65 lines
2.8 KiB
Python
65 lines
2.8 KiB
Python
from nio import RoomMessageImage
|
|
from openai import AsyncOpenAI
|
|
|
|
from matrix_gpt.chat_functions import download_mxc
|
|
from matrix_gpt.config import global_config
|
|
from matrix_gpt.generate_clients.api_client import ApiClient
|
|
from matrix_gpt.generate_clients.command_info import CommandInfo
|
|
from matrix_gpt.image import process_image
|
|
|
|
|
|
class OpenAIClient(ApiClient):
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def _create_client(self, api_base: str = None):
|
|
return AsyncOpenAI(
|
|
api_key=self._api_key,
|
|
base_url=api_base
|
|
)
|
|
|
|
def append_msg(self, content: str, role: str):
|
|
assert role in [self._HUMAN_NAME, self._BOT_NAME]
|
|
self._context.append({'role': role, 'content': content})
|
|
|
|
async def append_img(self, img_event: RoomMessageImage, role: str):
|
|
"""
|
|
We crop the largest dimension of the image to 512px and then let the AI decide
|
|
if it should use low or high res analysis.
|
|
"""
|
|
assert role in [self._HUMAN_NAME, self._BOT_NAME]
|
|
img_bytes = await download_mxc(img_event.url, self._client_helper.client)
|
|
encoded_image = await process_image(img_bytes, resize_px=512)
|
|
self._context.append({
|
|
"role": role,
|
|
'content': [{
|
|
'type': 'image_url',
|
|
'image_url': {
|
|
'url': f"data:image/png;base64,{encoded_image}",
|
|
'detail': 'auto'
|
|
}
|
|
}]
|
|
})
|
|
|
|
def prepare_context(self, context: list, system_prompt: str = None, injected_system_prompt: str = None):
|
|
assert not len(self._context)
|
|
self._context = context
|
|
if isinstance(system_prompt, str) and len(system_prompt):
|
|
self._context.insert(0, {"role": "system", "content": system_prompt})
|
|
if (isinstance(injected_system_prompt, str) and len(injected_system_prompt)) and len(self._context) >= 3:
|
|
# Only inject the system prompt if this isn't the first reply.
|
|
if self._context[-1]['role'] == 'system':
|
|
# Delete the last system message since we want to replace it with our inject prompt.
|
|
del self._context[-1]
|
|
self._context.insert(-1, {"role": "system", "content": injected_system_prompt})
|
|
|
|
async def generate(self, command_info: CommandInfo, matrix_gpt_data: str = None):
|
|
r = await self._create_client(command_info.api_base).chat.completions.create(
|
|
model=command_info.model,
|
|
messages=self._context,
|
|
temperature=command_info.temperature,
|
|
timeout=global_config['response_timeout'],
|
|
max_tokens=None if command_info.max_tokens == 0 else command_info.max_tokens,
|
|
)
|
|
return r.choices[0].message.content, None
|