fix(custom_llm.py): pass input params to custom llm

litellm/llms/custom_llm.py

@@ -59,16 +59,88 @@ class CustomLLM(BaseLLM):
     def __init__(self) -> None:
         super().__init__()
 
-    def completion(self, *args, **kwargs) -> ModelResponse:
+    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, *args, **kwargs) -> Iterator[GenericStreamingChunk]:
+    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, *args, **kwargs) -> ModelResponse:
+    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, *args, **kwargs) -> AsyncIterator[GenericStreamingChunk]:
+    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!")
 
 

litellm/main.py

@@ -2711,8 +2711,27 @@ def completion(
                 async_fn=acompletion, stream=stream, custom_llm=custom_handler
             )
 
+            headers = headers or litellm.headers
+
             ## CALL FUNCTION
-            response = handler_fn()
+            response = handler_fn(
+                model=model,
+                messages=messages,
+                headers=headers,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                api_key=api_key,
+                api_base=api_base,
+                acompletion=acompletion,
+                logging_obj=logging,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                timeout=timeout,  # type: ignore
+                custom_prompt_dict=custom_prompt_dict,
+                client=client,  # pass AsyncOpenAI, OpenAI client
+                encoding=encoding,
+            )
             if stream is True:
                 return CustomStreamWrapper(
                     completion_stream=response,

litellm/tests/test_custom_llm.py

@@ -17,7 +17,16 @@ sys.path.insert(
 import os
 from collections import defaultdict
 from concurrent.futures import ThreadPoolExecutor
-from typing import Any, AsyncGenerator, AsyncIterator, Coroutine, Iterator, Union
+from typing import (
+    Any,
+    AsyncGenerator,
+    AsyncIterator,
+    Callable,
+    Coroutine,
+    Iterator,
+    Optional,
+    Union,
+)
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import httpx
@@ -94,21 +103,75 @@ class CustomModelResponseIterator:
 
 
 class MyCustomLLM(CustomLLM):
-    def completion(self, *args, **kwargs) -> litellm.ModelResponse:
+    def completion(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        custom_prompt_dict: dict,
+        model_response: ModelResponse,
+        print_verbose: Callable[..., Any],
+        encoding,
+        api_key,
+        logging_obj,
+        optional_params: dict,
+        acompletion=None,
+        litellm_params=None,
+        logger_fn=None,
+        headers={},
+        timeout: Optional[Union[float, openai.Timeout]] = None,
+        client: Optional[litellm.HTTPHandler] = None,
+    ) -> ModelResponse:
         return litellm.completion(
             model="gpt-3.5-turbo",
             messages=[{"role": "user", "content": "Hello world"}],
             mock_response="Hi!",
         )  # type: ignore
 
-    async def acompletion(self, *args, **kwargs) -> litellm.ModelResponse:
+    async def acompletion(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        custom_prompt_dict: dict,
+        model_response: ModelResponse,
+        print_verbose: Callable[..., Any],
+        encoding,
+        api_key,
+        logging_obj,
+        optional_params: dict,
+        acompletion=None,
+        litellm_params=None,
+        logger_fn=None,
+        headers={},
+        timeout: Optional[Union[float, openai.Timeout]] = None,
+        client: Optional[litellm.AsyncHTTPHandler] = None,
+    ) -> litellm.ModelResponse:
         return litellm.completion(
             model="gpt-3.5-turbo",
             messages=[{"role": "user", "content": "Hello world"}],
             mock_response="Hi!",
         )  # type: ignore
 
-    def streaming(self, *args, **kwargs) -> Iterator[GenericStreamingChunk]:
+    def streaming(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        custom_prompt_dict: dict,
+        model_response: ModelResponse,
+        print_verbose: Callable[..., Any],
+        encoding,
+        api_key,
+        logging_obj,
+        optional_params: dict,
+        acompletion=None,
+        litellm_params=None,
+        logger_fn=None,
+        headers={},
+        timeout: Optional[Union[float, openai.Timeout]] = None,
+        client: Optional[litellm.HTTPHandler] = None,
+    ) -> Iterator[GenericStreamingChunk]:
         generic_streaming_chunk: GenericStreamingChunk = {
             "finish_reason": "stop",
             "index": 0,
@@ -126,7 +189,25 @@ class MyCustomLLM(CustomLLM):
         )
         return custom_iterator
 
-    async def astreaming(self, *args, **kwargs) -> AsyncIterator[GenericStreamingChunk]:  # type: ignore
+    async def astreaming(  # type: ignore
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        custom_prompt_dict: dict,
+        model_response: ModelResponse,
+        print_verbose: Callable[..., Any],
+        encoding,
+        api_key,
+        logging_obj,
+        optional_params: dict,
+        acompletion=None,
+        litellm_params=None,
+        logger_fn=None,
+        headers={},
+        timeout: Optional[Union[float, openai.Timeout]] = None,
+        client: Optional[litellm.AsyncHTTPHandler] = None,
+    ) -> AsyncIterator[GenericStreamingChunk]:  # type: ignore
         generic_streaming_chunk: GenericStreamingChunk = {
             "finish_reason": "stop",
             "index": 0,