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156 lines
4.6 KiB
Python
Executable file
156 lines
4.6 KiB
Python
Executable file
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"""
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Simple LLM service, performs text prompt completion using Mistral.
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Input is prompt, output is response.
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"""
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from mistralai import Mistral
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import os
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import logging
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# Module logger
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logger = logging.getLogger(__name__)
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from .... exceptions import TooManyRequests
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from .... base import LlmService, LlmResult
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default_ident = "text-completion"
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default_model = 'ministral-8b-latest'
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default_temperature = 0.0
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default_max_output = 4096
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default_api_key = os.getenv("MISTRAL_TOKEN")
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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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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("Mistral API key not specified")
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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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}
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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.mistral = Mistral(api_key=api_key)
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logger.info("Mistral LLM service initialized")
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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 = temperature if temperature is not None else self.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.mistral.chat.complete(
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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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top_p=1,
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frequency_penalty=0,
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presence_penalty=0,
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response_format={
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"type": "text"
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}
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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. The MistralAI library has retry logic
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# so retry-able errors are retried transparently. It means we
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# don't get rate limit events.
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# We could choose to turn off retry and handle all that here
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# or subclass BackoffStrategy to keep the retry logic, but
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# get the events out.
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# except Mistral.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"Mistral LLM 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=default_model,
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help=f'LLM model (default: ministral-8b-latest)'
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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'Mistral API Key'
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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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