mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-10-17 04:37:00 +00:00
d145a67843
- Changed default model from qwen3-30b-a3b-instruct-2507 to qwen-flash - Updated evaluation tools to use EVALUATION_PROMPT_FOR_QUESTION instead of QUESTION2 - Added new PROMPT_MEMZERO_JSON2 configuration with context priority rules - Modified llm_request_for_json to use qwen-flash as default model - Updated halumem evaluation to specify qwen3-max model explicitly for certain requests
88 lines
2.6 KiB
Python
88 lines
2.6 KiB
Python
import asyncio
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import json
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import logging
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import re
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from tenacity import retry, stop_after_attempt, wait_random_exponential, before_sleep_log
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from reme_ai.core.schema import Message
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from reme_ai.core.utils import load_env
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from reme_ai.reme import ReMe
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logger = logging.getLogger(__name__)
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load_env()
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WAIT_TIME_LOWER = 1
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WAIT_TIME_UPPER = 60
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RETRY_TIMES = 5
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# Use ReMe singleton's LLM instead of creating a separate instance
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reme = ReMe()
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@retry(
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wait=wait_random_exponential(min=WAIT_TIME_LOWER, max=WAIT_TIME_UPPER),
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stop=stop_after_attempt(3),
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reraise=True,
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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async def llm_request(prompt, model_name: str = "qwen3-max", **kwargs) -> str:
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"""Make an LLM request using ReMe's LLM with optional model override.
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Args:
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prompt: The prompt to send to the LLM
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model_name: Optional model name to override the default model (default: "qwen3-max")
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**kwargs: Additional arguments to pass to the chat method
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Returns:
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The assistant's response content
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"""
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assistant_message = await reme.llm.chat(
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messages=[
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Message(
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**{
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"role": "user",
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"content": prompt,
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},
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),
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],
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model_name=model_name,
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**kwargs,
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)
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return assistant_message.content
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@retry(
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wait=wait_random_exponential(min=WAIT_TIME_LOWER, max=WAIT_TIME_UPPER),
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stop=stop_after_attempt(RETRY_TIMES),
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reraise=True,
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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async def llm_request_for_json(prompt, model_name: str = "qwen-flash", **kwargs):
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# async def llm_request_for_json(prompt, model_name: str = "qwen3-max", **kwargs):
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"""Make an LLM request expecting JSON response using ReMe's LLM.
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Args:
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prompt: The prompt to send to the LLM
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model_name: Optional model name to override the default model (default: "qwen3-max")
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**kwargs: Additional arguments to pass to the chat method
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Returns:
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Parsed JSON object from the LLM response
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Raises:
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ValueError: If no JSON block is found in the model output
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"""
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content = await llm_request(prompt, model_name=model_name, **kwargs)
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match = re.search(r"```json\s*(\{.*?\})\s*```", content, re.DOTALL)
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if not match:
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raise ValueError(f"No JSON block found in model output: {content}")
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json_str = match.group(1).strip()
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return json.loads(json_str)
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if __name__ == "__main__":
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r = asyncio.run(llm_request_for_json('hello? answer in ```json\n{"answer": "..."}```'))
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print(r)
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