* 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
* 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(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
* fix(types/utils.py): support langfuse + humanloop routes on llm router
* fix(main.py): remove acompletion elif block
just await if coroutine returned
* 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
* 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(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(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
* 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
* feat(deepgram/): initial e2e support for deepgram stt
Uses deepgram's `/listen` endpoint to transcribe speech to text
Closes https://github.com/BerriAI/litellm/issues/4875
* fix: fix linting errors
* test: fix 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>
* refactor(prometheus.py): refactor to use a factory method for setting label values
allows for enforcing end user id disabling on prometheus e2e
* fix: fix linting error
* fix(prometheus.py): ensure label factory drops end-user value if disabled by user
* fix(prometheus.py): specify service_type in end user tracking get
* test: fix test
* test: add unit test for prometheus factory
* test: improve test (cover flag not set scenario)
* test(test_prometheus.py): e2e test covering if 'end_user_id' shows up in testing if disabled
scrapes the `/metrics` endpoint and scans text to check if id appears in emitted metrics
* fix(prometheus.py): stringify status code before logging it
* test: add new test image embedding to base llm unit tests
Addresses https://github.com/BerriAI/litellm/issues/6515
* fix(bedrock/embed/multimodal-embeddings): strip data prefix from image urls for bedrock multimodal embeddings
Fix https://github.com/BerriAI/litellm/issues/6515
* feat: initial commit for fireworks ai audio transcription support
Relevant issue: https://github.com/BerriAI/litellm/issues/7134
* test: initial fireworks ai test
* feat(fireworks_ai/): implemented fireworks ai audio transcription config
* fix(utils.py): register fireworks ai audio transcription config, in config manager
* fix(utils.py): add fireworks ai param translation to 'get_optional_params_transcription'
* refactor(fireworks_ai/): define text completion route with model name handling
moves model name handling to specific fireworks routes, as required by their api
* refactor(fireworks_ai/chat): define transform_Request - allows fixing model if accounts/ is missing
* fix: fix linting errors
* fix: fix linting errors
* fix: fix linting errors
* fix: fix linting errors
* fix(handler.py): fix linting errors
* fix(main.py): fix tgai text completion route
* refactor(together_ai/completion): refactors together ai text completion route to just use provider transform request
* refactor: move test_fine_tuning_api out of local_testing
reduces local testing ci/cd time
* fix(invoke_handler.py): fix mock response iterator to handle tool calling
returns tool call if returned by model response
* fix(prometheus.py): add new 'tokens_by_tag' metric on prometheus
allows tracking 'token usage' by task
* feat(prometheus.py): add input + output token tracking by tag
* feat(prometheus.py): add tag based deployment failure tracking
allows admin to track failure by use-case
* run azure testing on ci/cd
* update docs on azure batches endpoints
* add input azure.jsonl
* refactor - use separate file for batches endpoints
* fixes for passing custom llm provider to /batch endpoints
* pass custom llm provider to files endpoints
* update azure batches doc
* add info for azure batches api
* update batches endpoints
* use simple helper for raising proxy exception
* update config.yml
* fix imports
* add type hints to get_litellm_params
* update get_litellm_params
* update get_litellm_params
* update get slp
* QOL - stop double logging a create batch operations on custom loggers
* re use slp from og event
* _create_standard_logging_object_for_completed_batch
* fix linting errors
* reduce num changes in PR
* update BATCH_STATUS_POLL_MAX_ATTEMPTS
* fix(prometheus.py): support streaming end user litellm_proxy_total_requests_metric tracking
* fix(prometheus.py): add 'requested_model' and 'end_user_id' to 'litellm_request_total_latency_metric_bucket'
enables latency tracking by end user + requested model
* fix(prometheus.py): add end user, user and requested model metrics to 'litellm_llm_api_latency_metric'
* test: update prometheus unit tests
* test(test_prometheus.py): update tests
* test(test_prometheus.py): fix test
* test: reorder test
* build(model_prices_and_context_window.json): add gemini-1.5-flash context caching
* fix(context_caching/transformation.py): just use last identified cache point
Fixes https://github.com/BerriAI/litellm/issues/6738
* fix(context_caching/transformation.py): pick first contiguous block - handles system message error from google
Fixes https://github.com/BerriAI/litellm/issues/6738
* fix(vertex_ai/gemini/): track context caching tokens
* refactor(gemini/): place transformation.py inside `chat/` folder
make it easy for user to know we support the equivalent endpoint
* fix: fix import
* refactor(vertex_ai/): move vertex_ai cost calc inside vertex_ai/ folder
make it easier to see cost calculation logic
* fix: fix linting errors
* fix: fix circular import
* feat(gemini/cost_calculator.py): support gemini context caching cost calculation
generifies anthropic's cost calculation function and uses it across anthropic + gemini
* build(model_prices_and_context_window.json): add cost tracking for gemini-1.5-flash-002 w/ context caching
Closes https://github.com/BerriAI/litellm/issues/6891
* docs(gemini.md): add gemini context caching architecture diagram
make it easier for user to understand how context caching works
* docs(gemini.md): link to relevant gemini context caching code
* docs(gemini/context_caching): add readme in github, make it easy for dev to know context caching is supported + where to go for code
* fix(llm_cost_calc/utils.py): handle gemini 128k token diff cost calc scenario
* fix(deepseek/cost_calculator.py): support deepseek context caching cost calculation
* test: fix test
* fix(main.py): support 'mock_timeout=true' param
allows mock requests on proxy to have a time delay, for testing
* fix(main.py): ensure mock timeouts raise litellm.Timeout error
triggers retry/fallbacks
* fix: fix fallback + mock timeout testing
* fix(router.py): always return remaining tpm/rpm limits, if limits are known
allows for rate limit headers to be guaranteed
* docs(timeout.md): add docs on mock timeout = true
* fix(main.py): fix linting errors
* test: fix test
* fix(utils.py): e2e azure tts cost tracking working
moves tts response obj to include hidden params (allows for litellm call id, etc. to be sent in response headers) ; fixes spend_Tracking_utils logging payload to account for non-base model use-case
Fixes https://github.com/BerriAI/litellm/issues/7223
* fix: fix linting errors
* build(model_prices_and_context_window.json): add bedrock llama 3.3
Closes https://github.com/BerriAI/litellm/issues/7329
* fix(openai.py): fix return type for sync openai httpx response
* test: update test
* fix(spend_tracking_utils.py): fix if check
* fix(spend_tracking_utils.py): fix if check
* test: improve debugging for test
* fix: fix import
* fix(proxy_track_cost_callback.py): log to db if only end user param given
* fix: allows for jwt-auth based end user id spend tracking to work
* fix(utils.py): fix 'get_end_user_id_for_cost_tracking' to use 'user_api_key_end_user_id'
more stable - works with jwt-auth based end user tracking as well
* test(test_jwt.py): add e2e unit test to confirm end user cost tracking works for spend logs
* test: update test to use end_user api key hash param
* fix(langfuse.py): support end user cost tracking via jwt auth + langfuse
logs end user to langfuse if decoded from jwt token
* fix: fix linting errors
* test: fix test
* test: fix test
* fix: fix end user id extraction
* fix: run test earlier
* fix(health.md): add rerank model health check information
* build(model_prices_and_context_window.json): add gemini 2.0 for google ai studio - pricing + commercial rate limits
* build(model_prices_and_context_window.json): add gemini-2.0 supports audio output = true
* docs(team_model_add.md): clarify allowing teams to add models is an enterprise feature
* fix(o1_transformation.py): add support for 'n', 'response_format' and 'stop' params for o1 and 'stream_options' param for o1-mini
* build(model_prices_and_context_window.json): add 'supports_system_message' to supporting openai models
needed as o1-preview, and o1-mini models don't support 'system message
* fix(o1_transformation.py): translate system message based on if o1 model supports it
* fix(o1_transformation.py): return 'stream' param support if o1-mini/o1-preview
o1 currently doesn't support streaming, but the other model versions do
Fixes https://github.com/BerriAI/litellm/issues/7292
* fix(o1_transformation.py): return tool calling/response_format in supported params if model map says so
Fixes https://github.com/BerriAI/litellm/issues/7292
* fix: fix linting errors
* fix: update '_transform_messages'
* fix(o1_transformation.py): fix provider passed for supported param checks
* test(base_llm_unit_tests.py): skip test if api takes >5s to respond
* fix(utils.py): return false in 'supports_factory' if can't find value
* fix(o1_transformation.py): always return stream + stream_options as supported params + handle stream options being passed in for azure o1
* feat(openai.py): support stream faking natively in openai handler
Allows o1 calls to be faked for just the "o1" model, allows native streaming for o1-mini, o1-preview
Fixes https://github.com/BerriAI/litellm/issues/7292
* fix(openai.py): use inference param instead of original optional param
* fix(factory.py): skip empty text blocks for bedrock user messages
Fixes https://github.com/BerriAI/litellm/issues/7169
* Add support for Gemini 2.0 GoogleSearch tool (#7257)
* Add support for google_search tool in gemini 2.0
* Add/modify tests
* Fix grounding check
* Remove 2.0 grounding test; exclude experimental model in VERTEX_MODELS_TO_NOT_TEST
* Swap order of tools
* DFix formatting
* fix(get_api_base.py): return api base in streaming response
Fixes https://github.com/BerriAI/litellm/issues/7249
Closes https://github.com/BerriAI/litellm/pull/7250
* fix(cost_calculator.py): only set base model to model if not none
Fixes https://github.com/BerriAI/litellm/issues/7223
* fix(cost_calculator.py): enforce stricter order when picking model for cost calculation
* fix(cost_calculator.py): fix '_select_model_name_for_cost_calc' to return model name with region name prefix if provided
* fix(utils.py): fix 'get_model_info()' to handle edge case where model name starts with custom llm provider AND custom llm provider is given
* fix(cost_calculator.py): handle `custom_llm_provider-` scenario
* fix(cost_calculator.py): e2e working tts cost tracking
ensures initial message is passed in, to cost calculator
* fix(factory.py): suppress linting errors
* fix(cost_calculator.py): strip llm provider from model name after selecting cost calc model
* fix(litellm_logging.py): store initial request in 'input' field + accept base_model to be passed in litellm_params directly
* test: handle none env var value in flaky test
* fix(litellm_logging.py): fix linting errors
---------
Co-authored-by: Sam B <samlingx@gmail.com>
