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add coverage for rate limit errors to togetherai

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Krrish Dholakia 2023-08-29 12:54:56 -07:00
parent 88bd1df3e0
commit f11599e50c
7 changed files with 191 additions and 103 deletions
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85
litellm/main.py
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@ -23,6 +23,7 @@ from .llms.anthropic import AnthropicLLM
from .llms.huggingface_restapi import HuggingfaceRestAPILLM
from .llms.baseten import BasetenLLM
from .llms.ai21 import AI21LLM
from .llms.together_ai import TogetherAILLM
import tiktoken
from concurrent.futures import ThreadPoolExecutor
@ -540,78 +541,30 @@ def completion(
response = model_response
elif custom_llm_provider == "together_ai" or ("togethercomputer" in model):
custom_llm_provider = "together_ai"
import requests
TOGETHER_AI_TOKEN = (
get_secret("TOGETHER_AI_TOKEN")
or get_secret("TOGETHERAI_API_KEY")
or get_secret("TOGETHER_AI_API_KEY")
or api_key
together_ai_key = (
api_key
or litellm.togetherai_api_key
or get_secret("TOGETHER_AI_TOKEN")
or get_secret("TOGETHERAI_API_KEY")
)
headers = {"Authorization": f"Bearer {TOGETHER_AI_TOKEN}"}
endpoint = "https://api.together.xyz/inference"
prompt = " ".join(
[message["content"] for message in messages]
) # TODO: Add chat support for together AI
## LOGGING
logging.pre_call(input=prompt, api_key=TOGETHER_AI_TOKEN)
print(f"TOGETHER_AI_TOKEN: {TOGETHER_AI_TOKEN}")
if (
"stream_tokens" in optional_params
and optional_params["stream_tokens"] == True
):
res = requests.post(
endpoint,
json={
"model": model,
"prompt": prompt,
"request_type": "language-model-inference",
**optional_params,
},
stream=optional_params["stream_tokens"],
headers=headers,
)
together_ai_client = TogetherAILLM(encoding=encoding, api_key=together_ai_key, logging_obj=logging)
model_response = together_ai_client.completion(
model=model,
messages=messages,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
)
if "stream_tokens" in optional_params and optional_params["stream_tokens"] == True:
# don't try to access stream object,
response = CustomStreamWrapper(
res.iter_lines(), model, custom_llm_provider="together_ai", logging_obj=logging
model_response, model, custom_llm_provider="together_ai", logging_obj=logging
)
return response
else:
res = requests.post(
endpoint,
json={
"model": model,
"prompt": prompt,
"request_type": "language-model-inference",
**optional_params,
},
headers=headers,
)
## LOGGING
logging.post_call(
input=prompt, api_key=TOGETHER_AI_TOKEN, original_response=res.text
)
# make this safe for reading, if output does not exist raise an error
json_response = res.json()
if "error" in json_response:
raise Exception(json.dumps(json_response))
elif "error" in json_response["output"]:
raise Exception(json.dumps(json_response["output"]))
completion_response = json_response["output"]["choices"][0]["text"]
prompt_tokens = len(encoding.encode(prompt))
completion_tokens = len(encoding.encode(completion_response))
## RESPONSE OBJECT
model_response["choices"][0]["message"]["content"] = completion_response
model_response["created"] = time.time()
model_response["model"] = model
model_response["usage"] = {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": prompt_tokens + completion_tokens,
}
response = model_response
response = model_response
elif model in litellm.vertex_chat_models:
import vertexai
from vertexai.preview.language_models import ChatModel, InputOutputTextPair