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"""Anyscale embeddings wrapper."""
from __future__ import annotations
from typing import Dict
from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.utils.openai import is_openai_v1
DEFAULT_API_BASE = "https://api.endpoints.anyscale.com/v1"
DEFAULT_MODEL = "thenlper/gte-large"
class AnyscaleEmbeddings(OpenAIEmbeddings):
"""`Anyscale` Embeddings API."""
anyscale_api_key: SecretStr = Field(default=None)
"""AnyScale Endpoints API keys."""
model: str = Field(default=DEFAULT_MODEL)
"""Model name to use."""
anyscale_api_base: str = Field(default=DEFAULT_API_BASE)
"""Base URL path for API requests."""
tiktoken_enabled: bool = False
"""Set this to False for non-OpenAI implementations of the embeddings API"""
embedding_ctx_length: int = 500
"""The maximum number of tokens to embed at once."""
@property
def lc_secrets(self) -> Dict[str, str]:
return {
"anyscale_api_key": "ANYSCALE_API_KEY",
}
@root_validator()
def validate_environment(cls, values: dict) -> dict:
"""Validate that api key and python package exists in environment."""
values["anyscale_api_key"] = convert_to_secret_str(
get_from_dict_or_env(
values,
"anyscale_api_key",
"ANYSCALE_API_KEY",
)
)
values["anyscale_api_base"] = get_from_dict_or_env(
values,
"anyscale_api_base",
"ANYSCALE_API_BASE",
default=DEFAULT_API_BASE,
)
try:
import openai
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
if is_openai_v1():
# For backwards compatibility.
client_params = {
"api_key": values["anyscale_api_key"].get_secret_value(),
"base_url": values["anyscale_api_base"],
}
values["client"] = openai.OpenAI(**client_params).embeddings
else:
values["openai_api_base"] = values["anyscale_api_base"]
values["openai_api_key"] = values["anyscale_api_key"].get_secret_value()
values["client"] = openai.Embedding
return values
@property
def _llm_type(self) -> str:
return "anyscale-embedding"
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