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import os
import numpy as np
from transformers import AutoModel
from extensions.openai.errors import ServiceUnavailableError
from extensions.openai.utils import debug_msg, float_list_to_base64
from modules.logging_colors import logger
embeddings_params_initialized = False
def initialize_embedding_params():
'''
using 'lazy loading' to avoid circular import
so this function will be executed only once
'''
global embeddings_params_initialized
if not embeddings_params_initialized:
from extensions.openai.script import params
global st_model, embeddings_model, embeddings_device
st_model = os.environ.get("OPENEDAI_EMBEDDING_MODEL", params.get('embedding_model', 'all-mpnet-base-v2'))
embeddings_model = None
# OPENEDAI_EMBEDDING_DEVICE: auto (best or cpu), cpu, cuda, ipu, xpu, mkldnn, opengl, opencl, ideep, hip, ve, fpga, ort, xla, lazy, vulkan, mps, meta, hpu, mtia, privateuseone
embeddings_device = os.environ.get("OPENEDAI_EMBEDDING_DEVICE", params.get('embedding_device', 'cpu'))
if embeddings_device.lower() == 'auto':
embeddings_device = None
embeddings_params_initialized = True
def load_embedding_model(model: str):
try:
from sentence_transformers import SentenceTransformer
except ModuleNotFoundError:
logger.error("The sentence_transformers module has not been found. Please install it manually with pip install -U sentence-transformers.")
raise ModuleNotFoundError
initialize_embedding_params()
global embeddings_device, embeddings_model
try:
print(f"Try embedding model: {model} on {embeddings_device}")
if 'jina-embeddings' in model:
embeddings_model = AutoModel.from_pretrained(model, trust_remote_code=True) # trust_remote_code is needed to use the encode method
embeddings_model = embeddings_model.to(embeddings_device)
else:
embeddings_model = SentenceTransformer(model, device=embeddings_device)
print(f"Loaded embedding model: {model}")
except Exception as e:
embeddings_model = None
raise ServiceUnavailableError(f"Error: Failed to load embedding model: {model}", internal_message=repr(e))
def get_embeddings_model():
initialize_embedding_params()
global embeddings_model, st_model
if st_model and not embeddings_model:
load_embedding_model(st_model) # lazy load the model
return embeddings_model
def get_embeddings_model_name() -> str:
initialize_embedding_params()
global st_model
return st_model
def get_embeddings(input: list) -> np.ndarray:
model = get_embeddings_model()
debug_msg(f"embedding model : {model}")
embedding = model.encode(input, convert_to_numpy=True, normalize_embeddings=True, convert_to_tensor=False)
debug_msg(f"embedding result : {embedding}") # might be too long even for debug, use at you own will
return embedding
def embeddings(input: list, encoding_format: str) -> dict:
embeddings = get_embeddings(input)
if encoding_format == "base64":
data = [{"object": "embedding", "embedding": float_list_to_base64(emb), "index": n} for n, emb in enumerate(embeddings)]
else:
data = [{"object": "embedding", "embedding": emb.tolist(), "index": n} for n, emb in enumerate(embeddings)]
response = {
"object": "list",
"data": data,
"model": st_model, # return the real model
"usage": {
"prompt_tokens": 0,
"total_tokens": 0,
}
}
debug_msg(f"Embeddings return size: {len(embeddings[0])}, number: {len(embeddings)}")
return response
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