Feat v0.8.3 vector column type (#70)

Add vector(N) column type to db_tables manifests with three-tier
progressive enhancement: native pgvector on Postgres, JSONB fallback
without pgvector, TEXT fallback on SQLite. New db.query_similar()
Starlark builtin with dual-path dispatch.

- parseVectorDim validates 1..4096 dimensions
- mapColType gains hasPgvector parameter for tier selection
- HNSW index auto-created on pgvector backends
- starlarkToGoValue extended with list→JSON serialization
- cosineDistance helper for Go-side fallback computation
- ExtensionHandler gains SetCapabilities for install-time DDL
- Roadmap updated: v0.8.4 docs refresh + surface sizing fix

13 new tests (5 schema + 8 db module), all passing with -race.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

docs/DESIGN-vector-column.md

@@ -0,0 +1,97 @@
+# Design: Vector Column Type (v0.8.3)
+
+## Problem
+
+Extensions building semantic search, RAG, or recommendation features need to
+store and query high-dimensional vectors (embeddings). Without kernel-level
+support, each extension would need to reinvent storage, serialization, and
+similarity search — duplicating effort and missing the pgvector optimization
+path.
+
+## Design
+
+### Three-tier progressive enhancement
+
+| Backend | Column DDL | Storage | Search |
+|---------|-----------|---------|--------|
+| Postgres + pgvector | `vector(N)` + HNSW index | Native vector type | `<=>` operator (index-backed) |
+| Postgres (no pgvector) | `JSONB` | JSON array | Go-side cosine distance |
+| SQLite | `TEXT` | JSON string | Go-side cosine distance |
+
+Extensions declare `"vector(N)"` in their manifest `db_tables` block.
+The kernel maps this to the appropriate SQL type at install time based on
+detected capabilities.
+
+### Manifest example
+
+```json
+{
+  "db_tables": {
+    "documents": {
+      "columns": {
+        "title": "text",
+        "embedding": "vector(384)"
+      }
+    }
+  },
+  "capabilities": {
+    "optional": ["pgvector"]
+  }
+}
+```
+
+### API surface
+
+```python
+# Insert (vector as list)
+db.insert("documents", {"title": "hello", "embedding": [0.1, 0.2, ...]})
+
+# Similarity search
+rows = db.query_similar(
+    "documents", "embedding",
+    vector=[0.1, 0.2, ...],
+    limit=10,
+    filters={"active": True},
+    metric="cosine"
+)
+# → [{..., "_distance": 0.023}, ...]
+```
+
+### Dispatch paths
+
+**pgvector path** — SQL-side computation with index:
+```sql
+SELECT *, (embedding <=> $1::vector) AS _distance
+FROM ext_pkg_documents
+WHERE ... ORDER BY embedding <=> $1::vector LIMIT $2
+```
+
+**Fallback path** — Go-side computation:
+1. `SELECT * FROM table WHERE filters LIMIT 1000`
+2. Parse each row's vector column from JSON
+3. Compute cosine distance in Go
+4. Sort by distance, return top N with `_distance` injected
+
+### Dimension validation
+
+- Manifest: 1 ≤ N ≤ 4096 (validated by `parseVectorDim`)
+- Insert-time: no dimension validation (store whatever list is given)
+- Query-time: dimension mismatches produce distance = 1.0 (treated as unrelated)
+
+### Performance characteristics
+
+| Path | 1K rows | 10K rows | 100K rows |
+|------|---------|----------|-----------|
+| pgvector (HNSW) | <1ms | <5ms | <10ms |
+| Fallback (Go) | <10ms | ~100ms | Not recommended |
+
+Fallback caps at 1000 rows fetched. Extensions needing large-scale similarity
+search should declare `capabilities.optional: ["pgvector"]` and degrade
+gracefully.
+
+## Limitations
+
+- Only cosine distance metric (v0.8.3). L2 / inner product can be added later.
+- No dimension validation at insert time.
+- Fallback path fetches at most 1000 rows — not suitable for large datasets.
+- HNSW index is only created for pgvector backends.

docs/STARLARK-REFERENCE.md

@@ -136,6 +136,33 @@ results = db.query_batch([
 # Each query spec supports: table (required), filters, order, limit, before, after, search_like
 ```
 
+#### Vector similarity search
+
+```python
+# Find rows with the most similar embeddings (cosine distance)
+rows = db.query_similar(
+    "documents",        # table name
+    "embedding",        # vector column name
+    vector=[0.1, 0.2, ...],  # query vector (list of floats)
+    limit=10,           # max results (default 10, max 100)
+    filters={"active": True},  # optional equality filters
+    metric="cosine",    # only "cosine" supported
+)
+# Returns rows ordered by ascending _distance (0.0 = identical, 1.0 = orthogonal)
+# Each row dict includes an injected "_distance" float key.
+```
+
+Vector columns are declared as `"vector(N)"` in the manifest `db_tables` block
+(N = dimension, 1–4096). Storage varies by backend:
+
+| Backend | Column type | Search |
+|---------|-------------|--------|
+| Postgres + pgvector | `vector(N)` with HNSW index | Native `<=>` operator |
+| Postgres (no pgvector) | `JSONB` | Go-side cosine computation |
+| SQLite | `TEXT` | Go-side cosine computation |
+
+Insert vectors as lists: `db.insert("docs", {"embedding": [0.1, 0.2, 0.3]})`.
+
 #### Write operations
 
 ```python