import json
import logging
from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.pydantic_v1 import Extra, Field, root_validator
from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names
logger = logging.getLogger(__name__)
VALID_TASKS = (
"text2text-generation",
"text-generation",
"summarization",
"conversational",
)
[docs]@deprecated(
since="0.0.37",
removal="0.3",
alternative_import="from langchain_huggingface.llms import HuggingFaceEndpoint",
)
class HuggingFaceEndpoint(LLM):
""" HuggingFace终端。
要使用这个类，您应该已经安装了``huggingface_hub``包，并且
将环境变量``HUGGINGFACEHUB_API_TOKEN``设置为您的API令牌，
或者作为构造函数的一个命名参数给出。
示例:
.. code-block:: python
# 基本示例（无流式处理）
llm = HuggingFaceEndpoint(
endpoint_url="http://localhost:8010/",
max_new_tokens=512,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.01,
repetition_penalty=1.03,
huggingfacehub_api_token="my-api-key"
)
print(llm.invoke("什么是深度学习？"))
# 流式响应示例
from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
callbacks = [StreamingStdOutCallbackHandler()]
llm = HuggingFaceEndpoint(
endpoint_url="http://localhost:8010/",
max_new_tokens=512,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.01,
repetition_penalty=1.03,
callbacks=callbacks,
streaming=True,
huggingfacehub_api_token="my-api-key"
)
print(llm.invoke("什么是深度学习？"))""" # noqa: E501
endpoint_url: Optional[str] = None
"""要使用的端点URL。"""
repo_id: Optional[str] = None
"""要使用的存储库。"""
huggingfacehub_api_token: Optional[str] = None
max_new_tokens: int = 512
"""生成的令牌的最大数量"""
top_k: Optional[int] = None
"""保留最高概率词汇标记的数量，用于进行top-k过滤。"""
top_p: Optional[float] = 0.95
"""如果设置为<1，则仅保留概率相加达到“top_p”或更高的最小一组最有可能的标记。"""
typical_p: Optional[float] = 0.95
"""典型的解码质量。有关更多信息，请参阅[自然语言生成的典型解码](https://arxiv.org/abs/2202.00666)。"""
temperature: Optional[float] = 0.8
"""用于模块化对数分布的值。"""
repetition_penalty: Optional[float] = None
"""重复惩罚的参数。1.0表示没有惩罚。
更多细节请参见[此论文](https://arxiv.org/pdf/1909.05858.pdf)。"""
return_full_text: bool = False
"""生成的文本是否需要在前面添加提示符"""
truncate: Optional[int] = None
"""将输入的标记截断到给定的大小"""
stop_sequences: List[str] = Field(default_factory=list)
"""如果生成了`stop_sequences`中的一个成员，则停止生成令牌。"""
seed: Optional[int] = None
"""随机抽样种子"""
inference_server_url: str = ""
"""文本生成推断实例基本URL"""
timeout: int = 120
"""超时时间（秒）"""
streaming: bool = False
"""是否异步生成令牌流"""
do_sample: bool = False
"""激活logits采样"""
watermark: bool = False
"""使用[A Watermark for Large Language Models]进行水印处理
（https://arxiv.org/abs/2301.10226）"""
server_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""保存未明确指定的任何文本生成推理服务器参数"""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""保存任何在`call`中有效但未明确指定的模型参数"""
model: str
client: Any
async_client: Any
task: Optional[str] = None
"""任务：调用模型。
应该是一个返回`generated_text`或`summary_text`的任务。"""
class Config:
"""此pydantic对象的配置。"""
extra = Extra.forbid
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""从传入的额外参数构建额外的kwargs。"""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please make sure that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
if "endpoint_url" not in values and "repo_id" not in values:
raise ValueError(
"Please specify an `endpoint_url` or `repo_id` for the model."
)
if "endpoint_url" in values and "repo_id" in values:
raise ValueError(
"Please specify either an `endpoint_url` OR a `repo_id`, not both."
)
values["model"] = values.get("endpoint_url") or values.get("repo_id")
return values
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""验证包是否已安装并且API令牌是否有效。"""
try:
from huggingface_hub import login
except ImportError:
raise ImportError(
"Could not import huggingface_hub python package. "
"Please install it with `pip install huggingface_hub`."
)
try:
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
login(token=huggingfacehub_api_token)
except Exception as e:
raise ValueError(
"Could not authenticate with huggingface_hub. "
"Please check your API token."
) from e
from huggingface_hub import AsyncInferenceClient, InferenceClient
values["client"] = InferenceClient(
model=values["model"],
timeout=values["timeout"],
token=huggingfacehub_api_token,
**values["server_kwargs"],
)
values["async_client"] = AsyncInferenceClient(
model=values["model"],
timeout=values["timeout"],
token=huggingfacehub_api_token,
**values["server_kwargs"],
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""获取调用文本生成推理API的默认参数。"""
return {
"max_new_tokens": self.max_new_tokens,
"top_k": self.top_k,
"top_p": self.top_p,
"typical_p": self.typical_p,
"temperature": self.temperature,
"repetition_penalty": self.repetition_penalty,
"return_full_text": self.return_full_text,
"truncate": self.truncate,
"stop_sequences": self.stop_sequences,
"seed": self.seed,
"do_sample": self.do_sample,
"watermark": self.watermark,
**self.model_kwargs,
}
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""获取识别参数。"""
_model_kwargs = self.model_kwargs or {}
return {
**{"endpoint_url": self.endpoint_url, "task": self.task},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""llm的返回类型。"""
return "huggingface_endpoint"
def _invocation_params(
self, runtime_stop: Optional[List[str]], **kwargs: Any
) -> Dict[str, Any]:
params = {**self._default_params, **kwargs}
params["stop_sequences"] = params["stop_sequences"] + (runtime_stop or [])
return params
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""调用HuggingFace Hub的推理端点。"""
invocation_params = self._invocation_params(stop, **kwargs)
if self.streaming:
completion = ""
for chunk in self._stream(prompt, stop, run_manager, **invocation_params):
completion += chunk.text
return completion
else:
invocation_params["stop"] = invocation_params[
"stop_sequences"
] # porting 'stop_sequences' into the 'stop' argument
response = self.client.post(
json={"inputs": prompt, "parameters": invocation_params},
stream=False,
task=self.task,
)
try:
response_text = json.loads(response.decode())[0]["generated_text"]
except KeyError:
response_text = json.loads(response.decode())["generated_text"]
# Maybe the generation has stopped at one of the stop sequences:
# then we remove this stop sequence from the end of the generated text
for stop_seq in invocation_params["stop_sequences"]:
if response_text[-len(stop_seq) :] == stop_seq:
response_text = response_text[: -len(stop_seq)]
return response_text
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
invocation_params = self._invocation_params(stop, **kwargs)
if self.streaming:
completion = ""
async for chunk in self._astream(
prompt, stop, run_manager, **invocation_params
):
completion += chunk.text
return completion
else:
invocation_params["stop"] = invocation_params["stop_sequences"]
response = await self.async_client.post(
json={"inputs": prompt, "parameters": invocation_params},
stream=False,
task=self.task,
)
try:
response_text = json.loads(response.decode())[0]["generated_text"]
except KeyError:
response_text = json.loads(response.decode())["generated_text"]
# Maybe the generation has stopped at one of the stop sequences:
# then remove this stop sequence from the end of the generated text
for stop_seq in invocation_params["stop_sequences"]:
if response_text[-len(stop_seq) :] == stop_seq:
response_text = response_text[: -len(stop_seq)]
return response_text
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
invocation_params = self._invocation_params(stop, **kwargs)
for response in self.client.text_generation(
prompt, **invocation_params, stream=True
):
# identify stop sequence in generated text, if any
stop_seq_found: Optional[str] = None
for stop_seq in invocation_params["stop_sequences"]:
if stop_seq in response:
stop_seq_found = stop_seq
# identify text to yield
text: Optional[str] = None
if stop_seq_found:
text = response[: response.index(stop_seq_found)]
else:
text = response
# yield text, if any
if text:
chunk = GenerationChunk(text=text)
if run_manager:
run_manager.on_llm_new_token(chunk.text)
yield chunk
# break if stop sequence found
if stop_seq_found:
break
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
invocation_params = self._invocation_params(stop, **kwargs)
async for response in await self.async_client.text_generation(
prompt, **invocation_params, stream=True
):
# identify stop sequence in generated text, if any
stop_seq_found: Optional[str] = None
for stop_seq in invocation_params["stop_sequences"]:
if stop_seq in response:
stop_seq_found = stop_seq
# identify text to yield
text: Optional[str] = None
if stop_seq_found:
text = response[: response.index(stop_seq_found)]
else:
text = response
# yield text, if any
if text:
chunk = GenerationChunk(text=text)
if run_manager:
await run_manager.on_llm_new_token(chunk.text)
yield chunk
# break if stop sequence found
if stop_seq_found:
break