The base added router_metadata to SpendLogsMetadata in #39001 without
updating this fixture, and its CI run never executed logging_testing,
so the job now fails on every branch merged with current staging.
* fix(otel): emit cache token counts on OTel v2 LLM spans
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(otel): trim comment in LLMUsage adapter
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(otel): drop casts in LLMUsage cache token adapter
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(deps): bump restrictedpython to 8.3 for GHSA-ffg3-p8fm-mjx2
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The advisory was modified 2026-08-31 and flags mlflow 3.13.0 through
3.15.2 with no fixed release published, so every osv-scan run fails
with nothing to bump. Same treatment as the existing diskcache entry.
`ChatCompletionFileObject` is in the union `_count_content_list` accepts, but
`file` was missing from its match, so every local count of a Responses
`input_file` raised `Invalid content item type: file`. On
/v1/responses/input_tokens that surfaced as an opaque 500 whenever the model's
provider counting API refused the block and the local tokenizer took over.
Count it the way the module already counts the same thing in Anthropic's
dialect: the filename like a document title, the inline bytes through the
image pricer.
Vercel now lists video and embedding models without token pricing or token
limits, and the first of them KeyError'd the whole weekly run before any
provider was synced. Those rows are skipped, and a failed catalog fetch for
any provider now yields an empty transform instead of a crash.
The transform now declares reasoning_effort_levels straight from the catalog's
effort options instead of mapping them onto per-level support flags, which
mis-advertised efforts these models do not take. max_tokens mirrors
max_completion_tokens rather than context_length, supports_prompt_caching is
derived from cache-read pricing, video input is read from input_modalities, and
a failed Friendli fetch no longer breaks the weekly sync run. The committed
friendliai entries are regenerated from the live catalog: stale gemma-4 token
caps refreshed, the delisted K-EXAONE-236B-A23B entry dropped, and GLM-5.3 /
GLM-5.3-Flash picked up with current billed pricing.
Since the requires-python cap moved to <3.15, uv resolved the project
python to 3.14, downloaded a managed interpreter under
/root/.local/share/uv that the runtime stage never receives, and every
layer-cache-miss image build broke: first at uvloop's cp314 sdist
configure step, then, with file/make added, at the runtime stage where
the copied venv's python symlink dangles and prisma imports fall through
to the system python. UV_PYTHON_DOWNLOADS=0 (already the convention in
migrations/backend/gateway) roots the venv on the apk python3.
The wolfi-base digest bump is required alongside it: the pinned 08-22
base ships glibc-2.43 while the current apk python-3.13 needs
GLIBC_2.44, and wolfi version-names glibc packages so apk upgrade
cannot cross that boundary.
With the venv on system 3.13 every dependency installs from wheels
again, so the file and make packages added for the sdist build are
reverted.
The Responses-to-chat transform dropped the filename OpenAI requires next to
file_data, so a request carrying an inline PDF counted 13 tokens instead of 36
and a real completion through the chat bridge got a 400.
Assistant list content was forwarded to /v1/responses/input_tokens as chat
`text` blocks, which the Responses API rejects (it accepts only output_text
and refusal inside an assistant turn). The 400 sent the whole request to the
local tokenizer, so any conversation with an assistant turn silently lost
provider-exact counting, including the image counting added in 73ab647b1c.
Assistant content now collapses to the plain string the Responses API counts
identically, and image parts are kept to user turns where they are legal.
* fix(vertex_ai): graft default vertex path when api_base has a version-only path
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(vertex_ai): keep query and fragment placement when grafting vertex path
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(vertex_ai): merge alt=sse into existing query when streaming
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(spend_tracking): persist router metadata in spend logs for internal router models
* test(spend_tracking): expect router_metadata key in exact-payload tests, type the routed-kwargs helper
The chat-to-Responses reverse transform kept only text blocks, so an image
input was dropped before the count went to OpenAI. A 256x256 image request
counted 13 tokens instead of 268.
/v1/responses/input_tokens returned 200 with a count for an empty
"input" ("" or []), while OpenAI returns a 400 missing_required_parameter.
The route also went through optimistic budget reservation, which is only
released by LLM success/failure callbacks that a token count never
reaches, so every call leaked a reservation until TTL expiry and could
429 real traffic. Both routes plus the /openai alias now join
/utils/token_counter in the reservation exemption set.
