mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-15 23:31:29 +00:00
68f997dd09
Add an opt-in mode so a key that exceeds its own max_budget is throttled to a globally configured percentage of its TPM/RPM instead of being blocked entirely. A new litellm_settings global, budget_exceeded_throttle_percentage, sets the fraction (e.g. 0.1 = 10%). A per-key throttle_on_budget_exceeded flag (stored in key metadata via the existing management-endpoint metadata routing) opts the key in. When both are set and the key is over budget, the budget check records the percentage on a request-scoped budget_throttle_pct instead of raising, and the rate limiter scales the key's configured TPM/RPM by it. Keys without the flag keep hard-blocking; team/user/org budgets are unaffected. The throttle is recomputed from the key's original limits on every request and the decision is cleared before the auth object is cached, so it never compounds across requests. Both the budget read-time check and the budget reservation path honor the opt-in, and both the v3 and legacy rate limiters apply the scaling. Enabling throttle_on_budget_exceeded is proxy-admin only. It converts an admin-imposed hard budget block into a soft throttle that keeps spending past max_budget, so a non-admin must not be able to self-opt-in and bypass their own spend cap. Both /key/generate and /key/update reject a non-admin setting it to true (update only gates the transition to enabled, so a non-admin can still edit other fields and turn the flag off). This matches the feature being wholly proxy-admin operated: the global percentage is admin-only too. A key that opts in but has no TPM or RPM limit has nothing to scale, so it stays hard-blocked rather than serving unlimited requests past its budget (fail-safe). The global budget_exceeded_throttle_percentage is configurable from the admin UI (Settings -> General Settings), persisted through litellm_settings so it survives a restart, not only from config.yaml. Resolves LIT-3894. Scope for LIT-3893.
2313 lines
92 KiB
Python
2313 lines
92 KiB
Python
### Hide pydantic namespace conflict warnings globally ###
|
|
from __future__ import annotations
|
|
|
|
import warnings
|
|
|
|
warnings.filterwarnings("ignore", message=".*conflict with protected namespace.*")
|
|
# Suppress Pydantic 2.11+ deprecation warning about accessing model_fields on instances
|
|
# This warning can accumulate during streaming and cause memory leaks
|
|
warnings.filterwarnings("ignore", message=".*Accessing the.*attribute on the instance is deprecated.*")
|
|
### INIT VARIABLES #########################
|
|
import threading
|
|
import os
|
|
|
|
# Load .env before any other litellm imports so env vars (e.g. LITELLM_UI_SESSION_DURATION) are available
|
|
import dotenv as _dotenv
|
|
|
|
|
|
def _dev_env_hot_reload_enabled() -> bool:
|
|
"""The proxy exports this flag when started with ``--reload``. A reloaded
|
|
worker is a fresh process that inherits the reloader's environment, so an
|
|
edited ``.env`` value stays masked by the stale inherited one unless we
|
|
let the file win; overriding makes the edit take effect on reload."""
|
|
return os.getenv("LITELLM_DEV_ENV_HOT_RELOAD") == "True"
|
|
|
|
|
|
if os.getenv("LITELLM_MODE", "DEV") == "DEV":
|
|
_dotenv.load_dotenv(override=_dev_env_hot_reload_enabled())
|
|
|
|
from typing import (
|
|
Callable,
|
|
List,
|
|
Optional,
|
|
Dict,
|
|
Union,
|
|
Any,
|
|
Literal,
|
|
get_args,
|
|
TYPE_CHECKING,
|
|
Tuple,
|
|
overload,
|
|
Type,
|
|
)
|
|
from litellm.types.integrations.datadog import DatadogInitParams
|
|
from litellm.types.integrations.newrelic import NewRelicInitParams
|
|
from litellm._logging import (
|
|
set_verbose,
|
|
_turn_on_debug,
|
|
verbose_logger,
|
|
json_logs,
|
|
_turn_on_json,
|
|
log_level,
|
|
)
|
|
import re
|
|
from litellm.constants import (
|
|
DEFAULT_BATCH_SIZE,
|
|
DEFAULT_FLUSH_INTERVAL_SECONDS,
|
|
ROUTER_MAX_FALLBACKS,
|
|
DEFAULT_MAX_RETRIES,
|
|
DEFAULT_REPLICATE_POLLING_RETRIES,
|
|
DEFAULT_REPLICATE_POLLING_DELAY_SECONDS,
|
|
LITELLM_CHAT_PROVIDERS,
|
|
HUMANLOOP_PROMPT_CACHE_TTL_SECONDS,
|
|
OPENAI_CHAT_COMPLETION_PARAMS,
|
|
OPENAI_CHAT_COMPLETION_PARAMS as _openai_completion_params, # backwards compatibility
|
|
OPENAI_FINISH_REASONS,
|
|
OPENAI_FINISH_REASONS as _openai_finish_reasons, # backwards compatibility
|
|
openai_compatible_endpoints,
|
|
openai_compatible_providers,
|
|
openai_text_completion_compatible_providers,
|
|
_openai_like_providers,
|
|
replicate_models,
|
|
clarifai_models,
|
|
huggingface_models,
|
|
modelscope_models,
|
|
empower_models,
|
|
together_ai_models,
|
|
baseten_models,
|
|
WANDB_MODELS,
|
|
REPEATED_STREAMING_CHUNK_LIMIT,
|
|
request_timeout,
|
|
request_timeout_explicitly_set as request_timeout_explicitly_set,
|
|
open_ai_embedding_models,
|
|
cohere_embedding_models,
|
|
bedrock_embedding_models,
|
|
known_tokenizer_config,
|
|
BEDROCK_INVOKE_PROVIDERS_LITERAL,
|
|
BEDROCK_EMBEDDING_PROVIDERS_LITERAL,
|
|
BEDROCK_CONVERSE_MODELS,
|
|
DEFAULT_MAX_TOKENS,
|
|
DEFAULT_SOFT_BUDGET,
|
|
DEFAULT_ALLOWED_FAILS,
|
|
)
|
|
import httpx
|
|
|
|
# register_async_client_cleanup is lazy-loaded and called on first access
|
|
|
|
litellm_mode = os.getenv("LITELLM_MODE", "DEV") # "PRODUCTION", "DEV"
|
|
|
|
|
|
####################################################
|
|
if set_verbose:
|
|
_turn_on_debug()
|
|
####################################################
|
|
### Callbacks /Logging / Success / Failure Handlers #####
|
|
CALLBACK_TYPES = Union[str, Callable, "CustomLogger"] # CustomLogger is lazy-loaded
|
|
input_callback: List[CALLBACK_TYPES] = []
|
|
success_callback: List[CALLBACK_TYPES] = []
|
|
failure_callback: List[CALLBACK_TYPES] = []
|
|
service_callback: List[CALLBACK_TYPES] = []
|
|
audit_log_callbacks: List[CALLBACK_TYPES] = []
|
|
# logging_callback_manager is lazy-loaded via __getattr__
|
|
_custom_logger_compatible_callbacks_literal = Literal[
|
|
"lago",
|
|
"openmeter",
|
|
"logfire",
|
|
"literalai",
|
|
"litellm_agent",
|
|
"dynamic_rate_limiter",
|
|
"dynamic_rate_limiter_v3",
|
|
"langsmith",
|
|
"prometheus",
|
|
"otel",
|
|
"datadog",
|
|
"datadog_metrics",
|
|
"datadog_llm_observability",
|
|
"galileo",
|
|
"braintrust",
|
|
"arize",
|
|
"arize_phoenix",
|
|
"langtrace",
|
|
"gcs_bucket",
|
|
"azure_storage",
|
|
"opik",
|
|
"argilla",
|
|
"mlflow",
|
|
"langfuse",
|
|
"langfuse_otel",
|
|
"weave_otel",
|
|
"pagerduty",
|
|
"humanloop",
|
|
"azure_sentinel",
|
|
"gcs_pubsub",
|
|
"agentops",
|
|
"anthropic_cache_control_hook",
|
|
"generic_api",
|
|
"resend_email",
|
|
"sendgrid_email",
|
|
"smtp_email",
|
|
"deepeval",
|
|
"s3_v2",
|
|
"aws_sqs",
|
|
"vector_store_pre_call_hook",
|
|
"dotprompt",
|
|
"bitbucket",
|
|
"gitlab",
|
|
"cloudzero",
|
|
"focus",
|
|
"mavvrik",
|
|
"vantage",
|
|
"posthog",
|
|
"levo",
|
|
"compression_interception",
|
|
"newrelic",
|
|
]
|
|
cold_storage_custom_logger: Optional[_custom_logger_compatible_callbacks_literal] = None
|
|
logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None
|
|
_known_custom_logger_compatible_callbacks: List = list(get_args(_custom_logger_compatible_callbacks_literal))
|
|
callbacks: List[
|
|
Union[Callable, _custom_logger_compatible_callbacks_literal, "CustomLogger"] # CustomLogger is lazy-loaded
|
|
] = []
|
|
callback_settings: Dict[str, Dict[str, Any]] = {}
|
|
initialized_langfuse_clients: int = 0
|
|
langfuse_default_tags: Optional[List[str]] = None
|
|
langsmith_batch_size: Optional[int] = None
|
|
prometheus_initialize_budget_metrics: Optional[bool] = False
|
|
prometheus_latency_buckets: Optional[List[float]] = None
|
|
require_auth_for_metrics_endpoint: Optional[bool] = True
|
|
argilla_batch_size: Optional[int] = None
|
|
datadog_use_v1: Optional[bool] = False # if you want to use v1 datadog logged payload.
|
|
gcs_pub_sub_use_v1: Optional[bool] = False # if you want to use v1 gcs pubsub logged payload
|
|
generic_api_use_v1: Optional[bool] = False # if you want to use v1 generic api logged payload
|
|
argilla_transformation_object: Optional[Dict[str, Any]] = None
|
|
_async_input_callback: List[Union[str, Callable, "CustomLogger"]] = ( # CustomLogger is lazy-loaded
|
|
[]
|
|
) # internal variable - async custom callbacks are routed here.
|
|
_async_success_callback: List[Union[str, Callable, "CustomLogger"]] = ( # CustomLogger is lazy-loaded
|
|
[]
|
|
) # internal variable - async custom callbacks are routed here.
|
|
_async_failure_callback: List[Union[str, Callable, "CustomLogger"]] = ( # CustomLogger is lazy-loaded
|
|
[]
|
|
) # internal variable - async custom callbacks are routed here.
|
|
pre_call_rules: List[Callable] = []
|
|
post_call_rules: List[Callable] = []
|
|
turn_off_message_logging: Optional[bool] = False
|
|
standard_logging_payload_excluded_fields: Optional[List[str]] = (
|
|
None # Fields to exclude from StandardLoggingPayload before callbacks receive it
|
|
)
|
|
log_raw_request_response: bool = False
|
|
redact_messages_in_exceptions: Optional[bool] = False
|
|
redact_user_api_key_info: Optional[bool] = False
|
|
# When True (default — preserves historical behavior), the Router appends
|
|
# internal config names (model_group, fallback model groups, deployment
|
|
# timeouts, fallback failure details) onto exception messages and surfaces
|
|
# them to clients via ProxyException.message. Set to False if you do NOT
|
|
# want the proxy's internal model_name / fallback wiring visible to clients.
|
|
# Deprecation: planned to flip to False (redact by default) in a future
|
|
# major release; opt in early with `litellm.expose_router_debug_in_errors
|
|
# = False`.
|
|
expose_router_debug_in_errors: bool = True
|
|
filter_invalid_headers: Optional[bool] = False
|
|
add_user_information_to_llm_headers: Optional[bool] = (
|
|
None # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers
|
|
)
|
|
store_audit_logs = False # Enterprise feature, allow users to see audit logs
|
|
skip_system_message_in_guardrail: bool = False
|
|
skip_tool_message_in_guardrail: bool = False
|
|
### end of callbacks #############
|
|
|
|
email: Optional[str] = (
|
|
None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
|
|
)
|
|
token: Optional[str] = (
|
|
None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
|
|
)
|
|
telemetry = True
|
|
max_tokens: int = DEFAULT_MAX_TOKENS # OpenAI Defaults
|
|
drop_params = bool(os.getenv("LITELLM_DROP_PARAMS", False))
|
|
modify_params = bool(os.getenv("LITELLM_MODIFY_PARAMS", False))
|
|
use_chat_completions_url_for_anthropic_messages: bool = bool(
|
|
os.getenv("LITELLM_USE_CHAT_COMPLETIONS_URL_FOR_ANTHROPIC_MESSAGES", False)
|
|
) # When True, routes OpenAI /v1/messages requests to chat/completions instead of the Responses API
|
|
# When True, strip the OpenAI-flavored `usage.total_tokens` field that
|
|
# LiteLLM injects into non-streaming /v1/messages responses, bringing the
|
|
# wire response into line with the Anthropic spec (matches the streaming
|
|
# SSE path, which already omits total_tokens). Default False to preserve
|
|
# backward compatibility for clients that read the LiteLLM-shaped
|
|
# `usage.total_tokens` today. Planned to flip to True in a future major
|
|
# release; opt in early via Python:
|
|
# `litellm.strip_anthropic_total_tokens = True`
|
|
# Or via `litellm_settings.strip_anthropic_total_tokens: true` in
|
|
# config.yaml.
|
|
strip_anthropic_total_tokens: bool = False
|
|
route_all_chat_openai_to_responses: bool = (
|
|
os.getenv("LITELLM_ROUTE_ALL_CHAT_OPENAI_TO_RESPONSES", "false").lower() == "true"
|
|
) # When True, routes all OpenAI /chat/completions requests through the Responses API bridge
|
|
# When True, Gemini/Vertex Live setup is deferred until client `session.update`.
|
|
# Default False preserves historical behavior (auto-send setup on connect).
|
|
gemini_live_defer_setup: bool = os.getenv("LITELLM_GEMINI_LIVE_DEFER_SETUP", "false").lower() == "true"
|
|
use_legacy_interactions_schema: bool = (
|
|
os.getenv("LITELLM_USE_LEGACY_INTERACTIONS_SCHEMA", "false").lower() == "true"
|
|
) # When True, sends Api-Revision: 2026-05-07 to Google so responses use the legacy `outputs`
|
|
# schema instead of the new `steps` schema. Remove this flag after June 8, 2026.
|
|
retry = True
|
|
### AUTH ###
|
|
api_key: Optional[str] = None
|
|
openai_key: Optional[str] = None
|
|
groq_key: Optional[str] = None
|
|
gigachat_key: Optional[str] = None
|
|
xai_key: Optional[str] = None
|
|
databricks_key: Optional[str] = None
|
|
openai_like_key: Optional[str] = None
|
|
azure_key: Optional[str] = None
|
|
anthropic_key: Optional[str] = None
|
|
replicate_key: Optional[str] = None
|
|
bytez_key: Optional[str] = None
|
|
gdc_key: Optional[str] = None
|
|
gdc_api_base: Optional[str] = None
|
|
cohere_key: Optional[str] = None
|
|
infinity_key: Optional[str] = None
|
|
clarifai_key: Optional[str] = None
|
|
maritalk_key: Optional[str] = None
|
|
ai21_key: Optional[str] = None
|
|
ollama_key: Optional[str] = None
|
|
openrouter_key: Optional[str] = None
|
|
datarobot_key: Optional[str] = None
|
|
predibase_key: Optional[str] = None
|
|
huggingface_key: Optional[str] = None
|
|
vertex_project: Optional[str] = None
|
|
vertex_location: Optional[str] = None
|
|
predibase_tenant_id: Optional[str] = None
|
|
togetherai_api_key: Optional[str] = None
|
|
cloudflare_api_key: Optional[str] = None
|
|
vercel_ai_gateway_key: Optional[str] = None
|
|
baseten_key: Optional[str] = None
|
|
llama_api_key: Optional[str] = None
|
|
aleph_alpha_key: Optional[str] = None
|
|
nlp_cloud_key: Optional[str] = None
|
|
novita_api_key: Optional[str] = None
|
|
snowflake_key: Optional[str] = None
|
|
gradient_ai_api_key: Optional[str] = None
|
|
nebius_key: Optional[str] = None
|
|
wandb_key: Optional[str] = None
|
|
heroku_key: Optional[str] = None
|
|
cometapi_key: Optional[str] = None
|
|
ovhcloud_key: Optional[str] = None
|
|
lemonade_key: Optional[str] = None
|
|
sap_service_key: Optional[str] = None
|
|
amazon_nova_api_key: Optional[str] = None
|
|
inception_key: Optional[str] = None
|
|
common_cloud_provider_auth_params: dict = {
|
|
"params": ["project", "region_name", "token"],
|
|
"providers": ["vertex_ai", "bedrock", "watsonx", "azure", "vertex_ai_beta"],
|
|
}
|
|
use_litellm_proxy: bool = False # when True, requests will be sent to the specified litellm proxy endpoint
|
|
use_client: bool = False
|
|
ssl_verify: Union[str, bool] = True
|
|
ssl_security_level: Optional[str] = None
|
|
ssl_certificate: Optional[str] = None
|
|
user_url_validation: bool = True
|
|
user_url_allowed_hosts: List[str] = []
|
|
provider_url_destination_allowed_hosts: List[str] = []
|
|
ssl_ecdh_curve: Optional[str] = None # Set to 'X25519' to disable PQC and improve performance
|
|
disable_streaming_logging: bool = False
|
|
disable_token_counter: bool = False
|
|
disable_add_transform_inline_image_block: bool = False
|
|
disable_add_user_agent_to_request_tags: bool = False
|
|
disable_anthropic_gemini_context_caching_transform: bool = False
|
|
disable_vertex_batch_output_transformation: bool = False
|
|
extra_spend_tag_headers: Optional[List[str]] = None
|
|
in_memory_llm_clients_cache: "LLMClientCache"
|
|
safe_memory_mode: bool = False
|
|
enable_azure_ad_token_refresh: Optional[bool] = False
|
|
# Proxy Authentication - auto-obtain/refresh OAuth2/JWT tokens for LiteLLM Proxy
|
|
proxy_auth: Optional[Any] = None
|
|
### DEFAULT AZURE API VERSION ###
|
|
AZURE_DEFAULT_API_VERSION = "2025-02-01-preview" # this is updated to the latest
|
|
### DEFAULT WATSONX API VERSION ###
|
|
WATSONX_DEFAULT_API_VERSION = "2024-03-13"
|
|
### COHERE EMBEDDINGS DEFAULT TYPE ###
|
|
COHERE_DEFAULT_EMBEDDING_INPUT_TYPE: "COHERE_EMBEDDING_INPUT_TYPES" = "search_document"
|
|
### CREDENTIALS ###
|
|
credential_list: List["CredentialItem"] = []
|
|
### GUARDRAILS ###
|
|
llamaguard_model_name: Optional[str] = None
|
|
openai_moderations_model_name: Optional[str] = None
|
|
presidio_ad_hoc_recognizers: Optional[str] = None
|
|
google_moderation_confidence_threshold: Optional[float] = None
|
|
llamaguard_unsafe_content_categories: Optional[str] = None
|
|
blocked_user_list: Optional[Union[str, List]] = None
|
|
banned_keywords_list: Optional[Union[str, List]] = None
|
|
llm_guard_mode: Literal["all", "key-specific", "request-specific"] = "all"
|
|
guardrail_name_config_map: Dict[str, GuardrailItem] = {}
|
|
include_cost_in_streaming_usage: bool = False
|
|
reasoning_auto_summary: bool = False
|
|
### PROMPTS ####
|
|
from litellm.types.prompts.init_prompts import PromptSpec
|
|
|
|
prompt_name_config_map: Dict[str, PromptSpec] = {}
|
|
|
|
##################
|
|
### PREVIEW FEATURES ###
|
|
enable_preview_features: bool = False
|
|
return_response_headers: bool = False # get response headers from LLM Api providers - example x-remaining-requests,
|
|
enable_json_schema_validation: bool = False
|
|
enable_model_config_credential_overrides: bool = False
|
|
enable_key_alias_format_validation: bool = (
|
|
False # opt-in validation of key_alias format on /key/generate and /key/update
|
|
)
|
|
enable_gemini_default_thinking_level_low: bool = (
|
|
False # opt-in: force thinkingLevel low/minimal for Gemini 3 thinking param mapping
|
|
)
|
|
####################
|
|
logging: bool = True
|
|
enable_loadbalancing_on_batch_endpoints: Optional[bool] = None
|
|
require_managed_files: bool = False # proxy only - require target_model_names on POST /v1/files
|
|
enable_caching_on_provider_specific_optional_params: bool = (
|
|
False # feature-flag for caching on optional params - e.g. 'top_k'
|
|
)
|
|
caching: bool = False # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
|
|
caching_with_models: bool = False # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
|
|
cache: Optional["Cache"] = None # cache object <- use this - https://docs.litellm.ai/docs/caching
|
|
default_in_memory_ttl: Optional[float] = None
|
|
default_redis_ttl: Optional[float] = None
|
|
default_redis_batch_cache_expiry: Optional[float] = None
|
|
model_alias_map: Dict[str, str] = {}
|
|
model_group_settings: Optional["ModelGroupSettings"] = None
|
|
max_budget: float = 0.0 # set the max budget across all providers
|
|
budget_duration: Optional[str] = (
|
|
None # proxy only - resets budget after fixed duration. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d").
|
|
)
|
|
default_soft_budget: float = DEFAULT_SOFT_BUDGET # by default all litellm proxy keys have a soft budget of 50.0
|
|
budget_exceeded_throttle_percentage: Optional[float] = None
|
|
forward_traceparent_to_llm_provider: bool = False
|
|
|
|
|
|
_current_cost = 0.0 # private variable, used if max budget is set
|
|
error_logs: Dict = {}
|
|
add_function_to_prompt: bool = (
|
|
False # if function calling not supported by api, append function call details to system prompt
|
|
)
|
|
client_session: Optional[httpx.Client] = None
|
|
aclient_session: Optional[httpx.AsyncClient] = None
|
|
model_fallbacks: Optional[List] = None # Deprecated for 'litellm.fallbacks'
|
|
model_cost_map_url: str = os.getenv(
|
|
"LITELLM_MODEL_COST_MAP_URL",
|
|
"https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json",
|
|
)
|
|
blog_posts_url: str = os.getenv(
|
|
"LITELLM_BLOG_POSTS_URL",
|
|
"https://docs.litellm.ai/blog/rss.xml",
|
|
)
|
|
anthropic_beta_headers_url: str = os.getenv(
|
|
"LITELLM_ANTHROPIC_BETA_HEADERS_URL",
|
|
"https://raw.githubusercontent.com/BerriAI/litellm/main/litellm/anthropic_beta_headers_config.json",
|
|
)
|
|
suppress_debug_info: bool = False
|
|
dynamodb_table_name: Optional[str] = None
|
|
s3_callback_params: Optional[Dict] = None
|
|
s3_audit_callback_params: Optional[Dict] = None
|
|
datadog_llm_observability_params: Optional[Union[DatadogLLMObsInitParams, Dict]] = None
|
|
datadog_params: Optional[Union[DatadogInitParams, Dict]] = None
|
|
newrelic_params: Optional[Union[NewRelicInitParams, Dict]] = None
|
|
aws_sqs_callback_params: Optional[Dict] = None
|
|
generic_logger_headers: Optional[Dict] = None
|
|
default_key_generate_params: Optional[Dict] = None
|
|
default_key_max_budget_alert_emails: Optional[Dict[str, list]] = None
|
|
upperbound_key_generate_params: Optional[LiteLLM_UpperboundKeyGenerateParams] = None
|
|
key_generation_settings: Optional["StandardKeyGenerationConfig"] = None
|
|
default_internal_user_params: Optional[Dict] = None
|
|
default_team_params: Optional[Union[DefaultTeamSSOParams, Dict]] = None
|
|
default_team_settings: Optional[List] = None
|
|
max_user_budget: Optional[float] = None
|
|
default_max_internal_user_budget: Optional[float] = None
|
|
max_internal_user_budget: Optional[float] = None
|
|
max_ui_session_budget: Optional[float] = 0.25 # $0.25 USD budgets for UI Chat sessions
|
|
internal_user_budget_duration: Optional[str] = None
|
|
tag_budget_config: Optional[Dict[str, "BudgetConfig"]] = None
|
|
max_end_user_budget: Optional[float] = None
|
|
max_end_user_budget_id: Optional[str] = None
|
|
# When True, end-user IDs extracted from requests are validated against
|
|
# LiteLLM_EndUserTable / LiteLLM_UserTable. Values that do not resolve to a
|
|
# known row are dropped before reaching spend logs. Defaults to False for
|
|
# backwards compatibility — arbitrary client-supplied identifiers still
|
|
# pass through unchanged.
|
|
validate_end_user_id_in_db: bool = False
|
|
disable_end_user_cost_tracking: Optional[bool] = None
|
|
disable_end_user_cost_tracking_prometheus_only: Optional[bool] = None
|
|
enable_end_user_cost_tracking_prometheus_only: Optional[bool] = None
|
|
custom_prometheus_metadata_labels: List[str] = []
|
|
custom_prometheus_tags: List[str] = []
|
|
prometheus_metrics_config: Optional[List] = None
|
|
prometheus_emit_stream_label: bool = False
|
|
# Opt-in: emit `rate_limit_category` and `rate_limit_type` labels on
|
|
# `litellm_proxy_failed_requests_metric`. Off by default to preserve the
|
|
# pre-unification label set so existing dashboards / recording rules keyed on
|
|
# that metric keep matching after upgrade. Enable when downstream consumers
|
|
# are ready to split 429s by source (vendor vs. litellm) and dimension
|
|
# (RPM/TPM/concurrent/budget).
|
|
prometheus_emit_rate_limit_labels: bool = False
|
|
prometheus_user_budget_label_include_email_alias: bool = False
|
|
prometheus_end_user_metrics_max_series_per_metric: Optional[int] = 10000
|
|
prometheus_end_user_metrics_ttl_seconds: Optional[float] = 3600.0
|
|
prometheus_end_user_metrics_cleanup_interval_seconds: Optional[float] = 60.0
|
|
disable_add_prefix_to_prompt: bool = False # used by anthropic, to disable adding prefix to prompt
|
|
disable_copilot_system_to_assistant: bool = False # If false (default), converts all 'system' role messages to 'assistant' for GitHub Copilot compatibility. Set to true to disable this behavior.
|
|
public_mcp_servers: Optional[List[str]] = None
|
|
public_mcp_hub_strict_whitelist: bool = True
|
|
public_model_groups: Optional[List[str]] = None
|
|
public_agent_groups: Optional[List[str]] = None
|
|
# Supports both old format (Dict[str, str]) and new format (Dict[str, Dict[str, Any]])
|
|
# New format: { "displayName": { "url": "...", "index": 0 } }
|
|
# Old format: { "displayName": "url" } (for backward compatibility)
|
|
public_model_groups_links: Dict[str, Union[str, Dict[str, Any]]] = {}
|
|
#### REQUEST PRIORITIZATION #######
|
|
priority_reservation: Optional[Dict[str, Union[float, "PriorityReservationDict"]]] = None
|
|
# priority_reservation_settings is lazy-loaded via __getattr__
|
|
# Only declare for type checking - at runtime __getattr__ handles it
|
|
if TYPE_CHECKING:
|
|
priority_reservation_settings: Optional["PriorityReservationSettings"] = None
|
|
|
|
|
|
######## Networking Settings ########
|
|
use_aiohttp_transport: bool = True # Older variable, aiohttp is now the default. use disable_aiohttp_transport instead.
|
|
aiohttp_trust_env: bool = False # set to true to use HTTP_ Proxy settings
|
|
disable_aiohttp_transport: bool = False # Set this to true to use httpx instead
|
|
disable_aiohttp_trust_env: bool = False # When False, aiohttp will respect HTTP(S)_PROXY env vars
|
|
force_ipv4: bool = False # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
|
|
network_mock: bool = False # When True, use mock transport — no real network calls
|
|
|
|
####### STOP SEQUENCE LIMIT #######
|
|
disable_stop_sequence_limit: bool = False # when True, stop sequence limit is disabled
|
|
|
|
#### RETRIES ####
|
|
num_retries: Optional[int] = None # per model endpoint
|
|
max_fallbacks: Optional[int] = None
|
|
default_fallbacks: Optional[List] = None
|
|
fallbacks: Optional[List] = None
|
|
context_window_fallbacks: Optional[List] = None
|
|
content_policy_fallbacks: Optional[List] = None
|
|
allowed_fails: int = 3
|
|
allow_dynamic_callback_disabling: bool = True
|
|
num_retries_per_request: Optional[int] = None # for the request overall (incl. fallbacks + model retries)
|
|
####### SECRET MANAGERS #####################
|
|
secret_manager_client: Optional[Any] = (
|
|
None # list of instantiated key management clients - e.g. azure kv, infisical, etc.
|
|
)
|
|
_google_kms_resource_name: Optional[str] = None
|
|
_key_management_system: Optional["KeyManagementSystem"] = None
|
|
# Note: KeyManagementSettings must be eagerly imported because _key_management_settings
|
|
# is accessed during import time in secret_managers/main.py
|
|
# We'll import it after the lazy import system is set up
|
|
# We can't define it here because KeyManagementSettings is lazy-loaded
|
|
#### PII MASKING ####
|
|
output_parse_pii: bool = False
|
|
#############################################
|
|
from litellm.litellm_core_utils.get_model_cost_map import get_model_cost_map
|
|
|
|
model_cost = get_model_cost_map(url=model_cost_map_url)
|
|
cost_discount_config: Dict[str, float] = {} # Provider-specific cost discounts {"vertex_ai": 0.05} = 5% discount
|
|
cost_margin_config: Dict[
|
|
str, Union[float, Dict[str, float]]
|
|
] = {} # Provider-specific or global cost margins. Examples:
|
|
# Percentage: {"openai": 0.10} = 10% margin
|
|
# Fixed: {"openai": {"fixed_amount": 0.001}} = $0.001 per request
|
|
# Global: {"global": 0.05} = 5% global margin on all providers
|
|
# Combined: {"vertex_ai": {"percentage": 0.08, "fixed_amount": 0.0005}}
|
|
custom_prompt_dict: Dict[str, dict] = {}
|
|
check_provider_endpoint = False
|
|
|
|
|
|
####### THREAD-SPECIFIC DATA ####################
|
|
class MyLocal(threading.local):
|
|
def __init__(self):
|
|
self.user = "Hello World"
|
|
|
|
|
|
_thread_context = MyLocal()
|
|
|
|
|
|
def identify(event_details):
|
|
# Store user in thread local data
|
|
if "user" in event_details:
|
|
_thread_context.user = event_details["user"]
|
|
|
|
|
|
####### ADDITIONAL PARAMS ################### configurable params if you use proxy models like Helicone, map spend to org id, etc.
|
|
api_base: Optional[str] = None
|
|
headers = None
|
|
api_version: Optional[str] = None
|
|
organization = None
|
|
project = None
|
|
config_path = None
|
|
vertex_ai_safety_settings: Optional[dict] = None
|
|
|
|
####### COMPLETION MODELS ###################
|
|
from typing import Set
|
|
|
|
open_ai_chat_completion_models: Set = set()
|
|
open_ai_text_completion_models: Set = set()
|
|
cohere_models: Set = set()
|
|
cohere_chat_models: Set = set()
|
|
mistral_chat_models: Set = set()
|
|
text_completion_codestral_models: Set = set()
|
|
text_completion_inception_models: Set = set()
|
|
anthropic_models: Set = set()
|
|
openrouter_models: Set = set()
|
|
datarobot_models: Set = set()
|
|
vertex_language_models: Set = set()
|
|
vertex_vision_models: Set = set()
|
|
vertex_chat_models: Set = set()
|
|
vertex_code_chat_models: Set = set()
|
|
vertex_ai_image_models: Set = set()
|
|
vertex_ai_video_models: Set = set()
|
|
vertex_text_models: Set = set()
|
|
vertex_code_text_models: Set = set()
|
|
vertex_embedding_models: Set = set()
|
|
vertex_anthropic_models: Set = set()
|
|
vertex_llama3_models: Set = set()
|
|
vertex_deepseek_models: Set = set()
|
|
vertex_ai_ai21_models: Set = set()
|
|
vertex_mistral_models: Set = set()
|
|
vertex_openai_models: Set = set()
|
|
vertex_minimax_models: Set = set()
|
|
vertex_moonshot_models: Set = set()
|
|
vertex_zai_models: Set = set()
|
|
ai21_models: Set = set()
|
|
ai21_chat_models: Set = set()
|
|
nlp_cloud_models: Set = set()
|
|
aleph_alpha_models: Set = set()
|
|
bedrock_models: Set = set()
|
|
bedrock_converse_models: Set = set(BEDROCK_CONVERSE_MODELS)
|
|
fal_ai_models: Set = set()
|
|
fireworks_ai_models: Set = set()
|
|
fireworks_ai_embedding_models: Set = set()
|
|
deepinfra_models: Set = set()
|
|
perplexity_models: Set = set()
|
|
watsonx_models: Set = set()
|
|
gemini_models: Set = set()
|
|
xai_models: Set = set()
|
|
zai_models: Set = set()
|
|
deepseek_models: Set = set()
|
|
tencent_models: Set = set()
|
|
runwayml_models: Set = set()
|
|
azure_ai_models: Set = set()
|
|
jina_ai_models: Set = set()
|
|
voyage_models: Set = set()
|
|
infinity_models: Set = set()
|
|
heroku_models: Set = set()
|
|
databricks_models: Set = set()
|
|
cloudflare_models: Set = set()
|
|
codestral_models: Set = set()
|
|
friendliai_models: Set = set()
|
|
featherless_ai_models: Set = set()
|
|
palm_models: Set = set()
|
|
groq_models: Set = set()
|
|
azure_models: Set = set()
|
|
azure_anthropic_models: Set = set()
|
|
azure_text_models: Set = set()
|
|
anyscale_models: Set = set()
|
|
cerebras_models: Set = set()
|
|
galadriel_models: Set = set()
|
|
nvidia_nim_models: Set = set()
|
|
nvidia_riva_models: Set = set()
|
|
soniox_models: Set = set()
|
|
sambanova_models: Set = set()
|
|
sambanova_embedding_models: Set = set()
|
|
novita_models: Set = set()
|
|
assemblyai_models: Set = set()
|
|
snowflake_models: Set = set()
|
|
gradient_ai_models: Set = set()
|
|
llama_models: Set = set()
|
|
nscale_models: Set = set()
|
|
nebius_models: Set = set()
|
|
nebius_embedding_models: Set = set()
|
|
aiml_models: Set = set()
|
|
deepgram_models: Set = set()
|
|
elevenlabs_models: Set = set()
|
|
dashscope_models: Set = set()
|
|
moonshot_models: Set = set()
|
|
publicai_models: Set = set()
|
|
darkbloom_models: Set = set()
|
|
v0_models: Set = set()
|
|
morph_models: Set = set()
|
|
lambda_ai_models: Set = set()
|
|
inception_models: Set = set()
|
|
hyperbolic_models: Set = set()
|
|
black_forest_labs_models: Set = set()
|
|
recraft_models: Set = set()
|
|
cometapi_models: Set = set()
|
|
oci_models: Set = set()
|
|
vercel_ai_gateway_models: Set = set()
|
|
volcengine_models: Set = set()
|
|
wandb_models: Set = set(WANDB_MODELS)
|
|
ovhcloud_models: Set = set()
|
|
ovhcloud_embedding_models: Set = set()
|
|
lemonade_models: Set = set()
|
|
docker_model_runner_models: Set = set()
|
|
amazon_nova_models: Set = set()
|
|
stability_models: Set = set()
|
|
github_copilot_models: Set = set()
|
|
chatgpt_models: Set = set()
|
|
minimax_models: Set = set()
|
|
aws_polly_models: Set = set()
|
|
gigachat_models: Set = set()
|
|
llamagate_models: Set = set()
|
|
reducto_models: Set = set()
|
|
bedrock_mantle_models: Set = set()
|
|
|
|
|
|
def is_bedrock_pricing_only_model(key: str) -> bool:
|
|
"""
|
|
Excludes keys with the pattern 'bedrock/<region>/<model>'. These are in the model_prices_and_context_window.json file for pricing purposes only.
|
|
|
|
Args:
|
|
key (str): A key to filter.
|
|
|
|
Returns:
|
|
bool: True if the key matches the Bedrock pattern, False otherwise.
|
|
"""
|
|
# Regex to match 'bedrock/<region>/<model>'
|
|
bedrock_pattern = re.compile(r"^bedrock/[a-zA-Z0-9_-]+/.+$")
|
|
|
|
if "month-commitment" in key:
|
|
return True
|
|
|
|
is_match = bedrock_pattern.match(key)
|
|
return is_match is not None
|
|
|
|
|
|
def is_openai_finetune_model(key: str) -> bool:
|
|
"""
|
|
Excludes model cost keys with the pattern 'ft:<model>'. These are in the model_prices_and_context_window.json file for pricing purposes only.
|
|
|
|
Args:
|
|
key (str): A key to filter.
|
|
|
|
Returns:
|
|
bool: True if the key matches the OpenAI finetune pattern, False otherwise.
|
|
"""
|
|
return key.startswith("ft:") and not key.count(":") > 1
|
|
|
|
|
|
def add_known_models(model_cost_map: Optional[Dict] = None):
|
|
_map = model_cost_map if model_cost_map is not None else model_cost
|
|
for key, value in _map.items():
|
|
if value.get("litellm_provider") == "openai" and not is_openai_finetune_model(key):
|
|
open_ai_chat_completion_models.add(key)
|
|
elif value.get("litellm_provider") == "text-completion-openai":
|
|
open_ai_text_completion_models.add(key)
|
|
elif value.get("litellm_provider") == "azure_text":
|
|
azure_text_models.add(key)
|
|
elif value.get("litellm_provider") == "cohere":
|
|
cohere_models.add(key)
|
|
elif value.get("litellm_provider") == "cohere_chat":
|
|
cohere_chat_models.add(key)
|
|
elif value.get("litellm_provider") == "mistral":
|
|
mistral_chat_models.add(key)
|
|
elif value.get("litellm_provider") == "anthropic":
|
|
anthropic_models.add(key)
|
|
elif value.get("litellm_provider") == "empower":
|
|
empower_models.add(key)
|
|
elif value.get("litellm_provider") == "openrouter":
|
|
openrouter_models.add(key)
|
|
elif value.get("litellm_provider") == "vercel_ai_gateway":
|
|
vercel_ai_gateway_models.add(key)
|
|
elif value.get("litellm_provider") == "datarobot":
|
|
datarobot_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-text-models":
|
|
vertex_text_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-code-text-models":
|
|
vertex_code_text_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-language-models":
|
|
vertex_language_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-vision-models":
|
|
vertex_vision_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-chat-models":
|
|
vertex_chat_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-code-chat-models":
|
|
vertex_code_chat_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-embedding-models":
|
|
vertex_embedding_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-anthropic_models":
|
|
key = key.replace("vertex_ai/", "")
|
|
vertex_anthropic_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-llama_models":
|
|
key = key.replace("vertex_ai/", "")
|
|
vertex_llama3_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-deepseek_models":
|
|
key = key.replace("vertex_ai/", "")
|
|
vertex_deepseek_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-mistral_models":
|
|
key = key.replace("vertex_ai/", "")
|
|
vertex_mistral_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-ai21_models":
|
|
key = key.replace("vertex_ai/", "")
|
|
vertex_ai_ai21_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-image-models":
|
|
key = key.replace("vertex_ai/", "")
|
|
vertex_ai_image_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-video-models":
|
|
key = key.replace("vertex_ai/", "")
|
|
vertex_ai_video_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-openai_models":
|
|
key = key.replace("vertex_ai/", "")
|
|
vertex_openai_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-minimax_models":
|
|
key = key.replace("vertex_ai/", "")
|
|
vertex_minimax_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-moonshot_models":
|
|
key = key.replace("vertex_ai/", "")
|
|
vertex_moonshot_models.add(key)
|
|
elif value.get("litellm_provider") == "vertex_ai-zai_models":
|
|
key = key.replace("vertex_ai/", "")
|
|
vertex_zai_models.add(key)
|
|
elif value.get("litellm_provider") == "ai21":
|
|
if value.get("mode") == "chat":
|
|
ai21_chat_models.add(key)
|
|
else:
|
|
ai21_models.add(key)
|
|
elif value.get("litellm_provider") == "nlp_cloud":
|
|
nlp_cloud_models.add(key)
|
|
elif value.get("litellm_provider") == "aleph_alpha":
|
|
aleph_alpha_models.add(key)
|
|
elif value.get("litellm_provider") == "bedrock" and not is_bedrock_pricing_only_model(key):
|
|
bedrock_models.add(key)
|
|
elif value.get("litellm_provider") == "bedrock_converse":
|
|
bedrock_converse_models.add(key)
|
|
elif value.get("litellm_provider") == "deepinfra":
|
|
deepinfra_models.add(key)
|
|
elif value.get("litellm_provider") == "perplexity":
|
|
perplexity_models.add(key)
|
|
elif value.get("litellm_provider") == "watsonx":
|
|
watsonx_models.add(key)
|
|
elif value.get("litellm_provider") == "gemini":
|
|
gemini_models.add(key)
|
|
elif value.get("litellm_provider") == "fireworks_ai":
|
|
# ignore the 'up-to', '-to-' model names -> not real models. just for cost tracking based on model params.
|
|
if "-to-" not in key and "fireworks-ai-default" not in key:
|
|
fireworks_ai_models.add(key)
|
|
elif value.get("litellm_provider") == "fireworks_ai-embedding-models":
|
|
# ignore the 'up-to', '-to-' model names -> not real models. just for cost tracking based on model params.
|
|
if "-to-" not in key:
|
|
fireworks_ai_embedding_models.add(key)
|
|
elif value.get("litellm_provider") == "text-completion-codestral":
|
|
text_completion_codestral_models.add(key)
|
|
elif value.get("litellm_provider") == "text-completion-inception":
|
|
text_completion_inception_models.add(key)
|
|
elif value.get("litellm_provider") == "xai":
|
|
xai_models.add(key)
|
|
elif value.get("litellm_provider") == "zai":
|
|
zai_models.add(key)
|
|
elif value.get("litellm_provider") == "fal_ai":
|
|
fal_ai_models.add(key)
|
|
elif value.get("litellm_provider") == "deepseek":
|
|
deepseek_models.add(key)
|
|
elif value.get("litellm_provider") == "tencent":
|
|
tencent_models.add(key)
|
|
elif value.get("litellm_provider") == "runwayml":
|
|
runwayml_models.add(key)
|
|
elif value.get("litellm_provider") == "meta_llama":
|
|
llama_models.add(key)
|
|
elif value.get("litellm_provider") == "nscale":
|
|
nscale_models.add(key)
|
|
elif value.get("litellm_provider") == "azure_ai":
|
|
azure_ai_models.add(key)
|
|
elif value.get("litellm_provider") == "voyage":
|
|
voyage_models.add(key)
|
|
elif value.get("litellm_provider") == "infinity":
|
|
infinity_models.add(key)
|
|
elif value.get("litellm_provider") == "databricks":
|
|
databricks_models.add(key)
|
|
elif value.get("litellm_provider") == "cloudflare":
|
|
cloudflare_models.add(key)
|
|
elif value.get("litellm_provider") == "codestral":
|
|
codestral_models.add(key)
|
|
elif value.get("litellm_provider") == "friendliai":
|
|
friendliai_models.add(key)
|
|
elif value.get("litellm_provider") == "palm":
|
|
palm_models.add(key)
|
|
elif value.get("litellm_provider") == "groq":
|
|
groq_models.add(key)
|
|
elif value.get("litellm_provider") == "azure":
|
|
azure_models.add(key)
|
|
elif value.get("litellm_provider") == "azure_anthropic":
|
|
azure_anthropic_models.add(key)
|
|
elif value.get("litellm_provider") == "anyscale":
|
|
anyscale_models.add(key)
|
|
elif value.get("litellm_provider") == "cerebras":
|
|
cerebras_models.add(key)
|
|
elif value.get("litellm_provider") == "galadriel":
|
|
galadriel_models.add(key)
|
|
elif value.get("litellm_provider") == "nvidia_nim":
|
|
nvidia_nim_models.add(key)
|
|
elif value.get("litellm_provider") == "nvidia_riva":
|
|
nvidia_riva_models.add(key)
|
|
elif value.get("litellm_provider") == "soniox":
|
|
soniox_models.add(key)
|
|
elif value.get("litellm_provider") == "sambanova":
|
|
sambanova_models.add(key)
|
|
elif value.get("litellm_provider") == "sambanova-embedding-models":
|
|
sambanova_embedding_models.add(key)
|
|
elif value.get("litellm_provider") == "novita":
|
|
novita_models.add(key)
|
|
elif value.get("litellm_provider") == "nebius-chat-models":
|
|
nebius_models.add(key)
|
|
elif value.get("litellm_provider") == "nebius-embedding-models":
|
|
nebius_embedding_models.add(key)
|
|
elif value.get("litellm_provider") == "aiml":
|
|
aiml_models.add(key)
|
|
elif value.get("litellm_provider") == "assemblyai":
|
|
assemblyai_models.add(key)
|
|
elif value.get("litellm_provider") == "jina_ai":
|
|
jina_ai_models.add(key)
|
|
elif value.get("litellm_provider") == "snowflake":
|
|
snowflake_models.add(key)
|
|
elif value.get("litellm_provider") == "gradient_ai":
|
|
gradient_ai_models.add(key)
|
|
elif value.get("litellm_provider") == "featherless_ai":
|
|
featherless_ai_models.add(key)
|
|
elif value.get("litellm_provider") == "deepgram":
|
|
deepgram_models.add(key)
|
|
elif value.get("litellm_provider") == "elevenlabs":
|
|
elevenlabs_models.add(key)
|
|
elif value.get("litellm_provider") == "heroku":
|
|
heroku_models.add(key)
|
|
elif value.get("litellm_provider") == "dashscope":
|
|
dashscope_models.add(key)
|
|
elif value.get("litellm_provider") == "modelscope":
|
|
modelscope_models.add(key)
|
|
elif value.get("litellm_provider") == "moonshot":
|
|
moonshot_models.add(key)
|
|
elif value.get("litellm_provider") == "publicai":
|
|
publicai_models.add(key)
|
|
elif value.get("litellm_provider") == "darkbloom":
|
|
darkbloom_models.add(key)
|
|
elif value.get("litellm_provider") == "v0":
|
|
v0_models.add(key)
|
|
elif value.get("litellm_provider") == "morph":
|
|
morph_models.add(key)
|
|
elif value.get("litellm_provider") == "lambda_ai":
|
|
lambda_ai_models.add(key)
|
|
elif value.get("litellm_provider") == "inception":
|
|
inception_models.add(key)
|
|
elif value.get("litellm_provider") == "hyperbolic":
|
|
hyperbolic_models.add(key)
|
|
elif value.get("litellm_provider") == "black_forest_labs":
|
|
black_forest_labs_models.add(key)
|
|
elif value.get("litellm_provider") == "recraft":
|
|
recraft_models.add(key)
|
|
elif value.get("litellm_provider") == "cometapi":
|
|
cometapi_models.add(key)
|
|
elif value.get("litellm_provider") == "oci":
|
|
oci_models.add(key)
|
|
elif value.get("litellm_provider") == "volcengine":
|
|
volcengine_models.add(key)
|
|
elif value.get("litellm_provider") == "wandb":
|
|
wandb_models.add(key)
|
|
elif value.get("litellm_provider") == "ovhcloud":
|
|
ovhcloud_models.add(key)
|
|
elif value.get("litellm_provider") == "ovhcloud-embedding-models":
|
|
ovhcloud_embedding_models.add(key)
|
|
elif value.get("litellm_provider") == "lemonade":
|
|
lemonade_models.add(key)
|
|
elif value.get("litellm_provider") == "docker_model_runner":
|
|
docker_model_runner_models.add(key)
|
|
elif value.get("litellm_provider") == "amazon_nova":
|
|
amazon_nova_models.add(key)
|
|
elif value.get("litellm_provider") == "stability":
|
|
stability_models.add(key)
|
|
elif value.get("litellm_provider") == "github_copilot":
|
|
github_copilot_models.add(key)
|
|
elif value.get("litellm_provider") == "chatgpt":
|
|
chatgpt_models.add(key)
|
|
elif value.get("litellm_provider") == "minimax":
|
|
minimax_models.add(key)
|
|
elif value.get("litellm_provider") == "aws_polly":
|
|
aws_polly_models.add(key)
|
|
elif value.get("litellm_provider") == "gigachat":
|
|
gigachat_models.add(key)
|
|
elif value.get("litellm_provider") == "llamagate":
|
|
llamagate_models.add(key)
|
|
elif value.get("litellm_provider") == "reducto":
|
|
reducto_models.add(key)
|
|
elif value.get("litellm_provider") == "bedrock_mantle":
|
|
bedrock_mantle_models.add(key)
|
|
|
|
|
|
add_known_models()
|
|
# known openai compatible endpoints - we'll eventually move this list to the model_prices_and_context_window.json dictionary
|
|
|
|
# this is maintained for Exception Mapping
|
|
|
|
|
|
# used for Cost Tracking & Token counting
|
|
# https://azure.microsoft.com/en-in/pricing/details/cognitive-services/openai-service/
|
|
# Azure returns gpt-35-turbo in their responses, we need to map this to azure/gpt-3.5-turbo for token counting
|
|
azure_llms = {
|
|
"gpt-35-turbo": "azure/gpt-35-turbo",
|
|
"gpt-35-turbo-16k": "azure/gpt-35-turbo-16k",
|
|
"gpt-35-turbo-instruct": "azure/gpt-35-turbo-instruct",
|
|
"azure/gpt-41": "gpt-4.1",
|
|
"azure/gpt-41-mini": "gpt-4.1-mini",
|
|
"azure/gpt-41-nano": "gpt-4.1-nano",
|
|
}
|
|
|
|
azure_embedding_models = {
|
|
"ada": "azure/ada",
|
|
}
|
|
|
|
petals_models = [
|
|
"petals-team/StableBeluga2",
|
|
]
|
|
|
|
ollama_models = ["llama2"]
|
|
|
|
maritalk_models = ["maritalk"]
|
|
|
|
model_list = list(
|
|
open_ai_chat_completion_models
|
|
| open_ai_text_completion_models
|
|
| cohere_models
|
|
| cohere_chat_models
|
|
| anthropic_models
|
|
| set(replicate_models)
|
|
| openrouter_models
|
|
| datarobot_models
|
|
| set(huggingface_models)
|
|
| vertex_chat_models
|
|
| vertex_text_models
|
|
| ai21_models
|
|
| ai21_chat_models
|
|
| set(together_ai_models)
|
|
| set(baseten_models)
|
|
| aleph_alpha_models
|
|
| nlp_cloud_models
|
|
| set(ollama_models)
|
|
| bedrock_models
|
|
| deepinfra_models
|
|
| perplexity_models
|
|
| set(maritalk_models)
|
|
| runwayml_models
|
|
| vertex_language_models
|
|
| watsonx_models
|
|
| gemini_models
|
|
| text_completion_codestral_models
|
|
| text_completion_inception_models
|
|
| xai_models
|
|
| zai_models
|
|
| fal_ai_models
|
|
| deepseek_models
|
|
| modelscope_models
|
|
| azure_ai_models
|
|
| voyage_models
|
|
| infinity_models
|
|
| databricks_models
|
|
| cloudflare_models
|
|
| codestral_models
|
|
| friendliai_models
|
|
| palm_models
|
|
| groq_models
|
|
| azure_models
|
|
| azure_anthropic_models
|
|
| anyscale_models
|
|
| cerebras_models
|
|
| galadriel_models
|
|
| nvidia_nim_models
|
|
| nvidia_riva_models
|
|
| soniox_models
|
|
| sambanova_models
|
|
| azure_text_models
|
|
| novita_models
|
|
| assemblyai_models
|
|
| jina_ai_models
|
|
| snowflake_models
|
|
| gradient_ai_models
|
|
| llama_models
|
|
| featherless_ai_models
|
|
| nscale_models
|
|
| deepgram_models
|
|
| elevenlabs_models
|
|
| dashscope_models
|
|
| moonshot_models
|
|
| publicai_models
|
|
| darkbloom_models
|
|
| v0_models
|
|
| morph_models
|
|
| lambda_ai_models
|
|
| inception_models
|
|
| black_forest_labs_models
|
|
| recraft_models
|
|
| cometapi_models
|
|
| oci_models
|
|
| heroku_models
|
|
| vercel_ai_gateway_models
|
|
| volcengine_models
|
|
| wandb_models
|
|
| ovhcloud_models
|
|
| lemonade_models
|
|
| docker_model_runner_models
|
|
| reducto_models
|
|
| bedrock_mantle_models
|
|
| set(clarifai_models)
|
|
)
|
|
|
|
model_list_set = set(model_list)
|
|
|
|
# provider_list is lazy-loaded via __getattr__ to avoid importing LlmProviders at import time
|
|
|
|
|
|
models_by_provider: dict = {
|
|
"openai": open_ai_chat_completion_models | open_ai_text_completion_models,
|
|
"text-completion-openai": open_ai_text_completion_models,
|
|
"cohere": cohere_models | cohere_chat_models,
|
|
"cohere_chat": cohere_chat_models,
|
|
"anthropic": anthropic_models,
|
|
"replicate": replicate_models,
|
|
"huggingface": huggingface_models,
|
|
"together_ai": together_ai_models,
|
|
"baseten": baseten_models,
|
|
"openrouter": openrouter_models,
|
|
"vercel_ai_gateway": vercel_ai_gateway_models,
|
|
"datarobot": datarobot_models,
|
|
"vertex_ai": vertex_chat_models
|
|
| vertex_text_models
|
|
| vertex_anthropic_models
|
|
| vertex_vision_models
|
|
| vertex_language_models
|
|
| vertex_deepseek_models
|
|
| vertex_minimax_models
|
|
| vertex_moonshot_models
|
|
| vertex_zai_models,
|
|
"ai21": ai21_models,
|
|
"bedrock": bedrock_models | bedrock_converse_models,
|
|
"petals": petals_models,
|
|
"ollama": ollama_models,
|
|
"ollama_chat": ollama_models,
|
|
"deepinfra": deepinfra_models,
|
|
"perplexity": perplexity_models,
|
|
"maritalk": maritalk_models,
|
|
"watsonx": watsonx_models,
|
|
"gemini": gemini_models,
|
|
"fireworks_ai": fireworks_ai_models | fireworks_ai_embedding_models,
|
|
"aleph_alpha": aleph_alpha_models,
|
|
"text-completion-codestral": text_completion_codestral_models,
|
|
"text-completion-inception": text_completion_inception_models,
|
|
"xai": xai_models,
|
|
"zai": zai_models,
|
|
"fal_ai": fal_ai_models,
|
|
"deepseek": deepseek_models,
|
|
"tencent": tencent_models,
|
|
"runwayml": runwayml_models,
|
|
"mistral": mistral_chat_models,
|
|
"azure_ai": azure_ai_models,
|
|
"voyage": voyage_models,
|
|
"infinity": infinity_models,
|
|
"databricks": databricks_models,
|
|
"cloudflare": cloudflare_models,
|
|
"codestral": codestral_models,
|
|
"nlp_cloud": nlp_cloud_models,
|
|
"friendliai": friendliai_models,
|
|
"palm": palm_models,
|
|
"groq": groq_models,
|
|
"azure": azure_models | azure_text_models,
|
|
"azure_anthropic": azure_anthropic_models,
|
|
"azure_text": azure_text_models,
|
|
"anyscale": anyscale_models,
|
|
"cerebras": cerebras_models,
|
|
"galadriel": galadriel_models,
|
|
"nvidia_nim": nvidia_nim_models,
|
|
"nvidia_riva": nvidia_riva_models,
|
|
"soniox": soniox_models,
|
|
"sambanova": sambanova_models | sambanova_embedding_models,
|
|
"novita": novita_models,
|
|
"nebius": nebius_models | nebius_embedding_models,
|
|
"aiml": aiml_models,
|
|
"assemblyai": assemblyai_models,
|
|
"jina_ai": jina_ai_models,
|
|
"snowflake": snowflake_models,
|
|
"gradient_ai": gradient_ai_models,
|
|
"meta_llama": llama_models,
|
|
"nscale": nscale_models,
|
|
"featherless_ai": featherless_ai_models,
|
|
"deepgram": deepgram_models,
|
|
"elevenlabs": elevenlabs_models,
|
|
"heroku": heroku_models,
|
|
"dashscope": dashscope_models,
|
|
"modelscope": modelscope_models,
|
|
"moonshot": moonshot_models,
|
|
"publicai": publicai_models,
|
|
"darkbloom": darkbloom_models,
|
|
"v0": v0_models,
|
|
"morph": morph_models,
|
|
"lambda_ai": lambda_ai_models,
|
|
"inception": inception_models,
|
|
"hyperbolic": hyperbolic_models,
|
|
"black_forest_labs": black_forest_labs_models,
|
|
"recraft": recraft_models,
|
|
"cometapi": cometapi_models,
|
|
"oci": oci_models,
|
|
"volcengine": volcengine_models,
|
|
"wandb": wandb_models,
|
|
"ovhcloud": ovhcloud_models | ovhcloud_embedding_models,
|
|
"lemonade": lemonade_models,
|
|
"clarifai": clarifai_models,
|
|
"amazon_nova": amazon_nova_models,
|
|
"stability": stability_models,
|
|
"github_copilot": github_copilot_models,
|
|
"chatgpt": chatgpt_models,
|
|
"minimax": minimax_models,
|
|
"aws_polly": aws_polly_models,
|
|
"gigachat": gigachat_models,
|
|
"llamagate": llamagate_models,
|
|
"reducto": reducto_models,
|
|
"bedrock_mantle": bedrock_mantle_models,
|
|
}
|
|
|
|
# mapping for those models which have larger equivalents
|
|
longer_context_model_fallback_dict: dict = {
|
|
# openai chat completion models
|
|
"gpt-3.5-turbo": "gpt-3.5-turbo-16k",
|
|
"gpt-3.5-turbo-0301": "gpt-3.5-turbo-16k-0301",
|
|
"gpt-3.5-turbo-0613": "gpt-3.5-turbo-16k-0613",
|
|
"gpt-4": "gpt-4-32k",
|
|
"gpt-4-0314": "gpt-4-32k-0314",
|
|
"gpt-4-0613": "gpt-4-32k-0613",
|
|
# anthropic
|
|
"claude-instant-1": "claude-2",
|
|
"claude-instant-1.2": "claude-2",
|
|
# vertexai
|
|
"chat-bison": "chat-bison-32k",
|
|
"chat-bison@001": "chat-bison-32k",
|
|
"codechat-bison": "codechat-bison-32k",
|
|
"codechat-bison@001": "codechat-bison-32k",
|
|
# openrouter
|
|
"openrouter/openai/gpt-3.5-turbo": "openrouter/openai/gpt-3.5-turbo-16k",
|
|
"openrouter/anthropic/claude-instant-v1": "openrouter/anthropic/claude-2",
|
|
}
|
|
|
|
####### EMBEDDING MODELS ###################
|
|
|
|
all_embedding_models = (
|
|
open_ai_embedding_models
|
|
| set(cohere_embedding_models)
|
|
| set(bedrock_embedding_models)
|
|
| vertex_embedding_models
|
|
| fireworks_ai_embedding_models
|
|
| nebius_embedding_models
|
|
| sambanova_embedding_models
|
|
| ovhcloud_embedding_models
|
|
)
|
|
|
|
####### IMAGE GENERATION MODELS ###################
|
|
openai_image_generation_models = ["dall-e-2", "dall-e-3"]
|
|
|
|
####### VIDEO GENERATION MODELS ###################
|
|
openai_video_generation_models = ["sora-2"]
|
|
|
|
# timeout is lazy-loaded via __getattr__
|
|
# get_llm_provider is lazy-loaded via __getattr__
|
|
# remove_index_from_tool_calls is lazy-loaded via __getattr__
|
|
|
|
# Import KeyManagementSettings here (before utils import) because _key_management_settings
|
|
# is accessed during import time in secret_managers/main.py (via dd_tracing -> datadog -> _service_logger -> utils)
|
|
from litellm.types.secret_managers.main import KeyManagementSettings
|
|
|
|
_key_management_settings: KeyManagementSettings = KeyManagementSettings()
|
|
|
|
# client must be imported immediately as it's used as a decorator at function definition time
|
|
from .utils import client
|
|
|
|
# Note: Most other utils imports are lazy-loaded via __getattr__ to avoid loading utils.py
|
|
# (which imports tiktoken) at import time
|
|
|
|
from .llms.custom_llm import CustomLLM
|
|
from .llms.anthropic.common_utils import AnthropicModelInfo
|
|
from .llms.ai21.chat.transformation import AI21ChatConfig, AI21ChatConfig as AI21Config
|
|
from .llms.deprecated_providers.palm import (
|
|
PalmConfig,
|
|
) # here to prevent breaking changes
|
|
from .llms.deprecated_providers.aleph_alpha import AlephAlphaConfig
|
|
from .llms.gemini.common_utils import GeminiModelInfo
|
|
|
|
|
|
from .llms.vertex_ai.vertex_embeddings.transformation import (
|
|
VertexAITextEmbeddingConfig,
|
|
)
|
|
|
|
vertexAITextEmbeddingConfig = VertexAITextEmbeddingConfig()
|
|
|
|
|
|
from .llms.bedrock.embed.amazon_titan_v2_transformation import (
|
|
AmazonTitanV2Config,
|
|
)
|
|
from .llms.topaz.common_utils import TopazModelInfo
|
|
|
|
# OpenAIOSeriesConfig is lazy loaded - openaiOSeriesConfig will be created on first access
|
|
# OpenAIGPTConfig, OpenAIGPT5Config, etc. are lazy loaded - instances will be created on first access
|
|
from .llms.xai.common_utils import XAIModelInfo
|
|
|
|
# PublicAI now uses JSON-based configuration (see litellm/llms/openai_like/providers.json)
|
|
# All remaining configs are now lazy loaded - see _lazy_imports_registry.py
|
|
|
|
# Import LlmProviders here (before main import) because it's imported during import time
|
|
# in multiple places including openai.py (via main import)
|
|
from litellm.types.utils import LlmProviders
|
|
|
|
## Lazy loading this is not straightforward, will leave it here for now.
|
|
from .main import * # type: ignore
|
|
from .compression import compress # type: ignore[no-redef]
|
|
|
|
# Skills API
|
|
from .skills.main import (
|
|
create_skill,
|
|
acreate_skill,
|
|
list_skills,
|
|
alist_skills,
|
|
get_skill,
|
|
aget_skill,
|
|
delete_skill,
|
|
adelete_skill,
|
|
)
|
|
from .evals.main import (
|
|
create_eval,
|
|
acreate_eval,
|
|
list_evals,
|
|
alist_evals,
|
|
get_eval,
|
|
aget_eval,
|
|
delete_eval,
|
|
adelete_eval,
|
|
cancel_eval,
|
|
acancel_eval,
|
|
create_run,
|
|
acreate_run,
|
|
list_runs,
|
|
alist_runs,
|
|
get_run,
|
|
aget_run,
|
|
delete_run,
|
|
adelete_run,
|
|
cancel_run,
|
|
acancel_run,
|
|
)
|
|
from .integrations import *
|
|
from .llms.custom_httpx.async_client_cleanup import close_litellm_async_clients
|
|
from .exceptions import (
|
|
AuthenticationError,
|
|
InvalidRequestError,
|
|
BadRequestError,
|
|
ImageFetchError,
|
|
NotFoundError,
|
|
PermissionDeniedError,
|
|
RateLimitError,
|
|
RateLimitErrorCategory,
|
|
RateLimitType,
|
|
ServiceUnavailableError,
|
|
BadGatewayError,
|
|
OpenAIError,
|
|
ContextWindowExceededError,
|
|
ContentPolicyViolationError,
|
|
BudgetExceededError,
|
|
APIError,
|
|
Timeout,
|
|
APIConnectionError,
|
|
UnsupportedParamsError,
|
|
APIResponseValidationError,
|
|
UnprocessableEntityError,
|
|
InternalServerError,
|
|
JSONSchemaValidationError,
|
|
LITELLM_EXCEPTION_TYPES,
|
|
MockException,
|
|
)
|
|
from .budget_manager import BudgetManager
|
|
from .proxy.proxy_cli import run_server
|
|
from .router import Router
|
|
from .assistants.main import *
|
|
from .batches.main import *
|
|
from .images.main import *
|
|
from .videos.main import *
|
|
from .batch_completion.main import * # type: ignore
|
|
from .rerank_api.main import *
|
|
from .llms.anthropic.experimental_pass_through.messages.handler import *
|
|
from .responses.main import *
|
|
|
|
# Interactions API is available as litellm.interactions module
|
|
# Usage: litellm.interactions.create(), litellm.interactions.get(), etc.
|
|
from . import interactions
|
|
from .interactions.agents.main import (
|
|
acreate as acreate_agent,
|
|
create as create_agent,
|
|
alist as alist_agents,
|
|
list as list_agents,
|
|
aget as aget_agent,
|
|
get as get_agent,
|
|
adelete as adelete_agent,
|
|
delete as delete_agent,
|
|
alist_versions as alist_agent_versions,
|
|
list_versions as list_agent_versions,
|
|
)
|
|
from .skills.main import (
|
|
create_skill,
|
|
acreate_skill,
|
|
list_skills,
|
|
alist_skills,
|
|
get_skill,
|
|
aget_skill,
|
|
delete_skill,
|
|
adelete_skill,
|
|
)
|
|
from .containers.main import *
|
|
from .ocr.main import *
|
|
from .rust_bridge.ocr import use_litellm_rust
|
|
from .rag.main import *
|
|
from .sandbox.main import *
|
|
from .search.main import *
|
|
from .realtime_api.main import (
|
|
_arealtime,
|
|
acreate_realtime_client_secret,
|
|
acreate_realtime_transcription_session,
|
|
arealtime_calls,
|
|
)
|
|
from .responses.main import _aresponses_websocket
|
|
from .fine_tuning.main import *
|
|
from .files.main import *
|
|
from .vector_store_files.main import (
|
|
acreate as avector_store_file_create,
|
|
adelete as avector_store_file_delete,
|
|
alist as avector_store_file_list,
|
|
aretrieve as avector_store_file_retrieve,
|
|
aretrieve_content as avector_store_file_content,
|
|
aupdate as avector_store_file_update,
|
|
create as vector_store_file_create,
|
|
delete as vector_store_file_delete,
|
|
list as vector_store_file_list,
|
|
retrieve as vector_store_file_retrieve,
|
|
retrieve_content as vector_store_file_content,
|
|
update as vector_store_file_update,
|
|
)
|
|
from .scheduler import *
|
|
|
|
### ADAPTERS ###
|
|
from .types.adapter import AdapterItem
|
|
import litellm.anthropic_interface as anthropic
|
|
|
|
adapters: List[AdapterItem] = []
|
|
|
|
### Vector Store Registry ###
|
|
from .vector_stores.vector_store_registry import (
|
|
VectorStoreRegistry,
|
|
VectorStoreIndexRegistry,
|
|
)
|
|
|
|
vector_store_registry: Optional[VectorStoreRegistry] = None
|
|
vector_store_index_registry: Optional[VectorStoreIndexRegistry] = None
|
|
|
|
### RAG ###
|
|
from . import rag
|
|
|
|
### CUSTOM LLMs ###
|
|
from .types.llms.custom_llm import CustomLLMItem
|
|
|
|
custom_provider_map: List[CustomLLMItem] = []
|
|
_custom_providers: List[str] = [] # internal helper util, used to track names of custom providers
|
|
disable_hf_tokenizer_download: Optional[bool] = (
|
|
None # disable huggingface tokenizer download. Defaults to openai clk100
|
|
)
|
|
global_disable_no_log_param: bool = False
|
|
|
|
### CLI UTILITIES ###
|
|
from litellm.litellm_core_utils.cli_token_utils import get_litellm_gateway_api_key
|
|
|
|
### PASSTHROUGH ###
|
|
from .passthrough import allm_passthrough_route, llm_passthrough_route
|
|
from .google_genai import agenerate_content
|
|
|
|
### GLOBAL CONFIG ###
|
|
global_bitbucket_config: Optional[Dict[str, Any]] = None
|
|
|
|
|
|
def set_global_bitbucket_config(config: Dict[str, Any]) -> None:
|
|
"""Set global BitBucket configuration for prompt management."""
|
|
global global_bitbucket_config
|
|
global_bitbucket_config = config
|
|
|
|
|
|
### GLOBAL CONFIG ###
|
|
global_gitlab_config: Optional[Dict[str, Any]] = None
|
|
|
|
|
|
def set_global_gitlab_config(config: Dict[str, Any]) -> None:
|
|
"""Set global BitBucket configuration for prompt management."""
|
|
global global_gitlab_config
|
|
global_gitlab_config = config
|
|
|
|
|
|
# Lazy loading system for heavy modules to reduce initial import time and memory usage
|
|
|
|
if TYPE_CHECKING:
|
|
from litellm.types.utils import ModelInfo as _ModelInfoType
|
|
from litellm.types.utils import PriorityReservationSettings
|
|
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
|
|
from litellm.caching.caching import Cache
|
|
|
|
# Type stubs for lazy-loaded configs to help mypy
|
|
from .llms.bedrock.chat.converse_transformation import (
|
|
AmazonConverseConfig as AmazonConverseConfig,
|
|
)
|
|
from .llms.openai_like.chat.handler import (
|
|
OpenAILikeChatConfig as OpenAILikeChatConfig,
|
|
)
|
|
from .llms.galadriel.chat.transformation import (
|
|
GaladrielChatConfig as GaladrielChatConfig,
|
|
)
|
|
from .llms.github.chat.transformation import GithubChatConfig as GithubChatConfig
|
|
from .llms.azure_ai.anthropic.transformation import (
|
|
AzureAnthropicConfig as AzureAnthropicConfig,
|
|
)
|
|
from .llms.bytez.chat.transformation import BytezChatConfig as BytezChatConfig
|
|
from .llms.compactifai.chat.transformation import (
|
|
CompactifAIChatConfig as CompactifAIChatConfig,
|
|
)
|
|
from .llms.empower.chat.transformation import EmpowerChatConfig as EmpowerChatConfig
|
|
from .llms.minimax.chat.transformation import MinimaxChatConfig as MinimaxChatConfig
|
|
from .llms.aiohttp_openai.chat.transformation import (
|
|
AiohttpOpenAIChatConfig as AiohttpOpenAIChatConfig,
|
|
)
|
|
from .llms.huggingface.chat.transformation import (
|
|
HuggingFaceChatConfig as HuggingFaceChatConfig,
|
|
)
|
|
from .llms.huggingface.embedding.transformation import (
|
|
HuggingFaceEmbeddingConfig as HuggingFaceEmbeddingConfig,
|
|
)
|
|
from .llms.oobabooga.chat.transformation import OobaboogaConfig as OobaboogaConfig
|
|
from .llms.maritalk import MaritalkConfig as MaritalkConfig
|
|
from .llms.openrouter.chat.transformation import (
|
|
OpenrouterConfig as OpenrouterConfig,
|
|
)
|
|
from .llms.datarobot.chat.transformation import DataRobotConfig as DataRobotConfig
|
|
from .llms.anthropic.chat.transformation import AnthropicConfig as AnthropicConfig
|
|
from .llms.bedrock.claude_platform.transformation import (
|
|
BedrockClaudePlatformConfig as BedrockClaudePlatformConfig,
|
|
)
|
|
from .llms.bedrock.claude_platform.messages_transformation import (
|
|
BedrockClaudePlatformMessagesConfig as BedrockClaudePlatformMessagesConfig,
|
|
)
|
|
from .llms.anthropic.completion.transformation import (
|
|
AnthropicTextConfig as AnthropicTextConfig,
|
|
)
|
|
from .llms.groq.stt.transformation import GroqSTTConfig as GroqSTTConfig
|
|
from .llms.triton.completion.transformation import TritonConfig as TritonConfig
|
|
from .llms.triton.completion.transformation import (
|
|
TritonGenerateConfig as TritonGenerateConfig,
|
|
)
|
|
from .llms.triton.completion.transformation import (
|
|
TritonInferConfig as TritonInferConfig,
|
|
)
|
|
from .llms.triton.embedding.transformation import (
|
|
TritonEmbeddingConfig as TritonEmbeddingConfig,
|
|
)
|
|
from .llms.huggingface.rerank.transformation import (
|
|
HuggingFaceRerankConfig as HuggingFaceRerankConfig,
|
|
)
|
|
from .llms.databricks.chat.transformation import (
|
|
DatabricksConfig as DatabricksConfig,
|
|
)
|
|
from .llms.databricks.embed.transformation import (
|
|
DatabricksEmbeddingConfig as DatabricksEmbeddingConfig,
|
|
)
|
|
from .llms.predibase.chat.transformation import PredibaseConfig as PredibaseConfig
|
|
from .llms.replicate.chat.transformation import ReplicateConfig as ReplicateConfig
|
|
from .llms.snowflake.chat.transformation import SnowflakeConfig as SnowflakeConfig
|
|
from .llms.cohere.rerank.transformation import (
|
|
CohereRerankConfig as CohereRerankConfig,
|
|
)
|
|
from .llms.cohere.rerank_v2.transformation import (
|
|
CohereRerankV2Config as CohereRerankV2Config,
|
|
)
|
|
from .llms.azure_ai.rerank.transformation import (
|
|
AzureAIRerankConfig as AzureAIRerankConfig,
|
|
)
|
|
from .llms.infinity.rerank.transformation import (
|
|
InfinityRerankConfig as InfinityRerankConfig,
|
|
)
|
|
from .llms.jina_ai.rerank.transformation import (
|
|
JinaAIRerankConfig as JinaAIRerankConfig,
|
|
)
|
|
from .llms.deepinfra.rerank.transformation import (
|
|
DeepinfraRerankConfig as DeepinfraRerankConfig,
|
|
)
|
|
from .llms.hosted_vllm.rerank.transformation import (
|
|
HostedVLLMRerankConfig as HostedVLLMRerankConfig,
|
|
)
|
|
from .llms.nvidia_nim.rerank.transformation import (
|
|
NvidiaNimRerankConfig as NvidiaNimRerankConfig,
|
|
)
|
|
from .llms.nvidia_nim.rerank.ranking_transformation import (
|
|
NvidiaNimRankingConfig as NvidiaNimRankingConfig,
|
|
)
|
|
from .llms.vertex_ai.rerank.transformation import (
|
|
VertexAIRerankConfig as VertexAIRerankConfig,
|
|
)
|
|
from .llms.fireworks_ai.rerank.transformation import (
|
|
FireworksAIRerankConfig as FireworksAIRerankConfig,
|
|
)
|
|
from .llms.voyage.rerank.transformation import (
|
|
VoyageRerankConfig as VoyageRerankConfig,
|
|
)
|
|
from .llms.watsonx.rerank.transformation import (
|
|
IBMWatsonXRerankConfig as IBMWatsonXRerankConfig,
|
|
)
|
|
from .llms.clarifai.chat.transformation import ClarifaiConfig as ClarifaiConfig
|
|
from .llms.ai21.chat.transformation import AI21ChatConfig as AI21ChatConfig
|
|
from .llms.meta_llama.chat.transformation import LlamaAPIConfig as LlamaAPIConfig
|
|
from .llms.together_ai.completion.transformation import (
|
|
TogetherAITextCompletionConfig as TogetherAITextCompletionConfig,
|
|
)
|
|
from .llms.cloudflare.chat.transformation import (
|
|
CloudflareChatConfig as CloudflareChatConfig,
|
|
)
|
|
from .llms.novita.chat.transformation import NovitaConfig as NovitaConfig
|
|
from .llms.petals.completion.transformation import PetalsConfig as PetalsConfig
|
|
from .llms.ollama.chat.transformation import OllamaChatConfig as OllamaChatConfig
|
|
from .llms.ollama.completion.transformation import OllamaConfig as OllamaConfig
|
|
from .llms.sagemaker.completion.transformation import (
|
|
SagemakerConfig as SagemakerConfig,
|
|
)
|
|
from .llms.sagemaker.chat.transformation import (
|
|
SagemakerChatConfig as SagemakerChatConfig,
|
|
)
|
|
from .llms.sagemaker.nova.transformation import (
|
|
SagemakerNovaConfig as SagemakerNovaConfig,
|
|
)
|
|
from .llms.cohere.chat.transformation import CohereChatConfig as CohereChatConfig
|
|
from .llms.anthropic.experimental_pass_through.messages.transformation import (
|
|
AnthropicMessagesConfig as AnthropicMessagesConfig,
|
|
)
|
|
from .llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import (
|
|
AmazonAnthropicClaudeMessagesConfig as AmazonAnthropicClaudeMessagesConfig,
|
|
)
|
|
from .llms.bedrock.messages.mantle_transformation import (
|
|
AmazonMantleMessagesConfig as AmazonMantleMessagesConfig,
|
|
)
|
|
from .llms.together_ai.chat import TogetherAIConfig as TogetherAIConfig
|
|
from .llms.nlp_cloud.chat.handler import NLPCloudConfig as NLPCloudConfig
|
|
from .llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
|
|
VertexGeminiConfig as VertexGeminiConfig,
|
|
)
|
|
from .llms.gemini.chat.transformation import (
|
|
GoogleAIStudioGeminiConfig as GoogleAIStudioGeminiConfig,
|
|
)
|
|
from .llms.vertex_ai.vertex_ai_partner_models.anthropic.transformation import (
|
|
VertexAIAnthropicConfig as VertexAIAnthropicConfig,
|
|
)
|
|
from .llms.vertex_ai.vertex_ai_partner_models.llama3.transformation import (
|
|
VertexAILlama3Config as VertexAILlama3Config,
|
|
)
|
|
from .llms.vertex_ai.vertex_ai_partner_models.ai21.transformation import (
|
|
VertexAIAi21Config as VertexAIAi21Config,
|
|
)
|
|
from .llms.bedrock.chat.invoke_handler import (
|
|
AmazonCohereChatConfig as AmazonCohereChatConfig,
|
|
)
|
|
from .llms.bedrock.common_utils import (
|
|
AmazonBedrockGlobalConfig as AmazonBedrockGlobalConfig,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.amazon_ai21_transformation import (
|
|
AmazonAI21Config as AmazonAI21Config,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.amazon_nova_transformation import (
|
|
AmazonInvokeNovaConfig as AmazonInvokeNovaConfig,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.amazon_qwen2_transformation import (
|
|
AmazonQwen2Config as AmazonQwen2Config,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.amazon_qwen3_transformation import (
|
|
AmazonQwen3Config as AmazonQwen3Config,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.anthropic_claude2_transformation import (
|
|
AmazonAnthropicConfig as AmazonAnthropicConfig,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation import (
|
|
AmazonAnthropicClaudeConfig as AmazonAnthropicClaudeConfig,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.amazon_cohere_transformation import (
|
|
AmazonCohereConfig as AmazonCohereConfig,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.amazon_llama_transformation import (
|
|
AmazonLlamaConfig as AmazonLlamaConfig,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.amazon_deepseek_transformation import (
|
|
AmazonDeepSeekR1Config as AmazonDeepSeekR1Config,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.amazon_mistral_transformation import (
|
|
AmazonMistralConfig as AmazonMistralConfig,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.amazon_moonshot_transformation import (
|
|
AmazonMoonshotConfig as AmazonMoonshotConfig,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.amazon_titan_transformation import (
|
|
AmazonTitanConfig as AmazonTitanConfig,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.amazon_twelvelabs_pegasus_transformation import (
|
|
AmazonTwelveLabsPegasusConfig as AmazonTwelveLabsPegasusConfig,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
|
|
AmazonInvokeConfig as AmazonInvokeConfig,
|
|
)
|
|
from .llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import (
|
|
AmazonBedrockOpenAIConfig as AmazonBedrockOpenAIConfig,
|
|
)
|
|
from .llms.bedrock.image_generation.amazon_stability1_transformation import (
|
|
AmazonStabilityConfig as AmazonStabilityConfig,
|
|
)
|
|
from .llms.bedrock.image_generation.amazon_stability3_transformation import (
|
|
AmazonStability3Config as AmazonStability3Config,
|
|
)
|
|
from .llms.bedrock.image_generation.amazon_nova_canvas_transformation import (
|
|
AmazonNovaCanvasConfig as AmazonNovaCanvasConfig,
|
|
)
|
|
from .llms.bedrock.embed.amazon_titan_g1_transformation import (
|
|
AmazonTitanG1Config as AmazonTitanG1Config,
|
|
)
|
|
from .llms.bedrock.embed.amazon_titan_multimodal_transformation import (
|
|
AmazonTitanMultimodalEmbeddingG1Config as AmazonTitanMultimodalEmbeddingG1Config,
|
|
)
|
|
from .llms.cohere.chat.v2_transformation import (
|
|
CohereV2ChatConfig as CohereV2ChatConfig,
|
|
)
|
|
from .llms.bedrock.embed.cohere_transformation import (
|
|
BedrockCohereEmbeddingConfig as BedrockCohereEmbeddingConfig,
|
|
)
|
|
from .llms.bedrock.embed.twelvelabs_marengo_transformation import (
|
|
TwelveLabsMarengoEmbeddingConfig as TwelveLabsMarengoEmbeddingConfig,
|
|
)
|
|
from .llms.bedrock.embed.amazon_nova_transformation import (
|
|
AmazonNovaEmbeddingConfig as AmazonNovaEmbeddingConfig,
|
|
)
|
|
from .llms.openai.openai import (
|
|
OpenAIConfig as OpenAIConfig,
|
|
MistralEmbeddingConfig as MistralEmbeddingConfig,
|
|
)
|
|
from .llms.openai.image_variations.transformation import (
|
|
OpenAIImageVariationConfig as OpenAIImageVariationConfig,
|
|
)
|
|
from .llms.deepgram.audio_transcription.transformation import (
|
|
DeepgramAudioTranscriptionConfig as DeepgramAudioTranscriptionConfig,
|
|
)
|
|
from .llms.nvidia_riva.audio_transcription.transformation import (
|
|
NvidiaRivaAudioTranscriptionConfig as NvidiaRivaAudioTranscriptionConfig,
|
|
)
|
|
from .llms.topaz.image_variations.transformation import (
|
|
TopazImageVariationConfig as TopazImageVariationConfig,
|
|
)
|
|
from litellm.llms.openai.completion.transformation import (
|
|
OpenAITextCompletionConfig as OpenAITextCompletionConfig,
|
|
)
|
|
from .llms.groq.chat.transformation import GroqChatConfig as GroqChatConfig
|
|
from .llms.bedrock_mantle.chat.transformation import (
|
|
BedrockMantleChatConfig as BedrockMantleChatConfig,
|
|
)
|
|
from .llms.a2a.chat.transformation import A2AConfig as A2AConfig
|
|
from .llms.voyage.embedding.transformation import (
|
|
VoyageEmbeddingConfig as VoyageEmbeddingConfig,
|
|
)
|
|
from .llms.voyage.embedding.transformation_contextual import (
|
|
VoyageContextualEmbeddingConfig as VoyageContextualEmbeddingConfig,
|
|
)
|
|
from .llms.voyage.embedding.transformation_multimodal import (
|
|
VoyageMultimodalEmbeddingConfig as VoyageMultimodalEmbeddingConfig,
|
|
)
|
|
from .llms.infinity.embedding.transformation import (
|
|
InfinityEmbeddingConfig as InfinityEmbeddingConfig,
|
|
)
|
|
from .llms.perplexity.embedding.transformation import (
|
|
PerplexityEmbeddingConfig as PerplexityEmbeddingConfig,
|
|
)
|
|
from .llms.azure_ai.chat.transformation import (
|
|
AzureAIStudioConfig as AzureAIStudioConfig,
|
|
)
|
|
from .llms.mistral.chat.transformation import MistralConfig as MistralConfig
|
|
from .llms.openai.responses.transformation import (
|
|
OpenAIResponsesAPIConfig as OpenAIResponsesAPIConfig,
|
|
)
|
|
from .llms.azure.responses.transformation import (
|
|
AzureOpenAIResponsesAPIConfig as AzureOpenAIResponsesAPIConfig,
|
|
)
|
|
from .llms.azure.responses.o_series_transformation import (
|
|
AzureOpenAIOSeriesResponsesAPIConfig as AzureOpenAIOSeriesResponsesAPIConfig,
|
|
)
|
|
from .llms.xai.responses.transformation import (
|
|
XAIResponsesAPIConfig as XAIResponsesAPIConfig,
|
|
)
|
|
from .llms.litellm_proxy.responses.transformation import (
|
|
LiteLLMProxyResponsesAPIConfig as LiteLLMProxyResponsesAPIConfig,
|
|
)
|
|
from .llms.volcengine.responses.transformation import (
|
|
VolcEngineResponsesAPIConfig as VolcEngineResponsesAPIConfig,
|
|
)
|
|
from .llms.manus.responses.transformation import (
|
|
ManusResponsesAPIConfig as ManusResponsesAPIConfig,
|
|
)
|
|
from .llms.perplexity.responses.transformation import (
|
|
PerplexityResponsesConfig as PerplexityResponsesConfig,
|
|
)
|
|
from .llms.databricks.responses.transformation import (
|
|
DatabricksResponsesAPIConfig as DatabricksResponsesAPIConfig,
|
|
)
|
|
from .llms.openrouter.responses.transformation import (
|
|
OpenRouterResponsesAPIConfig as OpenRouterResponsesAPIConfig,
|
|
)
|
|
from .llms.bedrock_mantle.responses.transformation import (
|
|
BedrockMantleResponsesAPIConfig as BedrockMantleResponsesAPIConfig,
|
|
)
|
|
from .llms.gemini.interactions.transformation import (
|
|
GoogleAIStudioInteractionsConfig as GoogleAIStudioInteractionsConfig,
|
|
)
|
|
from .llms.openai.chat.o_series_transformation import (
|
|
OpenAIOSeriesConfig as OpenAIOSeriesConfig,
|
|
OpenAIOSeriesConfig as OpenAIO1Config,
|
|
)
|
|
from .llms.anthropic.skills.transformation import (
|
|
AnthropicSkillsConfig as AnthropicSkillsConfig,
|
|
)
|
|
from .llms.base_llm.skills.transformation import (
|
|
BaseSkillsAPIConfig as BaseSkillsAPIConfig,
|
|
)
|
|
from .llms.gradient_ai.chat.transformation import (
|
|
GradientAIConfig as GradientAIConfig,
|
|
)
|
|
from .llms.openai.chat.gpt_transformation import OpenAIGPTConfig as OpenAIGPTConfig
|
|
from .llms.openai.chat.gpt_5_transformation import (
|
|
OpenAIGPT5Config as OpenAIGPT5Config,
|
|
)
|
|
from .llms.openai.transcriptions.whisper_transformation import (
|
|
OpenAIWhisperAudioTranscriptionConfig as OpenAIWhisperAudioTranscriptionConfig,
|
|
)
|
|
from .llms.openai.transcriptions.gpt_transformation import (
|
|
OpenAIGPTAudioTranscriptionConfig as OpenAIGPTAudioTranscriptionConfig,
|
|
)
|
|
from .llms.openai.chat.gpt_audio_transformation import (
|
|
OpenAIGPTAudioConfig as OpenAIGPTAudioConfig,
|
|
)
|
|
from .llms.nvidia_nim.chat.transformation import NvidiaNimConfig as NvidiaNimConfig
|
|
from .llms.nvidia_nim.embed import (
|
|
NvidiaNimEmbeddingConfig as NvidiaNimEmbeddingConfig,
|
|
)
|
|
from .llms.gdc.chat.transformation import GDCGeminiConfig as GDCGeminiConfig
|
|
|
|
# Type stubs for lazy-loaded config instances
|
|
openaiOSeriesConfig: OpenAIOSeriesConfig
|
|
openAIGPTConfig: OpenAIGPTConfig
|
|
openAIGPTAudioConfig: OpenAIGPTAudioConfig
|
|
openAIGPT5Config: OpenAIGPT5Config
|
|
nvidiaNimConfig: NvidiaNimConfig
|
|
nvidiaNimEmbeddingConfig: NvidiaNimEmbeddingConfig
|
|
|
|
# Import config classes that need type stubs (for mypy) - import with _ prefix to avoid circular reference
|
|
from .llms.vllm.completion.transformation import VLLMConfig as _VLLMConfig
|
|
from .llms.deepseek.chat.transformation import (
|
|
DeepSeekChatConfig as _DeepSeekChatConfig,
|
|
)
|
|
from .llms.tencent.chat.transformation import (
|
|
TencentChatConfig as _TencentChatConfig,
|
|
)
|
|
from .llms.sap.chat.transformation import (
|
|
GenAIHubOrchestrationConfig as _GenAIHubOrchestrationConfig,
|
|
)
|
|
from .llms.sap.embed.transformation import (
|
|
GenAIHubEmbeddingConfig as _GenAIHubEmbeddingConfig,
|
|
)
|
|
from .llms.azure.chat.o_series_transformation import (
|
|
AzureOpenAIO1Config as _AzureOpenAIO1Config,
|
|
)
|
|
from .llms.perplexity.chat.transformation import (
|
|
PerplexityChatConfig as _PerplexityChatConfig,
|
|
)
|
|
from .llms.nscale.chat.transformation import NscaleConfig as _NscaleConfig
|
|
from .llms.watsonx.chat.transformation import (
|
|
IBMWatsonXChatConfig as _IBMWatsonXChatConfig,
|
|
)
|
|
from .llms.watsonx.completion.transformation import (
|
|
IBMWatsonXAIConfig as _IBMWatsonXAIConfig,
|
|
)
|
|
from .llms.litellm_proxy.chat.transformation import (
|
|
LiteLLMProxyChatConfig as _LiteLLMProxyChatConfig,
|
|
)
|
|
from .llms.deepinfra.chat.transformation import DeepInfraConfig as _DeepInfraConfig
|
|
from .llms.llamafile.chat.transformation import (
|
|
LlamafileChatConfig as _LlamafileChatConfig,
|
|
)
|
|
from .llms.lm_studio.chat.transformation import (
|
|
LMStudioChatConfig as _LMStudioChatConfig,
|
|
)
|
|
from .llms.lm_studio.embed.transformation import (
|
|
LmStudioEmbeddingConfig as _LmStudioEmbeddingConfig,
|
|
)
|
|
from .llms.watsonx.embed.transformation import (
|
|
IBMWatsonXEmbeddingConfig as _IBMWatsonXEmbeddingConfig,
|
|
)
|
|
from .llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
|
|
VertexGeminiConfig as _VertexGeminiConfig,
|
|
)
|
|
|
|
# Type stubs for lazy-loaded config classes (to help mypy understand types)
|
|
VLLMConfig: Type[_VLLMConfig]
|
|
DeepSeekChatConfig: Type[_DeepSeekChatConfig]
|
|
TencentChatConfig: Type[_TencentChatConfig]
|
|
GenAIHubOrchestrationConfig: Type[_GenAIHubOrchestrationConfig]
|
|
GenAIHubEmbeddingConfig: Type[_GenAIHubEmbeddingConfig]
|
|
AzureOpenAIO1Config: Type[_AzureOpenAIO1Config]
|
|
PerplexityChatConfig: Type[_PerplexityChatConfig]
|
|
NscaleConfig: Type[_NscaleConfig]
|
|
IBMWatsonXChatConfig: Type[_IBMWatsonXChatConfig]
|
|
IBMWatsonXAIConfig: Type[_IBMWatsonXAIConfig]
|
|
LiteLLMProxyChatConfig: Type[_LiteLLMProxyChatConfig]
|
|
DeepInfraConfig: Type[_DeepInfraConfig]
|
|
LlamafileChatConfig: Type[_LlamafileChatConfig]
|
|
LMStudioChatConfig: Type[_LMStudioChatConfig]
|
|
LmStudioEmbeddingConfig: Type[_LmStudioEmbeddingConfig]
|
|
IBMWatsonXEmbeddingConfig: Type[_IBMWatsonXEmbeddingConfig]
|
|
VertexAIConfig: Type[_VertexGeminiConfig] # Alias for VertexGeminiConfig
|
|
|
|
from .llms.featherless_ai.chat.transformation import (
|
|
FeatherlessAIConfig as FeatherlessAIConfig,
|
|
)
|
|
from .llms.cerebras.chat import CerebrasConfig as CerebrasConfig
|
|
from .llms.baseten.chat import BasetenConfig as BasetenConfig
|
|
from .llms.sambanova.chat import SambanovaConfig as SambanovaConfig
|
|
from .llms.sambanova.embedding.transformation import (
|
|
SambaNovaEmbeddingConfig as SambaNovaEmbeddingConfig,
|
|
)
|
|
from .llms.fireworks_ai.chat.transformation import (
|
|
FireworksAIConfig as FireworksAIConfig,
|
|
)
|
|
from .llms.fireworks_ai.completion.transformation import (
|
|
FireworksAITextCompletionConfig as FireworksAITextCompletionConfig,
|
|
)
|
|
from .llms.fireworks_ai.embed.fireworks_ai_transformation import (
|
|
FireworksAIEmbeddingConfig as FireworksAIEmbeddingConfig,
|
|
)
|
|
from .llms.friendliai.chat.transformation import (
|
|
FriendliaiChatConfig as FriendliaiChatConfig,
|
|
)
|
|
from .llms.jina_ai.embedding.transformation import (
|
|
JinaAIEmbeddingConfig as JinaAIEmbeddingConfig,
|
|
)
|
|
from .llms.xai.chat.transformation import XAIChatConfig as XAIChatConfig
|
|
from .llms.zai.chat.transformation import ZAIChatConfig as ZAIChatConfig
|
|
from .llms.aiml.chat.transformation import AIMLChatConfig as AIMLChatConfig
|
|
from .llms.volcengine.chat.transformation import (
|
|
VolcEngineChatConfig as VolcEngineChatConfig,
|
|
VolcEngineChatConfig as VolcEngineConfig,
|
|
)
|
|
from .llms.codestral.completion.transformation import (
|
|
CodestralTextCompletionConfig as CodestralTextCompletionConfig,
|
|
)
|
|
from .llms.inception.completion.transformation import (
|
|
InceptionTextCompletionConfig as InceptionTextCompletionConfig,
|
|
)
|
|
from .llms.azure.azure import (
|
|
AzureOpenAIAssistantsAPIConfig as AzureOpenAIAssistantsAPIConfig,
|
|
)
|
|
from .llms.heroku.chat.transformation import HerokuChatConfig as HerokuChatConfig
|
|
from .llms.cometapi.chat.transformation import CometAPIConfig as CometAPIConfig
|
|
from .llms.azure.chat.gpt_transformation import (
|
|
AzureOpenAIConfig as AzureOpenAIConfig,
|
|
)
|
|
from .llms.azure.chat.gpt_5_transformation import (
|
|
AzureOpenAIGPT5Config as AzureOpenAIGPT5Config,
|
|
)
|
|
from .llms.azure.completion.transformation import (
|
|
AzureOpenAITextConfig as AzureOpenAITextConfig,
|
|
)
|
|
from .llms.azure.audio_transcription.transformation import (
|
|
AzureSpeechAudioTranscriptionConfig as AzureSpeechAudioTranscriptionConfig,
|
|
)
|
|
from .llms.hosted_vllm.chat.transformation import (
|
|
HostedVLLMChatConfig as HostedVLLMChatConfig,
|
|
)
|
|
from .llms.hosted_vllm.embedding.transformation import (
|
|
HostedVLLMEmbeddingConfig as HostedVLLMEmbeddingConfig,
|
|
)
|
|
from .llms.hosted_vllm.responses.transformation import (
|
|
HostedVLLMResponsesAPIConfig as HostedVLLMResponsesAPIConfig,
|
|
)
|
|
from .llms.github_copilot.chat.transformation import (
|
|
GithubCopilotConfig as GithubCopilotConfig,
|
|
)
|
|
from .llms.github_copilot.responses.transformation import (
|
|
GithubCopilotResponsesAPIConfig as GithubCopilotResponsesAPIConfig,
|
|
)
|
|
from .llms.github_copilot.embedding.transformation import (
|
|
GithubCopilotEmbeddingConfig as GithubCopilotEmbeddingConfig,
|
|
)
|
|
from .llms.chatgpt.chat.transformation import ChatGPTConfig as ChatGPTConfig
|
|
from .llms.chatgpt.responses.transformation import (
|
|
ChatGPTResponsesAPIConfig as ChatGPTResponsesAPIConfig,
|
|
)
|
|
from .llms.gigachat.chat.transformation import GigaChatConfig as GigaChatConfig
|
|
from .llms.gigachat.embedding.transformation import (
|
|
GigaChatEmbeddingConfig as GigaChatEmbeddingConfig,
|
|
)
|
|
from .llms.nebius.chat.transformation import NebiusConfig as NebiusConfig
|
|
from .llms.wandb.chat.transformation import WandbConfig as WandbConfig
|
|
from .llms.dashscope.chat.transformation import (
|
|
DashScopeChatConfig as DashScopeChatConfig,
|
|
)
|
|
from .llms.dashscope.embed.transformation import (
|
|
DashScopeEmbeddingConfig as DashScopeEmbeddingConfig,
|
|
)
|
|
from .llms.dashscope.rerank.transformation import (
|
|
DashScopeRerankConfig as DashScopeRerankConfig,
|
|
)
|
|
from .llms.modelscope.chat.transformation import (
|
|
ModelScopeChatConfig as ModelScopeChatConfig,
|
|
)
|
|
from .llms.moonshot.chat.transformation import (
|
|
MoonshotChatConfig as MoonshotChatConfig,
|
|
)
|
|
from .llms.docker_model_runner.chat.transformation import (
|
|
DockerModelRunnerChatConfig as DockerModelRunnerChatConfig,
|
|
)
|
|
from .llms.v0.chat.transformation import V0ChatConfig as V0ChatConfig
|
|
from .llms.oci.chat.transformation import OCIChatConfig as OCIChatConfig
|
|
from .llms.oci.embed.transformation import OCIEmbeddingConfig as OCIEmbeddingConfig
|
|
from .llms.morph.chat.transformation import MorphChatConfig as MorphChatConfig
|
|
from .llms.ragflow.chat.transformation import RAGFlowConfig as RAGFlowConfig
|
|
from .llms.lambda_ai.chat.transformation import (
|
|
LambdaAIChatConfig as LambdaAIChatConfig,
|
|
)
|
|
from .llms.inception.chat.transformation import (
|
|
InceptionChatConfig as InceptionChatConfig,
|
|
)
|
|
from .llms.hyperbolic.chat.transformation import (
|
|
HyperbolicChatConfig as HyperbolicChatConfig,
|
|
)
|
|
from .llms.vercel_ai_gateway.chat.transformation import (
|
|
VercelAIGatewayConfig as VercelAIGatewayConfig,
|
|
)
|
|
from .llms.ovhcloud.chat.transformation import (
|
|
OVHCloudChatConfig as OVHCloudChatConfig,
|
|
)
|
|
from .llms.ovhcloud.embedding.transformation import (
|
|
OVHCloudEmbeddingConfig as OVHCloudEmbeddingConfig,
|
|
)
|
|
from .llms.cometapi.embed.transformation import (
|
|
CometAPIEmbeddingConfig as CometAPIEmbeddingConfig,
|
|
)
|
|
from .llms.lemonade.chat.transformation import (
|
|
LemonadeChatConfig as LemonadeChatConfig,
|
|
)
|
|
from .llms.snowflake.embedding.transformation import (
|
|
SnowflakeEmbeddingConfig as SnowflakeEmbeddingConfig,
|
|
)
|
|
from .llms.amazon_nova.chat.transformation import (
|
|
AmazonNovaChatConfig as AmazonNovaChatConfig,
|
|
)
|
|
from litellm.caching.llm_caching_handler import LLMClientCache
|
|
from litellm.types.llms.bedrock import COHERE_EMBEDDING_INPUT_TYPES
|
|
from litellm.types.utils import (
|
|
BudgetConfig,
|
|
CredentialItem,
|
|
PriorityReservationDict,
|
|
StandardKeyGenerationConfig,
|
|
)
|
|
from litellm.types.guardrails import GuardrailItem
|
|
from litellm.types.proxy.management_endpoints.ui_sso import (
|
|
DefaultTeamSSOParams,
|
|
LiteLLM_UpperboundKeyGenerateParams,
|
|
)
|
|
|
|
# Cost calculator functions
|
|
cost_per_token: Callable[..., Tuple[float, float]]
|
|
completion_cost: Callable[..., float]
|
|
response_cost_calculator: Any
|
|
modify_integration: Any
|
|
|
|
# Utils functions - type stubs for truly lazy loaded functions only
|
|
# (functions NOT imported via "from .main import *")
|
|
get_response_string: Callable[..., str]
|
|
supports_function_calling: Callable[..., bool]
|
|
supports_web_search: Callable[..., bool]
|
|
supports_url_context: Callable[..., bool]
|
|
supports_response_schema: Callable[..., bool]
|
|
supports_parallel_function_calling: Callable[..., bool]
|
|
supports_vision: Callable[..., bool]
|
|
supports_audio_input: Callable[..., bool]
|
|
supports_audio_output: Callable[..., bool]
|
|
supports_system_messages: Callable[..., bool]
|
|
supports_reasoning: Callable[..., bool]
|
|
acreate: Callable[..., Any]
|
|
get_max_tokens: Callable[..., int]
|
|
get_model_info: Callable[..., _ModelInfoType] # type: ignore[no-redef]
|
|
register_prompt_template: Callable[..., None]
|
|
validate_environment: Callable[..., dict]
|
|
check_valid_key: Callable[..., bool]
|
|
register_model: Callable[..., None]
|
|
encode: Callable[..., list]
|
|
decode: Callable[..., str]
|
|
_calculate_retry_after: Callable[..., float]
|
|
_should_retry: Callable[..., bool]
|
|
get_supported_openai_params: Callable[..., Optional[list]]
|
|
get_api_base: Callable[..., Optional[str]]
|
|
get_first_chars_messages: Callable[..., str]
|
|
get_provider_fields: Callable[..., List]
|
|
get_valid_models: Callable[..., list]
|
|
remove_index_from_tool_calls: Callable[..., None]
|
|
|
|
# Response types - truly lazy loaded only (not in main.py or elsewhere)
|
|
ModelResponseListIterator: Type[Any]
|
|
|
|
# HTTP handler singletons (created lazily via __getattr__ at runtime)
|
|
module_level_aclient: AsyncHTTPHandler
|
|
module_level_client: HTTPHandler
|
|
|
|
# Bedrock tool name mappings instance (lazy-loaded)
|
|
from litellm.caching.caching import InMemoryCache
|
|
|
|
bedrock_tool_name_mappings: InMemoryCache
|
|
|
|
# Azure exception class (lazy-loaded)
|
|
from litellm.llms.azure.common_utils import AzureOpenAIError
|
|
|
|
# Secret manager types (lazy-loaded)
|
|
from litellm.types.secret_managers.main import (
|
|
KeyManagementSystem,
|
|
KeyManagementSettings, # Not lazy-loaded - needed for _key_management_settings initialization
|
|
)
|
|
|
|
# Custom logger class (lazy-loaded)
|
|
from litellm.integrations.custom_logger import CustomLogger
|
|
|
|
# Datadog LLM observability params (lazy-loaded)
|
|
from litellm.types.integrations.datadog_llm_obs import DatadogLLMObsInitParams
|
|
|
|
# Logging callback manager class and instance (lazy-loaded)
|
|
from litellm.litellm_core_utils.logging_callback_manager import (
|
|
LoggingCallbackManager,
|
|
)
|
|
|
|
logging_callback_manager: LoggingCallbackManager
|
|
|
|
# provider_list is lazy-loaded
|
|
from litellm.types.utils import LlmProviders
|
|
|
|
provider_list: List[Union[LlmProviders, str]]
|
|
|
|
# Note: AmazonConverseConfig and OpenAILikeChatConfig are imported above in TYPE_CHECKING block
|
|
|
|
|
|
# Track if async client cleanup has been registered (for lazy loading)
|
|
_async_client_cleanup_registered = False
|
|
|
|
# Eager loading for backwards compatibility with VCR and other HTTP recording tools
|
|
# When LITELLM_DISABLE_LAZY_LOADING is set, lazy-loaded attributes are loaded at import time
|
|
# For now, this only affects encoding (tiktoken) as it was the only reported issue
|
|
# See: https://github.com/BerriAI/litellm/issues/18659
|
|
# This ensures encoding is initialized before VCR starts recording HTTP requests
|
|
if os.getenv("LITELLM_DISABLE_LAZY_LOADING", "").lower() in ("1", "true", "yes", "on"):
|
|
# Load encoding at import time (pre-#18070 behavior)
|
|
# This ensures encoding is initialized before VCR starts recording
|
|
from .main import encoding
|
|
|
|
|
|
def __getattr__(name: str) -> Any:
|
|
"""Lazy import handler with cached registry for improved performance."""
|
|
global _async_client_cleanup_registered
|
|
# Register async client cleanup on first access (only once)
|
|
if not _async_client_cleanup_registered:
|
|
from litellm.llms.custom_httpx.async_client_cleanup import (
|
|
register_async_client_cleanup,
|
|
)
|
|
|
|
register_async_client_cleanup()
|
|
_async_client_cleanup_registered = True
|
|
|
|
# Use cached registry from _lazy_imports instead of importing tuples every time
|
|
from ._lazy_imports import _get_lazy_import_registry
|
|
|
|
registry = _get_lazy_import_registry()
|
|
|
|
# Check if name is in registry and call the cached handler function
|
|
if name in registry:
|
|
handler_func = registry[name]
|
|
return handler_func(name)
|
|
|
|
# Lazy load encoding from main.py to avoid heavy tiktoken import
|
|
if name == "encoding":
|
|
from ._lazy_imports import _get_litellm_globals
|
|
|
|
_globals = _get_litellm_globals()
|
|
# Check if already cached
|
|
if "encoding" not in _globals:
|
|
from .main import encoding as _encoding
|
|
|
|
_globals["encoding"] = _encoding
|
|
return _globals["encoding"]
|
|
|
|
# Lazy load bedrock_tool_name_mappings instance
|
|
if name == "bedrock_tool_name_mappings":
|
|
from ._lazy_imports import _get_litellm_globals
|
|
|
|
_globals = _get_litellm_globals()
|
|
# Check if already cached
|
|
if "bedrock_tool_name_mappings" not in _globals:
|
|
from .llms.bedrock.chat.invoke_handler import (
|
|
bedrock_tool_name_mappings as _bedrock_tool_name_mappings,
|
|
)
|
|
|
|
_globals["bedrock_tool_name_mappings"] = _bedrock_tool_name_mappings
|
|
return _globals["bedrock_tool_name_mappings"]
|
|
|
|
# Lazy load AzureOpenAIError exception class
|
|
if name == "AzureOpenAIError":
|
|
from ._lazy_imports import _get_litellm_globals
|
|
|
|
_globals = _get_litellm_globals()
|
|
# Check if already cached
|
|
if "AzureOpenAIError" not in _globals:
|
|
from .llms.azure.common_utils import AzureOpenAIError as _AzureOpenAIError
|
|
|
|
_globals["AzureOpenAIError"] = _AzureOpenAIError
|
|
return _globals["AzureOpenAIError"]
|
|
|
|
# Lazy load openaiOSeriesConfig instance
|
|
if name == "openaiOSeriesConfig":
|
|
from ._lazy_imports import _get_litellm_globals
|
|
|
|
_globals = _get_litellm_globals()
|
|
if "openaiOSeriesConfig" not in _globals:
|
|
# Import the config class and instantiate it
|
|
config_class = __getattr__("OpenAIOSeriesConfig")
|
|
_globals["openaiOSeriesConfig"] = config_class()
|
|
return _globals["openaiOSeriesConfig"]
|
|
|
|
# Lazy load other config instances
|
|
_config_instances = {
|
|
"openAIGPTConfig": "OpenAIGPTConfig",
|
|
"openAIGPTAudioConfig": "OpenAIGPTAudioConfig",
|
|
"openAIGPT5Config": "OpenAIGPT5Config",
|
|
"nvidiaNimConfig": "NvidiaNimConfig",
|
|
"nvidiaNimEmbeddingConfig": "NvidiaNimEmbeddingConfig",
|
|
}
|
|
if name in _config_instances:
|
|
from ._lazy_imports import _get_litellm_globals
|
|
|
|
_globals = _get_litellm_globals()
|
|
if name not in _globals:
|
|
# Import the config class and instantiate it
|
|
config_class = __getattr__(_config_instances[name])
|
|
_globals[name] = config_class()
|
|
return _globals[name]
|
|
|
|
# Handle OpenAIO1Config alias
|
|
if name == "OpenAIO1Config":
|
|
return __getattr__("OpenAIOSeriesConfig")
|
|
|
|
# Lazy load provider_list
|
|
if name == "provider_list":
|
|
from ._lazy_imports import _get_litellm_globals
|
|
|
|
_globals = _get_litellm_globals()
|
|
# Check if already cached
|
|
if "provider_list" not in _globals:
|
|
# LlmProviders is eagerly imported above, so we can import it directly
|
|
from litellm.types.utils import LlmProviders
|
|
|
|
_globals["provider_list"] = list(LlmProviders)
|
|
return _globals["provider_list"]
|
|
|
|
# Lazy load priority_reservation_settings instance
|
|
if name == "priority_reservation_settings":
|
|
from ._lazy_imports import _get_litellm_globals
|
|
|
|
_globals = _get_litellm_globals()
|
|
# Check if already cached
|
|
if "priority_reservation_settings" not in _globals:
|
|
# Import the class and instantiate it
|
|
PriorityReservationSettings = __getattr__("PriorityReservationSettings")
|
|
_globals["priority_reservation_settings"] = PriorityReservationSettings()
|
|
return _globals["priority_reservation_settings"]
|
|
|
|
# Lazy load logging_callback_manager instance
|
|
if name == "logging_callback_manager":
|
|
from ._lazy_imports import _get_litellm_globals
|
|
|
|
_globals = _get_litellm_globals()
|
|
# Check if already cached
|
|
if "logging_callback_manager" not in _globals:
|
|
# Import the class and instantiate it
|
|
LoggingCallbackManager = __getattr__("LoggingCallbackManager")
|
|
_globals["logging_callback_manager"] = LoggingCallbackManager()
|
|
return _globals["logging_callback_manager"]
|
|
|
|
# Lazy load _service_logger module
|
|
if name == "_service_logger":
|
|
from ._lazy_imports import _get_litellm_globals
|
|
|
|
_globals = _get_litellm_globals()
|
|
# Check if already cached
|
|
if "_service_logger" not in _globals:
|
|
# Import the module lazily
|
|
import litellm._service_logger
|
|
|
|
_globals["_service_logger"] = litellm._service_logger
|
|
return _globals["_service_logger"]
|
|
|
|
# Lazy load evals module functions
|
|
if name in [
|
|
"acreate_eval",
|
|
"alist_evals",
|
|
"aget_eval",
|
|
"aupdate_eval",
|
|
"adelete_eval",
|
|
"acancel_eval",
|
|
"create_eval",
|
|
"list_evals",
|
|
"get_eval",
|
|
"update_eval",
|
|
"delete_eval",
|
|
"cancel_eval",
|
|
"acreate_run",
|
|
"alist_runs",
|
|
"aget_run",
|
|
"acancel_run",
|
|
"adelete_run",
|
|
"create_run",
|
|
"list_runs",
|
|
"get_run",
|
|
"cancel_run",
|
|
"delete_run",
|
|
]:
|
|
from litellm.evals.main import (
|
|
acreate_eval,
|
|
alist_evals,
|
|
aget_eval,
|
|
aupdate_eval,
|
|
adelete_eval,
|
|
acancel_eval,
|
|
create_eval,
|
|
list_evals,
|
|
get_eval,
|
|
update_eval,
|
|
delete_eval,
|
|
cancel_eval,
|
|
acreate_run,
|
|
alist_runs,
|
|
aget_run,
|
|
acancel_run,
|
|
adelete_run,
|
|
create_run,
|
|
list_runs,
|
|
get_run,
|
|
cancel_run,
|
|
delete_run,
|
|
)
|
|
|
|
return locals()[name]
|
|
|
|
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
|
|
|
|
|
# ALL_LITELLM_RESPONSE_TYPES is lazy-loaded via __getattr__ to avoid loading utils at import time
|