forked from phoenix/litellm-mirror
feat(anthropic_adapter.py): support for translating anthropic params to openai format
This commit is contained in:
Krrish Dholakia
parent
43d86528c1
commit
5d6e172d5c
10 changed files with 542 additions and 4213 deletions
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@ -881,3 +881,8 @@ from .assistants.main import *
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from .batches.main import *
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from .scheduler import *
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from .cost_calculator import response_cost_calculator, cost_per_token
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### ADAPTERS ###
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from .types.adapter import AdapterItem
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adapters: List[AdapterItem] = []
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43
litellm/adapters/anthropic_adapter.py
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43
litellm/adapters/anthropic_adapter.py
Normal file
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@ -0,0 +1,43 @@
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# What is this?
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## Translates OpenAI call to Anthropic `/v1/messages` format
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import json
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import os
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import traceback
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import uuid
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from typing import Literal, Optional
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import dotenv
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import httpx
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import litellm
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from litellm import ChatCompletionRequest, verbose_logger
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from litellm.integrations.custom_logger import CustomLogger
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from litellm.types.llms.anthropic import AnthropicMessagesRequest
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class AnthropicAdapter(CustomLogger):
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def __init__(self) -> None:
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super().__init__()
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def translate_completion_input_params(
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self, kwargs
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) -> Optional[ChatCompletionRequest]:
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"""
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- translate params, where needed
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- pass rest, as is
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"""
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request_body = AnthropicMessagesRequest(**kwargs) # type: ignore
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translated_body = litellm.AnthropicConfig().translate_anthropic_to_openai(
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anthropic_message_request=request_body
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)
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return translated_body
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def translate_completion_output_params(self, response: litellm.ModelResponse):
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return super().translate_completion_output_params(response)
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def translate_completion_output_params_streaming(self):
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return super().translate_completion_output_params_streaming()
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anthropic_adapter = AnthropicAdapter()
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@ -8,6 +8,8 @@ import dotenv
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from litellm.caching import DualCache
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from litellm.proxy._types import UserAPIKeyAuth
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from litellm.types.llms.openai import ChatCompletionRequest
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from litellm.types.utils import ModelResponse
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class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callback#callback-class
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@ -55,6 +57,28 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
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def pre_call_check(self, deployment: dict) -> Optional[dict]:
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pass
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#### ADAPTERS #### Allow calling 100+ LLMs in custom format - https://github.com/BerriAI/litellm/pulls
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def translate_completion_input_params(
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self, kwargs
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) -> Optional[ChatCompletionRequest]:
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"""
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Translates the input params, from the provider's native format to the litellm.completion() format.
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"""
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pass
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def translate_completion_output_params(self, response: ModelResponse):
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"""
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Translates the output params, from the OpenAI format to the custom format.
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"""
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pass
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def translate_completion_output_params_streaming(self):
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"""
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Translates the streaming chunk, from the OpenAI format to the custom format.
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"""
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pass
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#### CALL HOOKS - proxy only ####
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"""
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Control the modify incoming / outgoung data before calling the model
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@ -20,17 +20,36 @@ from litellm.llms.custom_httpx.http_handler import (
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_get_httpx_client,
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)
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from litellm.types.llms.anthropic import (
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AnthopicMessagesAssistantMessageParam,
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AnthropicMessagesRequest,
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AnthropicMessagesTool,
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AnthropicMessagesToolChoice,
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AnthropicMessagesUserMessageParam,
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ContentBlockDelta,
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ContentBlockStart,
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MessageBlockDelta,
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MessageStartBlock,
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)
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from litellm.types.llms.openai import (
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AllMessageValues,
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ChatCompletionAssistantMessage,
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ChatCompletionAssistantToolCall,
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ChatCompletionImageObject,
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ChatCompletionImageUrlObject,
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ChatCompletionRequest,
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ChatCompletionResponseMessage,
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ChatCompletionSystemMessage,
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ChatCompletionTextObject,
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ChatCompletionToolCallChunk,
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ChatCompletionToolCallFunctionChunk,
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ChatCompletionToolChoiceFunctionParam,
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ChatCompletionToolChoiceObjectParam,
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ChatCompletionToolChoiceValues,
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ChatCompletionToolMessage,
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ChatCompletionToolParam,
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ChatCompletionToolParamFunctionChunk,
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ChatCompletionUsageBlock,
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ChatCompletionUserMessage,
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)
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from litellm.types.utils import GenericStreamingChunk
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from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
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@ -168,6 +187,210 @@ class AnthropicConfig:
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optional_params["top_p"] = value
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return optional_params
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def translatable_anthropic_params(self) -> List:
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"""
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Which anthropic params, we need to translate to the openai format.
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"""
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return ["messages", "metadata", "system", "tool_choice", "tools"]
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def translate_anthropic_messages_to_openai(
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self,
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messages: List[
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Union[
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AnthropicMessagesUserMessageParam,
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AnthopicMessagesAssistantMessageParam,
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]
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],
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) -> List:
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new_messages: List[AllMessageValues] = []
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for m in messages:
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user_message: Optional[ChatCompletionUserMessage] = None
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tool_message_list: List[ChatCompletionToolMessage] = []
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## USER MESSAGE ##
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if m["role"] == "user":
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## translate user message
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if isinstance(m["content"], str):
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user_message = ChatCompletionUserMessage(
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role="user", content=m["content"]
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)
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elif isinstance(m["content"], list):
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new_user_content_list: List[
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Union[ChatCompletionTextObject, ChatCompletionImageObject]
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] = []
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for content in m["content"]:
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if content["type"] == "text":
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text_obj = ChatCompletionTextObject(
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type="text", text=content["text"]
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)
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new_user_content_list.append(text_obj)
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elif content["type"] == "image":
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image_url = ChatCompletionImageUrlObject(
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url=f"data:{content['type']};base64,{content['source']}"
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)
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image_obj = ChatCompletionImageObject(
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type="image_url", image_url=image_url
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)
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new_user_content_list.append(image_obj)
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elif content["type"] == "tool_result":
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if "content" not in content:
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tool_result = ChatCompletionToolMessage(
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role="tool",
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tool_call_id=content["tool_use_id"],
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content="",
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)
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tool_message_list.append(tool_result)
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elif isinstance(content["content"], str):
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tool_result = ChatCompletionToolMessage(
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role="tool",
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tool_call_id=content["tool_use_id"],
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content=content["content"],
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)
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tool_message_list.append(tool_result)
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elif isinstance(content["content"], list):
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for c in content["content"]:
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if c["type"] == "text":
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tool_result = ChatCompletionToolMessage(
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role="tool",
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tool_call_id=content["tool_use_id"],
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content=c["text"],
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)
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tool_message_list.append(tool_result)
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elif c["type"] == "image":
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image_str = (
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f"data:{c['type']};base64,{c['source']}"
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)
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tool_result = ChatCompletionToolMessage(
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role="tool",
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tool_call_id=content["tool_use_id"],
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content=image_str,
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)
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tool_message_list.append(tool_result)
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if user_message is not None:
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new_messages.append(user_message)
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if len(tool_message_list) > 0:
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new_messages.extend(tool_message_list)
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## ASSISTANT MESSAGE ##
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assistant_message_str: Optional[str] = None
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tool_calls: List[ChatCompletionAssistantToolCall] = []
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if m["role"] == "assistant":
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if isinstance(m["content"], str):
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assistant_message_str = m["content"]
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elif isinstance(m["content"], list):
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for content in m["content"]:
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if content["type"] == "text":
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if assistant_message_str is None:
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assistant_message_str = content["text"]
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else:
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assistant_message_str += content["text"]
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elif content["type"] == "tool_use":
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function_chunk = ChatCompletionToolCallFunctionChunk(
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name=content["name"],
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arguments=json.dumps(content["input"]),
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)
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tool_calls.append(
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ChatCompletionAssistantToolCall(
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id=content["id"],
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type="function",
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function=function_chunk,
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)
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)
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assistant_message = ChatCompletionAssistantMessage(
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role="assistant", content=assistant_message_str, tool_calls=tool_calls
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)
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new_messages.append(assistant_message)
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return new_messages
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def translate_anthropic_tool_choice_to_openai(
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self, tool_choice: AnthropicMessagesToolChoice
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) -> ChatCompletionToolChoiceValues:
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if tool_choice["type"] == "any":
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return "required"
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elif tool_choice["type"] == "auto":
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return "auto"
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elif tool_choice["type"] == "tool":
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tc_function_param = ChatCompletionToolChoiceFunctionParam(
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name=tool_choice.get("name", "")
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)
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return ChatCompletionToolChoiceObjectParam(
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type="function", function=tc_function_param
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)
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else:
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raise ValueError(
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"Incompatible tool choice param submitted - {}".format(tool_choice)
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)
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def translate_anthropic_tools_to_openai(
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self, tools: List[AnthropicMessagesTool]
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) -> List[ChatCompletionToolParam]:
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new_tools: List[ChatCompletionToolParam] = []
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for tool in tools:
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function_chunk = ChatCompletionToolParamFunctionChunk(
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name=tool["name"],
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parameters=tool["input_schema"],
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)
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if "description" in tool:
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function_chunk["description"] = tool["description"]
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new_tools.append(
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ChatCompletionToolParam(type="function", function=function_chunk)
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)
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return new_tools
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def translate_anthropic_to_openai(
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self, anthropic_message_request: AnthropicMessagesRequest
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) -> ChatCompletionRequest:
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"""
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This is used by the beta Anthropic Adapter, for translating anthropic `/v1/messages` requests to the openai format.
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"""
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new_messages: List[AllMessageValues] = []
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## CONVERT ANTHROPIC MESSAGES TO OPENAI
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new_messages = self.translate_anthropic_messages_to_openai(
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messages=anthropic_message_request["messages"]
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)
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## ADD SYSTEM MESSAGE TO MESSAGES
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if "system" in anthropic_message_request:
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new_messages.insert(
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0,
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ChatCompletionSystemMessage(
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role="system", content=anthropic_message_request["system"]
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),
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)
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new_kwargs: ChatCompletionRequest = {
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"model": anthropic_message_request["model"],
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"messages": new_messages,
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}
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## CONVERT METADATA (user_id)
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if "metadata" in anthropic_message_request:
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if "user_id" in anthropic_message_request["metadata"]:
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new_kwargs["user"] = anthropic_message_request["metadata"]["user_id"]
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## CONVERT TOOL CHOICE
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if "tool_choice" in anthropic_message_request:
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new_kwargs["tool_choice"] = self.translate_anthropic_tool_choice_to_openai(
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tool_choice=anthropic_message_request["tool_choice"]
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)
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## CONVERT TOOLS
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if "tools" in anthropic_message_request:
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new_kwargs["tools"] = self.translate_anthropic_tools_to_openai(
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tools=anthropic_message_request["tools"]
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)
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translatable_params = self.translatable_anthropic_params()
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for k, v in anthropic_message_request.items():
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if k not in translatable_params: # pass remaining params as is
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new_kwargs[k] = v # type: ignore
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return new_kwargs
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# makes headers for API call
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def validate_environment(api_key, user_headers):
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@ -48,6 +48,7 @@ from litellm import ( # type: ignore
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get_litellm_params,
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get_optional_params,
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)
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from litellm.integrations.custom_logger import CustomLogger
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.utils import (
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CustomStreamWrapper,
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@ -3943,6 +3944,33 @@ def text_completion(
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return text_completion_response
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###### Adapter Completion ################
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def adapter_completion(*, adapter_id: str, **kwargs) -> Any:
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translation_obj: Optional[CustomLogger] = None
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for item in litellm.adapters:
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if item["id"] == adapter_id:
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translation_obj = item["adapter"]
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if translation_obj is None:
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raise ValueError(
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"No matching adapter given. Received 'adapter_id'={}, litellm.adapters={}".format(
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adapter_id, litellm.adapters
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)
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)
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new_kwargs = translation_obj.translate_completion_input_params(kwargs=kwargs)
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response: ModelResponse = completion(**new_kwargs) # type: ignore
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translated_response = translation_obj.translate_completion_output_params(
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response=response
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)
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return translated_response
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##### Moderation #######################
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@ -2,17 +2,6 @@ model_list:
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- model_name: "*"
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litellm_params:
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model: "openai/*"
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- model_name: gemini-1.5-flash
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litellm_params:
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model: gemini/gemini-1.5-flash
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- model_name: whisper
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litellm_params:
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model: azure/azure-whisper
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api_version: 2024-02-15-preview
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api_base: os.environ/AZURE_EUROPE_API_BASE
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api_key: os.environ/AZURE_EUROPE_API_KEY
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model_info:
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mode: audio_transcription
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File diff suppressed because it is too large
Load diff
10
litellm/types/adapter.py
Normal file
10
litellm/types/adapter.py
Normal file
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@ -0,0 +1,10 @@
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from typing import List
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from typing_extensions import Dict, Required, TypedDict, override
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from litellm.integrations.custom_logger import CustomLogger
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class AdapterItem(TypedDict):
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id: str
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adapter: CustomLogger
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@ -9,25 +9,27 @@ class AnthropicMessagesToolChoice(TypedDict, total=False):
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name: str
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class AnthopicMessagesAssistantMessageTextContentParam(TypedDict, total=False):
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type: Required[Literal["text"]]
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class AnthropicMessagesTool(TypedDict, total=False):
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name: Required[str]
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description: str
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input_schema: Required[dict]
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class AnthropicMessagesTextParam(TypedDict):
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type: Literal["text"]
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text: str
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class AnthopicMessagesAssistantMessageToolCallParam(TypedDict, total=False):
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type: Required[Literal["tool_use"]]
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class AnthropicMessagesToolUseParam(TypedDict):
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type: Literal["tool_use"]
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id: str
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name: str
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input: dict
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AnthropicMessagesAssistantMessageValues = Union[
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AnthopicMessagesAssistantMessageTextContentParam,
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AnthopicMessagesAssistantMessageToolCallParam,
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AnthropicMessagesTextParam,
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AnthropicMessagesToolUseParam,
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]
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@ -46,6 +48,72 @@ class AnthopicMessagesAssistantMessageParam(TypedDict, total=False):
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"""
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class AnthropicImageParamSource(TypedDict):
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type: Literal["base64"]
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media_type: str
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data: str
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class AnthropicMessagesImageParam(TypedDict):
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type: Literal["image"]
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source: AnthropicImageParamSource
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class AnthropicMessagesToolResultContent(TypedDict):
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type: Literal["text"]
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text: str
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class AnthropicMessagesToolResultParam(TypedDict, total=False):
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type: Required[Literal["tool_result"]]
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tool_use_id: Required[str]
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is_error: bool
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content: Union[
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str,
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Iterable[
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Union[AnthropicMessagesToolResultContent, AnthropicMessagesImageParam]
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],
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]
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AnthropicMessagesUserMessageValues = Union[
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AnthropicMessagesTextParam,
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AnthropicMessagesImageParam,
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AnthropicMessagesToolResultParam,
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]
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class AnthropicMessagesUserMessageParam(TypedDict, total=False):
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role: Required[Literal["user"]]
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content: Required[Union[str, Iterable[AnthropicMessagesUserMessageValues]]]
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class AnthropicMetadata(TypedDict, total=False):
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user_id: str
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class AnthropicMessagesRequest(TypedDict, total=False):
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model: Required[str]
|
||||
messages: Required[
|
||||
List[
|
||||
Union[
|
||||
AnthropicMessagesUserMessageParam,
|
||||
AnthopicMessagesAssistantMessageParam,
|
||||
]
|
||||
]
|
||||
]
|
||||
max_tokens: Required[int]
|
||||
metadata: AnthropicMetadata
|
||||
stop_sequences: List[str]
|
||||
stream: bool
|
||||
system: str
|
||||
temperature: float
|
||||
tool_choice: AnthropicMessagesToolChoice
|
||||
tools: List[AnthropicMessagesTool]
|
||||
top_k: int
|
||||
top_p: float
|
||||
|
||||
|
||||
class ContentTextBlockDelta(TypedDict):
|
||||
"""
|
||||
'delta': {'type': 'text_delta', 'text': 'Hello'}
|
||||
|
|
|
|||
|
|
@ -305,7 +305,13 @@ class ChatCompletionToolCallFunctionChunk(TypedDict, total=False):
|
|||
arguments: str
|
||||
|
||||
|
||||
class ChatCompletionToolCallChunk(TypedDict):
|
||||
class ChatCompletionAssistantToolCall(TypedDict):
|
||||
id: Optional[str]
|
||||
type: Literal["function"]
|
||||
function: ChatCompletionToolCallFunctionChunk
|
||||
|
||||
|
||||
class ChatCompletionToolCallChunk(TypedDict): # result of /chat/completions call
|
||||
id: Optional[str]
|
||||
type: Literal["function"]
|
||||
function: ChatCompletionToolCallFunctionChunk
|
||||
|
|
@ -319,6 +325,107 @@ class ChatCompletionDeltaToolCallChunk(TypedDict, total=False):
|
|||
index: int
|
||||
|
||||
|
||||
class ChatCompletionTextObject(TypedDict):
|
||||
type: Literal["text"]
|
||||
text: str
|
||||
|
||||
|
||||
class ChatCompletionImageUrlObject(TypedDict, total=False):
|
||||
url: Required[str]
|
||||
detail: str
|
||||
|
||||
|
||||
class ChatCompletionImageObject(TypedDict):
|
||||
type: Literal["image_url"]
|
||||
image_url: ChatCompletionImageUrlObject
|
||||
|
||||
|
||||
class ChatCompletionUserMessage(TypedDict):
|
||||
role: Literal["user"]
|
||||
content: Union[
|
||||
str, Iterable[Union[ChatCompletionTextObject, ChatCompletionImageObject]]
|
||||
]
|
||||
|
||||
|
||||
class ChatCompletionAssistantMessage(TypedDict, total=False):
|
||||
role: Required[Literal["assistant"]]
|
||||
content: Optional[str]
|
||||
name: str
|
||||
tool_calls: List[ChatCompletionAssistantToolCall]
|
||||
|
||||
|
||||
class ChatCompletionToolMessage(TypedDict):
|
||||
role: Literal["tool"]
|
||||
content: str
|
||||
tool_call_id: str
|
||||
|
||||
|
||||
class ChatCompletionSystemMessage(TypedDict, total=False):
|
||||
role: Required[Literal["system"]]
|
||||
content: Required[str]
|
||||
name: str
|
||||
|
||||
|
||||
AllMessageValues = Union[
|
||||
ChatCompletionUserMessage,
|
||||
ChatCompletionAssistantMessage,
|
||||
ChatCompletionToolMessage,
|
||||
ChatCompletionSystemMessage,
|
||||
]
|
||||
|
||||
|
||||
class ChatCompletionToolChoiceFunctionParam(TypedDict):
|
||||
name: str
|
||||
|
||||
|
||||
class ChatCompletionToolChoiceObjectParam(TypedDict):
|
||||
type: Literal["function"]
|
||||
function: ChatCompletionToolChoiceFunctionParam
|
||||
|
||||
|
||||
ChatCompletionToolChoiceStringValues = Literal["none", "auto", "required"]
|
||||
|
||||
ChatCompletionToolChoiceValues = Union[
|
||||
ChatCompletionToolChoiceStringValues, ChatCompletionToolChoiceObjectParam
|
||||
]
|
||||
|
||||
|
||||
class ChatCompletionToolParamFunctionChunk(TypedDict, total=False):
|
||||
name: Required[str]
|
||||
description: str
|
||||
parameters: dict
|
||||
|
||||
|
||||
class ChatCompletionToolParam(TypedDict):
|
||||
type: Literal["function"]
|
||||
function: ChatCompletionToolParamFunctionChunk
|
||||
|
||||
|
||||
class ChatCompletionRequest(TypedDict, total=False):
|
||||
model: Required[str]
|
||||
messages: Required[List[AllMessageValues]]
|
||||
frequency_penalty: float
|
||||
logit_bias: dict
|
||||
logprobs: bool
|
||||
top_logprobs: int
|
||||
max_tokens: int
|
||||
n: int
|
||||
presence_penalty: float
|
||||
response_format: dict
|
||||
seed: int
|
||||
service_tier: str
|
||||
stop: Union[str, List[str]]
|
||||
stream_options: dict
|
||||
temperature: float
|
||||
top_p: float
|
||||
tools: List[ChatCompletionToolParam]
|
||||
tool_choice: ChatCompletionToolChoiceValues
|
||||
parallel_tool_calls: bool
|
||||
function_call: Union[str, dict]
|
||||
functions: List
|
||||
user: str
|
||||
|
||||
|
||||
class ChatCompletionDeltaChunk(TypedDict, total=False):
|
||||
content: Optional[str]
|
||||
tool_calls: List[ChatCompletionDeltaToolCallChunk]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue