mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 00:16:23 +02:00
142 lines
4 KiB
Python
Executable file
142 lines
4 KiB
Python
Executable file
|
|
"""
|
|
Simple LLM service, performs text prompt completion using OpenAI.
|
|
Input is prompt, output is response.
|
|
"""
|
|
|
|
from openai import OpenAI
|
|
import os
|
|
import logging
|
|
|
|
# Module logger
|
|
logger = logging.getLogger(__name__)
|
|
|
|
from .... exceptions import TooManyRequests
|
|
from .... base import LlmService, LlmResult
|
|
|
|
default_ident = "text-completion"
|
|
|
|
default_model = 'gemma3:9b'
|
|
default_url = os.getenv("LMSTUDIO_URL", "http://localhost:1234/")
|
|
default_temperature = 0.0
|
|
default_max_output = 4096
|
|
|
|
class Processor(LlmService):
|
|
|
|
def __init__(self, **params):
|
|
|
|
model = params.get("model", default_model)
|
|
url = params.get("url", default_url)
|
|
temperature = params.get("temperature", default_temperature)
|
|
max_output = params.get("max_output", default_max_output)
|
|
|
|
super(Processor, self).__init__(
|
|
**params | {
|
|
"model": model,
|
|
"temperature": temperature,
|
|
"max_output": max_output,
|
|
"url" : url,
|
|
}
|
|
)
|
|
|
|
self.default_model = model
|
|
self.url = url + "v1/"
|
|
self.temperature = temperature
|
|
self.max_output = max_output
|
|
self.openai = OpenAI(
|
|
base_url=self.url,
|
|
api_key = "sk-no-key-required",
|
|
)
|
|
|
|
logger.info("LMStudio LLM service initialized")
|
|
|
|
async def generate_content(self, system, prompt, model=None, temperature=None):
|
|
|
|
# Use provided model or fall back to default
|
|
model_name = model or self.default_model
|
|
# Use provided temperature or fall back to default
|
|
effective_temperature = temperature if temperature is not None else self.temperature
|
|
|
|
logger.debug(f"Using model: {model_name}")
|
|
logger.debug(f"Using temperature: {effective_temperature}")
|
|
|
|
prompt = system + "\n\n" + prompt
|
|
|
|
try:
|
|
|
|
logger.debug(f"Prompt: {prompt}")
|
|
|
|
resp = self.openai.chat.completions.create(
|
|
model=model_name,
|
|
messages=[
|
|
{"role": "user", "content": prompt}
|
|
],
|
|
temperature=effective_temperature,
|
|
max_tokens=self.max_output,
|
|
top_p=1,
|
|
frequency_penalty=0,
|
|
presence_penalty=0,
|
|
response_format={
|
|
"type": "text"
|
|
}
|
|
)
|
|
|
|
logger.debug(f"Full response: {resp}")
|
|
|
|
inputtokens = resp.usage.prompt_tokens
|
|
outputtokens = resp.usage.completion_tokens
|
|
|
|
logger.debug(f"LLM response: {resp.choices[0].message.content}")
|
|
logger.info(f"Input Tokens: {inputtokens}")
|
|
logger.info(f"Output Tokens: {outputtokens}")
|
|
|
|
resp = LlmResult(
|
|
text = resp.choices[0].message.content,
|
|
in_token = inputtokens,
|
|
out_token = outputtokens,
|
|
model = model_name
|
|
)
|
|
|
|
return resp
|
|
|
|
# SLM, presumably there aren't rate limits
|
|
|
|
except Exception as e:
|
|
|
|
logger.error(f"LMStudio LLM exception ({type(e).__name__}): {e}", exc_info=True)
|
|
raise e
|
|
|
|
@staticmethod
|
|
def add_args(parser):
|
|
|
|
LlmService.add_args(parser)
|
|
|
|
parser.add_argument(
|
|
'-m', '--model',
|
|
default=default_model,
|
|
help=f'LLM model (default: gemma3:9b)'
|
|
)
|
|
|
|
parser.add_argument(
|
|
'-u', '--url',
|
|
default=default_url,
|
|
help=f'LMStudio URL (default: {default_url})'
|
|
)
|
|
|
|
parser.add_argument(
|
|
'-t', '--temperature',
|
|
type=float,
|
|
default=default_temperature,
|
|
help=f'LLM temperature parameter (default: {default_temperature})'
|
|
)
|
|
|
|
parser.add_argument(
|
|
'-x', '--max-output',
|
|
type=int,
|
|
default=default_max_output,
|
|
help=f'LLM max output tokens (default: {default_max_output})'
|
|
)
|
|
|
|
def run():
|
|
|
|
Processor.launch(default_ident, __doc__)
|