import os from typing import List, Literal ROUTER_MAX_FALLBACKS = int(os.getenv("ROUTER_MAX_FALLBACKS", 5)) DEFAULT_BATCH_SIZE = int(os.getenv("DEFAULT_BATCH_SIZE", 512)) DEFAULT_FLUSH_INTERVAL_SECONDS = int(os.getenv("DEFAULT_FLUSH_INTERVAL_SECONDS", 5)) DEFAULT_S3_FLUSH_INTERVAL_SECONDS = int( os.getenv("DEFAULT_S3_FLUSH_INTERVAL_SECONDS", 10) ) DEFAULT_S3_BATCH_SIZE = int(os.getenv("DEFAULT_S3_BATCH_SIZE", 512)) DEFAULT_MAX_RETRIES = int(os.getenv("DEFAULT_MAX_RETRIES", 2)) DEFAULT_MAX_RECURSE_DEPTH = int(os.getenv("DEFAULT_MAX_RECURSE_DEPTH", 100)) DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER = int( os.getenv("DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER", 10) ) DEFAULT_FAILURE_THRESHOLD_PERCENT = float( os.getenv("DEFAULT_FAILURE_THRESHOLD_PERCENT", 0.5) ) # default cooldown a deployment if 50% of requests fail in a given minute DEFAULT_MAX_TOKENS = int(os.getenv("DEFAULT_MAX_TOKENS", 4096)) DEFAULT_ALLOWED_FAILS = int(os.getenv("DEFAULT_ALLOWED_FAILS", 3)) DEFAULT_REDIS_SYNC_INTERVAL = int(os.getenv("DEFAULT_REDIS_SYNC_INTERVAL", 1)) DEFAULT_COOLDOWN_TIME_SECONDS = int(os.getenv("DEFAULT_COOLDOWN_TIME_SECONDS", 5)) DEFAULT_REPLICATE_POLLING_RETRIES = int( os.getenv("DEFAULT_REPLICATE_POLLING_RETRIES", 5) ) DEFAULT_REPLICATE_POLLING_DELAY_SECONDS = int( os.getenv("DEFAULT_REPLICATE_POLLING_DELAY_SECONDS", 1) ) DEFAULT_IMAGE_TOKEN_COUNT = int(os.getenv("DEFAULT_IMAGE_TOKEN_COUNT", 250)) DEFAULT_IMAGE_WIDTH = int(os.getenv("DEFAULT_IMAGE_WIDTH", 300)) DEFAULT_IMAGE_HEIGHT = int(os.getenv("DEFAULT_IMAGE_HEIGHT", 300)) MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = int( os.getenv("MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB", 1024) ) # 1MB = 1024KB SINGLE_DEPLOYMENT_TRAFFIC_FAILURE_THRESHOLD = int( os.getenv("SINGLE_DEPLOYMENT_TRAFFIC_FAILURE_THRESHOLD", 1000) ) # Minimum number of requests to consider "reasonable traffic". Used for single-deployment cooldown logic. DEFAULT_REASONING_EFFORT_DISABLE_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_DISABLE_THINKING_BUDGET", 0) ) DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET", 1024) ) DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET", 2048) ) DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET", 4096) ) MAX_TOKEN_TRIMMING_ATTEMPTS = int( os.getenv("MAX_TOKEN_TRIMMING_ATTEMPTS", 10) ) # Maximum number of attempts to trim the message ########## 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 = int(os.getenv("MAX_REDIS_BUFFER_DEQUEUE_COUNT", 100)) MAX_SIZE_IN_MEMORY_QUEUE = int(os.getenv("MAX_SIZE_IN_MEMORY_QUEUE", 10000)) MAX_IN_MEMORY_QUEUE_FLUSH_COUNT = int( os.getenv("MAX_IN_MEMORY_QUEUE_FLUSH_COUNT", 1000) ) ############################################################################################### MINIMUM_PROMPT_CACHE_TOKEN_COUNT = int( os.getenv("MINIMUM_PROMPT_CACHE_TOKEN_COUNT", 1024) ) # minimum number of tokens to cache a prompt by Anthropic DEFAULT_TRIM_RATIO = float( os.getenv("DEFAULT_TRIM_RATIO", 0.75) ) # default ratio of tokens to trim from the end of a prompt HOURS_IN_A_DAY = int(os.getenv("HOURS_IN_A_DAY", 24)) DAYS_IN_A_WEEK = int(os.getenv("DAYS_IN_A_WEEK", 7)) DAYS_IN_A_MONTH = int(os.getenv("DAYS_IN_A_MONTH", 28)) DAYS_IN_A_YEAR = int(os.getenv("DAYS_IN_A_YEAR", 365)) REPLICATE_MODEL_NAME_WITH_ID_LENGTH = int( os.getenv("REPLICATE_MODEL_NAME_WITH_ID_LENGTH", 64) ) #### TOKEN COUNTING #### FUNCTION_DEFINITION_TOKEN_COUNT = int(os.getenv("FUNCTION_DEFINITION_TOKEN_COUNT", 9)) SYSTEM_MESSAGE_TOKEN_COUNT = int(os.getenv("SYSTEM_MESSAGE_TOKEN_COUNT", 4)) TOOL_CHOICE_OBJECT_TOKEN_COUNT = int(os.getenv("TOOL_CHOICE_OBJECT_TOKEN_COUNT", 4)) DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT = int( os.getenv("DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT", 10) ) DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT = int( os.getenv("DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT", 20) ) MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES = int( os.getenv("MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES", 768) ) MAX_LONG_SIDE_FOR_IMAGE_HIGH_RES = int( os.getenv("MAX_LONG_SIDE_FOR_IMAGE_HIGH_RES", 2000) ) MAX_TILE_WIDTH = int(os.getenv("MAX_TILE_WIDTH", 512)) MAX_TILE_HEIGHT = int(os.getenv("MAX_TILE_HEIGHT", 512)) OPENAI_FILE_SEARCH_COST_PER_1K_CALLS = float( os.getenv("OPENAI_FILE_SEARCH_COST_PER_1K_CALLS", 2.5 / 1000) ) MIN_NON_ZERO_TEMPERATURE = float(os.getenv("MIN_NON_ZERO_TEMPERATURE", 0.0001)) #### RELIABILITY #### REPEATED_STREAMING_CHUNK_LIMIT = int( os.getenv("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 = int(os.getenv("DEFAULT_MAX_LRU_CACHE_SIZE", 16)) INITIAL_RETRY_DELAY = float(os.getenv("INITIAL_RETRY_DELAY", 0.5)) MAX_RETRY_DELAY = float(os.getenv("MAX_RETRY_DELAY", 8.0)) JITTER = float(os.getenv("JITTER", 0.75)) DEFAULT_IN_MEMORY_TTL = int( os.getenv("DEFAULT_IN_MEMORY_TTL", 5) ) # default time to live for the in-memory cache DEFAULT_POLLING_INTERVAL = float( os.getenv("DEFAULT_POLLING_INTERVAL", 0.03) ) # default polling interval for the scheduler AZURE_OPERATION_POLLING_TIMEOUT = int(os.getenv("AZURE_OPERATION_POLLING_TIMEOUT", 120)) REDIS_SOCKET_TIMEOUT = float(os.getenv("REDIS_SOCKET_TIMEOUT", 0.1)) REDIS_CONNECTION_POOL_TIMEOUT = int(os.getenv("REDIS_CONNECTION_POOL_TIMEOUT", 5)) NON_LLM_CONNECTION_TIMEOUT = int( os.getenv("NON_LLM_CONNECTION_TIMEOUT", 15) ) # timeout for adjacent services (e.g. jwt auth) MAX_EXCEPTION_MESSAGE_LENGTH = int(os.getenv("MAX_EXCEPTION_MESSAGE_LENGTH", 2000)) BEDROCK_MAX_POLICY_SIZE = int(os.getenv("BEDROCK_MAX_POLICY_SIZE", 75)) REPLICATE_POLLING_DELAY_SECONDS = float( os.getenv("REPLICATE_POLLING_DELAY_SECONDS", 0.5) ) DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS = int( os.getenv("DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS", 4096) ) TOGETHER_AI_4_B = int(os.getenv("TOGETHER_AI_4_B", 4)) TOGETHER_AI_8_B = int(os.getenv("TOGETHER_AI_8_B", 8)) TOGETHER_AI_21_B = int(os.getenv("TOGETHER_AI_21_B", 21)) TOGETHER_AI_41_B = int(os.getenv("TOGETHER_AI_41_B", 41)) TOGETHER_AI_80_B = int(os.getenv("TOGETHER_AI_80_B", 80)) TOGETHER_AI_110_B = int(os.getenv("TOGETHER_AI_110_B", 110)) TOGETHER_AI_EMBEDDING_150_M = int(os.getenv("TOGETHER_AI_EMBEDDING_150_M", 150)) TOGETHER_AI_EMBEDDING_350_M = int(os.getenv("TOGETHER_AI_EMBEDDING_350_M", 350)) QDRANT_SCALAR_QUANTILE = float(os.getenv("QDRANT_SCALAR_QUANTILE", 0.99)) QDRANT_VECTOR_SIZE = int(os.getenv("QDRANT_VECTOR_SIZE", 1536)) CACHED_STREAMING_CHUNK_DELAY = float(os.getenv("CACHED_STREAMING_CHUNK_DELAY", 0.02)) MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = int( os.getenv("MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB", 512) ) DEFAULT_MAX_TOKENS_FOR_TRITON = int(os.getenv("DEFAULT_MAX_TOKENS_FOR_TRITON", 2000)) #### Networking settings #### request_timeout: float = float(os.getenv("REQUEST_TIMEOUT", 6000)) # time in seconds STREAM_SSE_DONE_STRING: str = "[DONE]" STREAM_SSE_DATA_PREFIX: str = "data: " ### SPEND TRACKING ### DEFAULT_REPLICATE_GPU_PRICE_PER_SECOND = float( os.getenv("DEFAULT_REPLICATE_GPU_PRICE_PER_SECOND", 0.001400) ) # price per second for a100 80GB FIREWORKS_AI_56_B_MOE = int(os.getenv("FIREWORKS_AI_56_B_MOE", 56)) FIREWORKS_AI_176_B_MOE = int(os.getenv("FIREWORKS_AI_176_B_MOE", 176)) FIREWORKS_AI_4_B = int(os.getenv("FIREWORKS_AI_4_B", 4)) FIREWORKS_AI_16_B = int(os.getenv("FIREWORKS_AI_16_B", 16)) FIREWORKS_AI_80_B = int(os.getenv("FIREWORKS_AI_80_B", 80)) #### Logging callback constants #### REDACTED_BY_LITELM_STRING = "REDACTED_BY_LITELM" MAX_LANGFUSE_INITIALIZED_CLIENTS = int( os.getenv("MAX_LANGFUSE_INITIALIZED_CLIENTS", 50) ) DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE = os.getenv( "DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE", "streaming.chunk.yield" ) ############### LLM Provider Constants ############### ### ANTHROPIC CONSTANTS ### ANTHROPIC_WEB_SEARCH_TOOL_MAX_USES = { "low": 1, "medium": 5, "high": 10, } DEFAULT_IMAGE_ENDPOINT_MODEL = "dall-e-2" LITELLM_CHAT_PROVIDERS = [ "openai", "openai_like", "xai", "custom_openai", "text-completion-openai", "cohere", "cohere_chat", "clarifai", "anthropic", "anthropic_text", "replicate", "huggingface", "together_ai", "datarobot", "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", "llamafile", "lm_studio", "galadriel", "novita", "meta_llama", "featherless_ai", "nscale", "nebius", ] LITELLM_EMBEDDING_PROVIDERS_SUPPORTING_INPUT_ARRAY_OF_TOKENS = [ "openai", "azure", "hosted_vllm", "nebius", ] 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", "web_search_options", ] OPENAI_TRANSCRIPTION_PARAMS = [ "language", "response_format", "timestamp_granularities", ] OPENAI_EMBEDDING_PARAMS = ["dimensions", "encoding_format", "user"] DEFAULT_EMBEDDING_PARAM_VALUES = { **{k: None for k in OPENAI_EMBEDDING_PARAMS}, "model": None, "custom_llm_provider": "", "input": None, } DEFAULT_CHAT_COMPLETION_PARAM_VALUES = { "functions": None, "function_call": None, "temperature": None, "top_p": None, "n": None, "stream": None, "stream_options": None, "stop": None, "max_tokens": None, "max_completion_tokens": None, "modalities": None, "prediction": None, "audio": None, "presence_penalty": None, "frequency_penalty": None, "logit_bias": None, "user": None, "model": None, "custom_llm_provider": "", "response_format": None, "seed": None, "tools": None, "tool_choice": None, "max_retries": None, "logprobs": None, "top_logprobs": None, "extra_headers": None, "api_version": None, "parallel_tool_calls": None, "drop_params": None, "allowed_openai_params": None, "additional_drop_params": None, "messages": None, "reasoning_effort": None, "thinking": None, "web_search_options": None, } 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", "api.llama.com/compat/v1/", "api.featherless.ai/v1", "inference.api.nscale.com/v1", "api.studio.nebius.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", "llamafile", "lm_studio", "galadriel", "novita", "meta_llama", "featherless_ai", "nscale", "nebius", ] openai_text_completion_compatible_providers: List = ( [ # providers that support `/v1/completions` "together_ai", "fireworks_ai", "hosted_vllm", "meta_llama", "llamafile", "featherless_ai", "nebius", ] ) _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 featherless_ai_models: List = [ "featherless-ai/Qwerky-72B", "featherless-ai/Qwerky-QwQ-32B", "Qwen/Qwen2.5-72B-Instruct", "all-hands/openhands-lm-32b-v0.1", "Qwen/Qwen2.5-Coder-32B-Instruct", "deepseek-ai/DeepSeek-V3-0324", "mistralai/Mistral-Small-24B-Instruct-2501", "mistralai/Mistral-Nemo-Instruct-2407", "ProdeusUnity/Stellar-Odyssey-12b-v0.0", ] nebius_models: List = [ "Qwen/Qwen3-235B-A22B", "Qwen/Qwen3-30B-A3B-fast", "Qwen/Qwen3-32B", "Qwen/Qwen3-14B", "nvidia/Llama-3_1-Nemotron-Ultra-253B-v1", "deepseek-ai/DeepSeek-V3-0324", "deepseek-ai/DeepSeek-V3-0324-fast", "deepseek-ai/DeepSeek-R1", "deepseek-ai/DeepSeek-R1-fast", "meta-llama/Llama-3.3-70B-Instruct-fast", "Qwen/Qwen2.5-32B-Instruct-fast", "Qwen/Qwen2.5-Coder-32B-Instruct-fast", ] nebius_embedding_models: List = [ "BAAI/bge-en-icl", "BAAI/bge-multilingual-gemma2", "intfloat/e5-mistral-7b-instruct", ] 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-v4.0", "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": "", "eos_token": "", }, "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 '' in content %}{% set content = content.split('')[-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｜>\\n'}}{% endif %}", }, "status": "success", }, } OPENAI_FINISH_REASONS = ["stop", "length", "function_call", "content_filter", "null"] HUMANLOOP_PROMPT_CACHE_TTL_SECONDS = int( os.getenv("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 = int( os.getenv("PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES", 5) ) MCP_TOOL_NAME_PREFIX = "mcp_tool" MAXIMUM_TRACEBACK_LINES_TO_LOG = int(os.getenv("MAXIMUM_TRACEBACK_LINES_TO_LOG", 100)) ########################### LiteLLM Proxy Specific Constants ########################### ######################################################################################## MAX_SPENDLOG_ROWS_TO_QUERY = int( os.getenv("MAX_SPENDLOG_ROWS_TO_QUERY", 1_000_000) ) # if spendLogs has more than 1M rows, do not query the DB DEFAULT_SOFT_BUDGET = float( os.getenv("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 = int( os.getenv("BATCH_STATUS_POLL_INTERVAL_SECONDS", 3600) ) # 1 hour BATCH_STATUS_POLL_MAX_ATTEMPTS = int( os.getenv("BATCH_STATUS_POLL_MAX_ATTEMPTS", 24) ) # for 24 hours HEALTH_CHECK_TIMEOUT_SECONDS = int( os.getenv("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" SPEND_LOG_CLEANUP_JOB_NAME = "spend_log_cleanup" SPEND_LOG_RUN_LOOPS = int(os.getenv("SPEND_LOG_RUN_LOOPS", 500)) SPEND_LOG_CLEANUP_BATCH_SIZE = int(os.getenv("SPEND_LOG_CLEANUP_BATCH_SIZE", 1000)) DEFAULT_CRON_JOB_LOCK_TTL_SECONDS = int( os.getenv("DEFAULT_CRON_JOB_LOCK_TTL_SECONDS", 60) ) # 1 minute PROXY_BUDGET_RESCHEDULER_MIN_TIME = int( os.getenv("PROXY_BUDGET_RESCHEDULER_MIN_TIME", 597) ) PROXY_BUDGET_RESCHEDULER_MAX_TIME = int( os.getenv("PROXY_BUDGET_RESCHEDULER_MAX_TIME", 605) ) PROXY_BATCH_WRITE_AT = int(os.getenv("PROXY_BATCH_WRITE_AT", 10)) # in seconds DEFAULT_HEALTH_CHECK_INTERVAL = int( os.getenv("DEFAULT_HEALTH_CHECK_INTERVAL", 300) ) # 5 minutes PROMETHEUS_FALLBACK_STATS_SEND_TIME_HOURS = int( os.getenv("PROMETHEUS_FALLBACK_STATS_SEND_TIME_HOURS", 9) ) DEFAULT_MODEL_CREATED_AT_TIME = int( os.getenv("DEFAULT_MODEL_CREATED_AT_TIME", 1677610602) ) # returns on `/models` endpoint DEFAULT_SLACK_ALERTING_THRESHOLD = int( os.getenv("DEFAULT_SLACK_ALERTING_THRESHOLD", 300) ) MAX_TEAM_LIST_LIMIT = int(os.getenv("MAX_TEAM_LIST_LIMIT", 20)) DEFAULT_PROMPT_INJECTION_SIMILARITY_THRESHOLD = float( os.getenv("DEFAULT_PROMPT_INJECTION_SIMILARITY_THRESHOLD", 0.7) ) LENGTH_OF_LITELLM_GENERATED_KEY = int(os.getenv("LENGTH_OF_LITELLM_GENERATED_KEY", 16)) SECRET_MANAGER_REFRESH_INTERVAL = int( os.getenv("SECRET_MANAGER_REFRESH_INTERVAL", 86400) ) LITELLM_SETTINGS_SAFE_DB_OVERRIDES = ["default_internal_user_params"] SPECIAL_LITELLM_AUTH_TOKEN = ["ui-token"] DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL = int( os.getenv("DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL", 60) )