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https://github.com/BerriAI/litellm.git
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Transform anthropic file content to openai file content
This commit is contained in:
Sameer Kankute
parent
854183e3b9
commit
ec3c9191f3
3 changed files with 269 additions and 32 deletions
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@ -197,25 +197,15 @@ class AnthropicBatchesConfig(BaseBatchesConfig):
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failed=request_counts_data.get("errored", 0),
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)
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# Extract results_url - this will be used for file content retrieval
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results_url = response_data.get("results_url")
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# Store results_url in output_file_id for later retrieval
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# We'll encode it in a way that we can detect it's an Anthropic results URL
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output_file_id = None
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if results_url:
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# Encode the batch_id and results_url so we can retrieve it later
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# Format: anthropic_batch_results:{batch_id}
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output_file_id = f"anthropic_batch_results:{batch_id}"
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return LiteLLMBatch(
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id=batch_id,
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object="batch",
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endpoint="/v1/messages",
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errors=None,
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input_file_id=None,
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input_file_id="None",
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completion_window="24h",
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status=openai_status,
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output_file_id=output_file_id,
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output_file_id=batch_id,
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error_file_id=None,
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created_at=created_at or int(time.time()),
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in_progress_at=created_at if processing_status == "in_progress" else None,
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@ -236,7 +226,13 @@ class AnthropicBatchesConfig(BaseBatchesConfig):
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"""Get the appropriate error class for Anthropic."""
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from ..common_utils import AnthropicError
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return AnthropicError(status_code=status_code, message=error_message, headers=headers)
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# Convert Dict to Headers if needed
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if isinstance(headers, dict):
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headers_obj: Optional[Headers] = Headers(headers)
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else:
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headers_obj = headers if isinstance(headers, Headers) else None
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return AnthropicError(status_code=status_code, message=error_message, headers=headers_obj)
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def transform_response(
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self,
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@ -1,21 +1,41 @@
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import asyncio
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from typing import Any, Coroutine, Optional, Union
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import json
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import time
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from typing import Any, Coroutine, Dict, List, Optional, Union
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import httpx
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import litellm
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from litellm._logging import verbose_logger
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from litellm._uuid import uuid
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from litellm.llms.custom_httpx.http_handler import (
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AsyncHTTPHandler,
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HTTPHandler,
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_get_httpx_client,
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get_async_httpx_client,
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)
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from litellm.litellm_core_utils.litellm_logging import Logging
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from litellm.types.llms.openai import (
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FileContentRequest,
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HttpxBinaryResponseContent,
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OpenAIBatchResult,
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OpenAIChatCompletionResponse,
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OpenAIErrorBody,
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)
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from litellm.types.utils import CallTypes, LlmProviders, ModelResponse
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from ..chat.transformation import AnthropicConfig
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from ..common_utils import AnthropicModelInfo
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# Map Anthropic error types to HTTP status codes
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ANTHROPIC_ERROR_STATUS_CODE_MAP = {
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"invalid_request_error": 400,
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"authentication_error": 401,
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"permission_error": 403,
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"not_found_error": 404,
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"rate_limit_error": 429,
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"api_error": 500,
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"overloaded_error": 503,
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"timeout_error": 504,
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}
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class AnthropicFilesHandler:
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"""
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@ -81,18 +101,29 @@ class AnthropicFilesHandler:
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}
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# Make the request to Anthropic
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async_client = get_async_httpx_client(llm_provider="anthropic")
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try:
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anthropic_response = await async_client.get(
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url=results_url,
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headers=headers
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)
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anthropic_response.raise_for_status()
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async_client = get_async_httpx_client(llm_provider=LlmProviders.ANTHROPIC)
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anthropic_response = await async_client.get(
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url=results_url,
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headers=headers
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)
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anthropic_response.raise_for_status()
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# Transform Anthropic batch results to OpenAI format
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transformed_content = self._transform_anthropic_batch_results_to_openai_format(
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anthropic_response.content
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)
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# Create a new response with transformed content
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transformed_response = httpx.Response(
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status_code=anthropic_response.status_code,
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headers=anthropic_response.headers,
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content=transformed_content,
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request=anthropic_response.request,
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)
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# Return the transformed response content
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return HttpxBinaryResponseContent(response=transformed_response)
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# Return the response content
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return HttpxBinaryResponseContent(response=anthropic_response)
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finally:
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await async_client.aclose()
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def file_content(
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self,
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@ -140,3 +171,197 @@ class AnthropicFilesHandler:
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)
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)
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def _transform_anthropic_batch_results_to_openai_format(
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self, anthropic_content: bytes
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) -> bytes:
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"""
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Transform Anthropic batch results JSONL to OpenAI batch results JSONL format.
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Anthropic format:
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{
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"custom_id": "...",
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"result": {
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"type": "succeeded",
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"message": { ... } // Anthropic message format
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}
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}
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OpenAI format:
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{
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"custom_id": "...",
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"response": {
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"status_code": 200,
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"request_id": "...",
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"body": { ... } // OpenAI chat completion format
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}
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}
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"""
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try:
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anthropic_config = AnthropicConfig()
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transformed_lines = []
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# Parse JSONL content
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content_str = anthropic_content.decode("utf-8")
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for line in content_str.strip().split("\n"):
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if not line.strip():
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continue
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anthropic_result = json.loads(line)
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custom_id = anthropic_result.get("custom_id", "")
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result = anthropic_result.get("result", {})
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result_type = result.get("type", "")
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# Transform based on result type
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if result_type == "succeeded":
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# Transform Anthropic message to OpenAI format
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anthropic_message = result.get("message", {})
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if anthropic_message:
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openai_response_body = self._transform_anthropic_message_to_openai_format(
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anthropic_message=anthropic_message,
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anthropic_config=anthropic_config,
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)
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# Create OpenAI batch result format
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openai_result: OpenAIBatchResult = {
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"custom_id": custom_id,
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"response": {
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"status_code": 200,
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"request_id": anthropic_message.get("id", ""),
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"body": openai_response_body,
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},
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}
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transformed_lines.append(json.dumps(openai_result))
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elif result_type == "errored":
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# Handle error case
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error = result.get("error", {})
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error_obj = error.get("error", {})
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error_message = error_obj.get("message", "Unknown error")
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error_type = error_obj.get("type", "api_error")
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status_code = ANTHROPIC_ERROR_STATUS_CODE_MAP.get(error_type, 500)
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error_body_errored: OpenAIErrorBody = {
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"error": {
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"message": error_message,
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"type": error_type,
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}
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}
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openai_result_errored: OpenAIBatchResult = {
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"custom_id": custom_id,
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"response": {
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"status_code": status_code,
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"request_id": error.get("request_id", ""),
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"body": error_body_errored,
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},
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}
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transformed_lines.append(json.dumps(openai_result_errored))
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elif result_type in ["canceled", "expired"]:
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# Handle canceled/expired cases
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error_body_canceled: OpenAIErrorBody = {
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"error": {
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"message": f"Batch request was {result_type}",
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"type": "invalid_request_error",
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}
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}
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openai_result_canceled: OpenAIBatchResult = {
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"custom_id": custom_id,
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"response": {
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"status_code": 400,
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"request_id": "",
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"body": error_body_canceled,
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},
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}
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transformed_lines.append(json.dumps(openai_result_canceled))
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# Join lines and encode back to bytes
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transformed_content = "\n".join(transformed_lines)
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if transformed_lines:
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transformed_content += "\n" # Add trailing newline for JSONL format
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return transformed_content.encode("utf-8")
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except Exception as e:
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verbose_logger.error(
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f"Error transforming Anthropic batch results to OpenAI format: {e}"
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)
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# Return original content if transformation fails
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return anthropic_content
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def _transform_anthropic_message_to_openai_format(
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self, anthropic_message: dict, anthropic_config: AnthropicConfig
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) -> OpenAIChatCompletionResponse:
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"""
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Transform a single Anthropic message to OpenAI chat completion format.
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"""
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try:
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# Create a mock httpx.Response for transformation
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mock_response = httpx.Response(
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status_code=200,
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content=json.dumps(anthropic_message).encode("utf-8"),
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)
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# Create a ModelResponse object
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model_response = ModelResponse()
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# Initialize with required fields - will be populated by transform_parsed_response
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model_response.choices = [
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litellm.Choices(
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finish_reason="stop",
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index=0,
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message=litellm.Message(content="", role="assistant"),
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)
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] # type: ignore
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# Create a logging object for transformation
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logging_obj = Logging(
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model=anthropic_message.get("model", "claude-3-5-sonnet-20241022"),
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messages=[{"role": "user", "content": "batch_request"}],
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stream=False,
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call_type=CallTypes.aretrieve_batch,
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start_time=time.time(),
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litellm_call_id="batch_" + str(uuid.uuid4()),
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function_id="batch_processing",
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litellm_trace_id=str(uuid.uuid4()),
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kwargs={"optional_params": {}},
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)
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logging_obj.optional_params = {}
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# Transform using AnthropicConfig
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transformed_response = anthropic_config.transform_parsed_response(
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completion_response=anthropic_message,
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raw_response=mock_response,
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model_response=model_response,
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json_mode=False,
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prefix_prompt=None,
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)
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# Convert ModelResponse to OpenAI format dict - it's already in OpenAI format
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openai_body: OpenAIChatCompletionResponse = transformed_response.model_dump(exclude_none=True)
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# Ensure id comes from anthropic_message if not set
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if not openai_body.get("id"):
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openai_body["id"] = anthropic_message.get("id", "")
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return openai_body
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except Exception as e:
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verbose_logger.error(
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f"Error transforming Anthropic message to OpenAI format: {e}"
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)
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# Return a basic error response if transformation fails
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error_response: OpenAIChatCompletionResponse = {
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"id": anthropic_message.get("id", ""),
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"object": "chat.completion",
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"created": int(time.time()),
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"model": anthropic_message.get("model", ""),
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"choices": [
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{
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"index": 0,
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"message": {"role": "assistant", "content": ""},
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"finish_reason": "error",
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}
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],
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"usage": {
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"total_tokens": 0,
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},
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}
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return error_response
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@ -437,10 +437,12 @@ class ListBatchRequest(TypedDict, total=False):
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"""
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after: Union[str, NotGiven]
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limit: Union[int, NotGiven]
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extra_headers: Optional[Dict[str, str]]
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extra_body: Optional[Dict[str, str]]
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timeout: Optional[float]
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# OpenAI Batch Result Types
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class OpenAIErrorBody(TypedDict, total=False):
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"""Error body in OpenAI batch response format."""
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error: Dict[str, str]
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BatchJobStatus = Literal[
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@ -1824,6 +1826,20 @@ class OpenAIChatCompletionResponse(TypedDict, total=False):
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service_tier: str
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# OpenAI Batch Result Types (defined after OpenAIChatCompletionResponse for forward reference)
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class OpenAIBatchResponse(TypedDict, total=False):
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"""Response wrapper in OpenAI batch result format."""
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status_code: int
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request_id: str
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body: Union[OpenAIChatCompletionResponse, OpenAIErrorBody]
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class OpenAIBatchResult(TypedDict, total=False):
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"""OpenAI batch result format."""
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custom_id: str
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response: OpenAIBatchResponse
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OpenAIChatCompletionFinishReason = Literal[
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"stop", "content_filter", "function_call", "tool_calls", "length"
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]
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