Review pass over the preceding commits.
- The activity export paged by OFFSET. The journals are append-only and the
feed is newest-first, so rows written mid-export shift the window down and
repeat rows already emitted. Pages by keyset on (created_at, feed, id) now.
- top_token_users and the per-user breakdown counted run-level rollup rows.
A scheduled run already has a row per LLM call, so its spend was billed
twice -- invisible while the column was tokens, obvious once it was dollars.
Both now exclude them, matching every other spend query.
- total_cost was summed from the per-model split, which drops rows with no
model_id and so undercounted the figure printed above the series. Summed
from the series instead.
- The CSV export encoded its detail cell without the fallback the NDJSON
branch had, so a non-JSON-native value would have failed the stream.
- The activity view reset pagination in an effect, which fetched the stale
page against the new filters before fetching again. Reset in the setters.
- The audit taxonomy is no longer re-exported from the helper module; the one
caller that wanted it imports from where it lives.
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.
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.
arc53-machine