MaskedHTTPStatusError constructs a new httpx.Response from the original
error. Two bugs surfaced under real HTTP error responses:
1. The new Response was created without request=, so response.request
raised RuntimeError("The .request property has not been set.") for
any downstream caller (e.g. exception_mapping_utils) that inspected it.
2. The decoded response bytes were passed together with the original
Content-Encoding header. On construction httpx tried to decompress
the already-decoded bytes and raised httpx.DecodingError
("Error -3 while decompressing data: incorrect header check").
Set response.request to the masked Request and strip Content-Encoding
(and the now-stale Content-Length) before rebuilding the Response.
URL/message masking is unchanged; the new request carries the already
masked URL.
Also update test_logging_key_masking_gemini: the security commit
25f93bed91 moved Gemini API keys from ?key=... URL params to the
x-goog-api-key header, so api_base no longer contains the key.
- streaming_iterator.py: adopted main's more defensive version of the
tool-arg queueing check (.get() instead of [], isinstance guard) —
same logic, same behavior, lower crash surface
- model_prices_and_context_window.json + backup: combined staging's
search_context_cost_per_query fields (PR #24372) with main's new
supports_service_tier field — both are independent additions to the
same Gemini model entries
- test_streaming_handler.py: kept Azure streaming regression test
(PR #24354) and added main's two new Gemini legacy vertex
finish_reason normalization tests
- test_gemini_batch_embeddings.py: kept staging's unsupported-params
filtering tests (PR #24370) and added main's index/order test
Resolved conflicts:
- streaming_handler.py: combined role check (PR #24354, Azure streaming)
with reasoning_items check (new in main) — both are independent OR
conditions in is_chunk_non_empty()
- CI/CD: accepted main's versions throughout
- Redis tests migrated to CircleCI (PR #25354): removed enable-redis
from GH Actions workflows
- E2E UI tests restructured (PR #25365): simplified CircleCI job
- Coverage via Codecov added to all GH Actions unit test workflows
- Deleted test-litellm-matrix.yml and test-proxy-e2e-azure-batches.yml
(removed in main)
* [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
---------
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.
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
* fix: emit input_json_delta for tool args bundled in first streaming chunk
Some providers (xAI, Gemini) include tool_call function arguments in the
same streaming chunk as the function name/id. The AnthropicStreamWrapper
was discarding the trigger chunk entirely when starting a new content
block, which silently dropped the input_json_delta carrying tool
arguments. This caused tool_use blocks to arrive with empty input {}.
Now queue the processed_chunk after content_block_start when it carries
non-empty input_json_delta data. Backward compatible: providers that send
empty arguments in the first chunk (OpenAI-style) are unaffected since
the condition checks for truthy partial_json.
* test: add tests for input_json_delta emission on bundled tool args
Covers the fix for providers (xAI, Gemini) that bundle tool_call
arguments in the same streaming chunk as the function name/id.
Verifies the AnthropicStreamWrapper emits input_json_delta after
content_block_start, and that empty-arg chunks (OpenAI-style) are
unaffected.
* style: apply Black formatting to streaming_iterator.py
* fix: mirror input_json_delta fix to sync __next__ and add sync tests
* test: make no_extra_delta tests assert explicitly instead of passing silently
- Omit messages whose list content is empty after stripping thinking blocks
- Retry only on HTTP 400 plus invalid-signature body match
- Return response inline from retry loop; drop unreachable None guard
- Tests: thinking-only turn dropped, non-400 no retry
Made-with: Cursor
Strip thinking blocks from the request body and retry once when Anthropic returns an invalid thinking signature error (e.g. after credential or deployment change). Applies to all BaseAnthropicMessagesConfig providers (direct Anthropic, Bedrock, Vertex, Azure AI).
Made-with: Cursor
- Add automatic conversion of Vertex AI batch prediction JSONL to OpenAI format
- Preserve custom_id via Vertex AI labels for request correlation
- Fix Content-Length header mismatch in transformed responses
- Add comprehensive tests for batch output transformation
Made-with: Cursor
When modify_params is true, Bedrock Converse setup no longer prepends or
appends the default user message if the boundary assistant turn has
prefix: true, so OpenAI-style assistant prefill reaches the API unchanged.
Made-with: Cursor
- Static Methods for Streaming Handler Function
- Remove the afile_content_streaming wrapper function. Enabled with a stream boolean in afile_content
- Cleaned up test cases after refactor
- Removed unused imports and streamlined type hints in `litellm/utils.py` and `litellm/files/main.py`.
- Moved `FileContentStreamingResult` to a new `litellm/files/types.py` for better organization.
- Updated `FileContentStreamingResponse` in `litellm/files/streaming.py` to include asynchronous close methods and improved logging capabilities.
- Enhanced tests to ensure proper closure of streaming iterators in `tests/test_litellm/llms/openai/test_openai_file_content_streaming.py` and `tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py`.
- Introduced `afile_content_streaming` and `file_content_streaming` functions in `litellm/files/main.py` to handle asynchronous and synchronous file content streaming.
- Added `FileContentStreamingResponse` class in `litellm/files/streaming.py` to manage streaming responses with logging capabilities.
- Updated OpenAI API integration in `litellm/llms/openai/openai.py` to support new streaming methods.
- Enhanced file content retrieval in `litellm/proxy/openai_files_endpoints/files_endpoints.py` to route requests for streaming.
- Added unit tests for the new streaming functionality in `tests/test_litellm/llms/openai/test_openai_file_content_streaming.py` and `tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py`.
- Refactored type hints and imports for better clarity and organization across modified files.
