CodeGeeX4/llamaindex_demo/models/embedding.py

38 lines
1.3 KiB
Python
Raw Permalink Normal View History

2024-07-05 01:33:53 +00:00
import os
from llama_index.core.base.embeddings.base import BaseEmbedding
from pydantic import Field
from zhipuai import ZhipuAI
class GLMEmbeddings(BaseEmbedding):
client = Field(description="embedding model client")
embedding_size: float = Field(description="embedding size")
def __init__(self):
super().__init__(model_name='GLM', embed_batch_size=64)
self.client = ZhipuAI(api_key=os.getenv("Zhipu_API_KEY"))
self.embedding_size = 1024
def _get_query_embedding(self, query: str) -> list[float]:
return self._get_text_embeddings([query])[0]
def _get_text_embedding(self, text: str) -> list[float]:
return self._get_text_embeddings([text])[0]
def _get_text_embeddings(self, texts: list[str]) -> list[list[float]]:
return self._get_len_safe_embeddings(texts)
async def _aget_query_embedding(self, query: str) -> list[float]:
return self._get_query_embedding(query)
def _get_len_safe_embeddings(self, texts: list[str]) -> list[list[float]]:
try:
# 获取embedding响应
response = self.client.embeddings.create(model="embedding-2", input=texts)
data = [item.embedding for item in response.data]
return data
except Exception as e:
print(f"Fail to get embeddings, caused by {e}")
return []