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325 | class NVIDIA(OpenAILike, FunctionCallingLLM):
"""NVIDIA's API Catalog Connector."""
_is_hosted: bool = PrivateAttr(True)
_mode: str = PrivateAttr(default="nvidia")
def __init__(
self,
model: Optional[str] = None,
nvidia_api_key: Optional[str] = None,
api_key: Optional[str] = None,
base_url: Optional[str] = BASE_URL,
max_tokens: Optional[int] = 1024,
**kwargs: Any,
) -> None:
"""
Initialize an instance of the NVIDIA class.
This class provides an interface to the NVIDIA NIM. By default, it connects to a hosted NIM,
but you can switch to an on-premises NIM by providing a `base_url`.
Args:
model (str, optional): The model to use for the NIM.
nvidia_api_key (str, optional): The API key for the NVIDIA NIM. Defaults to None.
api_key (str, optional): An alternative parameter for providing the API key. Defaults to None.
base_url (str, optional): The base URL for the NIM. Use this to switch to an on-premises NIM.
max_tokens (int, optional): The maximum number of tokens to generate. Defaults to 1024.
**kwargs: Additional keyword arguments.
API Keys:
- The recommended way to provide the API key is through the `NVIDIA_API_KEY` environment variable.
Raises:
DeprecationWarning: If an API key is not provided for a hosted NIM, a warning is issued. This will become an error in version 0.2.0.
"""
api_key = get_from_param_or_env(
"api_key",
nvidia_api_key or api_key,
"NVIDIA_API_KEY",
"NO_API_KEY_PROVIDED",
)
is_hosted = base_url in KNOWN_URLS
if base_url not in KNOWN_URLS:
base_url = self._validate_url(base_url)
if is_hosted and api_key == "NO_API_KEY_PROVIDED":
warnings.warn(
"An API key is required for the hosted NIM. This will become an error in 0.2.0.",
)
super().__init__(
api_key=api_key,
api_base=base_url,
max_tokens=max_tokens,
is_chat_model=is_chat_model(model),
default_headers={"User-Agent": "llama-index-llms-nvidia"},
is_function_calling_model=is_nvidia_function_calling_model(model),
**kwargs,
)
self.model = model
self._is_hosted = base_url in KNOWN_URLS
if self._is_hosted and api_key == "NO_API_KEY_PROVIDED":
warnings.warn(
"An API key is required for the hosted NIM. This will become an error in 0.2.0.",
)
if not model:
self.__get_default_model()
def __get_default_model(self):
"""Set default model."""
if not self._is_hosted:
valid_models = [
model.id
for model in self.available_models
if not model.base_model or model.base_model == model.id
]
self.model = next(iter(valid_models), None)
if self.model:
warnings.warn(
f"Default model is set as: {self.model}. \n"
"Set model using model parameter. \n"
"To get available models use available_models property.",
UserWarning,
)
else:
raise ValueError("No locally hosted model was found.")
else:
self.model = DEFAULT_MODEL
def _validate_url(self, base_url):
"""
Base URL Validation.
ValueError : url which do not have valid scheme and netloc.
Warning : v1/chat/completions routes.
ValueError : Any other routes other than above.
"""
expected_format = "Expected format is 'http://host:port'."
result = urlparse(base_url)
if not (result.scheme and result.netloc):
raise ValueError(f"Invalid base_url, {expected_format}")
if result.path:
normalized_path = result.path.strip("/")
if normalized_path == "v1":
pass
elif normalized_path == "v1/chat/completions":
warnings.warn(f"{expected_format} Rest is Ignored.")
else:
raise ValueError(f"Invalid base_url, {expected_format}")
return urlunparse((result.scheme, result.netloc, "v1", "", "", ""))
@property
def available_models(self) -> List[Model]:
models = [
Model(
id=model.id,
base_model=getattr(model, "params", {}).get("root", None),
)
for model in self._get_client().models.list().data
]
# only exclude models in hosted mode. in non-hosted mode, the administrator has control
# over the model name and may deploy an excluded name that will work.
if self._is_hosted:
exclude = {
"mistralai/mixtral-8x22b-v0.1", # not a /chat/completion endpoint
}
models = [model for model in models if model.id not in exclude]
return models
@classmethod
def class_name(cls) -> str:
return "NVIDIA"
@deprecated(
version="0.1.3",
reason="Will be removed in 0.2. Construct with `base_url` instead.",
)
def mode(
self,
mode: Optional[Literal["nvidia", "nim"]] = "nvidia",
*,
base_url: Optional[str] = None,
model: Optional[str] = None,
api_key: Optional[str] = None,
) -> "NVIDIA":
"""
Deprecated: use NVIDIA(base_url="...") instead.
"""
if mode == "nim":
if not base_url:
raise ValueError("base_url is required for nim mode")
if mode == "nvidia":
api_key = get_from_param_or_env(
"api_key",
api_key,
"NVIDIA_API_KEY",
)
base_url = base_url or BASE_URL
self._mode = mode
if base_url:
self.api_base = base_url
if model:
self.model = model
if api_key:
self.api_key = api_key
return self
@property
def _is_chat_model(self) -> bool:
return is_chat_model(self.model)
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,
**kwargs: Any,
) -> Dict[str, Any]:
"""Prepare the chat with tools."""
# misralai uses the same 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,
**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:
# if not isinstance(tool_call, ToolCall):
# raise ValueError("Invalid tool_call object")
argument_dict = json.loads(tool_call.function.arguments)
tool_selections.append(
ToolSelection(
tool_id=tool_call.id,
tool_name=tool_call.function.name,
tool_kwargs=argument_dict,
)
)
return tool_selections
def _stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
client = self._get_client()
all_kwargs = self._get_model_kwargs(**kwargs)
self._update_max_tokens(all_kwargs, prompt)
def gen() -> CompletionResponseGen:
text = ""
for response in client.completions.create(
prompt=prompt,
stream=True,
**all_kwargs,
):
if len(response.choices) > 0:
delta = response.choices[0].text
else:
delta = ""
text += delta
yield CompletionResponse(
delta=delta,
text=text,
raw=response,
additional_kwargs=self._get_response_token_counts(response),
)
return gen()
|