68 lines
2.6 KiB
Python
68 lines
2.6 KiB
Python
import os
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from collections.abc import Iterable
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from pathlib import Path
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from typing import Optional, Union
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from langchain.callbacks.stdout import StdOutCallbackHandler
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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from embedchain.config import BaseLlmConfig
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from embedchain.helpers.json_serializable import register_deserializable
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from embedchain.llm.base import BaseLlm
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@register_deserializable
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class GPT4ALLLlm(BaseLlm):
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def __init__(self, config: Optional[BaseLlmConfig] = None):
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super().__init__(config=config)
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if self.config.model is None:
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self.config.model = "orca-mini-3b-gguf2-q4_0.gguf"
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self.instance = GPT4ALLLlm._get_instance(self.config.model)
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self.instance.streaming = self.config.stream
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def get_llm_model_answer(self, prompt):
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return self._get_answer(prompt=prompt, config=self.config)
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@staticmethod
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def _get_instance(model):
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try:
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from langchain.llms.gpt4all import GPT4All as LangchainGPT4All
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except ModuleNotFoundError:
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raise ModuleNotFoundError(
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"The GPT4All python package is not installed. Please install it with `pip install --upgrade embedchain[opensource]`" # noqa E501
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) from None
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model_path = Path(model).expanduser()
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if os.path.isabs(model_path):
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if os.path.exists(model_path):
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return LangchainGPT4All(model=str(model_path))
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else:
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raise ValueError(f"Model does not exist at {model_path=}")
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else:
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return LangchainGPT4All(model=model, allow_download=True)
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def _get_answer(self, prompt: str, config: BaseLlmConfig) -> Union[str, Iterable]:
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if config.model and config.model != self.config.model:
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raise RuntimeError(
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"GPT4ALLLlm does not support switching models at runtime. Please create a new app instance."
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)
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messages = []
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if config.system_prompt:
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messages.append(config.system_prompt)
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messages.append(prompt)
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kwargs = {
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"temp": config.temperature,
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"max_tokens": config.max_tokens,
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}
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if config.top_p:
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kwargs["top_p"] = config.top_p
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callbacks = [StreamingStdOutCallbackHandler()] if config.stream else [StdOutCallbackHandler()]
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response = self.instance.generate(prompts=messages, callbacks=callbacks, **kwargs)
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answer = ""
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for generations in response.generations:
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answer += " ".join(map(lambda generation: generation.text, generations))
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return answer
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