* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
A name bound twice keeps only the second binding. In `tests/` that is nearly
always a repeated import, harmless but misleading, and the same rule is what
catches the cases that are not harmless: a local that shadows an import the
module still calls, and a second `def test_x` that quietly replaces the first.
311 of the 344 sites were repeated imports and came out with ruff's own fix.
The remaining 33 needed a decision. Four modules imported a name they never
used because a local definition below already shadowed it. Two comprehensions
bound `call` over `unittest.mock.call`, which those modules import and use.
One test rebound the two module handles its nested reload closure had captured.
One class attribute shadowed an unused `status` import.
The load-test fixtures move to a conftest, which is how pytest is meant to share
them, so the test module no longer imports three fixture names it never calls.
The nine `prisma_client` parameters keep a narrow `noqa`: pytest resolves that
fixture by name before the body runs, so the parameter never shadows anything.
a369cb0da7 made completion() hand the prefixed model back to responses(), so
that responses() running get_llm_provider() a second time becomes a no-op
instead of stripping a prefix the model id owns. That was deliberate, and it
shipped with its own unit test, but it left two older assertions behind still
expecting the bare id.
#37744 corrected the openai one in test_openai.py. This is its azure sibling,
which llm_translation_testing has been failing on ever since.
Only the expected value moves. The neighbouring custom_llm_provider assertion
already passes and stays as it is.
* [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>
* fix:fix: prompt_cache_key OAI + Azure OpenAI
* test_prompt_cache_key_supported
* test_azure_openai_with_prompt_cache_key
* fix: remove unnecessary async from test_azure_openai_with_prompt_cache_key
Addresses Greptile feedback: litellm.completion() is synchronous, so
async def is unnecessary and would silently pass without running.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: remove unused filter_and_transform_beta_headers imports
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test_azure_openai_with_prompt_cache_key
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* fix(handler.py): support routing custom llm's to chat completion handler
Adds custom llm support for anthropic
* test(test_anthropic_experimental_pass_through_messages_handler.py): add unit test confirming custom llm respected
* docs(custom_llm_server.md): document anthropic custom llm translation
* test(volcengine.py): map thinking in extra body
Fixes https://github.com/BerriAI/litellm/issues/11879
* feat(main.py): support `azure/responses/<deployment-name>` model string
this allows us to route the model correctly
Closes https://github.com/BerriAI/litellm/issues/11879
* docs(azure_responses.md): document calling azure responses api models via chat completions bridge
Closes https://github.com/BerriAI/litellm/issues/11917
* fix: fix custom provider check
* test: update tests
* fix(utils.py): convert stringified numbers to numbers
Closes https://github.com/BerriAI/litellm/issues/11266
* fix(convert_dict_to_model_response_object/): bubble up azure content_filter_results
* fix: fix linting error
* fix: fix linting errors
* fix(types/utils.py): ensure choices is correctly set
* fix: delete field if not set
* fix: expand scope of choicelogprobs value
* fix(azure.py): ensure max_retries=0 is respected
Fixes https://github.com/BerriAI/litellm/issues/6129
* fix(test_openai.py): add unit test to ensure openai sdk calls always respect max_retries = 0
* test(test_azure_openai.py): add unit testing for azure_text/ route
* fix(azure.py): fix passing max retries on streaming
* fix(azure.py): fix azure max retries on async completion + streaming
* fix(completion/handler.py): fix azure text async completion + streaming
* test(test_azure_openai.py): ensure azure openai max retries always respected
* test(test_azure_o_series.py): add testing to ensure max retries always respected
* Added gemini providers for 2.0-flash and 2.0-flash lite (#8321)
* Update model_prices_and_context_window.json
added gemini providers for 2.0-flash and 2.0-flash light
* Update model_prices_and_context_window.json
fixed URL
---------
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* Convert tool use arguments to string before counting tokens (#6989)
In at least some cases the `messages["tool_calls"]["function"]["arguments"]` is a dict, not a string. In order to tokenize it properly it needs to be a string. In the case that it is already a string this is a noop, which is also fine.
* build(model_prices_and_context_window.json): add gemini 2.0 flash lite pricing
* build(model_prices_and_context_window.json): add gemini commercial rate limits
* fix(utils.py): fix linting error
* refactor(utils.py): refactor to maintain function size
---------
Co-authored-by: Bardia Khosravi <bardiakhosravi95@gmail.com>
Co-authored-by: Josh Morrow <josh@jcmorrow.com>
* fix(convert_dict_to_response.py): only convert if response is the response_format tool call passed in
Fixes https://github.com/BerriAI/litellm/issues/8241
* fix(gpt_transformation.py): makes sure response format / tools conversion doesn't remove previous tool calls
* refactor(gpt_transformation.py): refactor out json schema converstion to base config
keeps logic consistent across providers
* fix(o_series_transformation.py): support o3 mini native streaming
Fixes https://github.com/BerriAI/litellm/issues/8274
* fix(gpt_transformation.py): remove unused variables
* test: update test
* fix(gpt_transformation.py): fix response_format translation check for 4o models
Fixes https://github.com/BerriAI/litellm/issues/7616
* feat(key_management_endpoints.py): support 'temp_budget_increase' and 'temp_budget_expiry' fields
Allow proxy admin to grant temporary budget increases to keys
* fix(proxy/_types.py): enforce temp_budget_increase and temp_budget_expiry are always passed together
* feat(user_api_key_auth.py): initial working temp budget increase logic
ensures key budget exceeded error checks for temp budget in key metadata
* feat(proxy_server.py): return the key max budget and key spend in the response headers
Allows clientside user to know their remaining limits
* test: add unit testing for new proxy utils
Ensures new key budget is correctly handled
* docs(temporary_budget_increase.md): add doc on temporary budget increase
* fix(utils.py): remove 3.5 from response_format check for now
not all azure 3.5 models support response_format
* fix(user_api_key_auth.py): return valid user api key auth object on all paths
* fix(azure/): support passing headers to azure openai endpoints
Fixes https://github.com/BerriAI/litellm/issues/6217
* fix(utils.py): move default tokenizer to just openai
hf tokenizer makes network calls when trying to get the tokenizer - this slows down execution time calls
* fix(router.py): fix pattern matching router - add generic "*" to it as well
Fixes issue where generic "*" model access group wouldn't show up
* fix(pattern_match_deployments.py): match to more specific pattern
match to more specific pattern
allows setting generic wildcard model access group and excluding specific models more easily
* fix(proxy_server.py): fix _delete_deployment to handle base case where db_model list is empty
don't delete all router models b/c of empty list
Fixes https://github.com/BerriAI/litellm/issues/7196
* fix(anthropic/): fix handling response_format for anthropic messages with anthropic api
* fix(fireworks_ai/): support passing response_format + tool call in same message
Addresses https://github.com/BerriAI/litellm/issues/7135
* Revert "fix(fireworks_ai/): support passing response_format + tool call in same message"
This reverts commit 6a30dc6929.
* test: fix test
* fix(replicate/): fix replicate default retry/polling logic
* test: add unit testing for router pattern matching
* test: update test to use default oai tokenizer
* test: mark flaky test
* test: skip flaky test
* fix(main.py): support passing max retries to azure/openai embedding integrations
Fixes https://github.com/BerriAI/litellm/issues/7003
* feat(team_endpoints.py): allow updating team model aliases
Closes https://github.com/BerriAI/litellm/issues/6956
* feat(router.py): allow specifying model id as fallback - skips any cooldown check
Allows a default model to be checked if all models in cooldown
s/o @micahjsmith
* docs(reliability.md): add fallback to specific model to docs
* fix(utils.py): new 'is_prompt_caching_valid_prompt' helper util
Allows user to identify if messages/tools have prompt caching
Related issue: https://github.com/BerriAI/litellm/issues/6784
* feat(router.py): store model id for prompt caching valid prompt
Allows routing to that model id on subsequent requests
* fix(router.py): only cache if prompt is valid prompt caching prompt
prevents storing unnecessary items in cache
* feat(router.py): support routing prompt caching enabled models to previous deployments
Closes https://github.com/BerriAI/litellm/issues/6784
* test: fix linting errors
* feat(databricks/): convert basemodel to dict and exclude none values
allow passing pydantic message to databricks
* fix(utils.py): ensure all chat completion messages are dict
* (feat) Track `custom_llm_provider` in LiteLLMSpendLogs (#7081)
* add custom_llm_provider to SpendLogsPayload
* add custom_llm_provider to SpendLogs
* add custom llm provider to SpendLogs payload
* test_spend_logs_payload
* Add MLflow to the side bar (#7031)
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
* (bug fix) SpendLogs update DB catch all possible DB errors for retrying (#7082)
* catch DB_CONNECTION_ERROR_TYPES
* fix DB retry mechanism for SpendLog updates
* use DB_CONNECTION_ERROR_TYPES in auth checks
* fix exp back off for writing SpendLogs
* use _raise_failed_update_spend_exception to ensure errors print as NON blocking
* test_update_spend_logs_multiple_batches_with_failure
* (Feat) Add StructuredOutputs support for Fireworks.AI (#7085)
* fix model cost map fireworks ai "supports_response_schema": true,
* fix supports_response_schema
* fix map openai params fireworks ai
* test_map_response_format
* test_map_response_format
* added deepinfra/Meta-Llama-3.1-405B-Instruct (#7084)
* bump: version 1.53.9 → 1.54.0
* fix deepinfra
* litellm db fixes LiteLLM_UserTable (#7089)
* ci/cd queue new release
* fix llama-3.3-70b-versatile
* refactor - use consistent file naming convention `AI21/` -> `ai21` (#7090)
* fix refactor - use consistent file naming convention
* ci/cd run again
* fix naming structure
* fix use consistent naming (#7092)
---------
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com>
Co-authored-by: ali sayyah <ali.sayyah2@gmail.com>
* fix(anthropic/chat/transformation.py): support anthropic disable_parallel_tool_use param
Fixes https://github.com/BerriAI/litellm/issues/6456
* feat(anthropic/chat/transformation.py): support anthropic computer tool use
Closes https://github.com/BerriAI/litellm/issues/6427
* fix(vertex_ai/common_utils.py): parse out '$schema' when calling vertex ai
Fixes issue when trying to call vertex from vercel sdk
* fix(main.py): add 'extra_headers' support for azure on all translation endpoints
Fixes https://github.com/BerriAI/litellm/issues/6465
* fix: fix linting errors
* fix(transformation.py): handle no beta headers for anthropic
* test: cleanup test
* fix: fix linting error
* fix: fix linting errors
* fix: fix linting errors
* fix(transformation.py): handle dummy tool call
* fix(main.py): fix linting error
* fix(azure.py): pass required param
* LiteLLM Minor Fixes & Improvements (10/24/2024) (#6441)
* fix(azure.py): handle /openai/deployment in azure api base
* fix(factory.py): fix faulty anthropic tool result translation check
Fixes https://github.com/BerriAI/litellm/issues/6422
* fix(gpt_transformation.py): add support for parallel_tool_calls to azure
Fixes https://github.com/BerriAI/litellm/issues/6440
* fix(factory.py): support anthropic prompt caching for tool results
* fix(vertex_ai/common_utils): don't pop non-null required field
Fixes https://github.com/BerriAI/litellm/issues/6426
* feat(vertex_ai.py): support code_execution tool call for vertex ai + gemini
Closes https://github.com/BerriAI/litellm/issues/6434
* build(model_prices_and_context_window.json): Add 'supports_assistant_prefill' for bedrock claude-3-5-sonnet v2 models
Closes https://github.com/BerriAI/litellm/issues/6437
* fix(types/utils.py): fix linting
* test: update test to include required fields
* test: fix test
* test: handle flaky test
* test: remove e2e test - hitting gemini rate limits
* Litellm dev 10 26 2024 (#6472)
* docs(exception_mapping.md): add missing exception types
Fixes https://github.com/Aider-AI/aider/issues/2120#issuecomment-2438971183
* fix(main.py): register custom model pricing with specific key
Ensure custom model pricing is registered to the specific model+provider key combination
* test: make testing more robust for custom pricing
* fix(redis_cache.py): instrument otel logging for sync redis calls
ensures complete coverage for all redis cache calls
* (Testing) Add unit testing for DualCache - ensure in memory cache is used when expected (#6471)
* test test_dual_cache_get_set
* unit testing for dual cache
* fix async_set_cache_sadd
* test_dual_cache_local_only
* redis otel tracing + async support for latency routing (#6452)
* docs(exception_mapping.md): add missing exception types
Fixes https://github.com/Aider-AI/aider/issues/2120#issuecomment-2438971183
* fix(main.py): register custom model pricing with specific key
Ensure custom model pricing is registered to the specific model+provider key combination
* test: make testing more robust for custom pricing
* fix(redis_cache.py): instrument otel logging for sync redis calls
ensures complete coverage for all redis cache calls
* refactor: pass parent_otel_span for redis caching calls in router
allows for more observability into what calls are causing latency issues
* test: update tests with new params
* refactor: ensure e2e otel tracing for router
* refactor(router.py): add more otel tracing acrosss router
catch all latency issues for router requests
* fix: fix linting error
* fix(router.py): fix linting error
* fix: fix test
* test: fix tests
* fix(dual_cache.py): pass ttl to redis cache
* fix: fix param
* fix(dual_cache.py): set default value for parent_otel_span
* fix(transformation.py): support 'response_format' for anthropic calls
* fix(transformation.py): check for cache_control inside 'function' block
* fix: fix linting error
* fix: fix linting errors
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* fix(azure.py): handle /openai/deployment in azure api base
* fix(factory.py): fix faulty anthropic tool result translation check
Fixes https://github.com/BerriAI/litellm/issues/6422
* fix(gpt_transformation.py): add support for parallel_tool_calls to azure
Fixes https://github.com/BerriAI/litellm/issues/6440
* fix(factory.py): support anthropic prompt caching for tool results
* fix(vertex_ai/common_utils): don't pop non-null required field
Fixes https://github.com/BerriAI/litellm/issues/6426
* feat(vertex_ai.py): support code_execution tool call for vertex ai + gemini
Closes https://github.com/BerriAI/litellm/issues/6434
* build(model_prices_and_context_window.json): Add 'supports_assistant_prefill' for bedrock claude-3-5-sonnet v2 models
Closes https://github.com/BerriAI/litellm/issues/6437
* fix(types/utils.py): fix linting
* test: update test to include required fields
* test: fix test
* test: handle flaky test
* test: remove e2e test - hitting gemini rate limits
* fix get_response_headers
* unit testing for get headers
* unit testing for anthropic / azure openai headers
* increase test coverage for test_completion_response_ratelimit_headers
* fix test rate limit headers
