"""Wrapper around YandexGPT embedding models."""
from __future__ import annotations
import logging
import time
from typing import Any, Callable, Dict, List
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
logger = logging.getLogger(__name__)
[docs]class YandexGPTEmbeddings(BaseModel, Embeddings):
"""YandexGPT Embeddings models.
To use, you should have the ``yandexcloud`` python package installed.
There are two authentication options for the service account
with the ``ai.languageModels.user`` role:
- You can specify the token in a constructor parameter `iam_token`
or in an environment variable `YC_IAM_TOKEN`.
- You can specify the key in a constructor parameter `api_key`
or in an environment variable `YC_API_KEY`.
To use the default model specify the folder ID in a parameter `folder_id`
or in an environment variable `YC_FOLDER_ID`.
Or specify the model URI in a constructor parameter `model_uri`
Example:
.. code-block:: python
from langchain_community.embeddings.yandex import YandexGPTEmbeddings
embeddings = YandexGPTEmbeddings(iam_token="t1.9eu...", model_uri="emb://<folder-id>/text-search-query/latest")
"""
iam_token: SecretStr = "" # type: ignore[assignment]
"""Yandex Cloud IAM token for service account
with the `ai.languageModels.user` role"""
api_key: SecretStr = "" # type: ignore[assignment]
"""Yandex Cloud Api Key for service account
with the `ai.languageModels.user` role"""
model_uri: str = ""
"""Model uri to use."""
folder_id: str = ""
"""Yandex Cloud folder ID"""
model_name: str = "text-search-query"
"""Model name to use."""
model_version: str = "latest"
"""Model version to use."""
url: str = "llm.api.cloud.yandex.net:443"
"""The url of the API."""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
sleep_interval: float = 0.0
"""Delay between API requests"""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that iam token exists in environment."""
iam_token = convert_to_secret_str(
get_from_dict_or_env(values, "iam_token", "YC_IAM_TOKEN", "")
)
values["iam_token"] = iam_token
api_key = convert_to_secret_str(
get_from_dict_or_env(values, "api_key", "YC_API_KEY", "")
)
values["api_key"] = api_key
folder_id = get_from_dict_or_env(values, "folder_id", "YC_FOLDER_ID", "")
values["folder_id"] = folder_id
if api_key.get_secret_value() == "" and iam_token.get_secret_value() == "":
raise ValueError("Either 'YC_API_KEY' or 'YC_IAM_TOKEN' must be provided.")
if values["iam_token"]:
values["_grpc_metadata"] = [
("authorization", f"Bearer {values['iam_token'].get_secret_value()}")
]
if values["folder_id"]:
values["_grpc_metadata"].append(("x-folder-id", values["folder_id"]))
else:
values["_grpc_metadata"] = (
("authorization", f"Api-Key {values['api_key'].get_secret_value()}"),
)
if values["model_uri"] == "" and values["folder_id"] == "":
raise ValueError("Either 'model_uri' or 'folder_id' must be provided.")
if not values["model_uri"]:
values[
"model_uri"
] = f"emb://{values['folder_id']}/{values['model_name']}/{values['model_version']}"
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed documents using a YandexGPT embeddings models.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
return _embed_with_retry(self, texts=texts)
[docs] def embed_query(self, text: str) -> List[float]:
"""Embed a query using a YandexGPT embeddings models.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return _embed_with_retry(self, texts=[text])[0]
def _create_retry_decorator(llm: YandexGPTEmbeddings) -> Callable[[Any], Any]:
from grpc import RpcError
min_seconds = 1
max_seconds = 60
return retry(
reraise=True,
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(retry_if_exception_type((RpcError))),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def _embed_with_retry(llm: YandexGPTEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the embedding call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
def _completion_with_retry(**_kwargs: Any) -> Any:
return _make_request(llm, **_kwargs)
return _completion_with_retry(**kwargs)
def _make_request(self: YandexGPTEmbeddings, texts: List[str]): # type: ignore[no-untyped-def]
try:
import grpc
from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2 import ( # noqa: E501
TextEmbeddingRequest,
)
from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2_grpc import ( # noqa: E501
EmbeddingsServiceStub,
)
except ImportError as e:
raise ImportError(
"Please install YandexCloud SDK with `pip install yandexcloud` \
or upgrade it to recent version."
) from e
result = []
channel_credentials = grpc.ssl_channel_credentials()
channel = grpc.secure_channel(self.url, channel_credentials)
for text in texts:
request = TextEmbeddingRequest(model_uri=self.model_uri, text=text)
stub = EmbeddingsServiceStub(channel)
res = stub.TextEmbedding(request, metadata=self._grpc_metadata) # type: ignore[attr-defined]
result.append(list(res.embedding))
time.sleep(self.sleep_interval)
return result