added vector_engine to fix name postgres name space issue

python/ebook_search/api/main.py

@@ -31,7 +31,7 @@ logger = logging.getLogger(__name__)
 async def lifespan(app: FastAPI) -> AsyncIterator[None]:
     """Manage application startup and shutdown resources."""
     logger.info("ebook_search_startup")
-    app.state.engine = get_postgres_engine(name="RICHIE")
+    app.state.engine = get_postgres_engine(name="RICHIE", vector_engine=True)
     with Session(app.state.engine) as session:
         ensure_bm25_corpus(session, app.state.config)
     try:

python/orm/common.py

@@ -31,8 +31,24 @@ def get_connection_info(name: str) -> tuple[str, str, str, str, str | None]:
     return cast("tuple[str, str, str, str, str | None]", (database, host, port, username, password))
 
 
-def get_postgres_engine(*, name: str = "POSTGRES", pool_pre_ping: bool = True) -> Engine:
-    """Create a SQLAlchemy engine from environment variables."""
+def get_postgres_engine(
+    *,
+    name: str = "POSTGRES",
+    pool_pre_ping: bool = True,
+    vector_engine: bool = False,
+) -> Engine:
+    """Create a SQLAlchemy engine from environment variables.
+
+    Args:
+        name (str, optional): The name of the environment variable prefix. Defaults to "POSTGRES".
+        pool_pre_ping (bool, optional): Whether to ping the database before each connection. Defaults to True.
+        This fixes the issue of trying to use a conection that has timed out on the database side.
+        vector_engine (bool, optional): Whether to use the vector search schema. Defaults to False.
+        This updates the search path the incldued the vecore types and operators.
+
+    Returns:
+        Engine: The SQLAlchemy engine.
+    """
     database, host, port, username, password = get_connection_info(name)
 
     url = URL.create(
@@ -44,8 +60,14 @@ def get_postgres_engine(*, name: str = "POSTGRES", pool_pre_ping: bool = True) -
         database=database,
     )
 
+    connect_args = {}
+    # There more better way to do this is with separate PG account and a dedicated vector schema for the vector types
+    if vector_engine:
+        connect_args["options"] = "-csearch_path=main,public"
+
     return create_engine(
         url=url,
         pool_pre_ping=pool_pre_ping,
         pool_recycle=1800,
+        connect_args=connect_args,
     )