Migration 0033 adds quota_policies (instance default, team per-member
allowance, user override; a token budget and a USD budget per row) and
token_usage.cost. The column is added IF NOT EXISTS so a database that
already carries it upgrades cleanly.
Every usage row now records the call's USD cost from the model catalog;
bring-your-own models are recorded at $0.
No code change needed: the server side (`FastMCP`, `@mcp.tool`,
`get_http_headers`, `http_app`) and the client side (`Client`, `BearerAuth`,
the three transports, and the `mcp.client.auth` / `mcp.shared.auth` OAuth
types imported directly) all kept their signatures.
Verified beyond the suite by driving the real `/mcp` mount over a live
uvicorn socket with the fastmcp client: tool discovery, a `search_docs` call
and bearer-token extraction all round-trip, with the token reaching
`search()` intact.
fastmcp 4 and mcp 2.2 also moved to httpx2, so the retry tuple's comment now
places them on that side of the split.
Both SDKs' 1.x/3.x majors run on httpx2 (the maintained fork of httpx by its
original author, published by Pydantic at github.com/pydantic/httpx2, version
line 2.x) instead of httpx, which is what makes these two bumps one change.
httpx2 is now a declared dependency because two modules import it directly.
Everything else in the 0.x -> 1.x / 2.x -> 3.x change lists is absent here:
no Text Completions, no `with_raw_response`, no raw `output_format` dicts, no
Bedrock client, and `requires-python` is already 3.12.
Three code changes, all forced by the bump:
The BYOM DNS-pinning client in `docsgpt/security/safe_url.py` is handed to
`OpenAI(http_client=...)`, and the SDK rejects an old-httpx client at
construction — which would have taken the SSRF guard offline. It is built on
httpx2 now, and its `sni_hostname` extension carries a `str` rather than
ascii bytes: httpcore passes the value straight to
`ssl.SSLContext.wrap_socket`, and the truststore backend httpx2 uses for the
default system trust store encodes it instead of accepting bytes. Bytes
therefore failed every real handshake while passing the existing tests, which
stub the transport out; the test now pins the type and says why.
The stream-retry error tuple in `docsgpt/llm/base.py` named `httpx`
exceptions only. The two libraries' exception classes are unrelated types, so
after the bump the retry silently stopped firing for openai and anthropic
while still working for google-genai and elevenlabs. It now covers both
stacks, with a parametrized test over each.
anthropic 1.x dropped temperature/top_p/top_k from `messages.create`'s
signature (passing one raises TypeError) without dropping them from the API,
so the provider forwards them through `extra_body`. The wire request is
unchanged and a model that rejects them 400s exactly as before.
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.
Alex