forked from phoenix/litellm-mirror
556 lines
17 KiB
Python
556 lines
17 KiB
Python
### INIT VARIABLES ###
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import threading, requests
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from typing import Callable, List, Optional, Dict, Union, Any
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from litellm.caching import Cache
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from litellm._logging import set_verbose
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from litellm.proxy._types import KeyManagementSystem
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import httpx
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input_callback: List[Union[str, Callable]] = []
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success_callback: List[Union[str, Callable]] = []
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failure_callback: List[Union[str, Callable]] = []
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callbacks: List[Callable] = []
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_async_input_callback: List[
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Callable
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] = [] # internal variable - async custom callbacks are routed here.
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_async_success_callback: List[
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Union[str, Callable]
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] = [] # internal variable - async custom callbacks are routed here.
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_async_failure_callback: List[
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Callable
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] = [] # internal variable - async custom callbacks are routed here.
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pre_call_rules: List[Callable] = []
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post_call_rules: List[Callable] = []
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email: Optional[
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str
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] = None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
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token: Optional[
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str
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] = None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
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telemetry = True
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max_tokens = 256 # OpenAI Defaults
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drop_params = False
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retry = True
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api_key: Optional[str] = None
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openai_key: Optional[str] = None
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azure_key: Optional[str] = None
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anthropic_key: Optional[str] = None
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replicate_key: Optional[str] = None
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cohere_key: Optional[str] = None
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maritalk_key: Optional[str] = None
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ai21_key: Optional[str] = None
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openrouter_key: Optional[str] = None
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huggingface_key: Optional[str] = None
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vertex_project: Optional[str] = None
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vertex_location: Optional[str] = None
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togetherai_api_key: Optional[str] = None
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cloudflare_api_key: Optional[str] = None
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baseten_key: Optional[str] = None
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aleph_alpha_key: Optional[str] = None
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nlp_cloud_key: Optional[str] = None
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use_client: bool = False
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logging: bool = True
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caching: bool = False # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
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caching_with_models: bool = False # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
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cache: Optional[
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Cache
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] = None # cache object <- use this - https://docs.litellm.ai/docs/caching
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model_alias_map: Dict[str, str] = {}
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model_group_alias_map: Dict[str, str] = {}
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max_budget: float = 0.0 # set the max budget across all providers
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_openai_completion_params = [
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"functions",
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"function_call",
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"temperature",
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"temperature",
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"top_p",
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"n",
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"stream",
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"stop",
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"max_tokens",
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"presence_penalty",
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"frequency_penalty",
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"logit_bias",
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"user",
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"request_timeout",
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"api_base",
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"api_version",
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"api_key",
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"deployment_id",
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"organization",
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"base_url",
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"default_headers",
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"timeout",
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"response_format",
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"seed",
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"tools",
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"tool_choice",
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"max_retries",
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]
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_litellm_completion_params = [
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"metadata",
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"acompletion",
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"caching",
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"mock_response",
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"api_key",
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"api_version",
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"api_base",
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"force_timeout",
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"logger_fn",
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"verbose",
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"custom_llm_provider",
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"litellm_logging_obj",
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"litellm_call_id",
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"use_client",
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"id",
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"fallbacks",
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"azure",
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"headers",
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"model_list",
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"num_retries",
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"context_window_fallback_dict",
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"roles",
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"final_prompt_value",
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"bos_token",
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"eos_token",
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"request_timeout",
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"complete_response",
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"self",
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"client",
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"rpm",
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"tpm",
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"input_cost_per_token",
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"output_cost_per_token",
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"hf_model_name",
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"model_info",
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"proxy_server_request",
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"preset_cache_key",
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]
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_current_cost = 0 # private variable, used if max budget is set
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error_logs: Dict = {}
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add_function_to_prompt: bool = False # if function calling not supported by api, append function call details to system prompt
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client_session: Optional[httpx.Client] = None
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aclient_session: Optional[httpx.AsyncClient] = None
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model_fallbacks: Optional[List] = None # Deprecated for 'litellm.fallbacks'
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model_cost_map_url: str = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
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suppress_debug_info = False
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dynamodb_table_name: Optional[str] = None
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#### RELIABILITY ####
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request_timeout: Optional[float] = 6000
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num_retries: Optional[int] = None # per model endpoint
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fallbacks: Optional[List] = None
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context_window_fallbacks: Optional[List] = None
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allowed_fails: int = 0
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num_retries_per_request: Optional[
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int
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] = None # for the request overall (incl. fallbacks + model retries)
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####### SECRET MANAGERS #####################
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secret_manager_client: Optional[
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Any
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] = None # list of instantiated key management clients - e.g. azure kv, infisical, etc.
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_google_kms_resource_name: Optional[str] = None
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_key_management_system: Optional[KeyManagementSystem] = None
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#############################################
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def get_model_cost_map(url: str):
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try:
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with requests.get(
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url, timeout=5
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) as response: # set a 5 second timeout for the get request
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response.raise_for_status() # Raise an exception if the request is unsuccessful
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content = response.json()
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return content
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except Exception as e:
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import importlib.resources
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import json
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with importlib.resources.open_text(
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"litellm", "model_prices_and_context_window_backup.json"
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) as f:
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content = json.load(f)
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return content
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model_cost = get_model_cost_map(url=model_cost_map_url)
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custom_prompt_dict: Dict[str, dict] = {}
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####### THREAD-SPECIFIC DATA ###################
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class MyLocal(threading.local):
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def __init__(self):
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self.user = "Hello World"
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_thread_context = MyLocal()
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def identify(event_details):
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# Store user in thread local data
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if "user" in event_details:
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_thread_context.user = event_details["user"]
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####### ADDITIONAL PARAMS ################### configurable params if you use proxy models like Helicone, map spend to org id, etc.
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api_base = None
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headers = None
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api_version = None
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organization = None
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config_path = None
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####### COMPLETION MODELS ###################
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open_ai_chat_completion_models: List = []
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open_ai_text_completion_models: List = []
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cohere_models: List = []
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anthropic_models: List = []
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openrouter_models: List = []
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vertex_language_models: List = []
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vertex_vision_models: List = []
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vertex_chat_models: List = []
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vertex_code_chat_models: List = []
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vertex_text_models: List = []
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vertex_code_text_models: List = []
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ai21_models: List = []
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nlp_cloud_models: List = []
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aleph_alpha_models: List = []
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bedrock_models: List = []
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deepinfra_models: List = []
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perplexity_models: List = []
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for key, value in model_cost.items():
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if value.get("litellm_provider") == "openai":
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open_ai_chat_completion_models.append(key)
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elif value.get("litellm_provider") == "text-completion-openai":
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open_ai_text_completion_models.append(key)
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elif value.get("litellm_provider") == "cohere":
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cohere_models.append(key)
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elif value.get("litellm_provider") == "anthropic":
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anthropic_models.append(key)
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elif value.get("litellm_provider") == "openrouter":
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openrouter_models.append(key)
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elif value.get("litellm_provider") == "vertex_ai-text-models":
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vertex_text_models.append(key)
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elif value.get("litellm_provider") == "vertex_ai-code-text-models":
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vertex_code_text_models.append(key)
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elif value.get("litellm_provider") == "vertex_ai-language-models":
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vertex_language_models.append(key)
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elif value.get("litellm_provider") == "vertex_ai-vision-models":
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vertex_vision_models.append(key)
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elif value.get("litellm_provider") == "vertex_ai-chat-models":
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vertex_chat_models.append(key)
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elif value.get("litellm_provider") == "vertex_ai-code-chat-models":
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vertex_code_chat_models.append(key)
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elif value.get("litellm_provider") == "ai21":
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ai21_models.append(key)
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elif value.get("litellm_provider") == "nlp_cloud":
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nlp_cloud_models.append(key)
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elif value.get("litellm_provider") == "aleph_alpha":
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aleph_alpha_models.append(key)
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elif value.get("litellm_provider") == "bedrock":
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bedrock_models.append(key)
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elif value.get("litellm_provider") == "deepinfra":
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deepinfra_models.append(key)
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elif value.get("litellm_provider") == "perplexity":
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perplexity_models.append(key)
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# known openai compatible endpoints - we'll eventually move this list to the model_prices_and_context_window.json dictionary
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openai_compatible_endpoints: List = [
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"api.perplexity.ai",
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"api.endpoints.anyscale.com/v1",
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"api.deepinfra.com/v1/openai",
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"api.mistral.ai/v1",
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]
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# this is maintained for Exception Mapping
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openai_compatible_providers: List = [
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"anyscale",
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"mistral",
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"deepinfra",
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"perplexity",
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"xinference",
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]
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# well supported replicate llms
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replicate_models: List = [
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# llama replicate supported LLMs
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"replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf",
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"a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52",
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"meta/codellama-13b:1c914d844307b0588599b8393480a3ba917b660c7e9dfae681542b5325f228db",
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# Vicuna
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"replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b",
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"joehoover/instructblip-vicuna13b:c4c54e3c8c97cd50c2d2fec9be3b6065563ccf7d43787fb99f84151b867178fe",
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# Flan T-5
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"daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f"
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# Others
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"replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5",
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"replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad",
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]
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huggingface_models: List = [
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"meta-llama/Llama-2-7b-hf",
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"meta-llama/Llama-2-7b-chat-hf",
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"meta-llama/Llama-2-13b-hf",
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"meta-llama/Llama-2-13b-chat-hf",
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"meta-llama/Llama-2-70b-hf",
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"meta-llama/Llama-2-70b-chat-hf",
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"meta-llama/Llama-2-7b",
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"meta-llama/Llama-2-7b-chat",
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"meta-llama/Llama-2-13b",
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"meta-llama/Llama-2-13b-chat",
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"meta-llama/Llama-2-70b",
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"meta-llama/Llama-2-70b-chat",
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] # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/providers
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together_ai_models: List = [
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# llama llms - chat
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"togethercomputer/llama-2-70b-chat",
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# llama llms - language / instruct
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"togethercomputer/llama-2-70b",
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"togethercomputer/LLaMA-2-7B-32K",
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"togethercomputer/Llama-2-7B-32K-Instruct",
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"togethercomputer/llama-2-7b",
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# falcon llms
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"togethercomputer/falcon-40b-instruct",
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"togethercomputer/falcon-7b-instruct",
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# alpaca
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"togethercomputer/alpaca-7b",
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# chat llms
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"HuggingFaceH4/starchat-alpha",
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# code llms
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"togethercomputer/CodeLlama-34b",
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"togethercomputer/CodeLlama-34b-Instruct",
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"togethercomputer/CodeLlama-34b-Python",
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"defog/sqlcoder",
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"NumbersStation/nsql-llama-2-7B",
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"WizardLM/WizardCoder-15B-V1.0",
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"WizardLM/WizardCoder-Python-34B-V1.0",
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# language llms
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"NousResearch/Nous-Hermes-Llama2-13b",
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"Austism/chronos-hermes-13b",
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"upstage/SOLAR-0-70b-16bit",
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"WizardLM/WizardLM-70B-V1.0",
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] # supports all together ai models, just pass in the model id e.g. completion(model="together_computer/replit_code_3b",...)
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baseten_models: List = [
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"qvv0xeq",
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"q841o8w",
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"31dxrj3",
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] # FALCON 7B # WizardLM # Mosaic ML
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# used for Cost Tracking & Token counting
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# https://azure.microsoft.com/en-in/pricing/details/cognitive-services/openai-service/
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# Azure returns gpt-35-turbo in their responses, we need to map this to azure/gpt-3.5-turbo for token counting
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azure_llms = {
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"gpt-35-turbo": "azure/gpt-35-turbo",
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"gpt-35-turbo-16k": "azure/gpt-35-turbo-16k",
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"gpt-35-turbo-instruct": "azure/gpt-35-turbo-instruct",
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}
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azure_embedding_models = {
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"ada": "azure/ada",
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}
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petals_models = [
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"petals-team/StableBeluga2",
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]
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ollama_models = ["llama2"]
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maritalk_models = ["maritalk"]
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model_list = (
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open_ai_chat_completion_models
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+ open_ai_text_completion_models
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+ cohere_models
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+ anthropic_models
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+ replicate_models
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+ openrouter_models
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+ huggingface_models
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+ vertex_chat_models
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+ vertex_text_models
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+ ai21_models
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+ together_ai_models
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+ baseten_models
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+ aleph_alpha_models
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+ nlp_cloud_models
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+ ollama_models
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+ bedrock_models
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+ deepinfra_models
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+ perplexity_models
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+ maritalk_models
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)
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provider_list: List = [
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"openai",
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"custom_openai",
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"text-completion-openai",
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"cohere",
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"anthropic",
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"replicate",
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"huggingface",
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"together_ai",
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"openrouter",
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"vertex_ai",
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"palm",
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"gemini",
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"ai21",
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"baseten",
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"azure",
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"sagemaker",
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"bedrock",
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"vllm",
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"nlp_cloud",
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"petals",
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"oobabooga",
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"ollama",
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"ollama_chat",
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"deepinfra",
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"perplexity",
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"anyscale",
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"mistral",
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"maritalk",
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"voyage",
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"cloudflare",
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"xinference",
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"custom", # custom apis
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]
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models_by_provider: dict = {
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"openai": open_ai_chat_completion_models + open_ai_text_completion_models,
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"cohere": cohere_models,
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"anthropic": anthropic_models,
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"replicate": replicate_models,
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"huggingface": huggingface_models,
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"together_ai": together_ai_models,
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"baseten": baseten_models,
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"openrouter": openrouter_models,
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"vertex_ai": vertex_chat_models + vertex_text_models,
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"ai21": ai21_models,
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"bedrock": bedrock_models,
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"petals": petals_models,
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"ollama": ollama_models,
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"deepinfra": deepinfra_models,
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"perplexity": perplexity_models,
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"maritalk": maritalk_models,
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}
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# mapping for those models which have larger equivalents
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longer_context_model_fallback_dict: dict = {
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# openai chat completion models
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"gpt-3.5-turbo": "gpt-3.5-turbo-16k",
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"gpt-3.5-turbo-0301": "gpt-3.5-turbo-16k-0301",
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"gpt-3.5-turbo-0613": "gpt-3.5-turbo-16k-0613",
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"gpt-4": "gpt-4-32k",
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"gpt-4-0314": "gpt-4-32k-0314",
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"gpt-4-0613": "gpt-4-32k-0613",
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# anthropic
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"claude-instant-1": "claude-2",
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"claude-instant-1.2": "claude-2",
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# vertexai
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"chat-bison": "chat-bison-32k",
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"chat-bison@001": "chat-bison-32k",
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"codechat-bison": "codechat-bison-32k",
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"codechat-bison@001": "codechat-bison-32k",
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# openrouter
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"openrouter/openai/gpt-3.5-turbo": "openrouter/openai/gpt-3.5-turbo-16k",
|
|
"openrouter/anthropic/claude-instant-v1": "openrouter/anthropic/claude-2",
|
|
}
|
|
|
|
####### EMBEDDING MODELS ###################
|
|
open_ai_embedding_models: List = ["text-embedding-ada-002"]
|
|
cohere_embedding_models: List = [
|
|
"embed-english-v3.0",
|
|
"embed-english-light-v3.0",
|
|
"embed-multilingual-v3.0",
|
|
"embed-english-v2.0",
|
|
"embed-english-light-v2.0",
|
|
"embed-multilingual-v2.0",
|
|
]
|
|
bedrock_embedding_models: List = [
|
|
"amazon.titan-embed-text-v1",
|
|
"cohere.embed-english-v3",
|
|
"cohere.embed-multilingual-v3",
|
|
]
|
|
|
|
all_embedding_models = (
|
|
open_ai_embedding_models + cohere_embedding_models + bedrock_embedding_models
|
|
)
|
|
|
|
####### IMAGE GENERATION MODELS ###################
|
|
openai_image_generation_models = ["dall-e-2", "dall-e-3"]
|
|
|
|
|
|
from .timeout import timeout
|
|
from .utils import (
|
|
client,
|
|
exception_type,
|
|
get_optional_params,
|
|
modify_integration,
|
|
token_counter,
|
|
cost_per_token,
|
|
completion_cost,
|
|
get_litellm_params,
|
|
Logging,
|
|
acreate,
|
|
get_model_list,
|
|
get_max_tokens,
|
|
get_model_info,
|
|
register_prompt_template,
|
|
validate_environment,
|
|
check_valid_key,
|
|
get_llm_provider,
|
|
completion_with_config,
|
|
register_model,
|
|
encode,
|
|
decode,
|
|
_calculate_retry_after,
|
|
_should_retry,
|
|
get_secret,
|
|
)
|
|
from .llms.huggingface_restapi import HuggingfaceConfig
|
|
from .llms.anthropic import AnthropicConfig
|
|
from .llms.replicate import ReplicateConfig
|
|
from .llms.cohere import CohereConfig
|
|
from .llms.ai21 import AI21Config
|
|
from .llms.together_ai import TogetherAIConfig
|
|
from .llms.cloudflare import CloudflareConfig
|
|
from .llms.palm import PalmConfig
|
|
from .llms.gemini import GeminiConfig
|
|
from .llms.nlp_cloud import NLPCloudConfig
|
|
from .llms.aleph_alpha import AlephAlphaConfig
|
|
from .llms.petals import PetalsConfig
|
|
from .llms.vertex_ai import VertexAIConfig
|
|
from .llms.sagemaker import SagemakerConfig
|
|
from .llms.ollama import OllamaConfig
|
|
from .llms.maritalk import MaritTalkConfig
|
|
from .llms.bedrock import (
|
|
AmazonTitanConfig,
|
|
AmazonAI21Config,
|
|
AmazonAnthropicConfig,
|
|
AmazonCohereConfig,
|
|
AmazonLlamaConfig,
|
|
)
|
|
from .llms.openai import OpenAIConfig, OpenAITextCompletionConfig
|
|
from .llms.azure import AzureOpenAIConfig
|
|
from .main import * # type: ignore
|
|
from .integrations import *
|
|
from .exceptions import (
|
|
AuthenticationError,
|
|
InvalidRequestError,
|
|
BadRequestError,
|
|
NotFoundError,
|
|
RateLimitError,
|
|
ServiceUnavailableError,
|
|
OpenAIError,
|
|
ContextWindowExceededError,
|
|
BudgetExceededError,
|
|
APIError,
|
|
Timeout,
|
|
APIConnectionError,
|
|
APIResponseValidationError,
|
|
UnprocessableEntityError,
|
|
)
|
|
from .budget_manager import BudgetManager
|
|
from .proxy.proxy_cli import run_server
|
|
from .router import Router
|