refactor: read settings directly instead of getattr with a second default

About 85 call sites read a setting as getattr(settings, "NAME", fallback),
each carrying its own copy of the default. Every one of those names is a
field with a default on the model, so the fallback could never apply to
the real settings object; it only masked drift. Two had drifted:

- OPENAI_PROMPT_CACHE_KEY defaults to True on the model but the reader
  fell back to False, and two test stubs relied on that.
- SharePoint's MICROSOFT_AUTHORITY fallback to
  https://login.microsoftonline.com/<tenant> never fired, because the
  attribute always exists (as None), so MSAL got authority=None. The
  connector now derives the tenant authority when the setting is unset,
  as its test always assumed.

Four places read EMBEDDINGS_KEY straight from os.environ, skipping the
"None"/"" normalisation the model applies; they read the setting now.
Test stubs that replaced a module's settings with a SimpleNamespace list
every setting the code under test reads.

docsgpt/vectorstore/base.py

@@ -277,7 +277,7 @@ def _delegation_enabled() -> bool:
     with a ``MagicMock``, whose every attribute is a truthy object, and
     ``bool()`` on that would silently route them through the broker.
     """
-    return getattr(settings, "EMBEDDINGS_DELEGATE_TO_WORKER", False) is True
+    return settings.EMBEDDINGS_DELEGATE_TO_WORKER is True
 
 
 def get_embeddings(

docsgpt/vectorstore/embeddings_delegated.py

@@ -173,8 +173,8 @@ class DelegatedEmbeddings:
         the full timeout at once -- at the shipped 60s and 96 WSGI threads, an
         API that serves nothing at all, health checks included.
         """
-        queue = getattr(settings, "EMBEDDINGS_QUEUE", "embeddings")
-        timeout = getattr(settings, "EMBEDDINGS_DELEGATE_TIMEOUT", 60)
+        queue = settings.EMBEDDINGS_QUEUE
+        timeout = settings.EMBEDDINGS_DELEGATE_TIMEOUT
 
         remaining = self._cooldown_remaining()
         if remaining > 0:

docsgpt/vectorstore/embeddings_local.py

@@ -188,8 +188,8 @@ def _describe_from_repo(repo: str) -> Optional[EmbeddingModel]:
 
 def _apply_overrides(spec: EmbeddingModel) -> EmbeddingModel:
     """Let ``EMBEDDINGS_POOLING``/``EMBEDDINGS_NORMALIZE`` win over any source."""
-    pooling = getattr(settings, "EMBEDDINGS_POOLING", None)
-    normalize = getattr(settings, "EMBEDDINGS_NORMALIZE", None)
+    pooling = settings.EMBEDDINGS_POOLING
+    normalize = settings.EMBEDDINGS_NORMALIZE
     changes = {}
     if isinstance(pooling, str) and pooling.strip().lower() in ("cls", "mean"):
         changes["pooling"] = pooling.strip().lower()
@@ -289,10 +289,10 @@ class EmbeddingsWrapper:
         try:
             _register(self.spec)
             init_kwargs = {"model_name": self.spec.repo}
-            threads = getattr(settings, "EMBEDDINGS_THREADS", None)
+            threads = settings.EMBEDDINGS_THREADS
             if isinstance(threads, int) and threads > 0:
                 init_kwargs["threads"] = threads
-            cache_dir = getattr(settings, "EMBEDDINGS_CACHE_DIR", None)
+            cache_dir = settings.EMBEDDINGS_CACHE_DIR
             if cache_dir:
                 init_kwargs["cache_dir"] = cache_dir
             self.model = TextEmbedding(**init_kwargs)
@@ -331,7 +331,7 @@ class EmbeddingsWrapper:
         if not documents:
             return []
         batch_size: Optional[int] = None
-        raw = getattr(settings, "EMBEDDINGS_MODEL_BATCH_SIZE", None)
+        raw = settings.EMBEDDINGS_MODEL_BATCH_SIZE
         if isinstance(raw, int) and not isinstance(raw, bool) and raw > 0:
             batch_size = raw
 

docsgpt/vectorstore/pgconn.py

@@ -59,7 +59,7 @@ def resolve_pool_max_size() -> int:
     """
     from docsgpt.core.settings import settings
 
-    value = getattr(settings, "PGVECTOR_POOL_MAX_SIZE", DEFAULT_POOL_MAX_SIZE)
+    value = settings.PGVECTOR_POOL_MAX_SIZE
     if isinstance(value, int) and not isinstance(value, bool) and value >= 0:
         return value
     return DEFAULT_POOL_MAX_SIZE

docsgpt/vectorstore/pgvector.py

@@ -54,9 +54,9 @@ class PGVectorStore(BaseVectorStore):
         # Use provided connection string or fall back to settings.
         # If PGVECTOR_CONNECTION_STRING is not set but POSTGRES_URI is,
         # reuse the same cluster — normalize from SQLAlchemy dialect to libpq form.
-        self._connection_string = connection_string or getattr(settings, 'PGVECTOR_CONNECTION_STRING', None)
+        self._connection_string = connection_string or settings.PGVECTOR_CONNECTION_STRING
 
-        if not self._connection_string and getattr(settings, 'POSTGRES_URI', None):
+        if not self._connection_string and settings.POSTGRES_URI:
             from docsgpt.core.db_uri import normalize_pgvector_connection_string
             self._connection_string = normalize_pgvector_connection_string(settings.POSTGRES_URI)