mirror of
https://github.com/trustgraph-ai/trustgraph.git
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139 lines
3.8 KiB
Python
Executable file
139 lines
3.8 KiB
Python
Executable file
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"""
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Simple LLM service, performs text prompt completion using OpenAI.
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Input is prompt, output is response.
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"""
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from openai import OpenAI
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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 = 'gemma3:9b'
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default_url = os.getenv("LMSTUDIO_URL", "http://localhost:1234/")
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default_temperature = 0.0
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default_max_output = 4096
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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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url = params.get("url", default_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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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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"url" : url,
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}
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)
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self.default_model = model
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self.url = url + "v1/"
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self.temperature = temperature
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self.max_output = max_output
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self.openai = OpenAI(
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base_url=self.url,
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api_key = "sk-no-key-required",
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)
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logger.info("LMStudio LLM service initialized")
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async def generate_content(self, system, prompt, model=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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logger.debug(f"Using model: {model_name}")
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prompt = system + "\n\n" + prompt
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try:
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logger.debug(f"Prompt: {prompt}")
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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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{"role": "user", "content": prompt}
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]
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#temperature=self.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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logger.debug(f"Full response: {resp}")
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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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# SLM, presumably there aren't rate limits
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except Exception as e:
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logger.error(f"LMStudio 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: gemma3:9b)'
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)
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parser.add_argument(
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'-u', '--url',
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default=default_url,
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help=f'LMStudio URL (default: {default_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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