from __future__ import annotations
import logging
import warnings
from typing import (
Any,
Dict,
List,
Literal,
Mapping,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import openai
from langchain_core.embeddings import Embeddings
from langchain_core.utils import from_env, get_pydantic_field_names, secret_from_env
from pydantic import (
BaseModel,
ConfigDict,
Field,
SecretStr,
model_validator,
)
from typing_extensions import Self
logger = logging.getLogger(__name__)
DEFAULT_EMBED_BATCH_SIZE = 10
MAX_EMBED_BATCH_SIZE = 100
[docs]
class UpstageEmbeddings(BaseModel, Embeddings):
"""UpstageEmbeddings embedding model.
To use, set the environment variable `UPSTAGE_API_KEY` with your API key or
pass it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain_upstage import UpstageEmbeddings
model = UpstageEmbeddings(model='solar-embedding-1-large')
"""
client: Any = Field(default=None, exclude=True) #: :meta private:
async_client: Any = Field(default=None, exclude=True) #: :meta private:
model: str = Field(...)
"""Embeddings model name to use. Do not add suffixes like `-query` and `-passage`.
Instead, use 'solar-embedding-1-large' for example.
"""
dimensions: Optional[int] = None
"""The number of dimensions the resulting output embeddings should have.
Not yet supported.
"""
upstage_api_key: SecretStr = Field(
default_factory=secret_from_env(
"UPSTAGE_API_KEY",
error_message=(
"You must specify an api key. "
"You can pass it an argument as `api_key=...` or "
"set the environment variable `UPSTAGE_API_KEY`."
),
),
alias="api_key",
)
"""Automatically inferred from env are `UPSTAGE_API_KEY` if not provided."""
upstage_api_base: Optional[str] = Field(
default_factory=from_env(
"UPSTAGE_API_BASE", default="https://api.upstage.ai/v1/solar"
),
alias="base_url",
)
"""Endpoint URL to use."""
embedding_ctx_length: int = 4096
"""The maximum number of tokens to embed at once.
Not yet supported.
"""
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE
allowed_special: Union[Literal["all"], Set[str]] = set()
"""Not yet supported."""
disallowed_special: Union[Literal["all"], Set[str], Sequence[str]] = "all"
"""Not yet supported."""
chunk_size: int = 1000
"""Maximum number of texts to embed in each batch.
Not yet supported.
"""
max_retries: int = 2
"""Maximum number of retries to make when generating."""
request_timeout: Optional[Union[float, Tuple[float, float], Any]] = Field(
default=None, alias="timeout"
)
"""Timeout for requests to Upstage embedding API. Can be float, httpx.Timeout or
None."""
show_progress_bar: bool = False
"""Whether to show a progress bar when embedding.
Not yet supported.
"""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
skip_empty: bool = False
"""Whether to skip empty strings when embedding or raise an error.
Defaults to not skipping.
Not yet supported."""
default_headers: Union[Mapping[str, str], None] = None
default_query: Union[Mapping[str, object], None] = None
# Configure a custom httpx client. See the
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
http_client: Union[Any, None] = None
"""Optional httpx.Client. Only used for sync invocations. Must specify
http_async_client as well if you'd like a custom client for async invocations.
"""
http_async_client: Union[Any, None] = None
"""Optional httpx.AsyncClient. Only used for async invocations. Must specify
http_client as well if you'd like a custom client for sync invocations."""
model_config = ConfigDict(
extra="forbid",
populate_by_name=True,
protected_namespaces=(),
)
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
warnings.warn(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
client_params: dict = {
"api_key": (
self.upstage_api_key.get_secret_value()
if self.upstage_api_key
else None
),
"base_url": self.upstage_api_base,
"timeout": self.request_timeout,
"max_retries": self.max_retries,
"default_headers": self.default_headers,
"default_query": self.default_query,
}
if not (self.client or None):
sync_specific: dict = {"http_client": self.http_client}
self.client = openai.OpenAI(**client_params, **sync_specific).embeddings
if not (self.async_client or None):
async_specific: dict = {"http_client": self.http_async_client}
self.async_client = openai.AsyncOpenAI(
**client_params, **async_specific
).embeddings
return self
@property
def _invocation_params(self) -> Dict[str, Any]:
self.model = self.model.replace("-query", "").replace("-passage", "")
params: Dict = {"model": self.model, **self.model_kwargs}
if self.dimensions is not None:
params["dimensions"] = self.dimensions
return params
[docs]
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed a list of document texts using passage model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
assert (
self.embed_batch_size <= MAX_EMBED_BATCH_SIZE
), f"The embed_batch_size should not be larger than {MAX_EMBED_BATCH_SIZE}."
if not texts:
return []
params = self._invocation_params
params["model"] = params["model"] + "-passage"
embeddings = []
batch_size = min(self.embed_batch_size, len(texts))
for i in range(0, len(texts), batch_size):
batch = texts[i : i + batch_size]
data = self.client.create(input=batch, **params).data
embeddings.extend([r.embedding for r in data])
return embeddings
[docs]
def embed_query(self, text: str) -> List[float]:
"""Embed query text using query model.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
params = self._invocation_params
params["model"] = params["model"] + "-query"
response = self.client.create(input=text, **params)
if not isinstance(response, dict):
response = response.model_dump()
return response["data"][0]["embedding"]
[docs]
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed a list of document texts using passage model asynchronously.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
assert (
self.embed_batch_size <= MAX_EMBED_BATCH_SIZE
), f"The embed_batch_size should not be larger than {MAX_EMBED_BATCH_SIZE}."
if not texts:
return []
params = self._invocation_params
params["model"] = params["model"] + "-passage"
embeddings = []
batch_size = min(self.embed_batch_size, len(texts))
for i in range(0, len(texts), batch_size):
batch = texts[i : i + batch_size]
response = await self.async_client.create(input=batch, **params)
embeddings.extend([r.embedding for r in response.data])
return embeddings
[docs]
async def aembed_query(self, text: str) -> List[float]:
"""Asynchronous Embed query text using query model.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
params = self._invocation_params
params["model"] = params["model"] + "-query"
response = await self.async_client.create(input=text, **params)
if not isinstance(response, dict):
response = response.model_dump()
return response["data"][0]["embedding"]
