ProxyExtrasDBManager.spend_logs_is_partitioned() (#38452) silently returns
False when psycopg can't be imported, and psycopg was never added to the
extra_proxy install, so every production image lacks it. Schema
reconciliation then generates the unfiltered primary-key rewrite against a
genuinely partitioned LiteLLM_SpendLogs and Postgres rejects it, exactly the
failure the fix was meant to prevent. Ships psycopg via extra_proxy and logs
a warning when it's still missing instead of failing silently.
At end of drain the pump enqueued the sentinel first and picked the
billing mode from client_detached afterward, so a client that consumed
the sentinel and tore the relay down before the pump resumed (possible
whenever the sentinel enqueue hit a full queue) had its fully delivered
response billed through the teardown path, skipping the proxy's
post-response hook. Bill or park before the sentinel goes out, and let
an unconsumed sentinel fall back to dispatching the parked billing.
Shadow eval jobs previously targeted only virtual keys, so deployments on
pure JWT auth (which present no key at all) could never sample their
traffic. Jobs now carry a typed (target_type, target_id) pair covering
keys, teams, and users; sampling matches the identity every request
resolves to at auth time, so team and user jobs cover JWT traffic with
no client changes.
Resolves LIT-6578
* feat(complexity_router): escalate oversized prompts to a tier that fits before dispatch
The classifier scores complexity and never prompt size, so a long agentic
session whose newest ask is trivial classifies SIMPLE onto a small-window
tier and the provider rejects it with a context-window 400 that nothing
retries. The gate runs after classification on every decision path
(classify tail and session-affinity pin), estimates prompt tokens
including the out-of-band carriers (top-level system, tools,
instructions), and when the decided tier provably cannot hold the prompt
moves the request to the lowest configured tier with a model whose
declared window fits, restricting the pick to fitting models when the
decided tier can keep it. Models with no resolvable window are never
escalated away from or onto, escalated decisions are never written as
session pins, and the decision records context_escalated plus the
original tier in spend logs.
Resolves LIT-6503
* fix(complexity_router): judge groups by smallest window, bound skips by bytes, filter adaptive picks
Review-round rework, one mechanism per finding. A group is judged by its
smallest resolvable deployment window, since the core router picks within
a group with no fit check. The counting skip is gated on UTF-8 byte
length, which BPE token counts can never exceed, so token-dense scripts
cannot slip past it; only a real tokenizer count ever moves a request and
a failed count leaves the placement alone. The fit facts now filter every
adaptive phase including cold start and the tier fallbacks. Window
questions adopt the declared provider and never resolve authenticating
providers, and a router instance without get_model_list degrades the gate
to a no-op. Tests rebuilt on real Router instances resolving deployment
model_info end to end, plus a full-path test through
async_get_available_deployment
UsersTable overrides DataTable's default noDataMessage with its own
EmptyState, so the row reads "No users found" rather than "No results".
Assert that, and pair it with the seeded user being absent so the check
cannot pass while the filter silently does nothing.
The batch start told the seed which LiteLLM_SpendLogs rows were its own, but
using it as a hard cutoff also dropped rows another pod had already persisted.
Those rows are only repaid by that pod's own increment, so if it died first the
window row stayed permanently under the recorded spend.
The seed now reads both sums in one scan and takes off this batch's own spend,
flooring at the pre-batch total for the case where its log rows have not landed
yet. Redis payloads keep an empty request_ids so a leader from before the field
was dropped can still merge what it pops during a rolling deploy.
Claude-Session: https://claude.ai/code/session_01QvQzYztinxj8ZuD5YxbVdL
Two assertions were checking the wrong thing. The anchoring tests read
getByRole("listbox"), which resolves to SelectPrimitive.List; that sits at
full content height inside the popup that clips and scrolls it, so the box
overlapped the trigger even when nothing visible did. Measure the popup.
The SSO-ID search expected zero rows, but DataTable renders a "No results"
message row when a filter matches nothing, so the count is one. Assert the
empty state the user actually sees.
GigaChat reports prompt_tokens and total_tokens after subtracting cached
tokens (the docs example is prompt_tokens=1, precached_prompt_tokens=37,
total_tokens=5, so the fields are disjoint, not a subset). Map to the
OpenAI convention by adding precached_prompt_tokens back onto prompt and
total while still surfacing it as prompt_tokens_details.cached_tokens.
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.
The consolidated popup test only asserted the options never cover the
trigger, so opening above the trigger with room below it, the regression
PR #38554 fixed, would have passed. Split it back into a below-trigger
case and a cramped-viewport case. The header test accepted a single pixel
of vertical intersection; require the refresh control's centre to sit
within the tab row instead.
The migration smoke waited on `getByRole("button", { expanded: false })`
after clicking it. Playwright re-resolves that locator on every retry, so
once the clicked group flipped to expanded it matched the next collapsed
group instead, and the assertion could never pass. Count the remaining
collapsed groups and wait for that count to drop by one.
precached_prompt_tokens is a subset of prompt_tokens (OpenAI cached_tokens
semantics), so map it to prompt_tokens_details.cached_tokens instead of
adding it on top of prompt/total. Emit stream usage from any final chunk
carrying it rather than only finish_reason stop, which dropped tokens for
function_call and length streams. Merge auth metadata into a new dict in
the gigachat router handler instead of mutating the shared parsed-body
cache in place.
The UI e2e suite had a class of assertions that pin how the dashboard is
built rather than what it does, so an ordinary refactor turns them red
without any user-visible change.
Geometry. The auto-router template select had two tests made of pixel
arithmetic plus a data-side="bottom" check, which is Base UI's own
positioner signal. The regression they guard (#38554) is a popup opening
on top of the control that spawned it, so both cases collapse to one
invariant: the options never cover the trigger. It now runs at both
viewport heights and reads the popup as role=listbox. The models header
test compared the tabs and refresh centers within 2px, which a padding
change flips; it now asserts the two share a row.
Structure. The logs drawer test walked xpath=../../.. from a text node
and read collapsed state off chevron icon classes. SectionHeader now
renders a real disclosure button with aria-expanded, and its two copy
buttons carry distinct names instead of both being "Copy". Sidebar group
toggles expose aria-expanded too, so the migration spec can ask for a
collapsed group by state rather than by nesting depth.
Positional lookups. keyRow.locator("button").first(), row.locator("td")
.first() and getByTestId(grid).locator("div").first() all named a
position where they meant an action; they now name the control. Table
scoping moves from "table tbody" to role=row.
Timing. Nine waitForTimeout calls are gone. Every assertion that followed
them already retried to its own timeout, so the sleeps only slowed the
run down.
Both files under tests/users/ were wrapped in test.skip("...", () => {}),
which registers one skipped test and never runs the body, so the four
tests inside had never executed and were written against a UI that has
since changed (the search placeholder is "Search by email…", the ID
filters moved into a drawer, pagination is labelled "Go to previous
page"). Rewritten against the current surface: the suite goes from 104
collected tests to 107.
Left in place deliberately: the chip and dialog-footer data-slot
selectors, because the accessible names they work around live in
components/ui/, which is shadcn CLI-managed and not hand-edited.
* 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>
`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.
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
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>
