feat: add a full example for sqlmodel and fix error in README.md.

README.md

@@ -186,10 +186,13 @@ Use `vector_ip_ops` for inner product and `vector_cosine_ops` for cosine distanc
 Add a vector column
 
 ```python
+from typing import List, Optional
+
 from pgvector.sqlalchemy import Vector
-from sqlalchemy import Column
+from sqlmodel import Column, Field, Session, SQLModel, create_engine, select
 
 class Item(SQLModel, table=True):
+    id: Optional[int] = Field(default=None, primary_key=True)
     embedding: List[float] = Field(sa_column=Column(Vector(3)))
 ```
 
@@ -209,6 +212,8 @@ session.exec(select(Item).order_by(Item.embedding.l2_distance([3, 1, 2])).limit(
 
 Also supports `max_inner_product` and `cosine_distance`
 
+See [examples/simple_sqlmodel_vector.py](examples/simple_sqlmodel_vector.py) for full code.
+
 ## Psycopg 3
 
 Register the vector type with your connection

examples/simple_sqlmodel_vector.py

@@ -0,0 +1,34 @@
+"""
+A simple sqlmodel vector demo via pgvector.
+
+For mac, if depdency missing or error, try `pip install pgvector-binary`
+"""
+
+from typing import List, Optional
+
+from pgvector.sqlalchemy import Vector
+from sqlmodel import Column, Field, Session, SQLModel, create_engine, select
+
+
+class Item(SQLModel, table=True):
+    id: Optional[int] = Field(default=None, primary_key=True)
+
+    embedding: List[float] = Field(sa_column=Column(Vector(3)))
+
+sqlite_url = f"postgresql://testuser:testuser@localhost:5432/testdb"
+
+engine = create_engine(sqlite_url, echo=False)
+
+SQLModel.metadata.create_all(engine)
+
+with Session(engine) as session:
+    item = Item(embedding=[1, 2, 3])
+    session.add(item)
+    session.commit()
+
+    res = session.exec(
+        select(Item).order_by(Item.embedding.l2_distance([3, 1, 2])).limit(5)
+    )
+
+    for i in res:
+        print(i)