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.
In store mode every user turn chained onto the previous response, so the
provider's stored transcript grew without bound (measured: 889k prompt
tokens for a 37k-token saved history) while every local guard, the
compression pipeline included, measured the saved history. Each chained
tool round also re-sent the system message, which the server appends rather
than dedupes, and a saved compression point was applied exactly once, in the
turn that made it.
Chaining is now bounded. A turn starts from the saved history when the
previous turn's reported prompt reached the chain budget (default: the
model's context window), when the conversation was compressed after that
turn was produced, or when OPENAI_RESPONSES_CHAIN_ACROSS_TURNS is off.
Chained rounds omit an unchanged system head (hash carried in the persisted
Responses state). truncation="auto" is available behind a setting as a
backstop against a chain that outgrows the model's window.
Compression: a saved point is applied at every turn start; the threshold
counts the summary plus the queries after the point instead of the raw
history; re-compression summarises only the tail on top of the last point;
the mid-execution path marks itself persisted and resets the provider chain
so the rebuilt messages are the context; an empty summary is rejected; the
visible "[Context Compression Summary]" rows are no longer replayed as
history; appending the same point twice is a no-op.
Cache hints: a per-user prompt_cache_key and an optional
prompt_cache_retention on Responses API calls.
Measured on Azure with the same client shape as production (stateless
OpenAI client, server-side tools, PDF part): tokens billed on the sixth turn
fell from 58k to 35k, tool rounds add tens of tokens instead of ~2.8k, the
turn after a compression reused the saved summary in under two seconds
instead of re-summarising, and the round after a mid-execution compression
started from the compressed context instead of the full stored transcript.
Alex