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297 | class SageMakerLLM(LLM):
r"""SageMaker LLM。
示例:
`pip install llama-index-llms-sagemaker-endpoint`
```python
from llama_index.llms.sagemaker import SageMakerLLM
# 用于HuggingFaceH4/zephyr-7b-beta的钩子
# 不同的模型可能需要不同的格式
def messages_to_prompt(messages):
prompt = ""
for message in messages:
if message.role == 'system':
prompt += f"<|system|>\n{message.content}</s>\n"
elif message.role == 'user':
prompt += f"<|user|>\n{message.content}</s>\n"
elif message.role == 'assistant':
prompt += f"<|assistant|>\n{message.content}</s>\n"
# 确保我们从系统提示开始，如果需要，插入空白
if not prompt.startswith("<|system|>\n"):
prompt = "<|system|>\n</s>\n" + prompt
# 添加最终的助手提示
prompt = prompt + "<|assistant|>\n"
return prompt
def completion_to_prompt(completion):
return f"<|system|>\n</s>\n<|user|>\n{completion}</s>\n<|assistant|>\n"
# SageMakerLLM类的附加设置
model_name = "HuggingFaceH4/zephyr-7b-beta"
api_key = "your_api_key"
region = "your_region"
llm = SageMakerLLM(
model_name=model_name,
api_key=api_key,
region=region,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
)
```"""
endpoint_name: str = Field(description="SageMaker LLM endpoint name")
endpoint_kwargs: Dict[str, Any] = Field(
default={},
description="Additional kwargs for the invoke_endpoint request.",
)
model_kwargs: Dict[str, Any] = Field(
default={},
description="kwargs to pass to the model.",
)
content_handler: BaseIOHandler = Field(
default=DEFAULT_IO_HANDLER,
description="used to serialize input, deserialize output, and remove a prefix.",
)
profile_name: Optional[str] = Field(
description="The name of aws profile to use. If not given, then the default profile is used."
)
aws_access_key_id: Optional[str] = Field(description="AWS Access Key ID to use")
aws_secret_access_key: Optional[str] = Field(
description="AWS Secret Access Key to use"
)
aws_session_token: Optional[str] = Field(description="AWS Session Token to use")
aws_region_name: Optional[str] = Field(
description="AWS region name to use. Uses region configured in AWS CLI if not passed"
)
max_retries: Optional[int] = Field(
default=3,
description="The maximum number of API retries.",
gte=0,
)
timeout: Optional[float] = Field(
default=60.0,
description="The timeout, in seconds, for API requests.",
gte=0,
)
_client: Any = PrivateAttr()
_completion_to_prompt: Callable[[str, Optional[str]], str] = PrivateAttr()
def __init__(
self,
endpoint_name: str,
endpoint_kwargs: Optional[Dict[str, Any]] = {},
model_kwargs: Optional[Dict[str, Any]] = {},
content_handler: Optional[BaseIOHandler] = DEFAULT_IO_HANDLER,
profile_name: Optional[str] = None,
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
aws_session_token: Optional[str] = None,
region_name: Optional[str] = None,
max_retries: Optional[int] = 3,
timeout: Optional[float] = 60.0,
temperature: Optional[float] = 0.5,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[
Callable[[Sequence[ChatMessage]], str]
] = LLAMA_MESSAGES_TO_PROMPT,
completion_to_prompt: Callable[
[str, Optional[str]], str
] = LLAMA_COMPLETION_TO_PROMPT,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
**kwargs: Any,
) -> None:
if not endpoint_name:
raise ValueError(
"Missing required argument:`endpoint_name`"
" Please specify the endpoint_name"
)
endpoint_kwargs = endpoint_kwargs or {}
model_kwargs = model_kwargs or {}
model_kwargs["temperature"] = temperature
content_handler = content_handler
self._completion_to_prompt = completion_to_prompt
self._client = get_aws_service_client(
service_name="sagemaker-runtime",
profile_name=profile_name,
region_name=region_name,
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_session_token=aws_session_token,
max_retries=max_retries,
timeout=timeout,
)
callback_manager = callback_manager or CallbackManager([])
super().__init__(
endpoint_name=endpoint_name,
endpoint_kwargs=endpoint_kwargs,
model_kwargs=model_kwargs,
content_handler=content_handler,
profile_name=profile_name,
timeout=timeout,
max_retries=max_retries,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
model_kwargs = {**self.model_kwargs, **kwargs}
if not formatted:
prompt = self._completion_to_prompt(prompt, self.system_prompt)
request_body = self.content_handler.serialize_input(prompt, model_kwargs)
response = self._client.invoke_endpoint(
EndpointName=self.endpoint_name,
Body=request_body,
ContentType=self.content_handler.content_type,
Accept=self.content_handler.accept,
**self.endpoint_kwargs,
)
response["Body"] = self.content_handler.deserialize_output(response["Body"])
text = self.content_handler.remove_prefix(response["Body"], prompt)
return CompletionResponse(
text=text,
raw=response,
additional_kwargs={
"model_kwargs": model_kwargs,
"endpoint_kwargs": self.endpoint_kwargs,
},
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
model_kwargs = {**self.model_kwargs, **kwargs}
if not formatted:
prompt = self._completion_to_prompt(prompt, self.system_prompt)
request_body = self.content_handler.serialize_input(prompt, model_kwargs)
def gen() -> CompletionResponseGen:
raw_text = ""
prev_clean_text = ""
for response in self._client.invoke_endpoint_with_response_stream(
EndpointName=self.endpoint_name,
Body=request_body,
ContentType=self.content_handler.content_type,
Accept=self.content_handler.accept,
**self.endpoint_kwargs,
)["Body"]:
delta = self.content_handler.deserialize_streaming_output(
response["PayloadPart"]["Bytes"]
)
raw_text += delta
clean_text = self.content_handler.remove_prefix(raw_text, prompt)
delta = clean_text[len(prev_clean_text) :]
prev_clean_text = clean_text
yield CompletionResponse(text=clean_text, delta=delta, raw=response)
return gen()
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response_gen = self.stream_complete(prompt, formatted=True, **kwargs)
return stream_completion_response_to_chat_response(completion_response_gen)
@llm_chat_callback()
async def achat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponse:
raise NotImplementedError
@llm_chat_callback()
async def astream_chat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponseAsyncGen:
raise NotImplementedError
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
raise NotImplementedError
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError
@classmethod
def class_name(cls) -> str:
return "SageMakerLLM"
@property
def metadata(self) -> LLMMetadata:
"""LLM元数据。"""
return LLMMetadata(
model_name=self.endpoint_name,
)
|