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import logging
import os
from typing import Iterable, List, Optional
import voyageai # type: ignore
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import (
BaseModel,
Extra,
Field,
SecretStr,
root_validator,
)
from langchain_core.utils import convert_to_secret_str
logger = logging.getLogger(__name__)
class VoyageAIEmbeddings(BaseModel, Embeddings):
"""VoyageAIEmbeddings embedding model.
Example:
.. code-block:: python
from langchain_voyageai import VoyageAIEmbeddings
model = VoyageAIEmbeddings()
"""
_client: voyageai.Client = Field(exclude=True)
_aclient: voyageai.client_async.AsyncClient = Field(exclude=True)
model: str
batch_size: int
show_progress_bar: bool = False
truncation: Optional[bool] = None
voyage_api_key: Optional[SecretStr] = None
class Config:
extra = Extra.forbid
@root_validator(pre=True)
def default_values(cls, values: dict) -> dict:
"""Set default batch size based on model"""
model = values.get("model")
batch_size = values.get("batch_size")
if batch_size is None:
values["batch_size"] = 72 if model in ["voyage-2", "voyage-02"] else 7
return values
@root_validator()
def validate_environment(cls, values: dict) -> dict:
"""Validate that VoyageAI credentials exist in environment."""
voyage_api_key = values.get("voyage_api_key") or os.getenv(
"VOYAGE_API_KEY", None
)
if voyage_api_key:
api_key_secretstr = convert_to_secret_str(voyage_api_key)
values["voyage_api_key"] = api_key_secretstr
api_key_str = api_key_secretstr.get_secret_value()
else:
api_key_str = None
values["_client"] = voyageai.Client(api_key=api_key_str)
values["_aclient"] = voyageai.client_async.AsyncClient(api_key=api_key_str)
return values
def _get_batch_iterator(self, texts: List[str]) -> Iterable:
if self.show_progress_bar:
try:
from tqdm.auto import tqdm # type: ignore
except ImportError as e:
raise ImportError(
"Must have tqdm installed if `show_progress_bar` is set to True. "
"Please install with `pip install tqdm`."
) from e
_iter = tqdm(range(0, len(texts), self.batch_size))
else:
_iter = range(0, len(texts), self.batch_size) # type: ignore
return _iter
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed search docs."""
embeddings: List[List[float]] = []
_iter = self._get_batch_iterator(texts)
for i in _iter:
embeddings.extend(
self._client.embed(
texts[i : i + self.batch_size],
model=self.model,
input_type="document",
truncation=self.truncation,
).embeddings
)
return embeddings
def embed_query(self, text: str) -> List[float]:
"""Embed query text."""
return self._client.embed(
[text], model=self.model, input_type="query", truncation=self.truncation
).embeddings[0]
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
embeddings: List[List[float]] = []
_iter = self._get_batch_iterator(texts)
for i in _iter:
r = await self._aclient.embed(
texts[i : i + self.batch_size],
model=self.model,
input_type="document",
truncation=self.truncation,
)
embeddings.extend(r.embeddings)
return embeddings
async def aembed_query(self, text: str) -> List[float]:
r = await self._aclient.embed(
[text],
model=self.model,
input_type="query",
truncation=self.truncation,
)
return r.embeddings[0]
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