53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
from injector import inject, singleton
|
|
from llama_index.llms import MockLLM
|
|
from llama_index.llms.base import LLM
|
|
|
|
from private_gpt.components.llm.prompt_helper import get_prompt_style
|
|
from private_gpt.paths import models_path
|
|
from private_gpt.settings.settings import Settings
|
|
|
|
|
|
@singleton
|
|
class LLMComponent:
|
|
llm: LLM
|
|
|
|
@inject
|
|
def __init__(self, settings: Settings) -> None:
|
|
match settings.llm.mode:
|
|
case "local":
|
|
from llama_index.llms import LlamaCPP
|
|
|
|
prompt_style_cls = get_prompt_style(settings.local.prompt_style)
|
|
prompt_style = prompt_style_cls(
|
|
default_system_prompt=settings.local.default_system_prompt
|
|
)
|
|
|
|
self.llm = LlamaCPP(
|
|
model_path=str(models_path / settings.local.llm_hf_model_file),
|
|
temperature=0.1,
|
|
# llama2 has a context window of 4096 tokens,
|
|
# but we set it lower to allow for some wiggle room
|
|
context_window=3900,
|
|
generate_kwargs={},
|
|
# All to GPU
|
|
model_kwargs={"n_gpu_layers": -1},
|
|
# transform inputs into Llama2 format
|
|
messages_to_prompt=prompt_style.messages_to_prompt,
|
|
completion_to_prompt=prompt_style.completion_to_prompt,
|
|
verbose=True,
|
|
)
|
|
|
|
case "sagemaker":
|
|
from private_gpt.components.llm.custom.sagemaker import SagemakerLLM
|
|
|
|
self.llm = SagemakerLLM(
|
|
endpoint_name=settings.sagemaker.llm_endpoint_name,
|
|
)
|
|
case "openai":
|
|
from llama_index.llms import OpenAI
|
|
|
|
openai_settings = settings.openai.api_key
|
|
self.llm = OpenAI(api_key=openai_settings)
|
|
case "mock":
|
|
self.llm = MockLLM()
|