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feat: add MiniMax as LLM text completion provider
- Add MiniMax chat model provider using OpenAI-compatible API - Support MiniMax-M2.7 and MiniMax-M2.7-highspeed models - Temperature clamping to MiniMax valid range (0.0, 1.0] - Streaming support with token usage tracking - MINIMAX_API_KEY environment variable support - Add text-completion-minimax entry point - Add 15 unit tests and 3 integration tests - Update README with MiniMax in LLM APIs list
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8 changed files with 960 additions and 4 deletions
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@ -108,6 +108,7 @@ text-completion-claude = "trustgraph.model.text_completion.claude:run"
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text-completion-cohere = "trustgraph.model.text_completion.cohere:run"
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text-completion-llamafile = "trustgraph.model.text_completion.llamafile:run"
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text-completion-lmstudio = "trustgraph.model.text_completion.lmstudio:run"
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text-completion-minimax = "trustgraph.model.text_completion.minimax:run"
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text-completion-mistral = "trustgraph.model.text_completion.mistral:run"
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text-completion-ollama = "trustgraph.model.text_completion.ollama:run"
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text-completion-openai = "trustgraph.model.text_completion.openai:run"
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@ -0,0 +1,2 @@
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from . llm import *
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@ -0,0 +1,6 @@
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#!/usr/bin/env python3
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from . llm import run
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if __name__ == '__main__':
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run()
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251
trustgraph-flow/trustgraph/model/text_completion/minimax/llm.py
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251
trustgraph-flow/trustgraph/model/text_completion/minimax/llm.py
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"""
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Simple LLM service, performs text prompt completion using MiniMax.
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Input is prompt, output is response.
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"""
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from openai import OpenAI, RateLimitError
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import os
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import logging
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from .... exceptions import TooManyRequests
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from .... base import LlmService, LlmResult, LlmChunk
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "text-completion"
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default_model = 'MiniMax-M2.7'
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default_temperature = 1.0
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default_max_output = 4096
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default_api_key = os.getenv("MINIMAX_API_KEY")
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default_base_url = os.getenv("MINIMAX_BASE_URL")
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if default_base_url is None or default_base_url == "":
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default_base_url = "https://api.minimax.io/v1"
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class Processor(LlmService):
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def __init__(self, **params):
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model = params.get("model", default_model)
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api_key = params.get("api_key", default_api_key)
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base_url = params.get("url", default_base_url)
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temperature = params.get("temperature", default_temperature)
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max_output = params.get("max_output", default_max_output)
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if api_key is None:
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raise RuntimeError("MiniMax API key not specified")
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# Clamp temperature to MiniMax's valid range (0.0, 1.0]
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if temperature is not None:
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if temperature <= 0.0:
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temperature = 0.01
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elif temperature > 1.0:
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temperature = 1.0
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super(Processor, self).__init__(
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**params | {
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"model": model,
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"temperature": temperature,
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"max_output": max_output,
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"base_url": base_url,
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}
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)
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self.default_model = model
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self.temperature = temperature
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self.max_output = max_output
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self.openai = OpenAI(base_url=base_url, api_key=api_key)
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logger.info("MiniMax LLM service initialized")
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def _clamp_temperature(self, temperature):
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"""Clamp temperature to MiniMax's valid range (0.0, 1.0]"""
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if temperature is None:
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return self.temperature
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if temperature <= 0.0:
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return 0.01
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if temperature > 1.0:
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return 1.0
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return temperature
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async def generate_content(self, system, prompt, model=None, temperature=None):
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# Use provided model or fall back to default
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model_name = model or self.default_model
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# Use provided temperature or fall back to default
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effective_temperature = self._clamp_temperature(temperature)
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logger.debug(f"Using model: {model_name}")
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logger.debug(f"Using temperature: {effective_temperature}")
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prompt = system + "\n\n" + prompt
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try:
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resp = self.openai.chat.completions.create(
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model=model_name,
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": prompt
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}
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]
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}
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],
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temperature=effective_temperature,
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max_tokens=self.max_output,
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)
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inputtokens = resp.usage.prompt_tokens
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outputtokens = resp.usage.completion_tokens
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logger.debug(f"LLM response: {resp.choices[0].message.content}")
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logger.info(f"Input Tokens: {inputtokens}")
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logger.info(f"Output Tokens: {outputtokens}")
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resp = LlmResult(
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text = resp.choices[0].message.content,
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in_token = inputtokens,
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out_token = outputtokens,
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model = model_name
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)
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return resp
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# FIXME: Wrong exception, don't know what this LLM throws
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# for a rate limit
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except RateLimitError:
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# Leave rate limit retries to the base handler
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raise TooManyRequests()
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except Exception as e:
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# Apart from rate limits, treat all exceptions as unrecoverable
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logger.error(f"MiniMax LLM exception ({type(e).__name__}): {e}", exc_info=True)
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raise e
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def supports_streaming(self):
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"""MiniMax supports streaming"""
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return True
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async def generate_content_stream(self, system, prompt, model=None, temperature=None):
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"""
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Stream content generation from MiniMax.
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Yields LlmChunk objects with is_final=True on the last chunk.
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"""
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# Use provided model or fall back to default
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model_name = model or self.default_model
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# Use provided temperature or fall back to default
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effective_temperature = self._clamp_temperature(temperature)
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logger.debug(f"Using model (streaming): {model_name}")
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logger.debug(f"Using temperature: {effective_temperature}")
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prompt = system + "\n\n" + prompt
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try:
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response = self.openai.chat.completions.create(
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model=model_name,
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": prompt
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}
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]
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}
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],
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temperature=effective_temperature,
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max_tokens=self.max_output,
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stream=True,
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stream_options={"include_usage": True}
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)
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total_input_tokens = 0
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total_output_tokens = 0
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# Stream chunks
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for chunk in response:
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if chunk.choices and chunk.choices[0].delta.content:
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yield LlmChunk(
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text=chunk.choices[0].delta.content,
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in_token=None,
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out_token=None,
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model=model_name,
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is_final=False
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)
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# Capture usage from final chunk
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if chunk.usage:
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total_input_tokens = chunk.usage.prompt_tokens
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total_output_tokens = chunk.usage.completion_tokens
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# Send final chunk with token counts
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yield LlmChunk(
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text="",
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in_token=total_input_tokens,
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out_token=total_output_tokens,
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model=model_name,
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is_final=True
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)
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logger.debug("Streaming complete")
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except RateLimitError:
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logger.warning("Hit rate limit during streaming")
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raise TooManyRequests()
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except Exception as e:
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logger.error(f"MiniMax streaming exception ({type(e).__name__}): {e}", exc_info=True)
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raise e
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@staticmethod
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def add_args(parser):
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LlmService.add_args(parser)
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parser.add_argument(
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'-m', '--model',
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default="MiniMax-M2.7",
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help=f'LLM model (default: MiniMax-M2.7)'
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)
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parser.add_argument(
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'-k', '--api-key',
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default=default_api_key,
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help=f'MiniMax API key'
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)
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parser.add_argument(
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'-u', '--url',
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default=default_base_url,
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help=f'MiniMax service base URL'
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)
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parser.add_argument(
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'-t', '--temperature',
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type=float,
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default=default_temperature,
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help=f'LLM temperature parameter (default: {default_temperature})'
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)
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parser.add_argument(
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'-x', '--max-output',
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type=int,
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default=default_max_output,
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help=f'LLM max output tokens (default: {default_max_output})'
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)
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def run():
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Processor.launch(default_ident, __doc__)
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