_image_sources had no test asserting what it extracts. The existing image tests
live on the Bedrock side and all use base64 without a media_type, which is the one
path the fix left unchanged, so both behaviors it does change went unverified: the
url shape reaching the guardrail at all, and base64 arriving as a data URI.
Against the pre-fix extractor the url case sees [] and the media_type case sees
['AAAA'] instead of ['data:image/png;base64,AAAA'].
The remaining three assert behavior the fix deliberately preserves -- bare base64
passed through, a file source yielding nothing, a malformed source dropped rather
than handed on for a consumer to choke on.
Each message carries a text block because a message with no text never reaches the
guardrail, which would make every source shape look equally dropped.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Resolves budget-ratchet conflicts by taking staging's tighter limits and reworks the embedding raw-response helpers so the branch stays net-negative on the LIT001/LIT002 ceilings staging lowered: the request methods now return the LegacyAPIResponse and each caller keeps a single dict(headers) conversion.
Buffered streams governed by post_call policy pipelines now deliver text
rewrites back into the stream per surface (chat SSE, responses SSE,
anthropic messages SSE) instead of rejecting the request with a 400
upfront. Rewrites chain across pipeline steps; tool-call rewrites and
translations without stream write-back still withhold the stream.
- forward unrouted /gigachat/* requests with env credentials like other passthrough providers (the old fallback returned 400 on any request without a routed model, /gigachat/models included)
- fix basedpyright budget breaches across the gigachat provider, common_request_processing, and llm_passthrough_endpoints with real narrowing, no new suppressions
- add regression tests for the fallback target, auth header, and model-less endpoints
_input_item_provenance converted every input prefix, so an n-item request paid
for n+1 full conversions. It now converts each item once, glues consecutive
function_call items (plus their trailing-assistant context) into units so the
transform's tool_call merging is reproduced inside the unit conversion, and
verifies the unit concatenation against one full conversion, bailing to the
full-conversion fallback on any mismatch. Messages from multi-item units are
tainted, which keeps parallel tool calls patchable exactly like the old prefix
pass while unpredicted merges fall back safely.
A guardrail handing back a non-list structured_messages payload (the
HiddenLayer v2 evaluation dict) previously fell through the length-mismatch
fallback and 500ed converting the dict's keys as messages. The write-back is
now skipped for non-list payloads, restoring the previous no-write-back
behavior on the Responses surface.
Also refreshes the compresr texts-mirror docstring, which still claimed the
Responses translation cannot round-trip structured_messages.
The route-level regression test returns a real prisma row from a mocked
update and asserts both routes serialize it to a 200 with the toggled
blocked flag, which is exactly the path that raised AttributeError before
the validator guard. Also binds the loop variable in the e2e poll lambda
(ruff B023).
API_ROUTE_TO_CALL_TYPES listed the sync llm_passthrough_route first, so every
call_types[0] consumer resolved /llm_passthrough to a call type with no
guardrail translation handler, and the {model}:generateContent patterns never
matched a concrete route because the placeholder segment carries a literal
suffix the matcher treated as an exact segment. Reorder the passthrough
entries async-first, teach the matcher placeholder-with-suffix segments plus
suffixed multi-segment tails (mirroring FastAPI's {model_name:path}), add the
missing /v1beta generateContent entries, and register a Google GenAI
guardrail translation handler so guardrails actually scan generateContent
requests, responses, and streams.
Azure's chat completions validator rejects tool parameters carrying a
top-level anyOf/oneOf/allOf for every model family. AzureOpenAIConfig and
the o-series config now flatten them via the shared helper moved to
prompt_templates common_utils. Requests bridged to the Responses API for
gpt-5.4+ with reasoning active keep the union, which that surface accepts
The sync __next__ exhaustion branch stores calculate_total_usage() in the
final chunk's _hidden_params when stream_options is None, but the async
__anext__ sibling branch never did. Converted (fake) streams, like the ones
the Headroom guardrail produces by flipping streaming /v1/responses calls to
non-streaming, are consumed async, so their real usage never reached the
completion-to-responses bridge and it token-counted from scratch, reporting
input_tokens=0. Mirror the sync branch's hidden-usage block into the async
exhaustion branch and add a regression test that async-iterates a
CustomStreamWrapper over a MockResponseIterator and asserts the final chunk
carries the mock response's usage.
