diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index ddeb5633f..07f05262f 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -113,7 +113,7 @@ jobs:
 
       - name: Install dependencies
         working-directory: ./projects/pgai
-        run: uv sync
+        run: uv sync --all-extras
 
       - name: Lint
         run: just pgai lint
diff --git a/docs/python-integration.md b/docs/python-integration.md
new file mode 100644
index 000000000..4b92ddcd5
--- /dev/null
+++ b/docs/python-integration.md
@@ -0,0 +1,184 @@
+# SQLAlchemy Integration with pgai Vectorizer
+
+The `vectorizer_relationship` is a SQLAlchemy helper that integrates pgai's vectorization capabilities directly into your SQLAlchemy models.
+Think of it as a normal SQLAlchemy [relationship](https://docs.sqlalchemy.org/en/20/orm/basic_relationships.html), but with a preconfigured model instance under the hood.
+This allows you to easily query vector embeddings created by pgai using familiar SQLAlchemy patterns.
+
+## Installation
+
+To use the SQLAlchemy integration, install pgai with the SQLAlchemy extras:
+
+```bash
+pip install "pgai[sqlalchemy]"
+```
+
+## Basic Usage
+
+Here's a basic example of how to use the `vectorizer_relationship`:
+
+```python
+from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
+from pgai.sqlalchemy import vectorizer_relationship
+
+class Base(DeclarativeBase):
+    pass
+
+class BlogPost(Base):
+    __tablename__ = "blog_posts"
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+    title: Mapped[str]
+    content: Mapped[str]
+
+    # Add vector embeddings for the content field
+    content_embeddings = vectorizer_relationship(
+        dimensions=768
+    )
+```
+Note if you work with alembics autogenerate functionality for migrations, also check [Working with alembic](#working-with-alembic).
+
+### Semantic Search
+
+You can then perform semantic similarity search on the field using [pgvector-python's](https://github.com/pgvector/pgvector-python) distance functions:
+
+```python
+from sqlalchemy import func, text
+
+similar_posts = (
+    session.query(BlogPost.content_embeddings)
+    .order_by(
+        BlogPost.content_embeddings.embedding.cosine_distance(
+            func.ai.openai_embed(
+                "text-embedding-3-small",
+                "search query",
+                text("dimensions => 768")
+            )
+        )
+    )
+    .limit(5)
+    .all()
+)
+```
+
+Or if you already have the embeddings in your application:
+
+```python
+similar_posts = (
+    session.query(BlogPost.content_embeddings)
+    .order_by(
+        BlogPost.content_embeddings.embedding.cosine_distance(
+            [3, 1, 2]
+        )
+    )
+    .limit(5)
+    .all()
+)
+```
+
+## Configuration
+
+The `vectorizer_relationship` accepts the following parameters:
+
+- `dimensions` (int): The size of the embedding vector (required)
+- `target_schema` (str, optional): Override the schema for the embeddings table. If not provided, inherits from the parent model's schema
+- `target_table` (str, optional): Override the table name for embeddings. Default is `{table_name}_embedding_store`
+
+Additional parameters are simply forwarded to the underlying [SQLAlchemy relationship](https://docs.sqlalchemy.org/en/20/orm/relationships.html) so you can configure it as you desire.
+
+Think of the `vectorizer_relationship` as a normal SQLAlchemy relationship, but with a preconfigured model instance under the hood.
+
+
+## Setting up the Vectorizer
+
+After defining your model, you need to create the vectorizer using pgai's SQL functions:
+
+```sql
+SELECT ai.create_vectorizer(
+    'blog_posts'::regclass,
+    embedding => ai.embedding_openai('text-embedding-3-small', 768),
+    chunking => ai.chunking_recursive_character_text_splitter(
+        'content',
+        50,  -- chunk_size
+        10   -- chunk_overlap
+    )
+);
+```
+
+We recommend adding this to a migration script and run it via alembic.
+
+
+## Querying Embeddings
+
+The `vectorizer_relationship` provides several ways to work with embeddings:
+
+### 1. Direct Access to Embeddings
+
+If you access the class proeprty of your model the `vectorizer_relationship` provide a SQLAlchemy model that you can query directly:
+
+```python
+# Get all embeddings
+embeddings = session.query(BlogPost.content_embeddings).all()
+
+# Access embedding properties
+for embedding in embeddings:
+    print(embedding.embedding)  # The vector embedding
+    print(embedding.chunk)      # The text chunk
+```
+The model will have the primary key fields of the parent model as well as the following fields:
+- `chunk` (str): The text chunk that was embedded
+- `embedding` (Vector): The vector embedding
+- `chunk_seq` (int): The sequence number of the chunk
+- `embedding_uuid` (str): The UUID of the embedding
+- `parent` (ParentModel): The parent model instance
+
+### 2. Relationship Access
+
+
+```python
+blog_post = session.query(BlogPost).first()
+for embedding in blog_post.content_embeddings:
+    print(embedding.chunk)
+```
+Access the original posts through the parent relationship
+```python
+for embedding in similar_posts:
+    print(embedding.parent.title)
+```
+
+### 3. Join Queries
+
+You can combine embedding queries with regular SQL queries using the relationship:
+
+```python
+results = (
+    session.query(BlogPost, BlogPost.content_embeddings)
+    .join(BlogPost.content_embeddings)
+    .filter(BlogPost.title.ilike("%search term%"))
+    .all()
+)
+
+for post, embedding in results:
+    print(f"Title: {post.title}")
+    print(f"Chunk: {embedding.chunk}")
+```
+
+## Working with alembic 
+
+
+The `vectorizer_relationship` generates a new SQLAlchemy model, that is available under the attribute that you specify. If you are using alembic's autogenerate functionality to generate migrations, you will need to exclude these models from the autogenerate process.
+These are added to a list in your metadata called `pgai_managed_tables` and you can exclude them by adding the following to your `env.py`:
+
+```python
+def include_object(object, name, type_, reflected, compare_to):
+    if type_ == "table" and name in target_metadata.info.get("pgai_managed_tables", set()):
+        return False
+    return True
+
+context.configure(
+      connection=connection,
+      target_metadata=target_metadata,
+      include_object=include_object
+  )
+```
+
+This should now prevent alembic from generating tables for these models when you run `alembic revision --autogenerate`.
diff --git a/projects/pgai/pgai/sqlalchemy/__init__.py b/projects/pgai/pgai/sqlalchemy/__init__.py
new file mode 100644
index 000000000..a669bbea6
--- /dev/null
+++ b/projects/pgai/pgai/sqlalchemy/__init__.py
@@ -0,0 +1,184 @@
+from typing import Any, Generic, TypeVar, overload
+
+from pgvector.sqlalchemy import Vector  # type: ignore
+from sqlalchemy import ForeignKeyConstraint, Integer, Text, event, inspect
+from sqlalchemy.orm import (
+    DeclarativeBase,
+    Mapped,
+    Mapper,
+    Relationship,
+    RelationshipProperty,
+    backref,
+    mapped_column,
+    relationship,
+)
+
+# Type variable for the parent model
+T = TypeVar("T", bound=DeclarativeBase)
+
+
+def to_pascal_case(text: str):
+    # Split on any non-alphanumeric character
+    words = "".join(char if char.isalnum() else " " for char in text).split()
+    # Capitalize first letter of all words
+    return "".join(word.capitalize() for word in words)
+
+
+class EmbeddingModel(DeclarativeBase, Generic[T]):
+    """Base type for embedding models with required attributes"""
+
+    embedding_uuid: Mapped[str]
+    chunk: Mapped[str]
+    embedding: Mapped[Vector]
+    chunk_seq: Mapped[int]
+    parent: T  # Type of the parent model
+
+
+class _Vectorizer:
+    def __init__(
+        self,
+        dimensions: int,
+        target_schema: str | None = None,
+        target_table: str | None = None,
+        **kwargs: Any,
+    ):
+        self.dimensions = dimensions
+        self.target_schema = target_schema
+        self.target_table = target_table
+        self.owner: type[DeclarativeBase] | None = None
+        self.name: str | None = None
+        self._embedding_class: type[EmbeddingModel[Any]] | None = None
+        self._relationship: RelationshipProperty[Any] | None = None
+        self._initialized = False
+        self.relationship_args = kwargs
+        event.listen(Mapper, "after_configured", self._initialize_all)
+
+    def _initialize_all(self):
+        """Force initialization during mapper configuration"""
+        if not self._initialized and self.owner is not None:
+            self.__get__(None, self.owner)
+
+    def set_schemas_correctly(self, owner: type[DeclarativeBase]) -> None:
+        table_args_schema_name = getattr(owner, "__table_args__", {}).get("schema")
+        self.target_schema = (
+            self.target_schema
+            or table_args_schema_name
+            or owner.registry.metadata.schema
+            or "public"
+        )
+
+    def create_embedding_class(
+        self, owner: type[DeclarativeBase]
+    ) -> type[EmbeddingModel[Any]]:
+        assert self.name is not None
+        table_name = self.target_table or f"{owner.__tablename__}_embedding_store"
+        self.set_schemas_correctly(owner)
+        class_name = f"{to_pascal_case(self.name)}Embedding"
+        registry_instance = owner.registry
+        base: type[DeclarativeBase] = owner.__base__  # type: ignore
+
+        # Check if table already exists in metadata
+        # There is probably a better way to do this
+        # than accessing the internal _class_registry
+        # Not doing this ends up in a recursion because
+        # creating the new class reconfigures tha parent mapper
+        # again triggering the after_configured event
+        key = f"{self.target_schema}.{table_name}"
+        if key in owner.metadata.tables:
+            # Find the mapped class in the registry
+            for cls in owner.registry._class_registry.values():  # type: ignore
+                if hasattr(cls, "__table__") and cls.__table__.fullname == key:  # type: ignore
+                    return cls  # type: ignore
+
+        # Get primary key information from the fully initialized model
+        mapper = inspect(owner)
+        pk_cols = mapper.primary_key
+
+        # Create the complete class dictionary
+        class_dict: dict[str, Any] = {
+            "__tablename__": table_name,
+            "registry": registry_instance,
+            # Add all standard columns
+            "embedding_uuid": mapped_column(Text, primary_key=True),
+            "chunk": mapped_column(Text, nullable=False),
+            "embedding": mapped_column(Vector(self.dimensions), nullable=False),
+            "chunk_seq": mapped_column(Integer, nullable=False),
+        }
+
+        # Add primary key columns to the dictionary
+        for col in pk_cols:
+            class_dict[col.name] = mapped_column(col.type, nullable=False)
+
+        # Create the table args with foreign key constraint
+        table_args_dict: dict[str, Any] = dict()
+        if self.target_schema and self.target_schema != owner.registry.metadata.schema:
+            table_args_dict["schema"] = self.target_schema
+
+        # Create the composite foreign key constraint
+        fk_constraint = ForeignKeyConstraint(
+            [col.name for col in pk_cols],  # Local columns
+            [
+                f"{owner.__tablename__}.{col.name}" for col in pk_cols
+            ],  # Referenced columns
+            ondelete="CASCADE",
+        )
+
+        # Add table args to class dictionary
+        class_dict["__table_args__"] = (fk_constraint, table_args_dict)
+
+        # Create the class using type()
+        Embedding = type(class_name, (base,), class_dict)
+
+        return Embedding  # type: ignore
+
+    @overload
+    def __get__(
+        self, obj: None, objtype: type[DeclarativeBase]
+    ) -> type[EmbeddingModel[Any]]: ...
+
+    @overload
+    def __get__(
+        self, obj: DeclarativeBase, objtype: type[DeclarativeBase] | None = None
+    ) -> Relationship[EmbeddingModel[Any]]: ...
+
+    def __get__(
+        self, obj: DeclarativeBase | None, objtype: type[DeclarativeBase] | None = None
+    ) -> Relationship[EmbeddingModel[Any]] | type[EmbeddingModel[Any]]:
+        assert self.name is not None
+        relationship_name = f"_{self.name}_relationship"
+        if not self._initialized and objtype is not None:
+            self._embedding_class = self.create_embedding_class(objtype)
+
+            mapper = inspect(objtype)
+            assert mapper is not None
+            pk_cols = mapper.primary_key
+            if not hasattr(objtype, relationship_name):
+                self.relationship_instance = relationship(
+                    self._embedding_class,
+                    foreign_keys=[
+                        getattr(self._embedding_class, col.name) for col in pk_cols
+                    ],
+                    backref=self.relationship_args.pop(
+                        "backref", backref("parent", lazy="select")
+                    ),
+                    **self.relationship_args,
+                )
+                setattr(objtype, f"{self.name}_model", self._embedding_class)
+                setattr(objtype, relationship_name, self.relationship_instance)
+            self._initialized = True
+        if obj is None and self._initialized:
+            return self._embedding_class  # type: ignore
+
+        return getattr(obj, relationship_name)
+
+    def __set_name__(self, owner: type[DeclarativeBase], name: str):
+        self.owner = owner
+        self.name = name
+
+        metadata = owner.registry.metadata
+        if not hasattr(metadata, "info"):
+            metadata.info = {}
+        metadata.info.setdefault("pgai_managed_tables", set()).add(self.target_table)
+
+
+vectorizer_relationship = _Vectorizer
diff --git a/projects/pgai/pyproject.toml b/projects/pgai/pyproject.toml
index 5d6da0d31..7819d9514 100644
--- a/projects/pgai/pyproject.toml
+++ b/projects/pgai/pyproject.toml
@@ -43,6 +43,11 @@ classifiers = [
     "Operating System :: POSIX :: Linux",
 ]
 
+[project.optional-dependencies]
+sqlalchemy=[
+    "sqlalchemy>=2.0.36",
+]
+
 [project.urls]
 Homepage = "https://github.com/timescale/pgai"
 Repository = "https://github.com/timescale/pgai"
@@ -110,4 +115,5 @@ dev-dependencies = [
     "testcontainers==4.8.1",
     "build==1.2.2.post1",
     "twine==5.1.1",
+    "psycopg2==2.9.10",
 ]
diff --git a/projects/pgai/tests/vectorizer/cassettes/test_joined_loading.yaml b/projects/pgai/tests/vectorizer/cassettes/test_joined_loading.yaml
new file mode 100644
index 000000000..b6e0ad07a
--- /dev/null
+++ b/projects/pgai/tests/vectorizer/cassettes/test_joined_loading.yaml
@@ -0,0 +1,298 @@
+interactions:
+- request:
+    body: '{"input": [[2028, 374, 1296, 2262, 220, 15, 430, 690, 387, 23711, 13],
+      [2028, 374, 1296, 2262, 220, 16, 430, 690, 387, 23711, 13], [2028, 374, 1296,
+      2262, 220, 17, 430, 690, 387, 23711, 13]], "model": "text-embedding-3-small",
+      "dimensions": 768, "encoding_format": "float"}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '273'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - AsyncOpenAI/Python 1.53.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - async:asyncio
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.53.0
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.10.15
+    method: POST
+    uri: https://api.openai.com/v1/embeddings
+  response:
+    body:
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diff --git a/projects/pgai/tests/vectorizer/conftest.py b/projects/pgai/tests/vectorizer/conftest.py
index 2ac1f3dc8..23b593535 100644
--- a/projects/pgai/tests/vectorizer/conftest.py
+++ b/projects/pgai/tests/vectorizer/conftest.py
@@ -60,7 +60,6 @@ def vcr_():
 def postgres_container_manager() -> (
     Generator[Callable[[bool], PostgresContainer], None, None]
 ):
-    load_dotenv()
     extension_dir = os.path.abspath(
         os.path.join(os.path.dirname(__file__), "../../../extension/")
     )
@@ -84,6 +83,7 @@ def get_container(load_openai_key: bool = True) -> PostgresContainer:
             )
 
             if load_openai_key:
+                load_dotenv()
                 openai_api_key = os.environ["OPENAI_API_KEY"]
                 container = container.with_env("OPENAI_API_KEY", openai_api_key)
 
diff --git a/projects/pgai/tests/vectorizer/extensions/__init__.py b/projects/pgai/tests/vectorizer/extensions/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/projects/pgai/tests/vectorizer/extensions/conftest.py b/projects/pgai/tests/vectorizer/extensions/conftest.py
new file mode 100644
index 000000000..3733575f2
--- /dev/null
+++ b/projects/pgai/tests/vectorizer/extensions/conftest.py
@@ -0,0 +1,46 @@
+from collections.abc import Generator
+from pathlib import Path
+
+import pytest
+from sqlalchemy import Engine, create_engine, text
+from testcontainers.postgres import PostgresContainer  # type: ignore
+
+# Get the path to the fixtures directory relative to this file
+FIXTURES_DIR = Path(__file__).parent / "fixtures"
+
+
+def drop_vectorizer_if_exists(id: int, engine: Engine):
+    with engine.connect() as conn:
+        vectorizer_exists = conn.execute(
+            text(f"SELECT EXISTS (SELECT 1 FROM ai.vectorizer WHERE id = {id})")
+        ).scalar()
+
+        if vectorizer_exists:
+            conn.execute(text(f"SELECT ai.drop_vectorizer({id}, drop_all=>true);"))
+        conn.commit()
+
+
+@pytest.fixture
+def initialized_engine(
+    postgres_container: PostgresContainer,
+) -> Generator[Engine, None, None]:
+    """Create a SQLAlchemy engine with the AI extension enabled.
+
+    Args:
+        postgres_container: Postgres test container fixture
+
+    Returns:
+        Engine: Configured SQLAlchemy engine
+    """
+    engine = create_engine(postgres_container.get_connection_url())
+    with engine.connect() as conn:
+        conn.execute(text("CREATE EXTENSION IF NOT EXISTS ai CASCADE;"))
+        conn.commit()
+
+    yield engine
+
+    drop_vectorizer_if_exists(1, engine)
+    drop_vectorizer_if_exists(2, engine)
+    with engine.connect() as conn:
+        conn.execute(text("DROP SCHEMA public CASCADE; CREATE SCHEMA public;"))
+        conn.commit()
diff --git a/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy.py b/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy.py
new file mode 100644
index 000000000..570d766a3
--- /dev/null
+++ b/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy.py
@@ -0,0 +1,129 @@
+from typing import Any
+
+from sqlalchemy import Column, Engine, Integer, Text
+from sqlalchemy.orm import Session, declarative_base
+from sqlalchemy.sql import text
+from testcontainers.postgres import PostgresContainer  # type: ignore
+
+from pgai.sqlalchemy import vectorizer_relationship
+from tests.vectorizer.extensions.utils import run_vectorizer_worker
+
+
+def test_sqlalchemy(
+    postgres_container: PostgresContainer, initialized_engine: Engine, vcr_: Any
+):
+    db_url = postgres_container.get_connection_url()
+    # Create engine and base
+    Base = declarative_base()
+
+    class BlogPost(Base):
+        __tablename__ = "blog_posts"
+
+        id = Column(Integer, primary_key=True)
+        title = Column(Text, nullable=False)
+        content = Column(Text, nullable=False)
+
+        content_embeddings = vectorizer_relationship(
+            dimensions=768,
+        )
+
+    # Create tables
+    Base.metadata.drop_all(initialized_engine)
+    Base.metadata.create_all(
+        initialized_engine, tables=[Base.metadata.sorted_tables[0]]
+    )
+
+    # Create vectorizer
+    with initialized_engine.connect() as conn:
+        conn.execute(
+            text("""
+            SELECT ai.create_vectorizer(
+                'blog_posts'::regclass,
+                embedding => ai.embedding_openai('text-embedding-3-small', 768),
+                chunking => ai.chunking_recursive_character_text_splitter('content',
+                50,
+                10)
+            );
+        """)
+        )
+        conn.commit()
+
+    # Insert test data
+    with Session(initialized_engine) as session:
+        posts = [
+            BlogPost(
+                title="Introduction to Machine Learning",
+                content="Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience.",  # noqa
+            ),
+            BlogPost(
+                title="Python Programming",
+                content="Python is a high-level programming language known for its simplicity and readability.",  # noqa
+            ),
+            BlogPost(
+                title="Data Science Basics",
+                content="Data science combines statistics, programming, and domain expertise to extract insights from data.",  # noqa
+            ),
+        ]
+        session.add_all(posts)
+        session.commit()
+
+    with vcr_.use_cassette("test_sqlalchemy.yaml"):
+        run_vectorizer_worker(db_url, 1)
+
+    with Session(initialized_engine) as session:
+        # Test 1: Access embedding class directly
+        assert BlogPost.content_embeddings.__name__ == "ContentEmbeddingsEmbedding"
+
+        # Get all embeddings directly
+        all_embeddings = session.query(BlogPost.content_embeddings).all()
+        assert len(all_embeddings) > 0
+        assert hasattr(all_embeddings[0], "embedding")
+        assert hasattr(all_embeddings[0], "chunk")
+
+        # Test 2: Access embeddings through relationship
+        blog_post = session.query(BlogPost).first()
+        assert blog_post is not None
+
+        embedding = session.query(BlogPost.content_embeddings).first()
+        assert embedding is not None
+        assert embedding.chunk in blog_post.content
+
+        # Test 4: Semantic search functionality
+        from sqlalchemy import func
+
+        # Search for content similar to "artificial intelligence"
+        similar_embeddings = (
+            session.query(BlogPost.content_embeddings)
+            .order_by(
+                BlogPost.content_embeddings.embedding.cosine_distance(
+                    func.ai.openai_embed(
+                        "text-embedding-3-small",
+                        "artificial intelligence",
+                        text("dimensions => 768"),
+                    )
+                )
+            )
+            .limit(2)
+            .all()
+        )
+
+        assert len(similar_embeddings) > 0
+        # The ML post should be most similar to "artificial intelligence"
+        assert "Machine learning" in similar_embeddings[0].parent.content
+
+        # Test 5: Join query example
+        # Find all blog posts with their embeddings where title contains "Python"
+        python_posts = (
+            session.query(BlogPost, BlogPost.content_embeddings)
+            .join(
+                BlogPost.content_embeddings,
+                BlogPost.id == BlogPost.content_embeddings.id,  # type: ignore
+            )
+            .filter(BlogPost.title.ilike("%Python%"))
+            .all()
+        )
+
+        assert len(python_posts) > 0
+        post, embedding = python_posts[0]
+        assert "Python" in post.title
+        assert hasattr(embedding, "embedding")
diff --git a/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy_composite_primary.py b/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy_composite_primary.py
new file mode 100644
index 000000000..f38347007
--- /dev/null
+++ b/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy_composite_primary.py
@@ -0,0 +1,121 @@
+from typing import Any
+
+import numpy as np
+from sqlalchemy import Column, Engine, Text
+from sqlalchemy.orm import DeclarativeBase, Session
+from sqlalchemy.sql import text
+from testcontainers.postgres import PostgresContainer  # type: ignore
+
+from pgai.sqlalchemy import vectorizer_relationship
+from tests.vectorizer.extensions.utils import run_vectorizer_worker
+
+
+class Base(DeclarativeBase):
+    pass
+
+
+class Author(Base):
+    __tablename__ = "authors"
+    first_name = Column(Text, primary_key=True)
+    last_name = Column(Text, primary_key=True)
+    bio = Column(Text, nullable=False)
+    bio_embeddings = vectorizer_relationship(
+        dimensions=768,
+    )
+
+
+def test_vectorizer_composite_key(
+    postgres_container: PostgresContainer,
+    initialized_engine: Engine,
+    vcr_: Any,
+):
+    """Test vectorizer with a composite primary key."""
+    db_url = postgres_container.get_connection_url()
+
+    # Create tables
+    metadata = Author.metadata
+    metadata.create_all(initialized_engine, tables=[metadata.sorted_tables[0]])
+
+    # Create vectorizer
+    with initialized_engine.connect() as conn:
+        conn.execute(
+            text("""
+                SELECT ai.create_vectorizer(
+                    'authors'::regclass,
+                    embedding => ai.embedding_openai('text-embedding-3-small', 768),
+                    chunking =>
+                    ai.chunking_recursive_character_text_splitter('bio', 50, 10)
+                );
+            """)
+        )
+        conn.commit()
+
+    # Insert test data
+    with Session(initialized_engine) as session:
+        author = Author(
+            first_name="Jane",
+            last_name="Doe",
+            bio="Jane is an accomplished researcher in artificial intelligence and machine learning. She has published numerous papers on neural networks.",  # noqa
+        )
+        session.add(author)
+        session.commit()
+
+    # Run vectorizer worker
+    with vcr_.use_cassette("test_vectorizer_composite_key.yaml"):
+        run_vectorizer_worker(db_url, 1)
+
+    # Verify embeddings were created
+    with Session(initialized_engine) as session:
+        assert Author.bio_embeddings.__name__ == "BioEmbeddingsEmbedding"
+
+        # Check embeddings exist and have correct properties
+        embedding = session.query(Author.bio_embeddings).first()
+        assert embedding is not None
+        assert isinstance(embedding.embedding, np.ndarray)
+        assert len(embedding.embedding) == 768
+        assert embedding.chunk is not None
+        assert isinstance(embedding.chunk, str)
+
+        # Check composite key fields were created
+        assert hasattr(embedding, "first_name")
+        assert hasattr(embedding, "last_name")
+        assert embedding.first_name == "Jane"  # type: ignore
+        assert embedding.last_name == "Doe"  # type: ignore
+
+        # Verify relationship works
+        author = session.query(Author).first()
+        assert author is not None
+        assert hasattr(author, "bio_embeddings")
+        assert author.bio_embeddings is not None
+        assert len(author.bio_embeddings) > 0  # type: ignore
+        assert author.bio_embeddings[0].chunk in author.bio
+
+        # Test that parent relationship works
+        embedding_entity = session.query(Author.bio_embeddings).first()
+        assert embedding_entity is not None
+        assert embedding_entity.chunk in author.bio
+        assert embedding_entity.parent is not None
+        assert embedding_entity.parent.first_name == "Jane"
+        assert embedding_entity.parent.last_name == "Doe"
+
+        # Test semantic search with composite keys
+        from sqlalchemy import func
+
+        # Search for content similar to "machine learning"
+        similar_embeddings = (
+            session.query(Author.bio_embeddings)
+            .order_by(
+                Author.bio_embeddings.embedding.cosine_distance(
+                    func.ai.openai_embed(
+                        "text-embedding-3-small",
+                        "machine learning",
+                        text("dimensions => 768"),
+                    )
+                )
+            )
+            .all()
+        )
+
+        assert len(similar_embeddings) > 0
+        # The bio should contain machine learning related content
+        assert "machine learning" in similar_embeddings[0].parent.bio
diff --git a/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy_large_embeddings.py b/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy_large_embeddings.py
new file mode 100644
index 000000000..7fd7ac44f
--- /dev/null
+++ b/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy_large_embeddings.py
@@ -0,0 +1,76 @@
+from typing import Any
+
+import numpy as np
+from sqlalchemy import Column, Engine, Integer, Text
+from sqlalchemy.orm import DeclarativeBase, Session
+from sqlalchemy.sql import text
+from testcontainers.postgres import PostgresContainer  # type: ignore
+
+from pgai.sqlalchemy import vectorizer_relationship
+from tests.vectorizer.extensions.utils import run_vectorizer_worker
+
+
+class Base(DeclarativeBase):
+    pass
+
+
+class BlogPost(Base):
+    __tablename__ = "blog_posts"
+    id = Column(Integer, primary_key=True)
+    title = Column(Text, nullable=False)
+    content = Column(Text, nullable=False)
+    content_embeddings = vectorizer_relationship(dimensions=1536)
+
+
+def test_vectorizer_embedding_creation(
+    postgres_container: PostgresContainer, initialized_engine: Engine, vcr_: Any
+):
+    """Test basic data insertion and embedding generation with default relationship."""
+    db_url = postgres_container.get_connection_url()
+    # Create tables
+    metadata = BlogPost.metadata
+    metadata.create_all(initialized_engine, tables=[metadata.sorted_tables[0]])
+    with initialized_engine.connect() as conn:
+        conn.execute(
+            text("""
+                SELECT ai.create_vectorizer(
+                    'blog_posts'::regclass,
+                    embedding => ai.embedding_openai('text-embedding-3-large', 1536),
+                    chunking => ai.chunking_recursive_character_text_splitter('content')
+                );
+            """)
+        )
+        conn.commit()
+
+    # Insert test data
+    with Session(initialized_engine) as session:
+        post = BlogPost(
+            title="Introduction to Machine Learning",
+            content="Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience.",  # noqa
+        )
+        session.add(post)
+        session.commit()
+
+    with vcr_.use_cassette("test_vectorizer_large_mbedding_creation.yaml"):
+        # Run vectorizer worker
+        run_vectorizer_worker(db_url, 1)
+
+    # Verify embeddings were created
+    with Session(initialized_engine) as session:
+        # Verify embedding class was created correctly
+        assert BlogPost.content_embeddings.__name__ == "ContentEmbeddingsEmbedding"
+
+        # Check embeddings exist and have correct properties
+        embedding = session.query(BlogPost.content_embeddings).first()
+        assert embedding is not None
+        assert isinstance(embedding.embedding, np.ndarray)
+        assert len(embedding.embedding) == 1536
+        assert embedding.chunk is not None  # Should have chunk text
+        assert isinstance(embedding.chunk, str)
+
+        blog_post = session.query(BlogPost).first()
+        assert blog_post is not None
+
+        embedding_entity = session.query(BlogPost.content_embeddings).first()
+        assert embedding_entity is not None
+        assert embedding_entity.chunk in blog_post.content
diff --git a/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy_lazy_strategies.py b/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy_lazy_strategies.py
new file mode 100644
index 000000000..f9bae9694
--- /dev/null
+++ b/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy_lazy_strategies.py
@@ -0,0 +1,86 @@
+from typing import Any
+
+from _pytest.logging import LogCaptureFixture
+from sqlalchemy import Column, Engine, Integer, Text
+from sqlalchemy.orm import DeclarativeBase, Session
+from sqlalchemy.sql import text
+from testcontainers.postgres import PostgresContainer  # type: ignore
+
+from pgai.sqlalchemy import vectorizer_relationship
+from tests.vectorizer.extensions.utils import run_vectorizer_worker
+
+
+class Base(DeclarativeBase):
+    pass
+
+
+class ArticleWithLazyStrategies(Base):
+    __tablename__ = "articles_lazy_test"
+    id = Column(Integer, primary_key=True)
+    title = Column(Text, nullable=False)
+    content = Column(Text, nullable=False)
+
+    # Different vectorizers with different lazy loading strategies
+    embeddings = vectorizer_relationship(dimensions=768, lazy="joined")
+
+
+def test_joined_loading(
+    postgres_container: PostgresContainer,
+    initialized_engine: Engine,
+    caplog: LogCaptureFixture,
+    vcr_: Any,
+):
+    """Test the difference between select and joined loading strategies."""
+    db_url = postgres_container.get_connection_url()
+
+    # Create tables
+
+    metadata = ArticleWithLazyStrategies.metadata
+    metadata.create_all(initialized_engine, tables=[metadata.sorted_tables[0]])
+
+    # Create vectorizers in database
+    with initialized_engine.connect() as conn:
+        conn.execute(
+            text("""
+                SELECT ai.create_vectorizer(
+                    'articles_lazy_test'::regclass,
+                    embedding => ai.embedding_openai('text-embedding-3-small', 768),
+                    chunking =>
+                    ai.chunking_recursive_character_text_splitter('content', 50, 10)
+                );
+                """)
+        )
+        conn.commit()
+
+    # Insert test data
+    with Session(initialized_engine) as session:
+        articles: list[ArticleWithLazyStrategies] = []
+        for i in range(3):
+            article = ArticleWithLazyStrategies(
+                title=f"Test Article {i}",
+                content=f"This is test content {i} that will be embedded.",
+            )
+            session.add(article)
+            articles.append(article)
+            # _ = article.embeddings
+        session.commit()
+
+    # Run vectorizer worker for each vectorizer
+    with vcr_.use_cassette("test_joined_loading.yaml"):
+        run_vectorizer_worker(db_url, 1)
+
+    with (
+        Session(initialized_engine) as session,
+        caplog.at_level("DEBUG", "sqlalchemy.engine"),
+    ):
+        articles = session.query(ArticleWithLazyStrategies).all()
+
+        initial_queries = [r.message for r in caplog.records if "SELECT" in r.message]
+        _ = [article.embeddings[0].chunk for article in articles]
+        after_select_queries = [
+            r.message for r in caplog.records if "SELECT" in r.message
+        ]
+        assert len(after_select_queries) == len(initial_queries), (
+            f"Should not trigger additional queries"
+            f" but queries were: {after_select_queries}"
+        )
diff --git a/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy_relationship.py b/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy_relationship.py
new file mode 100644
index 000000000..5ee5ad42a
--- /dev/null
+++ b/projects/pgai/tests/vectorizer/extensions/test_sqlalchemy_relationship.py
@@ -0,0 +1,90 @@
+from typing import Any
+
+import numpy as np
+from sqlalchemy import Column, Engine, Integer, Text
+from sqlalchemy.orm import DeclarativeBase, Session
+from sqlalchemy.sql import text
+from testcontainers.postgres import PostgresContainer  # type: ignore
+
+from pgai.sqlalchemy import vectorizer_relationship
+from tests.vectorizer.extensions.utils import run_vectorizer_worker
+
+
+class Base(DeclarativeBase):
+    pass
+
+
+class BlogPost(Base):
+    __tablename__ = "blog_posts"
+    id = Column(Integer, primary_key=True)
+    title = Column(Text, nullable=False)
+    content = Column(Text, nullable=False)
+    content_embeddings = vectorizer_relationship(
+        dimensions=768,
+    )
+
+
+def test_vectorizer_embedding_creation(
+    postgres_container: PostgresContainer, initialized_engine: Engine, vcr_: Any
+):
+    """Test basic data insertion and embedding generation with default relationship."""
+    db_url = postgres_container.get_connection_url()
+    # Create tables
+    metadata = BlogPost.metadata
+    metadata.create_all(initialized_engine, tables=[metadata.sorted_tables[0]])
+    with initialized_engine.connect() as conn:
+        conn.execute(
+            text("""
+                SELECT ai.create_vectorizer(
+                    'blog_posts'::regclass,
+                    embedding =>
+                    ai.embedding_openai('text-embedding-3-small', 768),
+                    chunking =>
+                    ai.chunking_recursive_character_text_splitter('content', 50, 10)
+                );
+            """)
+        )
+        conn.commit()
+
+    # Insert test data
+    with Session(initialized_engine) as session:
+        post = BlogPost(
+            title="Introduction to Machine Learning",
+            content="Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience.",  # noqa
+        )
+        session.add(post)
+        session.commit()
+
+    # Run vectorizer worker
+    with vcr_.use_cassette("test_vectorizer_embedding_creation_relationship.yaml"):
+        run_vectorizer_worker(db_url, 1)
+
+    # Verify embeddings were created
+    with Session(initialized_engine) as session:
+        # Verify embedding class was created correctly
+
+        blog_post = session.query(BlogPost).first()
+        assert blog_post is not None
+        assert blog_post.content_embeddings is not None
+        assert BlogPost.content_embeddings.__name__ == "ContentEmbeddingsEmbedding"
+
+        # Check embeddings exist and have correct properties
+        embedding = session.query(BlogPost.content_embeddings).first()
+        assert embedding is not None
+        assert isinstance(embedding.embedding, np.ndarray)
+        assert len(embedding.embedding) == 768
+        assert embedding.chunk is not None  # Should have chunk text
+        assert isinstance(embedding.chunk, str)
+
+        # Verify relationship works
+        blog_post = session.query(BlogPost).first()
+        assert blog_post is not None
+        assert hasattr(blog_post, "content_embeddings")
+        assert blog_post.content_embeddings is not None
+        assert len(blog_post.content_embeddings) > 0  # type: ignore
+        assert blog_post.content_embeddings[0].chunk in blog_post.content
+
+        embedding_entity = session.query(BlogPost.content_embeddings).first()
+        assert embedding_entity is not None
+        assert embedding_entity.chunk in blog_post.content
+        assert embedding_entity.parent is not None
diff --git a/projects/pgai/tests/vectorizer/extensions/utils.py b/projects/pgai/tests/vectorizer/extensions/utils.py
new file mode 100644
index 000000000..a1f3553fa
--- /dev/null
+++ b/projects/pgai/tests/vectorizer/extensions/utils.py
@@ -0,0 +1,19 @@
+from click.testing import CliRunner
+
+from pgai.cli import vectorizer_worker
+
+
+def run_vectorizer_worker(db_url: str, vectorizer_id: int) -> None:
+    CliRunner().invoke(
+        vectorizer_worker,
+        [
+            "--db-url",
+            db_url,
+            "--once",
+            "--vectorizer-id",
+            str(vectorizer_id),
+            "--concurrency",
+            "1",
+        ],
+        catch_exceptions=False,
+    )
diff --git a/projects/pgai/uv.lock b/projects/pgai/uv.lock
index 386ef1a5e..a0d7a885e 100644
--- a/projects/pgai/uv.lock
+++ b/projects/pgai/uv.lock
@@ -581,6 +581,57 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c6/b2/454d6e7f0158951d8a78c2e1eb4f69ae81beb8dca5fee9809c6c99e9d0d0/fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871", size = 179641 },
 ]
 
+[[package]]
+name = "greenlet"
+version = "3.1.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2f/ff/df5fede753cc10f6a5be0931204ea30c35fa2f2ea7a35b25bdaf4fe40e46/greenlet-3.1.1.tar.gz", hash = "sha256:4ce3ac6cdb6adf7946475d7ef31777c26d94bccc377e070a7986bd2d5c515467", size = 186022 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/25/90/5234a78dc0ef6496a6eb97b67a42a8e96742a56f7dc808cb954a85390448/greenlet-3.1.1-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:0bbae94a29c9e5c7e4a2b7f0aae5c17e8e90acbfd3bf6270eeba60c39fce3563", size = 271235 },
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