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300 | class NVIDIAEmbedding(BaseEmbedding):
"""NVIDIA embeddings."""
base_url: str = Field(
default_factory=lambda: os.getenv("NVIDIA_BASE_URL", BASE_URL),
description="Base url for model listing an invocation",
)
model: Optional[str] = Field(
description="Name of the NVIDIA embedding model to use.\n"
)
truncate: Literal["NONE", "START", "END"] = Field(
default="NONE",
description=(
"Truncate input text if it exceeds the model's maximum token length. "
"Default is 'NONE', which raises an error if an input is too long."
),
)
timeout: float = Field(
default=120, description="The timeout for the API request in seconds.", ge=0
)
max_retries: int = Field(
default=5,
description="The maximum number of retries for the API request.",
ge=0,
)
dimensions: Optional[int] = Field(
default=None,
description=(
"The number of dimensions for the embeddings. This parameter is not "
"supported by all models."
),
)
_client: Any = PrivateAttr()
_aclient: Any = PrivateAttr()
_is_hosted: bool = PrivateAttr(True)
def __init__(
self,
model: Optional[str] = None,
timeout: Optional[float] = 120,
max_retries: Optional[int] = 5,
dimensions: Optional[int] = 0,
nvidia_api_key: Optional[str] = None,
api_key: Optional[str] = None,
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE, # This could default to 50
callback_manager: Optional[CallbackManager] = None,
**kwargs: Any,
):
"""
Construct an Embedding interface for NVIDIA NIM.
This constructor initializes an instance of the NVIDIAEmbedding class, which provides
an interface for embedding text using NVIDIA's NIM service.
Parameters
----------
- model (str, optional): The name of the model to use for embeddings.
- timeout (float, optional): The timeout for requests to the NIM service, in seconds. Defaults to 120.
- max_retries (int, optional): The maximum number of retries for requests to the NIM service. Defaults to 5.
- dimensions (int, optional): The number of dimensions for the embeddings. This
parameter is not supported by all models.
- nvidia_api_key (str, optional): The API key for the NIM service. This is required if using a hosted NIM.
- api_key (str, optional): An alternative parameter for providing the API key.
- base_url (str, optional): The base URL for the NIM service. If not provided, the service will default to a hosted NIM.
- **kwargs: Additional keyword arguments.
API Keys:
- The recommended way to provide the API key is through the `NVIDIA_API_KEY` environment variable.
Note:
- Switch from a hosted NIM (default) to an on-premises NIM using the `base_url` parameter. An API key is required for hosted NIM.
"""
super().__init__(
model=model,
embed_batch_size=embed_batch_size,
callback_manager=callback_manager,
dimensions=dimensions,
**kwargs,
)
self.dimensions = dimensions
if embed_batch_size > 259:
raise ValueError("The batch size should not be larger than 259.")
api_key = get_from_param_or_env(
"api_key",
nvidia_api_key or api_key,
"NVIDIA_API_KEY",
"NO_API_KEY_PROVIDED",
)
self._is_hosted = self.base_url in KNOWN_URLS
if self._is_hosted: # hosted on API Catalog (build.nvidia.com)
if api_key == "NO_API_KEY_PROVIDED":
raise ValueError("An API key is required for hosted NIM.")
self._client = OpenAI(
api_key=api_key,
base_url=self.base_url,
timeout=timeout,
max_retries=max_retries,
)
self._client._custom_headers = {"User-Agent": "llama-index-embeddings-nvidia"}
self._aclient = AsyncOpenAI(
api_key=api_key,
base_url=self.base_url,
timeout=timeout,
max_retries=max_retries,
)
self._aclient._custom_headers = {"User-Agent": "llama-index-embeddings-nvidia"}
self.model = model
if not self.model:
if self._is_hosted:
self.model = DEFAULT_MODEL
else:
self.__get_default_model()
if not self.model.startswith("nvdev/"):
self._validate_model(self.model) ## validate model
def __get_default_model(self) -> None:
"""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 = self.model or DEFAULT_MODEL
def _validate_model(self, model_name: str) -> None:
"""
Validates compatibility of the hosted model with the client.
Skipping the client validation for non-catalogue requests.
Args:
model_name (str): The name of the model.
Raises:
ValueError: If the model is incompatible with the client.
"""
model = determine_model(model_name)
if self._is_hosted:
if not model:
warnings.warn(f"Unable to determine validity of {model_name}")
if model and model.endpoint:
self.base_url = model.endpoint
# Update client base_url for custom endpoints
self._client.base_url = self.base_url
self._aclient.base_url = self.base_url
# TODO: handle locally hosted models
@property
def available_models(self) -> List[str]:
"""Get available models."""
# TODO: hosted now has a model listing, need to merge known and listed models
if not self._is_hosted:
return [
Model(
id=model.id,
base_model=getattr(model, "params", {}).get("root", None),
)
for model in self._client.models.list()
]
else:
return [Model(id=id) for id in EMBEDDING_MODEL_TABLE]
@classmethod
def class_name(cls) -> str:
return "NVIDIAEmbedding"
def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
extra_body = {"input_type": "passage", "truncate": self.truncate}
if self.dimensions:
extra_body["dimensions"] = self.dimensions
return (
self._client.embeddings.create(
input=[query],
model=self.model,
extra_body=extra_body,
)
.data[0]
.embedding
)
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding."""
extra_body = {"input_type": "passage", "truncate": self.truncate}
if self.dimensions:
extra_body["dimensions"] = self.dimensions
return (
self._client.embeddings.create(
input=[text],
model=self.model,
extra_body=extra_body,
)
.data[0]
.embedding
)
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get text embeddings."""
assert len(texts) <= 259, "The batch size should not be larger than 259."
extra_body = {"input_type": "passage", "truncate": self.truncate}
if self.dimensions:
extra_body["dimensions"] = self.dimensions
data = self._client.embeddings.create(
input=texts,
model=self.model,
extra_body=extra_body,
).data
return [d.embedding for d in data]
async def _aget_query_embedding(self, query: str) -> List[float]:
"""Asynchronously get query embedding."""
return (
(
await self._aclient.embeddings.create(
input=[query],
model=self.model,
extra_body={"input_type": "query", "truncate": self.truncate},
)
)
.data[0]
.embedding
)
async def _aget_text_embedding(self, text: str) -> List[float]:
"""Asynchronously get text embedding."""
return (
(
await self._aclient.embeddings.create(
input=[text],
model=self.model,
extra_body={"input_type": "passage", "truncate": self.truncate},
)
)
.data[0]
.embedding
)
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Asynchronously get text embeddings."""
assert len(texts) <= 259, "The batch size should not be larger than 259."
data = (
await self._aclient.embeddings.create(
input=texts,
model=self.model,
extra_body={"input_type": "passage", "truncate": self.truncate},
)
).data
return [d.embedding for d in data]
|