mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
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"""Anyscale embeddings wrapper."""
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from __future__ import annotations
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from typing import Dict
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from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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from langchain_community.embeddings.openai import OpenAIEmbeddings
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from langchain_community.utils.openai import is_openai_v1
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DEFAULT_API_BASE = "https://api.endpoints.anyscale.com/v1"
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DEFAULT_MODEL = "thenlper/gte-large"
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class AnyscaleEmbeddings(OpenAIEmbeddings):
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"""`Anyscale` Embeddings API."""
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anyscale_api_key: SecretStr = Field(default=None)
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"""AnyScale Endpoints API keys."""
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model: str = Field(default=DEFAULT_MODEL)
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"""Model name to use."""
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anyscale_api_base: str = Field(default=DEFAULT_API_BASE)
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"""Base URL path for API requests."""
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tiktoken_enabled: bool = False
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"""Set this to False for non-OpenAI implementations of the embeddings API"""
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embedding_ctx_length: int = 500
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"""The maximum number of tokens to embed at once."""
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {
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"anyscale_api_key": "ANYSCALE_API_KEY",
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}
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@root_validator()
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def validate_environment(cls, values: dict) -> dict:
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"""Validate that api key and python package exists in environment."""
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values["anyscale_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(
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values,
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"anyscale_api_key",
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"ANYSCALE_API_KEY",
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)
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)
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values["anyscale_api_base"] = get_from_dict_or_env(
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values,
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"anyscale_api_base",
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"ANYSCALE_API_BASE",
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default=DEFAULT_API_BASE,
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)
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try:
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import openai
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except ImportError:
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raise ImportError(
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"Could not import openai python package. "
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"Please install it with `pip install openai`."
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)
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if is_openai_v1():
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# For backwards compatibility.
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client_params = {
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"api_key": values["anyscale_api_key"].get_secret_value(),
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"base_url": values["anyscale_api_base"],
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}
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values["client"] = openai.OpenAI(**client_params).embeddings
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else:
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values["openai_api_base"] = values["anyscale_api_base"]
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values["openai_api_key"] = values["anyscale_api_key"].get_secret_value()
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values["client"] = openai.Embedding
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return values
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@property
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def _llm_type(self) -> str:
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return "anyscale-embedding"
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