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from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.utils import get_from_dict_or_env
from FlagEmbedding import LLMEmbedder
logger = logging.getLogger(__name__)
class LocalEmbed(BaseModel, Embeddings):
"""`Zhipuai Embeddings` embedding models."""
zhipuai_api_key: Optional[str] = None
"""Zhipuai application apikey"""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""
Validate whether zhipuai_api_key in the environment variables or
configuration file are available or not.
Args:
values: a dictionary containing configuration information, must include the
fields of zhipuai_api_key
Returns:
a dictionary containing configuration information. If zhipuai_api_key
are not provided in the environment variables or configuration
file, the original values will be returned; otherwise, values containing
zhipuai_api_key will be returned.
Raises:
ValueError: zhipuai package not found, please install it with `pip install
zhipuai`
"""
values["zhipuai_api_key"] = get_from_dict_or_env(
values,
"zhipuai_api_key",
"ZHIPUAI_API_KEY",
)
values["client"] = LLMEmbedder('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True)
return values
def _embed(self, texts: str) -> List[float]:
print("cal embed:", texts)
embeddings = self.client.encode(texts)
return embeddings
def embed_query(self, text: str) -> List[float]:
"""
Embedding a text.
Args:
Text (str): A text to be embedded.
Return:
List [float]: An embedding list of input text, which is a list of floating-point values.
"""
resp = self.embed_documents([text])
return resp[0]
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""
Embeds a list of text documents.
Args:
texts (List[str]): A list of text documents to embed.
Returns:
List[List[float]]: A list of embeddings for each document in the input list.
Each embedding is represented as a list of float values.
"""
return [self._embed(text) for text in texts]
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
"""Asynchronous Embed search docs."""
raise NotImplementedError(
"Please use `embed_documents`. Official does not support asynchronous requests")
async def aembed_query(self, text: str) -> List[float]:
"""Asynchronous Embed query text."""
raise NotImplementedError(
"Please use `aembed_query`. Official does not support asynchronous requests") |