Feat v0.8.3 vector column (#70)

Co-authored-by: Jeffrey Smith <jasafpro@gmail.com>
Co-committed-by: Jeffrey Smith <jasafpro@gmail.com>

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