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318 | class VoyageEmbedding(MultiModalEmbedding):
"""
Class for Voyage embeddings.
Args:
model_name (str): Model for embedding.
Defaults to "voyage-01".
voyage_api_key (Optional[str]): Voyage API key. Defaults to None.
You can either specify the key here or store it as an environment variable.
"""
_client: voyageai.Client = PrivateAttr(None)
_aclient: voyageai.AsyncClient = PrivateAttr()
truncation: Optional[bool] = None
output_dtype: Optional[str] = None
output_dimension: Optional[int] = None
def __init__(
self,
model_name: str,
voyage_api_key: Optional[str] = None,
embed_batch_size: Optional[int] = None,
truncation: Optional[bool] = None,
output_dtype: Optional[str] = None,
output_dimension: Optional[int] = None,
callback_manager: Optional[CallbackManager] = None,
**kwargs: Any,
):
if model_name in [
"voyage-01",
"voyage-lite-01",
"voyage-lite-01-instruct",
"voyage-02",
"voyage-2",
"voyage-lite-02-instruct",
"voyage-large-2",
"voyage-large-2-instruct",
"voyage-multilingual-2",
"voyage-3",
"voyage-3-lite",
]:
logger.warning(
f"{model_name} is not the latest model by Voyage AI. Please note that `model_name` "
"will be a required argument in the future. We recommend setting it explicitly. Please see "
"https://docs.voyageai.com/docs/embeddings for the latest models offered by Voyage AI."
)
if embed_batch_size is None:
embed_batch_size = MAX_BATCH_SIZE
super().__init__(
model_name=model_name,
embed_batch_size=embed_batch_size,
callback_manager=callback_manager,
**kwargs,
)
self._client = voyageai.Client(api_key=voyage_api_key)
self._aclient = voyageai.AsyncClient(api_key=voyage_api_key)
self.truncation = truncation
self.output_dtype = output_dtype
self.output_dimension = output_dimension
@classmethod
def class_name(cls) -> str:
return "VoyageEmbedding"
@staticmethod
def _validate_image_format(file_type: str) -> bool:
"""Validate image format."""
return file_type.lower() in SUPPORTED_IMAGE_FORMATS
@classmethod
def _texts_to_content(cls, input_strs: List[str]) -> List[dict]:
return [{"content": [{"type": "text", "text": x}]} for x in input_strs]
def _build_batches(
self, texts: List[str]
) -> Generator[Tuple[List[str], int], None, None]:
"""Generate batches of texts based on token limits."""
max_tokens_per_batch = VOYAGE_TOTAL_TOKEN_LIMITS.get(self.model_name, 120_000)
index = 0
while index < len(texts):
batch: List[str] = []
batch_tokens = 0
while (
index < len(texts)
and len(batch) < min(self.embed_batch_size, MAX_BATCH_SIZE)
and batch_tokens < max_tokens_per_batch
):
n_tokens = len(
self._client.tokenize([texts[index]], model=self.model_name)[0]
)
if batch_tokens + n_tokens > max_tokens_per_batch and len(batch) > 0:
break
batch_tokens += n_tokens
batch.append(texts[index])
index += 1
yield batch, len(batch)
def _image_to_content(self, image_input: Union[str, Path, BytesIO]) -> Image:
"""Convert an image to a base64 Data URL."""
if isinstance(image_input, (str, Path)):
image = Image.open(str(image_input))
# If it's a string or Path, assume it's a file path
image_path = str(image_input)
file_extension = os.path.splitext(image_path)[1][1:].lower()
elif isinstance(image_input, BytesIO):
# If it's a BytesIO, use it directly
image = Image.open(image_input)
file_extension = image.format.lower()
image_input.seek(0) # Reset the BytesIO stream to the beginning
else:
raise ValueError("Unsupported input type. Must be a file path or BytesIO.")
if self._validate_image_format(file_extension):
return image
else:
raise ValueError(f"Unsupported image format: {file_extension}")
def _embed_image(
self, image_path: ImageType, input_type: Optional[str] = None
) -> List[float]:
"""Embed images using VoyageAI."""
if self.model_name not in MULTIMODAL_MODELS:
raise ValueError(
f"{self.model_name} is not a valid multi-modal embedding model. Supported models are {MULTIMODAL_MODELS}"
)
processed_image = self._image_to_content(image_path)
return self._client.multimodal_embed(
model=self.model_name,
inputs=[[processed_image]],
input_type=input_type,
truncation=self.truncation,
).embeddings[0]
async def _aembed_image(
self, image_path: ImageType, input_type: Optional[str] = None
) -> List[float]:
"""Embed images using VoyageAI."""
if self.model_name not in MULTIMODAL_MODELS:
raise ValueError(
f"{self.model_name} is not a valid multi-modal embedding model. Supported models are {MULTIMODAL_MODELS}"
)
processed_image = self._image_to_content(image_path)
return (
await self._aclient.multimodal_embed(
model=self.model_name,
inputs=[[processed_image]],
input_type=input_type,
truncation=self.truncation,
)
).embeddings[0]
def _get_image_embedding(self, img_file_path: ImageType) -> Embedding:
return self._embed_image(img_file_path)
async def _aget_image_embedding(self, img_file_path: ImageType) -> Embedding:
return await self._aembed_image(img_file_path)
def _embed(self, texts: List[str], input_type: str) -> List[List[float]]:
"""Embed texts with dynamic batching based on token limits."""
embeddings: List[List[float]] = []
for batch, _ in self._build_batches(texts):
if self.model_name in CONTEXT_MODELS:
r = self._client.contextualized_embed(
inputs=[batch],
model=self.model_name,
input_type=input_type,
output_dtype=self.output_dtype,
output_dimension=self.output_dimension,
).results
embeddings.extend(r[0].embeddings)
elif self.model_name in MULTIMODAL_MODELS:
batch_embeddings = self._client.multimodal_embed(
inputs=self._texts_to_content(batch),
model=self.model_name,
input_type=input_type,
truncation=self.truncation,
).embeddings
embeddings.extend(batch_embeddings)
else:
batch_embeddings = self._client.embed(
batch,
model=self.model_name,
input_type=input_type,
truncation=self.truncation,
output_dtype=self.output_dtype,
output_dimension=self.output_dimension,
).embeddings
embeddings.extend(batch_embeddings)
return embeddings
async def _aembed(self, texts: List[str], input_type: str) -> List[List[float]]:
"""Asynchronously embed texts with dynamic batching based on token limits."""
embeddings: List[List[float]] = []
for batch, _ in self._build_batches(texts):
if self.model_name in CONTEXT_MODELS:
ar = await self._aclient.contextualized_embed(
inputs=[batch],
model=self.model_name,
input_type=input_type,
output_dtype=self.output_dtype,
output_dimension=self.output_dimension,
)
r = ar.results
embeddings.extend(r[0].embeddings)
elif self.model_name in MULTIMODAL_MODELS:
r = await self._aclient.multimodal_embed(
inputs=self._texts_to_content(batch),
model=self.model_name,
input_type=input_type,
truncation=self.truncation,
)
embeddings.extend(r.embeddings)
else:
r = await self._aclient.embed(
batch,
model=self.model_name,
input_type=input_type,
truncation=self.truncation,
output_dtype=self.output_dtype,
output_dimension=self.output_dimension,
)
embeddings.extend(r.embeddings)
return embeddings
def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
return self._embed([query], input_type="query")[0]
async def _aget_query_embedding(self, query: str) -> List[float]:
"""The asynchronous version of _get_query_embedding."""
r = await self._aembed([query], input_type="query")
return r[0]
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding."""
return self._embed([text], input_type="document")[0]
async def _aget_text_embedding(self, text: str) -> List[float]:
"""Asynchronously get text embedding."""
r = await self._aembed([text], input_type="document")
return r[0]
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get text embeddings."""
return self._embed(texts, input_type="document")
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Asynchronously get text embeddings."""
return await self._aembed(texts, input_type="document")
def get_general_text_embedding(
self, text: str, input_type: Optional[str] = None
) -> List[float]:
"""Get general text embedding with input_type."""
return self._embed([text], input_type=input_type)[0]
async def aget_general_text_embedding(
self, text: str, input_type: Optional[str] = None
) -> List[float]:
"""Asynchronously get general text embedding with input_type."""
r = await self._aembed([text], input_type=input_type)
return r[0]
|