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/agents/base.py

@@ -382,7 +382,7 @@ class BaseAgent(ABC):
         when the conversation was compressed after that turn was produced —
         the compressed local history is the context then, not the server's.
         """
-        if not getattr(settings, "OPENAI_RESPONSES_CHAIN_ACROSS_TURNS", True):
+        if not settings.OPENAI_RESPONSES_CHAIN_ACROSS_TURNS:
             return None
         if not self.chat_history:
             return None
@@ -422,7 +422,7 @@ class BaseAgent(ABC):
             # No provider-reported usage on the previous turn (older rows,
             # estimate-only providers): nothing to bound against.
             return meta["response_id"]
-        budget = getattr(settings, "OPENAI_RESPONSES_CHAIN_BUDGET_TOKENS", None)
+        budget = settings.OPENAI_RESPONSES_CHAIN_BUDGET_TOKENS
         if not budget:
             from docsgpt.core.model_utils import get_token_limit
 

docsgpt/agents/tool_executor.py

@@ -53,7 +53,7 @@ def _dedupable_tool_names() -> frozenset:
     """
     from docsgpt.core.settings import settings
 
-    return frozenset(BUILTIN_AGENT_TOOLS) | frozenset(getattr(settings, "DEFAULT_CHAT_TOOLS", None) or [])
+    return frozenset(BUILTIN_AGENT_TOOLS) | frozenset(settings.DEFAULT_CHAT_TOOLS or [])
 
 
 def _requires_approval(tool: Dict, action: Dict) -> bool:

docsgpt/agents/tools/artifact_generator.py

@@ -829,7 +829,7 @@ class ArtifactGeneratorTool(Tool):
         spec_path = f"{token_dir}/spec.json"
         out_path = f"{token_dir}/out.{_KIND_INFO[kind]['ext']}"
         program = _RENDERERS[kind].format(spec_path=spec_path, out_path=out_path)
-        timeout = float(getattr(settings, "SANDBOX_EXEC_TIMEOUT", 60))
+        timeout = float(settings.SANDBOX_EXEC_TIMEOUT)
 
         manager = SandboxCreator.get_manager()
         try:

docsgpt/agents/tools/attachment_bridge.py

@@ -113,7 +113,7 @@ def bridge_attachment(
     # Reject oversize attachments BEFORE buffering them: the authoritative ``size``
     # column lets us avoid pulling a multi-hundred-MB file fully into worker memory,
     # and the bounded read below backstops a missing/lying ``size``.
-    max_bytes = int(getattr(settings, "ARTIFACT_MAX_BYTES", 0) or 0)
+    max_bytes = int(settings.ARTIFACT_MAX_BYTES or 0)
     declared_size = attachment.get("size")
     if max_bytes and isinstance(declared_size, (int, float)) and declared_size > max_bytes:
         raise AttachmentBridgeError(

docsgpt/agents/tools/code_executor.py

@@ -87,9 +87,9 @@ class CodeExecutorTool(Tool):
         baked in. Keep the package lists in sync with deployment/sandbox/Dockerfile
         (jupyter) and scripts/build_daytona_snapshot.py (daytona snapshot).
         """
-        backend = str(getattr(settings, "SANDBOX_BACKEND", "jupyter") or "jupyter").lower()
+        backend = str(settings.SANDBOX_BACKEND or "jupyter").lower()
         if backend == "daytona":
-            if getattr(settings, "DAYTONA_SNAPSHOT", None):
+            if settings.DAYTONA_SNAPSHOT:
                 return (
                     "Preinstalled beyond the stdlib: python-pptx, python-docx, openpyxl, "
                     "reportlab, lxml, pillow. pip install anything else from within the code "
@@ -330,7 +330,7 @@ class CodeExecutorTool(Tool):
             # Reject an oversize input BEFORE buffering it: the declared ``size``
             # avoids pulling a huge file into worker memory, and the bounded read
             # below backstops a missing/lying size column.
-            max_bytes = int(getattr(settings, "SANDBOX_MAX_INPUT_BYTES", 0) or 0)
+            max_bytes = int(settings.SANDBOX_MAX_INPUT_BYTES or 0)
             declared_size = version.get("size")
             if max_bytes and isinstance(declared_size, (int, float)) and declared_size > max_bytes:
                 return {"error": f"input artifact {artifact_id} exceeds the {max_bytes}-byte sandbox input limit."}
@@ -484,7 +484,7 @@ class CodeExecutorTool(Tool):
     @staticmethod
     def _exec_timeout() -> float:
         """Return the fixed per-run wall-clock cap (SANDBOX_EXEC_TIMEOUT; not caller-adjustable)."""
-        return float(getattr(settings, "SANDBOX_EXEC_TIMEOUT", 60))
+        return float(settings.SANDBOX_EXEC_TIMEOUT)
 
     @staticmethod
     def _is_timeout(result: ExecResult) -> bool:

docsgpt/agents/tools/mcp_tool.py

@@ -108,11 +108,11 @@ class MCPTool(Tool):
         if configured_redirect_uri:
             return configured_redirect_uri.rstrip("/")
 
-        explicit = getattr(settings, "MCP_OAUTH_REDIRECT_URI", None)
+        explicit = settings.MCP_OAUTH_REDIRECT_URI
         if explicit:
             return explicit.rstrip("/")
 
-        connector_base = getattr(settings, "CONNECTOR_REDIRECT_BASE_URI", None)
+        connector_base = settings.CONNECTOR_REDIRECT_BASE_URI
         if connector_base:
             parsed = urlparse(connector_base)
             if parsed.scheme and parsed.netloc:

docsgpt/agents/tools/read_document.py

@@ -255,7 +255,7 @@ class ReadDocumentTool(Tool):
         # The task's per-call time limits are raised to match the awaited window: bound to
         # the base timeout at import, the worker would otherwise self-terminate a large
         # parse long before this await gives up.
-        queue = getattr(settings, "DOCUMENT_PARSE_QUEUE", "parsing")
+        queue = settings.DOCUMENT_PARSE_QUEUE
         try:
             async_result = parse_document.apply_async(
                 args=[artifact_id, parent, self.user_id, options],

docsgpt/agents/workflow_agent.py

@@ -331,7 +331,7 @@ class WorkflowAgent(BaseAgent):
         from docsgpt.storage.storage_creator import StorageCreator
 
         storage = StorageCreator.get_storage()
-        max_bytes = int(getattr(settings, "ARTIFACT_MAX_BYTES", 0) or 0)
+        max_bytes = int(settings.ARTIFACT_MAX_BYTES or 0)
         dropped: List[str] = []
         if len(self.attachments) > _MAX_INPUT_DOCUMENTS:
             over = len(self.attachments) - _MAX_INPUT_DOCUMENTS

docsgpt/agents/workflows/workflow_engine.py

@@ -639,7 +639,7 @@ class WorkflowEngine:
         raw_ids = self._resolve_input_artifact_ids(inputs)
         if not raw_ids:
             return loaded
-        max_bytes = int(getattr(settings, "SANDBOX_MAX_INPUT_BYTES", 0) or 0)
+        max_bytes = int(settings.SANDBOX_MAX_INPUT_BYTES or 0)
         storage = StorageCreator.get_storage()
         # Two inputs whose current versions share a filename would clobber each other at the
         # same ``inputs/{name}`` path; track used paths and disambiguate deterministically.
@@ -749,15 +749,15 @@ class WorkflowEngine:
 
         supported = set(supported_types)
         supports_images = any(t.startswith("image/") for t in supported)
-        max_files = int(getattr(settings, "WORKFLOW_NODE_NATIVE_MAX_FILES", 5))
-        extract_max = int(getattr(settings, "WORKFLOW_NODE_EXTRACT_MAX_FILES", 5))
+        max_files = int(settings.WORKFLOW_NODE_NATIVE_MAX_FILES)
+        extract_max = int(settings.WORKFLOW_NODE_EXTRACT_MAX_FILES)
         # One wall clock for every blocking parse this node issues. The cap
         # above bounds how MANY parses run; this bounds how LONG they take in
         # total, so N documents cannot serialize N size-scaled windows.
         parse_deadline = time.monotonic() + float(
-            getattr(settings, "WORKFLOW_NODE_EXTRACT_BUDGET_SECONDS", 900)
+            settings.WORKFLOW_NODE_EXTRACT_BUDGET_SECONDS
         )
-        max_bytes = int(getattr(settings, "SANDBOX_MAX_INPUT_BYTES", 25 * 1024 * 1024))
+        max_bytes = int(settings.SANDBOX_MAX_INPUT_BYTES)
 
         # One read-only connection for the whole batch; the resolved-version
         # rows are collected, then storage reads happen outside the DB context.
@@ -976,7 +976,7 @@ class WorkflowEngine:
         if not user_id:
             return None
         options = {"output": "markdown", "include_tables": False, "persist": False}
-        queue = getattr(settings, "DOCUMENT_PARSE_QUEUE", "parsing")
+        queue = settings.DOCUMENT_PARSE_QUEUE
         # OCR cost scales with pages, so the window grows with the document's size
         # (floored at DOCUMENT_PARSE_TIMEOUT); the task's per-call time limits are
         # raised to match, else the worker would self-terminate mid-parse.
@@ -1084,7 +1084,7 @@ class WorkflowEngine:
         """Return the stricter of the node's requested timeout and the sandbox cap."""
         from docsgpt.core.settings import settings
 
-        cap = float(getattr(settings, "SANDBOX_EXEC_TIMEOUT", 60))
+        cap = float(settings.SANDBOX_EXEC_TIMEOUT)
         if requested is None:
             return cap
         try: