* fix(core_helpers.py): handle litellm_metadata instead of 'metadata'
* feat(batches/): ensure batches logs are written to db
makes batches response dict compatible
* fix(cost_calculator.py): handle batch response being a dictionary
* fix(batches/main.py): modify retrieve endpoints to use @client decorator
enables logging to work on retrieve call
* fix(batches/main.py): fix retrieve batch response type to be 'dict' compatible
* fix(spend_tracking_utils.py): send unique uuid for retrieve batch call type
create batch and retrieve batch share the same id
* fix(spend_tracking_utils.py): prevent duplicate retrieve batch calls from being double counted
* refactor(batches/): refactor cost tracking for batches - do it on retrieve, and within the established litellm_logging pipeline
ensures cost is always logged to db
* fix: fix linting errors
* fix: fix linting error
* fix(azure/chat/gpt_transformation.py): add 'prediction' as a support azure param
Closes https://github.com/BerriAI/litellm/issues/8500
* build(model_prices_and_context_window.json): add new 'gemini-2.0-pro-exp-02-05' model
* style: cleanup invalid json trailing commma
* feat(utils.py): support passing 'tokenizer_config' to register_prompt_template
enables passing complete tokenizer config of model to litellm
Allows calling deepseek on bedrock with the correct prompt template
* fix(utils.py): fix register_prompt_template for custom model names
* test(test_prompt_factory.py): fix test
* test(test_completion.py): add e2e test for bedrock invoke deepseek ft model
* feat(base_invoke_transformation.py): support hf_model_name param for bedrock invoke calls
enables proxy admin to set base model for ft bedrock deepseek model
* feat(bedrock/invoke): support deepseek_r1 route for bedrock
makes it easy to apply the right chat template to that call
* feat(constants.py): store deepseek r1 chat template - allow user to get correct response from deepseek r1 without extra work
* test(test_completion.py): add e2e mock test for bedrock deepseek
* docs(bedrock.md): document new deepseek_r1 route for bedrock
allows us to use the right config
* fix(exception_mapping_utils.py): catch read operation timeout
* fix(caching_routes.py): mask redis password on `/cache/ping` route
* fix(caching_routes.py): fix linting erro
* fix(caching_routes.py): fix linting error on caching routes
* fix: fix test - ignore mask_dict - has a breakpoint
* fix(azure.py): add timeout param + elapsed time in azure timeout error
* fix(http_handler.py): add elapsed time to http timeout request
makes it easier to debug how long request took before failing
* fix(azure.py): ensure max_retries=0 is respected
Fixes https://github.com/BerriAI/litellm/issues/6129
* fix(test_openai.py): add unit test to ensure openai sdk calls always respect max_retries = 0
* test(test_azure_openai.py): add unit testing for azure_text/ route
* fix(azure.py): fix passing max retries on streaming
* fix(azure.py): fix azure max retries on async completion + streaming
* fix(completion/handler.py): fix azure text async completion + streaming
* test(test_azure_openai.py): ensure azure openai max retries always respected
* test(test_azure_o_series.py): add testing to ensure max retries always respected
* Added gemini providers for 2.0-flash and 2.0-flash lite (#8321)
* Update model_prices_and_context_window.json
added gemini providers for 2.0-flash and 2.0-flash light
* Update model_prices_and_context_window.json
fixed URL
---------
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* Convert tool use arguments to string before counting tokens (#6989)
In at least some cases the `messages["tool_calls"]["function"]["arguments"]` is a dict, not a string. In order to tokenize it properly it needs to be a string. In the case that it is already a string this is a noop, which is also fine.
* build(model_prices_and_context_window.json): add gemini 2.0 flash lite pricing
* build(model_prices_and_context_window.json): add gemini commercial rate limits
* fix(utils.py): fix linting error
* refactor(utils.py): refactor to maintain function size
---------
Co-authored-by: Bardia Khosravi <bardiakhosravi95@gmail.com>
Co-authored-by: Josh Morrow <josh@jcmorrow.com>
* fix(convert_dict_to_response.py): only convert if response is the response_format tool call passed in
Fixes https://github.com/BerriAI/litellm/issues/8241
* fix(gpt_transformation.py): makes sure response format / tools conversion doesn't remove previous tool calls
* refactor(gpt_transformation.py): refactor out json schema converstion to base config
keeps logic consistent across providers
* fix(o_series_transformation.py): support o3 mini native streaming
Fixes https://github.com/BerriAI/litellm/issues/8274
* fix(gpt_transformation.py): remove unused variables
* test: update test
* fix(vertex_ai/gemini/transformation.py): handle 'http://' image urls
* test: add base test for `http:` url's
* fix(factory.py/get_image_details): follow redirects
allows http calls to work
* fix(codestral/): fix stream chunk parsing on last chunk of stream
* Azure ad token provider (#6917)
* Update azure.py
Added optional parameter azure ad token provider
* Added parameter to main.py
* Found token provider arg location
* Fixed embeddings
* Fixed ad token provider
---------
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* fix: fix linting errors
* fix(main.py): leave out o1 route for azure ad token provider, for now
get v0 out for sync azure gpt route to begin with
* test: skip http:// test for fireworks ai
model does not support it
* refactor: cleanup dead code
* fix: revert http:// url passthrough for gemini
google ai studio raises errors
* test: fix test
---------
Co-authored-by: bahtman <anton@baht.dk>
* fix(o_series_transformation.py): add 'reasoning_effort' as o series model param
Closes https://github.com/BerriAI/litellm/issues/8182
* fix(main.py): ensure `reasoning_effort` is a mapped openai param
* refactor(azure/): rename o1_[x] files to o_series_[x]
* refactor(base_llm_unit_tests.py): refactor testing for o series reasoning effort
* test(test_azure_o_series.py): have azure o series tests correctly inherit from base o series model tests
* feat(base_utils.py): support translating 'developer' role to 'system' role for non-openai providers
Makes it easy to switch from openai to anthropic
* fix: fix linting errors
* fix(base_llm_unit_tests.py): fix test
* fix(main.py): add missing param
* fix: support azure o3 model family for fake streaming workaround (#8162)
* fix: support azure o3 model family for fake streaming workaround
* refactor: rename helper to is_o_series_model for clarity
* update function calling parameters for o3 models (#8178)
* refactor(o1_transformation.py): refactor o1 config to be o series config, expand o series model check to o3
ensures max_tokens is correctly translated for o3
* feat(openai/): refactor o1 files to be 'o_series' files
expands naming to cover o3
* fix(azure/chat/o1_handler.py): azure openai is an instance of openai - was causing resets
* test(test_azure_o_series.py): assert stream faked for azure o3 mini
Resolves https://github.com/BerriAI/litellm/pull/8162
* fix(o1_transformation.py): fix o1 transformation logic to handle explicit o1_series routing
* docs(azure.md): update doc with `o_series/` model name
---------
Co-authored-by: byrongrogan <47910641+byrongrogan@users.noreply.github.com>
Co-authored-by: Low Jian Sheng <15527690+lowjiansheng@users.noreply.github.com>
* fix(gpt_transformation.py): fix response_format translation check for 4o models
Fixes https://github.com/BerriAI/litellm/issues/7616
* feat(key_management_endpoints.py): support 'temp_budget_increase' and 'temp_budget_expiry' fields
Allow proxy admin to grant temporary budget increases to keys
* fix(proxy/_types.py): enforce temp_budget_increase and temp_budget_expiry are always passed together
* feat(user_api_key_auth.py): initial working temp budget increase logic
ensures key budget exceeded error checks for temp budget in key metadata
* feat(proxy_server.py): return the key max budget and key spend in the response headers
Allows clientside user to know their remaining limits
* test: add unit testing for new proxy utils
Ensures new key budget is correctly handled
* docs(temporary_budget_increase.md): add doc on temporary budget increase
* fix(utils.py): remove 3.5 from response_format check for now
not all azure 3.5 models support response_format
* fix(user_api_key_auth.py): return valid user api key auth object on all paths
* 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
* 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
* init commit ft jobs logging
* add ft logging
* add logging for FineTuningJob
* simple FT Job create test
* simplify Azure fine tuning to use all methods in OAI ft
* update doc string
* add aretrieve_fine_tuning_job
* re use from litellm.proxy.utils import handle_exception_on_proxy
* fix naming
* add /fine_tuning/jobs/{fine_tuning_job_id:path}
* remove unused imports
* update func signature
* run ci/cd again
* ci/cd run again
* fix code qulity
* ci/cd run again
* use 1 file for azure batches handling
* add cancel_batch endpoint
* add a cancel batch on open ai
* add cancel_batch endpoint
* add cancel batches to test
* remove unused imports
* test_batches_operations
* update test_batches_operations
* 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(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(azure/): support passing headers to azure openai endpoints
Fixes https://github.com/BerriAI/litellm/issues/6217
* fix(utils.py): move default tokenizer to just openai
hf tokenizer makes network calls when trying to get the tokenizer - this slows down execution time calls
* fix(router.py): fix pattern matching router - add generic "*" to it as well
Fixes issue where generic "*" model access group wouldn't show up
* fix(pattern_match_deployments.py): match to more specific pattern
match to more specific pattern
allows setting generic wildcard model access group and excluding specific models more easily
* fix(proxy_server.py): fix _delete_deployment to handle base case where db_model list is empty
don't delete all router models b/c of empty list
Fixes https://github.com/BerriAI/litellm/issues/7196
* fix(anthropic/): fix handling response_format for anthropic messages with anthropic api
* fix(fireworks_ai/): support passing response_format + tool call in same message
Addresses https://github.com/BerriAI/litellm/issues/7135
* Revert "fix(fireworks_ai/): support passing response_format + tool call in same message"
This reverts commit 6a30dc6929.
* test: fix test
* fix(replicate/): fix replicate default retry/polling logic
* test: add unit testing for router pattern matching
* test: update test to use default oai tokenizer
* test: mark flaky test
* test: skip flaky test
* fix(main.py): support passing max retries to azure/openai embedding integrations
Fixes https://github.com/BerriAI/litellm/issues/7003
* feat(team_endpoints.py): allow updating team model aliases
Closes https://github.com/BerriAI/litellm/issues/6956
* feat(router.py): allow specifying model id as fallback - skips any cooldown check
Allows a default model to be checked if all models in cooldown
s/o @micahjsmith
* docs(reliability.md): add fallback to specific model to docs
* fix(utils.py): new 'is_prompt_caching_valid_prompt' helper util
Allows user to identify if messages/tools have prompt caching
Related issue: https://github.com/BerriAI/litellm/issues/6784
* feat(router.py): store model id for prompt caching valid prompt
Allows routing to that model id on subsequent requests
* fix(router.py): only cache if prompt is valid prompt caching prompt
prevents storing unnecessary items in cache
* feat(router.py): support routing prompt caching enabled models to previous deployments
Closes https://github.com/BerriAI/litellm/issues/6784
* test: fix linting errors
* feat(databricks/): convert basemodel to dict and exclude none values
allow passing pydantic message to databricks
* fix(utils.py): ensure all chat completion messages are dict
* (feat) Track `custom_llm_provider` in LiteLLMSpendLogs (#7081)
* add custom_llm_provider to SpendLogsPayload
* add custom_llm_provider to SpendLogs
* add custom llm provider to SpendLogs payload
* test_spend_logs_payload
* Add MLflow to the side bar (#7031)
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
* (bug fix) SpendLogs update DB catch all possible DB errors for retrying (#7082)
* catch DB_CONNECTION_ERROR_TYPES
* fix DB retry mechanism for SpendLog updates
* use DB_CONNECTION_ERROR_TYPES in auth checks
* fix exp back off for writing SpendLogs
* use _raise_failed_update_spend_exception to ensure errors print as NON blocking
* test_update_spend_logs_multiple_batches_with_failure
* (Feat) Add StructuredOutputs support for Fireworks.AI (#7085)
* fix model cost map fireworks ai "supports_response_schema": true,
* fix supports_response_schema
* fix map openai params fireworks ai
* test_map_response_format
* test_map_response_format
* added deepinfra/Meta-Llama-3.1-405B-Instruct (#7084)
* bump: version 1.53.9 → 1.54.0
* fix deepinfra
* litellm db fixes LiteLLM_UserTable (#7089)
* ci/cd queue new release
* fix llama-3.3-70b-versatile
* refactor - use consistent file naming convention `AI21/` -> `ai21` (#7090)
* fix refactor - use consistent file naming convention
* ci/cd run again
* fix naming structure
* fix use consistent naming (#7092)
---------
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com>
Co-authored-by: ali sayyah <ali.sayyah2@gmail.com>
