`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.
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.
- sync llm_passthrough_route: read and close an error-status streaming
response before mapping it, so upstream 4xx/5xx surface as the provider
error instead of httpx.ResponseNotRead
- AsyncPassthroughStreamingResponse: expose aiter_bytes() and carry
_hidden_params so the router attaches headers in place instead of
wrapping the stream in HiddenParamsAsyncIteratorWrapper, which 500'd
every streaming azure router-model passthrough request
- logging: swap the passthrough httpx result for the transformed
ModelResponse/EmbeddingResponse when firing success callbacks
- get_llm_provider: resolve gigachat from its api base and drop the dead
gigachat_models elif branch
- constants: register the gigachat api base in openai_compatible_endpoints
Adapts streaming_model_restamp.py to the LIT001/LIT010 gates that landed
on staging since this branch was cut (Final annotations, Mapping in
annotations instead of dict).
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
When the upstream errors while the client is still connected, the pump
forwards the exception through the relay queue so the proxy's failure
handling re-raises it. If the client disconnects before consuming that
queued exception, neither the failure hook nor billing ran and the spend
row was lost. The pump now waits for client detach and, if the exception
was never consumed, salvages partial spend like the post-disconnect
error path.
Also rewrites the bedrock disconnect logging test to the detached-pump
contract: billing fires after the upstream drain completes, not
synchronously at aclose().
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.
Resolving github_copilot/chatgpt names through get_llm_provider runs the
provider's OAuth device flow synchronously on the event loop. Adopt the
declared provider in PatternMatchRouter.get_pattern, which the auth
layer's zero-cost budget check walks on every request against wildcard
routers, and in /utils/supported_openai_params.
When the pump finishes draining while the client is still connected,
billing is deferred to the proxy's post-response hook, which only fires
on a normally completed response. A client disconnect before the relay
consumed the queued tail tore the generator down past that hook, so the
request logged no spend at all. The relay teardown now dispatches the
stored deferred billing whenever it never reached the end-of-stream
sentinel.
Also drops the live pass_through_tests script: that CI job runs against
a fixed config with no Bedrock model or AWS credentials, so it could
only fail there. The scenario is covered by unit tests on the
relay/pump seam.
* test(newrelic): cover static default_team_settings per-team routing
The dynamic POST /team/{team_id}/callback path for New Relic is tested, but
the static default_team_settings twin had no regression coverage. Add a test
that drives default_team_settings -> add_team_based_callbacks_from_config and
asserts the resolved trusted vars dispatch to BOTH the per-team metrics logger
(cost/usage) and the trace logger (LLM/agent spans), so a config-file customer
gets the same per-team routing as the API customer.
Also correct the /team/callback docstring: callback_name is a str validated
against the credential-capable callbacks, not a fixed langfuse/langsmith/gcs
Literal, and document the newrelic_api_key / newrelic_region vars.
* chore(ui): sync schema.d.ts with the /team/callback docstring
Regenerate the dashboard OpenAPI types for the add_team_callbacks description
change: callback_name is a validated str (not a langfuse/langsmith/gcs
Literal) and the newrelic_api_key / newrelic_region vars are documented.
* docs(newrelic): note LITELLM_OTEL_V2 prerequisite, trim test comments
Address review: team-scoped New Relic config is rejected with a 400 unless the
proxy runs with LITELLM_OTEL_V2=true, so document that in the /team/callback
endpoint and sync schema.d.ts. Drop the narrative setup comments in the new
test per the repo comment convention; the test name and docstring already say why.
