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litellm-mirror/litellm/constants.py
Krish Dholakia 36308a31be
Gemini-2.5-flash - support reasoning cost calc + return reasoning content (#10141) 
* build(model_prices_and_context_window.json): add vertex ai gemini-2.5-flash pricing

* build(model_prices_and_context_window.json): add gemini reasoning token pricing

* fix(vertex_and_google_ai_studio_gemini.py): support counting thinking tokens for gemini

allows accurate cost calc

* fix(utils.py): add reasoning token cost calc to generic cost calc

ensures gemini-2.5-flash cost calculation is accurate

* build(model_prices_and_context_window.json): mark gemini-2.5-flash as 'supports_reasoning'

* feat(gemini/): support 'thinking' + 'reasoning_effort' params + new unit tests

allow controlling thinking effort for gemini-2.5-flash models

* test: update unit testing

* feat(vertex_and_google_ai_studio_gemini.py): return reasoning content if given in gemini response

* test: update model name

* fix: fix ruff check

* test(test_spend_management_endpoints.py): update tests to be less sensitive to new keys / updates to usage object

* fix(vertex_and_google_ai_studio_gemini.py): fix translation
2025-04-19 09:20:52 -07:00

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from typing import List, Literal
ROUTER_MAX_FALLBACKS = 5
DEFAULT_BATCH_SIZE = 512
DEFAULT_FLUSH_INTERVAL_SECONDS = 5
DEFAULT_MAX_RETRIES = 2
DEFAULT_MAX_RECURSE_DEPTH = 10
DEFAULT_FAILURE_THRESHOLD_PERCENT = (
0.5 # default cooldown a deployment if 50% of requests fail in a given minute
)
DEFAULT_MAX_TOKENS = 4096
DEFAULT_ALLOWED_FAILS = 3
DEFAULT_REDIS_SYNC_INTERVAL = 1
DEFAULT_COOLDOWN_TIME_SECONDS = 5
DEFAULT_REPLICATE_POLLING_RETRIES = 5
DEFAULT_REPLICATE_POLLING_DELAY_SECONDS = 1
DEFAULT_IMAGE_TOKEN_COUNT = 250
DEFAULT_IMAGE_WIDTH = 300
DEFAULT_IMAGE_HEIGHT = 300
DEFAULT_MAX_TOKENS = 256 # used when providers need a default
MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = 1024 # 1MB = 1024KB
SINGLE_DEPLOYMENT_TRAFFIC_FAILURE_THRESHOLD = 1000 # Minimum number of requests to consider "reasonable traffic". Used for single-deployment cooldown logic.
DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET = 1024
DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET = 2048
DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET = 4096
########## Networking constants ##############################################################
_DEFAULT_TTL_FOR_HTTPX_CLIENTS = 3600 # 1 hour, re-use the same httpx client for 1 hour
########### v2 Architecture constants for managing writing updates to the database ###########
REDIS_UPDATE_BUFFER_KEY = "litellm_spend_update_buffer"
REDIS_DAILY_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_spend_update_buffer"
REDIS_DAILY_TEAM_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_team_spend_update_buffer"
REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_tag_spend_update_buffer"
MAX_REDIS_BUFFER_DEQUEUE_COUNT = 100
MAX_SIZE_IN_MEMORY_QUEUE = 10000
MAX_IN_MEMORY_QUEUE_FLUSH_COUNT = 1000
###############################################################################################
MINIMUM_PROMPT_CACHE_TOKEN_COUNT = (
1024 # minimum number of tokens to cache a prompt by Anthropic
)
DEFAULT_TRIM_RATIO = 0.75 # default ratio of tokens to trim from the end of a prompt
HOURS_IN_A_DAY = 24
DAYS_IN_A_WEEK = 7
DAYS_IN_A_MONTH = 28
DAYS_IN_A_YEAR = 365
REPLICATE_MODEL_NAME_WITH_ID_LENGTH = 64
#### TOKEN COUNTING ####
FUNCTION_DEFINITION_TOKEN_COUNT = 9
SYSTEM_MESSAGE_TOKEN_COUNT = 4
TOOL_CHOICE_OBJECT_TOKEN_COUNT = 4
DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT = 10
DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT = 20
MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES = 768
MAX_LONG_SIDE_FOR_IMAGE_HIGH_RES = 2000
MAX_TILE_WIDTH = 512
MAX_TILE_HEIGHT = 512
OPENAI_FILE_SEARCH_COST_PER_1K_CALLS = 2.5 / 1000
MIN_NON_ZERO_TEMPERATURE = 0.0001
#### RELIABILITY ####
REPEATED_STREAMING_CHUNK_LIMIT = 100 # catch if model starts looping the same chunk while streaming. Uses high default to prevent false positives.
DEFAULT_MAX_LRU_CACHE_SIZE = 16
INITIAL_RETRY_DELAY = 0.5
MAX_RETRY_DELAY = 8.0
JITTER = 0.75
DEFAULT_IN_MEMORY_TTL = 5 # default time to live for the in-memory cache
DEFAULT_POLLING_INTERVAL = 0.03 # default polling interval for the scheduler
AZURE_OPERATION_POLLING_TIMEOUT = 120
REDIS_SOCKET_TIMEOUT = 0.1
REDIS_CONNECTION_POOL_TIMEOUT = 5
NON_LLM_CONNECTION_TIMEOUT = 15 # timeout for adjacent services (e.g. jwt auth)
MAX_EXCEPTION_MESSAGE_LENGTH = 2000
BEDROCK_MAX_POLICY_SIZE = 75
REPLICATE_POLLING_DELAY_SECONDS = 0.5
DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS = 4096
TOGETHER_AI_4_B = 4
TOGETHER_AI_8_B = 8
TOGETHER_AI_21_B = 21
TOGETHER_AI_41_B = 41
TOGETHER_AI_80_B = 80
TOGETHER_AI_110_B = 110
TOGETHER_AI_EMBEDDING_150_M = 150
TOGETHER_AI_EMBEDDING_350_M = 350
QDRANT_SCALAR_QUANTILE = 0.99
QDRANT_VECTOR_SIZE = 1536
CACHED_STREAMING_CHUNK_DELAY = 0.02
MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = 512
DEFAULT_MAX_TOKENS_FOR_TRITON = 2000
#### Networking settings ####
request_timeout: float = 6000 # time in seconds
STREAM_SSE_DONE_STRING: str = "[DONE]"
### SPEND TRACKING ###
DEFAULT_REPLICATE_GPU_PRICE_PER_SECOND = 0.001400 # price per second for a100 80GB
FIREWORKS_AI_56_B_MOE = 56
FIREWORKS_AI_176_B_MOE = 176
FIREWORKS_AI_16_B = 16
FIREWORKS_AI_80_B = 80
LITELLM_CHAT_PROVIDERS = [
"openai",
"openai_like",
"xai",
"custom_openai",
"text-completion-openai",
"cohere",
"cohere_chat",
"clarifai",
"anthropic",
"anthropic_text",
"replicate",
"huggingface",
"together_ai",
"openrouter",
"vertex_ai",
"vertex_ai_beta",
"gemini",
"ai21",
"baseten",
"azure",
"azure_text",
"azure_ai",
"sagemaker",
"sagemaker_chat",
"bedrock",
"vllm",
"nlp_cloud",
"petals",
"oobabooga",
"ollama",
"ollama_chat",
"deepinfra",
"perplexity",
"mistral",
"groq",
"nvidia_nim",
"cerebras",
"ai21_chat",
"volcengine",
"codestral",
"text-completion-codestral",
"deepseek",
"sambanova",
"maritalk",
"cloudflare",
"fireworks_ai",
"friendliai",
"watsonx",
"watsonx_text",
"triton",
"predibase",
"databricks",
"empower",
"github",
"custom",
"litellm_proxy",
"hosted_vllm",
"lm_studio",
"galadriel",
]
OPENAI_CHAT_COMPLETION_PARAMS = [
"functions",
"function_call",
"temperature",
"temperature",
"top_p",
"n",
"stream",
"stream_options",
"stop",
"max_completion_tokens",
"modalities",
"prediction",
"audio",
"max_tokens",
"presence_penalty",
"frequency_penalty",
"logit_bias",
"user",
"request_timeout",
"api_base",
"api_version",
"api_key",
"deployment_id",
"organization",
"base_url",
"default_headers",
"timeout",
"response_format",
"seed",
"tools",
"tool_choice",
"max_retries",
"parallel_tool_calls",
"logprobs",
"top_logprobs",
"reasoning_effort",
"extra_headers",
"thinking",
]
openai_compatible_endpoints: List = [
"api.perplexity.ai",
"api.endpoints.anyscale.com/v1",
"api.deepinfra.com/v1/openai",
"api.mistral.ai/v1",
"codestral.mistral.ai/v1/chat/completions",
"codestral.mistral.ai/v1/fim/completions",
"api.groq.com/openai/v1",
"https://integrate.api.nvidia.com/v1",
"api.deepseek.com/v1",
"api.together.xyz/v1",
"app.empower.dev/api/v1",
"https://api.friendli.ai/serverless/v1",
"api.sambanova.ai/v1",
"api.x.ai/v1",
"api.galadriel.ai/v1",
]
openai_compatible_providers: List = [
"anyscale",
"mistral",
"groq",
"nvidia_nim",
"cerebras",
"sambanova",
"ai21_chat",
"ai21",
"volcengine",
"codestral",
"deepseek",
"deepinfra",
"perplexity",
"xinference",
"xai",
"together_ai",
"fireworks_ai",
"empower",
"friendliai",
"azure_ai",
"github",
"litellm_proxy",
"hosted_vllm",
"lm_studio",
"galadriel",
]
openai_text_completion_compatible_providers: List = (
[ # providers that support `/v1/completions`
"together_ai",
"fireworks_ai",
"hosted_vllm",
]
)
_openai_like_providers: List = [
"predibase",
"databricks",
"watsonx",
] # private helper. similar to openai but require some custom auth / endpoint handling, so can't use the openai sdk
# well supported replicate llms
replicate_models: List = [
# llama replicate supported LLMs
"replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf",
"a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52",
"meta/codellama-13b:1c914d844307b0588599b8393480a3ba917b660c7e9dfae681542b5325f228db",
# Vicuna
"replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b",
"joehoover/instructblip-vicuna13b:c4c54e3c8c97cd50c2d2fec9be3b6065563ccf7d43787fb99f84151b867178fe",
# Flan T-5
"daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f",
# Others
"replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5",
"replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad",
]
clarifai_models: List = [
"clarifai/meta.Llama-3.Llama-3-8B-Instruct",
"clarifai/gcp.generate.gemma-1_1-7b-it",
"clarifai/mistralai.completion.mixtral-8x22B",
"clarifai/cohere.generate.command-r-plus",
"clarifai/databricks.drbx.dbrx-instruct",
"clarifai/mistralai.completion.mistral-large",
"clarifai/mistralai.completion.mistral-medium",
"clarifai/mistralai.completion.mistral-small",
"clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1",
"clarifai/gcp.generate.gemma-2b-it",
"clarifai/gcp.generate.gemma-7b-it",
"clarifai/deci.decilm.deciLM-7B-instruct",
"clarifai/mistralai.completion.mistral-7B-Instruct",
"clarifai/gcp.generate.gemini-pro",
"clarifai/anthropic.completion.claude-v1",
"clarifai/anthropic.completion.claude-instant-1_2",
"clarifai/anthropic.completion.claude-instant",
"clarifai/anthropic.completion.claude-v2",
"clarifai/anthropic.completion.claude-2_1",
"clarifai/meta.Llama-2.codeLlama-70b-Python",
"clarifai/meta.Llama-2.codeLlama-70b-Instruct",
"clarifai/openai.completion.gpt-3_5-turbo-instruct",
"clarifai/meta.Llama-2.llama2-7b-chat",
"clarifai/meta.Llama-2.llama2-13b-chat",
"clarifai/meta.Llama-2.llama2-70b-chat",
"clarifai/openai.chat-completion.gpt-4-turbo",
"clarifai/microsoft.text-generation.phi-2",
"clarifai/meta.Llama-2.llama2-7b-chat-vllm",
"clarifai/upstage.solar.solar-10_7b-instruct",
"clarifai/openchat.openchat.openchat-3_5-1210",
"clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B",
"clarifai/gcp.generate.text-bison",
"clarifai/meta.Llama-2.llamaGuard-7b",
"clarifai/fblgit.una-cybertron.una-cybertron-7b-v2",
"clarifai/openai.chat-completion.GPT-4",
"clarifai/openai.chat-completion.GPT-3_5-turbo",
"clarifai/ai21.complete.Jurassic2-Grande",
"clarifai/ai21.complete.Jurassic2-Grande-Instruct",
"clarifai/ai21.complete.Jurassic2-Jumbo-Instruct",
"clarifai/ai21.complete.Jurassic2-Jumbo",
"clarifai/ai21.complete.Jurassic2-Large",
"clarifai/cohere.generate.cohere-generate-command",
"clarifai/wizardlm.generate.wizardCoder-Python-34B",
"clarifai/wizardlm.generate.wizardLM-70B",
"clarifai/tiiuae.falcon.falcon-40b-instruct",
"clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat",
"clarifai/gcp.generate.code-gecko",
"clarifai/gcp.generate.code-bison",
"clarifai/mistralai.completion.mistral-7B-OpenOrca",
"clarifai/mistralai.completion.openHermes-2-mistral-7B",
"clarifai/wizardlm.generate.wizardLM-13B",
"clarifai/huggingface-research.zephyr.zephyr-7B-alpha",
"clarifai/wizardlm.generate.wizardCoder-15B",
"clarifai/microsoft.text-generation.phi-1_5",
"clarifai/databricks.Dolly-v2.dolly-v2-12b",
"clarifai/bigcode.code.StarCoder",
"clarifai/salesforce.xgen.xgen-7b-8k-instruct",
"clarifai/mosaicml.mpt.mpt-7b-instruct",
"clarifai/anthropic.completion.claude-3-opus",
"clarifai/anthropic.completion.claude-3-sonnet",
"clarifai/gcp.generate.gemini-1_5-pro",
"clarifai/gcp.generate.imagen-2",
"clarifai/salesforce.blip.general-english-image-caption-blip-2",
]
huggingface_models: List = [
"meta-llama/Llama-2-7b-hf",
"meta-llama/Llama-2-7b-chat-hf",
"meta-llama/Llama-2-13b-hf",
"meta-llama/Llama-2-13b-chat-hf",
"meta-llama/Llama-2-70b-hf",
"meta-llama/Llama-2-70b-chat-hf",
"meta-llama/Llama-2-7b",
"meta-llama/Llama-2-7b-chat",
"meta-llama/Llama-2-13b",
"meta-llama/Llama-2-13b-chat",
"meta-llama/Llama-2-70b",
"meta-llama/Llama-2-70b-chat",
] # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/providers
empower_models = [
"empower/empower-functions",
"empower/empower-functions-small",
]
together_ai_models: List = [
# llama llms - chat
"togethercomputer/llama-2-70b-chat",
# llama llms - language / instruct
"togethercomputer/llama-2-70b",
"togethercomputer/LLaMA-2-7B-32K",
"togethercomputer/Llama-2-7B-32K-Instruct",
"togethercomputer/llama-2-7b",
# falcon llms
"togethercomputer/falcon-40b-instruct",
"togethercomputer/falcon-7b-instruct",
# alpaca
"togethercomputer/alpaca-7b",
# chat llms
"HuggingFaceH4/starchat-alpha",
# code llms
"togethercomputer/CodeLlama-34b",
"togethercomputer/CodeLlama-34b-Instruct",
"togethercomputer/CodeLlama-34b-Python",
"defog/sqlcoder",
"NumbersStation/nsql-llama-2-7B",
"WizardLM/WizardCoder-15B-V1.0",
"WizardLM/WizardCoder-Python-34B-V1.0",
# language llms
"NousResearch/Nous-Hermes-Llama2-13b",
"Austism/chronos-hermes-13b",
"upstage/SOLAR-0-70b-16bit",
"WizardLM/WizardLM-70B-V1.0",
] # supports all together ai models, just pass in the model id e.g. completion(model="together_computer/replit_code_3b",...)
baseten_models: List = [
"qvv0xeq",
"q841o8w",
"31dxrj3",
] # FALCON 7B # WizardLM # Mosaic ML
BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[
"cohere",
"anthropic",
"mistral",
"amazon",
"meta",
"llama",
"ai21",
"nova",
"deepseek_r1",
]
open_ai_embedding_models: List = ["text-embedding-ada-002"]
cohere_embedding_models: List = [
"embed-english-v3.0",
"embed-english-light-v3.0",
"embed-multilingual-v3.0",
"embed-english-v2.0",
"embed-english-light-v2.0",
"embed-multilingual-v2.0",
]
bedrock_embedding_models: List = [
"amazon.titan-embed-text-v1",
"cohere.embed-english-v3",
"cohere.embed-multilingual-v3",
]
known_tokenizer_config = {
"mistralai/Mistral-7B-Instruct-v0.1": {
"tokenizer": {
"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
"bos_token": "<s>",
"eos_token": "</s>",
},
"status": "success",
},
"meta-llama/Meta-Llama-3-8B-Instruct": {
"tokenizer": {
"chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
"bos_token": "<|begin_of_text|>",
"eos_token": "",
},
"status": "success",
},
"deepseek-r1/deepseek-r1-7b-instruct": {
"tokenizer": {
"add_bos_token": True,
"add_eos_token": False,
"bos_token": {
"__type": "AddedToken",
"content": "<｜begin▁of▁sentence｜>",
"lstrip": False,
"normalized": True,
"rstrip": False,
"single_word": False,
},
"clean_up_tokenization_spaces": False,
"eos_token": {
"__type": "AddedToken",
"content": "<｜end▁of▁sentence｜>",
"lstrip": False,
"normalized": True,
"rstrip": False,
"single_word": False,
},
"legacy": True,
"model_max_length": 16384,
"pad_token": {
"__type": "AddedToken",
"content": "<｜end▁of▁sentence｜>",
"lstrip": False,
"normalized": True,
"rstrip": False,
"single_word": False,
},
"sp_model_kwargs": {},
"unk_token": None,
"tokenizer_class": "LlamaTokenizerFast",
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜><think>\\n'}}{% endif %}",
},
"status": "success",
},
}
OPENAI_FINISH_REASONS = ["stop", "length", "function_call", "content_filter", "null"]
HUMANLOOP_PROMPT_CACHE_TTL_SECONDS = 60 # 1 minute
RESPONSE_FORMAT_TOOL_NAME = "json_tool_call" # default tool name used when converting response format to tool call
########################### Logging Callback Constants ###########################
AZURE_STORAGE_MSFT_VERSION = "2019-07-07"
PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES = 5
MCP_TOOL_NAME_PREFIX = "mcp_tool"
########################### LiteLLM Proxy Specific Constants ###########################
########################################################################################
MAX_SPENDLOG_ROWS_TO_QUERY = (
1_000_000 # if spendLogs has more than 1M rows, do not query the DB
)
DEFAULT_SOFT_BUDGET = (
50.0 # by default all litellm proxy keys have a soft budget of 50.0
)
# makes it clear this is a rate limit error for a litellm virtual key
RATE_LIMIT_ERROR_MESSAGE_FOR_VIRTUAL_KEY = "LiteLLM Virtual Key user_api_key_hash"
# pass through route constansts
BEDROCK_AGENT_RUNTIME_PASS_THROUGH_ROUTES = [
"agents/",
"knowledgebases/",
"flows/",
"retrieveAndGenerate/",
"rerank/",
"generateQuery/",
"optimize-prompt/",
]
BATCH_STATUS_POLL_INTERVAL_SECONDS = 3600 # 1 hour
BATCH_STATUS_POLL_MAX_ATTEMPTS = 24 # for 24 hours
HEALTH_CHECK_TIMEOUT_SECONDS = 60 # 60 seconds
UI_SESSION_TOKEN_TEAM_ID = "litellm-dashboard"
LITELLM_PROXY_ADMIN_NAME = "default_user_id"
########################### DB CRON JOB NAMES ###########################
DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job"
PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME = "prometheus_emit_budget_metrics_job"
DEFAULT_CRON_JOB_LOCK_TTL_SECONDS = 60 # 1 minute
PROXY_BUDGET_RESCHEDULER_MIN_TIME = 597
PROXY_BUDGET_RESCHEDULER_MAX_TIME = 605
PROXY_BATCH_WRITE_AT = 10 # in seconds
DEFAULT_HEALTH_CHECK_INTERVAL = 300 # 5 minutes
PROMETHEUS_FALLBACK_STATS_SEND_TIME_HOURS = 9
DEFAULT_MODEL_CREATED_AT_TIME = 1677610602 # returns on `/models` endpoint
DEFAULT_SLACK_ALERTING_THRESHOLD = 300
MAX_TEAM_LIST_LIMIT = 20
DEFAULT_PROMPT_INJECTION_SIMILARITY_THRESHOLD = 0.7
LENGTH_OF_LITELLM_GENERATED_KEY = 16
SECRET_MANAGER_REFRESH_INTERVAL = 86400
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