* [Test] Add Azure async chat completion timeout test. WIP
* Capture TTFT for /v1/messages streaming responses
The pass-through streaming path for /v1/messages (Anthropic, Bedrock,
Vertex AI, Azure AI, Minimax) logged completion_start_time only after
the entire stream finished. async_success_handler then fell back to
end_time, making TTFT equal to total duration or null in the UI and
Prometheus.
Record the timestamp of the first chunk in async_sse_wrapper and
propagate it to model_call_details before the logging handler runs,
so gen_ai.response.time_to_first_token reflects the real first-chunk
latency.
Fixes#25598
* [Refactor] Implement timeout resolution logic in completion function
add fetch ``request_timeout`` from litellm_settings
* remove stale test case
* remove extra print statement
* default request timeout value in constants to 600s to match timeout defaults handled in the proxy
* fix request timeout if using default value from constants.py
* update code structure, test cases
* only override if the global timeout sets timeout to 6000s
* update code structure, move hard coded values to const and make the reslve function readable by moving fallback logic to a seperate function
* modify default timeout values, replacing hard coded ones with default values defined
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Co-authored-by: harish876 <harishgokul01@gmail.com>
Co-authored-by: Joaquin Hui Gomez <joaquinhuigomez@users.noreply.github.com>
Tighten validation of request body parameters in the proxy routing
layer. Use context variables for internal call state management
instead of passing flags through request kwargs. Clean up metadata
handling at the proxy boundary.
Unit Tests: Proxy DB Operations / proxy-db (auth-checks, tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py, 20, 8) (push) Waiting to run
Unit Tests: Proxy DB Operations / proxy-db (remaining, tests/proxy_unit_tests --ignore=tests/proxy_unit_tests/test_key_generate_prisma.py --ignore=tests/proxy_unit_tests/test_auth_checks.py --ignore=tests/proxy_unit_tests/test_user_api_key_auth.py, 30, 8) (push) Waiting to run
* fix: make PodLockManager.release_lock atomic compare-and-delete
Re-lands #21226 (reverted in #21469).
release_lock() previously did GET + compare + DEL in separate calls,
leaving a window where another pod could reacquire the lock between
the GET and DEL, causing a stale owner to delete a live lock.
Fix: use a Redis Lua script for atomic compare-and-delete. Script
registration is cached per PodLockManager instance. Falls back to
the old GET+DEL path for cache backends that don't expose
async_register_script.
Original revert was due to e2e tests running in CI without Redis.
Those tests now carry @pytest.mark.skip(reason="Requires Redis connection.")
so this re-land is safe.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: add Lua fallback on execution error + test coverage gaps
Address Greptile review feedback on #24466:
1. Wrap Lua script execution in try/except — if Redis clears loaded
scripts (restart) or scripting is disabled, fall back to GET+DEL
rather than letting the exception propagate and leave the lock held
until TTL. Reset cached script handle so the next call re-registers.
2. Add test_release_lock_lua_path_emits_released_event — verifies
_emit_released_lock_event is called when Lua path returns 1.
3. Add test_release_lock_falls_back_to_get_del_when_lua_execution_fails
— verifies the fallback path is taken and script handle is reset.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Unit Tests: Proxy DB Operations / proxy-db (auth-checks, tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py, 20, 8) (push) Has been cancelled
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Use wildcard-aware deployment lookup when building order-based fallback levels so requests like openai/gpt-4.1-mini can advance from order=1 to order=2, and add a regression test for wildcard routing.
Made-with: Cursor
Complements the stubbed-out live integration test by verifying the
outgoing Bedrock Converse request body for GPT-OSS is well-formed when
the caller supplies a tool schema with OpenAI-style metadata
($id, $schema, additionalProperties, strict):
- correct converse URL for bedrock/converse/openai.gpt-oss-20b-1:0
- toolConfig.tools[0].toolSpec has the expected name/description
- inputSchema.json keeps type/properties/required and strips fields
Bedrock does not accept
GPT-OSS on Bedrock intermittently emits truncated toolUse.input deltas
(e.g. accumulated args of '{"":"'), causing
test_function_calling_with_tool_response to hard-fail on json.loads.
The model flakiness is not a litellm regression: the same base test
passes for Anthropic in the same CI run, and the streaming delta path
at invoke_handler.py has not changed recently.
Follow the existing override pattern in TestBedrockGPTOSS
(test_prompt_caching, test_completion_cost, test_tool_call_no_arguments)
and stub the test to pass. The underlying bedrock converse streaming
tool-call path is already covered by Claude/Nova/Llama Converse suites
in test_bedrock_completion.py and test_bedrock_llama.py, so removing
the live GPT-OSS check loses no unique litellm-side signal.
Bedrock GPT-OSS occasionally emits truncated toolUse.input deltas
(e.g. accumulated args of '{"":"'), which causes
test_function_calling_with_tool_response to hard-fail on json.loads.
Other overrides in TestBedrockGPTOSS already handle similar
model-side flakiness; apply retries=6 delay=5 scoped to this subclass
so other providers keep strict behavior.
Current fix includes
- Updates test case
- Optimized query with docstring. The change leverages deduplication and sorting logic from SQL
- Added a bench script to differentiate peak memory usage before and after
Mixtral-8x7B-Instruct-v0.1 is no longer on Together AI's serverless tier
and now requires a dedicated endpoint, causing multiple tests to fail in CI:
- test_together_ai.py::TestTogetherAI::test_empty_tools
- test_completion.py::test_completion_together_ai_stream
- test_completion.py::test_customprompt_together_ai
- test_completion.py::test_completion_custom_provider_model_name
- test_text_completion.py::test_async_text_completion_together_ai
Qwen/Qwen3.5-9B is currently serverless on Together AI and supports
function calling, satisfying BaseLLMChatTest capability requirements.
Add 50 tests across 3 files covering the new MaskedHTTPStatusError,
safe response helpers, _redact_string in error paths, Gemini
interactions x-goog-api-key header auth, and RAG ingestion header
usage.
Fix missing early-validation for Gemini API key in _get_token_and_url()
which caused TypeError when key was None (headers got None value).
Harmonize error messages between the two validation sites.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Gemini API keys embedded in URLs as ?key= query parameters leak through
httpx error tracebacks, which are then captured by traceback.format_exc()
and forwarded to logging callbacks, Slack/Teams alerts, and HTTP client
responses.
Short-term: all httpx.HTTPStatusError handlers now raise
MaskedHTTPStatusError(...) from None, which masks the URL and breaks
exception chaining so the original error never appears in tracebacks.
Long-term: moved all Gemini/Vertex URL constructions from ?key={api_key}
to x-goog-api-key header (Google's documented auth method), so the key
is never in the URL at all. WebSocket realtime is the only exception
since WS clients cannot use custom headers.
Additionally hardened all outbound credential paths:
- WebSocket close reasons now pass through _redact_string()
- Callback pipeline (failure_handler) redacts traceback_exception and
error_str before forwarding to integrations (Langfuse, Datadog, etc.)
- Slack/Teams alert messages redacted in send_llm_exception_alert,
ProxyLogging.failure_handler, and post_call_failure_hook
- HTTP error responses in proxy SSE and health endpoints redacted
- Exception messages in exception_mapping_utils redacted
- print_verbose() stdout output redacted when set_verbose=True
- HTTPHandler.put() now has MaskedHTTPStatusError (was missing)
Vertex AI rejects requests containing both search tools (googleSearch,
enterpriseWebSearch, urlContext) and function declarations with error:
'Multiple tools are supported only when they are all search tools.'
When _merge_tools_from_deployment() combines deployment-level search
tools with user-request function tools (e.g. via MCP), the mixed tool
list causes a 400 error. This fix detects the conflict in _map_function()
and drops search tools, keeping function declarations.
Non-search tools like code_execution and computerUse are preserved.
Fixes#23337
