chore: initial snapshot for gitea/github upload
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"""
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OpenAI Responses API token counting implementation.
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"""
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from litellm.llms.openai.responses.count_tokens.handler import (
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OpenAICountTokensHandler,
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)
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from litellm.llms.openai.responses.count_tokens.token_counter import (
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OpenAITokenCounter,
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)
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from litellm.llms.openai.responses.count_tokens.transformation import (
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OpenAICountTokensConfig,
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)
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__all__ = [
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"OpenAICountTokensHandler",
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"OpenAICountTokensConfig",
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"OpenAITokenCounter",
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]
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"""
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OpenAI Responses API token counting handler.
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Uses httpx for HTTP requests to OpenAI's /v1/responses/input_tokens endpoint.
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"""
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import json
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from typing import Any, 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.llms.custom_httpx.http_handler import get_async_httpx_client
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from litellm.llms.openai.common_utils import OpenAIError
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from litellm.llms.openai.responses.count_tokens.transformation import (
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OpenAICountTokensConfig,
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)
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class OpenAICountTokensHandler(OpenAICountTokensConfig):
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"""
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Handler for OpenAI Responses API token counting requests.
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"""
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async def handle_count_tokens_request(
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self,
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model: str,
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input: Union[str, List[Any]],
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api_key: str,
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api_base: Optional[str] = None,
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timeout: Optional[Union[float, httpx.Timeout]] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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instructions: Optional[str] = None,
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) -> Dict[str, Any]:
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"""
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Handle a token counting request to OpenAI's Responses API.
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Returns:
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Dictionary containing {"input_tokens": <number>}
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Raises:
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OpenAIError: If the API request fails
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"""
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try:
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self.validate_request(model, input)
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verbose_logger.debug(
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f"Processing OpenAI CountTokens request for model: {model}"
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)
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request_body = self.transform_request_to_count_tokens(
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model=model,
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input=input,
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tools=tools,
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instructions=instructions,
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)
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endpoint_url = self.get_openai_count_tokens_endpoint(api_base)
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verbose_logger.debug(f"Making request to: {endpoint_url}")
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headers = self.get_required_headers(api_key)
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async_client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.OPENAI
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)
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request_timeout = (
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timeout if timeout is not None else litellm.request_timeout
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)
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response = await async_client.post(
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endpoint_url,
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headers=headers,
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json=request_body,
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timeout=request_timeout,
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)
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verbose_logger.debug(f"Response status: {response.status_code}")
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if response.status_code != 200:
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error_text = response.text
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verbose_logger.error(f"OpenAI API error: {error_text}")
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raise OpenAIError(
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status_code=response.status_code,
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message=error_text,
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)
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openai_response = response.json()
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verbose_logger.debug(f"OpenAI response: {openai_response}")
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return openai_response
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except OpenAIError:
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raise
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except httpx.HTTPStatusError as e:
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verbose_logger.error(f"HTTP error in CountTokens handler: {str(e)}")
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raise OpenAIError(
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status_code=e.response.status_code,
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message=e.response.text,
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)
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except (httpx.RequestError, json.JSONDecodeError, ValueError) as e:
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verbose_logger.error(f"Error in CountTokens handler: {str(e)}")
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raise OpenAIError(
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status_code=500,
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message=f"CountTokens processing error: {str(e)}",
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)
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"""
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OpenAI Token Counter implementation using the Responses API /input_tokens endpoint.
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"""
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import os
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from typing import Any, Dict, List, Optional
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from litellm._logging import verbose_logger
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from litellm.llms.base_llm.base_utils import BaseTokenCounter
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from litellm.llms.openai.common_utils import OpenAIError
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from litellm.llms.openai.responses.count_tokens.handler import (
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OpenAICountTokensHandler,
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)
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from litellm.llms.openai.responses.count_tokens.transformation import (
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OpenAICountTokensConfig,
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)
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from litellm.types.utils import LlmProviders, TokenCountResponse
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# Global handler instance - reuse across all token counting requests
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openai_count_tokens_handler = OpenAICountTokensHandler()
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class OpenAITokenCounter(BaseTokenCounter):
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"""Token counter implementation for OpenAI provider using the Responses API."""
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def should_use_token_counting_api(
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self,
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custom_llm_provider: Optional[str] = None,
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) -> bool:
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return custom_llm_provider == LlmProviders.OPENAI.value
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async def count_tokens(
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self,
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model_to_use: str,
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messages: Optional[List[Dict[str, Any]]],
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contents: Optional[List[Dict[str, Any]]],
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deployment: Optional[Dict[str, Any]] = None,
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request_model: str = "",
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tools: Optional[List[Dict[str, Any]]] = None,
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system: Optional[Any] = None,
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) -> Optional[TokenCountResponse]:
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"""
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Count tokens using OpenAI's Responses API /input_tokens endpoint.
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"""
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if not messages:
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return None
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deployment = deployment or {}
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litellm_params = deployment.get("litellm_params", {})
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# Get OpenAI API key from deployment config or environment
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api_key = litellm_params.get("api_key")
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if not api_key:
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api_key = os.getenv("OPENAI_API_KEY")
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if not api_key:
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verbose_logger.warning("No OpenAI API key found for token counting")
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return None
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api_base = litellm_params.get("api_base")
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# Convert chat messages to Responses API input format
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input_items, instructions = OpenAICountTokensConfig.messages_to_responses_input(
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messages
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)
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# Use system param if instructions not extracted from messages
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if instructions is None and system is not None:
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instructions = system if isinstance(system, str) else str(system)
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# If no input items were produced (e.g., system-only messages), fall back to local counting
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if not input_items:
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return None
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try:
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result = await openai_count_tokens_handler.handle_count_tokens_request(
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model=model_to_use,
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input=input_items if input_items is not None else [],
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api_key=api_key,
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api_base=api_base,
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tools=tools,
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instructions=instructions,
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)
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if result is not None:
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return TokenCountResponse(
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total_tokens=result.get("input_tokens", 0),
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request_model=request_model,
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model_used=model_to_use,
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tokenizer_type="openai_api",
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original_response=result,
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)
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except OpenAIError as e:
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verbose_logger.warning(
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f"OpenAI CountTokens API error: status={e.status_code}, message={e.message}"
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)
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return TokenCountResponse(
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total_tokens=0,
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request_model=request_model,
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model_used=model_to_use,
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tokenizer_type="openai_api",
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error=True,
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error_message=e.message,
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status_code=e.status_code,
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)
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except Exception as e:
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verbose_logger.warning(f"Error calling OpenAI CountTokens API: {e}")
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return TokenCountResponse(
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total_tokens=0,
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request_model=request_model,
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model_used=model_to_use,
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tokenizer_type="openai_api",
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error=True,
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error_message=str(e),
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status_code=500,
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)
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return None
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@@ -0,0 +1,160 @@
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"""
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OpenAI Responses API token counting transformation logic.
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This module handles the transformation of requests to OpenAI's /v1/responses/input_tokens endpoint.
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"""
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from typing import Any, Dict, List, Optional, Union
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class OpenAICountTokensConfig:
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"""
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Configuration and transformation logic for OpenAI Responses API token counting.
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OpenAI Responses API Token Counting Specification:
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- Endpoint: POST https://api.openai.com/v1/responses/input_tokens
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- Response: {"input_tokens": <number>}
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"""
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def get_openai_count_tokens_endpoint(self, api_base: Optional[str] = None) -> str:
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base = api_base or "https://api.openai.com/v1"
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base = base.rstrip("/")
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return f"{base}/responses/input_tokens"
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def transform_request_to_count_tokens(
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self,
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model: str,
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input: Union[str, List[Any]],
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tools: Optional[List[Dict[str, Any]]] = None,
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instructions: Optional[str] = None,
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) -> Dict[str, Any]:
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"""
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Transform request to OpenAI Responses API token counting format.
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The Responses API uses `input` (not `messages`) and `instructions` (not `system`).
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"""
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request: Dict[str, Any] = {
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"model": model,
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"input": input,
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}
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if instructions is not None:
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request["instructions"] = instructions
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if tools is not None:
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request["tools"] = self._transform_tools_for_responses_api(tools)
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return request
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def get_required_headers(self, api_key: str) -> Dict[str, str]:
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return {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}",
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}
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def validate_request(self, model: str, input: Union[str, List[Any]]) -> None:
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if not model:
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raise ValueError("model parameter is required")
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if not input:
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raise ValueError("input parameter is required")
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@staticmethod
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def _transform_tools_for_responses_api(
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tools: List[Dict[str, Any]],
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) -> List[Dict[str, Any]]:
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"""
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Transform OpenAI chat tools format to Responses API tools format.
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Chat format: {"type": "function", "function": {"name": "...", "parameters": {...}}}
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Responses format: {"type": "function", "name": "...", "parameters": {...}}
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"""
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transformed = []
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for tool in tools:
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if tool.get("type") == "function" and "function" in tool:
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func = tool["function"]
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item: Dict[str, Any] = {
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"type": "function",
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"name": func.get("name", ""),
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"description": func.get("description", ""),
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"parameters": func.get("parameters", {}),
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}
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if "strict" in func:
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item["strict"] = func["strict"]
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transformed.append(item)
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else:
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# Pass through non-function tools (e.g., web_search, file_search)
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transformed.append(tool)
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return transformed
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@staticmethod
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def messages_to_responses_input(
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messages: List[Dict[str, Any]],
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) -> tuple:
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"""
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Convert standard chat messages format to OpenAI Responses API input format.
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Returns:
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(input_items, instructions) tuple where instructions is extracted
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from system/developer messages.
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"""
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input_items: List[Dict[str, Any]] = []
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instructions_parts: List[str] = []
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for msg in messages:
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role = msg.get("role", "")
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content = msg.get("content") or ""
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if role in ("system", "developer"):
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# Extract system/developer messages as instructions
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if isinstance(content, str):
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instructions_parts.append(content)
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elif isinstance(content, list):
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# Handle content blocks - extract text
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text_parts = []
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for block in content:
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if isinstance(block, dict) and block.get("type") == "text":
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text_parts.append(block.get("text", ""))
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elif isinstance(block, str):
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text_parts.append(block)
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instructions_parts.append("\n".join(text_parts))
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elif role == "user":
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if isinstance(content, list):
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# Extract text from content blocks for Responses API
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text_parts = []
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for block in content:
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if isinstance(block, dict) and block.get("type") == "text":
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text_parts.append(block.get("text", ""))
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elif isinstance(block, str):
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text_parts.append(block)
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content = "\n".join(text_parts)
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input_items.append({"role": "user", "content": content})
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elif role == "assistant":
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# Map tool_calls to Responses API function_call items
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tool_calls = msg.get("tool_calls")
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if content:
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input_items.append({"role": "assistant", "content": content})
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if tool_calls:
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for tc in tool_calls:
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func = tc.get("function", {})
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input_items.append(
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{
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"type": "function_call",
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"call_id": tc.get("id", ""),
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"name": func.get("name", ""),
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"arguments": func.get("arguments", ""),
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}
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)
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elif not content:
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input_items.append({"role": "assistant", "content": content})
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elif role == "tool":
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input_items.append(
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{
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"type": "function_call_output",
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"call_id": msg.get("tool_call_id", ""),
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"output": content if isinstance(content, str) else str(content),
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}
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)
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instructions = "\n".join(instructions_parts) if instructions_parts else None
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return input_items, instructions
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