litellm-mirror/litellm/__init__.py
2023-08-19 20:03:31 -07:00

236 lines
6.2 KiB
Python

import threading
from typing import Callable, List, Optional
input_callback: List[str] = []
success_callback: List[str] = []
failure_callback: List[str] = []
set_verbose = False
telemetry = True
max_tokens = 256 # OpenAI Defaults
retry = True
api_key: Optional[str] = None
openai_key: Optional[str] = None
azure_key: Optional[str] = None
anthropic_key: Optional[str] = None
replicate_key: Optional[str] = None
cohere_key: Optional[str] = None
openrouter_key: Optional[str] = None
huggingface_key: Optional[str] = None
vertex_project: Optional[str] = None
vertex_location: Optional[str] = None
hugging_api_token: Optional[str] = None
togetherai_api_key: Optional[str] = None
caching = False
caching_with_models = False # if you want the caching key to be model + prompt
model_cost = {
"gpt-3.5-turbo": {
"max_tokens": 4000,
"input_cost_per_token": 0.0000015,
"output_cost_per_token": 0.000002,
},
"gpt-35-turbo": {
"max_tokens": 4000,
"input_cost_per_token": 0.0000015,
"output_cost_per_token": 0.000002,
}, # azure model name
"gpt-3.5-turbo-0613": {
"max_tokens": 4000,
"input_cost_per_token": 0.0000015,
"output_cost_per_token": 0.000002,
},
"gpt-3.5-turbo-0301": {
"max_tokens": 4000,
"input_cost_per_token": 0.0000015,
"output_cost_per_token": 0.000002,
},
"gpt-3.5-turbo-16k": {
"max_tokens": 16000,
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.000004,
},
"gpt-35-turbo-16k": {
"max_tokens": 16000,
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.000004,
}, # azure model name
"gpt-3.5-turbo-16k-0613": {
"max_tokens": 16000,
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.000004,
},
"gpt-4": {
"max_tokens": 8000,
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.00006,
},
"gpt-4-0613": {
"max_tokens": 8000,
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.00006,
},
"gpt-4-32k": {
"max_tokens": 8000,
"input_cost_per_token": 0.00006,
"output_cost_per_token": 0.00012,
},
"claude-instant-1": {
"max_tokens": 100000,
"input_cost_per_token": 0.00000163,
"output_cost_per_token": 0.00000551,
},
"claude-2": {
"max_tokens": 100000,
"input_cost_per_token": 0.00001102,
"output_cost_per_token": 0.00003268,
},
"text-bison-001": {
"max_tokens": 8192,
"input_cost_per_token": 0.000004,
"output_cost_per_token": 0.000004,
},
"chat-bison-001": {
"max_tokens": 4096,
"input_cost_per_token": 0.000002,
"output_cost_per_token": 0.000002,
},
"command-nightly": {
"max_tokens": 4096,
"input_cost_per_token": 0.000015,
"output_cost_per_token": 0.000015,
},
}
####### 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 = None
headers = None
api_version = None
organization = None
config_path = None
####### Secret Manager #####################
secret_manager_client = None
####### COMPLETION MODELS ###################
open_ai_chat_completion_models = [
"gpt-4",
"gpt-4-0613",
"gpt-4-32k",
"gpt-4-32k-0613",
#################
"gpt-3.5-turbo",
"gpt-3.5-turbo-16k",
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-16k-0613",
]
open_ai_text_completion_models = ["text-davinci-003"]
cohere_models = [
"command-nightly",
"command",
"command-light",
"command-medium-beta",
"command-xlarge-beta",
]
anthropic_models = ["claude-2", "claude-instant-1", "claude-instant-1.2"]
replicate_models = [
"replicate/"
] # placeholder, to make sure we accept any replicate model in our model_list
openrouter_models = [
"google/palm-2-codechat-bison",
"google/palm-2-chat-bison",
"openai/gpt-3.5-turbo",
"openai/gpt-3.5-turbo-16k",
"openai/gpt-4-32k",
"anthropic/claude-2",
"anthropic/claude-instant-v1",
"meta-llama/llama-2-13b-chat",
"meta-llama/llama-2-70b-chat",
]
vertex_chat_models = ["chat-bison", "chat-bison@001"]
vertex_text_models = ["text-bison", "text-bison@001"]
huggingface_models = [
"meta-llama/Llama-2-7b-hf",
"meta-llama/Llama-2-7b-chat-hf",
"meta-llama/Llama-2-13b-hf",
"meta-llama/Llama-2-13b-chat-hf",
"meta-llama/Llama-2-70b-hf",
"meta-llama/Llama-2-70b-chat-hf",
"meta-llama/Llama-2-7b",
"meta-llama/Llama-2-7b-chat",
"meta-llama/Llama-2-13b",
"meta-llama/Llama-2-13b-chat",
"meta-llama/Llama-2-70b",
"meta-llama/Llama-2-70b-chat",
] # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/completion/supported
ai21_models = ["j2-ultra", "j2-mid", "j2-light"]
model_list = (
open_ai_chat_completion_models
+ open_ai_text_completion_models
+ cohere_models
+ anthropic_models
+ replicate_models
+ openrouter_models
+ huggingface_models
+ vertex_chat_models
+ vertex_text_models
+ ai21_models
)
provider_list = [
"openai",
"cohere",
"anthropic",
"replicate",
"huggingface",
"together_ai",
"openrouter",
"vertex_ai",
"ai21",
]
####### EMBEDDING MODELS ###################
open_ai_embedding_models = ["text-embedding-ada-002"]
from .timeout import timeout
from .testing import *
from .utils import (
client,
exception_type,
get_optional_params,
modify_integration,
token_counter,
cost_per_token,
completion_cost,
get_litellm_params,
Logging
)
from .main import * # type: ignore
from .integrations import *
from openai.error import (
AuthenticationError,
InvalidRequestError,
RateLimitError,
ServiceUnavailableError,
OpenAIError,
)