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import os
from gpt4all import GPT4All
from .models import get_models
from ..typing import Messages
MODEL_LIST: dict[str, dict] = None
def find_model_dir(model_file: str) -> str:
local_dir = os.path.dirname(os.path.abspath(__file__))
project_dir = os.path.dirname(os.path.dirname(local_dir))
new_model_dir = os.path.join(project_dir, "models")
new_model_file = os.path.join(new_model_dir, model_file)
if os.path.isfile(new_model_file):
return new_model_dir
old_model_dir = os.path.join(local_dir, "models")
old_model_file = os.path.join(old_model_dir, model_file)
if os.path.isfile(old_model_file):
return old_model_dir
working_dir = "./"
for root, dirs, files in os.walk(working_dir):
if model_file in files:
return root
return new_model_dir
class LocalProvider:
@staticmethod
def create_completion(model: str, messages: Messages, stream: bool = False, **kwargs):
global MODEL_LIST
if MODEL_LIST is None:
MODEL_LIST = get_models()
if model not in MODEL_LIST:
raise ValueError(f'Model "{model}" not found / not yet implemented')
model = MODEL_LIST[model]
model_file = model["path"]
model_dir = find_model_dir(model_file)
if not os.path.isfile(os.path.join(model_dir, model_file)):
print(f'Model file "models/{model_file}" not found.')
download = input(f"Do you want to download {model_file}? [y/n]: ")
if download in ["y", "Y"]:
GPT4All.download_model(model_file, model_dir)
else:
raise ValueError(f'Model "{model_file}" not found.')
model = GPT4All(model_name=model_file,
#n_threads=8,
verbose=False,
allow_download=False,
model_path=model_dir)
system_message = "\n".join(message["content"] for message in messages if message["role"] == "system")
if system_message:
system_message = "A chat between a curious user and an artificial intelligence assistant."
prompt_template = "USER: {0}\nASSISTANT: "
conversation = "\n" . join(
f"{message['role'].upper()}: {message['content']}"
for message in messages
if message["role"] != "system"
) + "\nASSISTANT: "
with model.chat_session(system_message, prompt_template):
if stream:
for token in model.generate(conversation, streaming=True):
yield token
else:
yield model.generate(conversation)
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