Greptile P2: ``asyncio.gather`` without ``return_exceptions=True`` lets
the first failing call propagate immediately, leaving the other in-flight
inspections running until they complete on their own. Pass
``return_exceptions=True`` so every inspection finishes, then re-raise
the first exception encountered while iterating results.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Greptile P1: Aim's ``_anonymize_request`` and Lakera v2's mask-PII path
both wrote redacted content only to ``data["messages"]``. The Responses
API backend reads ``data["input"]``, so when a request arrived via
``/v1/responses`` with a plain string ``input`` the hook would update
``messages`` (which the backend ignores) and leave ``input`` carrying
the original unredacted text. Net effect: anonymize/mask silently passed
PII through to the LLM.
Add ``apply_redacted_messages_back`` to ``_content_utils`` — it writes
the redacted messages back to ``data["messages"]`` AND, when present,
re-flattens the redacted content into ``data["input"]``. Aim and
Lakera v2 now route their mask writeback through this helper. List
``input`` (multimodal) is still handled by the upstream
block-on-multimodal guard.
Adds unit tests for the helper and regression tests asserting
``data["input"]`` is redacted for both hooks on Responses-API string
input.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two more in-place rewrite paths exhibit the same regression as Lakera v2:
overwriting ``data["messages"]`` with text-only redacted versions silently
strips image/audio parts from multimodal requests.
- ``LassoGuardrail._run_lasso_guardrail``: when ``mask=True`` AND input
is multimodal/Responses-API list, fall back to the classify endpoint
(which raises on BLOCK actions but never overwrites the payload).
- ``AimGuardrail._anonymize_request``: when input is multimodal, raise
the standard 400 instead of replacing ``data["messages"]`` with the
text-only ``redacted_chat`` from Aim. The error message tells the
user to either send plain string content or rely on block-mode.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Mask-in-place uses the offsets that Lakera returns for the inspection
payload. ``build_inspection_messages`` flattens multimodal content into
joined text before sending to Lakera, so the offsets refer to the
flattened representation. Writing those offsets back via
``_mask_pii_in_messages`` and overwriting ``data["messages"]`` would
silently strip image/audio parts from the original request — that is a
real functional regression for Lakera + mask mode + multimodal input.
Detect multimodal input (any list-format ``content`` or non-string
``data["input"]``) up front and skip the mask-in-place branch in that
case. The hook then falls into the standard block-on-detect path so PII
is still blocked but the multimodal payload is never silently rewritten.
Per-part masking that preserves multimodal structure is the right
long-term fix; tracking that as a follow-up.
Also: add ``has_non_string_content`` to ``_content_utils`` (with tests)
and a regression test that asserts multimodal+PII raises an HTTPException
instead of returning a flattened request body.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Greptile P2 follow-ups on _content_utils.py:
- Drop unreachable ``_resolve_messages``. The new
``_iter_inspection_messages`` walks ``messages`` AND ``input``
independently; leaving the old fallback-only variant around invited a
future maintainer to wire it back up and silently narrow coverage.
- Rename ``iter_user_text`` → ``iter_message_text``. The helper walks
every role (user, assistant, system); the old name implied user-turn
content only. Callers and tests updated.
- Close mixed-list coverage gap. When ``data["input"]`` was a list
mixing content-part dicts and bare strings, ``iter_message_text`` and
``build_inspection_messages`` only saw the dict parts while
``walk_user_text`` already inspected both. ``_iter_text_parts_in_content``
now treats bare strings inside a content list as text fragments, so
read and write helpers agree on coverage.
Adds two regression tests for the mixed-list shape.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
CI mypy flagged the two ``_mask_pii_in_messages(messages=new_messages, ...)``
call sites because ``build_inspection_messages`` returns
``List[Dict[str, str]]`` while ``_mask_pii_in_messages`` declares
``List[AllMessageValues]``. The runtime payload matches; widening the
return type to a Union of TypedDicts is a larger refactor than is
warranted by this hardening change, so add the localised ``# type: ignore``
to match the two we already added on the ``call_v2_guard`` calls above.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Several guardrail hooks short-circuit when ``message.content`` is a list
or when the request uses the Responses-API ``input`` field instead of
``messages``. Centralise the content-walking logic in a shared helper and
update the affected hooks so list-format and Responses-API payloads no
longer skip inspection.
Also: Aim's ``async_post_call_success_hook`` now inspects every choice
(via ``asyncio.gather``) instead of only ``choices[0]`` — the prior
behaviour let ``n>1`` callers hide content in subsequent completions.
Hooks updated to use the new helper:
- aim, lakera_ai_v2, lasso (post a synthesised messages list to a remote
guardrail service)
- azure_content_safety, ibm_detector, banned_keywords, openai_moderation,
google_text_moderation (iterate text fragments locally)
- secret_detection (walk-and-rewrite to redact in place)
Drive-by fix: the legacy ``data["prompt"]`` list-handling path in
secret_detection rebound the loop variable instead of mutating the list,
leaving secrets unredacted on text-completion calls; corrected to index
back into the list.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* Unify cost calc in success_handler dict and typed branches
* Trim verbose comments and docstrings
---------
Co-authored-by: Michael Riad Zaky <michaelr@Mac.localdomain>
Co-authored-by: Michael Riad Zaky <michaelr@Michaels-MacBook-Air.local>
Claude 3.5 Sonnet v2 reached EOL on Bedrock 2026-03-01, returning the same
404 EOL error as 3.7 Sonnet. Sonnet 4.5 supports both InvokeModel and
Converse APIs on Bedrock, so use the same model for both routes.
AWS Bedrock has reached end-of-life for `claude-3-7-sonnet-20250219-v1:0`,
returning 404s with "This model version has reached the end of its life."
Update test references to `claude-sonnet-4-5-20250929-v1:0` (same capability
surface: thinking, tools, prompt caching, PDF input, vision, computer use).
The bedrock/invoke pass-through tests stay on Sonnet 3.5 since Sonnet 4.5
is converse-only on Bedrock.
Companion to the prior commit. process_items only converted empty
`items: {}` to `{"type": "object"}`. But anyOf branches like
`{"type": "array"}` (no items field at all) were untouched, so after
convert_anyof_null_to_nullable stripped the null branch and added
nullable, the array branch was sent to Vertex as
`{"type": "array", "nullable": true}` — which Vertex rejects with
INVALID_ARGUMENT (`any_of[0].items: missing field`).
Make process_items synthesize `items: {"type": "object"}` for any
`type == "array"` schema where items is missing or empty.
Also:
- Convert test_gemini_tool_calling_working_demo to a hermetic mock
test asserting items is present on the array branch in the sent
body. Was previously a real-network call to Vertex and was the
test the user reported still failing in CI.
- Add unit test test_build_vertex_schema_array_branch_missing_items_in_anyof
covering the missing-items shape directly.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Cache provider config lookups for Vertex Anthropic messages so repeated requests reuse the same config object and preserve credential cache state. Add a regression test to catch any future loss of config reuse.
Made-with: Cursor
* fix(caching): preserve prompt_tokens_details through embedding cache round-trip
The embedding caching layer was dropping prompt_tokens_details (including
image_count) because CachedEmbedding had no field for usage metadata and
the cache retrieval code reconstructed Usage without it. This caused
inconsistent responses where the first call returned image_count but
cached responses did not, breaking cost tracking for multimodal embeddings.
Add prompt_tokens_details to CachedEmbedding, persist per-item details
during cache storage, aggregate them on retrieval, and merge them in
combine_usage() for partial cache hits.
* style: apply Black formatting to caching files
* fix(caching): address Greptile review — cyclic import, guarded construction, nested dict merge
Move PromptTokensDetailsWrapper to inline import to resolve CodeQL cyclic
import warning. Guard PromptTokensDetailsWrapper construction with
try/except to handle unexpected cached keys. Add recursive dict merging
in _merge_prompt_tokens_details for nested fields like
cache_creation_token_details.
convert_anyof_null_to_nullable was stripping the items field from array
branches inside anyOf when a sibling null branch was present, leaving
{"type": "array"} without items. Vertex requires items whenever
type == "array" (even inside anyOf) and rejects the call with
INVALID_ARGUMENT.
Leave the (possibly empty) items in place so the downstream process_items
step can convert {} to {"type": "object"}, which is what Vertex wants.
Also:
- Update test_build_vertex_schema expected output, which was codifying
the broken shape.
- Convert test_gemini_tool_calling_not_working to a hermetic mock test
that asserts the request body sent to Vertex includes items inside
the callbacks anyOf array branch. The previous form made a real
network call and was flaky in CI.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
vi.clearAllMocks does not reset mockImplementation, so the error-notification
test was inadvertently relying on a deleteField stub set up in earlier tests
and would time out when run in isolation.
Previously, useStoreRequestInSpendLogs and useDeleteProxyConfigField
did not refresh the proxyConfig cache on success, so the Logging
Settings form continued to render the pre-save values until React
Query refetched on its own. Wire both hooks to invalidate
proxyConfigKeys on success so any active observer (currently the
Logging Settings page) repulls fresh data.
Export proxyConfigKeys for cross-hook reuse.
Switch the spend-logs save flow from mutateAsync + try/catch to
mutate + callbacks. Errors now surface through a single onError path
(no more double toast on failure), and the delete-then-update sequencing
runs through onSettled instead of awaited promises. handleFormSubmit is
no longer async.
Tighten the corresponding test to assert exactly one error toast fires.
Adds a CLI flag (`--timeout_worker_healthcheck`, env `TIMEOUT_WORKER_HEALTHCHECK`)
that forwards to uvicorn's `timeout_worker_healthcheck` Config kwarg (added in
uvicorn 0.37.0). Lets operators raise the supervisor's worker-ping timeout above
the default 5s when triaging workers being killed and respawned under load.
The helper introspects `uvicorn.Config.__init__` and only sets the kwarg if
supported, otherwise prints a warning - so the existing uvicorn>=0.32.1,<1.0.0
floor pin is unaffected. Gunicorn and Hypercorn paths are unchanged (the uvicorn
supervisor isn't running there); the value is also not passed to the helper at
all on those paths so the "uvicorn too old" warning never fires spuriously.
* Use auth key name if there are no app id in in headers or in extra_data
* use key alias instead of key name
* Fix
* last priority key alias
* Fix
* Add tests
* [Feat] Day-0 support for GPT-5.5 and GPT-5.5 Pro (#26449)
* feat(openai): day-0 support for GPT-5.5 and GPT-5.5 Pro
Add pricing + capability entries for the new GPT-5.5 family launched by
OpenAI on 2026-04-24:
- gpt-5.5 / gpt-5.5-2026-04-23 (chat): $5/$30/$0.50 per 1M
input/output/cached input
- gpt-5.5-pro / gpt-5.5-pro-2026-04-23 (responses-only): $60/$360/$6
per 1M input/output/cached input
Other fees (long-context >272k, flex, batches, priority, cache
discounts) follow the same ratios as GPT-5.4, with context window
retained at 1.05M input / 128K output.
No transformation / classifier code changes are required:
OpenAIGPT5Config.is_model_gpt_5_4_plus_model() already matches 5.5+ via
numeric version parsing, and model registration is driven from the
JSON. The existing responses-API bridge for tools + reasoning_effort
(litellm/main.py:970) already covers gpt-5.5-pro.
Tests:
- GPT5_MODELS regression list now covers gpt-5.5-pro and dated variants
- New test_generic_cost_per_token_gpt55_pro cost-calc test
- Updated test_generic_cost_per_token_gpt55 for long-context fields
* fix(openai): mirror reasoning_effort flags onto gpt-5.5 dated variants
gpt-5.5-2026-04-23 and gpt-5.5-pro-2026-04-23 were missing the
supports_none_reasoning_effort, supports_xhigh_reasoning_effort, and
supports_minimal_reasoning_effort flags that their non-dated
counterparts define. Reasoning-effort routing in OpenAIGPT5Config is
fully capability-driven from these JSON flags — since an absent flag
is treated as False for opt-in levels (xhigh), users pinning to a
dated snapshot would silently lose xhigh support and diverge from the
base alias on logprobs + flexible temperature handling.
Copy the flags onto both dated variants so every dated snapshot
inherits the base model's reasoning-effort capability profile.
Adds a parametrized regression test that asserts
supports_{none,minimal,xhigh}_reasoning_effort parity between each
dated variant and its non-dated counterpart, preventing future drift
when new snapshots are added.
* [Feat] Add azure/gpt-5.5 + azure/gpt-5.5-pro entries (+ dated variants) (#26361)
* feat(azure): add azure/gpt-5.5 + azure/gpt-5.5-pro entries (+ dated variants)
Azure variants of OpenAI's GPT-5.5 family. Microsoft has not yet
shipped GPT-5.5 on Azure OpenAI (latest GA on the Foundry models page
is GPT-5.4 as of 2026-04-24), but adding the entries day-0 mirrors the
established precedent for azure/gpt-5.4* (which were in the cost map
before the Azure rollout) so cost tracking and capability flags work
the moment customers deploy.
Schema follows the existing azure/gpt-5.4* shape:
- Same base/long-context pricing as openai/gpt-5.5*: $5/$30 chat,
$60/$360 pro per 1M, with priority tier 2x base
- Azure variants drop the flex/batches keys (Azure has no flex tier)
but keep priority pricing, matching gpt-5.4* precedent
- mode=chat for the thinking model, mode=responses for pro
reasoning_effort capability flags mirror the OpenAI variants exactly
since Azure proxies the same API contract: minimal rejection on both
chat and pro, low/none rejection on pro. Once #26456 (which sets
supports_low_reasoning_effort + minimal=false on openai/gpt-5.5*)
lands, OpenAI and Azure flag profiles align.
Tests pin entry presence + pricing for all four Azure variants and
verify the live-API-derived reasoning_effort flags.
* test: register supports_low_reasoning_effort in cost-map JSON schema
azure/gpt-5.5-pro and azure/gpt-5.5-pro-2026-04-23 added in this branch
carry supports_low_reasoning_effort=false. The strict
'additionalProperties: false' schema in
test_aaamodel_prices_and_context_window_json_is_valid rejected the new
key. Register it alongside the other supports_*_reasoning_effort
entries.
Note: the runtime side of this flag (code that reads it) lands in
#26456. Until that PR merges the flag is inert for both Azure and
OpenAI pro entries, but having the schema accept it lets cost-map
tests pass on either merge order.
* Use sanitize deep copy style to replace deepcopy usage
* Added test checking error is not happening anymore
* Added warning log when json copy failed
* Reduce to one change
* Fix spaces
---------
Co-authored-by: Ido Lavi <ido@noma.security>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: TomAlon <tom@noma.security>
tool_calls on assistant messages were translated to OllamaToolCall format
but never copied into the outgoing OllamaChatCompletionMessage, so Ollama
received {role: assistant, content: ''} with no tool_calls. The model
then had no record of having made a tool call, causing it to re-issue
the identical call on every turn (infinite loop).
Similarly, tool_call_id on role:tool messages was silently dropped.
Ollama uses this field to resolve the tool name from conversation history.
Also add tool_call_id to OllamaChatCompletionMessage TypedDict.
Fixes#26094
user