Replaces the metadata transfer approach: that block read top-level
litellm_metadata which guardrail info never populates, and the SLP
builder already reads the nested bucket it lands in, so it was dead
code and is reverted.
Real cause of missing evaluations: process_input_messages skips
apply_guardrail entirely when message scoping (skip_system_message,
skip_tool, scan_only_tool_results) leaves no scannable content, so
the guardrail shows up in applied_guardrails with no
guardrail_information entry. Now records a not_run entry unless the
guardrail records its own information.
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.
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.
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 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.
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>
When the upstream errors while the client is still connected, the pump
forwards the exception through the relay queue so the proxy's failure
handling re-raises it. If the client disconnects before consuming that
queued exception, neither the failure hook nor billing ran and the spend
row was lost. The pump now waits for client detach and, if the exception
was never consumed, salvages partial spend like the post-disconnect
error path.
Also rewrites the bedrock disconnect logging test to the detached-pump
contract: billing fires after the upstream drain completes, not
synchronously at aclose().
The chat-to-Responses reverse transform kept only text blocks, so an image
input was dropped before the count went to OpenAI. A 256x256 image request
counted 13 tokens instead of 268.
When the pump finishes draining while the client is still connected,
billing is deferred to the proxy's post-response hook, which only fires
on a normally completed response. A client disconnect before the relay
consumed the queued tail tore the generator down past that hook, so the
request logged no spend at all. The relay teardown now dispatches the
stored deferred billing whenever it never reached the end-of-stream
sentinel.
Also drops the live pass_through_tests script: that CI job runs against
a fixed config with no Bedrock model or AWS credentials, so it could
only fail there. The scenario is covered by unit tests on the
relay/pump seam.
_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>
- 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
A deployment carrying reasoning_effort in its litellm_params on the
/v1/messages passthrough mapped the effort to a legacy thinking block
whose budget_tokens was forwarded as is, so any request whose max_tokens
sat at or below that budget was rejected upstream with a 400. The mapped
budget now runs through the same cap the adaptive-to-legacy branch and
the chat path already use: it is clamped to max_tokens - 1, and dropped
with a warning when even the minimum budget cannot fit.
The cap helper becomes public since three call sites outside
AnthropicConfig use it.
GPT-5 and later accept a top-level anyOf natively and call tools better with it intact, so the flattening now runs only for the gpt-4, gpt-3.5, chatgpt-4o, o1, o3, and o4 families. Non-dict tool entries pass through untouched, a typeless root that carries properties counts as an object, and the bounded $ref walker is listed in the recursion detector allowlist.
Reprice ten more retired xAI slugs (grok-3 and grok-3-mini families,
grok-4-1-fast) to the grok-4.3 rates they now bill at, with family-correct
deprecation dates. Restore cache_read_input_token_cost on the Bedrock Grok 4.6
entries so implicit cache hits bill at the cache-read rate while explicit
cachePoint stays unsupported. Drop the unsourced 1080p video rate and the
gemini/ live native-audio entry the Gemini API 404s on. Add Groq qwen3.8-27b
tool-use flags per Groq docs. Extend the xai and gemini tests to lock all of
this in
OpenAI's function-calling validator rejects tool parameters carrying
oneOf/anyOf/allOf/enum/const/not at the top level, while the ChatGPT
backend Codex talks to natively accepts them, so an MCP tool declaring a
top-level union 400s through the proxy. Merge the branches into the
object schema for OpenAI itself only, walking the namespace-nested tools
current Codex builds send, on both /v1/responses and /v1/responses/compact
