diff --git a/pylib/embedding/pgvector.py b/pylib/embedding/pgvector.py
index dba1e09..ee9ef63 100644
--- a/pylib/embedding/pgvector.py
+++ b/pylib/embedding/pgvector.py
@@ -26,7 +26,7 @@
     register_vector = object()  # Set up a dummy to satisfy the type hints
     POOL_TYPE = object
 
-# See: 
+# See: https://github.com/OoriData/OgbujiPT/issues/87
 DEFAULT_USER_SCHEMA = 'public'
 DEFAULT_SYSTEM_SCHEMA = 'pg_catalog'
 
@@ -84,7 +84,7 @@ def __init__(self, embedding_model, table_name: str, pool, sys_schema=DEFAULT_SY
                 using multiple schemata, you can run into `ERROR: type "vector" does not exist`
                 unless a schema with the extension is in the search path (via `SET SCHEMA`)
 
-            half_precision (bool) - if True, use halfvec type to store half-precision vectors (pgvector 0.7.0 & up only).
+            half_precision (bool) - if True, use halfvec type to store half-precision vectors (pgvector 0.7.0+ only).
                 Default is False (full precision)
 
             itypes (list <str>) - Index types (or empty for no indexing).
@@ -138,8 +138,9 @@ def __init__(self, embedding_model, table_name: str, pool, sys_schema=DEFAULT_SY
 
     @classmethod
     async def from_conn_params(cls, embedding_model, table_name, host, port, db_name, user, password,
-        sys_schema=DEFAULT_SYSTEM_SCHEMA, pool_min=DEFAULT_MIN_CONNECTION_POOL_SIZE, pool_max=DEFAULT_MAX_CONNECTION_POOL_SIZE,
-        half_precision=False, itypes=None, ifuncs=None, i_max_conn=16, ef_construction=64) -> 'PGVectorHelper': # noqa: E501
+        sys_schema=DEFAULT_SYSTEM_SCHEMA, pool_min=DEFAULT_MIN_CONNECTION_POOL_SIZE,
+        pool_max=DEFAULT_MAX_CONNECTION_POOL_SIZE, half_precision=False, itypes=None, ifuncs=None,
+        i_max_conn=16, ef_construction=64) -> 'PGVectorHelper': # noqa: E501
         '''
         Create PGVectorHelper instance from connection/pool parameters
 
@@ -165,8 +166,9 @@ async def from_conn_params(cls, embedding_model, table_name, host, port, db_name
 
     @classmethod
     async def from_conn_string(cls, conn_string, embedding_model, table_name,
-        sys_schema=DEFAULT_SYSTEM_SCHEMA, pool_min=DEFAULT_MIN_CONNECTION_POOL_SIZE, pool_max=DEFAULT_MAX_CONNECTION_POOL_SIZE,
-        half_precision=False, itypes=None, ifuncs=None, i_max_conn=16, ef_construction=64) -> 'PGVectorHelper': # noqa: E501
+        sys_schema=DEFAULT_SYSTEM_SCHEMA, pool_min=DEFAULT_MIN_CONNECTION_POOL_SIZE,
+        pool_max=DEFAULT_MAX_CONNECTION_POOL_SIZE, half_precision=False,
+        itypes=None, ifuncs=None, i_max_conn=16, ef_construction=64) -> 'PGVectorHelper': # noqa: E501
         '''
         Create PGVectorHelper instance from a connection string AKA DSN
 
@@ -201,7 +203,7 @@ async def init_pool(conn, schema=DEFAULT_SYSTEM_SCHEMA):
         try:
             await register_vector(conn, schema=schema)
         except ValueError as e:
-            raise RuntimeError(f'Unable to find the vector type in the database or schema. You might need to enable it: {e}')
+            raise RuntimeError(f'Unable to find vector type in the DB/schema. You might need to enable it: {e}')
         try:
             await conn.set_type_codec(  # Register a codec for JSON
                 'jsonb', encoder=json.dumps, decoder=json.loads, schema=schema)
diff --git a/pylib/embedding/pgvector_data.py b/pylib/embedding/pgvector_data.py
index 62d588b..edc8c68 100644
--- a/pylib/embedding/pgvector_data.py
+++ b/pylib/embedding/pgvector_data.py
@@ -7,8 +7,7 @@
 
 from typing import Iterable, Callable, List, Sequence
 
-from ogbujipt.embedding.pgvector import (PGVectorHelper, asyncpg, process_search_response,
-    DEFAULT_MIN_CONNECTION_POOL_SIZE, DEFAULT_MAX_CONNECTION_POOL_SIZE, DEFAULT_SYSTEM_SCHEMA, DEFAULT_USER_SCHEMA)
+from ogbujipt.embedding.pgvector import (PGVectorHelper, asyncpg, process_search_response)
 
 __all__ = ['DataDB']
 
diff --git a/pylib/embedding/pgvector_message.py b/pylib/embedding/pgvector_message.py
index 1893e47..12e9d85 100644
--- a/pylib/embedding/pgvector_message.py
+++ b/pylib/embedding/pgvector_message.py
@@ -12,7 +12,7 @@
 
 from ogbujipt.config import attr_dict
 from ogbujipt.embedding.pgvector import (PGVectorHelper, asyncpg, process_search_response,
-    DEFAULT_MIN_CONNECTION_POOL_SIZE, DEFAULT_MAX_CONNECTION_POOL_SIZE, DEFAULT_SYSTEM_SCHEMA, DEFAULT_USER_SCHEMA)
+    DEFAULT_MIN_CONNECTION_POOL_SIZE, DEFAULT_MAX_CONNECTION_POOL_SIZE, DEFAULT_SYSTEM_SCHEMA)
 
 __all__ = ['MessageDB']
 
@@ -146,8 +146,9 @@ def __init__(self, embedding_model, table_name: str, pool: asyncpg.pool.Pool, wi
 
     @classmethod
     async def from_conn_params(cls, embedding_model, table_name, host, port, db_name, user, password, window=0,
-        sys_schema=DEFAULT_SYSTEM_SCHEMA, pool_min=DEFAULT_MIN_CONNECTION_POOL_SIZE, pool_max=DEFAULT_MAX_CONNECTION_POOL_SIZE,
-        half_precision=False, itypes=None, ifuncs=None, i_max_conn=16, ef_construction=64) -> 'MessageDB': # noqa: E501
+        sys_schema=DEFAULT_SYSTEM_SCHEMA, pool_min=DEFAULT_MIN_CONNECTION_POOL_SIZE,
+        pool_max=DEFAULT_MAX_CONNECTION_POOL_SIZE, half_precision=False,
+        itypes=None, ifuncs=None, i_max_conn=16, ef_construction=64) -> 'MessageDB': # noqa: E501
         obj = await super().from_conn_params(embedding_model, table_name, host, port, db_name, user, password,
             sys_schema, pool_min, pool_max)
         obj.window = window
@@ -155,8 +156,9 @@ async def from_conn_params(cls, embedding_model, table_name, host, port, db_name
 
     @classmethod
     async def from_conn_string(cls, conn_string, embedding_model, table_name, window=0,
-        sys_schema=DEFAULT_SYSTEM_SCHEMA, pool_min=DEFAULT_MIN_CONNECTION_POOL_SIZE, pool_max=DEFAULT_MAX_CONNECTION_POOL_SIZE,
-        half_precision=False, itypes=None, ifuncs=None, i_max_conn=16, ef_construction=64) -> 'MessageDB': # noqa: E501
+        sys_schema=DEFAULT_SYSTEM_SCHEMA, pool_min=DEFAULT_MIN_CONNECTION_POOL_SIZE,
+        pool_max=DEFAULT_MAX_CONNECTION_POOL_SIZE, half_precision=False,
+        itypes=None, ifuncs=None, i_max_conn=16, ef_construction=64) -> 'MessageDB': # noqa: E501
         obj = await super().from_conn_string(conn_string, embedding_model, table_name,
             sys_schema, pool_min, pool_max)
         obj.window = window
diff --git a/test/embedding/test_pgvector_data.py b/test/embedding/test_pgvector_data.py
index 9d2e331..ffb3ae9 100644
--- a/test/embedding/test_pgvector_data.py
+++ b/test/embedding/test_pgvector_data.py
@@ -207,7 +207,8 @@ async def test_data_vector_half_index_half(DB_HALF_INDEX_HALF):
             metadata=meta,  # Tag metadata
         )
 
-    assert await DB_HALF_INDEX_HALF.count_items() == len(KG_STATEMENTS), Exception('Incorrect number of documents after insertion')
+    assert await DB_HALF_INDEX_HALF.count_items() == len(KG_STATEMENTS), \
+        Exception('Incorrect number of documents after insertion')
 
     # search table with perfect match
     result = await DB_HALF_INDEX_HALF.search(text=item1_text, limit=3)