8c503501
 tangwang
补充基于阿里云的embedding
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"""
Text embedding encoder using Aliyun DashScope API.
Generates embeddings via Aliyun's text-embedding-v4 model.
"""
import os
import logging
import threading
import time
import numpy as np
from typing import List, Union
from openai import OpenAI
logger = logging.getLogger(__name__)
class CloudTextEncoder:
"""
Singleton text encoder using Aliyun DashScope API.
Thread-safe singleton pattern ensures only one instance exists.
Uses text-embedding-v4 model for generating embeddings.
"""
_instance = None
_lock = threading.Lock()
def __new__(cls, api_key: str = None, base_url: str = None):
with cls._lock:
if cls._instance is None:
cls._instance = super(CloudTextEncoder, cls).__new__(cls)
# Get API key from parameter or environment variable
api_key = api_key or os.getenv("DASHSCOPE_API_KEY")
if not api_key:
raise ValueError("DASHSCOPE_API_KEY must be set in environment or passed as parameter")
# Use Beijing region by default
base_url = base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1"
cls._instance.client = OpenAI(
api_key=api_key,
base_url=base_url
)
cls._instance.model = "text-embedding-v4"
logger.info(f"Created CloudTextEncoder instance with base_url: {base_url}")
return cls._instance
def encode(
self,
sentences: Union[str, List[str]],
normalize_embeddings: bool = True,
device: str = 'cpu',
batch_size: int = 32
) -> np.ndarray:
"""
Encode text into embeddings via Aliyun DashScope API.
Args:
sentences: Single string or list of strings to encode
normalize_embeddings: Whether to normalize embeddings (handled by API)
device: Device parameter (ignored, for compatibility)
batch_size: Batch size for processing (currently processes all at once)
Returns:
numpy array of shape (n, dimension) containing embeddings
"""
# Convert single string to list
if isinstance(sentences, str):
sentences = [sentences]
if not sentences:
return np.array([])
try:
# Call DashScope API
start_time = time.time()
completion = self.client.embeddings.create(
model=self.model,
input=sentences
)
elapsed_time = time.time() - start_time
logger.info(f"Generated embeddings for {len(sentences)} texts in {elapsed_time:.3f}s")
# Extract embeddings from response
embeddings = []
for item in completion.data:
embeddings.append(item.embedding)
return np.array(embeddings, dtype=np.float32)
except Exception as e:
logger.error(f"Failed to encode texts via DashScope API: {e}", exc_info=True)
# Return zero embeddings as fallback (dimension based on text-embedding-v4)
# text-embedding-v4 typically returns 1024-dimensional vectors
return np.zeros((len(sentences), 1024), dtype=np.float32)
def encode_batch(
self,
texts: List[str],
batch_size: int = 32,
device: str = 'cpu'
) -> np.ndarray:
"""
Encode a batch of texts via Aliyun DashScope API.
Args:
texts: List of texts to encode
batch_size: Batch size for processing
device: Device parameter (ignored, for compatibility)
Returns:
numpy array of embeddings
"""
if not texts:
return np.array([])
# Process in batches to avoid API limits
all_embeddings = []
for i in range(0, len(texts), batch_size):
batch = texts[i:i + batch_size]
embeddings = self.encode(batch, device=device)
all_embeddings.append(embeddings)
# Small delay to avoid rate limiting
if i + batch_size < len(texts):
time.sleep(0.1)
return np.vstack(all_embeddings) if all_embeddings else np.array([])
def get_embedding_dimension(self) -> int:
"""
Get the dimension of embeddings produced by this encoder.
Returns:
Embedding dimension (1024 for text-embedding-v4)
"""
return 1024
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