97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
import importlib
|
|
import logging
|
|
import os
|
|
from typing import Optional
|
|
|
|
from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
|
|
from langchain_community.llms.huggingface_hub import HuggingFaceHub
|
|
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
|
|
|
|
from embedchain.config import BaseLlmConfig
|
|
from embedchain.helpers.json_serializable import register_deserializable
|
|
from embedchain.llm.base import BaseLlm
|
|
|
|
|
|
@register_deserializable
|
|
class HuggingFaceLlm(BaseLlm):
|
|
def __init__(self, config: Optional[BaseLlmConfig] = None):
|
|
if "HUGGINGFACE_ACCESS_TOKEN" not in os.environ:
|
|
raise ValueError("Please set the HUGGINGFACE_ACCESS_TOKEN environment variable.")
|
|
|
|
try:
|
|
importlib.import_module("huggingface_hub")
|
|
except ModuleNotFoundError:
|
|
raise ModuleNotFoundError(
|
|
"The required dependencies for HuggingFaceHub are not installed."
|
|
'Please install with `pip install --upgrade "embedchain[huggingface-hub]"`'
|
|
) from None
|
|
|
|
super().__init__(config=config)
|
|
|
|
def get_llm_model_answer(self, prompt):
|
|
if self.config.system_prompt:
|
|
raise ValueError("HuggingFaceLlm does not support `system_prompt`")
|
|
return HuggingFaceLlm._get_answer(prompt=prompt, config=self.config)
|
|
|
|
@staticmethod
|
|
def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
|
|
# If the user wants to run the model locally, they can do so by setting the `local` flag to True
|
|
if config.model and config.local:
|
|
return HuggingFaceLlm._from_pipeline(prompt=prompt, config=config)
|
|
elif config.model:
|
|
return HuggingFaceLlm._from_model(prompt=prompt, config=config)
|
|
elif config.endpoint:
|
|
return HuggingFaceLlm._from_endpoint(prompt=prompt, config=config)
|
|
else:
|
|
raise ValueError("Either `model` or `endpoint` must be set in config")
|
|
|
|
@staticmethod
|
|
def _from_model(prompt: str, config: BaseLlmConfig) -> str:
|
|
model_kwargs = {
|
|
"temperature": config.temperature or 0.1,
|
|
"max_new_tokens": config.max_tokens,
|
|
}
|
|
|
|
if 0.0 < config.top_p < 1.0:
|
|
model_kwargs["top_p"] = config.top_p
|
|
else:
|
|
raise ValueError("`top_p` must be > 0.0 and < 1.0")
|
|
|
|
model = config.model
|
|
logging.info(f"Using HuggingFaceHub with model {model}")
|
|
llm = HuggingFaceHub(
|
|
huggingfacehub_api_token=os.environ["HUGGINGFACE_ACCESS_TOKEN"],
|
|
repo_id=model,
|
|
model_kwargs=model_kwargs,
|
|
)
|
|
return llm.invoke(prompt)
|
|
|
|
@staticmethod
|
|
def _from_endpoint(prompt: str, config: BaseLlmConfig) -> str:
|
|
llm = HuggingFaceEndpoint(
|
|
huggingfacehub_api_token=os.environ["HUGGINGFACE_ACCESS_TOKEN"],
|
|
endpoint_url=config.endpoint,
|
|
task="text-generation",
|
|
model_kwargs=config.model_kwargs,
|
|
)
|
|
return llm.invoke(prompt)
|
|
|
|
@staticmethod
|
|
def _from_pipeline(prompt: str, config: BaseLlmConfig) -> str:
|
|
model_kwargs = {
|
|
"temperature": config.temperature or 0.1,
|
|
"max_new_tokens": config.max_tokens,
|
|
}
|
|
|
|
if 0.0 < config.top_p < 1.0:
|
|
model_kwargs["top_p"] = config.top_p
|
|
else:
|
|
raise ValueError("`top_p` must be > 0.0 and < 1.0")
|
|
|
|
llm = HuggingFacePipeline.from_model_id(
|
|
model_id=config.model,
|
|
task="text-generation",
|
|
pipeline_kwargs=model_kwargs,
|
|
)
|
|
return llm.invoke(prompt)
|