mirror of
https://github.com/trustgraph-ai/trustgraph.git
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154 lines
4.3 KiB
Python
Executable file
154 lines
4.3 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, RateLimitError
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import os
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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 = 'gpt-3.5-turbo'
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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("OPENAI_TOKEN")
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default_base_url = os.getenv("OPENAI_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.openai.com/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("OpenAI 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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"base_url": base_url,
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}
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)
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self.model = model
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self.temperature = temperature
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self.max_output = max_output
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if base_url:
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self.openai = OpenAI(base_url=base_url, api_key=api_key)
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else:
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self.openai = OpenAI(api_key=api_key)
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print("Initialised", flush=True)
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async def generate_content(self, system, prompt):
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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=self.model,
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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=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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inputtokens = resp.usage.prompt_tokens
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outputtokens = resp.usage.completion_tokens
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print(resp.choices[0].message.content, flush=True)
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print(f"Input Tokens: {inputtokens}", flush=True)
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print(f"Output Tokens: {outputtokens}", flush=True)
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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 = self.model
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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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print(f"Exception: {type(e)} {e}")
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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="gpt-3.5-turbo",
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help=f'LLM model (default: GPT-3.5-Turbo)'
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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'OpenAI 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'OpenAI 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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