* fix(snowflake): normalize Cortex Claude request shapes
Co-authored-by: Kamron Javaherpour <kamron@kargo.com>
Co-authored-by: Oleksandr Kononov <oleks.konov@kargo.com>
* style(snowflake): format Cortex request transformations
* fix(snowflake): annotate Cortex wire payloads
* fix(snowflake): route Cortex content through the shared Anthropic converters
* fix(snowflake): surface Cortex prompt-cache usage and thinking blocks
Parse Cortex's Anthropic-dialect responses and SSE with Anthropic's own parser so cache_creation/cache_read counts, thinking blocks and signatures reach the caller. Restore thinking for every Claude model: Cortex documents extended thinking broadly and only adaptive thinking is 4.6-gated.
* fix(snowflake): echo signed thinking blocks on every assistant turn
The reference converter extends signed thinking blocks on each assistant turn, not just tool-call turns, so a replayed thinking-plus-text response keeps its signed block. Content-less thinking turns send no empty text block.
* fix(snowflake): preserve thinking list content
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Co-authored-by: Oleksandr Kononov <oleks.konov@kargo.com>
The block accepts output_cost_per_reasoning_token and cache_creation_input_token_cost. The generic
cost path and the DashScope calculator swap them in while a window is open, and unset keys keep the
standard rate. One shared TokenRates value replaces the DashScope-local copy, and
apply_off_peak_pricing takes and returns it.
Streaming /v1/messages against a model served through the chat-completions
bridge (every non-Anthropic provider other than OpenAI) minted its msg_ id
inside the stream wrapper, so the spend row landed under the provider's own
completion id and the caller could not find the call by the only id it saw.
The wrapper now mints the id once in its constructor and hands it to the
logging object, the same way the Responses-API bridge does.
The documented DISABLE_AIOHTTP_TRANSPORT env var already selects the httpx transport, so the extra module-global write was redundant. Types the monkeypatch fixture while here.
A streaming /v1/messages call against a non-Anthropic model is served an SSE
message_start frame carrying a msg_ id the adapter mints locally, since the
Responses API upstream only issues a resp_ id. That value never left the
adapter, so the spend row was keyed on the bridged response id and
GET /spend/logs?request_id=msg_... came back empty.
The adapter now hands the id it minted to the logging object, and the
/v1/messages logging path keys the row on it.
AmazonMoonshotConfig.transform_request called
_get_boto_credentials_from_optional_params purely for its side effect of
popping the aws_* keys off optional_params, then threw the result away. On
a box whose default AWS profile uses login_session without botocore[crt],
that call raises, so a bearer-token bedrock/invoke/moonshot.* deployment
still 500s with MissingDependencyException even after the rest of this
branch skips the chain.
It now filters the aws_* keys into a local dict the way the Qwen, OpenAI
and Claude 3 invoke transformations already do, so no credentials are
resolved and the caller's optional_params keeps the keys sign_request
reads afterwards.
The Router executor only routed a query embedding when the vector store
carried extra embedding configuration, so a store registered with no
embedding model at all always went to the Router and 500'd on the
s3_vectors default text-embedding-3-small when no deployment served it.
Route on whether the Router serves the model, which is the rule the
executor had before, and keep the request metadata on the SDK fallback so
the embedding stays attributed either way.
S3 Vectors now subclasses BaseQueryEmbeddingVectorStoreConfig, so its query
embedding runs through the Router executor with the request metadata instead
of a private router lookup. embedding_model stays accepted as an alias of
litellm_embedding_model. The router kwarg is gone from the search handler and
every provider transform now that nothing but the executor fallback read it.
* feat(azure): support credential chain for storage
* test(azure): clarify credential seam suppressions
* fix(azure): read chain tokens in a worker thread
The credential chain walk (IMDS probe, CLI subprocess) is blocking I/O,
so reading the provider inline in async set_valid_azure_ad_token stalls
every request on the worker's event loop
The container retrieve, list, delete, create and file routes validated the
provider's error body against the success model, so a deleted or unknown
container and a rejected API key surfaced as 500 pydantic errors instead of
the upstream 404 or 401. The handlers now raise the provider error class with
the upstream status and message before transforming the response.
GET /v1/containers dropped after, limit and order before calling the
provider, and GET /v1/containers/{id}/files dropped the same three, so
paginated list calls ignored their pagination arguments. Both routes now
forward their declared query params.
* fix(proxy): keep SpendLogs and callback session ids in sync when the request has none
Add general_settings.missing_session_id (generate | reject). In generate mode one id is
stamped into litellm_session_id, litellm_trace_id and metadata.session_id before callbacks
run, so LiteLLM_SpendLogs.session_id and the Langfuse session id match. In reject mode such
requests get a 400. Unset keeps the legacy behavior. MCP routes are not affected
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(proxy): regenerate schema.d.ts and shorten mutable-ok comment for ruff format
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): mark generated session ids so affinity consumers do not pin on them
Fireworks x-session-affinity, the router session_affinity pre-call check and the
complexity router session pin all read metadata.session_id as a caller-chosen
stable key. A missing_session_id: generate id is fresh per request, so it now
carries metadata.litellm_session_id_generated and those consumers skip it
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(guardrails): skip streaming guardrail rounds that re-scan cleared output
Streaming guardrails scanned the finished answer twice at end of stream
whenever the chunk count landed on a multiple of the sampling rate, ran
sampled rounds whose payload was identical to the previous one, and on
/v1/messages could scan an empty text before the first content chunk.
Every redundant round is a paid guardrail provider call.
Each endpoint handler now exposes a scan key describing what a round
would hand to apply_guardrail (the text so far, plus tool calls once the
stream has ended), and the unified streaming hook skips a sampled or
end-of-stream round whose key equals the last scanned one or carries
nothing to scan yet. Rounds that carry tool calls are never skipped.
* test(guardrails): expect one end-of-stream scan when the terminal chunk is sampled
Update sampled cadence expectations and use tuple-backed scan state
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(http_handler): honor HTTP(S)_PROXY / NO_PROXY when force_ipv4 uses the httpx transport
Passing an explicit transport to httpx.AsyncClient / httpx.Client disables its
automatic environment proxy mounts, so force_ipv4 on the httpx path sent every
LLM request direct and silently bypassed HTTPS_PROXY. Mount the same env-derived
proxy transports next to the IPv4-pinned direct transport in AsyncHTTPHandler,
HTTPHandler and the OpenAI async client factory.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(http_handler): carry the client's TLS verify and cert settings onto env proxy mounts
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
