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420 | class TextGenerationInference(FunctionCallingLLM):
model_name: Optional[str] = Field(
default=None,
description=("The name of the model served at the TGI endpoint"),
)
temperature: float = Field(
default=DEFAULT_TEMPERATURE,
description=("The temperature to use for sampling."),
ge=0.0,
le=1.0,
)
max_tokens: int = Field(
default=DEFAULT_NUM_OUTPUTS,
description=("The maximum number of tokens to generate."),
gt=0,
)
token: Union[str, bool, None] = Field(
default=None,
description=(
"Hugging Face token. Will default to the locally saved token. Pass "
"token=False if you don’t want to send your token to the server."
),
)
timeout: float = Field(
default=120, description=("The timeout to use in seconds."), ge=0
)
max_retries: int = Field(
default=5, description=("The maximum number of API retries."), ge=0
)
headers: Optional[Dict[str, str]] = Field(
default=None,
description=(
"Additional headers to send to the server. By default only the"
" authorization headers are sent. Values in this dictionary"
" will override the default values."
),
)
cookies: Optional[Dict[str, str]] = Field(
default=None, description=("Additional cookies to send to the server.")
)
seed: Optional[str] = Field(
default=None, description=("The random seed to use for sampling.")
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description=("Additional kwargs for the TGI API.")
)
_sync_client: "TGIClient" = PrivateAttr()
_async_client: "TGIAsyncClient" = PrivateAttr()
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description=("Maximum input length in tokens returned from TGI endpoint"),
)
is_chat_model: bool = Field(
default=True,
description=(
LLMMetadata.model_fields["is_chat_model"].description
+ " TGI makes use of chat templating,"
" function call is available only for '/v1/chat/completions' route"
" of TGI endpoint"
),
)
is_function_calling_model: bool = Field(
default=False,
description=(
LLMMetadata.model_fields["is_function_calling_model"].description
+ " 'text-generation-inference' supports function call"
" starting from v1.4.3"
),
)
def __init__(
self,
model_url,
model_name: Optional[str] = None,
cookies: Optional[dict] = None,
temperature: float = DEFAULT_TEMPERATURE,
max_tokens: int = DEFAULT_NUM_OUTPUTS,
timeout: int = 120,
max_retries: int = 5,
seed: Optional[int] = None,
token: Optional[str] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
token = get_from_param_or_env("token", token, "HF_TOKEN", "")
headers = {}
if token:
headers.update({"Authorization": f"Bearer {token}"})
try:
is_function_calling_model = resolve_tgi_function_call(model_url)
except Exception as e:
logger.warning(f"TGI client has no function call support: {e}")
is_function_calling_model = False
context_window = get_max_input_length(model_url) or DEFAULT_CONTEXT_WINDOW
super().__init__(
context_window=context_window,
temperature=temperature,
max_tokens=max_tokens,
additional_kwargs=additional_kwargs,
timeout=timeout,
max_retries=max_retries,
seed=seed,
model_name=model_name,
is_function_calling_model=is_function_calling_model,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
self._sync_client = TGIClient(
base_url=model_url,
headers=headers,
cookies=cookies,
timeout=timeout,
)
self._async_client = TGIAsyncClient(
base_url=model_url,
headers=headers,
cookies=cookies,
timeout=timeout,
)
@classmethod
def class_name(cls) -> str:
return "TextGenerationInference"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=self.context_window,
num_output=self.max_tokens,
is_chat_model=True,
model_name=self.model_name,
random_seed=self.seed,
is_function_calling_model=self.is_function_calling_model,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"temperature": self.temperature,
"max_tokens": self.max_tokens,
"seed": self.seed,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
# convert to TGI Message
messages = to_tgi_messages(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._sync_client.chat(messages=messages, **all_kwargs)
tool_calls = response.choices[0].message.tool_calls
return ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT,
content=response.choices[0].message.content,
additional_kwargs=(
{"tool_calls": tool_calls} if tool_calls is not None else {}
),
),
raw=dict(response),
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
complete_fn = chat_to_completion_decorator(self.chat)
return complete_fn(prompt, **kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
# convert to TGI Message
messages = to_tgi_messages(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._sync_client.chat(messages=messages, stream=True, **all_kwargs)
def generator() -> ChatResponseGen:
content = ""
role = MessageRole.ASSISTANT
for chunk in response:
content_delta = chunk.choices[0].delta.content
if content_delta is None:
continue
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=chunk,
)
return generator()
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
stream_complete_fn = stream_chat_to_completion_decorator(self.stream_chat)
return stream_complete_fn(prompt, **kwargs)
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
# convert to TGI Message
messages = to_tgi_messages(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = await self._async_client.chat(messages=messages, **all_kwargs)
tool_calls = response.choices[0].message.tool_calls
return ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT,
content=response.choices[0].message.content,
additional_kwargs=(
{"tool_calls": tool_calls} if tool_calls is not None else {}
),
),
raw=dict(response),
)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
acomplete_fn = achat_to_completion_decorator(self.achat)
return await acomplete_fn(prompt, **kwargs)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
# convert to TGI Message
messages = to_tgi_messages(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = await self._async_client.chat(
messages=messages, stream=True, **all_kwargs
)
async def generator() -> ChatResponseAsyncGen:
content = ""
role = MessageRole.ASSISTANT
async for chunk in response:
content_delta = chunk.choices[0].delta.content
if content_delta is None:
continue
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=chunk,
)
return generator()
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
astream_complete_fn = astream_chat_to_completion_decorator(self.astream_chat)
return await astream_complete_fn(prompt, **kwargs)
def _prepare_chat_with_tools(
self,
tools: List["BaseTool"],
user_msg: Optional[Union[str, ChatMessage]] = None,
chat_history: Optional[List[ChatMessage]] = None,
verbose: bool = False,
allow_parallel_tool_calls: bool = False,
tool_choice: str = "auto",
**kwargs: Any,
) -> Dict[str, Any]:
"""Prepare the arguments needed to let the LLM chat with tools."""
# use openai tool format
tool_specs = [
tool.metadata.to_openai_tool(skip_length_check=True) for tool in tools
]
if isinstance(user_msg, str):
user_msg = ChatMessage(role=MessageRole.USER, content=user_msg)
messages = chat_history or []
if user_msg:
messages.append(user_msg)
return {
"messages": messages,
"tools": tool_specs or None,
"tool_choice": resolve_tool_choice(tool_specs, tool_choice),
**kwargs,
}
def _validate_chat_with_tools_response(
self,
response: ChatResponse,
tools: List["BaseTool"],
allow_parallel_tool_calls: bool = False,
**kwargs: Any,
) -> ChatResponse:
"""Validate the response from chat_with_tools."""
if not allow_parallel_tool_calls:
force_single_tool_call(response)
return response
def get_tool_calls_from_response(
self,
response: "ChatResponse",
error_on_no_tool_call: bool = True,
) -> List[ToolSelection]:
"""Predict and call the tool."""
tool_calls = response.message.additional_kwargs.get("tool_calls", [])
if len(tool_calls) < 1:
if error_on_no_tool_call:
raise ValueError(
f"Expected at least one tool call, but got {len(tool_calls)} tool calls."
)
else:
return []
tool_selections = []
for tool_call in tool_calls:
# TODO Add typecheck with ToolCall from TGI once the client is updated
if tool_call and (tc_type := tool_call["type"]) != "function":
raise ValueError(
f"Invalid tool type: got {tc_type}, expect 'function'."
)
argument_dict = tool_call["function"]["parameters"]
tool_selections.append(
ToolSelection(
tool_id=tool_call["id"],
tool_name=tool_call["function"][
"name"
], # NOTE for now the tool_name is hardcoded 'tools' in TGI
tool_kwargs=argument_dict,
)
)
return tool_selections
|
