* ci: warn on SQL IN lists with no written bound
Postgres caps a prepared statement at 32,767 bind parameters and a
membership filter binds one per value, so an IN list built from table
data breaks once the table outgrows the cap. That is how the budget reset
job froze every due budget (LIT-7535, #40564).
check_unbounded_in_lists.py reports every Prisma "in" / "not_in" filter
whose value has no fixed size and every raw SQL literal that splices a
list in after "IN (", unless the line carries "# bounded-ok: <reason>".
It only warns for now: the output is the inventory for RCA action item
AI-1, and it exits 0.
* ci: decide a constant IN list by its module binding, not its casing
An ALL_CAPS name imported or filled at runtime is as unbounded as any
other, so a name now passes only when the module binds it once to a
value of fixed size. Adds Final to the locals a loop does not forbid.
* ci: only a frozen module value makes an IN list constant
A module list bound once could still grow through append or extend, so
a name now counts as fixed only when it is bound to a tuple, frozenset
or constant. Trims the module docstring to what a reader needs.
* ci: chunk Prisma IN lists with a shared helper and fail on new unbounded ones
Add litellm.repositories.bounded_in: find_many_in, count_in, update_many_in
and delete_many_in split a deduplicated value list into 5,000-value chunks,
AND each chunk with the caller's where, run them in order (a transaction
handle works) and combine the results. Writes take a required atomicity
argument, and a where that already filters the chunked field is refused.
check_unbounded_in_lists.py now fails CI on any finding missing from
unbounded_in_baseline.txt and on any stale baseline entry, so the baseline
only shrinks. Entries are keyed by path, enclosing scope, kind, field and
occurrence, not line numbers. The helper module is exempt, a constant
spread into a frozen tuple counts as fixed, and messages point at the
helper for "in" and at an array parameter for "not_in" and raw SQL.
A real-Postgres integration test shows a raw 40,000-value filter rejected
for too many bind variables while the helpers handle it.
* refactor: rename bounded_in to chunked_in and let callers pick a chunk size
The helper module is litellm.repositories.chunked_in, and its unit and
integration tests, the checker's exemption path and its finding messages
follow the new name. The `# bounded-ok` marker is unchanged.
find_many_in, count_in, update_many_in and delete_many_in take a
keyword-only chunk_size, defaulting to IN_LIST_CHUNK_SIZE (5,000). A value
below 1 or above MAX_IN_LIST_CHUNK_SIZE (30,000) raises ValueError before
any query, which leaves the rest of the filter headroom under Postgres's
32,767 bind-parameter cap.
* refactor: flatten chunked_in's stacked comprehensions with chain.from_iterable
LIT014 (#42650) caps a comprehension at one for and one if clause. The four nested walks in the helper now chain their iterables instead, with the same order and results.
* refactor: recover user details with find_many_in, sending chunks as lists
_details_for_user_ids reads users through find_many_in instead of a raw
"in" filter, so its lookup stays under the bind-parameter cap for any
number of recovered keys. Up to 5,000 ids it still sends one find_many
with the same where dict, and a PrismaError from any chunk is still
logged and treated as no details.
The helper now sends each chunk as a list, so a chunked filter equals
the dict a hand-written call would send and a migrated call site's
existing assertions keep passing.
The site's baseline entry is gone.
* ci: skip functional TypedDict field maps in the unbounded IN list check
The dict passed as the field map of TypedDict("Name", {...}), or as its fields= keyword, names fields: an "in" or "notIn" key there is a type, not a filter. Only that dict is skipped, for TypedDict, typing.TypedDict and typing_extensions.TypedDict; a filter nested in a field value or passed to any other call is still reported. The two types/proxy/management_endpoints/team_endpoints.py entries leave the baseline, which is now 156.
* fix: refuse an update_many_in whose data writes the chunked field
Chunks run one after another, so an update that sets the chunked field can move a row into a later chunk, which updates it again and counts it twice: values ["old", "new"] with chunk_size=1 and data={"id": "new"} does exactly that. update_many_in now raises ChunkedFieldWriteError before any query when data has the chunked field as a top-level key, in any form, including Prisma operators such as {"set": ...}.
* docs: cut the unbounded IN list checker's docstring to what it flags and how to clear it
It now says what is reported, the three ways to clear a finding, and how the baseline and --update-baseline work, in 11 lines. The per-shape detail lives in the tests.
* ci: key an unbounded IN list finding by its filtered expression too
A baseline key of path, scope, kind, field and occurrence let a PR delete
a baselined filter and add a different unbounded one on the same field in
the same function, and the new one took over the old key. The key now
also carries the filtered expression's source, whitespace-normalized
(the Prisma value, or a raw-SQL `IN (...)` slot), so that swap reads as
one new and one stale entry and fails the run. The same expression
re-added in the same function is still the same finding.
Every baseline entry is rewritten in the new form; the 156 findings are
unchanged, and only occurrence indexes renumber where one field had
several different expressions.
* fix(spend): attribute CLI session spend to the per-user cli-session alias instead of the hashed session token
A CLI session token is a fresh random secret on every login, so since v1.99 each
login's spend rows carried a different sha256 hash as api_key and the usage APIs
could resolve neither key_alias nor user_email for them. Spend rows and logging
callbacks now attribute a session request to its stable alias,
cli-session-<user_id>, and the usage endpoints derive that alias and owner from
the key itself instead of scanning for a matching digest
* fix(spend): resolve the CLI session team from the user's first team in usage metadata
A cli-session key carries no team of its own in the DB, so the usage
breakdown showed team_id None for it and the export grouped it as
Unassigned. The login attaches the user's first team to the session, so
the recovery mirrors that rule for cli-session keys only.
* fix(spend): claim the session team only for a single-team user
The CLI login attaches a team on its own only when the user has exactly
one; a user in several teams picks one per login, so usage metadata for
the alias would otherwise name a team the login may not have used.
* test(pass_through): mark the mocked auth object as a plain key
The logged key follows the alias only for a session token; a bare
MagicMock reads as one, so the test names the field it relies on.
* fix(spend): attribute CLI session pass-through, queue, and managed batch spend to the cli-session alias
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(spend): only treat the exact cli-session-<created_by> value as a batch key alias
A managed object row written by an older build can still carry the raw per-login
session token, which shares the cli-session- prefix. Matching on the prefix alone
would have surfaced that token as a trusted alias and persisted it verbatim in the
batch cost spend log, so the alias check now requires the exact per-user value and
every other prefixed value keeps going through redaction
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(spend): log proxy executed batch rows under the cli-session alias instead of the session token
_row_metadata set user_api_key from the raw bearer token while user_api_key_hash carried the alias, so the spend log redaction rejected the alias as untrusted and hashed the random session token instead
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(spend): attribute semantic search embedding spend to the cli-session alias
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(spend): scope /key/spend/report for a CLI session to the cli-session alias
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(spend): use the cli-session alias for websearch spend, prometheus failure labels and the parallel limiter
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(spend): drop explanatory docstrings on get_logged_api_key and attach_user_details
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(spend): only recover cli-session usage keys whose suffix is a known user
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>
* feat(spend): capture-rate check of LiteLLM spend against the OpenAI bill
* fix(spend): claim the alert lock after the check, NaN gauge on no rate, 180-day range cap, live settings, OpenAI adapter under llms
* fix(spend): chart the capture-rate gauge in the all-metrics dashboard and clear it when the check is removed
* fix(prometheus): record the capture-rate gauge when api_provider is an excluded label
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(spend): return 400 from /spend/calculate for a model with no pricing row
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(spend): assert error type and param for unpriced /spend/calculate
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(spend): move the repro to tests/integration
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix: alias ModelNotMappedError re-export to satisfy F401
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(utils): raise ModelNotMappedError only when the pricing row is missing
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: kerry <kerry@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(cost-map): remove models past their deprecation date
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(cost-calc): drop the empty parametrize left behind by the gemini web search removal
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(cost-map): drop merge base block left by conflict resolution
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(cost-calc): drop gemini image cost tests pinned on removed model
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: kerry <kerry@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(logs): add span type filter to request logs
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(logs): look up span type sql conditions from a mapping
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Mubashir Osmani <mubashir@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: benchmark and gate an installed release wheel
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: simplify installed-wheel benchmark check
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(rust): add native tokenizer codec
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(tokenizer): route Python tokenization through the Rust extension
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(lint): format tokenizer call
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(packaging): restore runtime dependencies and native images
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(tokenizer): preserve Python SDK behavior with Rust tokenizers
* fix(tokenizer): restore compatibility paths
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(tokenizer): count custom tokenizers directly
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(tokenizer): preserve caller-supplied Python tokenizer counts
* fix(tokenizer): reuse packaged vocabularies in the native wheel
* refactor(rust_bridge): route token counting through the catalog as RUST_OPT_IN
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(spend_tracking): compare tokenizer groups by value
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(deps): re-resolve filelock under the <4.0 pin
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(llms): align transformation override signatures with base configs
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* build(rust): use fat LTO to keep the native wheel under the 35 MB limit
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(tokenizer): preserve Python defaults with opt-in Rust dispatch
* test(proxy): tolerate missing litellm.utils.Tokenizer when patching it
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): patch the tokenizer dispatch function instead of the removed alias
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(tokenizer): give the Rust wrappers the tiktoken and tokenizers surface
Callers of litellm.encoding and litellm.create_tokenizer must see the same
read-only API whichever backend the catalog selects.
- OpenAIEncoding mirrors tiktoken.Encoding: n_vocab, max_token_value,
token_byte_values, encode_single_token, encode_with_unstable,
encode_to_numpy, decode_with_offsets, is_special_token, repr; the Rust
tiktoken crate keeps a Vocabulary beside each CoreBPE and reports the
requested encoding name (gpt2 stays gpt2).
- HuggingFaceTokenizer mirrors the read-only tokenizers.Tokenizer surface
(token_to_id, id_to_token, get_vocab, get_vocab_size,
get_added_tokens_decoder, num_special_tokens_to_add, padding, truncation,
encode_special_tokens, from_buffer); HuggingFaceEncoding gains the
char/word/token lookups, pad, truncate, set_sequence_id and merge.
Mutators stay on the Python tokenizer.
- from_json/from_pretrained claim the fork gate only when the huggingface
feature is compiled in; the surrogate fallback matches on the Codec.
- Tokenizer caching is keyed on the same catalog Context the dispatch runs
on; rust_tokenizer reads the encoding name without loading an encoding;
LITELLM_RUST parsing is cached.
- Drop the unused tiktoken_encoding_for_model export and Error::Download.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
* fix(tokenizer): close the exhaustive matches with assert_never
CodeQL reads a `match` over a Literal with no default arm as an implicit
`None` return. `assert_never` makes the exhaustiveness explicit for both the
HuggingFace tokenizer loader and the Rust token-counter factory.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
* feat(tokenizer): derive the fast counter from the shared tokenizer
The count-only counter (`fast` feature) and the codec each parsed the same
artifact: TokenCounter took the Anthropic JSON and the tiktoken rank files
from Python while Tokenizer loaded them again. One parse now serves both.
- FastTokenizer builds from a model another loader holds: `from_shared`
takes the Arc<tokenizers::Tokenizer> the HF codec keeps, and
`from_*_pairs` take the ranks the tiktoken vocabulary already parsed.
- `FastCounter::fast_counter` in the core crate derives it from either codec;
encodings the fast scanner does not reproduce are refused.
- Native `Tokenizer.count(text, fast=False)` opts into that counter, built
once per tokenizer on first use; `TokenCounter.from_tokenizer(tokenizer,
fast=False)` replaces the JSON and rank-file constructors.
- The Python route counts over the native tokenizers the codec path shares
(`native_encoding`, `native_anthropic`) and no longer reads rank files;
the packaged Anthropic tokenizer has one loader, `tokenizer_dispatch.anthropic`.
- Public wrappers gain `count(text, fast=False)`.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---------
Co-authored-by: Yujong Lee <yujong@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
* fix(proxy): release unclaimed budget reservations at request end
* fix(proxy): release unclaimed budget reservations of websocket sessions too
* test(proxy): drop the structural middleware inheritance check
* fix(proxy): claim the budget reservation on streaming pass-through before its cost callback
The SSE chunk processor hands its success handler to the logging worker
after the response, so the request-end release freed the reservation
first and left the key unguarded until the worker drained. Claim it at
both end-of-stream hand-offs, the immediate enqueue and the coroutine
parked for deferred dispatch.
Give the xai realtime test double the litellm_params attribute every
real Logging object carries, since the wrapper now reads it.
* test(pass-through): give the vertex streaming test doubles a litellm_params dict
The spec'd Logging mocks in test_vertex_ai_anthropic_streaming_cost_injection.py
lacked the instance attribute the chunk processor now reads to claim the budget
reservation. Also restores main's _lazy_openapi_snapshot.json: the branch's copy
had been regenerated under Python 3.14, which dedents one docstring description
that the CI regeneration on Python 3.12 keeps indented, and the PR adds no lazily
loaded route, so main's file is the correct one.
* fix(pass-through): claim the budget reservation only after its cost callback is enqueued
Every pass-through success hand-off stamped callback_bound before handing the
coroutine to the logging worker. When that enqueue raised, the reservation stayed
claimed with no callback left to reconcile it, so the request-end release skipped it
and the reserved cost stayed pinned on the key's counter. Enqueue first, then claim,
so a failed hand-off leaves the reservation for the request-end release.
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
With store_prompts_in_spend_logs on, the persisted request body kept the client's model string even when the row's model, model_group, and error text had been replaced by the unknown-model placeholder. The body's model now takes the same placeholder on those rows. Also annotates the new test locals with Final and wraps the four test lines that ran past 120 characters.
A temporary-only member update no longer clones the team default budget into the private row. The row stores just the temp pair and auth, spend admission and reservation add the active increase to the current shared default, so a later lowering of the default reaches members with an active grant
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Adds temp_budget_increase and temp_budget_expiry to the team member edit form with pair validation,
seeds stored values into edit mode, sends both through /team/member_update, and adds cached-key auth
and reservation regression tests for active and expired increases
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
SpendLogsMetadata gains a typed azure_spillover key so a request Azure
served off pay-as-you-go capacity is visible in spend tracking, stamped
from the provider response headers or the processed llm_provider- headers
on the standard logging payload. The header parsing moves into a shared
azure_spillover() helper that is_spilled_over_ptu_request() now wraps.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
A request for a configured model group that the router rejects before picking a deployment (all deployments in cooldown, no healthy deployment) never gets a custom_llm_provider in its logging kwargs. The spend log payload persisted an empty provider, the daily spend tables carried it through, and the Admin UI Usage page rendered those requests under unknown even though every model in the group has a provider
get_logging_payload now takes the proxy router and, when the logged provider is missing, infers it from the model group's deployments. It only attributes when every deployment in the group resolves to the same provider; mixed groups, unknown groups and a missing router leave the value empty as before. Explicitly logged providers keep precedence
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Project-scoped keys never wrote spend to LiteLLM_ProjectTable, so
/project/info stayed at 0 and project budgets could not block. Wire the
PROJECT entity through the spend queue, redis buffer, and db writer,
reserve and increment a spend:project counter, reseed it from the
project row, reset project spend in the budget cascade, and read the
live counter in the project max budget check. Team member budgets keep
gating project-scoped keys alongside the project budget
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The reconcile now records the database clock of the scan behind the last complete
run and, on the next run, rewrites every closed day with per-key rows updated since
then, however old the day is. Replaying only the marker day and the one before it
missed a delayed flush or retry that landed on an older date, and reads through the
marker come from the global table alone, so that spend was never counted.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
