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204 | class YandexGPTEmbedding(BaseEmbedding):
"""
A class representation for generating embeddings using the Yandex Cloud API.
Args:
api_key (Optional[str]): An API key for Yandex Cloud.
model_name (str): The name of the model to be used for generating embeddings.
The class ensures that this model is supported. Defaults to "general:embedding".
embed_batch_size (int): The batch size for embedding. Defaults to DEFAULT_EMBED_BATCH_SIZE.
callback_manager (Optional[CallbackManager]): Callback manager for hooks.
Example:
. code-block:: python
from llama_index.embeddings.yandexgpt import YandexGPTEmbedding
embeddings = YandexGPTEmbedding(
api_key="your-api-key",
folder_id="your-folder-id",
)
"""
api_key: str = Field(description="The YandexGPT API key.")
folder_id: str = Field(description="The folder id for YandexGPT API.")
retries: int = 6
sleep_interval: float = 0.1
def __init__(
self,
api_key: Optional[str] = None,
folder_id: Optional[str] = None,
model_name: str = "general:embedding",
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
callback_manager: Optional[CallbackManager] = None,
**kwargs: Any,
) -> None:
if not api_key:
raise ValueError(
"You must provide an API key or IAM token to use YandexGPT. "
"You can either pass it in as an argument or set it `YANDEXGPT_API_KEY`."
)
if not folder_id:
raise ValueError(
"You must provide catalog_id to use YandexGPT. "
"You can either pass it in as an argument or set it `YANDEXGPT_CATALOG_ID`."
)
api_key = get_from_param_or_env("api_key", api_key, "YANDEXGPT_KEY")
folder_id = get_from_param_or_env(
"folder_id", folder_id, "YANDEXGPT_CATALOG_ID"
)
super().__init__(
model_name=model_name,
api_key=api_key,
folder_id=folder_id,
embed_batch_size=embed_batch_size,
callback_manager=callback_manager,
**kwargs,
)
def _getModelUri(self, is_document: bool = False) -> str:
"""Construct the model URI based on whether the text is a document or a query."""
return f"emb://{self.folder_id}/text-search-{'doc' if is_document else 'query'}/latest"
@classmethod
def class_name(cls) -> str:
"""Return the class name."""
return "YandexGPTEmbedding"
def _embed(self, text: str, is_document: bool = False) -> List[float]:
"""
Embeds text using the YandexGPT Cloud API synchronously.
Args:
text: The text to embed.
is_document: Whether the text is a document (True) or a query (False).
Returns:
A list of floats representing the embedding.
Raises:
YException: If an error occurs during embedding.
"""
payload = {"modelUri": self._getModelUri(is_document), "text": text}
header = {
"Content-Type": "application/json",
"Authorization": f"Api-Key {self.api_key}",
"x-data-logging-enabled": "false",
}
try:
for attempt in Retrying(
stop=stop_after_attempt(self.retries),
wait=wait_fixed(self.sleep_interval),
):
with attempt:
response = requests.post(
DEFAULT_YANDEXGPT_API_BASE, json=payload, headers=header
)
response = response.json()
if "embedding" in response:
return response["embedding"]
raise YException(f"No embedding found, result returned: {response}")
except RetryError:
raise YException(
f"Error computing embeddings after {self.retries} retries. Result returned:\n{response}"
)
async def _aembed(self, text: str, is_document: bool = False) -> List[float]:
"""
Embeds text using the YandexGPT Cloud API asynchronously.
Args:
text: The text to embed.
is_document: Whether the text is a document (True) or a query (False).
Returns:
A list of floats representing the embedding.
Raises:
YException: If an error occurs during embedding.
"""
payload = {"modelUri": self._getModelUri(is_document), "text": text}
header = {
"Content-Type": "application/json",
"Authorization": f"Api-Key {self.api_key}",
"x-data-logging-enabled": "false",
}
try:
for attempt in Retrying(
stop=stop_after_attempt(self.retries),
wait=wait_fixed(self.sleep_interval),
):
with attempt:
async with aiohttp.ClientSession() as session:
async with session.post(
DEFAULT_YANDEXGPT_API_BASE, json=payload, headers=header
) as response:
result = await response.json()
if "embedding" in result:
return result["embedding"]
raise YException(
f"No embedding found, result returned: {result}"
)
except RetryError:
raise YException(
f"Error computing embeddings after {self.retries} retries. Result returned:\n{result}"
)
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding sync."""
return self._embed(text, is_document=True)
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get list of texts embeddings sync."""
embeddings = []
for text in texts:
embeddings.append(self._embed(text, is_document=True))
time.sleep(self.sleep_interval)
return embeddings
def _get_query_embedding(self, text: str) -> List[float]:
"""Get query embedding sync."""
return self._embed(text, is_document=False)
async def _aget_text_embedding(self, text: str) -> List[float]:
"""Get query text async."""
return await self._aembed(text, is_document=True)
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get list of texts embeddings async."""
embeddings = []
for text in texts:
embeddings.append(await self._aembed(text, is_document=True))
await asyncio.sleep(self.sleep_interval)
return embeddings
async def _aget_query_embedding(self, text: str) -> List[float]:
"""Get query embedding async."""
return await self._aembed(text, is_document=False)
|