litellm/constants.py

from typing import List

ROUTER_MAX_FALLBACKS = 5
DEFAULT_BATCH_SIZE = 512
DEFAULT_FLUSH_INTERVAL_SECONDS = 5
DEFAULT_MAX_RETRIES = 2
DEFAULT_FAILURE_THRESHOLD_PERCENT = (
    0.5  # default cooldown a deployment if 50% of requests fail in a given minute
)
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
SINGLE_DEPLOYMENT_TRAFFIC_FAILURE_THRESHOLD = 1000  # Minimum number of requests to consider "reasonable traffic". Used for single-deployment cooldown logic.
#### RELIABILITY ####
REPEATED_STREAMING_CHUNK_LIMIT = 100  # catch if model starts looping the same chunk while streaming. Uses high default to prevent false positives.
#### Networking settings ####
request_timeout: float = 6000  # time in seconds

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",
]

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


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",
]


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"

########################### LiteLLM Proxy Specific Constants ###########################
########################################################################################
MAX_SPENDLOG_ROWS_TO_QUERY = (
    1_000_000  # if spendLogs has more than 1M rows, do not query the DB
)
# 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"