* 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>
The managed files hook's content read looped the file's model mappings and asked each deployment for the file. A file LiteLLM stored itself maps every model to its storage url, so the read sent that internal id to the upstream server, failed, and the batch rate limiter failed open: a key's TPM limit did not apply to a LiteLLM-executed batch. The hook now returns the stored bytes from the file's storage backend before it consults any deployment
vLLM serves no /v1/files or /v1/batches, so a hosted_vllm deployment can never
host a batch. Batch inputs for such a deployment now land in a LiteLLM-owned
storage backend, the batch is executed line by line through the deployment's
own chat, completion, embedding, or responses route, and the batch plus its
output and error files are served back from the database under the creating key
Narrow or remove reportAny / reportExplicitAny sites in provider
transformations, caching, guardrails, proxy endpoints and enterprise
batch-cost polling. Public parameters widen to Mapping/Sequence rather
than dict/list so no caller signature breaks, and runtime behavior is
unchanged.
Converge on staging's delete plumbing (_S3DeleteContext read from the logging call's additional_args, _sign_s3_request_without_body, the credential-stripping delete_data in the managed-files hook) and keep this PR's listing support, the 400 mapping for out-of-bucket file ids, the proxy-admin-only raw cloud id rule, and the OpenAI FileDeleted delete response.
Two staging tests move to this PR's contract: an out-of-bucket delete raises BedrockError 400 instead of ValueError, and deleting a stored provider output returns FileDeleted rather than the stored file object.
GET /v1/files through a provider config now returns the OpenAI page shape
(object list, data, first_id, last_id, has_more) instead of a bare array, and
DELETE /v1/files/{id} on a managed id answers the OpenAI FileDeleted shape with
deleted true instead of an empty body
Bedrock listing asks S3 for max-keys=0 when the purpose is one Bedrock never
stores under LiteLLM's prefixes, and batch_output listing no longer requires an
input bucket when only s3_output_bucket_name is configured. The mock request
behind the 400 for a foreign file id uses the same https://litellm.ai URL the
exception module uses
* fix(batches): register ownership for every batch create path
Since the team isolation change, the managed files hook decided whether a
response came from a create by looking for the managed input file id on it,
which only the unified input path sets. Batches created from a model-encoded
input file id, a model param, or a raw provider id with ?provider= never got
an ownership row, so they vanished from GET /v1/batches for the key that
created them.
The create endpoint now stamps a create marker on the response before the
hooks run, and the hook keys ownership registration and the batch-created
metric on that marker instead of on the input id format.
* test(batches): assert ownership registration through the managed files hook
The endpoint tests asserted the private create marker, which is wiring, not
behaviour. They now run the create through the real managed files hook and
assert the ownership row is written for the creating key on every create
path, with the unified path driven by a genuine encoded input file id
instead of patched decoders.
Batch creation snapshotted the team's organization with a direct
litellm_teamtable query on every create. Go through get_team_object
instead, which serves the team auth already cached and only falls back
to the database when the team was never cached.
Second pass over the highest-Any-density modules that the first pass left
untouched: guardrail hooks, the gemini and anthropic transformation layers,
the proxy spend-tracking and pass-through endpoints, and the caching clients.
Untyped `response.json()` bodies and `dict[str, Any]` request payloads are
described once at their boundary with a TypedDict or Protocol, so the fields
read downstream resolve to real types instead of Any. No cast, no type: ignore,
no noqa, and no new Any annotations.
The managed batch listing fetched one page of rows, derived has_more from
that raw fetch, then dropped every row whose stored blob would not parse.
last_id came from the survivors, so a page of corrupt or legacy rows came
back as data [], last_id null, has_more true, and a client following
last_id could not advance. The OpenAI SDK's auto-paginator, which cursors
off the last item in data, stopped silently and returned a truncated list.
Read chunks until page_size + 1 batches survive parsing and file-id
resolution or the caller's rows run out, the way the managed file listing
already does, so a page carries data and a usable cursor while parseable
rows remain and has_more only says true when another one exists. The first
chunk keeps the old page_size + 1 size so a healthy page still costs one
query; a scan that has to continue widens to the file listing's
continuation chunk and stops resolving rows once the page is full.
OpenAIFilesPurpose was missing evals, which OpenAI documents. The upload
route validates against that set, so POST /v1/files with purpose=evals was
already being rejected, and the new listing validator extended the same
rejection to GET /v1/files?purpose=evals, turning a purpose OpenAI accepts
into a hard 400. Nothing branches exhaustively on the type, so widening it
changes no routing.
The managed-file listing test fake only understood a created_by filter. The
OR filter a key carrying both a user_id and a team_id produces, the team_id
filter a service-account key produces, and the empty filter a proxy admin
produces all fell through it and returned every row, so the shapes most real
keys send went uncovered. The fake now applies the filter it is handed, and
the listing is tested against all three, including paging an OR filter
across a cursor.
Two docstrings claimed the continuation chunk bounds what a filtered page
costs. It bounds queries per row scanned; the walk is still linear in the
rows the caller owns.
The managed hook returned the plain dict build_list_page builds, while
every other GET /v1/files path returns an SDK page object. A post-call
success hook or a logging callback that reads response.data off the
listing raised AttributeError as soon as a request took the managed path
FileListPage is a pydantic model over the same five fields, so hooks read
.data again and the response body does not move: jsonable_encoder gives
the same keys in the same order for the model and for the dict. It sits
in litellm.types.llms.openai because base_llm/files/transformation.py
already imports from there and cannot import proxy modules. It is
deliberately not subscriptable, since the provider-backed path returns a
page object that is not either, and dict access would be a third contract
to keep alive
Also reject a purpose the Files API never accepts. An unknown purpose
matches no row, so the listing answered an empty page for what is really
a bad request, while the upload route in this same file already refuses
those values against get_args(OpenAIFilesPurpose). The check runs before
the first query, and only in the managed hook, so providers that define
their own purposes keep them
Also put back the route's original except tail. Sending every error
through handle_exception_on_proxy changed error.type on a bad
target_model_names from "None" to the exception class name, which a
caller matching on the body would read as a break. create_file in this
file already pairs base's tail with a ProxyException passthrough, so
list_files does the same and the handle_exception_on_proxy import is gone
The chunk loop read `limit + 1` rows at a time, so a small limit whose
matches sit far behind the newest rows advanced a couple of rows per
query. A `purpose` that matches only the last of 10000 owned rows at
`limit=1` cost 5001 sequential find_many calls for one HTTP request,
which any authenticated caller could ask for on purpose.
Once a scan has to continue past its first chunk, widen the chunk to
FILE_LIST_CONTINUATION_CHUNK_SIZE. That same case now costs 21 queries.
The first chunk keeps its `limit + 1` size, so a page the newest rows
already fill still costs exactly one query and reads nothing extra.
Rows whose blob will not parse drop out of a page the way a filter does,
so they get the bound too, not just the purpose filter.
The floor only changes how many round trips a page costs, never what it
returns: chunk boundaries do not affect a keyset scan, so the page is
still `matches[:page_size]`, `has_more` is still `len(matches) >
page_size`, and empty data still implies `has_more` false.
The managed file listing cut the page to `limit` first and applied the
purpose filter in Python afterwards, so a page whose rows all failed the
filter came back as `data: []` with `has_more: true`. openai-python stops
paging the moment `data` is empty, so `files.list(purpose="batch", limit=1)`
returned nothing at all instead of every batch file.
Read successive keyset chunks until the page holds `limit + 1` matches or
the caller's rows run out, then return at most `limit` of them. `data` is
now non-empty whenever matching files remain, its last id is always a
usable cursor, and `has_more: false` only ever means the caller has seen
everything. Rows whose stored blob will not parse drop out in the same
loop, so they cannot empty a page either.
That also makes the `next_cursor_id` escape hatch on `build_list_page`
dead, so it goes back to what it was for the batch and vector-store
listings that share it.
Also move `validate_file_list_limit` up into the list_files route, so the
target_model_names and provider branches reject an out-of-range limit the
same way the managed file store already did.
The unscoped GET /v1/files limit check accepted 0, which OpenAI's minimum
of 1 does not allow, and the route's except block rebuilt every error with
getattr(e, "status_code", 500). ProxyException has no status_code, so the
400 it raises went out as a 500 and the OpenAI SDK retried it three times.
Errors now go through handle_exception_on_proxy, the helper the sibling
batches route already uses, and the unknown-cursor error is a ProxyException
so it carries type invalid_request_error and param after instead of the
literal "None". The cursor still 400s whether the file belongs to someone
else or does not exist at all
A page whose rows are all dropped by the purpose filter, or by a row
that does not parse, used to come back with an empty data list, has_more
true and last_id null, so the caller had no cursor to advance with and
stopped one page short of files it owns. last_id now falls back to the
last row the page read.
Also drops the OpenAIFilesPurpose import that the widened purpose
annotation left unused.
The owner-scoped listing read every row the caller owns in one query, so an
admin key that owns every file on the proxy pulled the whole table into one
response. Page it with a keyset cursor on unified_file_id instead, and accept
limit and after on GET /v1/files so a client can walk the pages. limit follows
what OpenAI documents for that route: 1 to 10000, default 10000.
An after cursor is resolved inside the caller's own scope, so an id they do not
own gets a 400 rather than a page, and has_more now reflects whether another
row exists instead of always being false.
Refs #37714
The retrieve path now defers a managed batch's accounting to CheckBatchCost, which
bills the key, team, and tags stored on the managed object row. The /v1/batches
create hook never persisted api_key or request_tags there (only the passthrough
creates did), so the poller attributed the cost to the user alone and the creating
key's spend stayed at zero.
A poll of a Vertex passthrough batch wrote nothing to the managed-object row,
so status and file_object stayed frozen at the create-time snapshot and
GET /v1/batches served a stale status and an empty output file id for the life
of the batch. Only the create may claim a batch, but every observation of one
may refresh its state.
store_unified_object_id takes create_if_missing, which the poll clears: it
refreshes status and file_object through update_many, and leaves a row that is
absent absent rather than creating one owned by the observer, since created_by
and team_id are written by whoever reaches the create branch. The update payload
is now shared with the upsert so it cannot drift into writing api_key,
request_tags, created_by or team_id.
The passthrough identity re-assertion that was previously part of this PR ships
separately in #36121, so this PR keeps only the batch attribution work.
The creating key owns user_api_key_alias only when it actually has one. Guarding
the overwrite on the presence of a key rather than on a resolved alias nulled the
field out for every key generated without key_alias, and for any key rotated or
deleted before its batch finished, losing the creating user's alias that the spend
row previously carried. The guard now matches the team-alias line below it.
Typing-only pass over the 21 files with the highest reportAny and
reportExplicitAny density among self-contained modules: management
endpoints, guardrails, streaming internals, response transformations,
MCP server, enterprise managed files, and vector store management.
Whole-tree basedpyright drops from 148,648 to 146,984 errors (-1,664),
with reportAny -1,111 and reportExplicitAny -296. No rule increased
repo-wide and no file regressed on any rule. No cast(), type: ignore,
noqa, suppression comments, or new Any annotations anywhere in the diff,
and no runtime behavior changes.
Budgets ratcheted by make lint-budget-update: basedpyright -1,663 across
48 rules, ruff-strict -86, type-discipline -110.
