mirror of
https://github.com/BerriAI/litellm.git
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274 lines
7.8 KiB
Python
274 lines
7.8 KiB
Python
import threading
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from typing import Callable, List, Optional
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input_callback: List[str] = []
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success_callback: List[str] = []
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failure_callback: List[str] = []
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set_verbose = False
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email: Optional[str] = None # for hosted dashboard. Learn more - https://docs.litellm.ai/docs/debugging/hosted_debugging
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telemetry = True
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max_tokens = 256 # OpenAI Defaults
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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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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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caching = False
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caching_with_models = False # if you want the caching key to be model + prompt
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debugger = False
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model_cost = {
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"gpt-3.5-turbo": {
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"max_tokens": 4000,
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"input_cost_per_token": 0.0000015,
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"output_cost_per_token": 0.000002,
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},
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"gpt-35-turbo": {
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"max_tokens": 4000,
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"input_cost_per_token": 0.0000015,
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"output_cost_per_token": 0.000002,
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}, # azure model name
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"gpt-3.5-turbo-0613": {
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"max_tokens": 4000,
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"input_cost_per_token": 0.0000015,
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"output_cost_per_token": 0.000002,
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},
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"gpt-3.5-turbo-0301": {
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"max_tokens": 4000,
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"input_cost_per_token": 0.0000015,
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"output_cost_per_token": 0.000002,
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},
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"gpt-3.5-turbo-16k": {
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"max_tokens": 16000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.000004,
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},
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"gpt-35-turbo-16k": {
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"max_tokens": 16000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.000004,
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}, # azure model name
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"gpt-3.5-turbo-16k-0613": {
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"max_tokens": 16000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.000004,
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},
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"gpt-4": {
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"max_tokens": 8000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.00006,
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},
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"gpt-4-0613": {
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"max_tokens": 8000,
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"input_cost_per_token": 0.000003,
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"output_cost_per_token": 0.00006,
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},
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"gpt-4-32k": {
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"max_tokens": 8000,
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"input_cost_per_token": 0.00006,
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"output_cost_per_token": 0.00012,
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},
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"claude-instant-1": {
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"max_tokens": 100000,
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"input_cost_per_token": 0.00000163,
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"output_cost_per_token": 0.00000551,
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},
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"claude-2": {
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"max_tokens": 100000,
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"input_cost_per_token": 0.00001102,
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"output_cost_per_token": 0.00003268,
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},
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"text-bison-001": {
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"max_tokens": 8192,
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"input_cost_per_token": 0.000004,
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"output_cost_per_token": 0.000004,
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},
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"chat-bison-001": {
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"max_tokens": 4096,
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"input_cost_per_token": 0.000002,
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"output_cost_per_token": 0.000002,
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},
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"command-nightly": {
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"max_tokens": 4096,
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"input_cost_per_token": 0.000015,
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"output_cost_per_token": 0.000015,
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},
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}
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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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####### Secret Manager #####################
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secret_manager_client = None
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####### COMPLETION MODELS ###################
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open_ai_chat_completion_models = [
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"gpt-4",
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"gpt-4-0613",
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"gpt-4-32k",
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"gpt-4-32k-0613",
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#################
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"gpt-3.5-turbo",
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"gpt-3.5-turbo-16k",
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"gpt-3.5-turbo-0613",
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"gpt-3.5-turbo-16k-0613",
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]
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open_ai_text_completion_models = ["text-davinci-003"]
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cohere_models = [
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"command-nightly",
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"command",
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"command-light",
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"command-medium-beta",
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"command-xlarge-beta",
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]
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anthropic_models = ["claude-2", "claude-instant-1", "claude-instant-1.2"]
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replicate_models = [
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"replicate/",
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"replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781",
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"a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52",
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"joehoover/instructblip-vicuna13b:c4c54e3c8c97cd50c2d2fec9be3b6065563ccf7d43787fb99f84151b867178fe"
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"replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5",
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"a16z-infra/llama-2-7b-chat:7b0bfc9aff140d5b75bacbed23e91fd3c34b01a1e958d32132de6e0a19796e2c",
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"replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b",
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"daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f",
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"replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad",
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] # placeholder, to make sure we accept any replicate model in our model_list
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openrouter_models = [
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"google/palm-2-codechat-bison",
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"google/palm-2-chat-bison",
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"openai/gpt-3.5-turbo",
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"openai/gpt-3.5-turbo-16k",
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"openai/gpt-4-32k",
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"anthropic/claude-2",
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"anthropic/claude-instant-v1",
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"meta-llama/llama-2-13b-chat",
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"meta-llama/llama-2-70b-chat",
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]
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vertex_chat_models = ["chat-bison", "chat-bison@001"]
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vertex_text_models = ["text-bison", "text-bison@001"]
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huggingface_models = [
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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/completion/supported
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ai21_models = ["j2-ultra", "j2-mid", "j2-light"]
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together_ai_models = [
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"togethercomputer/llama-2-70b-chat",
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"togethercomputer/Llama-2-7B-32K-Instruct",
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"togethercomputer/llama-2-7b",
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]
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baseten_models = ["qvv0xeq", "q841o8w", "31dxrj3"] # FALCON 7B # WizardLM # Mosaic ML
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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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)
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provider_list = [
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"openai",
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"azure",
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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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"ai21",
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"baseten",
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]
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models_by_provider = {
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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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}
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####### EMBEDDING MODELS ###################
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open_ai_embedding_models = ["text-embedding-ada-002"]
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from .timeout import timeout
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from .testing import *
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from .utils import (
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client,
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exception_type,
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get_optional_params,
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modify_integration,
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token_counter,
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cost_per_token,
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completion_cost,
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get_litellm_params,
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Logging,
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acreate,
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get_model_list,
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)
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from .main import * # type: ignore
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from .integrations import *
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from openai.error import (
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AuthenticationError,
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InvalidRequestError,
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RateLimitError,
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ServiceUnavailableError,
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OpenAIError,
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)
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