diff --git a/README.md b/README.md
index 83ccc25..63bf351 100644
--- a/README.md
+++ b/README.md
@@ -57,9 +57,10 @@ An extension for VillageSQL Server that adds a vector data type with external co
 
 ### Loading the Extension
 
-Before using any SVECTOR features, load the extension in your session:
+Because this extension depends on preview APIs, you must enable preview extensions before installing:
 
 ```sql
+SET PERSIST vsql_allow_preview_extensions = ON;
 INSTALL EXTENSION vsql_vector;
 ```
 
@@ -97,20 +98,20 @@ INSERT INTO embeddings VALUES (5, '[9.0, 8.0, 7.0, 6.0]', NULL);  -- L1 dist fro
 
 | Function | Returns | Description |
 |---|---|---|
-| `SVECTOR_DIMENSION(v)` | INT | Declared dimension of the vector |
-| `SVECTOR_MAX_DIMENSION()` | INT | Maximum supported dimension (3072) |
-| `SVECTOR_NORM(v)` | REAL | L2 (Euclidean) norm |
-| `SVECTOR_FORMAT(v, precision)` | STRING | Vector as a fixed-precision decimal string |
-| `SVECTOR_HEX(v)` | STRING | Raw float bytes as uppercase hex (useful for debugging) |
+| `VECTOR_DIMENSION(v)` | INT | Declared dimension of the vector |
+| `VECTOR_MAX_DIMENSION()` | INT | Maximum supported dimension (3072) |
+| `VECTOR_NORM(v)` | REAL | L2 (Euclidean) norm |
+| `VECTOR_FORMAT(v, precision)` | STRING | Vector as a fixed-precision decimal string |
+| `VECTOR_HEX(v)` | STRING | Raw float bytes as uppercase hex (useful for debugging) |
 
 #### Distance and similarity functions
 
 | Function | Returns | Description |
 |---|---|---|
-| `SVECTOR_DISTANCE_L1(v1, v2)` | REAL | L1 (Manhattan) distance — sum of absolute differences |
-| `SVECTOR_DISTANCE_L2(v1, v2)` | REAL | L2 (Euclidean) distance — square root of sum of squared differences |
-| `SVECTOR_DISTANCE_COSINE(v1, v2)` | REAL | Cosine distance — `1 - cosine_similarity`; range [0, 2] |
-| `SVECTOR_INNER_PRODUCT(v1, v2)` | REAL | Dot product (similarity, not a metric); higher means more similar |
+| `L1_DISTANCE(v1, v2)` | REAL | L1 (Manhattan) distance — sum of absolute differences |
+| `L2_DISTANCE(v1, v2)` | REAL | L2 (Euclidean) distance — square root of sum of squared differences |
+| `COSINE_DISTANCE(v1, v2)` | REAL | Cosine distance — `1 - cosine_similarity`; range [0, 2] |
+| `INNER_PRODUCT(v1, v2)` | REAL | Dot product (similarity, not a metric); higher means more similar |
 
 Both arguments to a distance/similarity function must have the same dimension. All functions return NULL if either argument is NULL.
 
@@ -118,10 +119,10 @@ Both arguments to a distance/similarity function must have the same dimension. A
 
 ```sql
 -- Norm of a stored vector
-SELECT id, SVECTOR_NORM(vec) AS norm FROM embeddings ORDER BY id;
+SELECT id, VECTOR_NORM(vec) AS norm FROM embeddings ORDER BY id;
 
 -- L2 distance between two stored vectors
-SELECT SVECTOR_DISTANCE_L2(a.vec, b.vec) AS dist
+SELECT L2_DISTANCE(a.vec, b.vec) AS dist
 FROM embeddings a, embeddings b
 WHERE a.id = 1 AND b.id = 2;
 
@@ -129,7 +130,7 @@ WHERE a.id = 1 AND b.id = 2;
 -- Note: HNSW index support is planned; today this performs a sequential scan.
 -- The query vector is stored in a table row and joined in as a workaround
 -- for the current limitation on inline constant vectors (see Known Limitations).
-SELECT id, SVECTOR_DISTANCE_L1(vec, query.ref_vec) AS dist
+SELECT id, L1_DISTANCE(vec, query.ref_vec) AS dist
 FROM embeddings,
      (SELECT vec AS ref_vec FROM embeddings WHERE id = 1) AS query
 ORDER BY dist ASC
@@ -137,7 +138,7 @@ LIMIT 2;
 -- Expected result: id=1 dist=0, id=2 dist=1
 
 -- TODO: once inline constant vector support is added, the intended syntax is:
--- SELECT id, SVECTOR_DISTANCE_L1(vec, '[1.0, 2.0, 3.0, 4.0]') AS dist
+-- SELECT id, L1_DISTANCE(vec, '[1.0, 2.0, 3.0, 4.0]') AS dist
 -- FROM embeddings
 -- ORDER BY dist ASC
 -- LIMIT 2;
diff --git a/mysql-test/r/readme_examples.result b/mysql-test/r/readme_examples.result
new file mode 100644
index 0000000..dd5f69c
--- /dev/null
+++ b/mysql-test/r/readme_examples.result
@@ -0,0 +1,127 @@
+SET PERSIST vsql_allow_preview_extensions = ON;
+INSTALL EXTENSION vsql_vector;
+# ---- README: SVECTOR Type ----
+# Create a table with a vector column
+CREATE TABLE embeddings (
+id    INT PRIMARY KEY,
+vec   SVECTOR(4) NOT NULL
+) ENGINE=InnoDB;
+# Add a vector column to an existing table
+ALTER TABLE embeddings ADD COLUMN vec2 SVECTOR(4) NULL;
+# Insert vectors (number of elements must match the declared dimension)
+INSERT INTO embeddings VALUES (1, '[1.0, 2.0, 3.0, 4.0]', NULL);
+INSERT INTO embeddings VALUES (2, '[1.0, 2.0, 3.0, 5.0]', NULL);
+INSERT INTO embeddings VALUES (3, '[1.0, 2.0, 5.0, 4.0]', NULL);
+INSERT INTO embeddings VALUES (4, '[2.0, 4.0, 6.0, 8.0]', NULL);
+INSERT INTO embeddings VALUES (5, '[9.0, 8.0, 7.0, 6.0]', NULL);
+# ---- README: Scalar / utility functions ----
+SELECT VECTOR_MAX_DIMENSION() AS max_dim;
+max_dim
+3072
+SELECT id, VECTOR_DIMENSION(vec) AS dims FROM embeddings ORDER BY id;
+id	dims
+1	4
+2	4
+3	4
+4	4
+5	4
+SELECT id, VECTOR_NORM(vec) AS norm FROM embeddings ORDER BY id;
+id	norm
+1	5.477225575051661
+2	6.244997998398398
+3	6.782329983125268
+4	10.954451150103322
+5	15.165750888103101
+SELECT id, VECTOR_FORMAT(vec, 2) AS formatted FROM embeddings ORDER BY id;
+id	formatted
+1	[1,2,3,4]
+2	[1,2,3,5]
+3	[1,2,5,4]
+4	[2,4,6,8]
+5	[9,8,7,6]
+SELECT SUBSTRING(VECTOR_HEX(vec), 17) AS hex_data FROM embeddings WHERE id = 1;
+hex_data
+0000803F000000400000404000008040
+# ---- README: Distance and similarity functions ----
+CREATE TABLE func_exact (
+id INT PRIMARY KEY,
+v1 SVECTOR(4) NOT NULL,
+v2 SVECTOR(4) NOT NULL
+) ENGINE=InnoDB;
+INSERT INTO func_exact VALUES
+(1, '[1.0, 0.0, 0.0, 0.0]', '[1.0, 0.0, 0.0, 0.0]'),
+(2, '[1.0, 0.0, 0.0, 0.0]', '[0.0, 1.0, 0.0, 0.0]');
+# L1_DISTANCE
+SELECT id, L1_DISTANCE(v1, v2) AS l1 FROM func_exact ORDER BY id;
+id	l1
+1	0
+2	2
+# L2_DISTANCE
+SELECT id, L2_DISTANCE(v1, v2) AS l2 FROM func_exact ORDER BY id;
+id	l2
+1	0
+2	1.4142135623730951
+# COSINE_DISTANCE (0 = same direction, 1 = orthogonal)
+SELECT id, COSINE_DISTANCE(v1, v2) AS cos_dist FROM func_exact ORDER BY id;
+id	cos_dist
+1	0
+2	1
+# INNER_PRODUCT
+SELECT id, INNER_PRODUCT(v1, v2) AS ip FROM func_exact ORDER BY id;
+id	ip
+1	1
+2	0
+# NULL handling: distance functions return NULL when either argument is NULL
+SELECT id,
+L1_DISTANCE(vec, vec2)     AS l1,
+L2_DISTANCE(vec, vec2)     AS l2,
+COSINE_DISTANCE(vec, vec2) AS cos_dist,
+INNER_PRODUCT(vec, vec2)   AS ip
+FROM embeddings ORDER BY id;
+id	l1	l2	cos_dist	ip
+1	NULL	NULL	NULL	NULL
+2	NULL	NULL	NULL	NULL
+3	NULL	NULL	NULL	NULL
+4	NULL	NULL	NULL	NULL
+5	NULL	NULL	NULL	NULL
+DROP TABLE func_exact;
+# ---- README: Example Queries ----
+# Norm of a stored vector
+SELECT id, VECTOR_NORM(vec) AS norm FROM embeddings ORDER BY id;
+id	norm
+1	5.477225575051661
+2	6.244997998398398
+3	6.782329983125268
+4	10.954451150103322
+5	15.165750888103101
+# L2 distance between two stored vectors
+SELECT L2_DISTANCE(a.vec, b.vec) AS dist
+FROM embeddings a, embeddings b
+WHERE a.id = 1 AND b.id = 2;
+dist
+1
+# Nearest-neighbour search by L1 distance (full table scan)
+SELECT id, L1_DISTANCE(vec, query.ref_vec) AS dist
+FROM embeddings,
+(SELECT vec AS ref_vec FROM embeddings WHERE id = 1) AS query
+ORDER BY dist ASC
+LIMIT 2;
+id	dist
+1	0
+2	1
+# Update a vector value
+UPDATE embeddings SET vec = '[0.5, 0.5, 0.5, 0.5]' WHERE id = 1;
+# Delete a row containing a vector
+DELETE FROM embeddings WHERE id = 2;
+# Verify state after update and delete
+SELECT id, VECTOR_FORMAT(vec, 2) AS vec FROM embeddings ORDER BY id;
+id	vec
+1	[0.5,0.5,0.5,0.5]
+3	[1,2,5,4]
+4	[2,4,6,8]
+5	[9,8,7,6]
+# Clean up
+DROP TABLE embeddings;
+UNINSTALL EXTENSION vsql_vector;
+SET PERSIST vsql_allow_preview_extensions = OFF;
+RESET PERSIST vsql_allow_preview_extensions;
diff --git a/mysql-test/t/readme_examples.test b/mysql-test/t/readme_examples.test
new file mode 100644
index 0000000..ec6bb1b
--- /dev/null
+++ b/mysql-test/t/readme_examples.test
@@ -0,0 +1,107 @@
+# Install vsql-vector extension
+SET PERSIST vsql_allow_preview_extensions = ON;
+INSTALL EXTENSION vsql_vector;
+
+--replace_result $MYSQLTEST_VARDIR MYSQLTEST_VARDIR
+--replace_result $MYSQL_TEST_DIR MYSQL_TEST_DIR
+
+########################################################################
+#
+# Verify all SQL syntaxes mentioned in README.md
+#
+########################################################################
+
+--echo # ---- README: SVECTOR Type ----
+
+--echo # Create a table with a vector column
+CREATE TABLE embeddings (
+  id    INT PRIMARY KEY,
+  vec   SVECTOR(4) NOT NULL
+) ENGINE=InnoDB;
+
+--echo # Add a vector column to an existing table
+ALTER TABLE embeddings ADD COLUMN vec2 SVECTOR(4) NULL;
+
+--echo # Insert vectors (number of elements must match the declared dimension)
+INSERT INTO embeddings VALUES (1, '[1.0, 2.0, 3.0, 4.0]', NULL);
+INSERT INTO embeddings VALUES (2, '[1.0, 2.0, 3.0, 5.0]', NULL);
+INSERT INTO embeddings VALUES (3, '[1.0, 2.0, 5.0, 4.0]', NULL);
+INSERT INTO embeddings VALUES (4, '[2.0, 4.0, 6.0, 8.0]', NULL);
+INSERT INTO embeddings VALUES (5, '[9.0, 8.0, 7.0, 6.0]', NULL);
+
+--echo # ---- README: Scalar / utility functions ----
+
+SELECT VECTOR_MAX_DIMENSION() AS max_dim;
+SELECT id, VECTOR_DIMENSION(vec) AS dims FROM embeddings ORDER BY id;
+SELECT id, VECTOR_NORM(vec) AS norm FROM embeddings ORDER BY id;
+SELECT id, VECTOR_FORMAT(vec, 2) AS formatted FROM embeddings ORDER BY id;
+SELECT SUBSTRING(VECTOR_HEX(vec), 17) AS hex_data FROM embeddings WHERE id = 1;
+
+--echo # ---- README: Distance and similarity functions ----
+
+# Use unit-component vectors so sqrt() denominators are exact integers,
+# giving fully deterministic results across all IEEE 754 platforms.
+CREATE TABLE func_exact (
+  id INT PRIMARY KEY,
+  v1 SVECTOR(4) NOT NULL,
+  v2 SVECTOR(4) NOT NULL
+) ENGINE=InnoDB;
+
+INSERT INTO func_exact VALUES
+  (1, '[1.0, 0.0, 0.0, 0.0]', '[1.0, 0.0, 0.0, 0.0]'),
+  (2, '[1.0, 0.0, 0.0, 0.0]', '[0.0, 1.0, 0.0, 0.0]');
+
+--echo # L1_DISTANCE
+SELECT id, L1_DISTANCE(v1, v2) AS l1 FROM func_exact ORDER BY id;
+
+--echo # L2_DISTANCE
+SELECT id, L2_DISTANCE(v1, v2) AS l2 FROM func_exact ORDER BY id;
+
+--echo # COSINE_DISTANCE (0 = same direction, 1 = orthogonal)
+SELECT id, COSINE_DISTANCE(v1, v2) AS cos_dist FROM func_exact ORDER BY id;
+
+--echo # INNER_PRODUCT
+SELECT id, INNER_PRODUCT(v1, v2) AS ip FROM func_exact ORDER BY id;
+
+--echo # NULL handling: distance functions return NULL when either argument is NULL
+SELECT id,
+       L1_DISTANCE(vec, vec2)     AS l1,
+       L2_DISTANCE(vec, vec2)     AS l2,
+       COSINE_DISTANCE(vec, vec2) AS cos_dist,
+       INNER_PRODUCT(vec, vec2)   AS ip
+FROM embeddings ORDER BY id;
+
+DROP TABLE func_exact;
+
+--echo # ---- README: Example Queries ----
+
+--echo # Norm of a stored vector
+SELECT id, VECTOR_NORM(vec) AS norm FROM embeddings ORDER BY id;
+
+--echo # L2 distance between two stored vectors
+SELECT L2_DISTANCE(a.vec, b.vec) AS dist
+FROM embeddings a, embeddings b
+WHERE a.id = 1 AND b.id = 2;
+
+--echo # Nearest-neighbour search by L1 distance (full table scan)
+SELECT id, L1_DISTANCE(vec, query.ref_vec) AS dist
+FROM embeddings,
+     (SELECT vec AS ref_vec FROM embeddings WHERE id = 1) AS query
+ORDER BY dist ASC
+LIMIT 2;
+
+--echo # Update a vector value
+UPDATE embeddings SET vec = '[0.5, 0.5, 0.5, 0.5]' WHERE id = 1;
+
+--echo # Delete a row containing a vector
+DELETE FROM embeddings WHERE id = 2;
+
+--echo # Verify state after update and delete
+SELECT id, VECTOR_FORMAT(vec, 2) AS vec FROM embeddings ORDER BY id;
+
+--echo # Clean up
+DROP TABLE embeddings;
+
+UNINSTALL EXTENSION vsql_vector;
+SET PERSIST vsql_allow_preview_extensions = OFF;
+RESET PERSIST vsql_allow_preview_extensions;