One GitHub connection feeds both a Knowledge source and an agent tool.
Users connect with a personal access token, checked against GitHub and
named after the account, or, when an admin registers a GitHub App
(GITHUB_CLIENT_ID, GITHUB_CLIENT_SECRET, GITHUB_APP_SLUG), with Sign in
with GitHub. App tokens expire after eight hours and are refreshed before
each sync or tool call; tokens with no expiry are never treated as expired.
The catalog gains an optional second sign-in method (oauth_settings,
exposed as sign_in_methods) without changing any other connector.
Sync lists the repositories the connection can read (the token's own, or
the App installations') and ingests one with the connection's token. The
tool is GitHub's read-only MCP server (api.githubcopilot.com/mcp/readonly):
setup discovers its actions and creates it bound to the connection, and
the executor sends the connection's token only to that server.
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.
Enum-like settings whose allowed values were only listed in a comment are
now Literal types, so a typo fails at startup with a message naming the
allowed values instead of falling through to a default with a warning
(or, for VECTOR_STORE, failing on first use):
AUTH_TYPE, VECTOR_STORE, STORAGE_TYPE, URL_STRATEGY, OCR_BACKEND,
OCR_ENGINE, SANDBOX_BACKEND, DOC_PARSER_ENGINE, TTS_PROVIDER, STT_PROVIDER
Each keeps a before-validator that strips and lower-cases the value, since
the registries that consume them already lower-cased at the use site, and
AUTH_TYPE maps the "None"/"none"/"" spellings a .env file carries to None
(it was the string "None" before, which only worked because nothing
compared against it). An empty TTS/STT provider still means "off".
LLM_PROVIDER stays a plain str because providers are plugin-extensible.
Containers are typed (dict[str, int], list[str], dict[str, Any]) instead
of bare dict/list, six fields that were Optional with a non-None default
are plain, and integer settings whose description already states a range
carry it as a constraint (ge=0 for "0 disables", ge=1 for counts that
cannot be zero, 0 < threshold <= 1).
docsgpt/core/settings.py had grown to 258 fields in one 600-line class,
touched by about two commits a week, with related settings scattered
(GitHub ingest caps inside the embeddings block, API keys in four places,
the OpenAI Responses knobs 100 lines from the other OpenAI fields).
It is now a package: one module per domain (auth, llm, embeddings,
retrieval, vectorstores, database, workers, ingestion, ocr, storage,
connectors, server, events, agents, guardrails, scheduler, sandbox,
speech), each a SettingsGroup owning its fields and validators, composed
by multiple inheritance into the same flat Settings class. Every
attribute name, type, default, alias and constraint is unchanged, so
settings.NAME reads, .env files and test monkeypatches all keep working;
the import path docsgpt.core.settings is the package. Settings.normalize_api_key
is kept as a classmethod for callers that reuse it.
The comment above or beside each field became its Field(description=...),
so the definitions are visible to tooling; the next commit generates the
docs reference from them.
Pitfall recorded for future groups: pydantic collects validators by
method name across the MRO, so two groups naming a validator the same
would silently keep only one. Each group's validator has a unique name.
arc53-machine