mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-30 03:18:57 +01:00
66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
import os
|
|
from sentence_transformers import SentenceTransformer
|
|
import numpy as np
|
|
from extensions.openai.utils import float_list_to_base64, debug_msg
|
|
from extensions.openai.errors import *
|
|
|
|
st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "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", "cpu")
|
|
if embeddings_device.lower() == 'auto':
|
|
embeddings_device = None
|
|
|
|
def load_embedding_model(model: str) -> SentenceTransformer:
|
|
global embeddings_device, embeddings_model
|
|
try:
|
|
embeddings_model = 'loading...' # flag
|
|
# see: https://www.sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer
|
|
emb_model = SentenceTransformer(model, device=embeddings_device)
|
|
# ... emb_model.device doesn't seem to work, always cpu anyways? but specify cpu anyways to free more VRAM
|
|
print(f"\nLoaded embedding model: {model} on {emb_model.device} [always seems to say 'cpu', even if 'cuda'], max sequence length: {emb_model.max_seq_length}")
|
|
except Exception as e:
|
|
embeddings_model = None
|
|
raise ServiceUnavailableError(f"Error: Failed to load embedding model: {model}", internal_message=repr(e))
|
|
|
|
return emb_model
|
|
|
|
|
|
def get_embeddings_model() -> SentenceTransformer:
|
|
global embeddings_model, st_model
|
|
if st_model and not embeddings_model:
|
|
embeddings_model = load_embedding_model(st_model) # lazy load the model
|
|
return embeddings_model
|
|
|
|
|
|
def get_embeddings_model_name() -> str:
|
|
global st_model
|
|
return st_model
|
|
|
|
|
|
def get_embeddings(input: list) -> np.ndarray:
|
|
return get_embeddings_model().encode(input, convert_to_numpy=True, normalize_embeddings=True, convert_to_tensor=False, device=embeddings_device)
|
|
|
|
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
|