* feat(main.py): initial commit for `/image/variations` endpoint support
* refactor(base_llm/): introduce new base llm base config for image variation endpoints
* refactor(openai/image_variations/transformation.py): implement openai image variation transformation handler
* fix: test
* feat(openai/): working openai `/image/variation` endpoint calls via sdk
* feat(topaz/): topaz sync image variation call support
Addresses https://github.com/BerriAI/litellm/issues/7593
'
* fix(topaz/transformation.py): fix linting errors
* fix(openai/image_variations/handler.py): fix passing json data
* fix(main.py): image_variation/
support async image variation route - `aimage_variation`
* fix(test_get_model_info.py): fix test
* fix: cleanup unused imports
* feat(openai/): add async `/image/variations` endpoint support
* feat(topaz/): support async `/image/variations` calls
* fix: test
* fix(utils.py): fix get_model_info_helper for no model info w/ provider config
handles situation where model info is not known but provider config exists
* test(test_router_fallbacks.py): mark flaky test
* fix: fix unused imports
* test: bump otel load test perf threshold - accounts for current load tests hitting same server
* fix(__init__.py): fix init to exclude pricing-only model cost values from real model names
prevents bad health checks on wildcard routes
* fix(get_llm_provider.py): fix to handle calling bedrock_converse models
* 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
* test(test_basic_python_version.py): assert all optional dependencies are marked as extras on poetry
Fixes https://github.com/BerriAI/litellm/issues/7677
* docs(secret.md): clarify 'read_and_write' secret manager usage on aws
* docs(secret.md): fix doc
* build(ui/teams.tsx): add edit/delete button for updating user / team membership on ui
allows updating user role to admin on ui
* build(ui/teams.tsx): display edit member component on ui, when edit button on member clicked
* feat(team_endpoints.py): support updating team member role to admin via api endpoints
allows team member to become admin post-add
* build(ui/user_dashboard.tsx): if team admin - show all team keys
Fixes https://github.com/BerriAI/litellm/issues/7650
* test(config.yml): add tomli to ci/cd
* test: don't call python_basic_testing in local testing (covered by python 3.13 testing)
* test(test_get_model_info.py): add unit test confirming router deployment updates global 'get_model_info'
* fix(get_supported_openai_params.py): fix custom llm provider 'get_supported_openai_params'
Fixes https://github.com/BerriAI/litellm/issues/7668
* docs(azure.md): clarify how azure ad token refresh on proxy works
Closes https://github.com/BerriAI/litellm/issues/7665
* 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(main.py): fix lm_studio/ embedding routing
adds the mapping + updates docs with example
* docs(self_serve.md): update doc to show how to auto-add sso users to teams
* fix(streaming_handler.py): simplify async iterator check, to just check if streaming response is an async iterable
* fix(streaming_chunk_builder_utils.py): add test for groq tool calling + streaming + combine chunks
Addresses https://github.com/BerriAI/litellm/issues/7621
* fix(streaming_utils.py): fix modelresponseiterator for openai like chunk parser
ensures chunk parser uses the correct tool call id when translating the chunk
Fixes https://github.com/BerriAI/litellm/issues/7621
* build(model_hub.tsx): display cost pricing on model hub
* build(model_hub.tsx): show cost per token pricing + complete model information
* fix(types/utils.py): fix usage object handling
* feat(cost_calculator.py): add cost tracking ($0) for openai moderations endpoint
removes sentry cost tracking errors caused by this
* build(teams.tsx): allow assigning teams to orgs
* 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
* test(test_utils.py): initial test for valid models
Addresses https://github.com/BerriAI/litellm/issues/7525
* fix: test
* feat(fireworks_ai/transformation.py): support retrieving valid models from fireworks ai endpoint
* refactor(fireworks_ai/): support checking model info on `/v1/models` route
* docs(set_keys.md): update docs to clarify check llm provider api usage
* fix(watsonx/common_utils.py): support 'WATSONX_ZENAPIKEY' for iam auth
* fix(watsonx): read in watsonx token from env var
* fix: fix linting errors
* fix(utils.py): fix provider config check
* style: cleanup unused imports
* fix(redact_messages.py): fix redact messages for non-model response input to be dictionary
fixes issue with otel logging when message redaction is enabled
* fix(proxy_server.py): fix langfuse key leak in exception string
* test: fix test
* test: fix test
* test: fix tests
* test(azure_openai_o1.py): initial commit with testing for azure openai o1 preview model
* fix(base_llm_unit_tests.py): handle azure o1 preview response format tests
skip as o1 on azure doesn't support tool calling yet
* fix: initial commit of azure o1 handler using openai caller
simplifies calling + allows fake streaming logic alr. implemented for openai to just work
* feat(azure/o1_handler.py): fake o1 streaming for azure o1 models
azure does not currently support streaming for o1
* feat(o1_transformation.py): support overriding 'should_fake_stream' on azure/o1 via 'supports_native_streaming' param on model info
enables user to toggle on when azure allows o1 streaming without needing to bump versions
* style(router.py): remove 'give feedback/get help' messaging when router is used
Prevents noisy messaging
Closes https://github.com/BerriAI/litellm/issues/5942
* fix(types/utils.py): handle none logprobs
Fixes https://github.com/BerriAI/litellm/issues/328
* fix(exception_mapping_utils.py): fix error str unbound error
* refactor(azure_ai/): move to openai_like chat completion handler
allows for easy swapping of api base url's (e.g. ai.services.com)
Fixes https://github.com/BerriAI/litellm/issues/7275
* refactor(azure_ai/): move to base llm http handler
* fix(azure_ai/): handle differing api endpoints
* fix(azure_ai/): make sure all unit tests are passing
* fix: fix linting errors
* fix: fix linting errors
* fix: fix linting error
* fix: fix linting errors
* fix(azure_ai/transformation.py): handle extra body param
* fix(azure_ai/transformation.py): fix max retries param handling
* fix: fix test
* test(test_azure_o1.py): fix test
* fix(llm_http_handler.py): support handling azure ai unprocessable entity error
* fix(llm_http_handler.py): handle sync invalid param error for azure ai
* fix(azure_ai/): streaming support with base_llm_http_handler
* fix(llm_http_handler.py): working sync stream calls with unprocessable entity handling for azure ai
* fix: fix linting errors
* fix(llm_http_handler.py): fix linting error
* fix(azure_ai/): handle cohere tool call invalid index param error
* fix(internal_user_endpoints.py): fix team list sort - handle team_alias being set + None
* fix(key_management_endpoints.py): allow team admin to create key for member via admin ui
Fixes https://github.com/BerriAI/litellm/issues/7482
* fix(proxy_server.py): allow querying info on specific model group via `/model_group/info`
allows client-side user to get model info from proxy
* fix(proxy_server.py): add docstring on `/model_group/info` showing how to filter by model name
* test(test_proxy_utils.py): add unit test for returning model group info filtered
* fix(proxy_server.py): fix query param
* fix(test_Get_model_info.py): handle no whitelisted bedrock modells
* 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
* test(azure_openai_o1.py): initial commit with testing for azure openai o1 preview model
* fix(base_llm_unit_tests.py): handle azure o1 preview response format tests
skip as o1 on azure doesn't support tool calling yet
* fix: initial commit of azure o1 handler using openai caller
simplifies calling + allows fake streaming logic alr. implemented for openai to just work
* feat(azure/o1_handler.py): fake o1 streaming for azure o1 models
azure does not currently support streaming for o1
* feat(o1_transformation.py): support overriding 'should_fake_stream' on azure/o1 via 'supports_native_streaming' param on model info
enables user to toggle on when azure allows o1 streaming without needing to bump versions
* style(router.py): remove 'give feedback/get help' messaging when router is used
Prevents noisy messaging
Closes https://github.com/BerriAI/litellm/issues/5942
* test: fix azure o1 test
* test: fix tests
* fix: fix test
* refactor(utils.py): migrate amazon titan config to base config
* refactor(utils.py): refactor bedrock meta invoke model translation to use base config
* refactor(utils.py): move bedrock ai21 to base config
* refactor(utils.py): move bedrock cohere to base config
* refactor(utils.py): move bedrock mistral to use base config
* refactor(utils.py): move all provider optional param translations to using a config
* docs(clientside_auth.md): clarify how to pass vertex region to litellm proxy
* fix(utils.py): handle scenario where custom llm provider is none / empty
* fix: fix get config
* test(test_otel_load_tests.py): widen perf margin
* fix(utils.py): fix get provider config check to handle custom llm's
* fix(utils.py): fix check
* docs(sidebar.js): docs for support model access groups for wildcard routes
* feat(key_management_endpoints.py): add check if user is premium_user when adding model access group for wildcard route
* refactor(docs/): make control model access a root-level doc in proxy sidebar
easier to discover how to control model access on litellm
* docs: more cleanup
* feat(fireworks_ai/): add document inlining support
Enables user to call non-vision models with images/pdfs/etc.
* test(test_fireworks_ai_translation.py): add unit testing for fireworks ai transform inline helper util
* docs(docs/): add document inlining details to fireworks ai docs
* feat(fireworks_ai/): allow user to dynamically disable auto add transform inline
allows client-side disabling of this feature for proxy users
* feat(fireworks_ai/): return 'supports_vision' and 'supports_pdf_input' true on all fireworks ai models
now true as fireworks ai supports document inlining
* test: fix tests
* fix(router.py): add unit testing for _is_model_access_group_for_wildcard_route
* fix(azure_ai/transformation.py): route ai.services.azure calls to the azure provider route
requires token to be passed in as 'api-key'
Closes https://github.com/BerriAI/litellm/issues/7275
* fix(key_management_endpoints.py): enforce user is member of team, if team_id set and team_id exists in team table
* fix(key_management_endpoints.py): handle assigned_user_id = none
* feat(create_key_button.tsx): allow assigning keys to other users
allows proxy admin to easily assign other people keys
* build(create_key_button.tsx): fix error message display
don't swallow the error message for key creation failure
* build(create_key_button.tsx): allow proxy admin to edit team id
* build(create_key_button.tsx): allow proxy admin to assign keys to other users
* build(edit_user.tsx): clarify how 'user budgets' are applied
* test: remove dup test
* fix(key_management_endpoints.py): don't raise error if team not in db
'
* test: fix test
* feat(main.py): mock_response() - support 'litellm.ContextWindowExceededError' in mock response
enabled quicker router/fallback/proxy debug on context window errors
* feat(exception_mapping_utils.py): extract special litellm errors from error str if calling `litellm_proxy/` as provider
Closes https://github.com/BerriAI/litellm/issues/7259
* fix(user_api_key_auth.py): specify 'Received Proxy Server Request' is span kind server
Closes https://github.com/BerriAI/litellm/issues/7298
* fix(model_dashboard.tsx): support setting model_info params - e.g. mode on ui
Closes https://github.com/BerriAI/litellm/issues/5270
* fix(lowest_tpm_rpm_v2.py): deployment rpm over limit check
fixes selection error when getting potential deployments below known tpm/rpm limit
Fixes https://github.com/BerriAI/litellm/issues/7395
* fix(test_tpm_rpm_routing_v2.py): add unit test for https://github.com/BerriAI/litellm/issues/7395
* fix(lowest_tpm_rpm_v2.py): fix tpm key name in dict post rpm update
* test: rename test to run earlier
* test: skip flaky test
* build(model_prices_and_context_window.json): update groq models to specify 'supports_vision' parameter
Closes https://github.com/BerriAI/litellm/issues/7433
* docs(groq.md): add groq vision example to docs
Closes https://github.com/BerriAI/litellm/issues/7433
* fix(prometheus.py): refactor self.litellm_proxy_failed_requests_metric to use label factory
* feat(prometheus.py): new 'litellm_proxy_failed_requests_by_tag_metric'
allows tracking failed requests by tag on proxy
* fix(prometheus.py): fix exception logging
* feat(prometheus.py): add new 'litellm_request_total_latency_by_tag_metric'
enables tracking latency by use-case
* feat(prometheus.py): add new llm api latency by tag metric
* feat(prometheus.py): new litellm_deployment_latency_per_output_token_by_tag metric
allows tracking deployment latency by tag
* fix(prometheus.py): refactor 'litellm_requests_metric' to use enum values + label factory
* feat(prometheus.py): new litellm_proxy_total_requests_by_tag metric
allows tracking total requests by tag
* feat(prometheus.py): new metric litellm_deployment_successful_fallbacks_by_tag
allows tracking deployment fallbacks by tag
* fix(prometheus.py): new 'litellm_deployment_failed_fallbacks_by_tag' metric
allows tracking failed fallbacks on deployment by custom tag
* test: fix test
* test: rename test to run earlier
* test: skip flaky test
* 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>
