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refactor: replace 'traceback.print_exc()' with logging library

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allows error logs to be in json format for otel logging
This commit is contained in:
Krrish Dholakia 2024-06-06 13:47:43 -07:00
parent 3c1d4179c2
commit e391e30285
41 changed files with 542 additions and 225 deletions
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56
litellm/llms/ollama_chat.py
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@ -1,11 +1,15 @@
from itertools import chain
import requests, types, time
import json, uuid
import requests
import types
import time
import json
import uuid
import traceback
from typing import Optional
from litellm import verbose_logger
import litellm
import httpx, aiohttp, asyncio
from .prompt_templates.factory import prompt_factory, custom_prompt
import httpx
import aiohttp
class OllamaError(Exception):
@ -299,7 +303,10 @@ def get_ollama_response(
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
@ -307,7 +314,9 @@ def get_ollama_response(
model_response["choices"][0]["message"] = message
model_response["choices"][0]["finish_reason"] = "tool_calls"
else:
model_response["choices"][0]["message"]["content"] = response_json["message"]["content"]
model_response["choices"][0]["message"]["content"] = response_json["message"][
"content"
]
model_response["created"] = int(time.time())
model_response["model"] = "ollama/" + model
prompt_tokens = response_json.get("prompt_eval_count", litellm.token_counter(messages=messages)) # type: ignore
@ -361,7 +370,10 @@ def ollama_completion_stream(url, api_key, data, logging_obj):
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
@ -410,9 +422,10 @@ async def ollama_async_streaming(
first_chunk_content = first_chunk.choices[0].delta.content or ""
response_content = first_chunk_content + "".join(
[
chunk.choices[0].delta.content
async for chunk in streamwrapper
if chunk.choices[0].delta.content]
chunk.choices[0].delta.content
async for chunk in streamwrapper
if chunk.choices[0].delta.content
]
)
function_call = json.loads(response_content)
delta = litellm.utils.Delta(
@ -420,7 +433,10 @@ async def ollama_async_streaming(
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
@ -433,7 +449,8 @@ async def ollama_async_streaming(
async for transformed_chunk in streamwrapper:
yield transformed_chunk
except Exception as e:
traceback.print_exc()
verbose_logger.error("LiteLLM.gemini(): Exception occured - {}".format(str(e)))
verbose_logger.debug(traceback.format_exc())
async def ollama_acompletion(
@ -483,7 +500,10 @@ async def ollama_acompletion(
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
@ -491,7 +511,9 @@ async def ollama_acompletion(
model_response["choices"][0]["message"] = message
model_response["choices"][0]["finish_reason"] = "tool_calls"
else:
model_response["choices"][0]["message"]["content"] = response_json["message"]["content"]
model_response["choices"][0]["message"]["content"] = response_json[
"message"
]["content"]
model_response["created"] = int(time.time())
model_response["model"] = "ollama_chat/" + data["model"]
@ -509,5 +531,9 @@ async def ollama_acompletion(
)
return model_response
except Exception as e:
traceback.print_exc()
verbose_logger.error(
"LiteLLM.ollama_acompletion(): Exception occured - {}".format(str(e))
)
verbose_logger.debug(traceback.format_exc())
raise e