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#1	import os
#2	from collections.abc import Iterable
#3	from pathlib import Path
#4	from typing import Optional, Union
#5
#6	from langchain.callbacks.stdout import StdOutCallbackHandler
#7	from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
#8
#9	from embedchain.config import BaseLlmConfig
#10	from embedchain.helpers.json_serializable import register_deserializable
#11	from embedchain.llm.base import BaseLlm
#12
#13
#14	@register_deserializable
#15	class GPT4ALLLlm(BaseLlm):
#16	def __init__(self, config: Optional[BaseLlmConfig] = None):
#17	super().__init__(config=config)
#18	if self.config.model is None:
#19	self.config.model = "orca-mini-3b-gguf2-q4_0.gguf"
#20	self.instance = GPT4ALLLlm._get_instance(self.config.model)
#21	self.instance.streaming = self.config.stream
#22
#23	def get_llm_model_answer(self, prompt):
#24	return self._get_answer(prompt=prompt, config=self.config)
#25
#26	@staticmethod
#27	def _get_instance(model):
#28	try:
#29	from langchain_community.llms.gpt4all import GPT4All as LangchainGPT4All
#30	except ModuleNotFoundError:
#31	raise ModuleNotFoundError(
#32	"The GPT4All python package is not installed. Please install it with `pip install --upgrade embedchain[opensource]`" # noqa E501
#33	) from None
#34
#35	model_path = Path(model).expanduser()
#36	if os.path.isabs(model_path):
#37	if os.path.exists(model_path):
#38	return LangchainGPT4All(model=str(model_path))
#39	else:
#40	raise ValueError(f"Model does not exist at {model_path=}")
#41	else:
#42	return LangchainGPT4All(model=model, allow_download=True)
#43
#44	def _get_answer(self, prompt: str, config: BaseLlmConfig) -> Union[str, Iterable]:
#45	if config.model and config.model != self.config.model:
#46	raise RuntimeError(
#47	"GPT4ALLLlm does not support switching models at runtime. Please create a new app instance."
#48	)
#49
#50	messages = []
#51	if config.system_prompt:
#52	messages.append(config.system_prompt)
#53	messages.append(prompt)
#54	kwargs = {
#55	"temp": config.temperature,
#56	"max_tokens": config.max_tokens,
#57	}
#58	if config.top_p:
#59	kwargs["top_p"] = config.top_p
#60
#61	callbacks = [StreamingStdOutCallbackHandler()] if config.stream else [StdOutCallbackHandler()]
#62
#63	response = self.instance.generate(prompts=messages, callbacks=callbacks, **kwargs)
#64	answer = ""
#65	for generations in response.generations:
#66	answer += " ".join(map(lambda generation: generation.text, generations))
#67	return answer
#68

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