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Jina v3 Embeddings model for QAFD-RAG
"""
from typing import List, Optional
import numpy as np
import logging
logger = logging.getLogger(__name__)
class JinaV3EmbeddingModel:
"""Jina Embeddings v3 model - standalone version"""
def __init__(self, global_config, embedding_model_name: Optional[str] = None):
self.global_config = global_config
self.embedding_model_name = embedding_model_name or global_config.embedding_model_name
# Initialize Jina v3 model
logger.info(f"Initializing Jina v3: {self.embedding_model_name}")
from sentence_transformers import SentenceTransformer
self.embedding_model = SentenceTransformer(
self.embedding_model_name,
trust_remote_code=True
)
self.embedding_dim = 1024 # Jina v3 fixed dimension
self.batch_size = getattr(global_config, 'embedding_batch_size', 32)
self.max_seq_len = getattr(global_config, 'embedding_max_seq_len', 8192)
self.normalize = getattr(global_config, 'embedding_return_as_normalized', True)
logger.info(f"✅ Jina v3 model loaded: {self.embedding_dim}-dim")
def batch_encode(self, texts: List[str], **kwargs) -> np.ndarray:
"""Encode texts to embeddings"""
if isinstance(texts, str):
texts = [texts]
batch_size = kwargs.get('batch_size', self.batch_size)
# Encode with Jina v3
embeddings = self.embedding_model.encode(
texts,
batch_size=batch_size,
convert_to_numpy=True,
show_progress_bar=False,
normalize_embeddings=self.normalize
)
return embeddings
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