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from __future__ import annotations
import logging
from typing import Dict, List, Any
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, root_validator
logger = logging.getLogger(__name__)
class ZhipuAIEmbeddings(BaseModel, Embeddings):
"""`Zhipuai Embeddings` embedding models."""
client: Any
"""`zhipuai.ZhipuAI"""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""
实例化ZhipuAI为values["client"]
Args:
values (Dict): 包含配置信息的字典，必须包含 client 的字段.
Returns:
values (Dict): 包含配置信息的字典。如果环境中有zhipuai库，则将返回实例化的ZhipuAI类；否则将报错 'ModuleNotFoundError: No module named 'zhipuai''.
"""
from zhipuai import ZhipuAI
values["client"] = ZhipuAI()
return values
def embed_query(self, text: str) -> List[float]:
"""
生成输入文本的 embedding.
Args:
texts (str): 要生成 embedding 的文本.
Return:
embeddings (List[float]): 输入文本的 embedding，一个浮点数值列表.
"""
embeddings = self.client.embeddings.create(
model="embedding-2",
input=text
)
return embeddings.data[0].embedding
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""
生成输入文本列表的 embedding.
Args:
texts (List[str]): 要生成 embedding 的文本列表.
Returns:
List[List[float]]: 输入列表中每个文档的 embedding 列表。每个 embedding 都表示为一个浮点值列表。
"""
return [self.embed_query(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") |