* fix(prometheus.py): fix setting key budget metrics
ensures custom metadata works with key budget metric
this is a patch. root cause pr is written in a separate branch
* test: fix test
* test(base_llm_unit_tests.py): add test to ensure drop params is respected
* fix(types/prometheus.py): use typing_extensions for python3.8 compatibility
* build: add cherry picked commits
* fix(base_utils.py): supported nested json schema passed in for anthropic calls
* refactor(base_utils.py): refactor ref parsing to prevent infinite loop
* test(test_openai_endpoints.py): refactor anthropic test to use bedrock
* fix(langfuse_prompt_management.py): add unit test for sync langfuse calls
Resolves https://github.com/BerriAI/litellm/issues/7938#issuecomment-2613293757
* fix(bedrock/converse_handler.py): fix bedrock region name on async calls
* fix(utils.py): fix split model handling
Fixes bedrock cost calculation when region name is given
* feat(_health_endpoints.py): support health checking datadog integration
Closes https://github.com/BerriAI/litellm/issues/7921
* Fix: Problem with langfuse_tags when using litellm proxy with langfuse integration (#7801)
* Refactor: Create method get_langfuse_tags
* Fix: Exception Handling
* fix base aws llm
* fix auth with aws role
* test aws base llm
* fix base aws llm init
* run ci/cd again
* fix get_credentials
* ci/cd run again
* _auth_with_aws_role
* feat(langfuse.py): log the used prompt when prompt management used
* test: fix test
* docs(self_serve.md): add doc on restricting personal key creation on ui
* feat(s3.py): support s3 logging with team alias prefixes (if available)
New preview feature
* fix(main.py): remove old if block - simplify to just await if coroutine returned
fixes lm_studio async embedding error
* fix(langfuse.py): handle get prompt check
* fix(vertex_ai/gemini/transformation.py): handle 'http://' in gemini process url
* refactor(router.py): refactor '_prompt_management_factory' to use logging obj get_chat_completion logic
deduplicates code
* fix(litellm_logging.py): update 'get_chat_completion_prompt' to update logging object messages
* docs(prompt_management.md): update prompt management to be in beta
given feedback - this still needs to be revised (e.g. passing in user message, not ignoring)
* refactor(prompt_management_base.py): introduce base class for prompt management
allows consistent behaviour across prompt management integrations
* feat(prompt_management_base.py): support adding client message to template message + refactor langfuse prompt management to use prompt management base
* fix(litellm_logging.py): log prompt id + prompt variables to langfuse if set
allows tracking what prompt was used for what purpose
* feat(litellm_logging.py): log prompt management metadata in standard logging payload + use in langfuse
allows logging prompt id / prompt variables to langfuse
* test: fix test
* fix(router.py): cleanup unused imports
* fix: fix linting error
* fix: fix trace param typing
* fix: fix linting errors
* fix: fix code qa check
* 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
* refactor(prometheus.py): refactor to remove `_tag` metrics and incorporate in regular metrics
* fix(prometheus.py): handle label values not set in enum values
* feat(prometheus.py): working e2e custom metadata labels
* docs(prometheus.md): update docs to clarify how custom metrics would work
* test(test_prometheus_unit_tests.py): fix test
* test: add unit testing
* fix(prometheus.py): refactor litellm_input_tokens_metric to use label factory
makes adding new metrics easier
* feat(prometheus.py): add 'request_model' to 'litellm_input_tokens_metric'
* refactor(prometheus.py): refactor 'litellm_output_tokens_metric' to use label factory
makes adding new metrics easier
* feat(prometheus.py): emit requested model in 'litellm_output_tokens_metric'
* feat(prometheus.py): support tracking success events with custom metrics
* refactor(prometheus.py): refactor '_set_latency_metrics' to just use the initially created enum values dictionary
reduces scope for missing values
* feat(prometheus.py): refactor all tags to support custom metadata tags
enables metadata tags to be used across for e2e tracking
* fix(prometheus.py): fix requested model on success event enum_values
* test: fix test
* test: fix test
* test: handle filenotfound error
* docs(prometheus.md): add new values to prometheus
* docs(prometheus.md): document adding custom metrics on prometheus
* bump: version 1.56.5 → 1.56.6
* fix(langfuse_prompt_management.py): migrate dynamic logging to langfuse custom logger compatible class
* fix(langfuse_prompt_management.py): support failure callback logging to langfuse as well
* feat(proxy_server.py): support setting custom tokenizer on config.yaml
Allows customizing value for `/utils/token_counter`
* fix(proxy_server.py): fix linting errors
* test: skip if file not found
* style: cleanup unused import
* docs(configs.md): add docs on setting custom tokenizer
