mirror of
https://github.com/ParisNeo/lollms-webui.git
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126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
######
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# Project : GPT4ALL-UI
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# File : backend.py
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# Author : ParisNeo with the help of the community
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# Underlying backend : Abdeladim's pygptj backend
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# Supported by Nomic-AI
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# license : Apache 2.0
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# Description :
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# This is an interface class for GPT4All-ui backends.
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# This backend is a wrapper to marella's backend
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# Follow him on his github project : https://github.com/marella/ctransformers
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######
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from pathlib import Path
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from typing import Callable
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from api.backend import LLMBackend
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from api.config import load_config
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import yaml
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import re
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__author__ = "parisneo"
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__github__ = "https://github.com/nomic-ai/gpt4all-ui"
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__copyright__ = "Copyright 2023, "
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__license__ = "Apache 2.0"
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backend_name = "CustomBackend"
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class CustomBackend(LLMBackend):
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# Define what is the extension of the model files supported by your backend
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# Only applicable for local models for remote models like gpt4 and others, you can keep it empty
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# and reimplement your own list_models method
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file_extension='*.bin'
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def __init__(self, config:dict) -> None:
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"""Builds a LLAMACPP backend
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Args:
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config (dict): The configuration file
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"""
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super().__init__(config, False)
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# The local config can be used to store personal information that shouldn't be shared like chatgpt Key
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# or other personal information
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# This file is never commited to the repository as it is ignored by .gitignore
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self._local_config_file_path = Path(__file__).parent/"config_local.yaml"
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self.config = load_config(self._local_config_file_path)
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# Do your initialization stuff
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def tokenize(self, prompt):
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"""
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Tokenizes the given prompt using the model's tokenizer.
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Args:
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prompt (str): The input prompt to be tokenized.
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Returns:
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list: A list of tokens representing the tokenized prompt.
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"""
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return self.model.tokenize(prompt.encode())
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def detokenize(self, tokens_list):
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"""
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Detokenizes the given list of tokens using the model's tokenizer.
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Args:
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tokens_list (list): A list of tokens to be detokenized.
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Returns:
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str: The detokenized text as a string.
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"""
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return self.model.detokenize(tokens_list)
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def generate(self,
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prompt:str,
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n_predict: int = 128,
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new_text_callback: Callable[[str], None] = bool,
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verbose: bool = False,
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**gpt_params ):
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"""Generates text out of a prompt
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Args:
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prompt (str): The prompt to use for generation
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n_predict (int, optional): Number of tokens to prodict. Defaults to 128.
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new_text_callback (Callable[[str], None], optional): A callback function that is called everytime a new text element is generated. Defaults to None.
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verbose (bool, optional): If true, the code will spit many informations about the generation process. Defaults to False.
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"""
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try:
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output = ""
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self.model.reset()
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tokens = self.model.tokenize(prompt)
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count = 0
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generated_text = """
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This is an empty backend that shows how you can build your own backend.
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Find it in backends
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"""
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for tok in re.split(r' |\n', generated_text):
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if count >= n_predict or self.model.is_eos_token(tok):
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break
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word = self.model.detokenize(tok)
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if new_text_callback is not None:
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if not new_text_callback(word):
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break
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output += word
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count += 1
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except Exception as ex:
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print(ex)
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return output
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@staticmethod
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def list_models(config:dict):
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"""Lists the models for this backend
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"""
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return ["ChatGpt by Open AI"]
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@staticmethod
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def get_available_models():
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# Create the file path relative to the child class's directory
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backend_path = Path(__file__).parent
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file_path = backend_path/"models.yaml"
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with open(file_path, 'r') as file:
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yaml_data = yaml.safe_load(file)
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return yaml_data |