litellm-mirror/litellm/llms/custom_llm.py
Krish Dholakia 11f9df923a
LiteLLM Minor Fixes & Improvements (10/10/2024) (#6158)
* refactor(vertex_ai_partner_models/anthropic): refactor anthropic to use partner model logic

* fix(vertex_ai/): support passing custom api base to partner models

Fixes https://github.com/BerriAI/litellm/issues/4317

* fix(proxy_server.py): Fix prometheus premium user check logic

* docs(prometheus.md): update quick start docs

* fix(custom_llm.py): support passing dynamic api key + api base

* fix(realtime_api/main.py): Add request/response logging for realtime api endpoints

Closes https://github.com/BerriAI/litellm/issues/6081

* feat(openai/realtime): add openai realtime api logging

Closes https://github.com/BerriAI/litellm/issues/6081

* fix(realtime_streaming.py): fix linting errors

* fix(realtime_streaming.py): fix linting errors

* fix: fix linting errors

* fix pattern match router

* Add literalai in the sidebar observability category (#6163)

* fix: add literalai in the sidebar

* fix: typo

* update (#6160)

* Feat: Add Langtrace integration (#5341)

* Feat: Add Langtrace integration

* add langtrace service name

* fix timestamps for traces

* add tests

* Discard Callback + use existing otel logger

* cleanup

* remove print statments

* remove callback

* add docs

* docs

* add logging docs

* format logging

* remove emoji and add litellm proxy example

* format logging

* format `logging.md`

* add langtrace docs to logging.md

* sync conflict

* docs fix

* (perf) move s3 logging to Batch logging + async [94% faster perf under 100 RPS on 1 litellm instance] (#6165)

* fix move s3 to use customLogger

* add basic s3 logging test

* add s3 to custom logger compatible

* use batch logger for s3

* s3 set flush interval and batch size

* fix s3 logging

* add notes on s3 logging

* fix s3 logging

* add basic s3 logging test

* fix s3 type errors

* add test for sync logging on s3

* fix: fix to debug log

---------

Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Willy Douhard <willy.douhard@gmail.com>
Co-authored-by: yujonglee <yujonglee.dev@gmail.com>
Co-authored-by: Ali Waleed <ali@scale3labs.com>
2024-10-11 23:04:36 -07:00

199 lines
5.3 KiB
Python

# What is this?
## Handler file for a Custom Chat LLM
"""
- completion
- acompletion
- streaming
- async_streaming
"""
import copy
import json
import os
import time
import types
from enum import Enum
from functools import partial
from typing import (
Any,
AsyncGenerator,
AsyncIterator,
Callable,
Coroutine,
Iterator,
List,
Literal,
Optional,
Tuple,
Union,
)
import httpx # type: ignore
import requests # type: ignore
import litellm
from litellm.litellm_core_utils.core_helpers import map_finish_reason
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.types.utils import GenericStreamingChunk, ProviderField
from litellm.utils import (
CustomStreamWrapper,
EmbeddingResponse,
ImageResponse,
ModelResponse,
Usage,
)
from .base import BaseLLM
from .prompt_templates.factory import custom_prompt, prompt_factory
class CustomLLMError(Exception): # use this for all your exceptions
def __init__(
self,
status_code,
message,
):
self.status_code = status_code
self.message = message
super().__init__(
self.message
) # Call the base class constructor with the parameters it needs
class CustomLLM(BaseLLM):
def __init__(self) -> None:
super().__init__()
def completion(
self,
model: str,
messages: list,
api_base: str,
custom_prompt_dict: dict,
model_response: ModelResponse,
print_verbose: Callable,
encoding,
api_key,
logging_obj,
optional_params: dict,
acompletion=None,
litellm_params=None,
logger_fn=None,
headers={},
timeout: Optional[Union[float, httpx.Timeout]] = None,
client: Optional[HTTPHandler] = None,
) -> ModelResponse:
raise CustomLLMError(status_code=500, message="Not implemented yet!")
def streaming(
self,
model: str,
messages: list,
api_base: str,
custom_prompt_dict: dict,
model_response: ModelResponse,
print_verbose: Callable,
encoding,
api_key,
logging_obj,
optional_params: dict,
acompletion=None,
litellm_params=None,
logger_fn=None,
headers={},
timeout: Optional[Union[float, httpx.Timeout]] = None,
client: Optional[HTTPHandler] = None,
) -> Iterator[GenericStreamingChunk]:
raise CustomLLMError(status_code=500, message="Not implemented yet!")
async def acompletion(
self,
model: str,
messages: list,
api_base: str,
custom_prompt_dict: dict,
model_response: ModelResponse,
print_verbose: Callable,
encoding,
api_key,
logging_obj,
optional_params: dict,
acompletion=None,
litellm_params=None,
logger_fn=None,
headers={},
timeout: Optional[Union[float, httpx.Timeout]] = None,
client: Optional[AsyncHTTPHandler] = None,
) -> ModelResponse:
raise CustomLLMError(status_code=500, message="Not implemented yet!")
async def astreaming(
self,
model: str,
messages: list,
api_base: str,
custom_prompt_dict: dict,
model_response: ModelResponse,
print_verbose: Callable,
encoding,
api_key,
logging_obj,
optional_params: dict,
acompletion=None,
litellm_params=None,
logger_fn=None,
headers={},
timeout: Optional[Union[float, httpx.Timeout]] = None,
client: Optional[AsyncHTTPHandler] = None,
) -> AsyncIterator[GenericStreamingChunk]:
raise CustomLLMError(status_code=500, message="Not implemented yet!")
def image_generation(
self,
model: str,
prompt: str,
api_key: Optional[str],
api_base: Optional[str],
model_response: ImageResponse,
optional_params: dict,
logging_obj: Any,
timeout: Optional[Union[float, httpx.Timeout]] = None,
client: Optional[HTTPHandler] = None,
) -> ImageResponse:
raise CustomLLMError(status_code=500, message="Not implemented yet!")
async def aimage_generation(
self,
model: str,
prompt: str,
model_response: ImageResponse,
api_key: Optional[
str
], # dynamically set api_key - https://docs.litellm.ai/docs/set_keys#api_key
api_base: Optional[
str
], # dynamically set api_base - https://docs.litellm.ai/docs/set_keys#api_base
optional_params: dict,
logging_obj: Any,
timeout: Optional[Union[float, httpx.Timeout]] = None,
client: Optional[AsyncHTTPHandler] = None,
) -> ImageResponse:
raise CustomLLMError(status_code=500, message="Not implemented yet!")
def custom_chat_llm_router(
async_fn: bool, stream: Optional[bool], custom_llm: CustomLLM
):
"""
Routes call to CustomLLM completion/acompletion/streaming/astreaming functions, based on call type
Validates if response is in expected format
"""
if async_fn:
if stream:
return custom_llm.astreaming
return custom_llm.acompletion
if stream:
return custom_llm.streaming
return custom_llm.completion