* nvidia nim support embedding config
* add nvidia config in init
* nvidia nim embeddings
* docs nvidia nim embeddings
* docs embeddings on nvidia nim
* fix llm translation test
* init litellm langfuse / gcs credentials in litellm logging obj
* add gcs key based test
* rename vars
* save standard_callback_dynamic_params in model call details
* add working gcs bucket key based logging
* test_basic_gcs_logging_per_request
* linting fix
* add doc on gcs bucket team based logging
* fix doc on prometheus
* (docs) clean up prometheus docs
* docs show what metrics are deprectaed
* doc clarify labels used for bduget metrics
* add litellm_remaining_api_key_requests_for_model
* fix parallel request limiter - use one cache update call
* ci/cd run again
* run ci/cd again
* use docker username password
* fix config.yml
* fix config
* fix config
* fix config.yml
* ci/cd run again
* use correct typing for batch set cache
* fix async_set_cache_pipeline
* fix only check user id tpm / rpm limits when limits set
* fix test_openai_azure_embedding_with_oidc_and_cf
* add InstanceImage type
* fix vertex image transform
* add langchain vertex test request
* add new vertex test
* update multimodal embedding tests
* add test_vertexai_multimodal_embedding_base64image_in_input
* simplify langchain mm embedding usage
* add langchain example for multimodal embeddings on vertex
* fix linting error
* add check deployment_is_active_for_environment
* add test for test_init_router_with_supported_environments
* show good example config for environments
* docs clean up config.yaml
* docs cleanup
* docs configs
* docs specfic env
* LiteLLM Minor Fixes & Improvements (09/23/2024) (#5842)
* feat(auth_utils.py): enable admin to allow client-side credentials to be passed
Makes it easier for devs to experiment with finetuned fireworks ai models
* feat(router.py): allow setting configurable_clientside_auth_params for a model
Closes https://github.com/BerriAI/litellm/issues/5843
* build(model_prices_and_context_window.json): fix anthropic claude-3-5-sonnet max output token limit
Fixes https://github.com/BerriAI/litellm/issues/5850
* fix(azure_ai/): support content list for azure ai
Fixes https://github.com/BerriAI/litellm/issues/4237
* fix(litellm_logging.py): always set saved_cache_cost
Set to 0 by default
* fix(fireworks_ai/cost_calculator.py): add fireworks ai default pricing
handles calling 405b+ size models
* fix(slack_alerting.py): fix error alerting for failed spend tracking
Fixes regression with slack alerting error monitoring
* fix(vertex_and_google_ai_studio_gemini.py): handle gemini no candidates in streaming chunk error
* docs(bedrock.md): add llama3-1 models
* test: fix tests
* fix(azure_ai/chat): fix transformation for azure ai calls
* use /user/list endpoint on admin ui
* sso insert user with role when user does not exist
* add sso sign in test
* linting fix
* rename self serve doc
* add doc for self serve flow
* test - sso sign in default values
* add test for /user/list endpoint
* add test for using images with custom openai endpoints
* run all otel tests
* update name of test
* add custom openai model to test config
* add test for setting supports_vision=True for model
* fix test guardrails aporia
* docs supports vison
* fix yaml
* fix yaml
* docs supports vision
* fix bedrock guardrail test
* fix cohere rerank test
* update model_group doc string
* add better prints on test
* fix(vertex_llm_base.py): Handle api_base = ""
Fixes https://github.com/BerriAI/litellm/issues/5798
* fix(o1_transformation.py): handle stream_options not being supported
https://github.com/BerriAI/litellm/issues/5803
* docs(routing.md): fix docs
Closes https://github.com/BerriAI/litellm/issues/5808
* perf(internal_user_endpoints.py): reduce db calls for getting team_alias for a key
Use the list gotten earlier in `/user/info` endpoint
Reduces ui keys tab load time to 800ms (prev. 28s+)
* feat(proxy_server.py): support CONFIG_FILE_PATH as env var
Closes https://github.com/BerriAI/litellm/issues/5744
* feat(get_llm_provider_logic.py): add `litellm_proxy/` as a known openai-compatible route
simplifies calling litellm proxy
Reduces confusion when calling models on litellm proxy from litellm sdk
* docs(litellm_proxy.md): cleanup docs
* fix(internal_user_endpoints.py): fix pydantic obj
* test(test_key_generate_prisma.py): fix test
