* build(pyproject.toml): add new dev dependencies - for type checking
* build: reformat files to fit black
* ci: reformat to fit black
* ci(test-litellm.yml): make tests run clear
* build(pyproject.toml): add ruff
* fix: fix ruff checks
* build(mypy/): fix mypy linting errors
* fix(hashicorp_secret_manager.py): fix passing cert for tls auth
* build(mypy/): resolve all mypy errors
* test: update test
* fix: fix black formatting
* build(pre-commit-config.yaml): use poetry run black
* fix(proxy_server.py): fix linting error
* fix: fix ruff safe representation error
* fix(parallel_request_limiter.py): improve single instance rate limiting by updating in-memory cache instantly
Fixes issue where parallel request limiter had a leak
* fix(parallel_request_limiter.py): fix parallel request limiter to not decrement val on max limit being reached
* test(test_parallel_request_limiter.py): fix test
* test: fix test
* fix(parallel_request_limiter.py): move to using common enum
* test: fix test
* fix(parallel_request_limiter.py): add back parallel request information to max parallel request limiter
Resolves https://github.com/BerriAI/litellm/issues/8392
* test: mark flaky test to handle time based tracking issues
* feat(model_management_endpoints.py): expose new patch `/model/{model_id}/update` endpoint
Allows updating specific values of a model in db - makes it easy for admin to know this by calling it a PA
TCH
* feat(edit_model_modal.tsx): allow user to update llm provider + api key on the ui
* fix: fix linting error
* feat(proxy/utils.py): get associated litellm budget from db in combined_view for key
allows user to create rate limit tiers and associate those to keys
* feat(proxy/_types.py): update the value of key-level tpm/rpm/model max budget metrics with the associated budget table values if set
allows rate limit tiers to be easily applied to keys
* docs(rate_limit_tiers.md): add doc on setting rate limit / budget tiers
make feature discoverable
* feat(key_management_endpoints.py): return litellm_budget_table value in key generate
make it easy for user to know associated budget on key creation
* fix(key_management_endpoints.py): document 'budget_id' param in `/key/generate`
* docs(key_management_endpoints.py): document budget_id usage
* refactor(budget_management_endpoints.py): refactor budget endpoints into separate file - makes it easier to run documentation testing against it
* docs(test_api_docs.py): add budget endpoints to ci/cd doc test + add missing param info to docs
* fix(customer_endpoints.py): use new pydantic obj name
* docs(user_management_heirarchy.md): add simple doc explaining teams/keys/org/users on litellm
* Litellm dev 12 26 2024 p2 (#7432)
* (Feat) Add logging for `POST v1/fine_tuning/jobs` (#7426)
* init commit ft jobs logging
* add ft logging
* add logging for FineTuningJob
* simple FT Job create test
* (docs) - show all supported Azure OpenAI endpoints in overview (#7428)
* azure batches
* update doc
* docs azure endpoints
* docs endpoints on azure
* docs azure batches api
* docs azure batches api
* fix(key_management_endpoints.py): fix key update to actually work
* test(test_key_management.py): add e2e test asserting ui key update call works
* fix: proxy/_types - fix linting erros
* test: update test
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* fix: test
* fix(parallel_request_limiter.py): enforce tpm/rpm limits on key from tiers
* fix: fix linting errors
* test: fix test
* fix: remove unused import
* test: update test
* docs(customer_endpoints.py): document new model_max_budget param
* test: specify unique key alias
* docs(budget_management_endpoints.py): document new model_max_budget param
* test: fix test
* test: fix tests
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* fix(dual_cache.py): update in-memory check for redis batch get cache
Fixes latency delay for async_batch_redis_cache
* fix(service_logger.py): fix race condition causing otel service logging to be overwritten if service_callbacks set
* feat(user_api_key_auth.py): add parent otel component for auth
allows us to isolate how much latency is added by auth checks
* perf(parallel_request_limiter.py): move async_set_cache_pipeline (from max parallel request limiter) out of execution path (background task)
reduces latency by 200ms
* feat(user_api_key_auth.py): have user api key auth object return user tpm/rpm limits - reduces redis calls in downstream task (parallel_request_limiter)
Reduces latency by 400-800ms
* fix(parallel_request_limiter.py): use batch get cache to reduce user/key/team usage object calls
reduces latency by 50-100ms
* fix: fix linting error
* fix(_service_logger.py): fix import
* fix(user_api_key_auth.py): fix service logging
* fix(dual_cache.py): don't pass 'self'
* fix: fix python3.8 error
* fix: fix init]
* fix parallel request limiter - use one cache update call
* ci/cd run again
* run ci/cd again
* use docker username password
* fix config.yml
* fix config
* fix config
* fix config.yml
* ci/cd run again
* use correct typing for batch set cache
* fix async_set_cache_pipeline
* fix only check user id tpm / rpm limits when limits set
* fix test_openai_azure_embedding_with_oidc_and_cf
* fix parallel request limiter use correct user id
* async def get_user_object(
fix
* use safe get_internal_user_object
* fix store internal users in redis correctly
* fix(health_check.py): hide sensitive keys from health check debug information k
* fix(route_llm_request.py): fix proxy model not found error message to indicate how to resolve issue
* fix(vertex_llm_base.py): fix exception message to not log credentials
