feat: vector search for builder

AGENTS.md

@@ -239,6 +239,62 @@ config = AsyncpgConfig(
 )
 ```
 
+### Custom SQLGlot Expression Pattern
+
+For dialect-specific SQL generation (e.g., vector distance functions):
+
+```python
+# In sqlspec/builder/_custom_expressions.py
+from sqlglot import exp
+from typing import Any
+
+class CustomExpression(exp.Expression):
+    """Custom expression with dialect-aware SQL generation."""
+    arg_types = {"this": True, "expression": True, "metric": False}
+
+    def sql(self, dialect: "Any | None" = None, **opts: Any) -> str:
+        """Override sql() method for dialect-specific generation."""
+        dialect_name = str(dialect).lower() if dialect else "generic"
+
+        left_sql = self.left.sql(dialect=dialect, **opts)
+        right_sql = self.right.sql(dialect=dialect, **opts)
+
+        if dialect_name == "postgres":
+            return self._sql_postgres(left_sql, right_sql)
+        if dialect_name == "mysql":
+            return self._sql_mysql(left_sql, right_sql)
+        return self._sql_generic(left_sql, right_sql)
+
+# Register with SQLGlot generator system
+def _register_with_sqlglot() -> None:
+    from sqlglot.dialects.postgres import Postgres
+    from sqlglot.generator import Generator
+
+    def custom_sql_base(generator: "Generator", expression: "CustomExpression") -> str:
+        return expression._sql_generic(generator.sql(expression.left), generator.sql(expression.right))
+
+    Generator.TRANSFORMS[CustomExpression] = custom_sql_base
+    Postgres.Generator.TRANSFORMS[CustomExpression] = custom_sql_postgres
+
+_register_with_sqlglot()
+```
+
+**Use this pattern when**:
+- Database syntax varies significantly across dialects
+- Standard SQLGlot expressions don't match any database's native syntax
+- Need operator syntax (e.g., `<->`) vs function calls (e.g., `DISTANCE()`)
+
+**Key principles**:
+- Override `.sql()` method for dialect detection
+- Register with SQLGlot's TRANSFORMS for nested expression support
+- Store metadata (like metric) as `exp.Identifier` in `arg_types` for runtime access
+- Provide generic fallback for unsupported dialects
+
+**Example**: `VectorDistance` in `sqlspec/builder/_vector_expressions.py` generates:
+- PostgreSQL: `embedding <-> '[0.1,0.2]'` (operator)
+- MySQL: `DISTANCE(embedding, STRING_TO_VECTOR('[0.1,0.2]'), 'EUCLIDEAN')` (function)
+- Oracle: `VECTOR_DISTANCE(embedding, TO_VECTOR('[0.1,0.2]'), EUCLIDEAN)` (function)
+
 ### Error Handling
 
 - Custom exceptions inherit from `SQLSpecError` in `sqlspec/exceptions.py`

Makefile

@@ -117,7 +117,7 @@ release:                                           ## Bump version and create re
 	@make docs
 	@make clean
 	@make build
-	@uv lock --upgrade-package litestar-vite >/dev/null 2>&1
+	@uv lock --upgrade-package sqlspec >/dev/null 2>&1
 	@uv run bump-my-version bump $(bump)
 	@echo "${OK} Release complete 🎉"
 

docs/reference/builder.rst

@@ -514,6 +514,238 @@ Base Classes
    :undoc-members:
    :show-inheritance:
 
+Vector Distance Functions
+=========================
+
+The query builder provides portable vector similarity search functions that generate dialect-specific SQL across PostgreSQL (pgvector), MySQL 9+, Oracle 23ai+, BigQuery, DuckDB, and other databases.
+
+.. note::
+   Vector functions are designed for AI/ML similarity search with embedding vectors. The SQL is generated at ``build(dialect=X)`` time, enabling portable query definitions that execute against multiple database types.
+
+Column Methods
+--------------
+
+.. py:method:: Column.vector_distance(other_vector, metric="euclidean")
+
+   Calculate vector distance using the specified metric.
+
+   Generates dialect-specific SQL for vector distance operations.
+
+   :param other_vector: Vector to compare against (list, Column reference, or SQLGlot expression)
+   :type other_vector: list[float] | Column | exp.Expression
+   :param metric: Distance metric to use (default: "euclidean")
+   :type metric: str
+   :return: FunctionColumn expression for use in SELECT, WHERE, ORDER BY
+   :rtype: FunctionColumn
+
+   **Supported Metrics:**
+
+   - ``euclidean`` - L2 distance (default)
+   - ``cosine`` - Cosine distance
+   - ``inner_product`` - Negative inner product (for similarity ranking)
+   - ``euclidean_squared`` - L2² distance (Oracle only)
+
+   **Examples:**
+
+   .. code-block:: python
+
+      from sqlspec import sql
+      from sqlspec.builder import Column
+
+      query_vector = [0.1, 0.2, 0.3]
+
+      # Basic distance query
+      query = (
+          sql.select("id", "title", Column("embedding").vector_distance(query_vector).alias("distance"))
+          .from_("documents")
+          .where(Column("embedding").vector_distance(query_vector) < 0.5)
+          .order_by("distance")
+          .limit(10)
+      )
+
+      # Using dynamic attribute access
+      query = (
+          sql.select("*")
+          .from_("docs")
+          .order_by(sql.embedding.vector_distance(query_vector, metric="cosine"))
+          .limit(10)
+      )
+
+      # Compare two vector columns
+      query = (
+          sql.select("*")
+          .from_("pairs")
+          .where(Column("vec1").vector_distance(Column("vec2"), metric="euclidean") < 0.3)
+      )
+
+.. py:method:: Column.cosine_similarity(other_vector)
+
+   Calculate cosine similarity (1 - cosine_distance).
+
+   Convenience method that computes similarity instead of distance.
+   Returns values in range [-1, 1] where 1 = identical vectors.
+
+   :param other_vector: Vector to compare against
+   :type other_vector: list[float] | Column | exp.Expression
+   :return: FunctionColumn expression computing ``1 - cosine_distance(self, other_vector)``
+   :rtype: FunctionColumn
+
+   **Example:**
+
+   .. code-block:: python
+
+      from sqlspec import sql
+
+      query_vector = [0.5, 0.5, 0.5]
+
+      # Find most similar documents
+      query = (
+          sql.select("id", "title", sql.embedding.cosine_similarity(query_vector).alias("similarity"))
+          .from_("documents")
+          .order_by(sql.column("similarity").desc())
+          .limit(10)
+      )
+
+Database Compatibility
+----------------------
+
+Vector functions generate dialect-specific SQL:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 15 25 25 35
+
+   * - Database
+     - Euclidean
+     - Cosine
+     - Inner Product
+   * - PostgreSQL (pgvector)
+     - ``<->`` operator
+     - ``<=>`` operator
+     - ``<#>`` operator
+   * - MySQL 9+
+     - ``DISTANCE(..., 'EUCLIDEAN')``
+     - ``DISTANCE(..., 'COSINE')``
+     - ``DISTANCE(..., 'DOT')``
+   * - Oracle 23ai+
+     - ``VECTOR_DISTANCE(..., EUCLIDEAN)``
+     - ``VECTOR_DISTANCE(..., COSINE)``
+     - ``VECTOR_DISTANCE(..., DOT)``
+   * - BigQuery
+     - ``EUCLIDEAN_DISTANCE(...)``
+     - ``COSINE_DISTANCE(...)``
+     - ``DOT_PRODUCT(...)``
+   * - DuckDB (VSS extension)
+     - ``array_distance(...)``
+     - ``array_cosine_distance(...)``
+     - ``array_negative_inner_product(...)``
+   * - Generic
+     - ``VECTOR_DISTANCE(..., 'EUCLIDEAN')``
+     - ``VECTOR_DISTANCE(..., 'COSINE')``
+     - ``VECTOR_DISTANCE(..., 'INNER_PRODUCT')``
+
+Usage Examples
+--------------
+
+**Basic Similarity Search**
+
+.. code-block:: python
+
+   from sqlspec import sql
+
+   # Find documents similar to query vector
+   query_vector = [0.1, 0.2, 0.3]
+
+   query = (
+       sql.select("id", "title", sql.embedding.vector_distance(query_vector).alias("distance"))
+       .from_("documents")
+       .order_by("distance")
+       .limit(10)
+   )
+
+   # PostgreSQL generates: SELECT id, title, embedding <-> '[0.1,0.2,0.3]' AS distance ...
+   # MySQL generates: SELECT id, title, DISTANCE(embedding, STRING_TO_VECTOR('[0.1,0.2,0.3]'), 'EUCLIDEAN') AS distance ...
+   # Oracle generates: SELECT id, title, VECTOR_DISTANCE(embedding, TO_VECTOR('[0.1,0.2,0.3]'), EUCLIDEAN) AS distance ...
+
+**Threshold Filtering**
+
+.. code-block:: python
+
+   # Find documents within distance threshold
+   query = (
+       sql.select("*")
+       .from_("documents")
+       .where(sql.embedding.vector_distance(query_vector, metric="euclidean") < 0.5)
+       .order_by(sql.embedding.vector_distance(query_vector))
+   )
+
+**Similarity Ranking**
+
+.. code-block:: python
+
+   # Rank by cosine similarity (higher = more similar)
+   query = (
+       sql.select("id", "content", sql.embedding.cosine_similarity(query_vector).alias("score"))
+       .from_("articles")
+       .order_by(sql.column("score").desc())
+       .limit(5)
+   )
+
+**Multiple Metrics**
+
+.. code-block:: python
+
+   # Compare different distance metrics in single query
+   query = (
+       sql.select(
+           "id",
+           sql.embedding.vector_distance(query_vector, metric="euclidean").alias("l2_dist"),
+           sql.embedding.vector_distance(query_vector, metric="cosine").alias("cos_dist"),
+           sql.embedding.cosine_similarity(query_vector).alias("similarity")
+       )
+       .from_("documents")
+       .limit(10)
+   )
+
+**Combined Filters**
+
+.. code-block:: python
+
+   # Vector search with additional filters
+   query = (
+       sql.select("*")
+       .from_("products")
+       .where("category = ?")
+       .where("in_stock = TRUE")
+       .where(sql.embedding.vector_distance(query_vector) < 0.3)
+       .order_by(sql.embedding.vector_distance(query_vector))
+       .limit(20)
+   )
+
+Dialect-Agnostic Construction
+------------------------------
+
+Queries are constructed once and executed against multiple databases:
+
+.. code-block:: python
+
+   from sqlspec import sql
+
+   # Define query once
+   query = (
+       sql.select("id", "title", sql.embedding.vector_distance([0.1, 0.2, 0.3]).alias("distance"))
+       .from_("documents")
+       .order_by("distance")
+       .limit(10)
+   )
+
+   # Execute with different adapters
+   pg_result = await pg_session.execute(query)      # → PostgreSQL SQL with <-> operator
+   mysql_result = await mysql_session.execute(query)  # → MySQL SQL with DISTANCE()
+   oracle_result = await oracle_session.execute(query)  # → Oracle SQL with VECTOR_DISTANCE()
+
+The dialect is selected at ``build(dialect=X)`` time based on the driver, not at query construction time.
+
 Filter Integration
 ==================
 

sqlspec/adapters/adbc/driver.py

@@ -826,6 +826,10 @@ def get_adbc_statement_config(detected_dialect: str) -> StatementConfig:
         "type_coercion_map": type_map,
     }
 
+    if detected_dialect == "duckdb":
+        parameter_overrides["preserve_parameter_format"] = False
+        parameter_overrides["supported_execution_parameter_styles"] = {ParameterStyle.QMARK, ParameterStyle.NUMERIC}
+
     if detected_dialect in {"postgres", "postgresql"}:
         parameter_overrides["ast_transformer"] = build_null_pruning_transform(dialect=sqlglot_dialect)
 

sqlspec/builder/_base.py

@@ -319,11 +319,25 @@ def _parameterize_expression(self, expression: exp.Expression) -> exp.Expression
             A new expression with literals replaced by parameter placeholders
         """
 
+        from sqlspec.builder._vector_expressions import VectorDistance
+
         def replacer(node: exp.Expression) -> exp.Expression:
             if isinstance(node, exp.Literal):
                 if node.this in {True, False, None}:
                     return node
-                param_name = self._add_parameter(node.this, context="where")
+
+                parent = node.parent
+                if isinstance(parent, exp.Array) and node.find_ancestor(VectorDistance) is not None:
+                    return node
+
+                value = node.this
+                if node.is_number and isinstance(node.this, str):
+                    try:
+                        value = float(node.this) if "." in node.this or "e" in node.this.lower() else int(node.this)
+                    except ValueError:
+                        value = node.this
+
+                param_name = self._add_parameter(value, context="where")
                 return exp.Placeholder(this=param_name)
             return node