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anthropic and together ai streaming no longer waits till completion to return response

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Krrish Dholakia 2023-08-26 19:00:53 -07:00
parent 95ce560f6d
commit 320fe89a24
6 changed files with 78 additions and 114 deletions
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80
litellm/main.py
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@ -35,8 +35,6 @@ from litellm.utils import (
)
from litellm.utils import (
get_ollama_response_stream,
stream_to_string,
together_ai_completion_streaming,
)
####### ENVIRONMENT VARIABLES ###################
@ -542,46 +540,56 @@ def completion(
logging.pre_call(input=prompt, api_key=TOGETHER_AI_TOKEN)
print(f"TOGETHER_AI_TOKEN: {TOGETHER_AI_TOKEN}")
res = requests.post(
endpoint,
json={
"model": model,
"prompt": prompt,
"request_type": "language-model-inference",
**optional_params,
},
headers=headers,
)
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,
)
response = CustomStreamWrapper(
res.iter_lines(), model, custom_llm_provider="together_ai"
)
return response
## 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 "output" not in json_response:
raise Exception(
f"liteLLM: Error Making TogetherAI request, JSON Response {json_response}"
else:
res = requests.post(
endpoint,
json={
"model": model,
"prompt": prompt,
"request_type": "language-model-inference",
**optional_params,
},
headers=headers,
)
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
## 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 "output" not in json_response:
raise Exception(
f"liteLLM: Error Making TogetherAI request, JSON Response {json_response}"
)
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
elif model in litellm.vertex_chat_models:
# import vertexai/if it fails then pip install vertexai# import cohere/if it fails then pip install cohere
install_and_import("vertexai")