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.
The backend import package is now docsgpt, the name it will carry on PyPI;
application was far too generic to install into anyone's site-packages.
git mv plus a mechanical rewrite of every import, dotted string and path
reference: 734 Python files, the compose files, Dockerfile, workflows, docs,
setup scripts, devcontainer, k8s manifests, vscode config, pytest and coverage
config, .gitignore. Behaviour is unchanged.
Kept for one release:
- A top-level application package whose meta-path finder resolves
application.x.y to the already-imported docsgpt.x.y object, so old imports
and entry points (celery -A application.app.celery,
uvicorn application.asgi:asgi_app) keep working with a FutureWarning.
- Celery registers every application.* task name as an alias of its
docsgpt.* task on start-up, so messages queued by the previous release still
run. The redbeat key prefix moves to redbeat:docsgpt:v2: so schedule entries
the previous release wrote are left unread instead of firing twice.
The backend image builds from the repository root (docker build -f
docsgpt/Dockerfile .) so it can ship the alias package; a root .dockerignore
allow-lists docsgpt/ and application/ and keeps caches, local data, .env
files, the sample index files and the Dockerfile out. Compose and the image
workflows point at the new context.
Review follow-ups.
A BOM told the sniff which encoding to read, but was also taken as the
verdict: three prepended bytes let any binary through, including as
notes.txt. A BOM now only selects the test — UTF-8 falls through to the
byte rules on the remainder, UTF-16/32 decode and judge the characters
(NUL, unprintable, or replacement chars from bytes the decoder could not
read). Real Notepad-Unicode text still passes, mp4-behind-a-BOM does not,
in either language.
The gate treated the full parser table as a given, but without docling the
fallback extractor has no .tif/.tiff/.bmp/.webp/.vtt/.xml handler, so those
suffixes skipped the content check and reached the plain-text fallthrough —
the original bug, one install away. The worker now passes the keys of the
extractor it actually built, making the second gate stricter than the
route's static one rather than a copy of it.
Cache bins: extraction coerced a missing value to 0 and only non-zero bins
were recorded, so a provider reporting cached_tokens=0 persisted as NULL —
indistinguishable from "not reported", and OpenAI reports exactly that on
every uncached request. Bins are now carried as Optional and recorded when
not None, which is what the nullable columns and the NULL-means-unknown
comment already assumed. Anthropic's cache_read/cache_creation bins had the
same shape and are fixed alongside; the int-or-None coercion is shared in
llm/base.py.
Token accounting:
- Drain each tool round's provider stream to exhaustion before running
tools and recursing, so the usage decorator persists exactly one
token_usage row per LLM call, at call end. Previously every round's
generator was abandoned mid-iteration and flushed together at request
teardown, writing N near-identical rows stamped with the final
round's provider counts (duplicate billing).
- Consume the Chat Completions include_usage terminal chunk (it arrives
after finish_reason and was never read) so streamed calls record
provider-exact token counts instead of tiktoken estimates.
- Claim provider-reported usage once per call (_last_usage_claimed) so
a late-finalized generator can never adopt another call's counts.
Oversized-context guards:
- Enforce Responses API function_call/function_call_output pairing in
the input builder (drop unpaired items; bypassed for store-mode
previous_response_id chaining where calls are matched server-side).
- Hard pre-send context gate: shrink oversized tool results and refuse
payloads that cannot fit the model's window before dispatch, so a
hopeless request is never sent or billed.
- Cap a single tool result entering the LLM context
(TOOL_RESULT_MAX_TOKENS, default 20000); the tool journal and
persistence keep the full result. Applied on the resume/continuation
path too.
- Skip the fallback attempt when the payload cannot fit the fallback
model's context window (10% estimation slack).
- Compression: never save a compression point that does not reduce
tokens; bound oversized verbatim fields kept after a compression
point (COMPRESSION_RECENT_FIELD_MAX_TOKENS, default 8000).
Robustness fixes from review:
- Google parallel function calls: complete index-less ToolCalls are no
longer merged into one another (dict arguments raised TypeError on
+=; second call could execute with the first call's arguments).
- Trailing-frame failures after a delivered answer no longer error the
stream or restream the whole answer from the fallback
(_stream_reached_finish).
- In-memory compression falls back to minimal pruning when the summary
is not smaller than the original.
- keep<=0 guard in the middle-truncation helpers (a tiny cap returned
marker + full text).
Frontend: tooltip on the Analytics tokens stat card explaining that
agent tool loops re-send conversation context on every step.
Live /v1/responses rejects input_text for the assistant role
(input[N].content[0] -> "Supported values are: 'output_text' and
'refusal'"). Revert the previous review-driven change: encode assistant
turns as output_text and user/system turns as input_text. The SDK type
union suggested input_* only, but the API enforces output_text per-role —
confirmed against the live endpoint.
- Replay prior-turn assistant text as input_text, not output_text: a
Responses easy-input message only accepts input_* content parts, so the
old shape would 400 on the second turn of every conversation (default
store=false resends prior turns inline).
- Always request include=["reasoning.encrypted_content"] so in-turn
reasoning carryover works whether or not the response is also stored
server-side (OPENAI_RESPONSES_STORE).
- Add detail="auto" to input_image parts.
- Remove the now-dead azure_openai option from setup.sh / setup.ps1 and
the docs, completing the AzureOpenAILLM removal.
- Tests: correct the input_text / image-detail / include assertions and
add parallel tool-call and stream-error coverage.
Route opted-in models to /v1/responses while keeping Chat Completions as
the default. The flavor is selected per-model in the catalog YAML and
threaded through ModelCapabilities (with a new reasoning_effort field).
- Translate chat-shaped messages/tools/structured-output into the
Responses input / text.format / flattened-tool shapes at the provider
boundary; stored history and intra-turn tool round-trips need no
migration.
- Normalize the typed SSE event stream back into the existing handler
contract (content / thought / tool-call chunks) so the streaming
accumulator and OpenAI handler stay unchanged.
- Carry encrypted reasoning items across the in-turn tool loop
(provider-local, keyed by call_id) for stateless reasoning continuity.
- OPENAI_RESPONSES_STORE opt-in enables server-side state and
previous_response_id chaining across turns, persisted in message
metadata.
- Set gpt-5.5 to api_flavor: responses, reasoning_effort: medium.
- Remove AzureOpenAILLM and the azure_openai provider/catalog.
arc53-machine