 768 dimensions but got 3
+INSERT INTO items (id, embedding) VALUES (2, '[1.0, 2.0, 3.0]');
+```
+
+## Native Nim API
+
+For embedded or high-performance use, use the native Nim API directly:
 
 ```nim
 import barabadb/vector/engine
@@ -48,12 +129,12 @@ var ivfpq = newIVFPQIndex(
 
 ## Distance Metrics
 
-| Metric | Description |
-|--------|-------------|
-| `cosine` | Cosine similarity |
-| `euclidean` | L2 distance |
-| `dotproduct` | Dot product similarity |
-| `manhattan` | L1 distance |
+| Metric | SQL Function | Description |
+|--------|--------------|-------------|
+| `cosine` | `cosine_distance(a, b)` | Cosine dissimilarity (1 - similarity) |
+| `euclidean` | `euclidean_distance(a, b)` / `<->` | L2 distance |
+| `dotproduct` | `inner_product(a, b)` | Negative dot product |
+| `manhattan` | `l1_distance(a, b)` | L1 distance |
 
 ## Quantization