* fix(openai.py): ensure openai file object shows up on logs
* fix(managed_files.py): return unified file id as b64 str
allows retrieve file id to work as expected
* fix(managed_files.py): apply decoded file id transformation
* fix: add unit test for file id + decode logic
* fix: initial commit for litellm_proxy support with CRUD Endpoints
* fix(managed_files.py): support retrieve file operation
* fix(managed_files.py): support for DELETE endpoint for files
* fix(managed_files.py): retrieve file content support
supports retrieve file content api from openai
* fix: fix linting error
* test: update tests
* fix: fix linting error
* fix(files/main.py): pass litellm params to azure route
* test: fix test
* 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(custom_logger.py): expose new 'async_get_chat_completion_prompt' event hook
* fix(custom_logger.py): langfuse_prompt_management.py
remove 'headers' from custom logger 'async_get_chat_completion_prompt' and 'get_chat_completion_prompt' event hooks
* feat(router.py): expose new function for prompt management based routing
* feat(router.py): partial working router prompt factory logic
allows load balanced model to be used for model name w/ langfuse prompt management call
* feat(router.py): fix prompt management with load balanced model group
* feat(langfuse_prompt_management.py): support reading in openai params from langfuse
enables user to define optional params on langfuse vs. client code
* test(test_Router.py): add unit test for router based langfuse prompt management
* fix: fix linting errors
* 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
* feat(custom_logger.py): expose new `async_dataset_hook` for modifying/rejecting argilla items before logging
Allows user more control on what gets logged to argilla for annotations
* feat(google_ai_studio_endpoints.py): add new `/azure/*` pass through route
enables pass-through for azure provider
* feat(utils.py): support checking ollama `/api/show` endpoint for retrieving ollama model info
Fixes https://github.com/BerriAI/litellm/issues/6322
* fix(user_api_key_auth.py): add `/key/delete` to an allowed_ui_routes
Fixes https://github.com/BerriAI/litellm/issues/6236
* fix(user_api_key_auth.py): remove type ignore
* fix(user_api_key_auth.py): route ui vs. api token checks differently
Fixes https://github.com/BerriAI/litellm/issues/6238
* feat(internal_user_endpoints.py): support setting models as a default internal user param
Closes https://github.com/BerriAI/litellm/issues/6239
* fix(user_api_key_auth.py): fix exception string
* fix(user_api_key_auth.py): fix error string
* fix: fix test
* track api key and team in prom latency metric
* add test for latency metric
* test prometheus success metrics for latency
* track team and key labels for deployment failures
* add test for litellm_deployment_failure_responses_total
* fix checks for premium user on prometheus
* log_success_fallback_event and log_failure_fallback_event
* log original_exception in log_success_fallback_event
* track key, team and exception status and class on fallback metrics
* use get_standard_logging_metadata
* fix import error
* track litellm_deployment_successful_fallbacks
* add test test_proxy_fallback_metrics
* add log log_success_fallback_event
* fix test prometheus
* fix(vertex_endpoints.py): fix vertex ai pass through endpoints
* test(test_streaming.py): skip model due to end of life
* feat(custom_logger.py): add special callback for model hitting tpm/rpm limits
Closes https://github.com/BerriAI/litellm/issues/4096
run user calls through an llm api to check for prompt injection attacks. This happens in parallel to th
e actual llm call using `async_moderation_hook`
