litellm/litellm/__init__.py
2023-09-06 18:07:44 -07:00

245 lines
7.2 KiB
Python

import threading, requests
from typing import Callable, List, Optional, Dict
from litellm.caching import Cache
input_callback: List[str] = []
success_callback: List[str] = []
failure_callback: List[str] = []
set_verbose = False
email: Optional[
str
] = None # for hosted dashboard. Learn more - https://docs.litellm.ai/docs/debugging/hosted_debugging
token: Optional[
str
] = None # for hosted dashboard. Learn more - https://docs.litellm.ai/docs/debugging/hosted_debugging
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
ai21_key: Optional[str] = None
openrouter_key: Optional[str] = None
huggingface_key: Optional[str] = None
vertex_project: Optional[str] = None
vertex_location: Optional[str] = None
togetherai_api_key: Optional[str] = None
baseten_key: Optional[str] = None
aleph_alpha_key: Optional[str] = None
use_client = False
logging = True
caching = False # deprecated son
caching_with_models = False # if you want the caching key to be model + prompt # deprecated soon
cache: Optional[Cache] = None # cache object
model_alias_map: Dict[str, str] = {}
def get_model_cost_map():
url = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
try:
response = requests.get(url)
response.raise_for_status() # Raise an exception if request is unsuccessful
content = response.json()
return content
except requests.exceptions.RequestException as e:
print("Error occurred:", e)
return None
model_cost = get_model_cost_map()
custom_prompt_dict:Dict[str, dict] = {}
####### 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-4-0314",
"gpt-4-32k",
"gpt-4-32k-0314",
"gpt-4-32k-0613",
#################
"gpt-3.5-turbo",
"gpt-3.5-turbo-0301",
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-16k",
"gpt-3.5-turbo-16k-0613",
]
open_ai_text_completion_models = ["text-davinci-003", "curie-001", "babbage-001", "ada-001", "babbage-002", "davinci-002"]
cohere_models = [
"command-nightly",
"command",
"command-light",
"command-medium-beta",
"command-xlarge-beta",
]
anthropic_models = ["claude-2", "claude-instant-1", "claude-instant-1.2"]
# well supported replicate llms
replicate_models = [
# llama replicate supported LLMs
"replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf",
"a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52",
"meta/codellama-13b:1c914d844307b0588599b8393480a3ba917b660c7e9dfae681542b5325f228db",
# Vicuna
"replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b",
"joehoover/instructblip-vicuna13b:c4c54e3c8c97cd50c2d2fec9be3b6065563ccf7d43787fb99f84151b867178fe",
# Flan T-5
"daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f"
# Others
"replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5",
"replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad",
]
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"]
together_ai_models = [
"togethercomputer/llama-2-70b-chat",
"togethercomputer/Llama-2-7B-32K-Instruct",
"togethercomputer/llama-2-7b",
] # supports all together ai models, just pass in the model id e.g. completion(model="together_computer/replit_code_3b",...)
aleph_alpha_models = [
"luminous-base",
"luminous-base-control",
"luminous-extended",
"luminous-extended-control",
"luminous-supreme",
"luminous-supreme-control"
]
baseten_models = ["qvv0xeq", "q841o8w", "31dxrj3"] # FALCON 7B # WizardLM # Mosaic ML
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
+ together_ai_models
+ baseten_models
+ aleph_alpha_models
)
provider_list = [
"openai",
"cohere",
"anthropic",
"replicate",
"huggingface",
"together_ai",
"openrouter",
"vertex_ai",
"ai21",
"baseten",
"azure",
"sagemaker",
"bedrock",
"vllm",
"custom", # custom apis
]
models_by_provider = {
"openai": open_ai_chat_completion_models + open_ai_text_completion_models,
"cohere": cohere_models,
"anthropic": anthropic_models,
"replicate": replicate_models,
"huggingface": huggingface_models,
"together_ai": together_ai_models,
"baseten": baseten_models,
"openrouter": openrouter_models,
"vertex_ai": vertex_chat_models + vertex_text_models,
"ai21": ai21_models,
}
####### 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,
acreate,
get_model_list,
completion_with_split_tests,
get_max_tokens,
register_prompt_template
)
from .main import * # type: ignore
from .integrations import *
from .exceptions import (
AuthenticationError,
InvalidRequestError,
RateLimitError,
ServiceUnavailableError,
OpenAIError,
ContextWindowExceededError
)