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217 | class DatabricksEmbedding(BaseEmbedding):
"""Databricks class for text embedding.
Databricks adheres to the OpenAI API, so this integration aligns closely with the existing OpenAIEmbedding class.
Args:
model (str): The unique ID of the embedding model as served by the Databricks endpoint.
endpoint (Optional[str]): The url of the Databricks endpoint. Can be set as an environment variable (`DATABRICKS_SERVING_ENDPOINT`).
api_key (Optional[str]): The Databricks API key to use. Can be set as an environment variable (`DATABRICKS_TOKEN`).
Examples:
`pip install llama-index-embeddings-databricks`
```python
import os
from llama_index.core import Settings
from llama_index.embeddings.databricks import DatabricksEmbedding
# Set up the DatabricksEmbedding class with the required model, API key and serving endpoint
os.environ["DATABRICKS_TOKEN"] = "<MY TOKEN>"
os.environ["DATABRICKS_SERVING_ENDPOINT"] = "<MY ENDPOINT>"
embed_model = DatabricksEmbedding(model="databricks-bge-large-en")
Settings.embed_model = embed_model
# Embed some text
embeddings = embed_model.get_text_embedding("The DatabricksEmbedding integration works great.")
```
"""
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs as for the OpenAI API."
)
model: str = Field(
description="The ID of a model hosted on the databricks endpoint."
)
api_key: str = Field(description="The Databricks API key.")
endpoint: str = Field(description="The Databricks API endpoint.")
max_retries: int = Field(default=10, description="Maximum number of retries.", ge=0)
timeout: float = Field(default=60.0, description="Timeout for each request.", ge=0)
default_headers: Optional[Dict[str, str]] = Field(
default=None, description="The default headers for API requests."
)
reuse_client: bool = Field(
default=True,
description=(
"Reuse the client between requests. When doing anything with large "
"volumes of async API calls, setting this to false can improve stability."
),
)
_query_engine: str = PrivateAttr()
_text_engine: str = PrivateAttr()
_client: Optional[OpenAI] = PrivateAttr()
_aclient: Optional[AsyncOpenAI] = PrivateAttr()
_http_client: Optional[httpx.Client] = PrivateAttr()
def __init__(
self,
model: str,
endpoint: Optional[str] = None,
embed_batch_size: int = 100,
additional_kwargs: Optional[Dict[str, Any]] = None,
api_key: Optional[str] = None,
max_retries: int = 10,
timeout: float = 60.0,
reuse_client: bool = True,
callback_manager: Optional[CallbackManager] = None,
default_headers: Optional[Dict[str, str]] = None,
http_client: Optional[httpx.Client] = None,
num_workers: Optional[int] = None,
**kwargs: Any,
) -> None:
additional_kwargs = additional_kwargs or {}
api_key = get_from_param_or_env("api_key", api_key, "DATABRICKS_TOKEN")
endpoint = get_from_param_or_env(
"endpoint", endpoint, "DATABRICKS_SERVING_ENDPOINT"
)
super().__init__(
model=model,
endpoint=endpoint,
embed_batch_size=embed_batch_size,
callback_manager=callback_manager,
model_name=model,
additional_kwargs=additional_kwargs,
api_key=api_key,
max_retries=max_retries,
reuse_client=reuse_client,
timeout=timeout,
default_headers=default_headers,
num_workers=num_workers,
**kwargs,
)
self._client = None
self._aclient = None
self._http_client = http_client
def _get_client(self) -> OpenAI:
if not self.reuse_client:
return OpenAI(**self._get_credential_kwargs())
if self._client is None:
self._client = OpenAI(**self._get_credential_kwargs())
return self._client
def _get_aclient(self) -> AsyncOpenAI:
if not self.reuse_client:
return AsyncOpenAI(**self._get_credential_kwargs())
if self._aclient is None:
self._aclient = AsyncOpenAI(**self._get_credential_kwargs())
return self._aclient
@classmethod
def class_name(cls) -> str:
return "DatabricksEmbedding"
def _get_credential_kwargs(self) -> Dict[str, Any]:
return {
"api_key": self.api_key,
"base_url": self.endpoint,
"max_retries": self.max_retries,
"timeout": self.timeout,
"default_headers": self.default_headers,
"http_client": self._http_client,
}
def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
client = self._get_client()
return get_embedding(
client,
query,
engine=self.model,
**self.additional_kwargs,
)
async def _aget_query_embedding(self, query: str) -> List[float]:
"""The asynchronous version of _get_query_embedding."""
aclient = self._get_aclient()
return await aget_embedding(
aclient,
query,
engine=self.model,
**self.additional_kwargs,
)
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding."""
client = self._get_client()
return get_embedding(
client,
text,
engine=self.model,
**self.additional_kwargs,
)
async def _aget_text_embedding(self, text: str) -> List[float]:
"""Asynchronously get text embedding."""
aclient = self._get_aclient()
return await aget_embedding(
aclient,
text,
engine=self.model,
**self.additional_kwargs,
)
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get text embeddings.
By default, this is a wrapper around _get_text_embedding.
Can be overridden for batch queries.
"""
client = self._get_client()
return get_embeddings(
client,
texts,
engine=self.model,
**self.additional_kwargs,
)
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Asynchronously get text embeddings."""
aclient = self._get_aclient()
return await aget_embeddings(
aclient,
texts,
engine=self.model,
**self.additional_kwargs,
)
|
