diff --git a/litellm/llms/minimax/chat/transformation.py b/litellm/llms/minimax/chat/transformation.py index 69f228160f6..92987b794c5 100644 --- a/litellm/llms/minimax/chat/transformation.py +++ b/litellm/llms/minimax/chat/transformation.py @@ -2,12 +2,17 @@ MiniMax OpenAI transformation config - extends OpenAI chat config for MiniMax's OpenAI-compatible API """ -from typing import List, Optional, Tuple +import re +from typing import Any, Dict, List, Optional, Tuple import litellm -from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig +from litellm.llms.openai.chat.gpt_transformation import ( + OpenAIGPTConfig, + OpenAIChatCompletionStreamingHandler, +) from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam +from litellm.types.utils import ModelResponseStream class MinimaxChatConfig(OpenAIGPTConfig): @@ -25,20 +30,12 @@ class MinimaxChatConfig(OpenAIGPTConfig): @staticmethod def get_api_key(api_key: Optional[str] = None) -> Optional[str]: - """ - Get MiniMax API key from environment or parameters. - """ return api_key or get_secret_str("MINIMAX_API_KEY") or litellm.api_key @staticmethod def get_api_base( api_base: Optional[str] = None, ) -> str: - """ - Get MiniMax API base URL. - Defaults to international endpoint: https://api.minimax.io/v1 - For China, set to: https://api.minimaxi.com/v1 - """ return ( api_base or get_secret_str("MINIMAX_API_BASE") @@ -54,14 +51,7 @@ class MinimaxChatConfig(OpenAIGPTConfig): litellm_params: dict, stream: Optional[bool] = None, ) -> str: - """ - Get the complete URL for MiniMax OpenAI API. - Override to ensure we use MiniMax's endpoint. - """ - # Get the base URL (either provided or default MiniMax endpoint) base_url = self.get_api_base(api_base=api_base) - - # Ensure it ends with /chat/completions if base_url.endswith("/chat/completions"): return base_url elif base_url.endswith("/v1"): @@ -77,26 +67,100 @@ class MinimaxChatConfig(OpenAIGPTConfig): messages: List[AllMessageValues], tools: Optional[List[ChatCompletionToolParam]] = None, ) -> Tuple[List[AllMessageValues], Optional[List[ChatCompletionToolParam]]]: - """ - Override to preserve cache_control for MiniMax. - MiniMax supports cache_control - don't strip it. - """ - # MiniMax supports cache_control, so return messages and tools unchanged return messages, tools def get_supported_openai_params(self, model: str) -> list: - """ - Get supported OpenAI parameters for MiniMax. - Adds reasoning_split and thinking to the list of supported params. - """ base_params = super().get_supported_openai_params(model=model) additional_params = ["reasoning_split"] - - # Add thinking parameter if model supports reasoning try: if litellm.supports_reasoning(model=model, custom_llm_provider="minimax"): additional_params.append("thinking") except Exception: pass - return base_params + additional_params + + def get_model_response_iterator( + self, + streaming_response: Any, + sync_stream: Optional[bool] = None, + json_mode: Optional[bool] = None, + ): + return MinimaxChatResponseIterator( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=json_mode, + ) + + +class MinimaxChatResponseIterator(OpenAIChatCompletionStreamingHandler): + """ + MiniMax streaming response iterator that handles reasoning_content extraction. + + MiniMax streaming responses can contain: + 1. reasoning_details array - converts to reasoning_content + 2. ... tags in content - extracts to reasoning_content + 3. "reasoning" field (alias for reasoning_content) - clears from content + """ + + started_reasoning_content: bool = False + finished_reasoning_content: bool = False + pending_reasoning: str = "" + + def chunk_parser(self, chunk: dict) -> ModelResponseStream: + try: + choices = chunk.get("choices", []) + + for choice in choices: + delta = choice.get("delta", {}) + + # MiniMax uses "reasoning" (not "reasoning_content") in streaming chunks + # The parent class maps "reasoning" -> "reasoning_content" + # We clear content when either reasoning field is present + has_reasoning = ( + delta.get("reasoning_content") + or delta.get("reasoning") + ) + if has_reasoning: + delta["content"] = None + self.started_reasoning_content = True + + reasoning_details = delta.get("reasoning_details") + if reasoning_details and isinstance(reasoning_details, list): + reasoning_text = "" + for detail in reasoning_details: + if isinstance(detail, dict) and detail.get("text"): + reasoning_text += detail.get("text", "") + if reasoning_text: + delta["reasoning_content"] = reasoning_text + delta["content"] = None + self.started_reasoning_content = True + + content = delta.get("content", "") + if content and isinstance(content, str): + reasoning_matches = re.findall( + r"(.*?)", content, re.DOTALL + ) + if reasoning_matches: + self.pending_reasoning += "".join(reasoning_matches) + clean_content = re.sub( + r".*?", "", content, flags=re.DOTALL + ) + delta["content"] = clean_content if clean_content else None + if self.pending_reasoning: + delta["reasoning_content"] = self.pending_reasoning + self.started_reasoning_content = True + elif self.started_reasoning_content and not self.finished_reasoning_content: + self.finished_reasoning_content = True + self.pending_reasoning = "" + clean_content = re.sub( + r"", "", content + ) + delta["content"] = clean_content if clean_content else None + elif not self.started_reasoning_content: + if "" in content: + clean_content = content.replace("", "").replace("", "") + delta["content"] = clean_content if clean_content else None + + return super().chunk_parser(chunk) + except Exception: + return super().chunk_parser(chunk)