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refactor(proxy_server.py): print statement showing how to add debug for logs

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Krrish Dholakia 2023-11-03 17:41:02 -07:00
parent a2b9ffdd61
commit fa24a61976
8 changed files with 76 additions and 207 deletions
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80
litellm/proxy/proxy_server.py
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@ -87,6 +87,7 @@ print("\033[1;34mDocs: https://docs.litellm.ai/docs/proxy_server\033[0m")
print()
import litellm
litellm.suppress_debug_info = True
from fastapi import FastAPI, Request
from fastapi.routing import APIRouter
from fastapi.encoders import jsonable_encoder
@ -576,24 +577,29 @@ def model_list():
@router.post("/completions")
@router.post("/engines/{model:path}/completions")
async def completion(request: Request, model: Optional[str] = None):
body = await request.body()
body_str = body.decode()
try:
data = ast.literal_eval(body_str)
except:
data = json.loads(body_str)
data["model"] = (
server_settings.get("completion_model", None) # server default
or user_model # model name passed via cli args
or model # for azure deployments
or data["model"] # default passed in http request
)
if user_model:
data["model"] = user_model
data["call_type"] = "text_completion"
return litellm_completion(
**data
)
try:
body = await request.body()
body_str = body.decode()
try:
data = ast.literal_eval(body_str)
except:
data = json.loads(body_str)
data["model"] = (
server_settings.get("completion_model", None) # server default
or user_model # model name passed via cli args
or model # for azure deployments
or data["model"] # default passed in http request
)
if user_model:
data["model"] = user_model
data["call_type"] = "text_completion"
return litellm_completion(
**data
)
except Exception as e:
error_traceback = traceback.format_exc()
error_msg = f"{str(e)}\n\n{error_traceback}"
return {"error": error_msg}
@router.post("/v1/chat/completions")
@ -601,22 +607,28 @@ async def completion(request: Request, model: Optional[str] = None):
@router.post("/openai/deployments/{model:path}/chat/completions") # azure compatible endpoint
async def chat_completion(request: Request, model: Optional[str] = None):
global server_settings
body = await request.body()
body_str = body.decode()
try:
data = ast.literal_eval(body_str)
except:
data = json.loads(body_str)
data["model"] = (
server_settings.get("completion_model", None) # server default
or user_model # model name passed via cli args
or model # for azure deployments
or data["model"] # default passed in http request
)
data["call_type"] = "chat_completion"
return litellm_completion(
**data
)
try:
body = await request.body()
body_str = body.decode()
try:
data = ast.literal_eval(body_str)
except:
data = json.loads(body_str)
data["model"] = (
server_settings.get("completion_model", None) # server default
or user_model # model name passed via cli args
or model # for azure deployments
or data["model"] # default passed in http request
)
data["call_type"] = "chat_completion"
return litellm_completion(
**data
)
except Exception as e:
print(f"\033[1;31mAn error occurred: {e}\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`")
error_traceback = traceback.format_exc()
error_msg = f"{str(e)}\n\n{error_traceback}"
return {"error": error_msg}
def print_cost_logs():
with open("costs.json", "r") as f: