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240 | class MixedbreadAIEmbedding(BaseEmbedding):
"""
Class to get embeddings using the mixedbread ai embedding API with models such as 'mixedbread-ai/mxbai-embed-large-v1'.
Args:
api_key (Optional[str]): mixedbread ai API key. Defaults to None.
model_name (str): Model for embedding. Defaults to "mixedbread-ai/mxbai-embed-large-v1".
encoding_format (EncodingFormat): Encoding format for embeddings. Defaults to "float".
normalized (bool): Whether to normalize the embeddings. Defaults to True.
dimensions (Optional[int]): Number of dimensions for embeddings. Only applicable for models with matryoshka support.
prompt (Optional[str]): An optional prompt to provide context to the model.
embed_batch_size (Optional[int]): The batch size for embedding calls. Defaults to 128.
callback_manager (Optional[CallbackManager]): Manager for handling callbacks.
timeout (Optional[float]): Timeout for API calls.
max_retries (Optional[int]): Maximum number of retries for API calls.
httpx_client (Optional[httpx.Client]): Custom HTTPX client.
httpx_async_client (Optional[httpx.AsyncClient]): Custom asynchronous HTTPX client.
"""
api_key: str = Field(description="The mixedbread ai API key.", min_length=1)
model_name: str = Field(
default="mixedbread-ai/mxbai-embed-large-v1",
description="Model to use for embeddings.",
min_length=1,
)
encoding_format: EncodingFormat = Field(
default="float", description="Encoding format for the embeddings."
)
normalized: bool = Field(
default=True, description="Whether to normalize the embeddings."
)
dimensions: Optional[int] = Field(
default=None,
description="Number of dimensions for embeddings. Only applicable for models with matryoshka support.",
gt=0,
)
prompt: Optional[str] = Field(
default=None,
description="An optional prompt to provide context to the model.",
min_length=1,
)
embed_batch_size: int = Field(
default=128, description="The batch size for embedding calls.", gt=0, le=256
)
_client: Mixedbread = PrivateAttr()
_async_client: AsyncMixedbread = PrivateAttr()
def __init__(
self,
api_key: Optional[str] = None,
model_name: str = "mixedbread-ai/mxbai-embed-large-v1",
encoding_format: EncodingFormat = "float",
normalized: bool = True,
dimensions: Optional[int] = None,
prompt: Optional[str] = None,
embed_batch_size: Optional[int] = None,
callback_manager: Optional[CallbackManager] = None,
timeout: Optional[float] = None,
max_retries: Optional[int] = None,
httpx_client: Optional[httpx.Client] = None,
httpx_async_client: Optional[httpx.AsyncClient] = None,
**kwargs: Any,
):
if embed_batch_size is None:
embed_batch_size = 128 # Default batch size for mixedbread ai
try:
api_key = api_key or os.environ["MXBAI_API_KEY"]
except KeyError:
raise ValueError(
"Must pass in mixedbread ai API key or "
"specify via MXBAI_API_KEY environment variable "
)
super().__init__(
api_key=api_key,
model_name=model_name,
encoding_format=encoding_format,
normalized=normalized,
dimensions=dimensions,
prompt=prompt,
embed_batch_size=embed_batch_size,
callback_manager=callback_manager,
**kwargs,
)
self._client = Mixedbread(
api_key=api_key,
timeout=timeout,
http_client=httpx_client,
max_retries=max_retries if max_retries is not None else DEFAULT_MAX_RETRIES,
)
self._async_client = AsyncMixedbread(
api_key=api_key,
timeout=timeout,
http_client=httpx_async_client,
max_retries=max_retries if max_retries is not None else DEFAULT_MAX_RETRIES,
)
@classmethod
def class_name(cls) -> str:
return "MixedbreadAIEmbedding"
def _get_embedding(self, texts: List[str]) -> List[List[float]]:
"""
Get embeddings for a list of texts using the mixedbread ai API.
Args:
texts (List[str]): List of texts to embed.
Returns:
List[List[float]]: List of embeddings.
"""
response = self._client.embed(
model=self.model_name,
input=texts,
encoding_format=self.encoding_format,
normalized=self.normalized,
dimensions=self.dimensions,
prompt=self.prompt,
)
return [item.embedding for item in response.data]
async def _aget_embedding(self, texts: List[str]) -> List[List[float]]:
"""
Asynchronously get embeddings for a list of texts using the mixedbread ai API.
Args:
texts (List[str]): List of texts to embed.
Returns:
List[List[float]]: List of embeddings.
"""
response = await self._async_client.embed(
model=self.model_name,
input=texts,
encoding_format=self.encoding_format,
normalized=self.normalized,
dimensions=self.dimensions,
prompt=self.prompt,
)
return [item.embedding for item in response.data]
def _get_query_embedding(self, query: str) -> List[float]:
"""
Get embedding for a query using the mixedbread ai API.
Args:
query (str): Query text.
Returns:
List[float]: Embedding for the query.
"""
return self._get_embedding([query])[0]
async def _aget_query_embedding(self, query: str) -> List[float]:
"""
Asynchronously get embedding for a query using the mixedbread ai API.
Args:
query (str): Query text.
Returns:
List[float]: Embedding for the query.
"""
r = await self._aget_embedding([query])
return r[0]
def _get_text_embedding(self, text: str) -> List[float]:
"""
Get embedding for a text using the mixedbread ai API.
Args:
text (str): Text to embed.
Returns:
List[float]: Embedding for the text.
"""
return self._get_embedding([text])[0]
async def _aget_text_embedding(self, text: str) -> List[float]:
"""
Asynchronously get embedding for a text using the mixedbread ai API.
Args:
text (str): Text to embed.
Returns:
List[float]: Embedding for the text.
"""
r = await self._aget_embedding([text])
return r[0]
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""
Get embeddings for multiple texts using the mixedbread ai API.
Args:
texts (List[str]): List of texts to embed.
Returns:
List[List[float]]: List of embeddings.
"""
return self._get_embedding(texts)
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""
Asynchronously get embeddings for multiple texts using the mixedbread ai API.
Args:
texts (List[str]): List of texts to embed.
Returns:
List[List[float]]: List of embeddings.
"""
return await self._aget_embedding(texts)
|