* Add --only-models-matching-regex option
to `models import` which only processes models where
`litelllm_params.model` matches the regex
* Add test_models_import_only_models_matching_regex
* Print each model we're importing
* Add --only-access-groups-matching-regex option
to `models import` which only processes models where at least one item
in `model_info.access_groups` matches the regex. Add a unit test.
* Add `models import` examples to README.md
Add `models import` examples to proxy/client/cli/README.md
* ruff format litellm/proxy/client/cli/commands/models.py
* Make `models import` display tabular output
* models import refactoring
* Fix failing tests in test_models_commands.py
* Refactor import_models to make it shorter and more readable
* Extract from `import_models` a function called `get_model_list_from_yaml_file`
* Fix mypy error
* Add more specific typing
for better understandability and Intellisense
* More import_models refactoring
* More refactoring
* More refactoring
* Write unit tests for format_iso_datetime_str
* Add more unit tests
* ruff format tests/litellm/proxy/client/cli/test_models_commands.py
* ruff format litellm/proxy/client/cli/commands/models.py
* Make test_format_timestamp use UTC time
* fix(embeddings): use non default tokenizer when passing list of lists of tokens (int)
* feat(embeddings): allow for passthrough of list of lists of tokens to hosted_vllm models
* Revert "fix(embeddings): use non default tokenizer when passing list of lists of tokens (int)"
This reverts commit a48acd95f860c4fc85853e20668eabffff07cae7.
* refactor(embeddings): use a list to verify if provider accept as input a list of tokens
* fix(embeddings): verify the model name before validating if provider accept a arrays of tokens as input
When passing a list of tokens as input, verify the provider of the model by going through the list of models (`llm_model_list`). First, it check for model name then get the provider and verify if it accept or not arrays of tokens. If yes, then pass, else decode.
Previously, it was verifying provider and model name at the same time resulting in decoding even if the current model checked was not the target one (looping onto `llm_model_list`)
* test(embedding): add unit test to bypass decode for some providers with input as array of tokens
Ref: https://github.com/BerriAI/litellm/issues/10113
* fix(duration_parser.py): support `mo` unit
* test(test_key_management_endpoints.py): add test confirming generate_key_helper_fn uses predictable budgets
Closes https://github.com/BerriAI/litellm/issues/10800
* fix(anthropic/chat/transformation.py): add tool use cost tracking
* fix(anthropic/): refactor how hosted tool usage tracking is done
keep it separate from prompt / completion token details
* fix(anthropic/): add web search tool cost tracking
accurate cost tracking
* feat(anthropic/chat/transformation.py): map openai 'web_search_options' param to anthropic hosted tool
Allows calling anthropic web search in same format as openai
* feat(anthropic/chat/transformation.py): support unified anthropic 'web_search_options' param
Allows calling anthropic's web search tool in the openai format
* feat(anthropic/chat/transformation.py): map openai 'search_context_size' to anthropic 'max_uses' param
Translate search effort across both providers
* fix: mark web_search_options param as supported by openai + azure
* fix: fix linting error
* fix: fix linting errors
* fix: fix linting error
* fix: check if usage hasattr
* fix: pass web search options param
* add function to check config flag
* added unit tests
* convert to seconds support
* added in settings.md
* Updated config_settings.md
* remove extra point
* change config var
* resolve conflict
* Update Groq models on model_prices_and_context_window.json
Add support for 6 new models; add deprecations dates to 12 model; and update context-windows/max-tokens for 3 models
* Round model costs to 8 decimal places
* feat(cohere/embed): v2 embed api support
adds output_dimensions param support
* fix(cohere/embed): migrate to v2 embedding
Adds output dimension support
* fix: maintain /v1/embedding compatibility for bedrock cohere
Bedrock cohere is still using /v1 endpoints
* fix: fix linting error
* fix: fix passing extra headers
* test: update tests
* fix(litellm_logging.py): log custom headers in requester metadata
allows passing along custom headers from client to logging integration - e.g. `x-correlation-id`
* refactor: move enterprise code out of OSS package
work towards simplified CE version of docker image
* test: update test
* fix: fix linting error
